self‐healing in thermal interface materialsexperta-benelux.com/application notes/self‐healing in...

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Delft University of Technology - Faculty of Aerospace Engineering

2012

Self‐HealinginThermalInterfaceMaterials

Master of Science Thesis

Christian Moreno Belle ‐ LR

1011642

TU Delft

Delft University of Technology Novel Aerospace Materials

Self-Healing

in

Thermal Interface Materials

Master of Science Thesis

Author:

Christian Moreno Belle

Advisors:

Prof. Dr. Ir. S. van der Zwaag

Dr. U. Lafont

Date:

13 August 2012

Contact information:

[email protected]

i

Abstract

Thermal interface materials, TIM’s, are materials that serve to conduct heat from a heat source

surface to a heat sink surface. Aside from their thermal conductive property TIM’s should have other

valuable properties, like adhesive strength, to ascertain structural integrity of the assembled

compound structures. However, over time the thermal interface materials may loose their good

contact with both surfaces due to delamination. This can happen as a result of stresses like

contraction and expansion due to thermal cycling. The damage and integrity of the TIM’s are difficult

to spot and measure. Self‐healing polymers based on reversible chemistry may offer an interesting

new route to autonomously restore interfacial adhesion upon delamination. In this research project

several types of self‐healing polymer composites were synthesized with thermally conductive

granular materials either as random textured or as quasi‐aligned polymer composites, combining

good thermal conductivity with long lasting interfacial adhesion to be used in microelectronics and

LED applications. The experiments conducted in this project indicate how well the different

composites perform for different filler in thermal conductivity, adhesive strength and self‐healing

ability. The results of this project indicate that competitive TIM’s with excellent adhesive properties

can be fabricated with self‐healing abilities.

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Preface

This report is my master thesis for the conclusion of my Master program at the faculty of Aerospace

Engineering at Delft University of Technology, TUDelft.

I take this opportunity to thank both of my supervisors, starting with Prof. Dr. Ir. S. van der Zwaag for

his insights, his guidance and comments, for letting me join the Novel Aerospace Materials group and

giving me the opportunity to work on a very interesting topic with my second supervisor, Dr. U.L.

Lafont, who I thank for his patience and understandings, his guidance and support thru all my work

and for being strict when I needed it the most.

A second word of thanks goes for Nijesh James for his comments and guidance. Furthermore I thank

Daniella Deutz for her insight, her useful critics and reading part of my report.

I also thank all the members of the Novel Aerospace Materials group for their cheering, friendship

and wonderful experience at the TUDelft.

I also would like to thank my family and close friends for their patience and support as some of them

live countries away from The Netherlands.

I really enjoyed working on this topic and the persons involved but it is time to finish this report, this

is just the beginning.

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Table of Contents

Abstract .................................................................................................................................................... i

Preface ..................................................................................................................................................... iii

Table of Contents ..................................................................................................................................... v

Nomenclature ......................................................................................................................................... vii

1 Introduction ..................................................................................................................................... 1

1.1 Thermal interface material ...................................................................................................... 1

1.2 Self‐healing .............................................................................................................................. 6

1.2.1 Additive based self‐healing ............................................................................................. 7

1.2.2 Intrinsic based Self‐healing .............................................................................................. 8

1.3 Particle alignment in composite using dielectrophoresis ..................................................... 12

2 Materials and test methods .......................................................................................................... 16

2.1 Composite synthesis .............................................................................................................. 16

2.2 Composite synthesis using dielectrophoresis ....................................................................... 20

2.3 Cohesion healing test ............................................................................................................ 21

2.4 Adhesion recovery: lap shear test ......................................................................................... 22

2.5 Thermal conductivity test ...................................................................................................... 23

2.6 Other characterization techniques used ............................................................................... 24

3 Experimental results ...................................................................................................................... 26

3.1 Material characterization ...................................................................................................... 26

3.2 Cohesion recovery results ..................................................................................................... 29

3.2.1 Aluminium as filler ......................................................................................................... 29

3.2.2 Aluminium Nitride as filler............................................................................................. 32

3.2.3 Boron Nitride as filler .................................................................................................... 35

3.2.4 Copper as filler ............................................................................................................... 38

3.2.5 Graphite as filler ............................................................................................................ 40

3.2.6 Data summary for cohesion test ................................................................................... 43

3.3 Adhesion recovery results ..................................................................................................... 47

3.3.1 Aluminium as filler ......................................................................................................... 47

3.3.2 Aluminium Nitride as filler............................................................................................. 47

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3.3.3 Boron Nitride as filler .................................................................................................... 48

3.3.4 Copper as filler ............................................................................................................... 48

3.3.5 Graphite as filler ............................................................................................................ 49

3.3.6 Data summary for adhesion test ................................................................................... 50

3.4 Thermal conduction results ................................................................................................... 51

3.4.1 Specimen with non—aligned particles .......................................................................... 51

3.4.2 Comparison between aligned and non‐aligned samples .............................................. 53

3.4.3 SEM analysis of particulate composites/particles ......................................................... 54

4 Discussion ...................................................................................................................................... 59

4.1 Cohesion recovery ................................................................................................................. 59

4.2 Adhesion recovery ................................................................................................................. 62

4.3 Thermal conduction .............................................................................................................. 66

4.4 Valorization strategy ............................................................................................................. 69

5 Conclusions .................................................................................................................................... 73

References ............................................................................................................................................. 74

vii

Nomenclature

Acronyms DA Diels‐Alder

cl Crosslinker

DEP DiElectroPhoresis

DSC Differential Scanning Calorimetric

EPS Epoxidized PolySulfide

Im Imaginary

rDA Retro Diels‐Alder

Re Real

rms Root mean square

rpm Revolution per minute

SEM Scanning Electron Microscope

TIM Thermal Interface Material

TEM Transmission Electron Microscopy

UV Ultra Violet

List of Symbols Latin symbols

Symbols Unit Description

J/Kg ⋅K Heat capacity

V/m Electric field

eV eV Electron volt

F N Force

f Hz Frequency

g/eq Gram‐equivalent

H% ‐‐ Healing percentage

K ‐‐ Clausius–Mossotti factor

k W/mK Thermal conductivity

m Kg Mass

p Pa Pressure

r m radius

T K or oC Temperature in Kelvin or Celsius

Ti oC Order‐disorder transition temperature

Tg oC Glass transition temperature

Tm oC Melting temperature

t s Time

V V Voltage

v m/s Velocity

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Greek symbols

Symbols Unit Description

‐ Del operator

F/m Permittivity

Pa⋅s Viscosity

Kg/m3 Density

Ω⋅m Electrical conductivity

MPa Shear strength

0 Phase‐angle

rad/s Angular frequency

1

1 Introduction

At present there are very good interface materials designed for micro‐ and optoelectronics. These

interfaces can be tailored to fulfil one or several functions depending on the requirements of the

assembled compounds: Insulate or conduct electricity or heat, hold the compounds together etc.

Aside from these functions the interface materials can contain properties to enhance its functionality

and applicability: Corrosion resistance, very high thermal resistance, fluidity, self‐healing, etc.

If one of these interfaces deteriorates or fails in any other way it may cause damage or even cause

failure to the system they belong. Stresses between the interface bonding agent and the attached

components are the main reason for interfacial failures, i.e. stresses originated by different

expanding coefficients, corrosion stresses, electrical stresses, etc [1]. Those can cause damage to the

bonding interface and with time cause total debonding from its attached component(s).

This research was conducted to investigate an adhesive interface composite provided with good

thermal conductivity and self‐healing ability to restore all its cohesive/ adhesive and thermal

conduction properties upon damage.

The most significant concepts‐tools are introduced in this chapter; Thermal interfaces materials, self‐

healing and dielectrophoresis. The next chapter explains in detail the synthesis of the specimen, the

test methods and processes used on the specimens. The third chapter presents the data results in

the form of graphs, tables and images. The results are discussed in chapter four.

1.1 Thermalinterfacematerial

Heat generated within an electronic device must be removed to maintain the functional temperature

of the component within safe operating limits. Often this heat removal process involves conduction

from the component package surface to a radiator, heat sink or heat spreader, which can transfer

the heat to the ambient environment more efficiently. This radiator has to be joined carefully to the

package to maximize the radiated thermal emission [2].

Attaching a radiator to an electronic device requires that two generally not perfectly flat surfaces are

brought into intimate contact. These surfaces are usually characterized by a microscopic surface

roughness superimposed on a macroscopic non‐planarity that can give the surfaces a concave,

convex or twisted shape represented in Figure 1.1.

Figure 1.1 Magnified surface imperfections containing air gaps [3]

2

When two such surfaces are mechanically joined at moderate contact pressures, contact occurs only

at the high points or peaks of the surfaces. The low points in the surface form air‐filled voids. Typical

contact area can consist of more than 80 percent air voids, which creates a significant resistance to

heat flow [3, 4].

Thermal Interface Materials, or TIM’s, are used to eliminate these interstitial air gaps from the

junction by conforming to the rough and uneven mating surfaces. Since TIM’s have significantly

greater thermal conductivity than the air they replace, the resistance across the interface decreases

and the heat flux is greatly increased leading to a drop in the temperature of the component.

A variety of TIMs has been developed in response to the changing needs of the electronic packaging

market and can be categorized into the following families:

Elastomeric Pads/Insulators

Thermally Conductive Adhesive Tapes

Phase Change Materials

Thermally Conductive Gap Fillers

Thermally Conductive “Cure in Place” Compounds

Thermal Compounds or Greases

Gap Filling Liquids

Thermally Conductive Adhesives

Each of the mentioned families has their own set of advantages and disadvantages, also between

materials within the families. Closely looking at some materials of each family the next advantages

and disadvantages can be found. From Kaveh, A et al. [5]:

Elastomeric Pads/Insulators: Containing ceramic particles which are often reinforced with woven

glass fibers.

Pros:

Very high dielectric strength.

Very high volume resistivity against heat loads.

Long term electrical insulation.

Cons:

Require very high clamping pressures, higher than 1.4 kPa.

Can deform with time.

Thermally Conductive Adhesive Tapes: Fabricated with silicon, fiberglass or silicon elastomers.

Pros:

Easy to use, peel and press. No curing dispensing.

Good for mid‐range thermal performance conductivity.

Replace mounting hardware.

Good for bonding small heat sinks to components, eliminating the need for clips, screws or

mechanical fasteners.

Able to bond surfaces which are significantly rough or bowed.

Reduce stresses, even with relatively high compression, to allow for uneven surfaces.

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Relatively low pressure needed to apply, 35 to 100 Pa.

Cons:

Do not solve components flatness issues. Issues with air gaps.

Only moderate thermal conductivity.

Thermal performance highly dependent on contact area that can be achieved between

bonding surfaces.

Phase Change Materials: Including silicon and organic based versions.

Pros:

Fill interfacial air gaps and surface voids.

Create strong adhesive bonds that can handle high temperatures.

Can achieve complete wet‐out of surface after a mild melt temperature.

Very low mounting force required.

Low thermal resistance.

Will melt at temperatures just above 50oC.

Cons:

Require other forms of attachment.

Some older versions flow out of tight areas.

Require some compressive force.

Do not provide electrical isolation.

Thermally Conductive Gap Fillers: Silicon with optional foam.

Pros:

Can fill large areas.

Offer grease‐like thermal performance with pad‐like handling and installation convenience.

Can blanket multiple components of varying heights.

Useful for low compression force applications.

Can be custom moulded.

Often good for bonding large gaps between hot components and cold surfaces.

Easy application without curing or dispersing.

Can be easily reworked.

Can be provided in thicker sizes or larger offsets than tapes.

Cons:

Inferior to phase change materials and thermal grease in high dissipation situations.

High clamping pressures, higher than 1.4 kPa.

“Cure in Place” Compounds: includes silicon, silicon free, ceramic filled polyurethane and graphite

materials.

Pros:

Provide electrical isolation along with thermal conductivity.

Offer superior resistance to tear and cut‐through form burrs on heat sinks.

4

Cons:

Typically require special storage, have limited shelf life and require the final application work.

Greases and Gels: Including silicones, ceramic powder suspending in a liquid or gelatinous silicone, a

metal‐base version containing silver or aluminum particles and a carbon base version with diamond

powder.

Pros:

Easy to apply.

Do not need curing.

Can flow and conform to interfaces.

Offer re‐workable thermal interface layers.

Can achieve high thermal conductivity of 10 W/mK.

Since bond line thickness is very thin, less than 25 microns, the resulting thermal impedance

cross the interface can be low.

Silicon components do not leak out of the compounds.

No special storage requirements.

Insulated.

Cons:

Over time some can degrade, pump‐out or dry out.

Often present messy application issues.

Require careful application or it can lead to the formation of air gaps or leakages.

The actual bulk thermal conductivity is not high.

They have been regarded as “user unfriendly”.

No resistance to long term mechanical loads.

Gap Filling Liquids:

Pros:

Conforms like grease with respect to mating uneven surfaces.

Reduced material pomp out.

Low modulus.

Cons:

Curing time.

No resistance to long term mechanical loads.

Liquid Adhesives: Applied through dispensing or stencil printing.

Pros:

Provide structural support, eliminating the need for mechanical clip.

Cons:

Can be messy

5

After considering the different types of TIM together with the intention of making a polymer‐

granular conductor composite a closer look will be given to conductive adhesives. Thermal Adhesives

are one or two part adhesive systems which have been usually loaded with metallic or ceramic fillers

to enhance the thermal conductivity of the bulk polymer material. They are typically used to bond

small heat sinks to board level components, thus eliminating the need for clips, screws or other

mechanical fasteners to hold the heat sink in place. As a structural adhesive, thermal adhesives are

particularly useful for bonding surfaces which are significantly rough or curved.

Adhesives offer a number of advantages over mechanical fasteners:

Provide a relatively uniform distribution of stress across the joint.

Employ larger stress‐bearing areas.

Join similar or dissimilar materials of different thicknesses and shape.

Provide joints with smooth contours.

Seal joints against a variety of environments.

Can act as an insulator slowing heat transfer across the joint or act as a TIM facilitating heat

flow.

Five factors affect the choice, use, and performance of the interface material used between the

processor and the heat‐sink:

Thermal conductivity of the material.

Electrical conductivity of the material.

Gap Filling characteristics of the material.

