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Computational Design of Graphene–Nanoparticle Catalysts

Technical Report ·
DOI:https://doi.org/10.2172/1575127· OSTI ID:1575127
 [1]
  1. University of Massachusetts, Amherst, MA (United States); University of Massachusetts Amherst
+ Show Author Affiliations
Nanoclusters possess electronic properties that are inherently different from their bulk counterparts due to quantum effects that are strongly manifested at the nanoscale. Therefore, the catalytic properties of nanoclusters cannot be understood by a simple extrapolation of our understanding of catalysis on bulk metal surfaces. The presence of supports further modifies the electronic properties, morphology, and catalytic activity of a nanoparticle due to interplay between the electronic properties of the support and the nanoparticle. The objective of this research was to develop a systematic computational approach for designing and evaluating the activity and selectivity of supported catalyst nanoclusters, in the specific context of graphenesupported Pt and Pt-Ru nanoparticles for methanol decomposition. While the enhanced activity of nanoclusters has long been exploited in Pt–carbon black catalysts, recent experiments have shown that Pt–graphene catalysts can far outperform their predecessors, in particular, for methanol oxidation—a reaction of crucial importance for methanol fuel cells. However, the fundamental mechanisms by which graphene enhances the activity of nanoparticles for methanol oxidation as yet remain to be understood.
Research Organization:
University of Massachusetts, Amherst, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)
DOE Contract Number:
SC0010610
OSTI ID:
1575127
Report Number(s):
DOE-UMass--SC0010610
Country of Publication:
United States
Language:
English

References (8)

Tuning core–shell interactions in tungsten carbide–Pt nanoparticles for the hydrogen evolution reaction journal January 2018
Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models journal March 2017
Au-MoS 2 Hybrids as Hydrogen Evolution Electrocatalysts journal August 2019
CO Adsorption on Defective Graphene-Supported Pt 13 Nanoclusters journal September 2013
Self-Consistent Charge Density-Functional Tight-Binding Parametrization for Pt–Ru Alloys journal March 2017
Density Functional Theory Studies of the Methanol Decomposition Reaction on Graphene-Supported Pt 13 Nanoclusters journal July 2016
First-Principles Predictions of Structure–Function Relationships of Graphene-Supported Platinum Nanoclusters journal May 2016
Influence of Support Effects on CO Oxidation Kinetics on CO-Saturated Graphene-Supported Pt 13 Nanoclusters journal April 2015

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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
77 NANOSCIENCE AND NANOTECHNOLOGY