Results 91 to 100 of about 353,777 (261)

Effect of Metal Foam Structure on Pool Boiling Heat Transfer Characteristics

Zhileng xuebao, 2019
Pool boiling heat transfer characteristics of a refrigerant on heating surfaces of metal foams of different structures were experimentally studied. The test samples were copper foams of different pore densities (5–40 PPI), porosities (0.90 and0.95), and ...
Hu Chenyu, Lai Zhancheng, Hu Haitao, Han Weizhe   +3 more
doaj  

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

Electrochemical Behavior of Flame‐Sprayed Sc‐Doped AlCoCrFeMo High‐Entropy Alloy Coatings in 3.5% Sodium Chloride Solution

Advanced Engineering Materials, EarlyView.
Scandium (Sc)‐doped AlCoCrFeMo HEA coatings are fabricated via flame spraying with 0.1, 0.3, and 0.5 wt% Sc additions. Among these, the HEA‐Sc0.3 coating exhibits the highest corrosion resistance, indicated by a more positive corrosion potential and lower current density.
Pankaj Kumar, Akash Vyas, Mohammad Aatif Qazi, Nashit Jalal, Maria Ophelia Jarligo, Mostafa Yakout, Jing Liu, André McDonald   +7 more
wiley   +1 more source

Finite element analysis of heat transfer behavior in glass fiber/metal composite materials under constant heat load

AUTEX Research Journal
A finite element analysis model was employed to analyze the heat conduction behavior of glass fiber/metal (stainless steel, aluminum, or oxygen-free copper) composite materials under a constant thermal load of 100°C on the metal surface, i.e., the ...
Wang Ke, Zhu Ting, Ma Qian, Wang Shudong, Zhou Hongtao, Zhao Jumei, Jin Limin   +6 more
doaj   +1 more source

METALS AND CHARGE TRANSFER [PDF]

Proceedings of the National Academy of Sciences, 1961
openaire   +2 more sources

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

metal to metal charge transfer (MMCT) transition

, 2014
Citation: 'metal to metal charge transfer (MMCT) transition' in the IUPAC Compendium of Chemical Terminology, 3rd ed.; International Union of Pure and Applied Chemistry; 2006. Online version 3.0.1, 2019. 10.1351/goldbook.M03869 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for ...
openaire   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +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

A Fast Prediction Model for Liquid Metal Transfer Modes during the Wire Arc Additive Manufacturing Process. [PDF]

Materials (Basel), 2023
Ouyang J, Li M, Lian Y, Peng S, Liu C.
europepmc   +1 more source