Long‐term stability and reliability of the material.

Applicability.

When taking the above mentioned into account the ideal TIM see Figure 1.2, would be/have:

High thermal conductivity.

Easily conforms to both contours to be joined by small contact pressures .

Minimal thickness.

No leakage from the interface.

No deterioration over time.

Non‐toxic.

Easy to apply/remove.

However, when the actual performance of TIM is taken into account as in Figure 1.3, we can say that:

Gaps will not be completely filled, leaving some air pockets.

Some leakage may occur by the material by flowing out of its place due to pressure and/or

heat.

Performance may deteriorate over time.

Not always “manufacture friendly".

6

Figure 1.2 Ideal TIM gap filling performace [6] Figure 1.3 Actual TIM gap filling performance [6]

1.2 Self‐healing

Self‐Healing is a bio‐mimicking process to repair existing damage on a system by itself by using some

or no energy from outside the system. Currently there are not many self‐healing processes that can

restore the damaged system to its original state [7, 8]. We label a material to have “Self‐healing”

properties when it can restore some properties by itself to some extent to keep that material

fulfilling its requirements, even if it is just the action of stopping crack growth. In this last case

“damage management” would be a more accurate label rather than self‐healing [9]. It should be

realized that the conditions during damage accumulation are always different (e.g. lower stress or

higher temperature, or additional energy input) from those during healing.

Self‐healing systems are differentiated using the accumulation of damage over the elapsed time as

parameter. With this, three cases are distinguished:

a. Cases where self‐healing occurs only once, this means that the damage diminishes in some

degree from the material and it starts piling up again until it breaks see Figure 1.4 a.

b. Cases where multiple sessions of healing can occur, healing the material to some extent but

never perfect and/or never to a fixed amount of damage, eventually leading to failure of the

material see Figure 1.4 b.

c. The ideal case where the material gets multiple healing processes of some extent but never

allowing the material to reach critical damage value see Figure 1.4 c. The ideal case will be

when the material can do this an infinite number of times during its service life.

Figure 1.4 Damage over time [8]

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For self‐healing processes to occur self‐healing properties will have to be added to the material in the

form of additives or by manipulating the material granting it with intrinsic properties allowing self‐

healing to be externally triggered. The trigger can be temperature, light, electrolytes, pressure etc.

Either of these will bring engineering challenges to the system.

1.2.1 Additivebasedself‐healing

To obtain an autonomous self‐healing material using additives means imbuing the material with

healing agents. Healing agents are mainly kept in a fluid state, in a container, until damage occurs,

then flow to seal the damage and solidify using help form a catalyst or another fluid to trigger curing.

Two different types of systems to contain healing agents are:

Encapsulated systems: Using capsules which can be spherical or other specially designed forms as

shown in Figure 1.5. Forms can be tailored according to the matrix they are in, the forces they have

to endure without breaking and the direction of the forces to optimize crack encounters. A crack has

to encounter these capsules for the self‐healing mechanism to occur. After the capsule has been

broken and its healing agent has cured that particular sphere will be expended and will no longer

participate in future healing processes [10‐12].

Vascular systems: Hollow tubes, filled with a liquid healing agent, are used to create vascular

systems as represented in Figure 1.6. These tubes can be connected with each other to help supply

the healing agent [12]. These systems can be refilled and they can be interconnected. These vascular

systems take a great amount of volume from the surrounding matrix, altering the entire system

properties on a bigger scale than the encapsulated system.

Using additives will change the properties of that material to some extent. It may reduce the desired

properties on which that material was chosen in the first place. Two important parameters for these

systems are how much healing substance can be added onto the material while remaining usable and

if that amount is enough for self‐healing. Self‐healing means that something somehow needs to

“move” to the open space of the crack, fill it and restrain it from further opening. Movable parts or

fluids are not desired on a load carrying material but the mobility of the healing agent is a necessary

condition for autonomous self‐healing.

Figure 1.5 Encapsulated system [12]

Figure 1.6 Vascular system [12]

8

1.2.2 IntrinsicbasedSelf‐healing

The second approach to create a self‐healing material is to create materials with intrinsic self‐healing

properties, as represented in Figure 1.7, which have to be stimulated to trigger the healing

mechanism [12]. Heat is the most applied trigger for healing stimulation, by heating thermal reactive

self‐healing materials bonds will open up to later reform upon cooling down. This reversible bond

breaking lowers the viscosity making the material more or less fluid while at the same time giving the

material the mobility to seal the cracks. Surfaces need to be in contact with each other to close the

gap by creating new bonds and entangle, as shown in Figure 1.8. This holds for any healing

mechanism. After the temperature is lowered new bonds are created allowing the material to

recover an amount of damage and fixating the material as if new [12]. Problems related to

temperature stimulation are that the material may lose its shape and, during the healing process it

will not be able to carry loads.

Self‐healing is also a process that requires time. Instant healing responding to damage, using damage

as the trigger, would be the ideal process. For some self‐healing systems it will be almost impossible

to respond and heal as long as the material is carrying loads. Cyclic loads, against tensile static loads,

on the material can help in the self‐healing process, as healing can occur easier during resting periods

of cyclic loading or during long periods of compression loading which helps closing the crack making

surfaces to contact each other.

Self‐healing processes are very dependable on the material they are paired with and the

requirements of the material during its life. Some self‐healing materials can be seen as “tailored

products”, that means that when applying a known self‐healing system into a new material it should

be studied and tested to corroborate its compatibility.

Figure 1.7 Intrinsic principle [12]

Figure 1.8 Entanglement and reconnection options [13]

9

When talking about intrinsic self‐healing it is usually referred to reversible bonding ability based

system including supramolecular chemistry. Supramolecular chemistry includes all intermolecular

bonding using non‐covalent bonds, reversible covalent bonds and hydrogen bonding [12, 14].

Some of the supramolecular chemistry reactions, most commonly applied for self‐healing, are briefly

presented below:

Coordination complex processes: In chemistry a coordination complex or metal complex happens

when an atom or ion, usually metallic, is bonded to a surrounding array of molecules or ions, which

are in turn known as “ligands” or “complexing agents”. Many metal‐containing compounds consist of

coordination complexes. Anything that forms a bond with a metal ion is called a "ligand", and the

structure of the resulting complex may be termed the "ligation structure" or "coordination

structure". Coordination chemistry might be of interest if metallic additives are used to enhance

electric and or thermal conductivity of a polymer [15].

Diels‐Alder reaction: This reaction, see Figure 1.9, is named after its discoverers Otto Diels and Kurt

Alder. They discovered how a 4+2 cycloaddition reaction between an electron rich diene, in their

case a furan, and an electron poor dienophile, in their case a maleinide, join to form a cyclohexene

system [16]. Diels‐Alder reaction is an organic chemical reaction that not only forms carbon to

carbon bonds but also heteroatom‐heteroatom bonds, making it a highly prized reaction to

synthesize polymers. A quality of the DA reaction is their thermal reaction to debond the

cyclohexene system obtaining again free dienes and dienophiles. This reversible reaction is called

retro‐Diels‐Alder (rDA) reaction and has been well studied in the case of furan and maleimide

substances. These thermally activated polymers using rDA reaction are good candidates for self‐

healing materials, especially if the healing temperature lies not too far above the working

environmental temperature the product will sustain during its service life.

Figure 1.9 Diels‐Alder reaction [16]

Hydrogen bond interactions: The hydrogen bond is really a special case of dipole forces. A hydrogen

bond is the attractive force between the hydrogen attached to an electronegative atom of one

molecule and an electronegative atom of a different molecule. Usually the electronegative atom is

oxygen, nitrogen, or fluorine, which has a partial negative charge. The hydrogen then has the partial

positive charge [17].

Hydrogen bonding is usually stronger than normal dipole forces between molecules. Of course

hydrogen bonding is not nearly as strong as normal covalent bonds within a molecule, about 1/10 as

strong. This is still strong enough to have many important ramifications on the properties of the

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substances they occur i.e. water. Increase in the melting point, boiling point, solubility, and viscosity

of many compounds can be explained by the concept of hydrogen bonding [18].

When the hydrogen bond density is made to be locally high, such as in the hydrogen quadruple

material developed at the Tue by Prf. Meyer, adequate mechanical properties can be obtained..

Ionomeric interactions: Ionomers are a type of copolymers, with less than 20mol% ionizing

monomeric units usually less than 10%, and contains acids [19, 20]. This may have a heavy impact at

the chemical and physical properties of the material. What makes the Ionomers interesting for self‐

healing is that the acidic content is being neutralized, partially or completely, by ions creating ionic

groups. Ions concentrate in the material in groups of ion‐pairs known as multiplets seen Figure 1.10.

With increasing number of multiplets they will start to overlap with each other, creating clusters of

ionic content. It is because of these that the ionomers create regions of increased rigidity within the

material.

Figure 1.10 Ionomer multiplet [21]

Figure 1.11 Ionomer mapping [22]

These clusters are microphase separated from the rest of the material. This will cause these zones to

have a different glass transition temperature than de rest of the material. When this temperature is

reached multiplets will dislocate and gain mobility with respect to each other. This temperature is

known as “order‐disorder transition temperature” Ti, which normally lies lower than the melting

temperature Tm of the crystalline structure they are in. During Ti the physical properties of the

structure are reduced as the multiplets weaken [6].

As the semi‐crystalline structure of the ionomer, is heated towards Ti the bonds with the multiplets

are weaken and can “jump” creating bonds somewhere else. If bonds were already broken, i.e. due

to crack growth, separated cluster of ions are waiting to make contact with the material again to

reform new bonds repairing the damage. For such process the only thing that is needed for the self‐

healing process is some mobility after the damage occurs to bring surfaces together.

Photodimerization process: Photodimerization is a bimolecular photochemical process involving an

electronically excited unsaturated molecule that undergoes addition with an unexcited molecule of

the same kind.

Reversible photo‐cross‐linkable nano‐particles can be used as building blocks to create self‐healing

hydro gel films. The mobility necessary to close crack faces can be achieved through swelling

obtained during an exposure to a certain wavelength of UV light. During this exposure bonds are

broken and free to rearrange with other bonds. To promote swelling even further a solvent can be

used to some experiments with positive results. After an exposure time of a determined UV

wavelength another wavelength is applied to fixate the newly formed bonds. This method was used

11

by Cho et al. [21]. They targeted the reversible‐photo‐cycloadition bonded polymer gels for their test

subjects.

π‐interactions: In chemistry, π‐effects or π‐interactions are a type of non‐covalent reversible

interaction that involves so called π bonds between closely separated aromatic units in the polymer

backbone. Just like in an electrostatic interaction where a region of negative charge interacts with a

positive charge, the electron‐rich π system can interact with a metal, cationic or neutral, an anion,

another molecule and even another π system [23]. At elevated temperatures π‐ π associations tend

to find each other if close enough and form an intermediate state where all π systems are

momentarily together before separating with partner’s exchanged. This exchange gives the matrix a

certain degree of relaxation resulting in some fluidity and thus mobility to deform and repair[24].

Sulfur‐sulfur interaction: In terms of strength, non‐covalent bonds like hydrogen bonds are the

weakest in the supramolecular chemistry, even when they have good repairing properties for self‐

healing the strength obtained between the repaired crack surfaces might be insufficient for the

desired application. On the other hand there is i.e. the DA reaction bonding, these reactions generate

strong bonds that need an amount of heat to break and reverse, in other words, to heal. This amount

of heat or the needed duration for the healing might be too high for the surrounding structure. If the

end properties allow it, there are intermediate strength bonds that can be used for healing that

require less amount of heat for the reaction to take place. This type of self‐healing was chosen for

our experiments. Sulfur to sulfur in the form of disulfide to tetra‐sulfide bonds are such intermediate

bonds [25].Sulfur‐sulfur mechanism is shown in Figure 1.12

Figure 1.12 Mechanism of disulphide bond exchanges [25]

Sulfur bonds have the tendency to cleave under stress [26] they cleave and reattach as in Figure 1.13.

But after reaching a certain amount of stress there is more cleaving than re‐bonding creating

scissions in the material, stimulating degradation in its properties, it is also mentioned that disulfides

are more stable than other kind of sulfides having less problems from the sulphide scission

phenomena.

Figure 1.13 Sulfur bonds reattaching while under stress

Polymers containing sulfur‐sulfur bonds as the healing mechanism were chosen for the graduate

research project described in this thesis.

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1.3 Particlealignmentincompositeusingdielectrophoresis

As polymers and polymer adhesives have a low thermal conductivity by themselves, they are not

attractive candidate materials as TIMs.

To combine the good adhesive and thermo mechanical properties of the self‐healing polymer, with

adequate thermal conductivity values, granular materials with good intrinsic thermal conductivity,

such as Copper, Diamond or Boron Nitride, are mixed into the polymer matrix to obtain a functional

composite material.

It will be clear that the thermal conductivity will increase with the volume fraction of conductive

material. However high volume fractions of inert conductive material are likely to reduce the

adhesion and the attractive mechanical properties of the composite material.

However, in case of the granular particles are to be aligned in the direction of the leak flow, the heat

conduction is likely to go up, while the loss in other (mechanical) properties is likely to be small.

Recently, dielectrophoresis (DEP) was presented as an alternative and cheap method to align inert

functional particles into thread like structures in a low viscosity matrix, such as a polymer at an

elevated temperature [27].

In this chapter the physical principle of the DEP process is explained.

When an external electric field is applied across a particle suspended in a fluid medium, both the

particle and the suspending medium are being polarized. The result is the formation of unpaired

surface charges cumulated at the interface between the particle and the fluid medium. These

surface charges generate another electric field and distort the original electric field as represented in

Figure 1.14. The amount of charges at the interface depends on the field’s strength and the electrical

properties of the particle and the suspending medium. The most important electrical properties

involved are conductivity and permittivity, where conductivity is a measure of the ease with which

charges can move through a material, while permittivity is a measure of the energy storage or charge

accumulation in a system [28].

Figure 1.14 Dielectrophoresis typical electric field [28]

13

The surface charges interact with the electric field to produce Coulomb forces. Since the electric field

distribution is not uniform, drawn in Figure 1.14, the electric field density is higher on the right than

on the left, resulting in a net force in the drawn direction.

Few methods have been developed to find the total electrical force on the particle, including

effective moment method and Maxwell Stress Tensor method.

