Optimizing g‐C3$_3$N4$_4$‐Based Acceptor Materials Through Density Functional Theory‐Driven Bandgap Engineering for High‐Performance Solar Cells

sheet and the organic molecular plane became ≈ 90°, which minimizes the recombination rate of electron-hole pairs. By emphasizing the bandgap engineering of these composites, this work offers a strategy to enhance optoelectronic activity through the introduction of foreign organic moieties into the matrix.

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Publication Details

Journal
ChemPhysChem
Published
2026-04-22
DOI
https://doi.org/10.1002/cphc.202500859
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimizing g‐C3$_3$N4$_4$‐Based Acceptor Materials Through Density Functional Theory‐Driven Bandgap Engineering for High‐Performance Solar Cells

R DEKA, Dhruba Jyoti Kalita, Abhispa Saikia
ChemPhysChem
Machine Learning in Materials Science
article

Optimizing g‐C3$_3$N4$_4$‐Based Acceptor Materials Through Density Functional Theory‐Driven Bandgap Engineering for High‐Performance Solar Cells

R DEKA, Dhruba Jyoti Kalita, Abhispa Saikia
article en

Abstract

sheet and the organic molecular plane became ≈ 90°, which minimizes the recombination rate of electron-hole pairs. By emphasizing the bandgap engineering of these composites, this work offers a strategy to enhance optoelectronic activity through the introduction of foreign organic moieties into the matrix.

ChemPhysChemVol. 27(8)
Gauhati University (IN)
Affordable and clean energy
Openalex Percentile: Top 12%
Machine Learning in Materials Science
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