A Micro-Centrifugal Technique for Improved Assessment and Optimization of Nanomaterial Dispersions: The Case for Carbon Nanotubes

Simon G King1, Evandro Castaldelli2, Liam McCaffterty1, S Ravi P Silva1, Vlad Stolojan1[footnoteRef:1] [1: Corresponding author. Tel: +44 (0)1483 689411 Email: v.stolojan@surrey.ac.uk (Vlad Stolojan)]

1Advanced Technology Institute, University of Surrey, GU2 7XH

2Departamento de Química, FFCLRP, Universidade de São Paulo, Av. Bandeirantes 3900 CEP 14040-901, Ribeirão Preto, SP, Brazil

 

Abstract

Large-scale incorporation of nanomaterials into manufactured materials can only take place if they are suitably dispersed and mobile within the constituent components, typically within a solution/ink formulation so that the additive process can commence. Natural hydrophobicity of many nanomaterials must be overcome for their successful incorporation into any solution-based manufacturing process. To date, this has been typically achieved using polymers or surfactants, rather than chemical functionalization, to preserve the remarkable properties of the nanomaterials. Quantifying surfactant or dispersion technique efficacy has been challenging. Here we introduce a new methodology to quantify dispersions applicable to high-weight fraction suspensions of most nanomaterials. It’s based on centrifuging and weighing residue of undispersed material. This enables the determination of the efficacy of surfactants to disperse nanomaterials (e.g. ultrasonication power and duration) and leads to increased nanomaterial solution loading. To demonstrate this technique, we assessed carbon nanotube dispersions using popular surfactants: Benzalkonium chloride (ADBAC), Brij®52, Brij®58, Pluronic®F127, sodium dodecyl sulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), Triton™ X-100, Triton™X-405 and Tween®80, evaluating the dispersion outcome when varying sonicator power and horn depth, as well as imaging sono-intensity within the solution with luminol. The methodology is shown to be applicable for high-weight fraction nanomaterial suspensions, enabling greater deployment.

1. Introduction

Nanomaterials have been widely reported as materials of the future, increasing realisation in to their benefits has led to new technological innovation as well as enhancing existing technologies, maximising product efficacy and pushing performances previously thought not possible. Among many desirable attributes of nanomaterials, some passive and some by design, the primary properties of interest include, mechanical, electrical, surface area and biological. When incorporated into existing materials, such as carbon-fibre composites, the remarkable properties of the nanomaterial can be emulated in the final material.

As with many emerging technologies the transition between lab-scale to commercial manufacture can be challenging, especially with materials too small for conventional mechanical manipulation, as with nanomaterials. For this reason, solution processing is a necessity, but this comes with its own challenges. For many nanomaterials, natural hydrophobicity or lack of solubility is a limiting factor, in this case the chosen material must be functionalized to overcome these limits to achieve successful incorporation into any solution-based manufacturing process. Functionalization is typically achieved by either chemically altering the nanomaterials structure, known as covalent functionalization, or wrapping/coating the material with another material which is soluble. Typically, the choice of functionalization method depends on the intended application, with each method having its benefits and drawbacks.

Non-covalent functionalization has been used to disperse a variety of insoluble nanomaterials and has been demonstrated using a range of surfactants and polymers1,2. However, the degree of success has been varied, with publications showing contradictory dispersion results even when investigating the dispersion of the same nanomaterials3,4. A possible reason for these conflicting results on the efficiency of each of the reported surfactants is the lack of suitable standards or a technique allowing quantification of each methods’ effectiveness. Typically, previously reported techniques for the assessment nanomaterial dispersions, including carbon nanotubes (CNTs), involves either using optical transmission spectroscopy, which is only suitable for low-concentration solutions5, or applying a thin film of the dispersion to a substrate and imaging the particle distribution using atomic force microscopy (AFM) or scanning electron microscopy (SEM)6. Both these dispersion assessment techniques are not applicable for high loadings and/or highly optically-absorbent materials (e.g. CNT, graphene) and not suitable for use with all nanoparticle dispersions, particularly when formulating inks for additive manufacture with different optimisation parameters. Furthermore, the use of AFM or SEM requires the sample to be dried, which can induce particle aggregation and skew results, for example as in the case of the ‘coffee-ring’ effect7. To enable realistic evaluation of nanomaterial application, a universal dispersion assessment method that allows for high-weight nanomaterial loadings, which does not require specialist equipment nor expert technique, or the need for stringent process control, must be realised.

