ABSTRACT
The electronic properties of layered two-dimensional (2D) transition-metal dichalcogenide (TMD) van der Waals (vdW) heterostructures are strongly dependent on their layer number (N). However, extremely large computational resources are required to investigate the layer-dependent TMD vdW heterostructures for every possible combination if N varies in a large range. Fortunately, the machine learning (ML) technique provides a feasible way to probe this problem. In this work, based on the density functional theory (DFT) calculations combined with the ML technique, we effectively predict the layer-dependent electronic properties of TMD vdW heterostructures composed of MoS2, WS2, MoSe2, WSe2, MoTe2, or WTe2, in which the layer number varies from 2-10. The cross-validation scores of our trained ML models in predicting the bandgaps as well as the band edge positions exceed 90%, suggesting excellent performance. The predicted results show that in the case of a few-layer system, the number of layers has a significant effect on the electronic properties. The bandgap and band alignment could be dramatically changed from bilayer to triple-layer heterostructures. However, with the increase of the number of layers, the electronic properties change, and some general trends can be summarized. When the layer number is larger than 8, the properties of the TMD heterostructures tend to be stable, and the influence of the layer number decreases. Based on these results, our work not only sheds light on the understanding of the layer-dependent electronic properties of multi-layer TMD vdW heterostructures, but also provides an efficient way to accelerate the discovery of functional materials.
ABSTRACT
Searching for novel and high-performance two-dimensional (2D) materials is an important task for photocatalytic applications. Although multinary compounds exhibit more diversity in structure and properties in comparison to binary 2D materials, they are comparatively under-studied. Herein, using a machine-learning (ML) technique and high-throughput screening, we develop an efficient approach to accurately predict 2D multicomponent photocatalysts. Over 4000 monolayers are examined, and 75 multinary compounds are identified for photocatalytic applications. Considering our predictions, we find that the ternary and quaternary compounds A2P2X6 and ABP2X6 with A = Cu/Zn/Ge/Ag/Cd, B = Ga/In/Bi, and X = S/Se exhibit superior properties, making them promising candidates for overall water splitting. Thus, our work provides an efficient way to explore novel photocatalysts, which could stimulate further theoretical and experimental investigations on 2D multinary compounds for application in photocatalytic water splitting.
ABSTRACT
RAB7, a member of RAS oncogene family-like 1 (RAB7L1), is a GTPase belonging to the Rab family and acts as an upstream regulator to regulate the kinase activity of leucine-rich repeat kinase 2 (LRRK2). Although LRRK2 has been shown to aggravate secondary brain injury (SBI) after intracerebral hemorrhage (ICH), it is unknown whether RAB7L1 is also involved in this process. The purpose of the present study was to investigate the role of RAB7L1 in ICH-induced SBI in vivo. Autologous blood was injected into adult male Sprague-Dawley rats to induce an ICH model in vivo. The results showed that the protein levels of RAB7L1 increased after ICH. Overexpression of RAB7L1 induced neuronal apoptosis and damage, as demonstrated by TUNEL-positive and FJB-positive cells, and exacerbated ICH-induced learning and cognitive dysfunctions; in contrast, downregulation of RAB7L1 via RNA interference yielded comparatively opposite changes in these parameters. In summary, this study demonstrates that RAB7L1 promotes SBI after ICH and may represent a potential target for ICH therapy.