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Bi2Te3-based materials are remarkable semiconducting compounds widely employed for clean energy harvesting via thermoelectric effects. Their energy conversion efficiency is assessed based on their thermoelectric figure of merit which depends on the electrical and thermal properties. The measurement of electrical conductivity is often straightforward; however, the thermal conductivity (κ) measurement is incredibly arduous and requires sophisticated experimental devices. Herein, we propose a pioneering machine-learning technique that estimates the value of κ for such materials based on their electrical properties and structural lattice constants (a and c). Decision tree regression (DTR) and Support vector regression (SVR) algorithms were employed to build the thermal conductivity estimators. Afterward, the concept of adaptive boosting was applied to the conventional regressors to improve their prediction accuracy. The performance of the developed models was evaluated based on the R2-value, coefficient of correlation (CC) between the actual and experimental values of κ, mean absolute error, and mean square error. The results obtained based on these metrics indicate that the boosted decision tree outperforms the rest of the models with a CC of 99.4% and an R2-value of 98.8% in the testing phase. The models were also utilized to make predictions in actual physical situations, such as forecasting the thermal conductivities of compounds doped with transition metals or non-metals, and examining how the values of κ are affected by substrate temperature during pulsed laser deposition. The remarkable performance of the models in predicting the thermal conductivity of different configurations of the material with high accuracy underscores its importance to researchers and industry for renewable energy applications.

Graphitic carbon nitride (g-C3N4) is a remarkable semiconductor catalyst that has attracted widespread attention as a visible light photo-responsive, metal-free, low-cost photocatalytic material. Pristine g-C3N4 suffers fast recombination of photogenerated electron-hole pairs, low surface area, and insufficient visible light absorption, resulting in low photocatalytic efficiency. This review presents the recent progress, perspectives, and persistent challenges in the development of g-C3N4-based photocatalytic materials. Several approaches employed to improve the visible light absorption of the materials including metal and non-metal doping, co-doping, and heterojunction engineering have been extensively discussed. These approaches, in general, were found to decrease the material’s bandgap, increase the surface area, reduce charge carrier recombination, and promote visible light absorption, thereby enhancing the overall photocatalytic performance. The material has been widely used for different applications such as photocatalytic hydrogen production, water splitting, CO2 conversion, and water purification. The work has also identified various limitations and weaknesses associated with the material that hinders its maximum utilization under visible illumination and presented state-of-the-art solutions that have been reported recently. The summary presented in this review would add an invaluable contribution to photocatalysis research and facilitate the development of efficient visible light-responsive semiconducting materials.