Review on piezoelectric actuators:materials,classifications,applications,and recent trends

Piezoelectric actuators are a class of actuators that precisely transfer input electric energy into displacement,force,or movement outputs efficiently via inverse piezoelectric effect-based electromechanical coupling.Various types of piezoelectric actuators have sprung up and gained widespread use in various applications in terms of compelling attributes,such as high precision,flexibility of stoke,immunity to electromagnetic interference,and structural scalability.This paper systematically reviews the piezoelectric materials,operating principles,representative schemes,characteristics,and potential applications of each mainstream type of piezoelectric actuator.Herein,we intend to provide a more scientific and nuanced perspective to classify piezoelectric actuators into direct and indirect categories with several subcategories.In addition,this review outlines the pros and cons and the future development trends for all kinds of piezoelectric actuators by exploring the relations and mechanisms behind them.The rich content and detailed comparison can help build an in-depth and holistic understanding of piezoelectric actuators and pave the way for future research and the selection of practical applications.

Convolutional neural network-assisted recognition of nanoscale L1_(2) ordered structures in face-centred cubic alloys

Nanoscale L12-type ordered structures are widely used in face-centered cubic(FCC)alloys to exploit their hardening capacity and thereby improve mechanical properties.These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species,which makes them challenging to be characterized.Spatial distribution maps(SDMs)are used to probe local order by interrogating the three-dimensional(3D)distribution of atoms within reconstructed atom probe tomography(APT)data.However,it is almost impossible to manually analyze the complete point cloud(>10 million)in search for the partial crystallographic information retained within the data.Here,we proposed an intelligent L1_(2)-ordered structure recognition method based on convolutional neural networks(CNNs).The SDMs of a simulated L1_(2)-ordered structure and the FCC matrix were firstly generated.These simulated images combined with a small amount of experimental data were used to train a CNN-based L1_(2)-ordered structure recognition model.Finally,the approach was successfully applied to reveal the 3D distribution of L1_(2)–typeδ′–Al3(LiMg)nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system.The minimum radius of detectable nanodomain is even down to 5Å.The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.