DSpace Collection:https://hdl.handle.net/10356/22112024-03-28T09:03:04Z2024-03-28T09:03:04ZThermally evaporated perovskite ultrathin films for quantum well applicationsRachel Emmanuelle Raphaelhttps://hdl.handle.net/10356/1724762023-12-16T16:45:21Z2023-01-01T00:00:00ZTitle: Thermally evaporated perovskite ultrathin films for quantum well applications
Authors: Rachel Emmanuelle Raphael
Abstract: Our world is struggling with the consistently growing demand for energy, exacerbated by the challenges and depleting supply of fossil fuels, which have long served as the primary energy source. As a response to these challenges, mainly the environmental concerns associated with these sources, nations worldwide are transitioning away from fossil fuels and shifting towards cleaner and more sustainable energy solutions. In this global shift, solar photovoltaic (PV) technology stands out as a promising alternative, considering the abundant solar irradiance available on earth.
Over the past decade, researchers have explored perovskite materials due to their advantageous attributes, notably their cost-effectiveness and relatively simple processing. In addition to investigating novel materials, efforts have been made to explore innovative structures to further optimize device performance. This project delved into the exploration of integrating perovskite materials within an innovative PV structure, specifically, the quantum well structure. By combining the promising attributes of thermally evaporated perovskite materials with the optimistic potential of quantum well structure, this project extended its implication beyond mere performance enhancement.
This report thoroughly assesses the remarkable improvements in absorption capacity resulting from having two different materials in the implementation of a quantum well structure. It also discusses the quantum confinement effect, as an outcome of depositing ultra-thin films to achieve such structure. While highlighting the promising benefits, limitations and trade-offs associated with the structure are also carefully considered in this report.
By the end of the project, it was proven that substantial enhancement in light absorption could be achieved. The spectrum of light absorption range was successfully extended into the infrared region, starting from a wavelength of 1000 nm, a notable increase from only around 780 nm achievable with a single-material perovskite. This newfound capacity for light absorption holds promise for further advancements in PV device performance, given that absorption is a crucial factor in a solar cell device.2023-01-01T00:00:00ZDevelopment of deep neural network potential for studying water/diamond interfacesMelvin, Danielhttps://hdl.handle.net/10356/1723662023-12-09T16:45:28Z2023-01-01T00:00:00ZTitle: Development of deep neural network potential for studying water/diamond interfaces
Authors: Melvin, Daniel
Abstract: Diamond and Diamond-like carbon (DLC) are promising coating materials with high strength and outstanding tribological properties. The ultra-low friction and wear rate make diamonds attractive for real-world applications, especially micro- and nano- electromechanical systems (MEMS/NEMS). However, the termination species’ presence on the carbon surface and the interaction with the environment could significantly influence the tribological properties of the material. Plenty of studies have been dedicated to unveiling the main source of friction and improving the tribological properties of the carbon material surface. Nevertheless, our understanding of the interaction between terminated diamond surfaces and water molecules in the environment and its influence on the tribological properties of the material is still limited. In this study, we developed a deep neural network potential (DNNP) to accurately simulate the interaction between water and different diamond surface terminations. By utilizing a trained DNNP, the system size limitation can be overcome, allowing us to explore the influence of the water layer's structure on the tribological properties of terminated diamond surfaces. We found that the terminated specific terminational molecule species and sliding velocities could highly affect the tribological behavior of the carbon surface. These findings and studies help the advancement and understanding of the tribological properties of diamond-based material development.2023-01-01T00:00:00ZSynthesis of metal alloy catalysts using high-throughput experiments and machine learning optimizationCalista, Vaniahttps://hdl.handle.net/10356/1722702023-12-09T16:45:45Z2023-01-01T00:00:00ZTitle: Synthesis of metal alloy catalysts using high-throughput experiments and machine learning optimization
Authors: Calista, Vania
Abstract: The periodic table comprises over a hundred elements, offering numerous possibilities
for the discovery of novel materials that have superior properties and could therefore
be used to address current technological and societal challenges. However, exploring
the extensive range of combinations are resource-intensive: slow and costly,
particularly for materials significantly affected by the synthesis procedures. In this
final year project, a workflow for the high throughput synthesis of multimetallic alloys
is presented. The two-step workflow is comprised by a liquid mixing step and an
annealing step. An acceleration factor of 2.4 relative to the traditional auto combustion
sol gel synthesis method is achieved by synthesizing 24 samples in 620 minutes. To
evaluate the effectiveness of this methodology and with the assistance of previous
computational work carried out by collaborators at Meta AI, copper and three other
copper alloys, namely binary Cu-Ag, Cu-Zn, and ternary Cu-Zn-Ag, are synthesized,
due to their predicted promising use in CO2 reduction. The synthesized samples show
homogeneously distributed elemental composition and high phase purity. The catalytic
performance is evaluated by collaborators at the University of Toronto. The initial
findings from measuring pure Cu, which serves as a baseline, demonstrate consistent
performance when compared to commercially available Cu nanoparticles. Crucially,
the Faradaic efficiencies show different results compared to Cu nanoparticles. Firstly,
a substantial amount of H2 gas is produced, accompanied by low CO. This is due to
the large amount of carbon in our powders, stemming from the annealing step, and
large particle size of the pure Cu. To guide future experiments and optimize the
Faradaic efficiencies, the experimental data collected in this project is used to deploy
a Bayesian Optimization (BO) algorithm. Specifically, q-Noisy Expected
Hypervolume Improvement based Bayesian Optimization (qNEHVI-BO) model is
implemented, providing insight to guide the next experimental steps to achieve dry
samples and minimize the absolute difference between the obtained composition and
the target.2023-01-01T00:00:00ZFabrication, characterization, and data analysis of 2D perovskite for LED applicationGoh, Jun Jiehttps://hdl.handle.net/10356/1722332023-12-09T16:45:29Z2023-01-01T00:00:00ZTitle: Fabrication, characterization, and data analysis of 2D perovskite for LED application
Authors: Goh, Jun Jie
Abstract: This research investigates the degradation effects of 2D-organic inorganic halide
perovskite (PEA)2PbBr4 in the working conditions of Perovskite Light-Emitting Diodes
(PeLEDs) applications. Stability of (PEA)2PbBr4 is essential for the commercialization of
PeLEDs to ensure the longevity of the device.
The perovskite is exposed to different conditions to simulate utilisation and degradation
under real working conditions. The test environments include subjecting the 2D
perovskite thin films to thermal, ambient, photo and electrical bias conditions. X-ray
diffraction is used to analyse the crystal structure of the perovskite to understand its
stability and degradation under those conditions. Observations indicate that (PEA)2PbBr4
has a high degree of stability; however, harsher conditions should be use in future testing
to understand the limitations of the material.2023-01-01T00:00:00Z