Physics 495 - HonorsThesisMay 1, 2015

The Physics of Solar Cells for the Developing World

Lydia ThurmanInstructor: Professor Teitsworth

Abstract

As technology plays a larger and larger role in the global community, areas in the de-veloping world without consistent access to electricity are increasingly limited. Solar poweris a strong candidate for rural electrification projects because of its low emission operation,but it is difficult to get a sense of the true cost, both economically and energetically, thatoperating different types of solar power sources incurs. Here, we seek to develop a com-prehensive understanding of the resources required and energy expended in the manufactureof dye-sensitized solar cells (DSCs) and silicon solar cells, as well as the total energy pro-duction capabilities of these technologies. In order to do this, a procedure was adopted anditerated upon in order to fabricate DSCs using unprocessed natural dyes from blackberriesand beets, a measurement apparatus was designed to collect IV-curves and the life cyclesof the two technologies were examined. Comparing existing life cycle analyses (LCAs)for silicon cells with the analysis we conducted for dye-sensitized cells, we concluded thatdye-sensitized cells can compete with silicon cells, particularly in the developing world. Wefound that with certain improvements to the fabrication procedure, the energy payback life-time (the time to produce the same amount of energy expended in the production process)for a blackberry DSC could be as low as 4.2 years, and for a beet DSC could be as low as3.3 years. Meanwhile, a silicon cell has an energy payback lifetime of 7.7 years.

Contents

1 Introduction 2

2 Operational Principles of Solar Cells 4

2.1 Silicon solar cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Dye-sensitized solar cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 The Life Cycle Analysis 10

3.1 Developing a Methodology - Organic Photovoltaics . . . . . . . . . . . . . . . . . . . . 10

3.2 Silicon solar cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Dye-sensitized solar cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3.1 Goal and Scope Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3.2 Making an Energy Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4 Comparing Performance 22

4.1 Experimental Set-Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 Measuring methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.3 Data and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.3.1 The Silicon Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.3.2 The Dye Sensitized Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5 Conclusion 39

6 Acknowledgements 41

1

1 Introduction

With increased focus on renewable sources of energy, solar power has been the subject of significant

thought and research in recent years. Solar power seems like a great candidate for energy production,

given its practically nonexistant operational emmissions and its reliance on a naturally occurring and

largely uncaptured resource: incident sunlight. Over the course of the year, the sun supplies more than

enough to meet a significant amount of world energy demand if collected efficiently. For example, in

Nairobi, Kenya, the average power density is 234 Wm2 at the earth’s surface[16]. However, it is naıve to

disregard production and end-of-life considerations in asserting that a product has no or low emissions.

To truly evaluate and determine an energy source to be ”green,” all facets of its production, operation and

disposal must be considered.

The most common source of solar power is the silicon photovoltaic cell. First manufactured in the

1950s by Hoffman Electronics Corporation, silicon solar cells were believed to have incredibly long

lifetimes, but also incapable of competing with high voltage electricity sources[1]. Today, efficiencies

for single-junction silicon cells have reached 25%[6]. However, despite high mobility, and thus high

efficiency, the production process for silicon cells is incredibly energy intensive and environmentally

harmful. To produce a silicon cell a clean-room environment is required, given the sensitivity of the sili-

con crystal growth and doping processes. This, combined with the energy required to grow large silicon

crystals, is expensive and often translates to high emissions since fossil fuels produce the electricity used

in the process. In general, producing solar grade silicon and manufacturing silicon wafers account for

48% of the cost of a silicon solar module, with the remianing 52% distributed across module assembly,

cell manufacture, and installation[23].

As renewables are increasingly investigated, other solar options are becoming more prominent in

research environments. Dye-sensitized cells (DSCs), which rely on an organic dye for light absorption

and electron excitation, are one such option. First developed in the 1990s, state of the art DSCs have

been documented to reach efficiencies of 13% with carefully engineered dye [18]. However, a parallel

development has included the use of naturally occurring dyes, such as the anthocynanin pigment found in

berries and the beta vulgaris pigment found in beets to produce cheap and easily made, if low efficiency,

DSCs. These cells do not have long lifetimes, but given the low cost and limited energy required, it is

possible for them to still be competitive with silicon cells, particularly in parts of the developing world.

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As we consider different solar technologies and their appropriate uses, it is important to consider the

context in which they are being deployed. For example, an industrial environment requires high reliabil-

ity and a long lifetime. The alternative to implementing solar technology is usually reliance on fossil fuel

power plants, which are predictable and capable of producing large amounts of energy. Economic com-

petitiveness is important in an industrial environment, but in general large-scale manufacturing plants

have adequate funding to implement more costly projects. For these reasons, an industrial project would

be more likely to deploy high-efficiency and high-cost silicon modules. Alternatively, rural electrification

projects tend to prioritize low-cost implementation over high efficiencies and lifetimes. An inexpensive

project that doesn’t meet all energy needs, but provides the opportunity for lighting, phone-charging, and

other basic electric services in previously underserved areas, can be incredibly valuable. In the case of

sub-Saharan Africa, the electrification rate was only 30.5% in 2009. East African nations saw even lower

electrification rates, with Uganda and Kenya possessing rates of 9% and 16.1% respectively[3]. This sort

of environment, where economic and energy resources are limited, could present a good opportunity to

deploy DSCs.

Over the course of this past year, we have begun to look at some of these considerations. We were

able to fabricate dye-sensitized solar cells with repeatable results of photosensitivity. In fabricating the

cells, we were able to maintain complete control over the production process (and thus retain immediate

access to information about the life cycle of the cell). With this information, we were then be able to

compare the efficiency of the cell to the energy input required to construct and dispose of the cell.

In this paper, first we seek to outline the basic operational principles of silicon and DSCs. Then,

we consider life cycle analyses of silicon solar cells as they are found in the literature. Finally, we look

at the method of production of dye-sensitized cells and begin to discuss a potential life cycle analysis

and method for determining efficiency of this cell. We will conclude with recommendations for the

role DSCs can play moving forward with rural electrification projects in East Africa, and what from a

resource perspective, would make a silicon cell or a dye-sensitized cell more advantageous.

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2 Operational Principles of Solar Cells

The operation of solar cells relies on two fundamental steps: the photogeneration of electron-hole pairs

and the subsequent flow of the charge carriers to their respective electrodes. With multiple cells in series,

this uni-directional flow of electrons creates a current at a voltage high enough to be a usable power

source.

2.1 Silicon solar cells

Figure 1: A diagram of the energy bands in silicon. It is clear from this figure that silicon is an indirectsemiconductor, meaning that for incident wavelength to excite electrons from the highest valence stateto the lowest conduction state (i.e. only change the energy of an electron by 1.08 eV), they would haveto also change its momentum[17].