In the work reported here, we introduce a new approach for the assessment of effective nanomaterial dispersions, irrespective of the shape, size or quantity of material used, using only a microcentrifuge and a microbalance. There are many ultrasound horn shapes, centrifuge tube shapes and vessel materials which could all influence the dispersion result, and as such using an analytical method suitable for only one particular shape does not make it applicable generally. What is important is that our method of assessing dispersions can be used for any situation (e.g. surfactant, solvent choices) and ultrasonic arrangement, revealing the best dispersion for a given surfactant/nanomaterial/solvent/ultrasonic generator system.

The basis of this approach is that only successfully dispersed nanomaterials remain suspended in the solution during centrifugation. Centrifugation has been used empirically as a method for filtering large particles and agglomerates, but the quantity of material sedimented, i.e. the mass of material undispersed, has not been considered as a measure of dispersion efficiency. By demonstrating this technique with the popular nanomaterial, CNTs, we show that this method can be used to evaluate both the efficiency of a given surfactant and furthermore the effect of how the ultrasound conditions, such as horn depth, influence the final dispersion. Due to the nature of the technique, when applied correctly, this method reveals the optimum sonication parameters for that device, regardless of horn condition or sonicator calibration.

We demonstrate and apply this novel technique to the dispersion of single-walled CNTs using a variety of commonly reported ionic and non-ionic surfactants. In each case, the optimised sonication powers, to effectively utilise each surfactant is revealed, as well as how well each performed in dispersing the CNTs.

To further demonstrate usefulness of this assessment technique, the effect of horn depth on the resulting nanomaterial dispersion was also probed. Previously the use of a thermometer or hydrophone would have been required to do this, which would have only given an indication of the effect based on sono-hotspots and, furthermore, the presence of an external instrument would have skewed the results by disturbing the solution streaming8. By using the micro-centrifugal technique, we explore how varying the applied sonication configuration, in this case horn submersion depth, truly affects the final nanomaterial loading within the solution. The combined results of these two investigations present us with the optimum sonication power, and the best empirical sonication application depth.

Finally, for further visual verification of the horn-depth results, by using low-light imagery of the sonication of luminol solutions, we image ultrasound intensity within the solution itself, confirming how the ultrasound penetrates and distributes through the solution, and ultimately linking back to provide insight into the dispersion results.

The technique outlines a route for increasing the nanomaterial loadings within a solution, which then leads to greater material employment in commercial scale-up of device manufacture, while maintaining the ability to identify the optimum surfactant system and ultrasonication conditions for specific applications. Specifically, in the case of dispersing CNTs, this method is applicable to use with most manufactured CNTs; a significant advantage, as different species or brands of commercial CNT respond differently across available surfactants. The different interaction of dispersants with nanomaterials has resulted in the previously noted dispersion anomaly results despite using like-for-like surfactant systems and processing methods reported in the literature3,4.

CNTs, cylindrical tubes of graphene (whether it be one layer to multiple layers), have been an ever increasing presence in research, in both academic and commercial industries9–12. This strong interest is driven primarily by the unique electronic, chemical and mechanical properties that make them ideal for a number of applications, demonstrating both electrical and thermal performances which surpass those of copper, and mechanical performance that exceed the strongest steels13–15. Yet, the ability to translate these properties to the macroscale is still a challenge, mainly due to the inability to produce CNTs at any given lengths, forcing a dependency on charge conduction between weak CNT-to-CNT interactions, such as in the case of electrical conductivity for example. This requires an ex-situ manufacturing process, where CNT are dispersed in solvents to enable a more uniform ‘designer property’ to be incorporated into the desired application. However, a limiting factor with this approach is that CNTs are insoluble in almost all known solvents16. This insolubility leads to the necessity to functionalize the nanotubes in some degree before they can be manipulated into many useful applications or devices, such as for energy harvesting17,18 or in composites19,20.

Functionalization can be achieved via a number of routes. This includes covalently attaching a functional group, for example, by using an acid to add carboxyl (-COOH) groups to the chemical structure of the CNTs21; or non-covalently, through the wrapping/coating of the tubes with other molecules such as a surfactants, DNA/RNA22,23, or polymers1,24. In the case of CNTs, the choice of functionalization method depends on the intended application, with some methods altering specific properties of the CNTs. In some cases, the intended application for the CNTs relies on the ability to either retain or recover their remarkable properties, typically when these properties arise from tube-to-tube interactions, such as van der Waals attraction or ballistic conductance. Due to a current inability to mass produce large volumes of individual CNTs of significant lengths (compared to metals or carbon fibres which can be made to effectively any length required), inter-nanotube interactions are crucial for preserving these remarkable properties within a bulk material or a composite. Functionalizing the CNTs inhibits these interactions, which can allow for each individual nanotube to be manipulated. In order to reinstate these key interactions and maximise the final application’s desired properties, it is often required that the functionalization must be removed25. As covalent functionalization techniques involve chemical alteration of the nanotube, fully recovering the CNTs and returning them to their original state can be complicated, and in some cases will permanently degrade the final properties of the material, especially if the process involves strong acids (through a reduction in nanotube crystalline integrity) 26. However, for non-covalent functionalization techniques, which involve wrapping using surfactants or polymers, this is not the case, as no chemical alteration is involved allowing the functionalization to be safely removed after processing27,28. This makes non-covalent functionalization the preferred dispersion technique for applications such as composites.

