Results 121 to 130 of about 1,021,990 (293)
Rapidly Solidified High‐Strength Invar 36 Prepared by Planar‐Flow Melt Spinning
Advanced Engineering Materials, EarlyView.The Invar 36 alloy was rapidly solidified using the planar‐flow melt‐spinning technique. Ribbon samples with thicknesses ranging from 20 to 160 mm were produced. As the grain size of the ribbon decreased to sub‐micron levels, the hardness increased by more than 2 times.
Bekir Akgül, Mehmet Kul
wiley +1 more source
Modelling of the binary alloy solidification process [PDF]
, 1992 Bibliography: leaves 129-132.A thermodynamically consistent model capable of modelling a binary alloy undergoing solidification or melting is developed. The theory is continuum based, and the solid-liquid phase change system is described macroscopically ...
McAdie, R L
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Advanced Engineering Materials, EarlyView.Gallium microalloying redirects interfacial reactions in low‐temperature Bi–Sn solders from Cu–Sn toward Cu–Ga intermetallic formation. The resulting Cu–Ga layer suppresses intermetallic growth during thermal aging, alters fracture pathways, and improves interfacial stability.
Iva Králová +6 more
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Investigating the Low‐Temperature Phase Stability of the Binary Ta–W System
Advanced Engineering Materials, EarlyView.Atomistic simulations show that the binary Ta–W system forms ordered intermetallic phases, B2‐TaW and D03‐TaW3, as 0 K ground states. Configurational entropy, however, lowers the free energy of the disordered bcc solid solution, which becomes the stable phase above about 400 K.
Klemens Lechner +7 more
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Advanced Engineering Materials, EarlyView.HEA interlayers offer a versatile route for joining high‐performance structural materials. Their compositional and structural design regulates interfacial reactions, suppresses brittle IMCs, and improves metallurgical bonding. Sandwich interlayers further integrate defect healing with precipitation strengthening, enabling improved strength–ductility ...
Lin Yuan +4 more
wiley +1 more source
Effects of Mg on Microstructure and Solidification of a Hypereutectic Zn–8 wt.%Al Alloy
Advanced Engineering Materials, EarlyView.An appreciable set of results involving thermal data, microstructure, chemical composition, and microstructural growth laws is reported for ZnAlMg alloys. Such results demonstrate that ZnAlMg alloys have high potential for applications in automotive self‐lubricating components, batteries, and electrical systems.
Raí B. de Sousa +6 more
wiley +1 more source
, 2017 The densities and viscosities of binary mixtures of l-ethyl-3-methylimidazolium heptachlorodialuminate and tetrachloroaluminate ionic liquids with different molar compositions were measured by high-precision vibrating tube densimeter and automated ...
Zheng, Yong +9 more
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Supporting AI Readiness Through Digital Workflows in Materials Science
Advanced Engineering Materials, EarlyView.Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
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A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
Advanced Engineering Materials, EarlyView.High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source
Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Advanced Engineering Materials, EarlyView.Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source