Faculty of Science (ETDs)

Permanent URI for this community

https://hdl.handle.net/10539/37933

Browse

Now showing 1 - 10 of 10

Colloidal synthesis and characterization of molybdenum and tungsten-based phosphide electrocatalysts for hydrogen evolution reaction
(2022) Nkabinde, Siyabonga Sipho; Moloto , Nosipho
The production of hydrogen gas via hydrogen evolution reaction (HER) in acidic media has become an important area of research in light of the increasing demand for sustainable and environmentally friendly sources of energy. However, its large-scale production is currently being hindered by the lack of inexpensive and highly efficient non-noble electrocatalysts. Transition metal phosphides (TMPs) have transpired as favourable catalysts that can be prepared from cheap and readily available sources. Up to now, TMPs have been commonly prepared using solid-state and solid-gas reactions, which rely on the use of high temperatures and hence generate inhomogeneity in the prepared materials. Inhomogeneous materials are unattractive as catalysts because the correlation between a catalyst and its structural features cannot be systematically studied. For this reason, colloidal synthesis has emerged as a powerful method in the synthesis of TMPs as it allows for control over the resulting physical features (i.e. size, morphology, crystal phase, crystallinity etc.). The ability to tailor these physical properties provides room for improving the catalytic activity. By using the colloidal synthesis method, we have successfully prepared molybdenum and tungsten-based phosphide nanoparticles and studied the effect of their physical features on HER activity. In chapter 3, we report a facile colloidal synthesis method to produce an amorphous phase of molybdenum phosphide (MoP) by using trioctylphosphine (TOP) as a phosphorus source, molybdenum pentachloride (MoCl5) as a metal source and 1-octadecene (1-ODE) as a solvent/reducing agent. The use of the forementioned precursors promoted the formation of very small, shape controlled and well dispersed amorphous molybdenum phosphide (MoP) nanoparticles. Annealing (800 °C) of the amorphous MoP nanoparticles resulted in the formation of a crystalline MoP phase with a slightly bigger size but retained its dispersity and morphology upon exposure to high temperature. The amorphous and crystalline MoP phases were compared as HER electrocatalysts. HER results indicated that the amorphous MoP phase exhibited enhanced catalytic activity in hydrogen evolution reaction compared to the crystalline MoP phase. The high activity displayed by the amorphous MoP was attributed to the small sizes and the high density of unsaturated active sites characteristic of nanoparticles lacking long range crystalline order.
Deep learning models for defect detection in electroluminescence images of solar PV modules
(University of the Witwatersrand, 2024-05-29) Pratt, Lawrence; Klein, Richard
This thesis introduces multi-class solar cell defect detection (SCDD) in electroluminescence (EL) images of PV modules using semantic segmentation. The research is based on experimental results from training and testing existing deep-learning models on a novel dataset developed specifically for this thesis. The dataset consists of EL images and corresponding segmentation masks for defect detection and quantification in EL images of solar PV cells from mono crystalline and multi crystalline silicon wafer-based modules. While many papers have already been published on defect detection and classification in EL images, semantic segmentation is new to this field. The prior art was largely focused on methods to improve EL image quality, classify cells into normal or defective categories, statistical methods and machine learning models for classification, object detection, and some binary segmentation of cracks specifically. This research shows that multi-class semantic segmentation models have the potential to provide accurate defect detection and quantification in both high-quality lab-based EL images and lower-quality field-based EL images of PV modules. While most EL images are collected in factory and lab settings, advancements in imaging technology will lead to an increasing number of EL images taken in the field. Thus, effective methods for SCDD must be robust to various images taken in the labs and the real world, in the same way that deep-learning models for autonomous vehicles that navigate the city streets in some parts of the world today must be robust to real-world environments. The semantic segmentation of EL images, as opposed to image classification, yields