Results 111 to 120 of about 354,304 (262)

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Dynamic Changes of Microbial Communities and Chemical Compounds During the Dry Processing of <i>Coffea arabica</i>. [PDF]

Food Sci Nutr
Shen X, Wang Q, Yuan B, Yin Z, Li M, Shi R, Liu K, Yuan W.   +7 more
europepmc   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

Low‐Temperature Imidization of Polyimides (PI) for Scalable Manufacturing of Flexible Wearable Electronics

Advanced Engineering Materials, EarlyView.
Low‐temperature imidization of PEG‐modified polyimide enables fully screen‐printed, roll‐to‐roll fabrication of flexible electrodes on PET substrates, delivering robust mechanical durability and reliable ECG/EMG signal performance for wearable electronics.
Akib Abdullah Khan, Jong‐Hoon Kim
wiley   +1 more source

High shear dry extruded corn in feedlot receiving diets. [PDF]

Transl Anim Sci
Rients EL, Smerchek DT, Hansen SL.
europepmc   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Differential Effects of Household Thermal Processing on Extractable β-Glucan and Total Resistant Starch Across Processed Barley Forms. [PDF]

Foods
Shivanand SS, Sobhana PP, Epparapally S, Soma VK, Chowdavarapu RR, Sundaralingam G, Thenarangam S, Vishnoi S, Chithra PR, Vasireddy P, Madhari R.   +10 more
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Advanced Manufacturing Routes for Electrodes and Cells in All-Solid-State Batteries: From Powder to Power. [PDF]

Small Methods
Park H, Kong S, Jang J.
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source