Progress on mechanical and tribological characterization of 2D materials by AFM force spectroscopy

Two-dimensional(2D)materials are potential candidates for electronic devices due to their unique structures and exceptional physical properties,making them a focal point in nanotechnology research.Accurate assessment of the mechanical and tribological properties of 2D materials is imperative to fully exploit their potential across diverse applications.However,their nanoscale thickness and planar nature pose significant challenges in testing and characterizing their mechanical properties.Among the in situ characterization techniques,atomic force microscopy(AFM)has gained widespread applications in exploring the mechanical behaviour of nanomaterials,because of the easy measurement capability of nano force and displacement from the AFM tips.Specifically,AFM-based force spectroscopy is a common approach for studying the mechanical and tribological properties of 2D materials.This review comprehensively details the methods based on normal force spectroscopy,which are utilized to test and characterize the elastic and fracture properties,adhesion,and fatigue of 2D materials.Additionally,the methods using lateral force spectroscopy can characterize the interfacial properties of 2D materials,including surface friction of 2D materials,shear behaviour of interlayers as well as nanoflake-substrate interfaces.The influence of various factors,such as testing methods,external environments,and the properties of test samples,on the measured mechanical properties is also addressed.In the end,the current challenges and issues in AFM-based measurements of mechanical and tribological properties of 2D materials are discussed,which identifies the trend in the combination of multiple methods concerning the future development of the in situ testing techniques.

Hydrogel-based catalysts for hydrogen generation by the hydrolysis of B–H compounds under external physical fields

Hydrogen is a popular clean high-energy-density fuel.However,its utilization is limited by the challenges toward low-cost hydrogen production and safe hydrogen storage.Fortunately,these issues can be addressed using promising hydrogen storage materials such as B–H compounds.Hydrogen stored in B–H compounds can be released by hydrolysis at room temperature,which requires catalysts to increase the rate of the reaction.Recently,several effective approaches have been developed for hydrogen generation by catalyzing the hydrolysis of B–H compounds.This review summarizes the existing research on the use of nanoparticles loaded on hydrogels as catalysts for the hydrolysis of B–H compounds.First,the factors affecting the hydrolysis rate,such as temperature,p H,reactant concentration,and type of nano particles,were investigated.Further,the preparation methods(in situ reduction,one-pot method,template adsorption,etc.)for the hydrogel catalysts and the types of loaded catalysts were determined.Additionally,the hydrogel catalysts that can respond to magnetic fields,ultrasound fields,optical fields,and other physical fields are introduced.Finally,the issues and future developments of hydrogel-based catalysts are discussed.This review can inspire deeper investigations and provide guidance for the study of hydrogel catalysts in the field of hydrogen production via hydrolysis.

A Novel Deep Model with Meta-Learning for Rolling Bearing Few-Shot Fault Diagnosis

Machine learning,especially deep learning,has been highly successful in data-intensive applications;however,the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement.This leads to the so-called few-shot learning(FSL)problem,which requires the model rapidly generalize to new tasks that containing only a few labeled samples.In this paper,we proposed a new deep model,called deep convolutional meta-learning networks,to address the low performance of generalization under limited data for bearing fault diagnosis.The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data.The proposed method was compared to several FSL methods,including methods with and without pre-training the embedding mapping,and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain.The comparisons are carried out on 1-shot and 10-shot tasks using the Case Western Reserve University bearing dataset and a cylindrical roller bearing dataset.The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions.In addition,we found that the pretraining process does not always improve the prediction accuracy.

Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares

Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery,thereby influencing safety of entire electric vehicles.Precise estimation of battery model parameters using key measured signals is essential.However,measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors,potentially diminishing model estimation accuracy.Addressing the challenge of accuracy reduction caused by noise,this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares(BCFFRLS)method.Initially,a variational error model is crafted to estimate the average weighted variance of random noise.Subsequently,an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors,compensating for bias in the parameter estimates.To assess the proposed method's effectiveness in improving parameter identification accuracy,lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule(UDDS),Dynamic Stress Test(DST),and Hybrid Pulse Power Characterization(HPPC).The proposed method,alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares(FFRLS)—was employed for battery model parameter identification.Comparative analysis reveals substantial improvements,with the mean absolute error reduced by 25%,28%,and 15%,and the root mean square error reduced by 25.1%,42.7%,and 15.9%in UDDS,HPPC,and DST operating conditions,respectively,when compared to the FFRLS method.

Strain engineering in electrocatalysis:Strategies,characterization,and insights

Strain engineering,as a cutting-edge method for modulating the electronic structure of catalysts,plays a crucial role in regulating the interaction between the catalytic surface and the adsorbed molecules.The electrocatalytic performance is influenced by the electronic structure,which can be achieved by introducing the external forces or stresses to adjust interatomic spacing between surface atoms.The challenges in strain engineering research lie in accurately understanding the mechanical impact of strain on performance.This paper first introduces the basic strategy for generating the strain,summarizes the different strain generation forms and their advantages and disadvantages.The progress in researching the characterization means for the lattice strains and their applications in the field of electrocatalysis is also emphasized.Finally,the challenges of strain engineering are introduced,and an outlook on the future research directions is provided.