statistical data that can then be correlated to the power output for large batches of PV modules. This research evaluates the effectiveness of semantic segmentation to provide a quantitative analysis of PV module quality based on qualitative EL images. The raw EL image is translated into tabular datasets for further downstream analysis. First, we developed a dataset that included 29 classes in the ground truth masks in which each pixel was coloured according to the class. The classes were grouped into intrinsic “features” of solar cells and extrinsic “defects.” Next, a fully-supervised U-Net trained on the small dataset showed that SCDD using semantic segmentation was a viable approach. Next, additional fully-supervised deep-learning models(U-Net, PSPNet, DeepLabV3, DeepLabV3+) were trained using equal, inverse, and custom class weights to identify the best model for SCDD. A benchmark dataset was published along with benchmark performance metrics. The model performance was measured using mean recall, mean precision, and the mean intersection over union (mIoU) for a subset of the most common defects (cracks, inactive areas, and gridline defects) and features (ribbon interconnects and cell spacing) in the dataset. This work focused on developing a deep-learning method for SCDD independent of the imaging equipment, PV module design, and image quality that would be broadly applicable to EL images from any source. The initial experiment showed that semantic segmentation was a viable method for SCDD. The U-Net trained on the initial dataset with 108 images in the training dataset produced good representations of the features common to most of the cells and good representations of the defects with a reasonable sample size. Other defects with only a few examples in the training dataset were not effectively detected in this model. The U-Net results also showed that themIoU measured higher for the features compared to the defects across all models, which correlated with the size of the large features compared to the small defects that each class occupies in the images. The next set of experiments showed that the DeepLabv3+ trained with custom class weights scored the highest in terms of mIoU for the selected defects and features when compared to the alternative fully-supervised models. While the mIoU for cracks was still low (25%), the recall was high (86%). While increasing the recall substantially, the many long, narrow defects (e.g. cracks and gridlines) and features (e.g. ribbon interconnects and spacing) in the dataset were challenging to segment, especially at the borders. The custom class weights also tended to dilate the long, narrow features, which led to low precision. However, the resulting representations reliably located these defects in the complex images with both large and small objects, and the dilation proved effective at visually highlighting the long-narrow defects when the cell-level images were combined into module-level images. Therefore, the model prove useful in the context of detecting critical defects and quantifying the relative size of the defects in EL images of PV cells and modules despite the relatively low mIoU. The dataset was also published along with this paper. The final set of experiments focused on semi-supervised and self-supervised models. The results suggested that supervised training on a large out of-domain (OOD) dataset (COCO), self supervised pretraining on a large OOD dataset (ImageNet), and semi-supervised pretraining (CCT) were statistically equivalent as measured by the mIoU on a subset of critical defects and features. A new state-of-the-art (SOTA) for SCDD was achieved, exceeding the mIoU from the DeeplabV3+ with custom weights. The experiments also demonstrated that certain pretraining schemes resulted in the ability to detect and quantify underrepresented classes, such as the round ring defect. The unique contributions from this work include two benchmark datasets for multi-class semantic segmentation in EL images of solar PV cells. The smaller dataset consists of 765 images with corresponding ground truth masks. The larger dataset consists of more than 20,000 unlabelled EL images. The thesis also documents the performance metrics from various deep learning models based on fully-supervised, semi-supervised, and self-supervised architectures
Development of eco-friendly building bricks derived from carbon nanotube-reinforced coal ash and low-density polyethylene waste materials
(University of the Witwatersrand, Johannesburg, 2024) Makgabutlane, Boitumelo; Maubane-Nkadimeng, M.S.; Coville, N.J.
