Results 231 to 240 of about 11,619,519 (302)

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch   +3 more
wiley   +1 more source

PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley   +1 more source

Workflow for Design of Experiments‐Based Modeling of Species Transport and Growth Kinetics in GaN Hydride Vapor Phase Epitaxy

open access: yesAdvanced Engineering Materials, EarlyView.
A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič   +7 more
wiley   +1 more source

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

open access: yesAdvanced 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   +7 more
wiley   +1 more source

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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

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