For basic silicon photovoltaics, this fundamental photogeneration occurs as a result of semiconductor

properties. Silicon at cold temperatures has an unoccupied conduction band, but small amounts of energy

can excite valence band electrons into the conduction band, leaving behind a mobile hole in the valence

band. With incident light, incoming photons are absorbed by electrons in the valence band and these

electrons are then excited into conduction band states.

Silicon solar cells consist of a PN-junction: one region of silicon doped with atoms like Boron

which have excess holes, and one region of silicon doped with atoms like Phosphorus which have excess

electrons. This means that one region has a net positive charge and one has a net negative charge, and

this creates an electric field. Electron-hole pairs are generated in the depletion region in between the two,

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and then flow, respectively, towards the p- and n-type regions which are doped with acceptors and donors

respectively. Electrical contacts on the cell conduct the current towards a load.

In analyzing the data from efficiency testing on these silicon photovoltaic cells, it is first important

to recognize that since we are testing a physical cell, we do not expect to see results that match an ideal

photodiode. Instead of considering the silicon cell to be ideal, we consider it represented by an equivalent

circuit model, which is shown in Figure 2.

Figure 2: The equivalent circuit model for a silicon photovoltaic cell consists of a current source, a diode,and a shunt resistance in parallel with each other, and in series with a series resistance.

Figure 3: The IV curve for an ideal silicon photo-

voltaic cell.

This model is comprised of a series resis-

tances, a shunt resistance, a current source, and

an ideal diode. The total series resistance is the

accumulation of resistances in the path of the pho-

tocurrent. For instance, the series resistance rep-

resents the resistance of the wires and the resis-

tance of the silicon itself. The shunt resistance is

the resistance of any path that is an alternate to the

path of the photogenerated current. For instance,

current that leaks around the PN-junction travels

along a path that has relatively low resistance and

offers an alternative to the normal path in a single

direction that generates power. The current source

and ideal diode represent the ideal solar cell. In

ideal operation, a photocurrent is generated (represented here by the current source) and that current

only flows in a single direction without being diverted (represented by an ideal diode).

An ideal IV-curve for a silicon solar cell, then, would look like the IV-curve shown in Figure 3.

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However, for a cell that has a consequential shunt or series resistance, this would no longer be the

case. For a cell with a large series resistance, current flow would create a large voltage drop across the

equivalent series resistance, meaning that the output voltage of the cell is significantly reduced, while the

short circuit current is slightly reduced. A low shunt resistance would mean a significant portion of the

photogenerated current could flow through an alternate path, reducing significantly the overall current

produced by the cell. The open circuit voltage would also be slightly lower. Identifying these shifts on

an IV-curve are important in order to determine how close to ideal a silicon photovoltaic cell is, and if

there are significantly large resistances (either shunt or series) that are affecting the maximum operating

power, then this can be important in attempts to determine why maximum operating power is low and if

there are physical changes that can be made to improve it.

2.2 Dye-sensitized solar cells

The function of a dye-sensititzed solar cell relies on a dye-sensitized large band-gap semiconductor in

the place of the silicon PN-junction of conventional solar cells. When dye has been adsorbed onto a

semiconductor, light incident on the dye will generate excitons, after which the electrons are injected

into the conduction band of the semiconductor and the holes are conducted through an iodine electrolyte,

creating a photocurrent [13]. Figure 4 shows the geometry of a dye-sensitized solar cell made with our

specifications.

Figure 4: A diagram of the cross-section of a dye-sensitized solar cell[25].

Here, the layer of TiO2 (a large band-gap semiconductor) has adsorbed dye, and this layer acts as

the PN-junction of the cell. Electron-hole pairs are generated when incident light excites electrons in the

dye (see Figure 5 for a magnified image of this process), and electrons generated here then are conducted

through the TiO2 crystals to the conducting glass and collected there.

The layer of iodine solution serves to improve conduction and maintain strong contact within the cell,

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Figure 5: A magnified diagram of the dye-sensitized solar cell. Here you can clearly see the dyemolecules attached to TiO2 crystals, where photogeneration occurs and then electrons are conductedaway[25].

allowing the flow of electrons and holes without interruption due to small pockets of air or imperfections.

The iodine electrolyte solution undergoes oxidation at the surface of the dye, losing an electron, and

regeneration at the surface of the counter electrode. This regenerates the dye by enabling electron transfer

between the electrolyte and the dye.

We fabricated two different dye cells: one with blackberry juice acting as the sensitizer, and one

with beet juice acting as a sensitizer. The uses of the different dyes have obvious implications for the

life cycle analysis (i.e. the relative water-intensity of growing blackberries and beets), and in measuring

the efficiency, we sought to determine if the capacity to produce power differed greatly between the two.

Blackberries contain the dye molecule anthocyanin (shown in Figure 6), which has been demonstrated to

work well as a sensitizer in dye-sensitized nanocrystalline cells[25]. Fresh blackberries have been shown

to contain 245±68.0mg anthocyanins per 100g weight[27], relatively high compared to other similarly

colored foods.

Figure 6: The anthocyanin dye molecule. The presence of =O or -OH groups capable of bonding to thesurface of the titanium dioxide makes it a good candidate as a sensitizer.

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Beets contain a red pigment, betanin, which has also been demonstrated effective as a sensitizer. The

molecular make-up of betanin is very similar to anthocyanins, but differs in that it contains nitrogen[22].

Betanin has been investigated less than blackberries as a natural dye-sensitizer, but in some cases, dye

from beets has been shown to have more favorable light absorption properties than anthocyanins[10].

Fresh beets contain approximately 200mg of betanin per 100g weight, a concentration comparable to

that of blackberries[9].

Figure 7: The betanin dye molecule. The presence of =O or -OH groups capable of bonding to thesurface of the titanium dioxide makes it a good candidate as a sensitizer.

The concentration of the dyes is relevant because the maximum number of electrons that can be

injected into the TiO2 is proportional to the number of dye molecules attached to active sites on the TiO2

surface since these molecules are responsible for absorbing light and exciting electrons. In the process

of cell fabrication, dyes produced from blackberries and beets are diluted with water, and it is important

to ensure that the TiO2 adequately chelates with the dye molecules present in solution.

Figure 8: The equivalent circuit model for a dye-sensitized solar cell consists of a current source, adiode, and a shunt resistance in parallel with each other, and in series with resistances and capacitances inparallel. This model was designed to account for the voltage dependence of internal resistance elementsin DSCs[14].

Analyzing the equivalent circuit of a dye-sensitized cell is slightly more complex than that of the

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silicon cell shown in the previous section. Figure 8 shows the equivalent circuit proposed in a paper pub-

lished by Koide et al. This equivalent circuit looks similar to that of silicon: a current source and ideal

diode represent the single-direction photocurrent generation, and the shunt resistance represents alternate

paths current can take (here, mostly the backwards flow of electrons across the TiO2/dye/electrolyte junc-

tion). R1 and C1 then model the redox reaction at the counter electrode, R2 and C2 represent the carrier

trasnport by ions within the electrolyte, and Rh describes the sheet resistance of transparent conducting

oxide, which in the cells we are discussing is SnO2 deposited on glass.

This model integrates something that becomes very important with measuring IV curves for DSCs:

a time constant. The capacitance in this equivalent circuit ultimately has a large time constant, implying

that when changing the bias voltage, the time for the system to respond and stabilize is larger than a

silicon cell. Therefore, IV-curves must be collected with longer wait times between points.

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3 The Life Cycle Analysis

When it comes to comparing the performance of competing technologies there are a host of difficulties.

There are few sources of data that provide holistic information, summarizing the efficiency of a tech-

nology as well as its greenhouse gas production and embedded energy. However, a single descriptor,

whether efficiency or emissions or input energy, is not sufficient to evaluate a technology. This first truly

became clear with the advent of nuclear power in the mid-20th century. Nuclear power requires yellow

cake production and uranium mining, both of which have large associated energy (and therefore envi-

ronmental) costs. Nuclear power also has long-term waste implications; if the disposal of used uranium

requires transport to a storage facility and processing at the facility, the energy to power those processes

must be considered to understand the true efficiency of the technology. This awareness highlighted the

need for a better way to evaluate energy production and the functional impact of different technologies,

ultimately creating the field of Life Cycle Analysis [12].

Life cycle analyses are fundamentally questions of energy usage: how many watts go into the produc-

tion of a single watt by different energy process? Answering this question is not straightforward because

the same technologies are often produced through slightly different processes or using different materials

by different manufacturers. Furthermore, for a manufacturer closer to a resource supply, less energy will

be used in resource transport prior to production. Publishing reliable life cycle analyses either requires

focusing very closely on a single product and manufacturer, or accessing a great amount of industry data

to produce a meaningful average [12]. Here, we include some samples of different life cycle analyses for

silicon technology, recent work applying LCA to organic photovoltaic (OPV) solar cells, and an LCA

that we conducted for the dye-sensitized cells we fabricated.

3.1 Developing a Methodology - Organic Photovoltaics

Researchers from Universidad Politecnica de Cartagena, Imperial College of London, and the Technical

University of Denmark have published a paper outlining the life cycle analysis of an organic photovoltaic

(OPV) module produced via a roll-to-roll manufacturing process[5].

Theirs is one of the first and most comprehensive analyses of OPVs, and the methodology that they

describe is one that matches the literature consensus, and thus we have chosen to adopt it here to analyze

the dye-sensitized cells we have fabricated. Their methodology for performing a life cycle analysis of

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emerging technologies can be divided into two main steps: goal and scope definition and making an

inventory.

In discussing the goal and scope of a life cycle analysis, it is important to determine what the main

objectives of the analysis are (i.e. is the goal to understand all energy inputs and outputs? is it to assess

emissions?) as well as characterize what the actual product being analyzed is. This entails determining

which specific technology is being analyzed, or justifying why a single analysis can encompass multiple

technologies or multiple comparable products. It is important here to fully determine the boundaries of

the system being analyzed. For instance, in considering a silicon solar cell, it is reasonable to include

the entire production process (i.e. growing the silicon crystal and all subsequent clean room work, panel

assembly, etc.) in a life cycle analysis. However, it is a judgment call on the part of the analysts whether

or not to include transporting the Silicon PV module from the producer to the user in the analysis. For

the OPV LCA conducted by Espinosa, et al., recycling was not included as a part of the life cycle. This is

due to the relative novelty of OPVs and an overall lack of experience and research into end-of-life options

for the modules. In this part of the analysis, it is also important to identify and justify all assumptions

made from a system level[5].

Once the goals and scope have been specified, an inventory is constructed. This entails specifying all

materials and processes that go into making a module, and determining either the embedded energy in

each material (a measure which includes all energy required to extract, produce, or prepare that material)

or the energy utilized in each process. With this information, a total amount of energy required for

production is attained. Usually, this is scaled as energy input required per m2 of a module.

With this data obtained in a life cycle analysis, the energy payback time can be determined (i.e. the

time that it takes a module to produce the energy required over the course of its lifetime). For example,

the paper by Espinosa, et al, concludes that the OPV modules they study have an energy payback time of

2.02 years (assuming 2% efficiency)[5]. Alternatively, results can be expressed as a ratio of total energy

produced and total energy input.

Using this procedure, we conduct our own life cycle analysis for the beet and blackberry dye-

sensitized cells we fabricated in Section 3.3.

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3.2 Silicon solar cells

One of the most complete life cycle analyses for silicon solar cells comes from the National Renew-

able Energy Laboratory. The NREL approach, however, doesn’t consider the life cycle analysis to be

a measure of energy, but rather a measure of emissions. Their approach is to create a process of ”har-

monization,” by which different performance ratios, module efficiencies and solar irradiation values are

made consistent by adjusting published greenhouse gas emission estimates. The harmonized data deter-

mined an average production of 40 g of CO2 equivalent emitted per kWh of power produced. Comparing

this to the estimated 1,000 g of CO2 equivalent emitted per kWh with coal, silicon photovoltaics offer a

clear environmental advantage, with, as expected, the majority of emissions occurring during upstream

processing. Figure 9 shows the distribution of emissions for silicon photovoltaics.

Figure 9: The distribution of greenhouse gas emissions between different life-stages of Silicon photo-voltaic technology[15].

This analysis from NREL gives compelling reason to believe that silicon solar cells offer many ben-

efits for future power generation. However, given the goals of this specific LCA, they do not examine

the quantity of energy required to produce a module. Further exploring the literature, we consider a

LCA from Stoppato at the University of Padova[26]. This paper considers a process that begins with

silica extraction and includes the material and energy inputs for all aspects of manufacture through panel

assembly. This LCA seeks to determine the overall energy input, which is the goal of our DSC LCA,

and thus provides us a means of comparing the silicon photovoltaic module to the DSC ones which we

fabricate.

Here, after analyzing all of the process energies and embedded energies of materials, it is determined

that 1494 MJ are required to produce each photovoltaic panel, where each panel is .65 m2[26]. This is

equivalent to 2298 MJ per m2 of module. Upon completing our DSC LCA, we have equivalent energy

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inputs to compare.

