Results 101 to 110 of about 50,829 (262)

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

Stable Isotope Ratios of Herbs and Spices Commonly Used as Herbal Infusions in the Italian Market

ACS Omega, 2021
Purna K. Khatri, Roberto Larcher, Federica Camin, Luca Ziller, Agostino Tonon, Tiziana Nardin, Luana Bontempo   +6 more
doaj   +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

Rapid coronary artery stent computational simulation using the simplex deformable model. [PDF]

Med Eng Phys
Lee C, Zhao S, Wu W, Ramesh Bhat R, Patel P, Vardhan Trivedi Y, Munjal P, Murasato Y, Chatzizisis YS.   +8 more
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

Origins of Self-Fertility in Three <i>Caenorhabditis</i> Nematodes. [PDF]

Biology (Basel)
Bobinski M, Kennedy J, Pilgrim D, Ellis RE.   +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

Proposal of <i>Allogeochorda</i> gen. nov., <i>Allogeochorda subterranea</i> comb. nov., <i>Allostigmatella</i> gen. nov. and four new combinations and <i>Allosphingorhabdus</i> gen. nov. and <i>Allosphingorhabdus lutea</i> comb. nov. as replacement names for the illegitimate genus names <i>Geochorda</i>, <i>Stigmatella</i> and <i>Alterisphingorhabdus</i> and their species. [PDF]

Int J Syst Evol Microbiol
Kostovski M, Oren A.
europepmc   +1 more source

SIDBRINT: Fem memòria, fem xarxa

Podeu consultar la Jornada completa al camp Recurs relacionat.
openaire   +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