Assuming that the observation point is far enough, relative to the size of the particle, the surface

charges on the particle in Figure 1.14 can be approximated as a dipole, which is oriented with the

direction of the electric field.

With this approximation, the total electrical force on the particle is found as in equation (1.1).

∙ (1.1)

where is the effective dipole moment specific to the particle‐fluid system and is a del operator.

This electrical force is termed as dielectrophoretic (DEP) force [28].

When the dipole approximation is not accurate, higher order multipoles have to be considered. The

general solution has been solved by Jones and Washizu.

One of the most important applications of dielectrophoresis is particle separation as represented in

Figure 1.15. It relies on the fact that one particular sub‐population of particles has unique frequency‐

dependent dielectric properties, which is different from any other population. The relative

magnitude and direction of the dielectrophoretic force exerted on a given population of particles

depends on the conductivity and permittivity of the suspending medium, together with the

frequency and magnitude of the applied field. Therefore, differences in the dielectric properties of

particles manifest themselves as variations in the dielectrophoretic force magnitude or direction,

resulting in separation of particles. Particle manipulation is achieved by controlling the applied

frequency, to change the direction of the movement of the particles. The design of the electrodes,

the choice of the suspending medium, and the applied peak voltage can be pre‐determined to

optimize the operation of the device.

Figure 1.15 Representation of the particle distribution a) Normally distributed b) Distribution after DEP

14

Dielectrophoretically aligned composites can be obtained by applying an electric field to a composite

medium of particles dispersed in a low medium such as an uncured thermoset resin or a intrinsically

self‐healing polymer heated to a temperature above the reversible bond dissociation temperature.

The time averaged DEP force acting upon a dielectric sphere with a complex permittivity ∗ and

radius r, suspended in a medium with a complex permittivity ∗ and subjected to an electric field

Erms, can be generalized using the previous equation as in equation (1.2) [29],

2 ′ ∗ ∗ (1.2)

where ′ is the real part of the complex permittivity of the matrix and Erms is the applied sinusoidal

electric field having magnitudes , , and phases , , . Re[] and Im[] denote the real

and the imaginary part [30]. ∗ is the complex Clausius–Mossotti factor which is a function, see

equation (1.3), of the complex permittivities ′ , ′ and of the direct current conductivities of both

phases , of the ceramic particles and the polymer matrix, respectively, and of the angular

frequency of the electric field , where 2 .

∗

∗ ∗ /∗ 2 ∗ 2 /

(1.3)

Bellow the Clausius–Mossotti factor is plotted for spherical particles and oblate spheroid models in

Figure 1.16. It can be seen how the Clausius–Mossotti factor changes according to the frequency. The

frequency changes the phase of the electrical field which the Clausius–Mossotti factor. The Clausius–

Mossotti factor has the biggest impact on the force applied on the particles when the phase‐angle is

closest to 900. The Clausius–Mossotti factor directly affects the force exerted on the particle by the

electric field. This force will try to move the particle resulting in a translation, a rotation or a rotation

by cause of the translation. The real and imaginary parts of the Clausius–Mossotti factor affect the

translation and the rotation of the particle respectively. With this, translation only or rotation only of

the particles can be achieved by tuning the Clausius–Mossotti factor.

The translational motion of particles due to DEP is strongest for particles with diameters ranging

from approximately 1 to 1000 µm, since above 1000 µm, gravity overwhelms DEP, while below 1 µm

the Brownian motion becomes the dominant force [30].

The sign on the Clausius–Mossotti also have significance in the movement of the particles. It seems

that the positive Clausius–Mossotti factor will force the particles to orientate parallel to the electrical

field while negative will orientate then perpendicular to the electrical field.

15

Figure 1.16 Clausius–Mossotti factors plotted for spherical particles and oblate spheroid models for . ⁄ , Left the real part Right the imaginary part [29]

As stated the Another important parameter affecting the alignment of the particles is the viscosity of

the polymer matrix. An increase in viscosity results in a higher drag force acting on the particles. The

drag force can be approximated using Stokes’s law for flow at low Reynolds numbers, equation (1.4).

6 (1.4)

Where is the viscosity of the medium, r is the radius of the particle, and v is the velocity of the

particle. A high drag force leads to minimal alignment, but the high viscosity will be beneficial for the

final mechanical properties.

With this it can be said that the factors affecting the dielectrophoresis are:

The viscosity of the medium

The frequency of the electrical field.

The voltage of the electrical field

The phase of the electrical field

The size of the particle

The electrical conductivity of the medium and the particles

16

2 Materials and test methods

In this chapter the structure and properties of the two polymers used are presented. Furthermore

the processing of the unstructured and DEP structured composites is described.

Finally the test methods used to determine the cohesive and adhesive self‐healing ability as well as

the thermal conductivity and other physical properties are presented.

2.1 Compositesynthesis

For our experiment a self‐healing material based on sulphur‐sulphur supramolecular chemistry has

been chosen as the matrix for the composite matrix while several inert, granular materials with

different conductive properties were chosen as filler.

The matrix is prepared mixing epoxidized polysulfide, Thioplast EPS25 or EPS70 as the primary

material together with a thiol based cross‐linker , pentaerythritol tetrakis (3‐mercaptopropionate) or

4‐SH, together they form the self‐healing polymer. 1 weight percentage of 4‐Dimethylamino pyridine

(DMAP) was added to the mixture as catalyst for the curing reaction.

Figure 2.1 EPS Chemistry Figure 2.2 Crosslinker

With the Thioplast as start point the amount of cross‐linker is calculated by equation (2.1).

(2.1)

where m is the mass of the material [g], ‘eps’ for the Thioplast and ‘cl’ for the cross‐linker, and the

gram‐equivalent value [g/eq]. Examples of some values can be seen in Table 2.1.

Table 2.1 Typical synthesis of the pristine matrix

Precursors EPS25[g] EPS70[g] 4SH[g] Catalyst[g]

Matrix1 10 ‐ 1.9088 0.11908

Matrix2 ‐ 10 3.9408 0.13940

Gram‐eq

reaction

linker ha

giving th

Before m

composi

are put t

is expres

mixer. T

were mi

and the

where m

quivalent is c

. The Thiopl

as a molar m

he values of

mixing the T

ite compoun

together ins

ssed in volu

he amount o

ixed for test

filler materia

m is the mass

calculated di

ast EPS25 ha

mass of 488.

640

Figure 2.3 Co

Thioplast and

nd. The Thiop

ide a special

ume percent

of filler is det

ting. For this

al has to be k

s, the dens

Figure 2

ividing the m

as a molar m

66 g/mol wi

0 and

ombination of T

d the 4‐SH a

plast EPS25

l container a

age. This co

termined by

s calculation

known and is

ity and % th

.4 Mixing cont

molar mass b

mass of 1280

ith 4 active r

122.165.

Thioplast EPS25

a filler mate

or EPS70, th

as seen in Fig

ontainer is d

its volume p

n the densiti

s calculated

he desired vo

tainer Fig

by the numb

0 g/mol with

radicals as s

5 or EPS70 + cr

erial is added

he crosslinke

gure 2.4. In a

designed to b

percentage a

es of the m

by equation

% 100⁄

1 % 10⁄

olume perce

gure 2.5 Speed

ber of radica

2 active rad

een in Figur

rosslinker 4SH

d to the mo

r 4‐SH, the c

all this work

be used in a

and different

atrix, Thiopl

(2.2).

0

entage of fille

Mixer

ls taking pla

dicals while t

re 2.1 and Fi

onomers to

catalyst and

the filler pe

a speed mix

t volume per

last plus cro

er.

17

ace in the

he cross‐

gure 2.2,

form the

the filler

ercentage

xer speed

rcentages

oss‐linker,

(2.2)

18

The composite compound was mixed using the Speed Mixer DAC 150.1 shown in Figure 2.5, at 2110

rpm for 80 s. After that they are set on a 1 mm thick Teflon mould as arranged as shown in Figure

2.6.

The mould contains six rectangular cavities of 78x30x1 mm for six specimens in case multiple

specimens are made at the same time.

Figure 2.6 Mould set‐up Figure 2.7 Oven

After the material Is placed into the Teflon mould, two big aluminium plates holds the mould and the

material fixed. This setup is taken into de oven shown in Figure 2.7 to cure for 2 hours at 650C. In the

oven a weight is used to inflict pressure and obtain good specimen surfaces.

Many kinds of fillers were used to manufacture several kinds on specimens. What is left of the

samples after testing shows the variety of the fabricated specimens in Figure 2.8. The types of

composites produced are listed in Table 2.2.

Figure 2.8 Examples of prepared specimens containing the following fillers‐Silver colour: Aluminium‐ Grey colour: Aluminium Nitride ‐ Brown: Copper – Transparent: No filler – White: Boron Nitride – Black: Graphite

The samples in Figure 2.8 were all synthesised using EPS25. At 10% they were prone to retain some

bubbles form mixing. Surfaces were smooth on low values of fillers and very flexible. This flexibility

was reduced significantly with the increase of filler content.

19

Table 2.2 shows all the materials prepared for testing.

Graphite Aluminum Copper AlN BN Diamond

EPS25‐4SH‐2,5% X

EPS70‐4SH‐2,5% X

EPS25‐4SH‐4% X

EPS70‐4SH‐4% X

EPS25‐4SH‐5% XA

EPS70‐4SH‐5% XA

EPS25‐4SH‐7,5% X

EPS70‐4SH‐7,5% X

EPS25‐4SH‐10% XA X X X XA

EPS70‐4SH‐10% X X X X X

EPS25‐4SH‐20% XA X X X XA

EPS70‐4SH‐20% X X X X X

EPS25‐4SH‐30% XA X X XA X

EPS70‐4SH‐30% X X X X

EPS25‐4SH‐40% XA X X XA

EPS70‐4SH‐40% X X X X

Table 2.2 Prepared materials. X: normal; A: Aligned

20

2.2 Compositesynthesisusingdielectrophoresis

After the compound has being mixed it is poured into the DEP mould Figure 2.10, a Teflon sheet with

a form of 25x25x1 mm. Two aluminium sheets are placed under and on top of the material where

the current will be applied. The material is strongly pressed using two heavy plates to improve the

contact of the aluminium sheets. This set up is represented in Figure 2.10. The heavy plates are

fastened using several bolts for an uniform pressure distribution. The set‐up is then placed in a safety

box where a function generator coupled with an high voltage amplifier is used to apply the electrical

field for the DEP alignment. The voltage for each specimen was chosen beforehand and the

frequency was set according to the phase‐angle between the voltage‐ current before and after going

through the set‐up.

Figure 2.9 Equipment for DEP a) Function generator b) Amplifier c) Oscilloscope d) Heater

Figure 2.10 DEP test Set‐up

The oscilloscope seen in Figure 2.9 gives the representative output voltage across the composite. The

sinusoidal voltage‐currents applied on the specimen and, the phase‐angle difference is graphically

given as seen in Figure 2.11 and Figure 2.12. The phase‐angle, determined by the frequency, affects

the Clausius–Mossotti factor witch directly affect the strength of the force the electrical field,

generated by the sinusoidal voltage‐current, exerts on the particles as mentioned in section 1.3. The

higher the force the better the alignment and that is achieve by applying a phase angle as close to

900 as possible [30].The set‐up was placed on top of a heater at 500C and left for 2 hours while under

current.

Figure 2.11 View of the sinusoidal electrical currents

Figure 2.12 Phase‐angle between the electrical currents

21

2.3 Cohesionhealingtest

A small piece of material, 10x15 mm, is prepared by inflicting two cuts of around 5 mm using a razor

blade. The cuts inflicted forms a cross with one of its ends meeting an edge of the sample. The first

part of the test consists on monitoring the state of the cut as function of the temperature and time.

An oven is set to simulate two different operating environments, 65oC or 100oC. Both are below the

decomposition temperature of the material.

For monitoring a microscope was used to observe the changes using a 2.5x zooming lens.

Figure 2.13 Microscope Figure 2.14 Setup

For the test Procedure

Cut small piece of the specimen

Cut a cross form incision to monitor during healing

Take an image of the incision

Sandwich the specimen using two microscope slides

Fix it with to clamps to simulate operating situations

Introduce the specimens into the oven to simulate operating environment

Wait a predetermined amount of time

Remove the specimen from the oven

Remove clamps and one crystal to expose the incision

Take an image operating the microscope to record the healing progress

This procedure keeps monitoring the progress of the healing until the cut disappears or a maximum

time is exceeded.

The cohesive test was realized by recording images of the healing progress. To translate al the

analogue data to digital data the surface separation of the cut was measured and compared to its

original aperture at 0 min. With this the healing efficiency is defined and 100% healing means that

the cut has closed completely.

22

2.4 Adhesionrecovery:lapsheartest

Lap shear test was conducted to measure the adhesion properties of the material as it may change

with the volume percentage of the filler added. The test took ASTM D1002 “Apparent Shear Strength

of Single‐Lap‐Joint Adhesively Bonded Metal Specimens by Tension Loading” as a model for

preparation and conduction of the test.

The prepared lap shear specimen consists of two plates of aluminium 6082 T6 that have been joined

by using a current specimen on each end where the two plates overlap, Figure 2.16. The Aluminium

plates were cleaned using acetone. From a synthesised TIM, 3 pieces were cut, while avoiding

surface imperfections like air bubbles. The obtained pieces were around 25x12x1 mm. To initially

hold the aluminium plates in place, clamps were used. The specimens where placed in an oven at

65oC for 2 hours to simulate working conditions and promote adhesion.

During the lap shear test, the aluminium plates are pulled in opposite directions at a rate of 1

mm/min to produce a shearing stress on the adhesive. An overlap of around 12 ±3 mm is used since

the yield point of the aluminium plate is not expected to be exceeded during the test. The distance

between the jaws was kept constant for all tests.

While careful consideration should be given to the grips used to hold the lap shear specimen, since

improper grips and setting alignment can lead to grip slippage and cleavage stresses, for our

specimens vice grips were chosen while looking at the maximum expected force.

Vice grips with serrated inserts were used for the lap shear tests. The serrated grip inserts are

designed to dig into the material and prevent it from slipping. If slippage occurs with a vice grip, self‐

tightening grips of pneumatic or wedge design may have been used. The specimen was mounted in

the tensile bench shown in Figure 2.15 Tensile bench, so that the centre line of the adhesive is

aligned with the centreline of the force exerted by the machine.