1.1. The Micro-Centrifugal Assessment Technique

The basis of our dispersion assessment technique consists of three core steps; i) solution dispersion, ii) solution centrifugation, and iii) the weighing of any residue material that failed to disperse (see Figure 1). In Figure 1 (a), the process begins with the mixing of the nanomaterial solution, where measured quantities of the chosen surfactant and nanomaterial are added to the solvent. In Figure 1 (b), this solution is tip-sonicated for a fixed length of time, at a specified power. Varying the sonication power used across a sample set, allows the sonication power-per-volume to be calculated, producing a range of dispersions subjected to different power concentrations (W mL-1). After sonication, Figure 1 (c), 1 mL of each sample is transferred to a micro-centrifuge tube of a premeasured weight, where these are centrifuged at fixed time and speed. Figure 1 (d) illustrates how after step (c), samples of dispersed nano-material in solution is left above solid material which failed to disperse and fell out from the suspension during centrifugation. Coalesced nanomaterials which failed to disperse have a larger mass, causing them to aggregate at the bottom during the centrifugation. Finally, in Figure 1 (e), by draining the dispersed nano-material solution off of the top of the bottom solid without disturbing the residual, results in only the dropped-out material remaining in the tube. This was executed by pipette-removing the supernatant, without disturbing the residual, followed by using an absorbent lint-free ‘bud’ to collect any droplets of liquid that remained within the tube (avoiding accidently removing any dropped-out material). We found that the residual material was well adhered to the bottom of the vial, so removing the supernatant was repeatable. We anticipate that this may not be the case if I lower centrifugal acceleration is used and we recommend a minimum of 5000G. It is not necessary to dry the dropped-out material entirely, as any residual liquid within its mass should also be included in measurements; in practical terms, this liquid trapped within the residual is forming a sol-gel that can be removed through evaporation and would typically be very difficult to weigh. Much the same as the solid residual, this trapped liquid would be proportional to the mass of residual material (for like-to-like materials) and would also be unfit for use with the rest of the useable dispersion, making weighing the trapped liquid just as important as the solid.

Figure 1 – A schematic diagram of the micro-centrifugal dispersion assessment technique. (a) The nanomaterial, surfactant and solvent are added to a vessel at a specific recipe. (b) The solution is tip sonicated at the specified power within an ice bath for a fixed time. (c) 1 mL of the dispersion is added to a micro-centrifuge tube of a known weight, this is then centrifuged. (d) After centrifuging, the solution will contain both the dispersed material in solution and any material the failed to disperse at the bottom. (e) The supernatant is carefully removed, leaving only the dropped-out material which failed to disperse, which can now be weighed.

Weighing the remaining dropped-out material within the micro-centrifuge tube (where the tube’s pre-measured weight could be subtracted), allows for the weight of the residue to be directly measured. Note, this can occasionally be problematic if the residue weight is significantly smaller than the weight of a tube. Typical micro-centrifuge tube weight is around 1 g, so if the residue is smaller than this by ~two orders of magnitude, then removal of the dropped-out material is advised to ensure accuracy in weighing its mass. Finally, to conclude the dispersion assessment, plotting the weight of the residue as a function of sonication power yields a measure of surfactant efficiency, indicating the optimum power that should be used for each respective surfactant or dispersion technique. Using this method allows us to determine the optimum dispersion parameters for any surfactant or polymer. Although not covered in this article, this method can also be used to probe the optimum surfactant-to-nanomaterial ratios, which are important for high-loading nanomaterial dispersions4.

 

2. Experimental

2.1. Materials

The CNTs used were Elicarb® single walled CNT ‘Wetcake’ product (large mm bundles of CNTs in DI water) supplied by Thomas Swan & Co. Ltd. and used without further processing. The titanium(IV) oxide (TiO2) was supplied Sigma-Aldrich (Merck) as the product ‘Aeroxide® P25’, which had a particle size of 21 nm. The carbon black was supplied Columbian Chemicals (Germany) as the product ‘Raven® P’, which had a particle size of 22 nm.

