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- All Subjects: engineering
- Creators: Alford, Terry
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High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.
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ii
aged modules also show that humidity accelerates the formation of IMC as they showed thicker AgxSny layer and weak interconnect-contact interfaces as compared to Arizona modules. It is also shown that climatic conditions have different effects on rate at which CuxSny and AgxSny intermetallic compounds are formed.
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As these two aspects of the solar cell framework improve, the need for a thorough analysis of their respective contribution under varying operation conditions has emerged along with challenges related to the lack of sensitivity of available characterization techniques. The main objective of my thesis work has been to establish a deep understanding of both “intrinsic” and “extrinsic” recombination processes that govern performance in high-quality silicon absorbers. By studying each recombination mechanism as a function of illumination and temperature, I strive to identify the lifetime limiting defects and propose a path to engineer the ultimate silicon solar cell.
This dissertation presents a detailed description of the experimental procedure required to deconvolute surface recombination contributions from bulk recombination contributions when performing lifetime spectroscopy analysis. This work proves that temperature- and injection-dependent lifetime spectroscopy (TIDLS) sensitivity can be extended to impurities concentrations down to 109 cm-3, orders of magnitude below any other characterization technique available today. A new method for the analysis of TIDLS data denominated Defect Parameters Contour Mapping (DPCM) is presented with the aim of providing a visual and intuitive tool to identify the lifetime limiting impurities in silicon material. Surface recombination velocity results are modelled by applying appropriate approaches from literature to our experimentally evaluated data, demonstrating for the first time their capability to interpret temperature-dependent data. In this way, several new results are obtained which solve long disputed aspects of surface passivation mechanisms. Finally, we experimentally evaluate the temperature-dependence of Auger lifetime and its impact on a theoretical intrinsically limited solar cell. These results decisively point to the need for a new Auger lifetime parameterization accounting for its temperature-dependence, which would in turn help understand the ultimate theoretical efficiency limit for a solar cell under real operation conditions.