Predicting Free Energies of Exfoliation and Solvation for Graphitic Carbon Nitrides Using Machine Learning.

As a metal-free and visible-light-responsive photocatalyst, graphitic carbon nitride (g-CN) has emerged as a new research hotspot and has attracted broad attention in the field of solar energy conversion and thin-film transistors. Liquid-phase exfoliation (LPE) is the best-known method for the synthesis of 2D g-CN nanosheets. In LPE, bulk g-CN is exfoliated in a solvent via high-shear mixing or sonication in order to produce a stable suspension of individual nanosheets.

Liquid-phase exfoliation of graphitic carbon nitrides studied by molecular dynamics simulation.

Hypothesis: The superiority of graphitic carbon nitride (g-CN) nanosheet results from its large specific surface area, which can be achieved by exfoliation of the bulk layered structure. Liquid-phase exfoliation (LPE) is the best-known method for the synthesis of two-dimensional (2D) g-CN nanosheets. However, experimental investigations do not allow for a molecular-level understanding of the process.

BiO Nanosheets Grown on Carbon Nanofiber with Inherent Hydrophobicity for High-Performance CO Electroreduction in a Wide Potential Window.

The ever-increasing concern for adverse climate changes has propelled worldwide research on the reduction of CO emission. In this regard, CO electroreduction (CER) to formate is one of the promising approaches to converting CO to a useful product. However, to achieve a high production rate of formate, the existing catalysts for CER fall short of expectation in maintaining the high formate selectivity and activity over a wide potential window.