[en] The adsorption characteristics of O₂ and NO on Co-anchored different graphene-based substrates (single vacancy, double vacancy and N atoms doped) have been investigated using density functional theory. The geometric stability of the single atom catalysts, adsorption configurations of gas molecules, adsorption energies, electronic structure and thermodynamic analysis have been performed. Co/vacancy-graphene shows high thermodynamic stability through calculating and comparing the binding energy of Co-anchored single atom catalysts and the cohesive energy of Co bulk. For O₂ adsorption, it prefers to form two chemical bonds with the Co atom, and electron transfer dominates the formation of the strong chemical ionic bonds. While on Co single and double vacancy graphene substrates, N atom in NO invariably bonds to the Co atom, with electron transfer and orbital hybridization dominating the process of bonding formation respectively, afterwards ionic and covalent bonds formed between gas molecule and the metal atom. Additionally, electro-negativity and partial d-band centre are good descriptors of adsorption energies and can well reveal the relationship of adsorption energy with adsorption activity and the electronic structure. Co/single vacancy-graphene substrate with three pyridine nitrogen atoms (Co/SV-N123) is a promising catalyst in catalytic oxidation of NO. The results can provide reference for the further study of the NO oxidation mechanism on the Co/GN surface as well as the new non-noble-metal catalysts design.

[en] Density functional theory calculations were used to study the adsorption of NO on Ni_n cluster (n = 1, 2, 3 and 4) doped graphene with different graphene-based support (single vacancy, one nitrogen decorated, two nitrogen decorated and three nitrogen decorated). The adsorption configuration, adsorption energy, charge transfer, density of states of NO on Ni_n/graphene are thoroughly studied. In addition, the d-band center and Fermi softness have been performed to consider the support effect. It is found that the support effect has a significant effect on the adsorption characteristics of NO molecule, which depends on the electronic structure of graphene-based support. The electronic structure can be characterized by the Fermi softness of the catalyst. Ni atom plays a more and more obvious role in NO adsorption process, with the increase of the number of Ni atoms. The Fermi softness is a great descriptors for the adsorption activity of the Ni_n/graphene. This result can contribute to the systematic study of graphene catalysts supported on metal clusters.

[en] Highlights: • The heterogeneous reduction mechanism of N₂O by char is revealed. • Char provides free sites and reduces the energy barrier of N₂O reduction. • The char edge structure has a notable influence on N₂O decomposition. • The participation of CO can reduce activation energy of N₂O reduction. - Abstract: In order to investigate heterogeneous reduction of N₂O by char, quantum chemistry theoretical calculation based on zigzag and armchair char edge model was conducted. Thermodynamics and kinetics calculation were carried out combined with density functional theory (DFT) and conventional transition state theory (TST). Theoretical calculation results indicate that heterogeneous reduction of N₂O by char undergoes two stages: N₂O decomposition on char edge and residual oxygen desorption from char edge. N₂O decomposition process is an exothermic reaction and takes place spontaneously and irreversibly. Char acts as an important catalyst which not only provides free sites for the heterogeneous reduction but also reduces the reaction energy barrier. The participation of CO can significantly reduce reaction activation energy of residual oxygen desorption. The char edge structure has notable influence on activation energy of N₂O decomposition on char edge. Activation energy values of N₂O decomposition on zigzag and armchair char edge are 33.91 kJ/mol and 163.58 kJ/mol, respectively. The calculation results can not only deepen understanding of the reaction mechanism but also provide theoretical guidance for operation optimization of low N₂O emission.

[en] As important parts of simultaneous removal of multiple pollutants in flue gas, catalytic oxidation of Hg⁰ and adsorption removal of SO₃, Pb species and As₂O₃ on the surface of (Fe,Co)@N-GN catalyst were systematically investigated in this work, using density functional theory. As a result, we found (Fe,Co)@N-GN not only showed great performance on Hg⁰ oxidation, but also exhibited outstanding removal capacity for SO₃, Pb species and As₂O₃ molecules. Besides, PDOS and EDD analysis were carried out and the result indicated that electron transfer and orbit hybridization played key roles in gas adsorption. In addition, thermodynamics analysis further evidenced (Fe,Co)@N-GN was a qualified sorbent under 550 K, and competitive analysis suggested that the adsorption order of pollution gas was determined by adsorption energy, rather than the volume fraction of corresponding gases in flue gas. We hoped this work can not only lay a foundation for theoretical investigation of Hg⁰ oxidation, but also provide an important guideline for simultaneous removal of multiple pollutants released from coal-fired power plants.

[en] Highlights: • Two novel high-capacity and high-stability anode materials for MXenes lithium-ion batteries. • A functional relationship that makes it easy to explore more MXenes materials. As an advanced battery technology, lithium-ion batteries have attracted extensive attention, especially in electric vehicles. However, low capacity and poor stability are two factors hindering more extensive application of lithium-ion batteries. Herein, we performed a screening study on MXenes including M₂C, MC₂, M₂N, MN₂ (M = Sc, Ti, V, Cr), in the search for promising lithium-ion battery anode materials by using density functional theory (DFT) calculations and ab initio molecular dynamic (AIMD) simulations. The theoretical capacities of Ti₂N and V₂N are 975 mAh/g and 924 mAh/g, respectively. Based on low deformation rate, and small energy variation, Ti₂N and V₂N have higher stability than that of graphite electrodes. The lower diffusion barrier accelerates the charging and discharging process of the battery. Considering their low diffusion barrier, excellent conductivity, high theoretical capacity, low deformation rate and high thermal stability, Ti₂N and V₂N are proposed as promising anode materials for lithium-ion batteries. The adsorption of lithium-ion belongs in the category of weak adsorption where the d-band center is negative. However, it is strong adsorption when the d-band center is positive. A linear relationship between the lithium-ion concentration and adsorption energy is developed for strong adsorption, which can be used to guide the design of high-capacity electrode materials.

[en] Highlights: • The eXtreme Gradient Boosting Regression (XGBR) algorithm was first applied to build a robust and predictive machine learning (ML) model for perovskite materials. • The method of combining machine learning and DFT calculation used in this article can greatly save computing resources and time, and accelerate the discovery of renewable energy materials. • Two lead-free perovskite structures screened out by the combination of machine learning and DFT calculation have reasonable band gap values, high environmental stability and good optical absorption properties. To accelerate the application of perovskite materials in photovoltaic solar cells, developing novel lead-free perovskite materials with suitable band gaps and high stability is vital. However, laborious experiment and density functional theory (DFT) calculation are time-consuming and incapable to screen promising perovskites rapidly and efficiently. Here, we proposed a novel search strategy combining machine learning and DFT calculation to screen 5,796 inorganic double perovskites. The eXtreme Gradient Boosting Regression (XGBR) algorithm was first applied to build a robust and predictive machine learning (ML) model for perovskite materials. XGBR algorithm yielded a lower mean square error (MSE) than both Artificial Neural Network (ANN) algorithm and Support Vector Regression (SVR) algorithm. From the ML model, two novel lead-free inorganic double perovskites: Na₂MgMnI₆, K₂NaInI₆, were obtained, suitable direct bandgaps of 1.46 eV for K₂NaInI₆ and 1.89 eV for Na₂MgMnI₆, which are similar to the organic–inorganic perovskite (MAPI3) CH₃NH₃PbI₃ (E_g = 1.6 eV), high thermal stability and good optical properties were also confirmed by DFT calculation. The method of combining ML and DFT exhibits high accuracy and significantly speeds up the discovery of promising perovskite materials.