3.3 Dye-sensitized solar cells

3.3.1 Goal and Scope Definition

The main objective of this life cycle analysis is to determine the total energy input per m2 of dye-

sensitized cell modules. The specifics of the product being characterized are altered slightly from the

actual cells we fabricated in order to provide a more realistic example of a system that might provide

energy. Here, we consider the product to have a total surface area of 1 m2, and since the total surface

of our cells is only slightly larger than the total active area, it is fair to assume a total active area of 1

m2 as well. The cells that we fabricated were the size of microscope slides (7.72e-4 m2), but rather than

conduct a life cycle analysis for a module consisting of individual cells in series, we consider a cell that

has been constructed with two glass sheets, each 1 m2 and with a layer of tin oxide deposited.

Figure 10: A diagram displaying what we consider to be contained within the DSC system, and what isexternal.

The procedure that we used for fabrication is included in the appendix to this paper. Each step is

detailed there, and a list of steps requiring electrical energy, as well as a complete list of materials used,

can be found in Section 3.3.2. Because we conduct the analysis for a cell that is 1m2, some of the

steps do not match. For instance, in curing the layer of TiO2, we use a heatplate which has an area of

approximately 25 in2. This is 162nd of the area of the 1m2 module. Here, we make the assumption that the

electrical energy required to heat a heatplate has a linear relationship with the area being heated. Thus,

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it is adequate to determine the energy used by a heatplate to cure 25 in2 of a module, and then multiply

that by a factor of 62. The only other electrical implement used is a balance, and here we assume that the

time to weigh 2g of TiO2, which is how much TiO2 is used to construct 5 small cells, is the same time

that it takes to weigh the .518 grams required to construct a cell of 1m2.

Finally, we must establish a clear system boundary for what energy inputs we attribute to constructing

the final product. Figure 10 shows which processes we include within our analysis, and which processes

are external. Here, we exclude the material and module transport process from our analysis, because

of the great variability. One application for dye-sensitized cells would be easy, ”make-at-home” deploy-

ment, meaning an individual desiring energy access would construct the cell themselves and thus module

transport would be zero. In an ideal scenario, given that dye-sensitized cells are thought to be a good

option for this cheap, locally acquirable energy sources, materials would also be locally sourced, thus

adding negligible emissions and requiring minimal energy expenditures. It is possible that manufacture

in a single location or factory and the accompanying economies of scale could create a more efficient

process. However, the price would be greater for the consumer than if the consumer constructed their

own cells. By constructing the cells on site, the consumer does not have to pay for external labor or trans-

port, and is able to limit the costs incurred by themselves. As discussed previously, low-cost deployment

is a priority for rural electrification projects with limited funding. With the assumption that the cells are

made on site, we exclude transportation processes from our analysis.

Included in our analysis, then, is all of the raw material extraction and production (which appears in

our analysis as the embedded energy of those materials), the cell production, and the end of life treatment.

The cell operation is included as well, but since dye-sensitized cells have no emissions or energy inputs

during operation, it is only a source of energy outputs. The cell production process is fully described in

the appendix, but the recycling process is not. For these cells, recycling is simple and materials can be

re-assembled into new dye-sensitized cells at the end of their lifetime. After these naturally dyed DSCs

lose efficiency, the glass plates can be separated, and cleaned with ethanol and water. The glass plates

can then be reused and the materials washed off of the glass can be disposed of easily because of its

low-hazard and low-risk properties.

It is important to note that the energy inputs for this life-cycle analysis could be reduced by consid-

ering the cell to be made with reused glass deposited with tin oxide, but here we consider using brand

new glass in order to understand the maximum energy input. Thus, the energy payback time that we

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determine will be a worst case scenario figure and can be expected to be improved upon for recycled

versions of the DSC.

3.3.2 Making an Energy Inventory

With these assumptions and product specifications in place, we continue to inventory all of the materials

and processes, and determine the total energy embedded or expended in each. Table 1 shows the embed-

ded energies for individual materials, as well as the total embedded energy in a 1 m2 module. Table 2

shows the energy expended in individual processes, as well as the total energy expended in constructing

a module.

Material Embedded Energy (MJ/kg) Total Embedded Energy (MJ/module)Glass microscope slides with tin oxide 15 204

TiO2 in powder form 73.8 38.2Tide dish soap 3.6 0.05

Ethanol 27.6 56.3Acetic acid 1.7 1.4Kim wipes 28.2 21.49Scotch tape 91.0 0.34Blackberries 18.7 33.7

Beets 9.18 21.4Graphite stick 300 2.99

Deionized water 0.12 .15Copper Tape 42.0 0.65

Gloves 91 1.20Weigh boat 81.5 0.34

Plastic pipette 81.5 0.09Washing materials after construction .01 1.53

Total (Blackberry cell) - 363Total (Beet cell) - 350

Table 1: The embedded energy for relevant materials of fabrication[7],[24],[8],[2],[20],[4]. For data onthe embedded energy of a given material for module, a module is assumed to have a surface area of 1m2.

In looking at these embodied energies, some assumptions were made. For many of the implements

used in fabricating the cell (rubber gloves, weigh boats, plastic pipettes, etc.), no comprehensive life

cycle analysis has previously been conducted, so embedded energies are unclear. For the purposes

of our life cycle analysis, we have made the assumption that the embedded energies in the materials

themselves is roughly equivalent to the embedded energy of the consumer product. For example, we

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consider the embedded energy of a rubber glove to be the embedded energy of that quantity of rub-

ber. Another assumption made was to consider the embedded energy of the tin oxide layer of the glass

negligible. The mass of tin oxide included in the coating layer is orders of magnitude smaller than the

glass, such that it is adequate to include the embedded energy of the glass. The embedded energy data

comes from databases frequently relied upon in LCA literature as a source of the embedded energies in

materials[7],[24],[8],[2],[20],[4]. Many of these databases focus on the embedded energy in materials

required for construction and energy production (i.e. biodiesel production). In the absence of collecting

holistic and harmonized data for a database specific to the materials included in DSCs, some assumptions

were made about materials we were unable to locate in these databases. The value used for embedded en-

ergy of acetic acid was a published value for hydrochloric acid, but we assume that the embedded energy

in the two acids are of similar magnitudes (which is low relative to other materials) and use the available

data[2]. We also make the assumption here that the povidine iodine solution contributes a negligible

amount of embedded energy to the materials. Previous papers have established that this contribution is

less than 2% of the production process, so we can consider that contribution to be included within the

uncertainty in our result[21].

The data on blackberries and beets come from an analysis by Dr. Paul McNulty in the Department

of Agricultural and Food Engineering at the University College of Dublin. Here, the intended purpose

of the data was to determine the energy intensity of different agricultural products, as well as different

food processing procedures (i.e. curing meat, preparing frozen vegetables, etc.). The energy intensities

published describe the energy input required per unit of metabolizable food energy produced. Therefore,

this figure is dimensionless and independent of relative water, ash and fiber concentrations[19]. These

data then had to be converted from a dimensionless energy intensity to a value of energy input (in mega-

joules) per kilogram of material. The energy intensities published were 5.1 for fresh vegetables and 10.4

for fresh fruit. We assume here that the difference in energy intensities across fruit and vegetable culti-

vation are representative of the difference between blackberry and beet production specifically. This is a

fair assumption because the cultivation process of blackberries is significantly more water intensive than

that of beets, and so such a difference is expected. In general, fruit production is more water intensive

than vegetables, so this parallel provides justification for our assumption.