This study reports on the incorporation of carbon nanotubes (CNTs) into the all-waste derived building bricks. The focus was on waste management and beneficiation of plastic waste and coal ash, which are generated in large volumes without sufficient recycling. The waste materials were characterized using a range of techniques to ascertain their properties for application. Multiwalled carbon nanotubes (MWCNTs) were synthesized using a facile floating chemical vapour deposition method (CVD) and their physicochemical properties were tested. Bricks with dimensions of 220 x 105 x 70mm were developed with an optimum 85:15 coal ash to plastic waste ratio respectively using a specialized reactor. The bricks were tested for compressive strength, split tensile strength, water absorption, strain, thermal stability and durability using oxygen permeability index, chloride conductivity index and water sorptivity index as indicators. Furthermore, environmental and financial sustainability and ecotoxicology were tested. At optimum conditions, high quality MWCNTs with a diameter of 83 nm, length of 414 μm and a carbon yield of 73% were obtained. The ID/IG ratio of 0.44, an oxidation temperature of 649 °C, a purity of 94% and surface area of 50.9 m2/g were achieved. Coal fly ash with a spherical shape, particle size of below 10 micron and a thermal stability of 680 °C was used as an aggregate for the bricks. The bricks (without CNTs) developed their maximum compressive strength of 11.9 MPa at 14 days. The incorporation of the CNTs improved the microstructure of the bricks by filling the voids and providing a bridging effect as reinforcement mechanisms. The optimum CNT loading of 0.05 wt.% produced bricks with a compressive strength of 22 MPa and tensile strength of 8.7 MPa, which exceeded the South African National Standards (SANS227:2007) requirements for building bricks by 450% and 625% respectively. The durability properties were improved as the CNT dosage was increased from 0-10 wt.%. The 0.05 wt.% bricks were categorized as “good” for all the durability indexes. The CNT containing bricks showed improved thermal stability and maintained their structural integrity. The chemical resistance also improved and the efflorescence was minimal on all the bricks. The utilization of waste in the bricks enabled resource conservation, reduced pollution and reduced cost compared to conventional bricks. When only considering the raw materials used, the cost of production per brick was $0.091. The ecotoxicology of the powdered brick samples was tested on Raphidocelis subcapitata (microalga) and Daphnia magna (aquatic organism) using leachates from neutral, acidic and basic mediums. Some heavy metals were leached above the threshold limit especially in acidic medium. The leachates were toxic to the test species at low concentrations and resulted in growth inhibition of the microalga and immobization of the aquatic organisms. The toxicity of the CNTs was inconclusive and dedicated tests are required to study their effect. With appropriate treatment of CFA, the waste derived CNT bricks have a great potential of being a sustainable alternative to the conventional bricks based on cost, properties and environmental impact
From Coal to Renewable Energy: Perspectives on South Africa's Energy Transition for a Sustainable Future.
(University of the Witwatersrand, Johannesburg, 2024) Sebele, Temperance; Simatele, Mulala Danny
South Africa has been experiencing an unstable electricity supply for years, leading to periods of load shedding from 2007 up to the present date. The electricity shortages have been attributed to distinct reasons, ranging from inefficient coal supply, skills shortages, sabotage by employees and lack of maintenance for nearly sixteen years. In addition to the electricity supply shortages, coal-fired electricity generation is responsible for roughly 80 per cent of South Africa’s total greenhouse gas emissions due to fossil fuel dependence, leading to many health, climate, and environmental challenges. To address the challenges related to fossil fuel dependency, moving to Renewable Energy sources that are climate and environmentally friendly is a necessity. The aim of this study was to investigate the optimal approaches that South Africa can embark on for a successful transition from coal to renewables. The institutional, policy, and strategic frameworks that exist within which South Africa can embark on for a successful transition were explored. Furthermore, the study sought to identify the challenges, and opportunities that exist or hinder the transition in South Africa. Lastly, the study explored how developments in the international policy frameworks influence South Africa’s ambitions to transition to renewables. The study is best suited to the pragmatism approach, and data were collected through the reviewing of literature, key-informant interviews, and questionnaires. A mixed-methods strategy that involved gathering both qualitative and quantitative data was employed and primary and secondary sources of data were used. The primary data sources used included key informants from various private and public institutions with an interest in South Africa’s energy matters such as ESKOM, SANEDI, SANEA, SAREC, SAPVIA, SAWEA, SAIPPA and NECSA. The non-probability sampling method was used in the participants’ selection from the sampled study institutions, with a combination of judgmental, snowballing and convenience sampling procedures employed at distinct phases of the research. Data collected was analysed both quantitatively and qualitatively, with interviews text data transcribed and analysed through