The surfactants used were all purchased from Sigma Aldrich (Merck) and used as received: Benzalkonium chloride (ADBAC), Brij 52, Brij 58, Pluronic F127, sodium dodecylsulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), Triton™ X-100, Triton™ X-405 and Tween 80. As well as the surfactants, the luminol, hydrogen peroxide, sodium carbonate, acetic acid and sodium hydroxide were all purchased from Sigma-Aldrich (Merck) and used as received, with no further processing. The details of each surfactant can be found in Table 1.

Table 1 – A list of all the surfactants chosen to disperse carbon nanotubes, where in each case the elemental composition has been detailed, along with the surfactant type. Please note: †polyethylene glycol is also known as polyethylene oxide (PEO). *Triblock copolymer PEO-b-polypropylene glycol-b-PEO. **In the case of polymeric surfactants, this not a true molecular weight but a mean molecular weight.

Surfactant:

Chemical name:

Composition:

Molecular Weight**:

Type:

ADBAC

Benzalkonium chloride

C, H, N, Cl

284

Cationic

Brij 52

Polyethylene glycol hexadecyl ether†

C, H, O

330

Non-ionic

Brij 58

Polyethylene glycol hexadecyl ether†

C, H, O

1,124

Non-ionic

Pluronic F127

Poloxamer 407*

C, H, O

12,500

Non-ionic

SDS

Sodium dodecyl sulfate

C, H, O, S, Na

288

Anionic

SDBS

Sodium dodecylbenzenesulfonate

C, H, O, S, Na

348

Anionic

Triton™ X-100

Polyethylene glycol tert-octylphenyl ether†

C, H, O

625

Non-ionic

Triton™ X-405

Polyethylene glycol tert-octylphenyl ether†

C, H, O

1968

Non-ionic

Tween 80

Polyethylene glycol sorbitan monooleate†

C, H, O

1310

Non-ionic

 

2.2. Solutions

For the CNT solutions, to allow a direct comparison between each surfactant, 15 mL aqueous-based dispersions were mixed so that there was 0.1% weight fraction CNTs and 1% weight fraction of the chosen surfactant, achieving a CNT to surfactant ratio of 1:10. The surfactant concentration was at least a minimum of half its critical micellar concentration (CMC)29.

For TiO2 and carbon black samples, 15 mL aqueous-based dispersions were mixed so that there was 2% weight fraction nanomaterial and 2.5% weight fraction surfactant. For these trials the chosen surfactant was SDS as it was previously reported as being suitable for both nanomaterials30,31.

For the CNT solutions used to probe the effects of ultrasonic horn depth, 20 mL aqueous-based dispersions of the same weight fractions of 0.1% CNT to 1% surfactant were used, where the chosen surfactant in this case was Tween 80.

For the luminol testing, a 1 L aqueous based solution of 1 mmol of luminol, 0.1 mol of hydrogen peroxide, 0.1 mol of acetic acid and 0.1 mol L-1 sodium carbonate was blended with a magnetic stirrer. To finish the solution, sodium hydroxide was slowly added until the solution reached a pH = 12, 32.

2.3. Methods

The tip sonicator used throughout this investigation was a ‘Cole Palmer CPX750’ (750 W max.), equipped with a straight-tipped 13 mm diameter horn. Calorimetry, which measures the heating of water by cavitation, was conducted on 15 mL of water (as this was the average sample size for this investigation), using an identical vessel chosen for this study to provide indication of how much energy is being absorbed by the solution33. From the test, the rate of temperature increase of the water related to an average energy efficiency of approximately 9.3%. However, due to the nature of this method, the sonicator condition, vessel type or configuration bear no influence on the results, provided that the chosen set-up is kept constant throughout the test. For example, using an alternative vessel material, e.g. glass, will change how the vessel interacts with the ultrasound, either increasing ultrasound reflection or absorption34. If the new vessel reflects more ultrasound back into the solution, the required sonicator output energy for an optimum dispersion will be reduced, whereas if the new vessel absorbs more ultrasound, the result will be the opposite. As long as the set-up is the same throughout the test, the effect on the efficiency of the dispersion will be the same.

Centrifugation was carried out using an Accuspin 400, with plastic 1 mL micro-centrifuge tubes. Finally, the luminol images were captured using a Nikon D5100 DSLR, equipped with an 18 – 55 mm Nikkor Zoom lens.