For the test procedure, all these steps represent 1 healing cycle;

Measure the contact area where both aluminium plates overlap.

Load each end of the specimen in the tensile grips and fasten them. The starting grip

distances was 90 mm. The grip area was around 25x43 mm per grip.

Apply a force that causes a displacement of 1mm/min between the plates until the specimen

breaks and, record the force history during the displacement and the type of joint failure.

After the test the specimens were prepared again by joining those using clamps with enough

force to keep the setup in place and prevent misalignment between the aluminium plates.

They were then placed in an oven for 2 hours at 65oC.

From each TIM created, 3 specimens for lap shear testing were prepared and, this procedure was

conducted seven times per specimen.

23

2.5 Thermalconductivitytest

Thermal conductivity testing was performed using the C‐Therm TCi thermal sensor, Figure 2.17.

This sensor is based on the Modified Transient Plane Source Method to determine the thermal

resistivity and effusivity of the material.

Figure 2.17 C‐Therm TCi [32]

Figure 2.18 Experiment set up [32]

The prepared specimen for this test must have a diameter of around 17 mm to cover the entire

sensor. The sensor works by being heated up by a small current and monitoring how it responses

while being in contact with the specimen. The resistivity and the effusivity of the specimen are

measured and obtained directly from the sensor. From the inverse of the resistivity the conductivity

is obtained. Using the effusivity concept other thermal properties like heat capacity and diffusivity

can be derived. The effusivity is given by equation (2.3).

kρc . (2.3)

where k is the thermal conductivity / ∙ , ρ is the density / and is the heat

capacity / ∙ .

Figure 2.15 Tensile bench Figure 2.16 Specimen set up [31]

24

This heating, reading and cooling will be repeated 6‐10 times per specimen to obtain an average of

the readings.

For the test procedure;

Set up the sensor

Wet the sensor with distilled water to improve contact

Apply de specimen on the sensor and a weight above it to press the specimen on the sensor,

Figure 2.18

Commence the sensing by heating up the sensor several times for several data points and

obtain an average

2.6 Othercharacterizationtechniquesused

To better understand the prepared specimens, their characteristics and parameters, the next set of

tests were conducted.

Differential scanning calorimetric (DSC) techniques were conducted using the PerkinElmer Sapphire

DSC shown in Figure 2.19 from ‐80 to 100°C at 3°C /min. This was conducted on a pristine specimen,

no filler added, and on a specimen with 30% volume of graphite to determine if there was a change

in the value of the glass transition temperature.

Figure 2.19 DSC

Figure 2.20 TEM Figure 2.21 SEM

Transmission electron microscopy (TEM) was performed using the FEI Tecnai TF20 electron

microscope seen in Figure 2.20, operated at 200 kV. Samples were mounted on Quantifoil® microgrid

carbon polymer supported on a copper grid by dropping sample suspension on the grid. This was

done to determine particle morphology of the added filler.

25

Scanning electron microscope (SEM) was used by the JEOL JSM‐7500F scanning electron micrograph

seen in Figure 2.21. Since our specimens are soft the specimens prepared for SEM were first frozen

by submerging them into liquid nitrogen and then breaking them. Since these specimens where

prepared with electrically conductive fillers they were directly mounted on the SEM for surface

observation.

Figure 2.22 Gold sputter

Figure 2.23 Set up

Later specimens were prepared differently. The material was first embedded in a polymer base

holder polished and covered by gold using the gold sputter seen Figure 2.22 for sputtering gold to

make the surface. This makes the surface conductive and more responsive for the image processing.

This is done because some specimens are not electric conductive.

26

3 Experimental results

In this chapter the results obtained by the tests explained on the previous chapter are presented.

First the materials characteristics are presented followed by the cohesion recovery results, the

adhesion recovery results and the thermal conduction results of the made specimens.

3.1 Materialcharacterization

All the data given by the DSC was plotted and presented below. Two cycles were performed per

sample.

In Figure 3.1 the plotted results of EPS25‐4SH for both cycles are given. The Tg for EPS25‐4SH was

determined, from both graphs, to be around ‐460 C.

Figure 3.1 DSC results for EPS25‐4SH 1st and 2nd cycle respectively

In Figure 3.2 the plotted results of EPS70‐4SH for both cycles are given. The Tg for EPS25‐4SH was

determined, from both graphs, to be around ‐3.50C.

Figure 3.2 DSC results for EPS70‐4SH 1st and 2nd cycle respectively

‐700

‐200

300

800

1300

1800

‐140 ‐120 ‐100 ‐80 ‐60 ‐40 ‐20 0 20

DSC

‐DDSC

Temperature [0C]

EPS25‐4SH 1st cycle

DSC [uW]DDSC [uW/min]

‐700

‐200

300

800

1300

1800

‐140 ‐120 ‐100 ‐80 ‐60 ‐40 ‐20 0 20

DSC

‐DDSC

Temperature [0C]

EPS25‐4SH 2nd cycle


‐700

‐200

300

800

1300

1800

‐130 ‐110 ‐90 ‐70 ‐50 ‐30 ‐10 10

DSC

‐DDSC

Temperature [0C]

EPS70‐4SH 1st cycle


‐700

‐200

300

800

1300

1800

‐130 ‐110 ‐90 ‐70 ‐50 ‐30 ‐10 10

DSC

‐DDSC

Temperature [0C]

EPS70‐4SH 2nd cycle


27

In Figure 3.3 the plotted results of EPS25‐4SH with 40% Graphite for both cycles are given. The Tg for

EPS25‐4SH‐40% Graphite was determined, from both graphs, to be around ‐430C.

Figure 3.3 DSC results for EPS25‐4SH‐40%Graphite 1st and 2nd cycle respectively

From the DSC around same values for the glass transition temperature Tg was obtained for the 0%

and 40% of graphite content. As expected, the fraction of interfacial material and the possible shift of

Tg due to molecular confinement at the interface was too small to lead to a measurable shift in glass

transition temperature. No DSC measurement were conducted for other samples.

‐1500

‐1000

‐500

0

500

1000

1500

2000

‐120 ‐100 ‐80 ‐60 ‐40 ‐20 0 20

DSC

‐DDSC

Temperature [0C]

EPS25‐4SH‐40%Gra 1st cycle

DSC…DDSC…

‐1500

‐1000

‐500

0

500

1000

1500

2000

‐120 ‐100 ‐80 ‐60 ‐40 ‐20 0 20DSC

‐DDSC

Temperature [0C]

EPS25‐4SH‐40%Gra 2nd cycle


28

From the TEM the next pictures were obtained, shown in Figure 3.4. These TEM pictures were taken

to characterize the powders and to know about morphology of the crystal agglomeration for several

of the used fillers.

Figure 3.4 TEM micrographs of (a) graphite (b) hBN and (c) Al (d) AlN particles used in the composites

From the images above it was observed that the particles of graphite and Aluminium are flakes and

that they heavily clustered together. Boron Nitride particles are more spherical and don’t tend to

cluster that much. Aluminium Nitride seems to have similar characteristics as Boron Nitride.

29

3.2 Cohesionrecoveryresults

The values obtained are plotted according to the time they were in the oven. The dots plotted are

the experimental data while the lines are calculated trend lines to simulate the healing progress of

that specimen. This section is arranged by the filler used to fabricate the TIM composite and ends

with section 3.2.6 comparing the filler used. Typical uncertainty in the healing fraction is ± 10 % and

an accuracy of 2.2 µm.

3.2.1 Aluminiumasfiller

The following graphs represent the results obtained using Aluminium as filler content.

Cohesion recovery at 65oC

Below it shows in Figure 3.5 the results for EPS25‐4SH and EPS70‐4SH combined with a) 10%, b) 20%,

c) 30% and d) 40% of Aluminium for a temperature of 65oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% Al at 65

oC

b) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 20 vol% Al at 65

oC

c) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 30 vol% Al at 65

oC

d) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 40 vol% Al at 65oC

Figure 3.5 Healing performance for EPS25‐4SH and EPS70‐4SH with Aluminium at 650C. Typical uncertainty in the healing fraction is ± 10 %.

0102030405060708090100

0 50 100 150 200 250

Healing %

Time [min]

10% Aluminium at 65o C

EPS25 + 10 vol% Al

EPS70 + 10 vol% Al

0

20

40

60

80

100

0 50 100 150 200 250

Healing %

Time [min]


EPS25 + 20 vol% Al

EPS70 + 20 vol% Al

0

20

40

60

80

100

0 50 100 150 200 250

Healing %

Time [min]


EPS25 + 30 vol% Al

EPS70 + 30 vol% Al0

20

40

60

80

100

0 50 100 150 200 250

Healing %

Time [min]


EPS25 + 40 vol% Al

EPS70 + 40 vol% Al

30


Below it shows in Figure 3.6 the results for EPS25‐4SH and EPS70‐4SH combined with a) 10%, b) 20%,

c) 30% and d) 40% of Aluminium for a temperature of 1000C.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% Al at 100oC

b) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 20 vol% Al at 100oC

c) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 30 vol% Al at 100

oC

d) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 40 vol% Al at 100

oC

Figure 3.6 Healing performance for EPS25‐4SH and EPS70‐4SH with Aluminium at 1000 C. Typical uncertainty in the healing fraction is ± 10 %.

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 10 vol% Al

EPS70 + 10 vol% Al

0

20

40

60

80

100

0 50 100 150 200Healing %

Time [min]


EPS25 + 20 vol% Al

EPS70 + 20 vol% Al

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

30% Aluminium at 100oC

EPS25 + 30 vol% Al

EPS70 + 30 vol% Al

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

40% Aluminium at 100oC

EPS25 + 40 vol% Al

EPS70 + 40 vol% Al

31

In Figure 3.7 the results are given for Aluminium based EPS70‐4SH composite. All the specimens

experience high recovery rate at the start. Only the specimens of 30% and 40% at 1000C do not reach

80% of healing recovery.

Figure 3.7 Healing performance for EPS70‐4SH with different concentration of Aluminium. Typical uncertainty in the healing fraction is ± 10 %.

In Figure 3.8 the results are given for Aluminium based EPS70‐4SH composite. Specimens show a high

recovery rate stopping at much lower healing values than for EPS25‐4SH. Only the specimen

containing 10% of Aluminium at 1000C shows healing percentage values higher than 50%.

Figure 3.8 Healing performance for EPS70‐4SH with different concentration of Aluminium. Typical uncertainty in the healing fraction is ± 10 %.

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250

Healing %

Time [min]

EPS25‐4SH with Aluminum

EPS25 + 10vol% Al 65CEPS25 + 20vol% Al 65CEPS25 + 30vol% Al 65CEPS25 + 40vol% Al 65CEPS25 + 10vol% Al 100CEPS25 + 20vol% Al 100CEPS25 + 30vol% Al 100CEPS25 + 40vol% Al 100C

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250

Healing %

Time [min]


EPS70 + 10vol% Al 65CEPS70 + 20vol% Al 65CEPS70 + 30vol% Al 65CEPS70 + 40vol% Al 65CEPS70 + 10vol% Al 100CEPS70 + 20vol% Al 100CEPS70 + 30vol% Al 100CEPS70 + 40vol% Al 100C

32

3.2.2 AluminiumNitrideasfiller

The following graphs represent the results obtained using Aluminium Nitride as filler.


Below it shows in Figure 3.9 the results for EPS25‐4SH and EPS70‐4SH combined a) 10%, b) 20% and

c) 30% of Aluminium Nitride for a temperature of 65oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% AlN at 65

oC

b) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 20 vol% AlN at 65

oC

c) Healing performance for EPS25‐4SH and EPS70‐

4SH combined with 30 vol% AlN at 65oC

Figure 3.9 Healing performance for EPS25‐4SH and EPS70‐4SH with Aluminium Nitride at 650C. Typical uncertainty in the healing fraction is ± 10 %.

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

10% Aluminum Nitride at 65oC

EPS25 + 10 vol% AlN

EPS70 + 10 vol% AlN

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

20% Aluminum Nitride at 65o C

EPS25 + 20 vol% AlN

EPS70 + 20 vol% AlN

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 30 vol% AlN

EPS70 + 30 vol% AlN

33


Below it shows in Figure 3.10 the results for EPS25‐4SH and EPS70‐4SH combined a) 10%, b) 20% and

c) 30% of Aluminium Nitride for a temperature of 100oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% AlN at 100oC

b) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 20 vol% AlN at 100oC

c) Healing performance for EPS25‐4SH and EPS70‐

4SH combined with 30 vol% AlN at 100°C

Figure 3.10 Healing performance for EPS25‐4SH and EPS70‐4SH with Aluminium Nitride at 1000 C. Typical uncertainty in the healing fraction is ± 10 %.

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 10 vol% AlN

EPS70 + 10 vol% AlN0

20

40

60

80

100

0 50 100 150 200Healing %

Time [min]


EPS25 + 20 vol% AlN

EPS70 + 20 vol% AlN

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 30 vol% AlN

EPS70 + 30 vol% AlN

34

In Figure 3.11 the results are given for Aluminium Nitride based EPS70‐4SH composite. Except for the

30% specimen at 650C all the specimens experience a high recovery and high recovery rates reaching

complete or near complete recovery.

Figure 3.11 Healing performance for EPS25‐4SH with all the different filler concentrations

In Figure 3.12 the results are given for Aluminium Nitride based EPS70‐4SH composite. All the

specimens experience high recovery rate at the start of the recovery except the 30% at 650C. At

1000C all the specimens reach or almost reach complete recovery while at 650C the recovery seems

to stop before 80% for all the specimens.

Figure 3.12 Healing performance for EPS70‐4SH with all the different filler concentrations

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200

Healing %

Time [min]

EPS25‐4SH with Aluminium Nitride

EPS25 + 10 vol% AlN 65C






0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200

Healing %

Time [min]

EPS70‐4SH with Aluminium Nitride







35

3.2.3 BoronNitrideasfiller

The following graphs represent the results obtained using Boron Nitride as filler content.


