Data-driven rational design of single-atom materials for hydrogen evolution and sensing

Herein we proposed a data-driven high-throughput principle to screen high-performance single-atom materials for hydrogen evolution reaction(HER)and hydrogen sensing by combing the theoretical computations and a topology-based multi-scale convolution kernel machine learning algorithm.After the rational training by 25 groups of data and prediction of all 168 groups of single-atom materials for HER and sensing,respectively,a high prediction accuracy(>0.931 R^(2) score)was achieved by our model.Results show that the promising HER catalysts include Pt atoms in C_(4) and Sc atoms in C_(1)N_(3) coordination environment.Moreover,Y atoms in C_(4) coordination environment and Cd atoms in C_(2)N_(2)-ortho coordination environment were predicted with great potential as hydrogen sensing materials.This method provides a way to accelerate the discovery of innovative materials by avoiding the time-consuming empirical principles in experiments.

Accurate atomic scanning transmission electron microscopy analysis enabled by deep learning

Currently,the machine learning(ML)-based scanning transmission electron microscopy(STEM)analysis is limited in the simulative stage,its application in experimental STEM is needed but challenging.Herein,we built up a universal model to analyze the vacancy defects and single atoms accurately and rapidly in experimental STEM images using a full convolution network.In our model,the unavoidable interference factors of noise,aberration,and carbon contamination were fully considered during the training,which were difficult to be considered in the past.Even toward the simultaneous identification of various vacancy types and low-contrast single atoms in the low-quality STEM images,our model showed rapid process speed(45 images per second)and high accuracy(>95%).This work represents an improvement in experimental STEM image analysis by ML.

Effects of Stress Level and Stress State on Creep Ductility:Evaluation of Different Models

The last few decades have witnessed an increasing emphasis on the development of strain-based ap- proach for predicting the creep life or damage of components operating at elevated temperatures. Creep ductility, as a key parameter in this approach, may vary with a number of factors including strain rate, state of stress, operating temperature, material microstructure, etc. The present paper, however, is focused on reviewing the state-of-the-art understanding of the effects of stress level and stress state on the creep ductility. Mechanisms involving the void growth and coalescence are presented to describe the role of stress level in the variation of uniaxial creep ductility. The prediction capacity of existing empirical duc- tility models is also assessed in light of uniaxial test data. On the other hand, a vast body of multiaxial creep test data, collected from open literature, is utilized to examine the influence of the state of stress on the creep ductility. Then, a variety of multiaxial ductility factor models are introduced and evaluated with the available experimental data. Finally, a brief discussion on the dependence of creep ductility on the stress triaxiality and Lode parameter, predicted by numerical methods, is provided.

Numerical Studies of Estimation of Fracture Parameters for Mismatched Welded Joints with HAZ Cracks

An idealized tri-material assumption is established to describe the constitutive relationship of mismatched welded joints by considering the influence of heat affected zone（HAZ）.The fracture parameters Jand C＊are estimated for mismatched welded joints with HAZ cracks by finite element analysis with ABAQUS.A middle crack tension（M（T））specimen is utilized in the analysis for different material properties and geometries of the weldment.The influence of mechanical property and geometry on the fracture parameters Jand C＊of the specimen is discussed for the welded joints with HAZ crack.The results suggest that the HAZ property is a significant factor in the estimation of Jand C＊for the mismatched welded joint with HAZ crack.

Single-atom Ni-N_(4)for enhanced electrochemical sensing

Single-atom catalysts(SACs)attract widespread attention in heterogeneous catalysis due to their maximum atomic utilization efficiency and unique physical and chemical properties.However,their applications in chemical sensing keep huge potential but remain unclear.Herein,a Ni-N_(4)-C SAC was synthesized for the trace detection of dopamine(DA)and uric acid(UA).The Ni-N_(4)-C SAC exhibited superior sensing performance compared to the Ni clusters.The detection range for DA and UA were 0.05–75μM and 5–90μM with detection limits of 0.027 and 0.82μM,respectively.Density functional theory(DFT)computations indicate that Ni-N_(4)-C has a lower reaction barrier during electrochemical process,indicating that the atomic Ni sites possess higher intrinsic activity than Ni clusters.Moreover,DA and UA show strong potential dependency on the Ni-N_(4)-C catalyst,indicating its applicability for their concurrent detection.This work extends the application of SACs in chemical sensing.