Figures 11 and 12 show the distribution of the magnitude of embedded energies contained in indi-

vidual materials used to fabricate the cells, where it is clear to see the that large majority of embedded

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Process Wattage of Equipment (W) Time Used (hrs) Total Energy (MJ/module)Using scale 1.8 .08 5.4e-4

Heat plate at 540 180 31 20.1Total - - 20.1

Table 2: The process energy for steps requiring electrical energy. Data on wattage and time utilized perm2 comes from the specific implements and procedure we used in fabrication.

Figure 11: The distribution of embedded energy in materials used for the beet DSC.

energy is contributed by the glass. The next highest contributions, TiO2 and the dyes themselves, are

approximately a factor of ten smaller. This suggests that massive improvements could be made with

a switch to a conductive plastic or some other less energy-intensive material as the substrate for these

cells. Figure 13 shows the distribution of overall energy inputs between process energy and energy em-

bedded in materials. This figure, again, emphasizes that the embedded energy of the glass has a massive

contribution, given that the process energy itself accounts for approximately 10% of the total energy

input.

One important consideration in any life cycle analysis is the end-of-life analysis. Currently, our

model accounts for the water and ethanol used to clean the module and dispose of materials that cannot

be reused. Therefore, this model considers the creation, use and disposal of a dye-sensitized cell, given

17

Figure 12: The distribution of embedded energy in materials used for the blackberry DSC.

that all materials are newly manufactured or produced prior to fabrication. However, as we have proven

with experimental testing, after cleaning with ethanol and water, the glass with deposited tin dioxide can

be reused. Therefore, the 204 MJ of energy embedded in the glass per m2 module are a one time energy

input that can serve for the fabrication of multiple modules after the lifetime of one has expired. One

way to incorporate this consideration into our analysis is to assume that the quantity of glass used in a

single m2 module can be reused five times. Here, five is somewhat arbitrary. We have demonstrated that

the tin oxide coated slides retain their conductivity after five fabrications and cleanings, but presumably

at some point conductivity would drop from scratches on the tin layer and other imperfections that arise

from processing and recycling. It is likely that the glass could be used more than five times, but here

we use a number of recyclings that we have proven to be usable. To incorporate this, we consider the

embedded energy of glass contained in a single m2 module to be one fifth that of the embedded energy

in that same quantity of new glass since it is now distributed across 5 modules. Figures 14 and 15 show

the new distribution of embedded energies of materials with this adjustment in place. Figure 16 shows

the distribution of energy input between embedded and process energies for both the blackberry and the

beet DSCs.

18

Figure 13: The breakdown between embedded and process energy for the beet and blackberry DSCs. Alldata labels are in MJ.

It is clear that the ease with which this fabrication process enables the recycle and reuse of DSC

materials is key to its energy efficiency. Ongoing research into dye-sensitized cells often looks at how

to replace the energy intensive tin dioxide glass as a substrate with plastic or other options with low

energy intensity. However, since tin oxide glass doesn’t present concerns of toxicity, and given that its

recycle and reuse is so easy, glass might remain the best option. Overall, it is clear that dye-sensitized

cells require low energy inputs. Considering the silicon LCA presented in the previous section from

Stoppato, we can see that the silicon cell requires approximately 6 times the energy input of a DSC,

without considering recycling, and 10 times the energy input of a DSC assuming 5 recycles. However,

this energy input differential is irrelevant if the DSC doesn’t produce an adequate amount of energy. In

the next section we look at the efficiencies of these cells in order to calculate energy pay back times

(EPBT) and the overall ratio of units of energy produced per units of energy required.

19

Figure 14: The distribution of embedded energy in materials used for the beet DSC assuming the con-ductive glass is recycled five times.

Figure 15: The distribution of embedded energy in materials used for the blackberry DSC assuming theconductive glass is recycled five times.

20

Figure 16: The breakdown between embedded and process energy for the beet and blackberry DSCswith an adjustment made for recycling. All data labels are in MJ.

21

4 Comparing Performance

In order to compare the performance of the silicon cells and the DSCs, we designed a simple measuring

apparatus to collect the current response for the cells at different applied voltages. In this way, we

constructed a curve of current versus voltage, from which we were able to extract the open circuit voltage,

the short circuit current and the values of voltage and current for maximum power output. By considering

the maximum power output and the total power incident on the devices, efficiencies can be calculated.

4.1 Experimental Set-Up

The circuit diagram for the system we used to collect data is shown in Figure 17.

Figure 17: A schematic circuit diagram of the system used to collect IV curves.

The data collection can essentially be reduced to two steps: voltage application and current mea-

surement. A Rigol DG1022 Function Generator, controlled by a LabView program, was used to apply

different voltages, and a Rigol DM3058E digital multimeter measured the current output of the cell. A

preliminary experimental set-up was designed to display the IV-curves on an oscilloscope, for the pur-

poses of demonstrations, but without a LabView program to more finely control wait time between steps,

problems of hysteresis in the measurements arose. With the LabView program in place, current values

measured by the ammeter are recorded and written to a file by the LabView program. The voltage applied

by the function generator was not the actual voltage applied to the solar cell because the ammeter has a

50Ω resistance to ground. Therefore, the actual voltage applied to the solar cell is

Vactual = Vset − Imeas ∗ (50Ω) (1)

22

The LabView program made this 50Ω correction and recorded the values of Vactual and the corre-

sponding Imeas.

The LabView program also allowed the variation of 4 variables: voltage start, voltage stop, voltage

step size, and wait time between measurements. It became very clear in the collection of data that the

time required for current output to stabilize after changing the applied voltage was very different between

the dye-sensitized cell and the silicon cell, indicating drastically different time constants. Therefore, in

collecting IV curves, it was necessary to manually change the voltage and observe the stabilization time

prior to setting the wait time to collect an IV curve.

The short circuit current is the current for which voltage is zero, and the open circuit voltage is

expected to be approximately 500 mV for both the silicon and the dye-sensitized cell, so to capture

the full IV-curve we need to step over voltages beginning below zero, and ending above the open circuit

voltage. For some of the data collection with the dye-sensitized cells it was clear that the cell was hooked

up backwards (i.e. the measured short circuit current was positive and became more positive as incident

light intensity increased). For this data, we flipped the sign of the recorded voltage and current to get the

IV-curve in the standard form.