Below it shows in Figure 3.13 the results for EPS25‐4SH and EPS70‐4SH combined a) 10%, b) 20%,

c) 30% and d) 40% of Boron Nitride for a temperature of 65oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% BN at 65

oC b) Healing performance for EPS25‐4SH and EPS70‐4SH combined

with 20 vol% BN at 65oC

c) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 30 vol% BN at 65oC

d) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 40 vol% BN at 65oC

Figure 3.13 Healing performance for EPS25‐4SH and EPS70‐4SH with Boron Nitride at 650 C. Typical uncertainty in the healing fraction is ± 10 %.

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

10% Boron Nitride at 65o C

EPS25 + 10 vol% BN

EPS70 + 10 vol% BN0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 20 vol% BN

EPS70 + 20 vol% BN

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 30 vol% BN

EPS70 + 30 vol% BN0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 40 vol% BN

EPS70 + 40 vol% BN

36



c) 30% and d) 40% of Boron Nitride for a temperature of 100oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% BN at 100oC

b) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 20 vol% BN at 100oC

c) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 30 vol% BN at 100

oC

d) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 40 vol% BN at 100

oC

Figure 3.14 Healing performance for EPS25‐4SH and EPS70‐4SH with Boron Nitride at 1000C. Typical uncertainty in the healing fraction is ± 10 %.

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 10 vol% BN

EPS70 + 10 vol% BN

EPS70 + 10 vol% BN H20

20

40

60

80

100

0 50 100 150 200Healing %

Time [min]


EPS25 + 20 vol% BN

EPS25 + 20 vol% BN H2

EPS70 + 20 vol% BN

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 30 vol% BN

EPS70 + 30 vol% BN0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]


EPS25 + 40 vol% BN

EPS70 + 40 vol% BN

37

In Figure 3.15 the results are given for Boron Nitride based EPS25‐4SH composite. Most of the

samples experience a high recovery rate at the start, clearly higher for those at 1000C. Most of them

reach healing values higher than 90% within 80 min. The specimen containing 40% at 65oC recovers

very slow after a recovery of 30%.

Figure 3.15 Healing performance for EPS25‐4SH with all the different filler concentrations. Typical uncertainty in the healing fraction is ± 10 %.

In Figure 3.16 the results are given for Boron Nitride based EPS25‐4SH composite. All the samples

experience a right healing rate before 30 min. The specimen of 40% at 65oC switches to slow

recovery after 20 min and cannot get higher than 20% of recovery. The other specimens reach 70%

or higher after 150 min


0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280

Healing %

Time [min]

EPS25‐4SH with Boron Nitride

EPS25 + 10 vol% BN 65C




EPS25 + 10 vol% BN 100C

EPS25 + 20 vol% BN 100C

EPS25 + 30 vol% BN 100C

EPS25 + 40 vol% BN 100C

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280

Healing %

Time [min]






EPS70 + 10 vol% BN 100C

EPS70 + 20 vol% BN 100C

EPS70 + 30 vol% BN 100C

EPS70 + 40 vol% BN 100C

38

3.2.4 Copperasfiller

The following graphs represent the results obtained for applying Copper as the filler content and

healing under an environment of 65oC or 100oC


Below it shows in Figure 3.17 the results for EPS25‐4SH and EPS70‐4SH combined a) 10%, b) 20 of

Copper for a temperature of 65oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% Cu at 65oC

b) Healing performance for EPS70‐4SH combined with 20 vol% Cu at 65oC

Figure 3.17 Healing performance for EPS25‐4SH and EPS70‐4SH with Copper at 650 C. Typical uncertainty in the healing fraction is ± 10 %.


Below it shows in Figure 3.18 the results for EPS25‐4SH and EPS70‐4SH combined a) 10%, b) 20 of

Copper for a temperature of 100oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% Cu at 100oC

b) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 20 vol% Cu at 100oC

Figure 3.18 Healing performance for EPS25‐4SH and EPS70‐4SH with Copper at 1000 C. Typical uncertainty in the healing fraction is ± 10 %.

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

10% Copper at 65o C

EPS25 + 10 vol% Cu

EPS70 + 10 vol% Cu0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

20% Copper at 65o C

EPS70 + 20 vol% Cu

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

10% Copper at 100o C

EPS25 + 10 vol% Cu

EPS70 + 10 vol% Cu0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

20% Copper at 100o C

EPS25 + 20 vol% Cu

EPS70 + 20 vol% Cu

39

In Figure 3.19 the results are given for all Copper based EPS25‐4SH composite. The healing rate

experienced appear to be very linear. They all reach healing values above 50%.


In Figure 3.20 the results are given for all Copper based EPS25‐4SH composite. They show a fast

healing rate at the beginning and much slower after at 20 min all the specimens seem to stabilize

between 40 and 70 healing percentage.


0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180

Healing %

Time [min]

EPS25 with Copper

EPS25 + 10 vol% Cu 65C

EPS25 + 10 vol% Cu 100C

EPS25 + 20 vol% Cu 100C

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180

Healing %

Time [min]

EPS70 with Copper



EPS70 + 10 vol% Cu 100C

EPS70 + 20 vol% Cu 100C

40

3.2.5 Graphiteasfiller

The following graphs represent the results obtained for applying Graphite as the filler content and

healing under an environment of 65oC or 100oC



c) 30% and d) 40% of Graphite for a temperature of 65oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% Graphite at 65

oC b) Healing performance for EPS25‐4SH and EPS70‐4SH combined

with 20 vol% Graphite at 65oC

c) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 30 vol% Graphite at 65oC

d) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 40 vol% Graphite at 65oC

Figure 3.21 Healing performance for EPS25‐4SH and EPS70‐4SH with Graphite at 650 C. Typical uncertainty in the healing fraction is ±10 %.

0

20

40

60

80

100

0 20 40 60 80 100

Healing %

Time [min]

10% Graphite at 65o C

EPS25 + 10 vol% Graphite

EPS70 + 10 vol% Graphite0

20

40

60

80

100

0 20 40 60 80 100

Healing %

Time [min]




0

20

40

60

80

100

0 20 40 60 80 100

Healing %

Time [min]



EPS25 + 30 vol% Graphite H2EPS70 + 30 vol% GraphiteEPS70 + 30 vol% Graphite H2

0

20

40

60

80

100

0 20 40 60 80 100

Healing %

Time [min]



EPS25 + 40 vol% Graphite H2


EPS70 + 40 vol% Graphite H2

41



c) 30% and d) 40% of Graphite for a temperature of 100oC.

a) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 10 vol% Graphite at 100oC

b) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 20 vol% Graphite at 100oC

c) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 30 vol% Graphite at 100

oC

d) Healing performance for EPS25‐4SH and EPS70‐4SH combined with 40 vol% Graphite at 100

oC

Figure 3.22 Healing performance for EPS25‐4SH and EPS70‐4SH with Graphite at 1000C. Typical uncertainty in the healing fraction is ± 10 %.

0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]




20

40

60

80

100

0 50 100 150 200Healing %

Time [min]




0

20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]




20

40

60

80

100

0 50 100 150 200

Healing %

Time [min]

40% Graphite at 100o CEPS25 + 40 vol% Graphite


42

In Figure 3.23 the results are given for all Graphite based EPS25‐4SH composite. Every specimen

show fast healing rate at the start. Only 40% experience an improvement in healing due to the higher

temperature. Most of the specimens reach healing values higher than 70%.


In Figure 3.24 the results are given for all Graphite based EPS25‐4SH. Every specimen show fast

healing rate at the start as in EPS25‐4SH. Many specimens do not experience an improvement in

healing due to the higher temperature. Specimens containing less than 30% of Graphite reach

healing values higher than 60%.


0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]

EPS25‐4SH with Graphite

EPS25 + 10 vol% Gra 65C








0

20

40

60

80

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]










43

3.2.6 Datasummaryforcohesiontest

In Figure 3.25 all the fillers tested with a volume percentage of 10% are plotted for EPS25‐4SH. All

specimens get high values of healing percentage in a short amount of time making their healing

curve very linear.

Figure 3.25 Healing performance for EPS25‐4SH with all the different fillers at 10 vol% . Typical uncertainty in the healing fraction is ± 10 %.

In Figure 3.26 all the fillers tested with a volume percentage of 20% are plotted for EPS25‐4SH. All

specimens get high values of healing percentage in a short amount of time but not as good with 10%

of filler. Already some fillers exit the fast recovery rate period before reaching 90% and stabilizing

around that value.

Figure 3.26 Healing performance for EPS25‐4SH with all the different fillers at 20 vol%. Typical uncertainty in the healing fraction is ± 10 %.

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]

EPS25‐4SH with 10% filler

EPS25 + 10 vol% Al 65CEPS25 + 10 vol% AlN 65CEPS25 + 10 vol% BN 65CEPS25 + 10 vol% Cu 65CEPS25 + 10 vol% Gra 65CEPS25 + 10 vol% Al 100CEPS25 + 10 vol% AlN 100CEPS25 + 10 vol% BN 100CEPS25 + 10 vol% Cu 100C

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]


EPS25 + 20 vol% Al 65CEPS25 + 20 vol% AlN 65CEPS25 + 20 vol% BN 65CEPS25 + 20 vol% Gra 65CEPS25 + 20 vol% Al 100CEPS25 + 20 vol% AlN 100CEPS25 + 20 vol% BN 100CEPS25 + 20 vol% Cu 100CEPS25 + 20 vol% Gra 100C

44

In Figure 3.27 all the fillers tested with a volume percentage of 30% are plotted for EPS25‐4SH. Most

of the specimens lose a great amount of recovery rate at 30% of filler. Specimens containing Nitride

show better healing than the rest for both temperature settings. Their behaviour for this volume

percentage seem to widen the differences between fillers.


In Figure 3.28 all the fillers tested with a volume percentage of 40% are plotted for EPS25‐4SH. They

all show a fast recovery rate at the start. Boron Nitride shows a good recovery for its filler content.

The differences between fillers are more noticeable.


0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]


EPS25 + 30 vol% Al 65CEPS25 + 30 vol% AlN 65CEPS25 + 30 vol% BN 65CEPS25 + 30 vol% Gra 65CEPS25 + 30 vol% Al 100CEPS25 + 30 vol% AlN 100CEPS25 + 30 vol% BN 100CEPS25 + 30 vol% Gra 100C

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]


EPS25 + 40 vol% Al 65C



EPS25 + 40 vol% Al 100C

EPS25 + 40 vol% BN 100C


45


all show a fast recovery rate at start. Their healing values are spread between 50% and 100% giving

already clear differences between fillers.



all show a fast recovery rate at start. Their healing values are spread between 40% and 100%.

Widening the differences between fillers with respect to 10%.


0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]


EPS70 + 10 vol% Al 65C EPS70 + 10 vol% AlN 65CEPS70 + 10 vol% BN 65C EPS70 + 10 vol% Cu 65CEPS70 + 10 vol% Gra 65C EPS70 + 10 vol% Al 100CEPS70 + 10 vol% AlN 100C EPS70 + 10 vol% BN 100CEPS70 + 10 vol% Cu 100C EPS70 + 10 vol% Gra 100C

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]


EPS70 + 20 vol% Al 65C EPS70 + 20 vol% AlN 65CEPS70 + 20 vol% BN 65C EPS70 + 20 vol% Cu 65CEPS70 + 20 vol% Gra 65C EPS70 + 20 vol% Al 100CEPS70 + 20 vol% AlN 100C EPS70 + 20 vol% BN 100CEPS70 + 20 vol% Cu 100C EPS70 + 20 vol% Gra 100C

46


all show a fast recovery rate at start but entering the slow rate before reaching a healing percentage

of 50%. Their healing values are spread between 20% and 80% for most of them.



all show a fast recovery rate at start but entering the slow rate early in the healing. Their healing

values are spread between 5% and 25% for most of them.


0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]


EPS70 + 30 vol%Al 65CEPS70 + 30 vol%AlN 65CEPS70 + 30 vol%BN 65CEPS70 + 30 vol%Gra 65CEPS70 + 30 vol%Al 100CEPS70 + 30 vol%AlN 100CEPS70 + 30 vol%BN 100CEPS70 + 30 vol%Gra 100C

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Healing %

Time [min]


EPS70 + 40 vol%Al 65C

EPS70 + 40 vol%BN 65C

EPS70 + 40 vol%Gra 65C

EPS70 + 40 vol%Al 100C

EPS70 + 40 vol%BN 100C

EPS70 + 40 vol%Gra 100C

47

3.3 Adhesionrecoveryresults

The results are first presented divided by the type of filler used and later a comparison between

fillers is given. A typical uncertainty of 15% can be applied.

3.3.1 Aluminiumasfiller

The adhesion recovery data for the specimens containing Aluminium as filler divided in two graphics

sorted by the composite matrix EPS25‐4SH and EPS70‐4SH with all volume percentages of

Aluminium, both shown in Figure 3.33 a) for EPS25‐4SH and b) for EPS70‐4SH.

a) Shear stress of EPS25‐4SH with Aluminium for several healing events

b) Shear stress of EPS70‐4SH with Aluminium for several healing events

Figure 3.33 Shear stress for the matrixes a) EPS25‐4SH and b) EPS70‐4SH with all Aluminium filler content. Typical uncertainty in the healing fraction is ±15 %.

3.3.2 AluminiumNitrideasfiller

The adhesion recovery data for the specimens containing Aluminium Nitride as filler divided in two

graphics sorted by the composite matrix EPS25‐4SH and EPS70‐4SH with all volume percentages of

Aluminium Nitride, both shown in Figure 3.34, a) for EPS25‐4SH and b) for EPS70‐4SH.

a) Shear stress of EPS25‐4SH with Aluminium Nitride for several healing events

b) Shear stress of EPS70‐4SH with Aluminium nitride for several healing events

Figure 3.34 Shear stress for the matrixes a) EPS25‐4SH and b) EPS70‐4SH with all Aluminium Nitride filler percentages. Typical uncertainty in the healing fraction is ±15 %.

