Results 181 to 190 of about 2,998,978 (246)

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

open access: yesAdvanced 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   +5 more
wiley   +1 more source

Graded chronic noncancer pain distribution using the Graded Chronic Pain Scale-Revised framework: a cross-sectional study. [PDF]

open access: yesPain Rep
Hellmann SS   +7 more
europepmc   +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

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

Enhanced Fog Water Harvesting on Superhydrophobic Steel Meshes

open access: yesAdvanced 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   +3 more
wiley   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

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

An Examination of Aerosol Jet‐Printed Surface Roughness and its Impact on the Performance of High‐Frequency Electronics

open access: yesAdvanced Engineering Materials, EarlyView.
This study explores aerosol jet‐printed (AJP) surface roughness, its effects on the performance of microwave electronics, and its process contributors. First, an electromagnetic model is vetted for AJP's unique roughness signature. Simulations are built which show process‐induced roughness is as significant as conductor resistivity in driving microwave
Christopher Areias, Alkim Akyurtlu
wiley   +1 more source

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