For the various CNT/surfactant dispersions, sonication was run at various power densities (defined here as power-per-unit-volume of sonicated solution) ranging from 5 to 40 W mL-1, in 5 W mL-1 increments, for a total of 30 minutes. However, before any experiments were completed, a thermocouple was used to check the temperature of a pure aqueous solution during ultrasonic operation when run on the highest ultrasonic intensity (see supporting information Figure S1). This test confirmed that the solution did not exceed 65 °C during the maximum output power of the process, when run with a pulsed interval of 3 seconds ‘on’ and 3 seconds ‘off’. Should a more volatile solvent be used, increasing the ‘off’ interval of the pulse will lower the operating temperature, e.g. 1 second ‘on’ and 3 seconds ‘off’ will lower the operating temperature to approximately 45 °C (see supporting information Figure S1). With the sonicator operating in defined pulse mode, the total processing time was 1 hour per sample. To further prevent the sample overheating, which leads to a loss of solvent, each of the samples were submerged in a 400 mL glass beaker ice bath throughout the duration of sonication, at the higher sonication powers the ice needed to be replaced more frequently to compensate for the increased sample heating.

Once the sonication parameters were defined and sonication applied to the samples, 1 mL

of each dispersion was transferred to a pre-weighed 1 mL micro-centrifuge tube using a micropipette. The samples were then centrifuged at 5000G for 30 minutes, before being drained and weighed (Figure 1).

For the CNT solutions used to probe the effects of ultrasonic horn depth, sonication was conducted at 17.5 W mL-1 (optimum ultrasonication power density identified for Tween 80, Figure 3b), at various pre-defined depths into the solution, as illustrated in Figure 2. This was measured as the percentage of solution which remained below the tip of the horn. All other sonication parameters remained the same as previously detailed, including duration, pulsing profile and ice bath immersion. The samples were then centrifuged, drained and weighed as per the micro-centrifugation dispersion assessment technique.

For the luminol solutions, sonication was conducted at 22.5 W mL-1 (60% power), at various pre-defined depths into the solution. Sonication was applied continuously with no pulsing throughout image acquisitions, without the use of any ice bath. For each horn depth, images where acquired in darkness at 320 ISO, at maximum aperture for 30 second exposures. Each image was analysed in ImageJ® image processing software to measure the luminescence intensity.

Figure 2 – A photograph illustrating the various depths that the ultrasonic horn was submerged to for the depth test. For the 100% position, the horn was placed just below the surface.

3. Results and Analysis

3.1. Investigating Surfactant Performance: CNT dispersing efficiency and optimum ultrasonication power density

All surfactants were assessed for their ability to disperse CNTs using the new micro-centrifugal technique as outlined previously. Brij 52 appeared to be the only surfactant which failed to disperse the CNTs to any degree, and this was clear even without centrifuging, where significant dropout was observed immediately after sonication. This poor performance has been reported in other publications35. Centrifuging any of the Brij 52 solutions resulted in nearly total material dropout to the bottom of the centrifuge tube, leaving a clear aqueous solution at the top. For this reason, the data for Brij 52 was omitted from the collective analysis in Figure 3.

a)

b)

Figure 3 – (a) The amount of non-dispersed material (as a residual CNT/surfactant sol-gel) for Triton X-100 and ADBAC is significantly higher than for all other surfactants for the power densities available. (b) The rescaled plot of the non-dispersed CNT residue shows that Triton X-405 and SDS are equally efficient surfactants, but at different power densities (17 W/mL for SDS and 22-27 W/mL for Triton X-405). Pluronic F127 may achieve similar dispersion efficiency, but at much higher ultrasonication power.

Figure 3 displays the mass of residual material which has dropped out of the solution suspension during centrifuging, for each of the surfactants listed in Table 1. For most surfactants we can identify a power density at which the dispersion is optimum (minimal drop-out), all except for the surfactants Pluronic F127 and Triton™ X-100; these may indeed reach an optimum, but at power densities beyond the capabilities of our equipment. The determination of the ideal sonication power density was made using a Gaussian function, to give an estimate for the distribution minimum. The samples made using surfactants Brij 58 and Triton™ X-405 do not follow a Gaussian distribution which may have been due to a change in the surfactants’ structure as a result of ultrasonic damage e.g. cleaving of the molecular chains36. In these cases, polynomial fittings were found to best follow the data and provide an approximation of any minima.

For the surfactants tested, three scenarios were identified based on the sonication power density applied. Surfactant micelle formation was not considered, as the concentrations used in this work were at least one order of magnitude lower than their CMC. In the first scenario, where the sonication power is not sufficient to fully de-bundle the CNTs, the persistent bundling results in a reduced accessible CNT surface area for the surfactant to interact. This leads to poor non-uniform surfactant coating of the CNT surface and the CNT remain in agglomerates, which are then pulled from suspension during centrifugation. As the sonication power density increases, in scenario 2, the poor de-bundling is resolved, at which point the maximum dispersion achievable with that surfactant is obtained (resulting in the lowest residue after centrifugation). This optimum sonication power density corresponds to the lowest point of the curves seen in Figure 3. As the sonication power density increases beyond the ideal we enter scenario 3, where damage starts to be introduced to the surfactant molecules from generated ·OH radicals, lowering their effectiveness37,38. With this increased damage to the surfactant, the mass of material that drops out of the solution during centrifugation increases, indicating a lower dispersion efficiency. Raman spectroscopic analysis confirms that this result was not arising from damage inflicted to the CNTs, through comparison of the G to D ratios before and after processing (see supporting information Figure S2).

