Results 121 to 130 of about 184,967,875 (212)

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

Identification of ice loads on ship structure using a hybrid regularization strategy. [PDF]

Sci Rep
Zhou C, Chen L, Li X, Liu W, Yin Q, Zhang J.   +5 more
europepmc   +1 more source

Characterizing Biopolymer Electrolytes in Zinc–Air Batteries: Challenges, Best Practices, and a Robust Workflow Guiding Future Research Paths

Advanced Energy Materials, EarlyView.
Bio‐based gel polymer electrolytes promise sustainable, mechanically adaptable zinc–air batteries, yet their progress is constrained by inconsistent characterization. This review critically links formulation, structure, interfaces, and cell performance, identifies methodological gaps under alkaline operating conditions, and proposes application ...
Matteo Milanesi, Hiba Ali, Barbara Belza, Mattia F. Palermo, Claudio Gerbaldi, Giuseppe Antonio Elia, Nagore Ortiz‐Vitoriano   +6 more
wiley   +1 more source

Long-Term Bending Behavior of Laminated Glass Plate with Temperature-Dependent Viscoelastic Interlayer. [PDF]

Materials (Basel)
Zhu X, Ni K, Xu C, Zhao A, Wu P.
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

Spectral inference of arterial stiffness from a single distal velocity waveform. [PDF]

PNAS Nexus
Chahal MS, Aghilinejad A.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Asymmetric Flow Dynamics in a Pneumatically Actuated Polydimethylsiloxane (PDMS) Micropump: Numerical Simulation and Experimental Validation. [PDF]

Micromachines (Basel)
Hsu PP, Lin JL.
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Robust in-situ stress inversion in an underground powerhouse using tensor synthesis and surrogate-assisted differential evolution. [PDF]

Sci Rep
Dong Z, Hu S, Qian Y, Zhang X, Fu P, Zhang K.   +5 more
europepmc   +1 more source