In this work, we study the surface energy of monolayer, bilayer and multilayer graphene coatings, produced through exfoliation of natural graphite flakes and chemical vapor deposition. We employ bimodal atomic force microscopy and micro-Raman spectroscopy for high spatial resolution and large area scanning of force of adhesion on the regions of the graphene/SiO2 surface with different graphene layers. Our measurements show that the interface conditions between graphene and SiO2 dominate the experimentally observed graphene surface energy. This finding sheds new light on the controversy surrounding graphene transparency studies. By separating the surface energy into polar and non-polar interactions, our findings suggest that monolayer graphene is nearly van der Waals opaque but partially transparent (near 60%) to polar interactions, which is further supported by characterizing graphene on the copper surface and two levels of density functional theory simulation. In addition to providing quantitative insight into the surface interactions of complicated graphene coatings, this work demonstrates a new route to nondestructively monitor the interface between graphene and coated substrates.
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http://dx.doi.org/10.1039/c9nr00155g | DOI Listing |
J Mol Model
January 2025
Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China.
A prevalent challenge in particulate photocatalytic water splitting lies in the fact that while numerous photocatalysts exhibit outstanding hydrogen evolution reaction (HER) activity in organic sacrificial reagents, their performance diminishes markedly in a Z-scheme water splitting system using electronic mediators. This underlying reason remains undefined, posing a long-standing issue in photocatalytic water splitting. Herein, we unveiled that the primary reason for the decreased HER activity in electronic mediators is due to the strong adsorption of shuttle ions on cocatalyst surfaces, which inhibits the initial proton reduction and results in a severe backward reaction of the oxidized shuttle ions.
View Article and Find Full Text PDFInorg Chem
January 2025
State Key Laboratory of Clean and Efficient Coal Utilization, Taiyuan University of Technology, Taiyuan 030024, China.
The low sulfur selectivity of Fe-based HS-selective catalytic oxidation catalysts is still a problem, especially at a high O content. This is alleviated here through anchoring FeO nanoclusters on UiO-66 via the formation of Fe-O-Zr bonds. The introduced FeO species exist in the form of Fe and Fe.
View Article and Find Full Text PDFLangmuir
January 2025
Hubei Key Laboratory of Oil and Gas Exploration and Development Theory and Technology (China University of Geosciences), Wuhan 430074, China.
The strong solid-liquid interaction leads to the complicated occurrence characteristics of shale oil. However, the solid-liquid interface interaction and its controls of the occurrence state of shale oil are poorly understood on the molecular scale. In this work, the adsorption behavior and occurrence state of shale oil in pores of organic/inorganic matter under reservoir conditions were investigated by using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Preferred Networks, Inc., Tokyo 100-0004, Japan.
Mapping the chemical reaction pathways and their corresponding activation barriers is a significant challenge in molecular simulation. Given the inherent complexities of 3D atomic geometries, even generating an initial guess of these paths can be difficult for humans. This paper presents an innovative approach that utilizes neural networks to generate initial guesses for reaction pathways based on the initial state and learning from a database of low-energy transition paths.
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