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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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Reduction of Alkenes: Catalytic Hydrogenation02:13

Reduction of Alkenes: Catalytic Hydrogenation

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Alkenes undergo reduction by the addition of molecular hydrogen to give alkanes. Because the process generally occurs in the presence of a transition-metal catalyst, the reaction is called catalytic hydrogenation.
Metals like palladium, platinum, and nickel are commonly used in their solid forms — fine powder on an inert surface. As these catalysts remain insoluble in the reaction mixture, they are referred to as heterogeneous catalysts.
The hydrogenation process takes place on the...
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Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

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Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
The metal catalyst used can be either heterogeneous or homogeneous. When hydrogenation of an alkene generates a chiral center, a pair of enantiomeric products is expected to form. However, an enantiomeric excess of one of the products can be facilitated using an enantioselective reaction or an...
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Reduction of Benzene to Cyclohexane: Catalytic Hydrogenation01:28

Reduction of Benzene to Cyclohexane: Catalytic Hydrogenation

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Unlike the easy catalytic hydrogenation of an alkene double bond, hydrogenation of a benzene double bond under similar reaction conditions does not take place easily. For example, in the reduction of stilbene, the benzene ring remains unaffected while the alkene bond gets reduced. Hydrogenation of an alkene double bond is exothermic and a favorable process. In contrast, to hydrogenate the first unsaturated bond of benzene, an energy input is needed; that is, the process is endothermic. This is...
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Reduction of Alkynes to cis-Alkenes: Catalytic Hydrogenation02:24

Reduction of Alkynes to cis-Alkenes: Catalytic Hydrogenation

8.5K
Introduction
Like alkenes, alkynes can be reduced to alkanes in the presence of transition metal catalysts such as Pt, Pd, or Ni. The reaction involves two sequential syn additions of hydrogen via a cis-alkene intermediate.
8.5K
Structural Isomerism02:34

Structural Isomerism

20.7K
Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly, SCN− can...
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Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction
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Latent Representation Learning for Structural Characterization of Catalysts.

Prahlad K Routh1, Yang Liu1, Nicholas Marcella1

  • 1Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States.

The Journal of Physical Chemistry Letters
|February 23, 2021
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Summary

We introduce a new unsupervised machine learning method, latent space analysis of spectra (LSAS), to analyze X-ray absorption near edge structure (XANES) spectra. LSAS effectively maps spectral data to structural properties, aiding nanocatalyst research.

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Area of Science:

  • Materials Science
  • Catalysis
  • Machine Learning

Background:

  • X-ray absorption near edge structure (XANES) spectroscopy is crucial for understanding nanocatalyst structure and function.
  • Supervised machine learning (ML) offers methods to correlate XANES spectra with local structural descriptors.
  • Existing ML approaches for XANES analysis require further development for comprehensive interpretation.

Purpose of the Study:

  • To present an unsupervised ML approach for learning latent representations of XANES spectra.
  • To demonstrate a novel method for interpreting complex spectral data and uncovering hidden information.
  • To establish a new tool for analyzing nanocatalyst behavior during reactions.

Main Methods:

  • Developed an autoencoder-based unsupervised ML model for XANES spectral analysis.
  • Implemented a latent space analysis of spectra (LSAS) approach.
  • Applied LSAS to supported Palladium (Pd) nanoparticle catalysts during Pd hydride formation.

Main Results:

  • The autoencoder successfully generated a lower-dimensional latent representation of XANES spectra.
  • This latent representation retained the critical spectrum-structure relationship.
  • LSAS effectively identified key factors influencing spectral changes in Pd K-edge XANES during hydride formation.

Conclusions:

  • Unsupervised ML, specifically LSAS, provides a powerful new avenue for XANES spectral analysis.
  • LSAS facilitates the mapping of spectral features to physicochemical properties of nanocatalysts.
  • This method enhances the understanding of dynamic processes in working catalysts.