Finally, during the collection of IV curves we collected data with and without light. To take data for

cells without incident light, we covered the cell with a piece of cardboard and placed a Newport Model

835 Optical Power Meter under the cardboard to record the incident light intensity. To take data for cells

with incident light, we shone a flashlight on the cells. Figure 18 shows the apparatus for shining light.

The light was clamped at a height such that power was still approximately 10,000x more than incident

light when the samples were covered, but also such that the light was as uniform as possible. (When you

shine a flashlight closer to the sample, the curvature of the cover causes there to be pockets that are darker

than others.) Figure 19 shows the manner in which we aligned cells for measurement. The circle shows

the placement for the optical power meter and the ”tail” drawn shows the placement of the meter’s cord.

The two parallel lines show the alignment for the long edges of the DSC and silicon cells. The black tape

was later placed with its edge on the lower line to make placement easier.

4.2 Measuring methodology

Using the apparatus described in the previous section, we used the following procedure to collect the

values of short circuit current and open circuit voltage:

23

Figure 18: A picture of the light set-up.

1. For a run with light, turn on flashlight, being careful to hold clamp system in place so it will not

shift. For a run without light, place cardboard over the area of the table where the cell and the

optical power meter will be placed.

2. Place the sensor of the optical power meter in the traced position on the table and turn the optical

power meter to measure wavelengths at 500 nm. Record the incident power.

3. Remove the optical power meter and align the cell with the traced position on the table. Turn on

the function generator and manually set it to apply a sine wave with frequency 100Hz, amplitude

4.0 mV, and offset 0V. This is essentially applying a 0V signal to the cell and the resulting current

output will show the short-circuit current of the cell.

4. Set ammeter to measure DC current and wait until measurement stabilizes (approximately 10 sec-

onds for DSC, 1 second for silicon cell).

24

Figure 19: A picture of the alignment set-up. The picture on the left shows what is traced on the tabletop. The picture on the right shows a DSC sample aligned with the two parallel lines, the position usedfor taking measurements.

5. Turn off the output of the function generator and disconnect the ammeter. With a multimeter in

parallel with the cell, wait until voltage stabilizes and then measure the open circuit voltage.

6. Remove cell and replace with optical power meter. Record the incident power to check for drift in

the output of the flashlight over time.

7. Record the end time and, for the DSC, the age of the cell.

Once these data have been collected and it is clear roughly how long it takes for the system to

stabilize, a full IV-curve can be collected, using the following procedure:

1. As in the first set of procedure, set up the flashlight or cardboard and record incident power using

the optical power meter.

2. Replace the optical power meter with the cell and set the function generator to output a sine wave

with frequency 100Hz, amplitude 4.0 mV, and offset 0V. Set the ammeter to measure DC current.

3. Open LabView program and set voltage start, voltage stop, voltage step size, and wait time. Wait

time is the number of miliseconds to wait between changing the voltage and measuring the current

response, and should change proportional to the size of the voltage step. The collection of Isc and

Voc should give an idea of how long to make the voltage step.

4. LabView will prompt for a file location and name. The naming protocol used is SAMPLE-

NAME.light.SAMPLEAGE for DSCs with incident light, and ”light” is replaced with ”nolight”

for those with cardboard. Silicon cells do not have a sample age and are just named SAMPLE-

NAME.light or SAMPLENAME.nolight.

25

4.3 Data and Results

4.3.1 The Silicon Cell

In considering the efficiencies of the silicon cells tested, it is important to recall the discussion of equiva-

lent circuits in Section 2. Our measurement system does tell us the actual power we would produce if we

were to hook up the cells being measured to a load and operate it at the optimal voltage, but in looking

at IV curves and considering the values of shunt and series resistance, it is possible that we could see

ways in which to improve the cell’s efficiency (i.e. seeing a large series resistance might suggest there

is larger than expected bulk resistance in the connections, while seeing a large shunt resistance might

suggest there is a short somewhere we can’t see). This doesn’t change the maximum power that can be

produced by this specific cell, but it does suggest that limitations on power, and thus efficiency, aren’t

inherent to the technology, but rather could be improved upon.

Ultimately, in testing the silicon cell, we determined that shunt and series resistances were not sig-

nificant, since the IV-curves produced looked very similar to the expected ideal curve.

Figure 20: The IV curve for the Silicon cell. The illuminated cell had an incident power of 39mW, andthe non-illuminated cell had an incident power of 9nW.

Figure 20 shows the IV curve obtained for a silicon cell with a 1.5cm x 2cm surface area. The power

produced by a solar cell is the product of voltage and current, so by calculating IV for each data point on

the curve, we are able to plot the power versus voltage, which shows us the maximum power production

of the cell, as well as the ideal voltage to operate at in order to achieve this power. This plot of power vs.

26

voltage is shown in Figure 21.

From this plot, it is clear that the Silicon cell has a max possible power of .744 mW, which occurs

at a voltage of 300 mV. From this, and the measurement of incident power, we are able to calculate the

efficiency.

Figure 21: The plot of power vs. voltage for the Silicon cell. The illuminated cell had an incident powerof 39mW and the non-illuminated cell had an incident power of 9nW.

E f f iciency =Pout,max

Pin=.744mW39mW

= 1.89% (2)

This efficiency is significantly lower than that we might expect from a silicon cell. After analyzing

the system, we determined that this is likely due to our measurement of the input power. The optical

power meter we use is set to measure wavelengths of 500nm, and the spectrum of the flashlight we use

is different than that of the sun. Here, the result is that we measure a higher power for the incident

irradiation of the flashlight than that power actually available for solar conversion by the silicon cell. We

compared tests with natural sunlight and with the flashlight, and produced the results in Table 3.

The efficiency calculated with the sun as a light source is 2.3 times as high as the efficiency calcu-

lated using the flashlight as the light source. Since this is due to a discrepancy between the ratios of

measured power to actual incident power for the two different light sources, we apply this correction to

27

Light source Isc (A) Voc (V) Pin (W/cm2) EfficiencySun .094 .55 .135 12.77%

Flashlight .0056 .450 .015 5.6%

Table 3: Here, the ”efficiency” values we calculate are not the true efficiencies of the solar cells we have,but rather they are the product of the short circuit current and open circuit voltage divided by the inputpower. This is the efficiency if you assume a fill factor of 100, and is an adequate assumption to find theratio between efficiencies calculated with sunlight and efficiencies calculated with the flashlight as thelight source.

the efficiency we calculated, and determine that the efficiency of the silicon cell that we tested is in fact

4.3%. This still appears low for a standard silicon cell, but it could be reasonable given that the cells

we are testing are not state of the art and not optimized in standard ways. In future testing, given the

time and budget, it would be good to examine alternate light sources to replace the flashlight. Using a

source that had a spectrum more closely matching the standard solar spectrum and power density would

produce a more accurate IV curve and a better understanding of the actual efficiencies. For now, we use

this efficiency value. Any results produced from the same light source will provide results appropriate

for comparison.

