Identification of optimally stable nanocluster geometries via mathematical optimization and density-functional theory-Reference-Cited by-同舟云学术

Identification of optimally stable nanocluster geometries via mathematical optimization and density-functional theory

Published:2020 Issue:1 Volume:5 Page:232-244
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Isenberg Natalie M.¹²³⁴,Taylor Michael G.⁵⁶³⁴,Yan Zihao⁵⁶³⁴,Hanselman Christopher L.¹²³⁴,Mpourmpakis Giannis⁵⁶³⁴^ORCID,Gounaris Chrysanthos E.¹²³⁴^ORCID

Affiliation:

1. Department of Chemical Engineering

2. Carnegie Mellon University

3. Pittsburgh

4. USA

5. Department of Chemical and Petroleum Engineering

6. University of Pittsburgh

Abstract

A novel methodology combining mathematical optimization with DFT calculations is developed to determine highly cohesive transition metal nanoclusters.

Funder

Division of Civil, Mechanical and Manufacturing Innovation

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2020/ME/C9ME00108E

Reference44 articles.

1. A review of modern transition-metal nanoclusters: their synthesis, characterization, and applications in catalysis

2. Correlating Particle Size and Shape of Supported Ru/γ-Al2O3 Catalysts with NH3 Decomposition Activity

3. The Impact of Nanoscience on Heterogeneous Catalysis

4. Catalyst Design Based on Morphology- and Environment-Dependent Adsorption on Metal Nanoparticles

5. Plastic-Compatible Low Resistance Printable Gold Nanoparticle Conductors for Flexible Electronics

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