3-D phase-field simulations of self-organized composite morphologies in physical vapor deposited phase-separating binary alloys-Reference-Cited by-同舟云学术

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3-D phase-field simulations of self-organized composite morphologies in physical vapor deposited phase-separating binary alloys

Published:2019-08-21 Issue:7 Volume:126 Page:075306
ISSN:0021-8979
Container-title:Journal of Applied Physics
language:en
Short-container-title:Journal of Applied Physics

Author:

Ankit Kumar¹²^ORCID,Derby Benjamin³,Raghavan Rahul¹,Misra Amit³,Demkowicz Michael J.²

Affiliation:

1. School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, Arizona 85287, USA

2. Department of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, USA

3. Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA

Funder

National Science Foundation

Publisher

AIP Publishing

Subject

General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.5110410

Reference26 articles.

1. Nanoscale phase separation in amorphous immiscible copper-niobium alloy thin films

2. Nanowire array fabricated by Al–Ge phase separation

3. Phase-separated Al–Si thin films

4. Self-assembled nano-scale multilayer formation using physical vapor deposition methods

5. Nanoscale Cu/Ta multilayer deposition by co-sputtering on a rotating substrate. Empirical model and experiment

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1. Self-organized composite morphologies and interface instability of immiscible alloy thin films during physical vapor deposition: Insights from phase-field simulations;Surface and Coatings Technology;2024-07

2. Density Functional Theory-Fed Phase Field Model for Semiconductor Nanostructures: The Case of Self-Induced Core–Shell InAlN Nanorods;Crystal Growth & Design;2024-05-14

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4. Strong Coupling in Two‐Phase Metamaterials Fabricated by Sequential Self‐Assembly;Advanced Optical Materials;2023-07-28

5. Emulating microstructural evolution during spinodal decomposition using a tensor decomposed convolutional and recurrent neural network;Computational Materials Science;2023-05

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