Results 171 to 180 of about 16,417 (265)

Strain‐Assembled Crystalline SrRuO3 Microtube and Emergent Curvilinear Magnetism

Advanced Science, EarlyView.
The interfacial‐strain assembly based on planar nanomembranes presents a promising approach for constructing novel magnetic microstructures. By employing the perpendicular magnetic anisotropic SrRuO3 freestanding nanomembrane as a model system, we exploit the interfacial strain effect within the crystalline SrTiO3/SrRuO3 bilayer, and successfully ...
Lei Gao, Yuqian Wang, Xiangyu Lyu, Pengyu Liu, Mingtong Zhu, Jin Liu, Mengcheng Li, Ailing Ji, Qinghua Zhang, Lin Gu, Libo Ma, Zexian Cao, Nianpeng Lu   +12 more
wiley   +1 more source

Novel design of tubular metamaterials with sign-switchable Poisson's ratio and tunable mechanical properties for intestinal stents. [PDF]

Front Bioeng Biotechnol
Lyu Y, Siniauskaya V, Jiang J, Wang H, Meng L, Bosiakov S, Abdul Kadir MR.   +6 more
europepmc   +1 more source

Pacinian Corpuscle‐Inspired Strain Conversion Enables Ultrasensitive, Linear, and Broad‐Range Piezoelectric Sensing for Cardiovascular Health Monitoring

Advanced Science, EarlyView.
A bioinspired piezoelectric sensor mimicking Pacinian corpuscles is developed to enable ultrasensitive and linear pressure sensing. A multilayer grooved architecture converts normal pressure into in‐plane strain, delivering high sensitivity, wide linear range, and efficient energy harvesting, enabling high‐fidelity wrist pulse monitoring and ...
Qi Yang, Zhongqian Song, Shengjie Liu, Yingming Ma, Weiyan Li, Huijun Kong, Cuiyu Liu, Yu Bao, Li Niu   +8 more
wiley   +1 more source

AI‐Assisted Self‐Powered Wearable Dual‐Mode Sensor With TENG and Stretchable Optical Fiber for Neurological Disorder Diagnostics

Advanced Science, EarlyView.
This manuscript presents the WDMS platform, an AI‐assisted, self‐powered wearable dual‐mode sensor for tele‐neurology. It integrates a contact–separation TENG insole with stretchable polyurethane optical‐fiber strain sensors to synchronously track plantar pressure and lower‐limb muscle deformation.
Tianliang Li, Han Liu, Guoxu Liu, Qian'ao Wang, Haotian Zhou, Guiyi Liu, Yan Xu, Jun Wang, Zuqiang Wang, Feng Zhu, Feiling Luo, Zhong Lin Wang, Chi Zhang   +12 more
wiley   +1 more source

AE-based classification and fracture process analysis of flawed marble with stress intensity factor validation. [PDF]

Sci Rep
Sha P, Fan J, Lv Y, Chen J.
europepmc   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

Biomechanical Variation of the Vessel Wall Along the Length of the Healthy Aorta-Linking Geometric, Flow-, and Pressure-Mediated Adaptations. [PDF]

J Biomech Eng
Badal RM, Barocas VH.
europepmc   +1 more source

Magneto‐X Effects in Magnetic Soft Materials and Their Applications

Advanced Science, EarlyView.
This review systematically explores magnetic soft materials (MSMs), a novel class of composites that transform under magnetic fields. It catalogs fundamental “Magneto‐X” effects, classifies materials by their matrix and magnetic fillers, and highlights transformative applications in soft robotics, biomedical devices, flexible electronics, etc. Finally,
Ziyin Xiang, Xiangling Xia, Benjamin Ducharne, Yujie Ye, Jie Shang, Run‐Wei Li   +5 more
wiley   +1 more source

Numerical Simulation of Failure Modes of Solid Propellants with Internal Cavities Under Various Loading Conditions. [PDF]

Polymers (Basel)
Liu K, Yang Q, Cao L, Wang J, Cao P.
europepmc   +1 more source

Machine‐Learning‐Guided Design of Incommensurate Antiferroelectrics via Field‐Driven Phase Engineering

Advanced Science, EarlyView.
The key to enhancing the energy storage performance of antiferroelectrics lies in regulating the phase transition and reverse phase transition. A phase‐field‐machine learning framework is employed to predict the energy storage performance of Pb‐based incommensurate antiferroelectrics with multi‐scale regulation strategy, thereby revealing the dynamic ...
Ke Xu, Xiaoming Shi, Zhaochen Xi, Shouzhe Dong, Changqing Guo, Rongzhen Gao, Letao Yang, Jing Wang, Di Zhou, Houbing Huang   +9 more
wiley   +1 more source