Dataset Information

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts.

ABSTRACT: Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO₂(111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap.

SUBMITTER: Liao V

PROVIDER: S-EPMC10082041 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts.

Liao Vinson V Cohen Maximilian M Wang Yifan Y Vlachos Dionisios G DG

Nature communications 20230408 1

Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-princ ...[more]

PMID: 37029140

Similar Datasets

Project description:ABSTRACT Thermally activated ultrafast diffusion, collision and combination of metal atoms comprise the fundamental processes of synthesizing burgeoning subnanometer metal clusters for diverse applications. However, so far, no method has allowed the kinetically controllable synthesis of subnanometer metal clusters without compromising metal loading. Herein, we have developed, for the first time, a graphene-confined ultrafast radiant heating (GCURH) method for the synthesis of high-loading metal cluster catalysts in microseconds, where the impermeable and flexible graphene acts as a diffusion-constrained nanoreactor for high-temperature reactions. Originating from graphene-mediated ultrafast and efficient laser-to-thermal conversion, the GCURH method is capable of providing a record-high heating and cooling rate of ∼109°C/s and a peak temperature above 2000°C, and the diffusion of thermally activated atoms is spatially limited within the confinement of the graphene nanoreactor. As a result, due to the kinetics-dominant and diffusion-constrained condition provided by GCURH, subnanometer Co cluster catalysts with high metal loading up to 27.1 wt% have been synthesized by pyrolyzing a Co-based metal-organic framework (MOF) in microseconds, representing one of the highest size-loading combinations and the quickest rate for MOF pyrolysis in the reported literature. The obtained Co cluster catalyst not only exhibits an extraordinary activity similar to that of most modern multicomponent noble metal counterparts in the electrocatalytic oxygen evolution reaction, but is also highly convenient for catalyst recycling and refining due to its single metal component. Such a novel GCURH technique paves the way for the kinetically regulated, limited diffusion distance of thermally activated atoms, which in turn provides enormous opportunities for the development of sophisticated and environmentally sustainable metal cluster catalysts. In order to overcome the trade-off between ultrasmall size and high loading in synthesizing high-performance metal cluster catalysts, a novel graphene-confined ultrafast radiant heating (GCURH) method is developed.

Dataset Information

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts.

Publications

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets