Induced spin polarization in graphene via interactions with halogen doped MoS2 and MoSe2 monolayers by DFT calculations

Spin Polarization and Flat Bands in Eu-Doped Nanoporous and Twisted Bilayer Graphenes

Advanced two-dimensional spin-polarized heterostructures based on twisted (TBG) and nanoporous (NPBG) bilayer graphenes doped with Eu ions were theoretically proposed and studied using Periodic Boundary Conditions Density Functional theory electronic structure calculations. The significant polarization of the electronic states at the Fermi level was discovered for both Eu/NPBG(AA) and Eu/TBG lattices. Eu ions' chemi- and physisorption to both graphenes may lead to structural deformations, drop of symmetry of low-dimensional lattices, interlayer fusion, and mutual slides of TBG graphene fragments. The frontier bands in the valence region at the vicinity of the Fermi level of both spin-polarized 2D Eu/NPBG(AA) and Eu/TBG lattices clearly demonstrate flat dispersion laws caused by localized electronic states formed by TBG Moiré patterns, which could lead to strong electron correlations and the formation of exotic quantum phases.

Hybrid G/BN@2H-MoS2 Nanomaterial Composites: Structural, Electronic and Molecular Adsorption Properties

Hybrid structures often possess superior properties to those of their component materials. This arises from changes in the structural or physical properties of the new materials. Here, we investigate the structural, electronic, and gas-adsorption properties of hybrid structures made from graphene/hexagonal boron nitride and 2H-molybdenum disulfide (G/BN@MoS₂) monolayers. We consider hybrid systems in which the G/BN patch is at the Mo plane (model I) and the S plane (model II). We find that the implanted hexagon of G or BN in MoS₂ alters its electronic properties: G@MoS₂ (I,II) are metallic, while BN@MoS₂ (I) is an n-type conducting and BN@MoS₂ (II) is semiconducting. We study the molecular adsorption of some diatomic gases (H₂, OH, N₂, NO, CO), triatomic gases (CO₂, NO₂, H₂S, SO₂), and polyatomic gases (COOH, CH₄, and NH₃) on our hybrid structures while considering multiple initial adsorption sites. Our results suggest that the hybrid systems may be suitable materials for some applications: G@MOS₂ (I) for oxygen reduction reactions, BN@MoS₂ (I,II) for NH₃-based hydrogen production, and G@MoS₂ (I) and BN@MoS₂ (I,II) for filtration of No, Co, SO₂, H₂S, and NO₂.

Bandgap prediction of two-dimensional materials using machine learning

The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

Exploring promising gas sensing and highly active catalysts for CO oxidation: transition-metal (Fe, Co and Ni) adsorbed Janus MoSSe monolayers

From first-principles calculations, the transition-metal (TM) atom (Fe, Co and Ni) adsorbed Janus MoSSe monolayer, toxic gas molecules (CO, NH3 and H2S) adsorbed on the Ni-MoSSe monolayer and CO catalytic oxidation on the Fe-MoSSe monolayer are systematically investigated. An increasing order (Fe-MoSSe < Co-MoSSe < Ni-MoSSe) is found for the stability and band gap of the TM atom adsorbed Janus MoSSe monolayer. These toxic gas molecules are found to be weakly physisorbed and strongly chemisorbed on the pristine and Ni-MoSSe monolayers, respectively. The electronic structure and gas molecular adsorption properties of the Janus MoSSe monolayer can be modulated by adsorbing different TM atoms and gas molecules. Particularly, the CO catalytic oxidation can be realized on the Fe-MoSSe monolayer in light of the more preferable Eley-Rideal (ER) mechanism with the two-step route (CO + O2 → OOCO → CO2 + Oads, CO + Oads → CO2) with highly exothermic processes in each step. The adsorption of TM atoms which may greatly enhance gas sensing performance and catalytic performance of CO oxidation based on the Janus MoSSe monolayer is further discussed.