As an alternative to efficiency, the cell performance can be expressed as watts produced per m2

assuming the standard amount of sunlight in. Available data show that in Nairobi, Kenya, the average

solar radiation is 5.3 kWh/m2/day[11]. Considering Nairobi to be a representative average irradiation

for East Africa, we can use this as the input power to consider the energy payback time (EPBT) for the

silicon cell.

Eout = 5.3kWh

m2day∗ 4.3% ∗ 1m2 = 19.08

MJday∗ 4.3% = .82

MJday

(3)

From Section 3.2, we know that the total energy inputs are:

Ein = 2298MJ (4)

Finally, we determine that the EPBT for this silicon cell is:

EPBT =EinEout

=2298MJ.82 MJ

day

= 2802days = 7.7years (5)

28

4.3.2 The Dye Sensitized Cell

Analyzing the dye-sensitized cell becomes slightly more complex. It is possible to simply consider the

IV-curves produced, but these curves seem to suggest that some parameter of the circuit is significantly

affecting its efficiency, since dye-sensitized cells in the literature have been shown to produce signifi-

cantly more power at a higher open circuit voltage.

Figures 22 and 23 show the unaltered data collected from a blackberry DSC sample.

Figure 22: The unaltered blackberry DSC IV curves. The light run is taken with an illumination of15mW/cm2, and the dark run with less than 5µW/cm2 of incident irradiation.

Figures 24 and 25 show the unaltered data collected from a beet DSC sample.

It is important to note that in the process of fabrication it was clear that the DSCs constructed using

beet dye did not adsorb the dye as well as those with blackberry dye. After soaking cells in blackberry

juice for ten minutes, the cells were a dark purple that did not disappear when rinsed with deionized

water and ethanol. However, the beet cells after ten minutes when rinsed lost all coloration. In order

to ensure even minimal adsorption, we soaked the beet cells overnight, and even after this process they

were not as dark as the blackberry soaked slides. We would expect the magnitude of adsorption to have a

proportional relationship with photoresponse and thus power produced, an effect we ultimately see when

we compare Figures 32 and 33.

29

Figure 23: Zooming in on the origin of the blackberry IV curves - here you can see the linear behaviorof the IV characteristic, implying an ohmic device. The slope of the line here is 1.9e-4 A

V , and the inverseof the slope (which has units of Ω) is 5,279Ω.

Figures 26 and 27 show the unaltered data for the power output of the blackberry and beet DSCs as

a function of voltage.

In the article by Gratzel, et al. that established dye-sensitized cells with anthocyanin from blackber-

ries as the sensitizer, it was established that the the current density produced is between 1 and 2mA/cm2,

where the relevant area is that of semiconductor saturated with anthocyanin. With an open circuit voltage

between 300 and 500mV, this translated to an efficiency between 0.5% and 1%[25].

Similarly, looking at the results of Hernandez-Martinez, et al., we see the demonstration that a DSC

prepared with beet juice acting as the sensitizer results in a current density of Jsc = 2.71 ± .003mA/cm2

and an efficiency of 0.89% ± .006%[10].

Knowing that such efficiencies and such short circuit currents are achievable, we have sought to

analyze our data to see what in the fabrication of our cells could be causing such low efficiencies and

such little differences between runs with and without incident light. Looking at the data, it seems as

though very low parallel shunt resistances, providing a path for current to flow in the opposite direction

30

Figure 24: The unaltered beet DSC IV curves. The light run is taken with an illumination of 15mW/cm2,and the dark run with less than 5µW/cm2 of incident irradiation.

of the diode, could be the problem with our cells. A very low shunt resistance, if you consider the

model presented in Section 2.2, would lower measured current and voltage values. The lower open

circuit voltage from our data (our measured Vocs are essentially zero, whereas previous experimental

work shows hundreds of mVs) is the biggest difference between expected and actual data, given that our

Isc is nonzero, albeit very low. In our data in particular, it seems as though this shunt resistance effect is

strongest around zero current and zero voltage, suggesting that the presence of a large net field in the cell

might affect which component of the cell has a dominating affect. Figures 23 and 25 show that for these

low voltages, the IV curve is completely linear, suggesting an ohmic relationship and perhaps the fact

that the photodiode is having negligible effect while the shunt resistance as a current path dominates.

In order to see if there is diode-like behavior in our data underneath the effect of this shunt resistance,

we adjust the measured current as though there is a fixed resistance in parallel with the diode. Where the

graph is linear, if that truly is ohmic behavior, the resistance in parallel is the inverse of the slope. Taking

this resistance, we add the current across the shunt (I = V/R, where V is the applied voltage and R is the

resistance found for low voltages) back to the current we measured. Thus, the adjusted current values

31

Figure 25: Zooming in on the origin of the beet IV curves - here you can see the linear behavior of theIV characteristic, implying an ohmic device. The slope of the line here is 6.4e-7 A

V , and the inverse of theslope (which has units of Ω) is 1,561,670Ω.

reflect the total current generated as the light absorbed by the dye creates and separates excitons.

This shunt resistance could come from a few different issues with fabrication. Multiple small shorts

across the cell would provide low resistance paths for current, and could be caused by small patches in

which the TiO2 layer is missing or deteriorated where the tin dioxide surfaces are in contact. This would

occur mostly around the edges of the TiO2 layer, where it is possible that the binder clips clamp the two

slides together at an angle such that there is a short. Another potential source of low shunt resistance

would be the backwards flow of electrons across the TiO2/dye/electrolyte, which could be caused by too

wide of an active area. For longer widths, more distance must be traveled by electrons in order to be

collected, so the likelihood of recombining with a hole and diminishing the current is higher.

Figures 28 and 29 show the adjusted IV curves for the blackberry and beet DSCs.

Figures 30 and 31 show the adjusted data for the power output of the blackberry and beet DSCs as a

function of voltage.

However, even with these adjustments the characteristics of the data do not closely resemble those

of the expected IV curves. Here, the curves shown in Figures 28 and 29 look roughly symmetrically,

whereas the expected IV characteristic for a diode would show a distinct difference between applying a

forward and reverse bias. Instead of seeing a nonzero turn-on voltage, and negligible current values for

32

Figure 26: The power vs. voltage curve for the blackberry DSC. This plot suggests that the maximumpower produced by the DSC is essentially zero, and for other applied voltages there is a power loss. Thisindicates that there is a very large shunt or series resistance that is dominating the effect of the diode inthe equivalent circuit model.

small reverse bias voltages, here we see curves which pass through the origin, whether or not there is

incident light. This could be a result of significantly lower shunt resistance than we determined from our

calculation of slope (which was 1.6 MΩ for the beet cell and 5.3kΩ for the blackberry cell), or from the

effect of other aspects of the cell, like high series resistances.