‐0,5

0,5

1,5

2,5

3,5

0

0,1

0,2

0,3

0 2 4 6 8

Shear strength [Kg/cm

2]

Shear strength [MPa]

Healing events

EPS25‐4SH with Aluminium10%

20%

30%

‐0,5

0,5

1,5

2,5

3,5

0

0,1

0,2

0,3

0 2 4 6 8


2]


Healing events


10%20%30%40%

0

1

2

3

4

5

6

7

0

0,1

0,2

0,3

0,4

0,5

0,6

0 2 4 6 8


2]


Healing events

EPS25‐4SH with Aluminum Nitride

10 vol% Al

20 vol% Al

30 vol% Al

0

1

2

3

4

5

6

7

0

0,1

0,2

0,3

0,4

0,5

0,6

0 2 4 6 8


2]


Healing events

EPS70‐4SH with Aluminum Nitride

10 vol% Al20 vol% Al30 vol% Al

48

3.3.3 BoronNitrideasfiller

The adhesion recovery data for the specimens containing Boron Nitride as filler divided in two

graphics sorted by the composite matrix EPS25‐4SH and EPS70‐4SH with all volume percentages of

Boron Nitride, both shown in Figure 3.35, a) for EPS25‐4SH and b) for EPS70‐4SH.

a) Shear stress of EPS25‐4SH with Boron Nitride for several healing events

b) Shear stress of EPS70‐4SH with Boron Nitride for several healing events

Figure 3.35 Shear stress for the matrixes a) EPS25‐4SH and b) EPS70‐4SH with all Boron Nitride filler content. Typical uncertainty in the healing fraction is ±15 %.

3.3.4 Copperasfiller

The adhesion recovery data for the specimens containing Copper as filler divided in two graphics

sorted by the composite matrix EPS25‐4SH and EPS70‐4SH with all volume percentages of Copper,

both shown in Figure 3.36, a) for EPS25‐4SH and b) for EPS70‐4SH.

0

1

2

3

4

5

6

7

0

0,1

0,2

0,3

0,4

0,5

0,6

0 2 4 6 8


2]


Healing events


10%

20%

30%

40%

0

1

2

3

4

5

6

7

0

0,1

0,2

0,3

0,4

0,5

0,6

0 2 4 6 8


2]


Healing events


10%

20%

30%

40%

49

a) Shear stress of EPS25‐4SH with Copper for several healing events

b) Shear stress of EPS70‐4SH with Copper for several healing events

Figure 3.36 Shear stress for the matrixes a) EPS25‐4SH and b) EPS70‐4SH with all Copper filler content. Typical uncertainty in the healing fraction is ±15 %.

3.3.5 Graphiteasfiller

The adhesion recovery data for the specimens containing Graphite as filler divided in two graphics

sorted by the composite matrix EPS25‐4SH and EPS70‐4SH with all volume percentages of Graphite,

both shown in Figure 3.37, a) for EPS25‐4SH and b) for EPS70‐4SH.

a) Shear stress of EPS25‐4SH with Graphite for several healing events

b) Shear stress of EPS70‐4SH with Graphite for several healing events

Figure 3.37 Shear stress for the matrixes a) EPS25‐4SH and b) EPS70‐4SH with all Graphite filler content. Typical uncertainty in the healing fraction is ±15 %.

0

0,5

1

1,5

2

2,5

0

0,05

0,1

0,15

0,2

0 2 4 6 8


2]


Healing events

EPS25‐4SH with Copper

10%

20%0

0,5

1

1,5

2

2,5

0

0,05

0,1

0,15

0,2

0 2 4 6 8


2]


Healing events

EPS70‐4SH with Copper

10%l

20%

0

1

2

3

4

5

6

7

0

0,1

0,2

0,3

0,4

0,5

0,6

0 2 4 6 8 10


2]


Adhesion Cycle


2.5%

3.5%

5%

7.5%

10%

20%

30%

40%

0

1

2

3

4

5

6

7

0

0,1

0,2

0,3

0,4

0,5

0,6

0 2 4 6 8 10


2]


Adhesion Cycle


2.5%

3.55%

5%

7.5%

10%

20%

30%

40%

50

3.3.6 Datasummaryforadhesiontest

In Figure 3.38 the adhesion shear strength of fillers tested are plotted for EPS25‐4SH. It shows how

the values for metallic fillers go down linearly while the other fillers experience an improvement at

start resulting on a maximum.

Figure 3.38 Shear strength of EPS25‐4SH with several fillers

In Figure 3.39 the adhesion shear strength of fillers tested are plotted for EPS70‐4SH. It shows how

the values for metallic fillers go down linearly while the other fillers experience an improvement at

start resulting on a maximum. The position of this maximum is spread in a larger range than in

EPS25‐4SH.

Figure 3.39 Shear strength of EPS70‐4SH with several fillers

0

1

2

3

4

5

6

0

0,1

0,2

0,3

0,4

0,5

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%


2]

Shear strength [Mpa]

Filler volume precentage

EPS25‐4SH with FillerAluminiumAlNBNCopperDiamond

0

1

2

3

4

5

6

0

0,1

0,2

0,3

0,4

0,5

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%


2]

Shear strength [Mpa]

Filler volume percentage

EPS70‐4SH with fillerAluminium

AlN

BN

Copper

51

3.4 Thermalconductionresults

The next results were found for the thermal test. First the data for the specimens prepared without

alignment are presented. Then the aligned specimens are presented together with the results of the

not aligned specimens for comparison. Typical uncertainty is ± 1%.

3.4.1 Specimenwithnon—alignedparticles

In Figure 3.40 the thermal conductivity is given for EPS25‐4SH and EPS70‐4SH with all the volume

percentages for the fillers: a) Aluminium b).Aluminium Nitride c) Boron Nitride d) Copper .e) Graphite

a) Thermal conductivity for EPS25‐4SH containing Aluminium b) Thermal conductivity for EPS25‐4SH containing Aluminium Nitride

c) Thermal conductivity for EPS25‐4SH containing Boron Nitride

d) Thermal conductivity for EPS25‐4SH containing Copper

e) Thermal conductivity for EPS25‐4SH containing Graphite

Figure 3.40 Thermal conductivity for EPS25‐4SH and EPS0‐4SH with the fillers: a) Aluminium b).Aluminium Nitride c) Boron Nitride d) Copper e) Graphite. Typical uncertainty is ± 1%.

‐0,5

0,5

1,5

2,5

0 10 20 30 40

Therm

al conductivity

[W/m

K]

Volume percentage of filler

Aluminium

EPS25

EPS70

‐0,5

0,5

1,5

2,5

0 10 20 30 40Th

erm

al conductivity

[W/m

K]


Aluminium Nitride

EPS25

EPS70

‐0,5

0,5

1,5

2,5

0 10 20 30 40

Therm

al conductivity

[W/m

K]


Boron Nitride

EPS25

EPS70

‐0,5

0,5

1,5

2,5

0 10 20 30 40Therm

al conductivity

[W/m

K]


Copper

EPS25

EPS70

0

0,5

1

1,5

2

2,5

0 10 20 30 40Therm

al conductivity

[W/m

K]

Volume percentage of graphite

Graphite

EPS25

EPS70

52

In Figure 3.41 the thermal conductivity for EPS25‐4SH is plotted for all the different filler used. All the

specimens experience a linear increase in thermal conduction The different between filler seem to

remain constant under 40%. At 40% Graphite experiences a major improvement compared to

previous volume fractions.

Figure 3.41 Thermal conductivity for EPS25‐4SH and filler. Typical uncertainty is ± 1%.

In Figure 3.42 the thermal conductivity for EPS70‐4SH is plotted for all the different filler used. All the

specimens experience a linear increase in thermal conduction. For this matrix the difference between

fillers increase after 20% where Graphite improves faster than the other fillers.

Figure 3.42 Thermal conductivity for EPS70‐4SH and filler. Typical uncertainty is ± 1%.

0

0,5

1

1,5

2

2,5

0 10 20 30 40

Therm

al conductivity [W/m

K]

Volume percentage of Filler

Thioplast EPS25

Graphite

Boron Nitride

Copper

Aluminium Nitride

Aluminium

0

0,5

1

1,5

2

2,5

0 5 10 15 20 25 30 35 40

Therm

al conductivity [W/m

K]

Volume percentage of Filler

Thioplast EPS70

Graphite

Boron Nitride

Copper

Aluminium Nitride

Aluminium

53

3.4.2 Comparisonbetweenalignedandnon‐alignedsamples

The data for the aligned specimens, processed using DEP, are plotted below together with their not‐

aligned version for comparison. In Figure 3.43 he thermal conductivity for EPS25 is plotted for:

a) Graphite aligned and not aligned and b) Boron nitride aligned and non‐aligned.

From both graphs the difference between alignment gets noticeable after a high amount of filler.

Larger amount that 20% for Graphite and 30% for Boron Nitride.

a) Thermal conductivity for EPS25‐4SH and Graphite, aligned and not‐aligned

b) Thermal conductivity for EPS25‐4SH and Boron Nitride, aligned and not‐aligned

Figure 3.43 Thermal conductivity for EPS25‐4SH with the fillers: a) Graphite b)) Boron Nitride. Typical uncertainty is ± 1%.

0

0,5

1

1,5

2

2,5

0 10 20 30 40

Therm

al conductivity

[W/m

K]


Aligning Graphite

EPS25

EPS25 aligned

0

0,5

1

1,5

2

2,5

0 10 20 30 40

Therm

al conductivity

[W/m

K]


Aligning Boron Nitride

EPS25

EPS25 aligned

54

3.4.3 SEManalysisofparticulatecomposites/particles

The results, obtained using the Scanning Electron Microscope, are presented below. These results

consist on surface images, Energy dispersion spectroscopy data and images and, weight analysis

using the atom count from the energy dispersion spectroscopy.

In Figure 3.44 the surface images for EPS25‐4SH containing not aligned Graphite for 40% of the

specimen volume is presented.

Figure 3.44 SEM for EPS25‐4SH with 40vol% Graphite‐Not Aligned. a) 60x b)200x c) 500x

In Figure 3.45 the energy dispersion spectroscopy electron count and surface mapping images for

EPS25‐4SH containing not aligned Graphite for 40% of the specimen volume is presented.

Figure 3.45 Energy Dispersion Spectroscopy for EPS25‐4SH with 40vol% Graphite‐Not Aligned. Left: element detection. Right: element mapping (Grey picture, CK Carbon, OK Oxygen, SK sulphur).

55

In Table 3.1 the weight analysis using the atom count from the energy dispersion spectroscopy SEM

for EPS25‐4SH with 40vol% Graphite, not aligned, is presented.

Element

Line

Net

Counts

Net Counts

Error

Weight %

Weight %

Error

Atom %

Atom %

Error

C K 69879 +/‐ 323 84.12 +/‐ 0.39 90.66 +/‐ 0.42

O K 7143 +/‐ 185 7.22 +/‐ 0.19 5.85 +/‐ 0.15

S K 73169 +/‐ 452 8.66 +/‐ 0.05 3.50 +/‐ 0.02

S L 0 +/‐ 58 ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐

Total 100.00 100.00

Table 3.1 Energy Dispersion Spectroscopy weight analysis for EPS25‐4SH containing not aligned 40 vol% Graphite

In Figure 3.46 and Figure 3.47 the surface images for EPS25‐4SH containing Graphite for 40% of the

specimen volume is presented.

Figure 3.46 SEM for EPS25‐4SH with 40 vol% of Graphite‐Aligned. a) 500x b) 2500x c) 10000x

(c)

56

Figure 3.47 SEM for EPS25‐4SH with 40 vol% of Graphite‐Aligned. a) 500x b) 2500x c) 2500x


EPS25‐4SH containing aligned Graphite for 40% of the specimen volume is presented.

Figure 3.48 Energy Dispersion Spectroscopy for EPS25‐4SH with 40 vol% Graphite aligned‐Aligned. Left: element detection. Right: element mapping (Grey: picture, SK: Sulphur, CK: Carbon, Au M: Gold, OK: Oxygen)

In Table 3.2the weight analysis using the atom count from the energy dispersion spectroscopy SEM

for EPS25‐4SH with 40 vol% Graphite, aligned, is presented.

Element

Line

Net

Counts

Net Counts

Error

Weight %

Weight %

Error

Atom %

Atom %

Error

C K 12658 +/‐ 118 67.05 +/‐ 0.63 94.28 +/‐ 0.88

O K 388 +/‐ 77 1.40 +/‐ 0.28 1.48 +/‐ 0.29

S K 676 +/‐ 131 3.48 +/‐ 0.67 1.83 +/‐ 0.36

S L 0 +/‐ 13 ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐

Au M 3975 +/‐ 188 28.07 +/‐ 1.33 2.41 +/‐ 0.11

Total 100.00 100.00

Table 3.2 Energy Dispersion Spectroscopy weight analysis for EPS25‐4SH containing 40 vol% Graphite‐Aligned

57

In Figure 3.49 the surface images for EPS25‐4SH containing Boron Nitride for 20% of the specimen

volume is presented.

Figure 3.49 SEM for EPS25‐4SH with 20 vol% of Boron Nitride. a) 60x b) 500x c) 3000x d) 10000x e) 30000x f) 50000x

In Figure 3.50 the surface images for EPS25‐4SH containing Boron Nitride for 300% of the specimen

volume is presented.

Figure 3.50 SEM for EPS25‐4SH with 30 vol% of Boron Nitride‐Aligned. a) 1000x b) 2000x c) 2000x

58


EPS25‐4SH containing aligned Boron Nitride for 40% of the specimen volume is presented.

Figure 3.51 Energy Dispersion Spectroscopy for EPS25‐4SH with 40% Boron Nitride‐Aligned. Left: element detection. Right: element mapping (Grey: picture, NK: Nitrogen, BK: Boron, CK: Carbon, OK: Oxygen, SK: Sulphur, Au M: Gold).

In Table 3.3 the weight analysis using the atom count from the energy dispersion spectroscopy SEM

for EPS25‐4SH with 40vol% Boron Nitride, aligned, is presented.

Element

Line

Net

Counts

Net Counts

Error

Weight %

Weight %

Error

Atom %

Atom %

Error

B K 2858 +/‐ 116 15.79 +/‐ 0.64 20.80 +/‐ 0.84

C K 16359 +/‐ 243 31.85 +/‐ 0.47 37.75 +/‐ 0.56

N K 14934 +/‐ 240 30.69 +/‐ 0.49 31.20 +/‐ 0.50

O K 8084 +/‐ 259 6.73 +/‐ 0.22 5.99 +/‐ 0.19

S K 44198 +/‐ 480 8.54 +/‐ 0.09 3.80 +/‐ 0.04

S L 0 +/‐ 68 ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐

Au L 0 0 ‐‐‐ ‐‐‐ ‐‐‐ ‐‐‐

Au M 21718 +/‐ 600 6.40 +/‐ 0.18 0.46 +/‐ 0.01

Total 100.00 100.00

Table 3.3 Energy Dispersion Spectroscopy weight analysis for EPS25‐4SH containing 40 vol% Boron Nitride

59

4 Discussion

In this chapter the results given in chapter 3 are treated and discussed. First the results concerning

the cohesion ability of the specimens are discussed followed by the adhesion ability and the thermal

conductivity of the composed materials. The chapter ends with a valorisation and characterization of

the different types of composites tested giving a tool with the purpose of identifying the most

suitable composite for the engineering needs of the application.

