Non-Monotonic Trends of Hydrogen Adsorption on Single Atom Doped g-C3N4

A descriptor for the structural stability of organic-inorganic hybrid perovskites based on binding mechanism in electronic structure

The poor stability of organic-inorganic hybrid perovskites hinders its commercial application, which motivates a need for greater theoretical insight into its binding mechanism. To date, the binding mode of organic cation and anion inside organic-inorganic hybrid perovskites is still unclear and even contradictory. Therefore, in this work based on density functional theory (DFT), the binding mechanism between organic cation and anion was systematically investigated through electronic structure analysis including an examination of the electronic localization function (ELF), electron density difference (EDD), reduced density gradient (RDG), and energy decomposition analysis (EDA). The binding strength is mainly determined by Coulomb effect and orbital polarization. Based on the above analysis, a novel 2D linear regression descriptor that E_b =  - 9.75Q²/R₀ + 0.00053 V∙E_HL - 6.11 with coefficient of determination R² = 0.88 was proposed to evaluate the binding strength (the units for Q, R₀, V, and E_HL are |e|, Å, bohr³, and eV, respectively), revealing that larger Coulomb effect (Q²/R₀), smaller volume of perovskite (V), and narrower energy difference (E_HL) between the lowest unoccupied molecular orbital (LUMO) of organic cation and the highest occupied molecular orbital (HOMO) of anion correspond to the stronger binding strength, which guides the design of highly stable organic-inorganic hybrid perovskites.

Superior lithium-storage properties derived from a g-C3N4-embedded honeycomb-shaped meso@mesoporous carbon nanofiber anode loaded with Fe2O3 for Li-ion batteries

In this work, a honeycomb-shaped meso@mesoporous carbon nanofiber material incorporating homogeneously dispersed ultra-fine Fe2O3 nanoparticles (denoted as Fe2O3@g-C3N4@H-MMCN) is synthesised through a pyrolysis process. The honeycomb-shaped configuration of the meso@mesoporous carbon nanofiber material derived from a natural bio-carbon source (crab shell) acts as a support for an anode material for Li-ion batteries. Graphitic carbon nitride (g-C3N4) is produced via the one-step pyrolysis of urea at high temperature under an N2 atmosphere without the assistance of additives. The resulting favorable electrochemical performance, with superior rate capabilities (1067 mA h g-1 at 1000 mA g-1), a remarkable specific capacity (1510 mA h g-1 at 100 mA g-1), and steady cycling performance (782.9 mA h g-1 after 500 cycles at 2000 mA g-1), benefitted from the advantages of both the host material and the Fe2O3 nanoparticles, which play an important role due to their ultra-fine particle size of 5 nm. The excellent cycle life and high capacity demonstrate that this strategy of strong synergistic effects represents a new pathway for pursuing high-electrochemical-performance materials for lithium-ion batteries.

Two-Dimensional Carbon-Based Auxetic Materials for Broad-Spectrum Metal-Ion Battery Anodes

Auxetic materials possess special applications due to their unique negative Poisson's ratios (NPRs). As a classic 2D carbon material, the NPR of graphene is still deliberated. Introducing the NPR in graphene would increase its extraordinary properties, and the NPR together with other properties would bring more significant applications for graphene. In this Letter, on the basis of first-principles calculations, we reconfigure the structure of graphene, and, as an example, we propose a new 2D planar carbon allotrope, xgraphene, which is constructed by 5-6-7 carbon rings. Our theoretical calculations indicate that xgraphene has an NPR and constitutes a broad spectrum of metal ion battery anodes with high performance. Its maximum storage capacities are 930/1302/744/1488 mAh/g for Li/Na/K/Ca-ion batteries. It has low metal-ion diffusion energy barriers (≤0.49 eV) and low average open-circuit voltages (≤0.53 V). Our density functional theory results also showed that it is intrinsically metallic and possesses dynamic, thermal, and mechanical stabilities. Its intrinsic NPR, which stems from the weakness of coupling of carbon-carbon bonds, is found upon loading the uniaxial strain along the armchair direction. This work not only opens up a new direction for the design of the next-generation broad-spectrum energy-storage materials with low cost and high performance but also offers a class application for auxetic materials.

Data-Mining for Processes in Chemistry, Materials, and Engineering

With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although the research and application targets are various, many important common points still exist in their data-mining. We then propose a generalized strategy based on the philosophy of data-mining, which should be applicable for the design and optimization targets for processes in various fields with both scientific and industrial purposes.