Results 301 to 310 of about 1,958,447 (346)

Powder Metallurgy and Additive Manufacturing of High‐Nitrogen Alloyed FeCr(Si)N Stainless Steel

Advanced Engineering Materials, EarlyView.
The alloying element Nitrogen enhances stainless steel strength, corrosion resistance, and stabilizes austenite. This study develops austenitic FeCr(Si)N steel production via powder metallurgy. Fe20Cr and Si3N4 are hot isostatically pressed, creating an austenitic microstructure.
Louis Becker, Felix Radtke, Jonathan Lentz, Santiago Benito, Christoph Broeckmann, Sebastian Weber   +5 more
wiley   +1 more source

Annotation of phenotypic diversity: decoupling data curation and ontology curation using Phenex. [PDF]

J Biomed Semantics, 2014
Balhoff JP, Dahdul WM, Dececchi TA, Lapp H, Mabee PM, Vision TJ.   +5 more
europepmc   +1 more source

Peer Review #1 of "A reference cytochrome c oxidase subunit I database curated for hierarchical classification of arthropod metabarcoding data (v0.1)"

, 2018
Rodney T. Richardson, Johan Bengtsson‐Palme, Mary M. Gardiner, Reed M. Johnson   +3 more
openalex   +2 more sources

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing—Part II: Processability by Powder Bed Fusion of Metals Using a Laser Beam

Advanced Engineering Materials, EarlyView.
In this manuscript, the processability of X2CrNiMo17‐12‐2 powder coated with silicon carbide, silicon, and silicon nitride nanoparticles is investigated. The amount of nanoparticles varies from 0.25 to 1 vol%. By coating the powder feedstock material with nanoparticles, an enlargement of the process window and an increase in the build rate are achieved.
Nick Hantke, Tim R. Brocksieper, Arne Lüddecke, Tobias Grimm, Arno Kwade, Jan T. Sehrt   +5 more
wiley   +1 more source

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing – Part I: Surface, Flowability, and Optical Properties of SiC, Si, and Si3N4 Coated Metal Powders

Advanced Engineering Materials, EarlyView.
Herein, silicon‐based nanoparticle coatings on X2CrNiMo17‐12‐2 metal powder are presented. The coating process scale, process parameters, nanoparticle size (65–200 nm) as well as the coating amount are discussed regarding powder properties. The surface roughness affects the flowability, while reflectance depends on the coating material and surface ...
Arne Lüddecke, Nick Hantke, Harald Zetzener, Jan T. Sehrt, Arno Kwade   +4 more
wiley   +1 more source

ChemEx: information extraction system for chemical data curation. [PDF]

BMC Bioinformatics, 2012
Tharatipyakul A, Numnark S, Wichadakul D, Ingsriswang S.   +3 more
europepmc   +1 more source

United States LEMIS wildlife trade data curated by EcoHealth Alliance

, 2019
Evan A. Eskew, Allison M. White, Noam Ross, Kristine M. Smith, Katherine F. Smith, Jon Paul Rodrı́guez, Carlos Zambrana‐Torrelio, William B. Karesh, Peter Daszak   +8 more
openalex   +2 more sources

Enhanced Fog Water Harvesting on Superhydrophobic Steel Meshes

Advanced Engineering Materials, EarlyView.
Fog harvesting using mesh designs offers a sustainable solution to water scarcity. This study highlights key considerations for fog harvesting research and develops a methodology for a standardized protocol reflecting fog characteristics and environmental conditions.
Pegah Sartipizadeh, Mohammad Reza Mohammadizadeh, Carlo Antonini, Raziyeh Akbari   +3 more
wiley   +1 more source

Deformation Behavior of La2O3‐Doped Copper during Equal Channel Angular Pressing

Advanced Engineering Materials, EarlyView.
By additions of strengthening elements and/or structure optimization, the mechanical properties of copper can be increased while keeping favorable electric conductivity. By combining addition of La2O3 and processing by equal channel angular pressing, substructure development is achieved, leading to increase in microhardness to more than double the ...
Lenka Kunčická, Josef Walek, Radim Kocich   +2 more
wiley   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source