Authors:

Deepanjali Chowdhury

Date Created:

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About Paper:

Solar cells hold great promise for the future of energy production. Among the different generations of solar cells, the third generation marks the latest and most significant improvement in solar cell technology, showcasing a notable leap forward in modernizing energy capture and utilization. Dye- Sensitized Solar Cells (DSSCs) hold great potential as third-generation solar cells, primarily due to their unique features, such as the use of a dye-absorbed semiconductor material, which enhances cost- effectiveness and flexibility. They exhibit promising results when paired with materials like carbon-based materials, and ongoing experiments are exploring other semiconductor oxides to enhance their performance, with a particular focus on improving the photoanode for increased efficiency. Semi- conducting oxides show excellent conductivity and porosity, making them promising for electrochemical energy storage and integration into solar cells. Despite their promise, testing various material variations in solar cell and energy storage systems is challenging due to time and resource constraints. This research builds on past studies by introducing machine learning as a cost-effective way to identify high- performance configurations for DSSCs. This study aims to apply to all materials in the periodic table, with a focus on semiconducting oxides. The proposed machine learning model aims to predict the system's performance across various parameters, including Power Conversion Efficiency and Fill Factor. By using machine learning, this study aims to simplify the evaluation process and speed up the identification of optimal configurations of carbon-based materials and semiconductor oxides, contributing to the advancement of sustainable and efficient solar energy technologies. Poster #2 Clinical Note Embeddings for Dynamic Survival Analysis Aaron Su

Source:

Texas A&M University / 2024

Topics:

No topics listed

Co-authors:

Deepanjali Chowdhury