The earth abundant and non-toxic solar absorber material kesterite Cu2ZnSn(S/Se)4 has been studied to achieve high power conversion efficiency beyond various limitations, such as secondary phases, antisite defects, band gap adjustment and microstructure. To alleviate these hurdles, we employed screening based approach to find suitable cationic dopant that can promote the current density and the theoretical maximum upper limit of the energy conversion efficiency (P(%)) of CZTS/Se solar devices. For this task, the hybrid functional (Heyd, Scuseria and Ernzerhof, HSE06) were used to study the electronic and optical properties of cation (Al, Sb, Ga, Ba) doped CZTS/Se. Our in-depth investigation reveals that the Sb atom is suitable dopant of CZTS/CZTSe and also it has comparable bulk modulus as of pure material. The optical absorption coefficient of Sb doped CZTS/Se is considerably larger than the pure materials because of easy formation of visible range exciton due to the presence of defect state below the Fermi level, which leads to an increase in the current density and P(%). Our results demonstrate that the lower formation energy, preferable energy gap and excellent optical absorption of the Sb doped CZTS/Se make it potential component for relatively high efficient solar cells.

Two-dimensional (2D) transition metal carbides/nitrides (MXenes) has attracted intensive attention in electrochemical reduction of CO2 into renewable fuels and chemical feedstock. Although encouraging progress has been made so far, but...

Functionalized carbon dots (CDs) exhibit intriguing photo-exciton dynamics. CDs can donate or accept electrons depending on their functional groups and electrostatic characteristics. We exploited the electron-accepting capability of nitrogendoped graphitic carbon dots (N-g-CDs) to improve the chargecarrier separation of the polymeric carbon nitride (PCN) photocatalyst. L-Aspartic acid pyrolyzed at 320 °C yielded ∼25 nm size N-g-CDs that were embedded with PCN. The in-plane infiltration of nanosized N-g-CDs increased the surface area of PCN from 11.5 to 104.9 m 2 g −1 . The N-g-CDs/PCN hybrid catalyst tested for photocatalytic chromium reduction evidenced about a 3-fold higher rate than PCN. Also, the antibiotic tetracycline and rhodamine B dye rapidly degraded with faster degradation kinetics. The carrier dynamic analysis and computational investigations suggest that the electron acceptor feature of N-g-CDs governs the effective separation of photo-excitons and the high surface area of N-g-CDs/PCN contributes to photoactivity enhancement. This study offers insights into designing high-performance metal-free photocatalysts for water treatment applications.

Two-dimensional materials supported by single atom catalysis (SAC) is foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and nonmetal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of graphitic carbon nitride (g-C3N4) are screened by density functional theory (DFT) calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on formation energy, Gibbs free energy, and bandgap analysis demonstrate that the single atoms of B, Mn, and Co anchored on g-C3N4 can serve as highly efficient active sites for hydrogen production. The ML model based on support vector regression (SVR) exhibits the best performance to accurately and rapidly predict the Gibbs free energy of hydrogen adsorption (ΔG H) with a lower mean absolute error (MAE) and a high coefficient of determination (R 2) of 0.08 eV and 0.95, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valence electron as key descriptors. Overall, the multistep workflow employed through DFT calculations combined with ML models for efficient screening of potential candidates for hydrogen evolution reaction (HER) from g-C3N4-based single atom catalysis can significantly contribute to the catalyst design and fabrication.

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