4.1 Cohesionrecovery

In this subsection the results of the cohesion recovery from section 3.1 are treated. The cohesion

recovery is directly related to healing of the damage by reconnecting the surfaces forming the crack.

In this section first the reproducibility of the test is discussed, and then the theories concerning the

cohesion recovery are presented, starting by discussing the influence of the matrix, the influence of

the filler and finishing with a discussion of the temperature dependence during healing.

Reproducibility

As explained in section 2.3, the data of the cohesion results were obtained from images taken using a

microscope equipped with a digital camera. To translate al the analog data to digital data the

distance between surfaces was measured and compared to its original aperture at 0 min. With this

100% healing means that the cut has closed completely. The values obtained are plotted according to

the healing time: the time they were in the oven.

There are several points that should be mentioned before discussing the results which may affect the

reliability of the data obtained. These points may affect the reproducibility of the tests.

‐ The thickness of the samples: All the samples do start with an approximate thickness of 1

mm. During the test, pressure was applied to the samples by the clamps in thickness

direction reducing this thickness during the test, the lower the viscosity the thinner the

thickness of the specimen at the end. This increase of flow helps to seal the damaged area

before healing can take place.

‐ The removal of the microscope slide: To successfully take an image of the sample the

microscope slide from the top side was removed, since the microscope light was reflected

disturbing the imaging. While removing the slide stresses are applied to the specimen. While

this procedure was carefully made it may produce separation of the healing surfaces as it

bonded on the slide. Separation of the surfaces by removing the microscope slide negatively

affects in sample performance. Saying this, the sample of copper for 20 volume percentage

was lost this way several times as it bonded too well on the slide to be removed.

‐ Sharpness and light of the image: While recording the image the image should be sharp and

well lit to distinguish the scission and its edges. Shadows make it difficult to locate the

scission edge. The sharpness of the image was also found to be a variable for the location of

the surface edges. Focusing the image was done by moving the sample closer or further

away from the camera. The edge of the scission has to be at the exact same distance from

the camera for every sample since the further the sample the smaller the perceived damage.

60

‐ Damage of samples: The initial damage of the samples was all reproduced using the same

cutting edge but the cuts procedure yielded samples containing more or less damage to heal

with respect to each other. This difference in damage is not reflected in the results. Two

samples completely healed after the same amount of time are seeing as having the same

healing properties.

‐ Surface contact: The contact of the surface is a must for self‐healing, especially if the material

has poor flow properties. Surfaces that were found to separate during test and not coming

into contact where not count in the results. The cut inflicted into the material was cross

formed counting 4 pair of surfaces that can move and heal at their own individual rate. This

can be seen as 4 healing reading per sample.

‐ The number of tests: Due to the short time to do all the tests, each test was performed only

once for most of the samples. Normally a number of tests of the same sample is always

desired to filter errors as much as possible.

Influence of the matrix

From the figures in section 3.2 it can be deduced that the healing depends on the matrix, the filler

content, the temperature of the environment, the type of filler and the time spent in the oven.

‐ In sections 3.2.1‐3.2.5 it can be observed that EPS25‐4SH has generally better healing recovery

properties than EPS70‐4SH. Many of the EPS25‐4SH samples manage to completely heal, as shown in

Figure 4.1. On the other hand many of the EPS70‐4SH do not completely heal leaving a scar, as

shown in Figure 4.2, or not heal at all if there was no connection between surfaces. There is healing,

but not enough flow for the material to move and seal the damage completely in most of the EPS70

cases.

Figure 4.1 EPS25‐4SH‐10%BN‐130min Figure 4.2 EPS70‐4SH‐10%BN‐190min

From the molecular architecture of the Thioplast [33, 34] EPS25‐4SH and EPS70, as shown in Figure

2.1, it is expected that EPS25‐4SH, to have better self‐healing properties than EPS70‐4SH. This can be

explained by two factors concerning their chemistry; their chain flexibility and their glass transition

temperature Tg.

61

Chain flexibility factor: Looking at the chemistry of the EPS25 [33] and EPS70 [34], shown Figure 2.1,

it can be seen that EPS70 is composed of molecules with a high aromatic content while EPS25 is

compose by aliphatic molecules. The aromatic rings of the EPS70 increases the chain rigidity and

reduces the flexibility of the chains and subsequently of the entire material, as compared to EPS25.

The molecular rigidity and flexibility will affect the viscosity of the compound [35]. The healing of the

material will happen when surfaces meet giving the chance to the molecules to entangle or

reconnect as shown in Figure 1.8 [12]. The less flexible the chains are the more difficult it will be for

the healing to take place since viscosity is a key parameter in healing. These factors make the EPS25

more favourable for self‐healing than EPS70.

Glass transition temperature factor: Viscosity deals with the relative displacement of entire chains

along each other and only occurs at high temperatures and in no‐crosslinked systems.

For the slightly crosslinked systems explored here, the glass transition temperature, which gives the

minimum temperature for substantial motion of molecular segments may be more relevant [26, 36].

The DSC measurements yielded Tg of ‐400 C and ‐100 C for EPS25‐4SH and EPS70‐4SH materials

respectively. See section 3.1. Clearly The EPS70 has the higher Tg. Heat temperatures of 650 C and

1000 C are well above the respective Tg values.

The difference for EPS25, being lower than for EPS70, contributes to the faster healing for EPS25 at

the fixed healing temperatures.

Influence of the filler

It is also expected that the rigidity of the compound to be significantly affected by the type and

volume fraction. Fillers are basically impurities added into a compound, making the compound the

matrix and the impurity the filler. A filler particle, or conglomerate, will obstruct a matrix molecule

from moving and making bonds to other molecules that are in the other side. If it happens that the

other side forms part of the damage surface, it will prevent healing to take place on that part of the

surface. Hanneman et al. [37] studied the influence of alumina fillers on unsaturated polyester resin

polymer matrix with results increasing viscously along with the amount of filler.

It should be pointed out that the filler particles themselves have no healing ability, given their

covalent or metallic bond structure and the healing temperatures well below the particulate

materials melting temperatures.

Environment influence

‐ From section 3.2.6 the differences between tested fillers can be observed in Figure 3.25 to Figure

3.28 It can be seen that graphite tends to be the best in EPS25 for 650C. While for 1000C Boron

Nitride and Aluminum Nitride performances increases substantially compared to the rest.

Graphite and Aluminum Nitride seem to be negatively affected by the increase of temperature for

less than 30 volume percentage.

‐ From Figure 3.29 to Figure 3.32 it can be observed that graphite is a good choice for a temperature

of 650C. Aluminum Nitride has a good performance compared to 10 and 20 volume percentages.

Increasing the temperature to 1000C seems to increase the recovery of Boron Nitride significantly,

especially at 30 volume percentage. Graphite is in EPS70 also negatively affected under 30 volume

percentage.

62

It can be concluded that from this section that EPS25‐4SH is the best choice when looking at the

cohesion recovery, this was found when looking at the matrix, the type of filler and environment

applied. It was also found that the results obtained are not completely reproducible, especially when

only one sample per material configuration was tested. More tests should be made with the same

material configuration to filter errors and increase its reliability.

4.2 Adhesionrecovery

The results of the adhesion test shown in section 3.3 were conducted using the procedure explained

in section 2.4.

In this section, the influence of amount of filler on adhesion will be discussed first. The reliability of

the bond will be next mentioned followed by the influence of time and the type of filler.

It can be seen from all figures from section 3.3, Figure 3.33 to Figure 3.39 that the adhesion

properties of the composite does change with the amount of filler used. As a compound gets more

filler inside, decreasing the amount of matrix per volume percentage, a decrease of the adhesive

properties was expected for all the specimens

In Table 4.1 lap shear strength of several materials is given to compare how well the synthesized

materials perform with respect to commercial TIM’s.

LapshearStrength[MPa]

ThermallyConductiveAdhesives(TechFilm®) 7‐28

EPS70‐4SH10%Al 0.1142

EPS70‐4SH20%AlN 0.544

EPS70‐4SH30%BN 0.517

EPS70‐4SH20%Cu 0.138

EPS70‐4SH20%Graphite 0.51

EPS25‐4SH10%Al 0.2078

EPS25‐4SH20%AlN 0.276

EPS25‐4SH30%BN 0.484

EPS25‐4SH10%Cu 0.199

EPS25‐4SH30%Graphite 0.39

Pads(Chomerics®) 0.3‐1.1

Tapes(Chomerics®) 0.1‐1.1

Table 4.1 Lap shear strength of several commercial TIM’s and best synthesized specimens per filler

63

‐ It can be observed that for some of the fillers that the adhesive property of the compound

increased with the volume percentage of the material up to an optimum where it decreased again,

best seen in Figure 3.38 and Figure 3.39.

To explain why this phenomenon occurs, two possible approaches are exposed for discussion.

Stiffness approach: The fillers used for the experiments are stiff and hard compared to the polymer

matrix. These are metals, semimetals or ceramic particles. Filler particles move within the matrix and

along with the flow of the matrix but they do not deform. Clusters of particle do deform but not the

particles. Fillers and clusters made of the filler material do block and impede the matrix to flow and

interact with the material at the other side, reducing the kinetic energy of the material. This means

increasing the viscosity. The matrix responds to the fillers positively increasing the viscosity [38] and

sequentially the rigidity of the overall compound. Rigidity of the material can increase the adhesive

property of the composite until a maximum optimum is reached and it decreases from that point

downwards. The form of the particle, their cluster formation affinity and the bonding strength

between filler and matrix can explain to understand why some filler did get better while increasing

the amount of fillers while other fillers were weaken from the start.

With this said it should be noted that from the fillers used only the non‐metallic fillers did get an

improvement and a maximum in their adhesive properties.

Figure 4.3 Effect of stiffness on adhesive bonds

More rigidity to a soft material can mean that the stresses going thru the material are better

distributed within the material becoming less concentrated at the edges where adhesives are the

weakest as represented in Figure 4.3. From Figure 4.3 it can be deduced that the deformation caused

by the soft material approaches mode I seen in Figure 4.4. Mode I is the weakest mode of the

material increasing the chances for failure of the bond between the adhesive and the adherent.

Different forms of failure between adhesive and adherent are presented in Figure 4.5. After a certain

amount of filler the adhesion of the specimen fails because it is too stiff and the stresses concentrate

again at the edges instead of evenly distributed through the entire adhesive causing a Mode II

failure. This could explain the maximum in the shear strength.

It was noted that the failures of the specimens where Adhesive or interfacial failures as seen in

Figure 4.5 in the right‐up corner and adhesive or interfacial failure with jumping fractures as seen in

Figure 4.5 in the button‐center position. This makes the bonding between the adhesive and the

adherent surface the weakness in the specimen.

64

Figure 4.4 Stress modes on a adhesive juncture

Figure 4.5 Failure modes of adhesives

Filler interaction approach: For this approach we look at the fact how the filler added into the matrix

will bond with the matrix and how this may affect the macroscopic adhesion. The strength of the

bonds, the type of bonds and the amount of bonds depend on the type of filler added. When looking

at the surface of the specimen to be bonded to the adherent it can be found that the surface has a

population of filler in it. This filler has an effect on the matrix in contact with him and the adherent as

schematized in Figure 4.6. Different cases could be resulting from the presence of the filler.

Case where the filler has a good bonding affinity with the matrix taking all the free radical of

the matrix around him for itself leaving none near him to interact with the adherent

surface.

Case where the matrix does have poor affinity with the matrix. The filler only takes a few

bonds and the rest are free to interact with the adherent surface.

Case where the filler does have good bonding affinity with the adherent surface and make

some kind of non‐covalent bonding with it, h‐bonding, ionic bonding, etc., reducing the

bonds that the matrix can make with the adherent around the filler. This can have positive

or negative effect depending on the strength of the bonds between the filler and the

adherent surface compared to the bonds between the matrix and the adherent surface as.

Case where the filler has bad bonding affinity, or even experiencing repulsion, giving more

chances for the matrix to create more bonds in the area directly around the filler.

Figure 4.6 Filler‐surface iteration representation

65

These theoretical cases may help understand why the metallic fillers were significantly worse

compared to the non‐metallic and semi‐metallic fillers. Metallic materials do not really like to make

bonds with new surfaces unless greatly heated up or being affected by a magnetic field.

‐ From the results exposed in Figure 3.33 to Figure 3.37 it can be seen that there is a good recovery of

the adhesion strength for all the fillers. In other words, the adhesion strength remains around the

same value for every made cycle. This behavior in favorable is cases where the adhesive separates

from the surfaces and has the opportunity to re‐bond with the surface, as long as there is contact

and sufficient time. This means that the adhesion recovery can heal up to a 100% of its original

strength. From this observation it follows that there is no need to increase the temperature of the

recovery periods to values above 650 C, unless healing time is critical.

‐ From the same figures last mentioned it can observed that most specimens experiences peak in

their 4th testing period. This is caused by a longer resting period between tests since not all the tests

were able to be performed at the same day. This demonstrate that time is a variable affecting

adhesion. When the adhesive and the adherent surface have contact bonds between both

substances start to create. When they first meet there is a fixating time where adhesive and

adherent sticks to each other but then time is needed to strengthen the bonding by increasing the

number of bonds during time. Akabori et al. [39] researched the effect time on the strength of an

adhesive concluding that times is indeed a variable for bonding strength.

‐ From section 3.3.6 looking at Figure 3.38 and Figure 3.39 the difference between fillers in bonding

strength can be observed. Graphite can be seen as the best filler overall. Boron‐Nitride exposed very

good results for its 30% compound. A big difference between these two fillers is that graphite is

electric conductor while Boron Nitride acts as an insulator. This makes both fillers a good choice to

make depending on the desired electrical conductivity of the TIM. Aluminum Nitride also yielded

good values when combined with EPS25 at 20 volume percentage.

