Scholarlink

<p>For their improved photovoltaic (PV) performance, this study addresses the electronic and structural basis of silicon carbide quantum dots (SQs) structures. For this, we employ Density Functional Theory (DFT) to develop and optimize donor-π-acceptor based Si<sub>8</sub>C<sub>8</sub>H<sub>8</sub> derived SQs. Using RDKit of Python programming language, their molecular descriptors are calculated. Their Topological Polar Surface Area (TPSA) emerges to be the most influential parameter by their correlational analysis, having a substantial correlation with their maximum absorption (λ<sub>max</sub>). The Random Forest Regression model predicts their open circuit voltage (V<sub>oc</sub>) with its R-Squared (R<sup>2</sup>) of 0.82. Additionally, their λ<sub>max</sub> as descriptor (Max_Abs) displays its strong correlations with both Short-Circuit Current Density (J<sub>sc</sub>, R = 0.71) and Light Harvesting Efficiency (LHE, R = 0.83) which suggests that a longer λ<sub>max</sub> can promote higher PV efficiency. There are also moderate correlations which existed for open circuit voltage (V<sub>oc</sub>, R = 0.56) and Max_Abs with its LogP and V<sub>oc</sub> (R = 0.63). These results guide the design of high-performance SQ-based PV materials by offering insightful information on structure–property relationships.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A DFT and Molecular Correlational Analysis on Newly Designed Silicon-Carbide Quantum Dots with Extended Acceptors for their Photovoltaic Performance

  • 期刊论文

  • 发表日期：

    2025年5月23日

查看全文
  • Sadaf Noreen,
  • Sajjad H. Sumrra,
  • Abrar U. Hassan,
  • Maria Afzaal,
  • Ashraf Y. Elnaggar,
  • Islam H. El Azab,
  • Mohamed H. H. Mahmoud

摘要

For their improved photovoltaic (PV) performance, this study addresses the electronic and structural basis of silicon carbide quantum dots (SQs) structures. For this, we employ Density Functional Theory (DFT) to develop and optimize donor-π-acceptor based Si8C8H8 derived SQs. Using RDKit of Python programming language, their molecular descriptors are calculated. Their Topological Polar Surface Area (TPSA) emerges to be the most influential parameter by their correlational analysis, having a substantial correlation with their maximum absorption (λmax). The Random Forest Regression model predicts their open circuit voltage (Voc) with its R-Squared (R2) of 0.82. Additionally, their λmax as descriptor (Max_Abs) displays its strong correlations with both Short-Circuit Current Density (Jsc, R = 0.71) and Light Harvesting Efficiency (LHE, R = 0.83) which suggests that a longer λmax can promote higher PV efficiency. There are also moderate correlations which existed for open circuit voltage (Voc, R = 0.56) and Max_Abs with its LogP and Voc (R = 0.63). These results guide the design of high-performance SQ-based PV materials by offering insightful information on structure–property relationships.