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Related Concept Videos

Catalysis02:50

Catalysis

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.
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes a mild...
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes a mild...
Catalysis01:27

Catalysis

Catalysis influences the rate of chemical reactions by providing an alternative reaction pathway with lower activation energy. A catalyst speeds up a reaction, but it is not consumed during the process. The fundamental principle of catalysis is the ability of a catalyst to alter the reaction mechanism, often introducing a more efficient pathway than the uncatalyzed process.In a catalyzed reaction, the catalyst participates directly in the reaction mechanism. It interacts with reactants to form...
Heterogeneous Catalysis01:22

Heterogeneous Catalysis

Heterogeneous catalysis involves a catalyst in a different phase from the reactants. It is a process where the catalyst and the reactants are in distinct phases, typically solid and gas or liquid.Most heterogeneous catalysts are metals, metal oxides, or acids. The list includes transition metals like iron (Fe), cobalt (Co), nickel (Ni), palladium (Pd), platinum (Pt), chromium (Cr), manganese (Mn), tungsten (W), silver (Ag), and copper (Cu). These metals possess partially vacant d orbitals that...

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Updated: Jun 14, 2026

Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

Integrating Physical Principles with Machine Learning for Predicting Field-Enhanced Catalysis.

Runze Zhao1, Qiang Li1, Jiaqi Yang1

  • 1Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.

JACS Au
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

We developed a machine learning approach to predict how electric fields affect molecule adsorption on catalyst nanoparticles. This method accelerates catalyst design for sustainable technologies by accurately modeling field-dependent energetics.

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Precise Electrochemical Sizing of Individual Electro-Inactive Particles
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Last Updated: Jun 14, 2026

Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

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Published on: July 1, 2016

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Precise Electrochemical Sizing of Individual Electro-Inactive Particles
05:03

Precise Electrochemical Sizing of Individual Electro-Inactive Particles

Published on: August 4, 2023

Area of Science:

  • Catalysis
  • Materials Science
  • Computational Chemistry

Background:

  • Field-dipole interactions tune catalyst nanoparticle (NP) energetics for sustainable technologies, boosting reaction efficiency.
  • Local electric field accumulation and field-dependent adsorption on NPs are poorly understood, posing computational challenges.

Purpose of the Study:

  • To develop an efficient computational method for mapping local electric fields and predicting field-dependent adsorption on catalyst NPs.
  • To integrate physics principles with machine learning for accurate and rapid prediction of adsorption energetics.

Main Methods:

  • Combined density functional theory (DFT) calculations with DFT-based CO vibrational Stark effects.
  • Employed physics-enhanced machine learning (ML) incorporating first-order Taylor expansion principles.
  • Investigated the influence of external electric field (EEF), generalized coordination number (GCN), and NP size.

Main Results:

  • Low-coordinated sites and smaller NP sizes significantly enhanced local electric field (LEF) strength (approx. 4-fold vs. flat surfaces).
  • ML models accurately and efficiently predicted field-driven adsorption energetics at specific NP sites.
  • Identified EEF, GCN, and NP size as key determinants of LEF strength.

Conclusions:

  • The integrated DFT and ML approach enables precise mapping of LEF and prediction of field-dependent adsorption.
  • This methodology facilitates rapid catalyst development for field-enhanced catalysis.
  • Offers a new paradigm for catalyst design based on fundamental principles, moving beyond trial and error.