manual tabulation and thematic analysis, and presented in graphs generated from Microsoft Excel, and the data from questionnaires analysed through the IBM Statistical Package for the Social Sciences (SPSS) software. The study revealed that the government played mainly four leading roles in the energy transition, which were providing financial support, legislative direction, institutional direction, and project oversight. Financial support is provided through financing projects and setting up financing policies that promote renewable energy investment, and legislative direction is provided through policy development and ensuring efficient implementation. Providing institutional direction is ensured through ensuring coordination across all spheres of government and capacitating institutions involved in the transition, and project oversight is provided through setting out renewable energy capacity determinations. The study further identified key energy transition elements, namely infrastructure, governance, legislation, stakeholders’ perceptions, and skills and strategies for a successful transition, which included channelling adequate financial resources to the renewable energy sector, privatisation of the electricity utility, diversification, rolling out bid windows, improving the legislative framework, improving grid access and integration, skills development, localisation of RE components manufacturing, providing incentives, and increasing consumer awareness about renewables. Several barriers to the transition were also identified, which included political interference and corruption, lack of financial investment, policies/legislation inadequacy, inconsistency in rolling out bidding windows, ESKOM’s monopoly, high cost of renewables, deficiency of incentives, skills and technology, labour unions, and deficiency of awareness on alternatives. The study recommends multisector reskilling of employees, since not all employees in the coal value chain may be interested in or able to be absorbed in the Renewable Energy sector. Furthermore, the government should fund and support progressive technologies and business models, improve the quality of institutions through merit-based appointments and uprooting corruption, privatisation of ESKOM to create opportunities for new entrants in the electricity market and improve stakeholder engagement and community support programmes. The UNFCCC must develop and ensure the implementation of enforcement strategies for holding countries accountable for their climate commitments for the transition to be realised
Integration of Sustainable Development Principles and Climate Change Adaptation Measures in Energy Optimization in Gold Mining in South Africa
(University of the Witwatersrand, Johannesburg, 2023-08) Nadunga, Irene; Simatele, Mulala Danny
South Africa located in the sub-Saharan African region and being a mining-intense country, is reported to be affected by extreme weather events which are increasing the country’s vulnerability to climate change impacts and therefore reducing the chances of achieving sustainable development. In light of this, mining companies are being pressured to make strong commitments towards implementation of sustainable development principles for sustainable mining. This study therefore aimed at investigating how sustainable development principles and climate change adaptation measures are interlinked and structured; and embedded in a gold mining company’s policies and strategies, in an effort to build the mining operations’ adaptive capacity and resilience against the impacts of climate change and achieve energy optimization. The challenges that can potentially prevent the effective integration of the sustainability principles and adaptation measures were also explored. Using a case study approach, this study was centered on the gold mining operations located within the Witwatersrand Basin of South Africa. Research data was collected from multiple sources, therefore employing a mixed method approach by applying the concurrent triangulation technique. Different analytical tools of policy, content and inductive data analysis, and descriptive statistical data analysis were applied. The empirical evidence shows that the gold mining operations are faced with increasing operating costs associated with the increased energy consumption and implementation of costly mining practices in an effort to combat the impacts of extreme weather events caused by climate change. This affirms that a relationship exists between climate change and energy use in gold mining. In an effort to address climate related risks and energy security, gold mining operations are implementing energy efficiency measures and using renewable energy in their energy mix; which measures are seen to integrate sustainability principles, therefore adopted as sustainability adaptation measures. In addition, some mining company policies and strategies are also seen to integrate sustainability principles and adaptation measures, in an effort to guide the mining operations in effectively developing and implementing sustainability adaptation measures, designed to holistically address climate related risks and energy security. This affirms that a relationship exists between sustainable development principles, climate change adaptation measures and energy optimization. This therefore, implies that sustainability principles and adaptation measures can be integrated to form sustainability adaptation measures, and that gold mining companies have the potential to achieve sustainable mining and contribute to sustainable development, particularly achieving SDG 7 and SDG 13.