In order to determine if this effect is a product of low shunt resistance, we attempted further rounds of

fabrication, this time introducing the use of parafilm to segregate the conducting surfaces of the cell from

each other, as well as seal the cell against rapid evaporation. Rapid evaporation would result in limited

mobility (given that the electrolyte enables electron-hole movement), creating a large series resistance.

If the conducting surfaces are not fully isolated from each other, this would produce shorts in the cell

which would create paths of low shunt resistance. By introducing parafilm, we hope to fix both of these

sources of unwanted resistance. The procedure for applying parafilm is included in the Appendix, and

Figures 32 and 33 show the un-altered IV-curves produced with this new procedure.

These curves look significantly better, and show non-negligible (although low) values for Voc and Isc.

Still, when we consider the efficiency values, we find a power output of 2.56e-9 W/cm2 for the blackberry

cell and 9.45e-16 W/cm2 for the beet cell. With a measured input of 15mW/cm2 for each of these cells,

these output powers correspond to efficiencies many orders of magnitude smaller than 1%. However,

33

Figure 27: The power vs. voltage curve for the beet DSC. Like the blackberry power curve, this plotsuggests that the maximum power produced by the DSC is essentially zero, and for other applied voltagesthere is a power loss. This indicates that there is a very large shunt or series resistance that is dominatingthe effect of the diode in the equivalent circuit model.

we find it heartening that a small change to fabrication procedure resulted in such a large improvement

in output power and efficiency, and with continued adjustment in order to improve upon the unwanted

resistance values, this basic set of material inputs and processes could produce DSCs with efficiencies

that match those shown by Gratzel, et al. and Hernandez-Martinez, et al. Therefore, in a preliminary

comparison of energy inputs from our LCA and efficiencies for the beet and blackberry DSC, we consider

the efficiency values presented by Gratzel, et al. and Hernandez-Martinez, et al.: .75% efficiency for the

blackberry cell and 0.89% ± .006% efficiency for the beet cell.

Using the same method of calculation as in Section 4.3.1 we determine the energy payback time for

the DSC technologies for the case of deployment in Nairobi, Kenya, where average solar radiation is 5.3

kWh/m2/day[11]. Considering Nairobi to be a representative average irradiation for East Africa, we can

use this as the input power to consider the energy payback time for the dye cells. For efficiencies from

state-of-the-art and expensive fabrication methodologies, as well as the low energy inputs and process

energies, the EPBT appears competitive. Table 4 shows a summary of the results.

34

Figure 28: The adjusted blackberry DSC IV curves. The light run is taken with an illumination of15mW/cm2, and the dark run with less than 5µW/cm2 of incident irradiation.

Figure 29: The adjusted beet DSC IV curves. The light run is taken with an illumination of 15mW/cm2,and the dark run with less than 5µW/cm2 of incident irradiation.

35

Figure 30: The power vs. voltage curve for adjusted beet IV curves. This run is for an illuminated cellwith an incident light intensity of 15mW.

Figure 31: The power vs. voltage curve for adjusted blackberry IV curves. This run is for an illuminatedcell with an incident light intensity of 15mW.

36

Figure 32: The IV curve for a blackberry DSC with parafilm integrated into the fabrication procedure.

Figure 33: The IV curve for a beet DSC with a parafilm integrated into the fabrication procedure.

37

Type of Cell Ein (MJ) Eout (MJ/day) EPBT (years)Blackberry (no recycle) 383 .14 7.3

Blackberry (recycle) 219 .14 4.2Beet (no recycle) 371 .17 6.0

Beet (recycle) 207 .17 3.3

Table 4: The energy inputs, outputs and payback times for the dye-sensitized cells fabricated in this paper.The energy inputs are determined by our LCA in Section 3.3, whereas the energy outputs are determinedbased upon the efficiencies published by Gratzel et al. for the blackberry cells and Hernandez-Martinez,et al. for the beet cells.

38

5 Conclusion

When we consider the efficiencies of different technologies, it is often difficult to assess the true costs and

true inputs. The globalization of resource supply and materials manufacture makes it even more difficult

to ascertain the amount of energy required to produce a single unit of energy from a given technology.

In this paper we have sought to evaluate the detailed sum of embedded energy of materials as well as the

process energy of fabrication for dye-sensitized solar cells constructed with natural dyes as sensitizers,

and compare these results with that of traditional silicon solar cells.

Table 5 shows a summary of our findings, showing the energy inputs, as assessed by this paper in

Section 3, the energy outputs, as published in papers by Gratzel, et al. and Hernandez-Martinez, et al.,

and the energy payback lifetimes as evaluated with these inputs and outputs.

Type of Cell Ein (MJ) Eout (MJ/day) EPBT (years)Blackberry (no recycle) 383 .14 7.3

Blackberry (recycle) 219 .14 4.2Beet (no recycle) 371 .17 6.0

Beet (recycle) 207 .17 3.3Silicon 2298 .82 7.7

Table 5: Energy inputs and outputs for all of the photovoltaic cells considered in this paper. For the fourdye-sensitized cells, the energy inputs are determined by our LCA in Section 3.3, whereas the energyoutputs are determined based upon the efficiencies published by Gratzel et al. for the blackberry cellsand Hernandez-Martinez, et al. for the beet cells.

Looking forward, further improvement of the fabrication methodology is required. Over the course

of our work, small adjustments were made to the fabrication procedure such that the efficiency improved

by multiple orders of magnitude. This is in due largely to reduction in some of the series resistance

of the DSC and eliminating small shorts in the cell, which contributed alternate paths for current to

flow with low shunt resistance. One further improvement that could be made would be to shorten the

spacing between the cathode and anode in our cell. At present, it is on the order of 100 microns given

the use of relatively thick parafilm as a sealant and electric insulator. The fact that this length is so

long (compared to DSCs fabricated by Gratzel, et al. and Hernandez-Martinez, et al.) means that free

electrons have a longer time to recombine with holes before reaching the electrode, decreasing the short

circuit current and open circuit voltage. In general, investigating the sources of high series resistance and

low shunt resistance and diminishing their effect via adjustments to the fabrication procedure could bring

39

the efficiencies of our low energy intensive cells up to the level of the state-of-the-art and expensive cells

produced in other papers, with efficiencies nearing 1%. All in all, dye-sensitized cells are a promising

technology that could provide low cost energy with low energy inputs in areas where high costs and high

energy inputs are barriers to electrification.

40

6 Acknowledgements

Special thanks to Dr. Stephen Teitsworth for advising this project and Dr. Yuriy Bomze for assistingwith the experimental design and theoretical considerations.

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