Results 141 to 150 of about 13,007,525 (289)

The Hydrostatic Pressure Distribution in the Periodontal Ligament and the Risk of Root Resorption-A Finite Element Method (FEM) Study on the Nonlinear Innovative Model. [PDF]

Materials (Basel)
Kuc AE, Sybilski K, Kotuła J, Piątkowski G, Kowala B, Lis J, Saternus S, Sarul M.   +7 more
europepmc   +1 more source

Force‐Electric Nanotentacles of Tantalum Implant Accelerate Cellular Energy Metabolism/Crosstalk for Immunomodulation‐Vascularized Bone Integration

Advanced Science, EarlyView.
A biomimetic force‐electric nanotentacle‐enabled tantalum (Ta) implant with surface‐nanostructured LiTaO3 (n‐LiTaO3) was first developed via laser nanofabrication combined with Li+‐induced in situ reaction, enabling the transduction of ultrasound into bioelectrical cues.
Taixing Zhang, Ke Ma, Kangqing Zuo, Pandong Lin, Tailong Zhang, Rongliang Ding, Linbo Zhang, Aonan Li, Yinchuan Wang, Yanling Huang, Jichao Feng, Guiyong Xiao, Yupeng Lu, Bing Han, Zhiqiang Wang, Ningbo Li   +15 more
wiley   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Research on topology optimization method of bone plate for rabbit femur. [PDF]

Sci Prog
Shang Y, Chen W, Zhang T, Liang H.
europepmc   +1 more source

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Advanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source

An Adaptive Coupling of Edge-Based Smoothed FEM and SPH with a Bidirectional Element-Particle Transformation Algorithm for Laser Powder Bed Fusion. [PDF]

Materials (Basel)
Suo M, Long T.
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Flexural behaviour of RC shear wall using enhanced finite element model. [PDF]

Sci Rep
Nasr O, Moustafa A, Ghallab AH.
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source