Magnetic enhancement of a high entropy spinel oxide electrocatalyst for rechargeable zinc-air batteries
(University of the Witwatersrand, Johannesburg, 2024) Hechter, Ernst Heznz; Ozoemena, Kenneth
The exploration of high entropy materials (HEMs) as electrocatalyst materials has only recently begun to accelerate. Similarly, the effect of magnetic fields on the oxygen evolution and reduction reactions has recently begun to attract great interest. In this work nanoparticles of the high entropy oxide (CuCoFeMnNi)3O4 were synthesized and supported on Vulcan carbon for use as a bifunctional OER/ORR catalyst in a rechargeable zinc-air battery (RZAB). The products were characterized to confirm and investigate the solid solution high entropy phase, and the electrochemistry was investigated with and without an external magnetic field. The HEMs demonstrated moderate intrinsic electrochemical properties, with overpotentials and current densities comparable to commercial platinum on carbon catalysts even at low loadings. Here is reported the most significant magnetic enhancement in RZAB power profile in literature at the time of writing, as well as improved RZAB stability and areal energy. This work offers insight into the mechanism of magnetic enhancement in the case of high entropy materials, and pioneers the use of combined strategies to achieve stable, cost-efficient and effective bifunctional OER/ORR electrocatalysis.
Optimization of gallium oxide (ga2o3) nanomaterials for gas sensing applications
(University of the Witwatersrand, Johannesburg, 2024) Gatsi, Nyepudzai Charsline
Gas sensors are needed for monitoring different gases in indoor and outdoor environments, food quality assessment, and health diagnostics. Among materials studied for these applications, semiconducting metal oxides (SMOs) have generated a lot of interest due to their excellent sensitivity, simple circuit, and low cost. One-dimensional (1𝐷) 𝐺𝑎2𝑂3 nanomaterials are part of the promising candidates explored for the sensing of different gases due to their excellent electrical conductivity, high catalytic behavior, and chemical and thermal stability. This study reports the optimization of crystal structure, morphology, and surface chemistry of 𝐺𝑎2𝑂3 nanostructures for use in the detection of various gases. A set of unmodified and noble metal modified 1𝐷 𝐺𝑎2𝑂3 nanomaterials were synthesized by microwave-assisted hydrothermal method followed by heat-treatment at different temperatures and their gas sensing performances were systematically studied. The samples were characterized by thermogravimetric analysis (TGA), X-ray diffraction (XRD), Raman analysis, scanning electron microscope (SEM), transmission electron microscope (TEM), Brunauer-Emmett-Teller (BET), photoluminescence (PL), diffuse reflectance spectroscopy (DRS), and X-ray photoelectron spectroscopy (XPS) methods. The effects of heat-treatment temperatures on phase transformations and gas sensing performances of various 𝐺𝑎2𝑂3 polymorphs were investigated. The 𝛼 − 𝐺𝑎2𝑂3, 𝛽 − 𝐺𝑎2𝑂3 and 𝛼/𝛽 − 𝐺𝑎2𝑂3 crystal structures were synthesized and evaluated for gas sensing. The 𝛽 − 𝐺𝑎2𝑂3 sensing layers presented selective response coupled with fast response/recovery times towards carbon monoxide (𝐶𝑂) compared to the 𝛼 − 𝐺𝑎2𝑂3 and 𝛼/𝛽 − 𝐺𝑎2𝑂3 crystal structures. The observed variations in the gas sensing performances of these three crystal structures were attributed to controlled properties of different 𝐺𝑎2𝑂3 polymorphs. Furthermore, the 𝛽 − 𝐺𝑎2𝑂3 polymorph was prepared in the form of regular and hierarchical nanorod-based morphological features which demonstrated different gas sensing behaviors. The 𝛽 − 𝐺𝑎2𝑂3 regular nanorods showed better capabilities of detecting isopropanol than the nanobundle-like and nanodandelion-like features, and these differences were attributed to changes in textural, porosity, and compositional properties related to different morphologies. The effects of incorporating 𝐴𝑔 and 𝐴𝑢 noble metal nanocrystals on regular 𝛽 − 𝐺𝑎2𝑂3 nanorods surfaces on their gas sensing behaviour were also investigated. The results revealed that surface modification of 𝛽 − 𝐺𝑎2𝑂3 nanorods with 0.5 and 1.0 𝑚𝑜𝑙% 𝐴𝑔 and 𝐴𝑢 noble metals significantly lowered the sensor operating temperature compared to that of unmodified 𝛽 − 𝐺𝑎2𝑂3 nanorods towards the detection of ethylene. In addition, surface incorporation of 1.0 𝑚𝑜𝑙% 𝐴𝑔 dramatically increased the sensor sensitivity and selectivity and reduced the response/recovery times towards ethylene gas, and these positive changes were attributed to the electronic and chemical sensitization effects stimulated by the catalytic activity of 𝐴𝑔 nanocrystals incorporated on the surface of 𝛽 − 𝐺𝑎2𝑂3 nanorods. This study unambiguously optimized the crystal structure, morphology, and surface chemistry of 𝐺𝑎2𝑂3 nanostructures for the detection of carbon monoxide, ethylene and isopropanol gases. These sensors may potentially be used in real-time detection of carbon monoxide and isopropanol for indoor air quality monitoring to improve human health. In additional they have also demonstrated capabilities for the precise and economical detection of ethylene around plants and fruits, which could be beneficial to the horticultural and agricultural industries
