Multilayer Fluorine-Free MoBTx MBene with Hydrophilic Structural-Modulating for the Fabrication of a Low-Resistance and High-Resolution Humidity Sensor.

2D transition metal borides (MBenes) with abundant surface terminals hold great promise in molecular sensing applications. However, MBenes from etching with fluorine-containing reagents present inert -fluorine groups on the surface, which hinders their sensing capability. Herein, the multilayer fluorine-free MoBTx MBene (where Tx represents O, OH, and Cl) with hydrophilic structure is prepared by a hydrothermal-assisted hydrochloric acid etching strategy based on guidance from the first-principle calculations. Significantly, the fluorine-free MoBTx-based humidity sensor is fabricated and demonstrates low resistance and excellent humidity performance, achieving a response of 90% to 98%RH and a high resolution of 1%RH at room temperature. By combining the experimental results with the first-principles calculations, the interactions between MoBTx and H2O, including the adsorption and intercalation of H2O, are understood first in depth. Finally, the portable humidity early warning system for real-time monitoring and early warning of infant enuresis and back sweating illustrates its potential for humidity sensing applications. This work not only provides guidance for preparation of fluorine-free MBenes, but also contributes to advancing their exploration in sensing applications.

Improving Molecular Dynamics Simulations for Solid-Liquid Interface with Machine Learning Interatomic Potentials.

Emerging developments in artificial intelligence have opened infinite possibilities for material simulation. Depending on the powerful fitting of machine learning algorithms to first-principles data, machine learning interatomic potentials (MLIPs) can effectively balance the accuracy and efficiency problems in molecular dynamics (MD) simulations, serving as powerful tools in various complex physicochemical systems. Consequently, this brings unprecedented enthusiasm for researchers to apply such novel technology in multiple fields to revisit the major scientific problems that have remained controversial owing to the limitations of previous computational methods. Herein, we introduce the evolution of MLIPs, provide valuable application examples for solid-liquid interfaces, and present current challenges. Driven by solving multitudinous difficulties in terms of the accuracy, efficiency, and versatility of MLIPs, this booming technique, combined with molecular simulation methods, will provide an underlying and valuable understanding of interdisciplinary scientific challenges, including materials, physics, and chemistry.

A general strategy to stabilize 1T-MoS2 using MXene heterostructures and unlock its hydrogen evolution reaction capabilities.

The two-dimensional (2D) metallic phase of MoS2, 1T-MoS2, has extraordinary electrical conductivity in contrast to the common 2D semiconducting phase, 2H-MoS2. However, the thermodynamic instabilities of 1T-MoS2 hinder its application. In this work, we investigate the possibilities of stabilizing 1T-MoS2 through heterostructure design using first-principles calculations. We found that MXene-based heterostructures could hamper phase transitions from 1T-MoS2 to 2H-MoS2 enabled by a larger phase transition kinetic energy barrier. Based on this finding, we propose a general and effective strategy for stabilizing 1T-MoS2, that is, building heterostructures using 1T-MoS2 and oxygen-functionalized MXenes. Besides, we have also observed that due to the occurrence of electron transfer in the heterostructure, 1T-MoS2 in the heterostructure exhibits improved hydrogen adsorption free energy and more active sites compared to the monolayer 1T-MoS2. These findings provide guidance for promoting and developing 1T-MoS2 for practical applications. In addition, the proposed heterostructure design strategy could inspire the study of phase transition behaviors and electrochemical properties of materials using interfaces.

Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by Active-Learning-Potential Molecular-Dynamics Simulation.

MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super-sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes.