Material Design and Nano-Patterning for Improved Solar Cell Light Harvesting [electronic resource].

9781088308998

Ann Arbor : ProQuest Dissertations & Theses, 2019.

1 online resource (126 p.)

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The continuing depletion of the world's fossil fuel resources is driving the development of alternative energy sources and technologies. The conversion of light into electricity via photovoltaics (PV) could make a considerable contribution to solving the imminent energy crisis. However, existing PV technologies suffer from optical and electrical losses, which reduce power conversion efficiency (PCE)—the efficiency with which light is converted to useable energy. Integration of light trapping structures in PV is the most promising technique to suppress losses and increase conversion efficiency. Nature utilizes countless designs to trap and harvest solar energy, which we can study and replicate in pursuit of useful solutions. We demonstrate the use of earth-abundant, fossilized diatom algae as light traps in regioregular poly(3-hexylthiophene) (P3HT) and fullerene derivative [6,6]-phenyl-C60-butyric acid methyl ester (PCBM) solar cells. Diatoms, the most common type of phytoplankton found in nature, are optimized for light absorption through millions of years of adaptive evolution.This thesis explores both the integration of bio-inspired nanostructures and patterns for light trapping and the development of new materials for solar applications, particularly metallic glasses (MGs) as nanostructured solar collectors for trapping visible light. We experimentally determine the optical constants of Pd77.5Cu6Si16.5 metallic glass alloy as a probe into the viability of incorporating MGs into optical devices. MGs are ideally suited materials for the replication of bio-inspired nano-patterns, such as the diatom frustule, due to their superior formability, tuneability in atomic composition, and ability to achieve atomic smoothness.We demonstrate a high throughput combinatorial approach to determine the heretofore-unexplored material properties of AuAgAl alloy libraries. Specifically, we explore the opto-electronic properties as a function of atomic composition via high throughput spectral ellipsometry (SE) and high throughput x-ray diffraction (XRD) measurements. In addition to the previously established tunable electrical and mechanical properties of alloys, we reveal their tunable optical properties. Determination of the optical properties of a novel material is a crucial step toward realizing promising candidates for enhanced light trapping in the next generation of optoelectronic devices. Furthermore, we implement machine learning algorithms to demonstrate the first application of a neural net for the prediction of optical constants as a function of material composition. The neural net was trained using 80% of the SE and composition data, and 20% of the data was withheld to be used for testing. The resulting neural net presented here can predict the optical constants of AuAgAl alloy compositions with only 7.84% error when tested on the foreign 20% of the data. Pairing this high throughput experimental methodology with advanced machine learning yields a fast and efficient method of identifying novel materials for optical applications.This thesis suggests that superior properties of bio-inspired light trapping schemes can be realized by metallic glass fabrication processes, hence implying a great potential for low-cost, high-efficiency PV. Moreover, the fact that high PCE can be achieved without the need for complicated processing of noble metals has important implications for further utilization of metallic glasses and designer alloys in various optoelectronic applications.

Dissertations & Theses @ Yale University.

Books / Online / Dissertations & Theses

English

January 17, 2020

Thesis (Ph.D.)--Yale University, 2019.

Yale University. Chemical and Environmental Engineering.