The best surfactants were found to be Brij 58, SDS and Triton™ X-405, with SDBS and Pluronic F127 also proving to be effective at dispersing the CNT wetcake. SDS was deemed to be the best at dispersing the CNTs, however the solution foamed when subject to high sonication powers. Pluronic F127 also displayed an interesting trend by revealing increasing effectiveness with increasing sonication power. It is thought that this surfactant would follow similar Gaussian distributions as the other typical surfactants, with an optimum power intensity higher than that achievable with the equipment used in this investigation. However, using a Gaussian approximation, the ideal sonication power for Pluronic F127 was estimated to be 42.5 W mL-1. It is thought that this effect is a result of the significantly higher molecular weight of the block-co-polymer compared to the other surfactants used.

The worst performing surfactants when dispersing CNTs were found to be Brij 52 and Triton™ X-100, with a high volume of dropped-out CNTs (compared to the other samples). In the case of Triton™ X-100, this result contradicts some literature, where it states its efficiency at dispersing CNTs3, but equally agreed with others that show its problems4. The reported variations are likely to have arisen due to the CNTs tested in these investigations, as opposed to the dispersion process, including quality at synthesis or the resulting tube morphologies (i.e. number of walls, size, aspect ratio, etc.). This also shows that a like-for-like comparison of surfactant efficiency as a function of ultrasonication power density must be performed based on the source/prior treatment of the nanomaterial (CNT, graphene, nanoparticles, etc), which can be now done reliably using the method described in this research study.

Lastly, Triton™ X-405 showed a similar trend to the Pluronic F127, with increased effectiveness with sonication intensity. Unlike the other surfactants tested, Triton™ X-405 proved to be the least affected by high sonication powers densities and failed to show much of an increase in drop-out weight after an optimum. Much the same as the research conducted by Blanch et al. (2010), Triton™ X-405 also revealed to be significantly more resilient to ultrasonic degradation, and substantially more effective at dispersing CNTs than Triton™ X-100.

Figure 4 – This graph displays the results from the sonication investigation which assess surfactant effectiveness as a function of sonication power intensity. The error is calculated from the standard error in the mean across a minimum of five repeat measurements. Triton™ X-100 was omitted as no result was obtained. *The ideal power density for Pluronic F127 was estimated due to equipment limitations.

A summary of the ideal sonication power densities for each surfactant can be found in Figure 4 (squares), with a comparison of the surfactant efficiency at dispersing CNTs, as derived from the measured lowest residual weight. Errors in these measurements were calculated on a surfactant-by-surfactant basis. This resulted in some samples having a very soft residual drop out which was hard to separate from the dispersed solution, leading to higher errors on some surfactant systems. To minimise this effect, five identical samples were measured at each sonication power, providing a mean dropout weight percentage and standard deviation, which we then used to calculate standard error. In addition to the errors stated, further demonstration of the accuracy of this dispersion assessment technique is included in the supporting information, where a complete sample of results is included for one of the investigations conducted in this report (see supporting information Figure S3).

It is important to note that the optimum sonication power densities will vary depending on several situations. For example, the shape of the sonication tip used to define these values; a tip with a different shape/profile would alter the energy transferred into the solution. In the work reported here, we ensured that the sonication device used was calibrated by the manufacturer, with only brand-new tips used. Furthermore, in this investigation it was ensured that the same centrifuge tubes were used during sonication. This was because the dimensions of the vessel, and its composition material, will also affect how much vibrational energy is either reflected back into the sample or absorbed by the vessel itself 34. From the results we obtained, we propose that the best surfactants to be used for the dispersion of the CNTs are either Brij 58, SDS or Triton™ X-405, with their relevant optimum sonication power densities detailed in Figure 4. However, these results only reflect on the degree of success at which each surfactant disperses the CNT material.

3.2. The Assessment Methods Suitability with Other Nanomaterials

To demonstrate the micro-centrifugal dispersion assessment methods suitability for use with other nanomaterials, the micro-centrifugal technique was further tested by dispersing titanium dioxide (TiO2), commonly used as a white pigment, in sunscreen and in biological devices. We also tested separately carbon black, which is commonly used as a black pigment and as a conductive additive. Both materials were dispersed, with SDS as the surfactant, across a range of sonication powers. Figure 5 displays the results from the dispersion using these nanomaterials, revealing behaviour in similar manner to the CNTs. Due to having a density of almost four times higher than CNTs or carbon black, to apply the micro-centrifuge method to the TiO2 dispersion the centrifuge was lowered to 200G.