Solar cell simulation using ab initio methods
(University of the Witwatersrand, Johannesburg, 2024) Zdravkovi´c, Milica; Quand, Alexander; Warmbier, Robert
Solar cells are a great source of renewable energy, but they are yet to reach their thermodynamic efficiency limits. Common commercial solar cells run at approximately 20% power conversion efficiency, and almost all efficiency loss comes from thermalisation. Ab initio simulations can reduce the need for physical experiments to quantify these losses while also providing insights into the quantum mechanical properties of materials. Note that density functional theory reformulates the expression for the ground state energy of a many particle system such that it is a functional of the electron density, thereby allowing the electronic energy to be solved for numerically. But the underlying mechanism behind thermalisation is the electron-phonon interaction. Using the theory of Green’s functions, the electron-phonon interaction self-energy and charge-carrier life times can be calculated. A method of approximating the charge-carrier lifetimes using the hydrostatic deformation potential interaction, which is only valid for longitudinal acoustic phonons, is presented. Deformation potentials of -10.125eV for Silicon and 18.663eV for Gallium Arsenide, commonly used solar cell materials, are calculated in good agreement with literature. Furthermore, the electron-phonon interaction life- times were calculated to be in the order of 2.0 × 10−15s for Si and 4.0 × 10−16s for GaAs, which could have indications that the optimal thickness of a GaAs absorption layer is much thinner than for Si. Thus the deformation potential method provides a satisfactory approximation for the electron-phonon quasiparticle lifetimes based on ab initio methods
The development of a burn-in test station for the ATLAS Tile Calorimeter Low Voltage Power Supplies for phase II upgrades
(University of the Witwatersrand, Johannesburg, 2022) Lepota, Thabo James; Mellado, Bruce
It is planned that the High Luminosity (HL) function of the Large Hadron Collider (LHC) will begin operation in 2027 with an integrated luminosity of 4000 fb−1.By using the HL-LHC scientists will be able to investigate new physics beyond the Standard Model (SM), examine electroweak symmetry in more detail, and examine the characteristics of the Higgs boson. The ATLAS Tile Calorimeter’s on and off detector electronics will be completely redesigned during phase II upgrade, run 3. Due to the high radiation levels, trigger rates, and high pile-up conditions associated with the HL-LHC era, it will be necessary to accommodate its acquisition system. The Institute of Collider Particle Physics is responsible for developing and manufacturing over a thousand transformer-coupled buck converters, known as Bricks, for the Low Voltage Power Supply (LVPS) system. The LVPS is critical to the TileCAL on detector electronics as it powers them by converting 200 V high voltage to 10 V. The Bricks are located within the inner barrel, they can only be accessed once a year. If an LVPS box fails, it can be down for up to a year, causing the Front-End electronics it supports to remain offline for the same amount of time. As a result, the Bricks’ reliability is of critical concern that must be addressed throughout their manufacturing process. In addition to the burn-in test station, the Bricks that pass the quality assurance tests are sent to the European Organization for Nuclear Research (CERN), to be installed in the ATLAS detector. In this manuscript, we show how we programmed the Peripheral Interface Controller (PIC) firmware, which is an integral part of the Brick Interface board functionality in the burn-in test station. We further give detail as to how the software framework (LabVIEW) used as a control program was modified and used to operate the burn-in test station during the burn-in process. The purpose of the test results discussed is to demonstrate the burn-in test station is functional according to the preliminary protocols prescribed for Bricks
The microwave assisted synthesis of doped carbon dot/carbon nano-onion composites: A novel all-carbon counter electrode for dye-sensitized solar cell
(University of the Witwatersrand, Johannesburg, 2023) Masemola, Khanyisile; Moloto, Nosipho; Maubane-Nkadimeng, Manoko S.; Coville, Neil J.
