Motivated by the recent successful synthesis of Janus monolayer of transition metal (TM) dichalcogenides, MXenes with Janus structures are worthy of further study, concerning its electronic structure and magnetic properties. Here, we study the effect of different transition metal atoms on the structure stability and magnetic and electronic properties of M’MCO2 (M’ and M = V, Cr and Mn). The result shows the output magnetic moment is contributed mainly by the d orbitals of the V, Cr, and Mn atoms. The total magnetic moments of ferromagnetic (FM) configuration and antiferromagnetic (AFM) configuration are affected by coupling types. FM has a large magnetic moment output, while the total magnetic moments of AFM2’s (intralayer AFM/interlayer FM) configuration and AFM3’s (interlayer AFM/intralayer AFM) configuration are close to 0. The band gap widths of VCrCO2, VMnCO2, CrMnCO2, V2CO2, and Cr2CO2 are no more than 0.02 eV, showing metallic properties, while Mn2CO2 is a semiconductor with a 0.7071 eV band gap width. Janus MXenes can regulate the size of band gap, magnetic ground state, and output net magnetic moment. This work achieves the control of the magnetic properties of the available 2D materials, and provides theoretical guidance for the extensive design of novel Janus MXene materials.
The possibility of using transition metal (TM)/MXene as a catalyst for the nitrogen reduction reaction (NRR) was studied by density functional theory, in which TM is an Fe atom, and MXene is pure Ti3C2O2 or Ti3C2O2−x doped with N/F/P/S/Cl. The adsorption energy and Gibbs free energy were calculated to describe the limiting potentials of N2 activation and reduction, respectively. N2 activation was spontaneous, and the reduction potential-limiting step may be the hydrogenation of N2 to *NNH and the desorption of *NH3 to NH3. The charge transfer of the adsorbed Fe atoms to N2 molecules weakened the interaction of N≡N, which indicates that Fe/MXene is a potential catalytic material for the NRR. In particular, doping with nonmetals F and S reduced the limiting potential of the two potential-limiting steps in the reduction reaction, compared with the undoped pure structure. Thus, Fe/MXenes doped with these nonmetals are the best candidates among these structures.
2D MXenes have been found to be one of the most promising catalysts for hydrogen evolution reaction (HER) due to their excellent electronic conductivity, hydrophilic nature, porosity and stability. Nonmetallic (NM) element doping is an effective approach to enhance the HER catalytic performance. By using the density functional theory (DFT) method, we researched the effect of nonmetallic doping (different element types, variable doping concentrations) and optimal hydrogen absorption concentration on the surface of NM-Ti3C2O2 for HER catalytic activity and stability. The calculation results show that doping nonmetallic elements can improve their HER catalytic properties; the P element dopants catalyst especially exhibits remarkable HER performance (∆GH = 0.008 eV when the P element doping concentration is 100% and the hydrogen absorption is 75%). The origin mechanism of the regulation of doping on stability and catalytic activity was analyzed by electronic structures. The results of this work proved that by controlling the doping elements and their concentrations we can tune the catalytic activity, which will accelerate the further research of HER catalysts.
As a key part of data preprocessing, namely attribute reduction, is effectively applied in the rough set field. The purpose of attribute reduction is to prevent too many attributes from affecting classifier operations and reduce the dimensionality of data space. Presently, in order to further improve the simplification performance of attribute reduction, numerous researchers have proposed a variety of methods. However, given the current findings, the challenges are: to reasonably compress the search space of candidate attributes; to fulfill multi-perspective evaluation; and to actualize attribute reduction based on guidance. In view of this, forward greedy searching to κ-reduct based on granular ball is proposed, which has the following advantages: (1) forming symmetrical granular balls to actualize the grouping of the universe; (2) continuously merging small universes to provide guidance for subsequent calculations; and (3) combining supervised and unsupervised perspectives to enrich the viewpoint of attribute evaluation and better improve the capability of attribute reduction. Finally, based on three classifiers, 16 UCI datasets are used to compare our proposed method with six advanced algorithms about attribute reduction and an algorithm without applying any attribute reduction algorithms. The experimental results indicate that our method can not only ensure the result of reduction has considerable performance in the classification test, but also improve the stability of attribute reduction to a certain degree.
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