Although we have only tested this dispersion assessment method on two other nanoparticle systems, we believe that the fundamental principles of the method can be applied to most nanomaterial dispersions to reveal optimum dispersion technique.

Figure 5 – These graphs display the results when the micro-centrifugal dispersion assessment method is applied to either TiO2 or carbon black. The optimum sonication power for TiO2 and carbon black was to be 13.4 W/mL and 16.1 W/mL respectively.

3.3. Investigating Horn Depth Effect

As a further demonstration into the insights this dispersion assessment method can provide, a second investigation was carried out to probe the effects of the sonicator horn depth on the dispersion of CNT, complementing the previously optimised sonication power with horn position. Previously, the only way to measure the effect of horn position was through using a thermometer or a hydrophone, where the very presence of the device would have interrupted solution streaming and influenced the result8. Using the micro-centrifugal technique does not involve any additional equipment, and therefore will provide efficient characterisation of the ultrasound dispersing conditions.

Manufacturers typically recommend immersing the horn to a central position, to a point where direct contact with the container is avoided and foaming of the solution is minimal. However, with incorrect placement, a significant part of the ultrasound energy is confined to a limited volume, reducing the efficiency of the sonication step and making like-for-like comparisons invalid. Applying the micro-centrifugal technique to measure the mass of CNT drop-out from solutions dispersed with Tween 80, at a fixed power density but sonicated at various horn depths, as outlined in the experimental section, revealed how this affected the nanomaterial loading in the final solution. Figure 6 shows the residual mass remaining after the supernatant is removed, as a function of sonicator horn depth. When the horn is positioned higher in the solution, the dispersion efficiency is improved, reducing the waste by almost a factor of 3, and most of the ultrasound energy is dispersed within the solution. We found that the ideal horn depth was approximately 20% into the solution (with 80% remaining directly under the horn), as opposed to the traditionally used 50% which produced almost twice as much undispersed, residual material. This result is agreement with other publications which have investigated the effects of horn position on other chemical reaction33,39.

Figure 6 – The micro-centrifugal technique revealed that the most efficient depth to submerge the ultrasonic horn is approximately 20% into the solution.

To better understand these results, and explain the physical processes behind them, another investigation was conducted by once more sonicating a solution at various horn depths, but this time containing luminol. Luminol has been used previously to effectively map ultrasonic cavitation, where luminol displays photoluminescence when exposed to hydroxyl radicals (·OH) caused by the sonolysis of water32,40,41. Since Luminol only undergoes photoluminescence during ultrasonic cavitation, it is an effective way to image ultrasound without a reactor. Figure 7 displays the photographs taken using a slow shutter DSLR digital camera during the sonication of luminol in an identical fashion to the CNT solutions. These photographs directly reveal the sono-hotspots of our experimental set-up and expose how the horn delivers the ultrasonic sound waves as well as how they propagate through the solution. Other than through the CNT dispersion results in Figure 6, further quantification of the effects of horn depth can only be achieved by visually comparing the photographs for each configuartion, where a higher luminesece from the luminol corresponds to a greater sono-intensity.

Figure 7 – Photographs of the ultrasonic horn at different depths during sonication. The illumination intensity of the luminol solution highlights the areas of highest ultrasound intensity.

The dispersion results from varying horn depth are expected if we analyse the observed physical process of ultra-sonication, and how the horn delivers the sound waves. For this investigation, we chose an un-tapered cylindrical horn without any design features, which then resulted in all the ultrasound energy only being emitted from the end of the horn into the immediate solution below it. Figure 7 photographs this in real-time. With these mechanical factors in consideration, it is then only logical that the optimum position for the horn is so that the sound waves are emitted down through as much of the solution as possible, without relying on solution flow around the vessel to achieve a homogeneous dispersion. Therefore, the horn must be positioned as high as possible to provide a larger immediate interaction volume immediately below it. Conversely, the horn needs to be sufficiently below the surface to achieve a good sound wave transmission, avoiding energy loss through the surface by foaming or splashing. The image taken with the horn just under the surface shows a significant reduction in the overall ultrasonic energy delivered to the sample, with the immediate high intensity ‘jet’ directly under the horn appearing faded, when compared to the other images. Although not visible in Figure 7, this was due to the enegy being lost through splashing and spray from the solution surface, which was only observable when sonicating with the lab lights on.