Human society's development is heavily reliant on stable energy supply, and fossil energy sources have long been a very reliable energy source for this objective. However, being a non-renewable energy source, fossil fuel depletion is unavoidable and impending in this or future generations. To solve this issue, renewable energy, particularly solar energy, has received a lot of attention since it directly turns solar energy into electrical power with no environmental consequences. Various photovoltaic technologies based on organic, inorganic, and hybrid solar cells have been successfully manufactured to date. However, much study has been concentrated on organic solar cells for household and other commercial uses due to its inherent cheap module cost and ease of production. But dye-sensitized solar cells (DSSCs) have been reported to be the most efficient and simplest applied organic solar cell technology. In this study, carbon dot: onion-like carbon nanomaterial composites (Cdots: OLCNs) were synthesized for possible future application in electronic devices with particular attention to dye-sensitized solar cells. The nitrogen-doped carbon dots (NCdots) and functionalized onion-like carbon nanomaterials (OLCNs) were synthesized using a one-step hydrothermal microwave assisted irradiation method and flame pyrolysis method using liquid fuels, respectively. The as-synthesized OLCNs where purified and washed using an organic solvent n-pentane to obtain pristine OLCNs (p-OLCNs) which were further functionalized with N2 gas to obtained nitrogen-doped CNOs (N- OLCNs) and H2O2 to give oxygenated OLCNs (ox-OLCNs). For the synthesis of NCdots, various precursors (ethylenediamine, urea and fumaronitrile) were used to evaluate the effect of different nitrogen sources on the properties of these materials. Photoluminescence spectroscopy showed that the resulting NCdots exhibited the conventional excitation-dependency behavior. The NCdots which presented with the highest fluorescence quantum yield (made from ethylenediamine) were used to make the subsequent NCdot: OLCNs composites. The as-prepared p-/ox-/N-OLCNs all showed similar morphologies typical of chain-like carbon nanostructures, according to transmission and scanning electron microscopy studies, but with varying particle sizes of 42 nm, 125nm and 85 nm, respectively. The corresponding nanocomposites were used as counter electrode materials in DSSCs. The application of all the nanocomposites in the DSSCs resulted in cell efficiencies, current densities, open circuit voltages and fill factors that were lower than that of a conventional platinum (Pt) electrode. All nanocomposites tested presented with cell efficiencies <1%. Furthermore, the cells displayed some photovoltaic effect of minimal activity in the absence of light, under dark field conditions implying it is still a photovoltaic material. This photocurrent generated by the cell in the dark is suggested to be a dominant contributor to the low performance of the cells. However, what was remarkable was that this photovoltaic effect, primarily due to the thermal activity from the long lasting glow of the NCdots specifically, was found to be stable and efficient in response as infrared radiation even without being illuminated with light for 5 minutes. This suggests that the NCdots: OLCNs composites have potential application, possibly as efficient diodes rather than for use in DSSCs.

Browse

Browsing Faculty of Science (ETDs) by SDG "SDG-7: Affordable and clean energy"

Results Per Page

Sort Options