It can be concluded when looking at these results that the EPS70‐4SH matrix is the best choice when

comparing the adhesive strengths, both from a relative and absolute perspective.

66

4.3 Thermalconduction

In section 2.5 the test for thermal conductivity is explained and in section 3.4.1 the thermal

conduction can be observed.

In Table 4.2 thermal conductivity of several materials is given to compare how well the synthesized

materials perform with respect to commercial TIM’s.

TIM’s(Source) Thermalconductivity[W/mK]

ThermallyConductiveAdhesives(TechFilm®) 0.31‐2.63

EPS70‐4SH‐40%Al 1.447

EPS70‐4SH‐30%AlN 0.952

EPS70‐4SH‐40%BN 1.191

EPS70‐4SH‐20%Copper 0.883

EPS70‐4SH‐40%Graphite 2.215

EPS25‐4SH‐40%Al 1.456

EPS25‐4SH‐30%AlN 0.919

EPS25‐4SH‐40%BN(aligned) 1.318

EPS25‐4SH‐20%Copper 0.793

EPS25‐4SHGraphite(aligned) 2.35

Pads(Chonerics®) 0.6‐0.7

Tapes(Chomerics®) 0.35‐0.4

Table 4.2 Thermal conductivity of several commercial TIM’s and best synthesized specimens per filler

First, the results for the specimens that were not aligned are discussed followed by the discussion of

the aligned specimens together with the SEM discussion

Discussion about the specimens not aligned

‐ As the value of the volume percentage of the filler increases it is expected to obtain higher

conductivity as seen in Figure 3.40. This is because the filler is a thermal conductor. Thermal

conduction refers to the easy of which the heat is transferred through the material. The more

67

material is present in the specimens that make the heat transfers easier, compared to a specimen

composed to only of Thioplast and a Crosslinker, the higher the thermal conductivity.

‐ Also from Figure 3.41 and Figure 3.42 it can be seen that EPS70‐4SH performs better than EPS25‐

4SH for all the fillers and their volume percentages.

‐ The difference in thermal conductivity between filler used can be seen in Figure 3.41 and Figure

3.42, from these graph it can be said that graphite is the best filler for thermal conductivity at high

volume percentages. Looking at Table 4.3 it can be seen that graphite has superior thermal

conductivity compared to the other fillers.

Material(Source) Thermalconductivity[W/mk]

Aluminum 237 (120‐180 alloys)

AluminumNitride 285

BoronNitride 600║; 30 ┴

Copper 401

Graphite 200–2000║; 2–800┴

Table 4.3 Thermal conductivity of the used fillers [40]

Discussion about the aligned specimens

To test if the filler particles could be aligned and if they would improve the conductivity of the

material materials where synthesized using dielectrophoresis. See section 1.3

From Figure 3.43 it can be seen that the dielectrophoresis did make a difference but only at higher

volume fractions. At low volume fractions the effect of the redistribution in average particle

separation distance in the direction of heat flow is too small to have a measurable effect.

Dielectrophoresis was used with the intention to not only orientate the particles to facilitate the

conductivity but also to try and cluster the particles in a way to facilitate the conduction as seen in

Figure 4.7.

Figure 4.7 Particle distribution a) Normally distributed b) Distribution after DEP

68

To find out if there was an alignment SEM pictures were conducted to see if there was cluster

formations.

‐ In section 3.4.3 the results from the SEM are given. First the results of a not aligned graphite sample

were given for comparison against aligned samples. A normal surface scanning image is given in

Figure 3.44. Disperse spectroscopy was performed to try to differentiate the different regions

composed by the matrix polymer and the filler, in this case graphite. Figure 3.45 gives the number of

hits obtaining for each element on the left side while the right side plots the founded elements in the

scanned surface.

It can be deduced form these images that the graphite is well dispersed through the matrix. Knowing

the amount of molecules in the surface the SEM gives as an approximation of the amount of each

founded elements. This can be found in Table 3.1. These values give an idea of the composition of

the surface in that particular area.

The next results given are from another graphite sample, the surface was polished to facilitate the

detection of particles and their alignment if any. This was done since the previous results were not

clear to differentiate what was polymer and what the graphite. Figure 3.46 and Figure 3.47 give

images for Graphite with 40%. Looking at then no clear particle alignment is spotted. Energy

Dispersion X‐Ray Spectroscopy was performed using low energy, up to around 5eV shown in Figure

3.48 left side. This was done to not to penetrate the surface much during the scanning as it. The

element distribution found can be seen from Figure 3.48 right side. No clear alignment was found

from the images taken and the data obtained. The volume percentage obtained from the SEM for

this specimen was given on Table 3.2.

Next the results for Boron Nitride were given. First images for 20% and 30% of Boron Nitride are

given followed by the Dispersion Spectroscopy for the 40%. A table of the volume percentages found

for the 40 volume percentage of Boron is also given, Table 3.3.

From the above results no alignment was spotted for Boron Nitride same as with graphite.

No particle separation or clusters were spotted. While looking for particles it was clear that they

were smaller than 1 µm, maybe in the nanometer range, when they were supposed to be around

5µm. This makes DEP difficult as mentioned in section 1.3.

Even so it was found that DEP worked and the results show an increase of thermal conductivity as

seen in Figure 3.43. Two theories could be applied here.

Particle rotation: It could be that the particles were not able to translate through the medium, in our

case the polymer matrix. Either the DEP force was too weak to produce translation or the viscosity

was too high. The increased effect can be explained in this situation by the rotation of the particles.

Some particles might have been rotated in line with the current [30].

Alignment in the surface: It has been observed that the surface of the specimens may contain less

filler content that the rest of the material. DEP might just affect the alignment on the surface by

bringing more filler content to the surface area. This facilitates the entrance and exit of the thermal

energy.

Various models have been tried and tested on different researches for thermal conductivity for best

fitting on their results i.e. Wattanakul et al. [41].For future work it would be a good idea to try model

69

the results obtained for our specimens. It was found that the Lewis–Nielsen model, equation (4.1),

has good predictability for thermal conductivity models [41]. This model however does utilize the

size of the particles. After looking at the SEM results it was concluded that the graphite morphology

differenced from the TEM results. Its particle size was greatly reduced during the specimen

preparation. Investigation is necessary on the mixed particle sizes. This is for modelling preparation.

11

(4.1)

where A and B are geometry factors, is the volume fraction of filler, is the maximum packing

density of filler, is the thermal conductivity of the polymer and k the thermal conductivity of the

compound.

It can be concluded that the Graphite makes the best filler when focusing on the thermal

conductivity. It can also be said that the alignment was favorable for amounts of 30% and higher. It is

recommended to model the thermal conductivity of aligned particulate composites to determine

requested degrees of alignment for substantial improvements in thermal conduction.

4.4 Valorizationstrategy

In this chapter a strategy is proposed to help choosing a compound between the many tested. To this

end all the results are handled to try to find an acceptable formula for their efficiency to

accommodate all the variations and give an idea of how well that material will perform compared

between the self‐made specimens.

The experiments conducted in the material were those that heals find out the characteristics of the

material according to the goal of the experiment. The goal of the experiment was to create a good

composite adhesive material to be used a thermal interface material while at the same time

containing self–healing and thermal conductivity properties. This means that the three major factors

involve to choose our material is their performance in:

Self‐sealing for their self‐healing property

Thermal conduction for their thermal conductivity property

The adhesive strength for their adhesive functionality.

The variations on the specimens will determine the variables for the quantification of the different

materials. These are:

The type of filler used: Aluminium, Aluminium Nitride, Boron Nitride, Copper and Graphite

The type of Matrix: EPS70‐4SH and EPS25‐4SH.

The volume percentage of filler present in the matrix

The alignment of the particles

These variables determine Table 4.4 shown below.

70

Form the results the efficiency determination of each specimen according to their three required

functionalities are explained.

Cohesion recovery efficiency: To obtain the efficiency of for the cohesion recovery, which represents

the self‐healing of the material, the next formula was used, see equation (4.2)

% (4.2)

Where H% is the healing recovery founded at the maximum exposed time te and tmin is the minimum

time a specimen was healed or the time the best specimen was healed.

Something to note is that this particular efficiency is not the real efficiency for all the materials

were % 100%. Since the healing is not linear, but is forms a curve when plotted see section 3.2,

it greatly depends on the when the test was stopped before complete healing. If the healing of a

specimen shows a healing recovery curve similar to a logarithmic function the end value of the

efficiency will become really low at % of 100 while the efficiency found will be much bigger.

Because of the above mentioned this efficiency should be taken as a soft referential value.

Adhesion recovery efficiency: To obtain this efficiency from the results the adhesive strength of all

the specimens was normalized against the largest value found. This was the case for EPS70‐4 SH with

20% volume percentage of Aluminium Nitride, its value will be called ∗ . The next formula,

equation (4.3) was used to calculate the efficiency for all the specimens:

∗ (4.3)

Where is the maximum adhesion strength for the current specimen.

Thermal recovery efficiency: To obtain these efficiency their founded conductivity values, k, where

normalized against the best value found, . This value belongs to EPS25‐4HS + 40vol% of

Graphite. This makes the formula:

(4.4)

71

All these efficiencies are separately shown on Table 4.4.

EPS25 EPS70

10% 20% 30% 40% 10% 20% 30% 40%

Aluminum

17 12 6 5 9 4 3 2

21 16 4 2 38 25 2 0

24 34 44 62 32 37 45 61

Aluminum Nitride

58 52 12 11 11 11

37 51 50 ‐‐ 72 100 73 ‐‐

20 28 40 21 30 39

Boron Nitride

92 42 11 8 35 15 9 7

24 43 90 53 26 49 95 60

18 (17) 25 (27) 37(40) 47 (56) 19 29 40 51

Copper

92 40 6 5

37 22 ‐‐ ‐‐ 23 26 ‐‐ ‐‐

29 34 31 37

Graphite

100 46 9 8 19 19 11 4

35 64 72 63 91 94 33 4

25 (25) 38 (41) 44 (75) 84 (100) 96 (86) 44 (44) 67 94

Table 4.4 Material valorisation – Cohesion (dark grey), Adhesion (light gray) and Thermal Conductivity empirical efficiencies‐ (Aligned)

This table only miss one detail, the desired properties a specific engineer or customer may want or

need for the material.

To further reduce these values to a single value per filler vs. their volume percentage the next

equation can be used on Table 4.4.

∗ ∗ ∗ (4.5)

Where a, b and c are factors representing the importance of their corresponding efficiency for

choosing a specimen. The next should hold:

0 , , , 1 1

72

An example of the reduction of Table 4.4 using equation (4.5) is given in Table 4.5, taking a=b=c=1/3.

EPS25 EPS70

0 0 0 0 0 0 0 0

Aluminum

21 21 18 23 26 22 17 21

Aluminum Nitride

39 44 34 ‐‐ 35 47 41 ‐‐

Boron Nitride

45 (44) 36 (37) 46 (47) 36 (39)

26 31 48 39

Copper 53

32 ‐‐ ‐‐ 20 23 ‐‐ ‐‐

Graphite 53 (35) 49(50) 42 (52) 52 (57) 69 (65) 52 (52)

37 34

Table 4.5 Example for factors a=b=c=1/3

Looking at the above table EPS70 + 10%Graphite would be taken as the best choice.

A valorisation technique was found that can be used as an indication for the most suitable material

depending on the engineering needs for a given application. The accuracy of this valorisation

technique will improve with the reliability of the cohesion results.

73

5 Conclusions

Throughout this research the results of this project have indicated that competitive TIM’s with

adhesive properties can be synthesized to have self‐healing properties. The cohesion, adhesion and

thermal conductivity experiments support this result.

The two polymer matrices used in this work, deriving their self‐healing ability from the reversible

sulfur‐sulfur bond formation, both showed attractive and fast cohesive and adhesive self‐healing

behaviour upon healing to 65‐1000C. Furthermore, the polymers had good processability

characteristics making it possible to create sound composite materials with loading fraction of up to

40% inert granular particle loadings.

From the cohesion experiments it can be concluded that Graphite and Boron Nitride both impact the

efficiency of the self‐healing effect the least, and are therefore the best suited fillers to be used for

the manufacturing self‐healing TIM’s. In order to confirm these results, it is recommended to conduct

more test with the same material configuration. The best material for cohesion was found to be

EPS25‐4SH‐10% Graphite completely recovering within 10 min at 650C and EPS25‐4SH‐10% Boron

Nitride completely recovering within 10 min at 1000C.

There is a maximum of adhesive strength, for non‐metallic fillers, between a volume percentage of

15% and 30%. The adhesion tests have shown that the amount of filler necessary to increase the

adhesive strength depends on the type of filler used. For Aluminium Nitride and Boron Nitride a filler

content of 20% and 30% respectively have been shown to facilitate this increase. EPS70‐4SH‐20% AlN

was the best composite with 5.44 MPa lap hear strength followed by EPS70‐4SH‐30% BN with 5.17

MPa lap hear strength.

The thermal conductivity of the manufactured TIM’s increases with the filler content. At these high

volume contents of filler, the thermal conductivity increases even more by structuring with DEP. The

increase in thermal conductivity, as compared to the non‐aligned samples, only occurs above 20% for

Graphite and 30% for Boron Nitride. This makes DEP a recommended process to increase the thermal

conductivity without having to increase the filler content of the compound. EPS25‐4SH‐40% Graphite

had an increase of 19.1% in thermal conductivity using DEP process, while EPS25‐4SH‐40% BN had an

increase of 19.5%. Construction of a model of the thermal conductivity as a function of the filler

content is recommended. The best synthesized specimen was EPS70‐4SH‐40% Graphite aligned with

a thermal conductivity of 2.35 W/mK.

Both Graphite and Boron Nitride favourably affect the thermal conductivity, self‐healing and

adhesive properties as TIM’s as compared to all the other investigated fillers. From the results it can

be said that Graphite is the most favourable filler material to increase the thermal conductivity of

TIM’s up to 20%. Higher values of volume percentages show that Boron Nitride becomes more

favourable. This should be corroborated with more empirical results.

The engineering application itself should be the deciding factor on which filler to use since Graphite

is an electrical conductor while Boron Nitride is an insulator.

74

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