Submerging the horn further into the solution, the photograph taken with 87.5% of the solution under the horn tip, reveals a much brighter luminescence. This indicates that there is now little energy loss through surface interaction, and that ultra-sound is being efficiently transmitted into the bulk of the solution through the immediate interaction volume below the horn tip. This observation agrees with the CNT dispersion results seen in Figure 6, where a larger interaction volume immediately under the ultrasonic horn resulted in the best dispersion. As the horn is submerged deeper, the solution under the horn tip does appear to glow brighter. At first it was thought that this was a result of sound waves reflecting off the vessel bottom, however, as explained later this was not the case. The increase in sono-luminescence is thought to be due to reduction in the immediate interaction volume below the horn, which effectively increases the applied power density quantified earlier in this work. Taking this into consideration, it is now suggested that the reduction in Tween 80’s dispersion efficiency as the horn was submerged deeper, as observed in Figure 6, was due to increases in the applied power density, damaging the polymer molecules in this area and reducing their dispersion effectiveness.

In some cases, it can also be observed how there is luminescence all the way up the shaft of the horn, this is due to solution flow. It was considered that perhaps some observations were the result of the long narrow shape of the vessel chosen for our investigation. It has been previously reported that the chosen vessel used in which to sonicate a solution can influence the result and its interaction with the sound waves. Size, shape and the material can play a crucial role in how the vessel reflects and absorbs sound34. Although the effects of the vessel are not directly investigated in the work reported here, comparing the photographs in Figure 7, does provide insight into how it is interacting with the sound energy. It was noticed in the photographs that there is a lack of patterned areas of high intensity, suggesting that the plastic vessel is absorbing energy rather than reflecting it, which isn’t ideal as reflection would increase solution interaction and improve the process efficiency. If the vessel was reflecting the sound energy, there would be evidence of patterns in the luminescence cause by standing waves, which is imaged in other publications when harder materials such as glass are used for the container34. It was also noticed that there is a slight highlight in luminescence at the edges of the vial bottom which increase as the horn submerged deeper. However, rather than a reflection this was found to be a topographical optical effect caused by the tapered cone at the bottom of the vessel. This suggests once more that most of the sound energy reaching the edge is absorbed by the plastic rather than reflected. This result could be very different if the vessel was made of a harder material such as glass.

4. Conclusions

In the work reported here, we have presented a new approach for the assessment of nanomaterial dispersion, irrespective of the materials morphology or the volumes used, using only a microcentrifuge and a microbalance. There are many dispersion configurations which can vary the outcome of the nanomaterial loading. What is important is that our method of assessing dispersions can be used for any arrangement, revealing the optimum dispersion configuration for a given surfactant/nanomaterial/solvent/ultrasonic generator system.

It is an inexpensive and efficient alternative to an optical transmission spectrometer or an SEM or AFM and, unlike current techniques, it is suitable for use with high weight fraction nano-particle suspensions. By demonstrating this technique on dispersions of CNTs using a variety of surfactants, we have shown that it can provide both an indication of dispersant efficiency, as well as revealing the optimum ultra-sonication conditions in which a dispersant would provide the best performance. Using this purposed technique will allow better employment of surfactants to nanomaterial systems, leading to higher, more stable dispersions, for more efficient application in composites and other advanced materials27.

Applying our technique to a variety of commercially available surfactants including Benzalkonium chloride (ADBAC), Brij 52, Brij 58, Pluronic F127, sodium dodecyl sulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), Triton™ X-100, Triton™ X-405 and Tween 80, has found that SDS was the most efficient at dispersing our CNT material. Following this with a second demonstration of this dispersion assessment technique, but this time to a fixed solution, allowed the effects of sonicator horn depth within the solution to be explored, revealing how shallower depths, when approximately 20% into the solution, produce much better dispersions. Luminol luminescence imaging was finally used to explain how and why shallower horn depths provide more efficient application of ultrasound.

Conflicts of interest

There are no conflicts to declare.

Supporting Information

Supporting information includes temperature profiles for the heating of the solution at full power (3 seconds ON, 3 seconds OFF cycle) as a function of time (S1), Raman spectroscopy of before and after the CNTs were sonicated (S2) and all the raw data points for the sonicator depth investigation (S3).

Acknowledgements

The work reported here was funded by both EPSRC, United Kingdom, Grant Numbers EP/G037388/1 and EP/N006372/1, Micro and NanoMaterials and Technologies (MiNMaT) Industrial Doctorate Centre (IDC) and Thomas Swan and Co. Ltd. The author would also like to thank the FAPESP, Brazil, for the financial support on the grants 2011/22379-6 and 2012/09719-5, and CNPq for grant 150025/2017-3.

 

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