Dataset Information

Controllable Encapsulation of "Clean" Metal Clusters within MOFs through Kinetic Modulation: Towards Advanced Heterogeneous Nanocatalysts.

ABSTRACT: Surfactant-free tiny Pt clusters were successfully encapsulated within MOFs with controllable size and spatial distribution by a novel kinetically modulated one-step strategy. Our synthesis relies on the rational manipulation of the reduction rate of Pt ions and/or the growth rate of MOFs by using H2 as assistant reducing agent and/or acetic acid as MOF-formation modulator. The as-prepared Pt@MOF core-shell composites exhibited exceedingly high activity and excellent selectivity in the oxidation of alcohols as a result of the ultrafine "clean" Pt clusters, as well as interesting molecular-sieving effects derived from the outer platinum-free MOF shell.

SUBMITTER: Liu H

PROVIDER: S-EPMC5069584 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Controllable Encapsulation of "Clean" Metal Clusters within MOFs through Kinetic Modulation: Towards Advanced Heterogeneous Nanocatalysts.

Liu Hongli H Chang Lina L Bai Cuihua C Chen Liyu L Luque Rafael R Li Yingwei Y

Angewandte Chemie (International ed. in English) 20160311 16

Surfactant-free tiny Pt clusters were successfully encapsulated within MOFs with controllable size and spatial distribution by a novel kinetically modulated one-step strategy. Our synthesis relies on the rational manipulation of the reduction rate of Pt ions and/or the growth rate of MOFs by using H2 as assistant reducing agent and/or acetic acid as MOF-formation modulator. The as-prepared Pt@MOF core-shell composites exhibited exceedingly high activity and excellent selectivity in the oxidation ...[more]

PMID: 26970412

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Project description:AbstractThe performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data," containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design.Impact statementArtificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding.Supplementary informationThe online version contains supplementary material available at 10.1557/s43577-021-00165-6.

Dataset Information

Controllable Encapsulation of "Clean" Metal Clusters within MOFs through Kinetic Modulation: Towards Advanced Heterogeneous Nanocatalysts.

Publications

Controllable Encapsulation of "Clean" Metal Clusters within MOFs through Kinetic Modulation: Towards Advanced Heterogeneous Nanocatalysts.

Similar Datasets

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