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Related Concept Videos

Catalysis02:50

Catalysis

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The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.
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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
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Quantifying Competitive Degradation Processes in Supported Nanocatalyst Systems.

James P Horwath1, Peter W Voorhees2, Eric A Stach1

  • 1University of Pennsylvania, Department of Materials Science and Engineering, Philadelphia, Pennsylvania 19104, United States.

Nano Letters
|June 10, 2021
PubMed
Summary
This summary is machine-generated.

We developed a model to understand how gold nanoparticles change over time, revealing a balance between evaporation and surface diffusion. This helps predict catalyst stability and performance.

Keywords:
catalyst stabilitydata miningin situ transmission electron microscopysupported catalysts

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Chemical Engineering

Background:

  • Supported metal nanoparticles are crucial for heterogeneous catalysts, but their stability dictates catalyst performance and longevity.
  • Catalyst destabilization occurs via complex thermodynamic and kinetic pathways, making it challenging to predict nanoparticle evolution.
  • Understanding these destabilization mechanisms is key to designing more robust and efficient catalysts.

Purpose of the Study:

  • To quantify the real-time destabilization of supported gold nanoparticles.
  • To develop a predictive model for nanoparticle evolution based on competing mechanisms.
  • To gain quantitative insights into factors influencing nanoparticle stability in reactive environments.

Main Methods:

  • Utilized *in situ* transmission electron microscopy (TEM) to observe nanoparticle dynamics.
  • Applied unsupervised machine learning to analyze data from hundreds of nanoparticles.
  • Developed a kinetic model incorporating evaporation and surface diffusion pathways.

Main Results:

  • Quantified nanoparticle destabilization in real-time, identifying evaporation and surface diffusion as key competing processes.
  • Determined model parameters through data mining, enabling accurate prediction of particle evolution.
  • Identified the critical particle size where Gibbs-Thomson pressure accelerates evaporation.

Conclusions:

  • The developed model accurately describes supported gold nanoparticle evolution.
  • This approach provides a quantitative framework for understanding nanoparticle stability.
  • The methodology is applicable to diverse supported nanoparticle systems for catalyst design.