Adsorption Characteristics of Gas Molecules Adsorbed on Graphene Doped with Mn: A First Principle Study

Density Functional Theory-Based Indicators to Estimate the Corrosion Potentials of Zinc Alloys in Chlorine-, Oxidizing-, and Sulfur-Harsh Environments

Nowadays, biodegradable metals and alloys, as well as their corrosion behavior, are of particular interest. The corrosion process of metals and alloys under various harsh conditions can be studied via the investigation of corrosion atom adsorption on metal surfaces. This can be performed using density functional theory-based simulations. Importantly, comprehensive analytical data obtained in simulations including parameters such as adsorption energy, the amount of charge transferred, atomic coordinates, etc., can be utilized in machine learning models to predict corrosion behavior, adsorption ability, catalytic activity, etc., of metals and alloys. In this work, data on the corrosion indicators of Zn surfaces in Cl-, S-, and O-rich harsh environments are collected. A dataset containing adsorption height, adsorption energy, partial density of states, work function values, and electronic charges of individual atoms is presented. In addition, based on these corrosion descriptors, it is found that a Cl-rich environment is less harmful for different Zn surfaces compared to an O-rich environment, and more harmful compared to a S-rich environment.

DFT-assisted low-dimensional carbon-based electrocatalysts design and mechanism study: a review

Low-dimensional carbon-based (LDC) materials have attracted extensive research attention in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relieve environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). Traditional "trial and error" strategies greatly slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus revealing the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The commonly used calculation methods and the basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge linking the structure and activity and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlated multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. Furthermore, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.

On the Use of Graphene Nanosheets for Drug Delivery: A Case Study of Cisplatin and Some of Its Analogs

Graphene (GN) nanosheets have been widely exploited in biomedical applications as potential nanocarriers for various drugs due to their distinct physical and chemical properties. In this regard, the adsorption behavior of cisplatin (cisPtCl₂) and some of its analogs on a GN nanosheet was investigated in perpendicular and parallel configurations by using density functional theory (DFT). According to the findings, the most significant negative adsorption energies (E_ads) within the cisPtX₂⋯GN complexes (where X = Cl, Br, and I) were observed for the parallel configuration, with values up to -25.67 kcal/mol at the H@GN site. Within the perpendicular configuration of the cisPtX₂⋯GN complexes, three orientations were investigated for the adsorption process, namely, X/X, X/NH₃, and NH₃/NH₃. The negative E_ads values of the cisPtX₂⋯GN complexes increased with the increasing atomic weight of the halogen atom. The Br@GN site showed the largest negative E_ads values for the cisPtX₂⋯GN complexes in the perpendicular configuration. The Bader charge transfer outcomes highlighted the electron-accepting properties of cisPtI₂ within the cisPtI₂⋯GN complexes in both configurations. The electron-donating character of the GN nanosheet increased as the electronegativity of the halogen atom increased. The band structure and density of state plots revealed the occurrence of the physical adsorption of the cisPtX₂ on the GN nanosheet, which was indicated by the appearance of new bands and peaks. Based on the solvent effect outlines, the negative E_ads values generally decreased after the adsorption process in a water medium. The recovery time results were in line with the E_ads findings, where the cisPtI₂ in the parallel configuration took the longest time to be desorbed from the GN nanosheet with values of 61.6 × 10⁸ ms at 298.15 K. The findings of this study provide better insights into the utilization of GN nanosheets in drug delivery applications.

Al-Decorated C2N Monolayer as a Potential Catalyst for NO Reduction with CO Molecules: A DFT Investigation

Developing efficient and economical catalysts for NO reduction is of great interest. Herein, the catalytic reduction of NO molecules on an Al-decorated C2N monolayer (Al-C2N) is systematically investigated using density functional theory (DFT) calculations. Our results reveal that the Al-C2N catalyst is highly selective for NO, more so than CO, according to the values of the adsorption energy and charge transfer. The NO reduction reaction more preferably undergoes the (NO)2 dimer reduction process instead of the NO direct decomposition process. For the (NO)2 dimer reduction process, two NO molecules initially co-adsorb to form (NO)2 dimers, followed by decomposition into N2O and Oads species. On this basis, five kinds of (NO)2 dimer structures that initiate four reaction paths are explored on the Al-C2N surface. Particularly, the cis-(NO)2 dimer structures (Dcis-N and Dcis-O) are crucial intermediates for NO reduction, where the max energy barrier along the energetically most favorable pathway (path II) is as low as 3.6 kcal/mol. The remaining Oads species on Al-C2N are then easily reduced with CO molecules, being beneficial for a new catalytic cycle. These results, combined with its low-cost nature, render Al-C2N a promising catalyst for NO reduction under mild conditions.