Results 201 to 210 of about 191,927 (272)

Jean-Marie Souriau's Symplectic Foliation Model of Sadi Carnot's Thermodynamics. [PDF]

Entropy (Basel)
Barbaresco F.
europepmc   +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

Direct and Inverse Steady-State Heat Conduction in Materials with Discontinuous Thermal Conductivity: Hybrid Difference/Meshless Monte Carlo Approaches. [PDF]

Materials (Basel)
Milewski S.
europepmc   +1 more source

Modeling the separation of water‐in‐oil emulsions in continuously fed gravity settlers using millifluidic experiments

AIChE Journal, EarlyView.
Abstract Emulsion separation remains a persistent challenge in chemical and process industries due to the metastable nature of dispersed droplets. In gravity separators, the overall separation rate is governed by the formation of a densely packed zone (DPZ) of deforming and coalescing droplets that mediates between the dispersed and continuous phases ...
Andrei Zlobin, José Esteban Andino‐Enriquez, Thomas Gouëzec, Maurice Bourrel, Enric Santanach‐Carreras, Nicolas Passade‐Boupat, Laurence Talini, François Lequeux, Pascal Panizza   +8 more
wiley   +1 more source

From Molecular Insight to Mesoscale Membrane Remodeling: Curvature Generation by Arginine-Rich Cell-Penetrating Peptides. [PDF]

Small Methods
Baxová KL, Koikkara J, Allolio C.
europepmc   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Discovering the Effects of Superior-Surface Vocal Fold Lesions via Fluid-Structure Interaction Analysis. [PDF]

Bioengineering (Basel)
Neves M, Niyingenera A, Delaney N, Zakerzadeh R.   +3 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Saturation behavior and full-field reconstruction of residual stress in quenched AISI 304 stainless steel via the contour method. [PDF]

Sci Rep
Meng L, Khan AM, Shan Y, Al-Ghamdi KA.
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source