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Related Concept Videos

Regioselectivity of Electrophilic Additions to Alkenes: Markovnikov's Rule02:17

Regioselectivity of Electrophilic Additions to Alkenes: Markovnikov's Rule

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If a set of reactants can yield multiple constitutional isomers, but one of the isomers is obtained as the major product, the reaction is said to be regioselective. In such reactions, bond formation or breaking is favored at one reaction site over others.
The hydrohalogenation of an unsymmetrical alkene can yield two haloalkane products, depending on which vinylic carbon takes up the halogen. However, one product usually predominates, where hydrogen adds to the vinylic carbon bearing the...
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Limiting Reactant02:27

Limiting Reactant

69.3K
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in reality, the reactants are not always present in the stoichiometric amounts indicated by the balanced equation.
69.3K
Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

3.8K
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...
3.8K
Reduction of Alkenes: Catalytic Hydrogenation02:13

Reduction of Alkenes: Catalytic Hydrogenation

13.9K
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...
13.9K
Reduction of Alkynes to cis-Alkenes: Catalytic Hydrogenation02:24

Reduction of Alkynes to cis-Alkenes: Catalytic Hydrogenation

8.9K
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.
8.9K
Regioselectivity and Stereochemistry of Hydroboration02:36

Regioselectivity and Stereochemistry of Hydroboration

9.4K
A significant aspect of hydroboration–oxidation is the regio- and stereochemical outcome of the reaction.
Hydroboration proceeds in a concerted fashion with the attack of borane on the π bond, giving a cyclic four-centered transition state. The –BH2 group is bonded to the less substituted carbon and –H to the more substituted carbon. The concerted nature requires the simultaneous addition of –H and –BH2 across the same face of the alkene giving syn stereochemistry.
9.4K

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Knowledge Graph for Methane Selective Conversion: Revisiting and Predicting Product Selectivity and Methane

Boyu Xu1, Gaoyang Li2, Bohan Wang3

  • 1Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, Utrecht, 3584 CC, The Netherlands.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|October 6, 2025
PubMed
Summary
This summary is machine-generated.

This study uses a knowledge graph to analyze methane conversion, identifying effective catalysts for industrial methanol production. The findings guide catalyst development for scalable chemical synthesis.

Keywords:
deep neural networksknowledge graphlarge language modelsmethane selective conversionpredicting catalytic performance

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Area of Science:

  • Chemical Engineering
  • Catalysis Science
  • Data Science in Chemistry

Background:

  • Selective methane conversion to valuable carbon-based compounds is crucial but faces scalability challenges.
  • Understanding the complex interplay between product selectivity and methane conversion rates is key to process optimization.
  • Existing literature contains vast data on methane conversion over diverse catalysts and conditions, yet lacks integrated analysis.

Purpose of the Study:

  • To construct a comprehensive knowledge graph (KG) for methane conversion using a large language model.
  • To analyze the KG for identifying optimal catalytic processes, reaction conditions, and development trends.
  • To provide insights for targeted catalyst design and industrial application of methane conversion technologies.

Main Methods:

  • Literature data on methane conversion was systematically collected and processed.
  • A knowledge graph was built with 11 entity types and 32 relationship types.
  • Deep neural network analysis was applied to the constructed knowledge graph.

Main Results:

  • The knowledge graph effectively structures and analyzes advancements in methane conversion.
  • Catalysts featuring metal active sites and multifunctional supports were identified as highly effective for methanol production.
  • Optimal reaction conditions suitable for industrial-scale applications were highlighted.

Conclusions:

  • The developed knowledge graph provides a powerful tool for understanding and advancing methane conversion processes.
  • Specific catalyst designs (metal active sites, multifunctional supports) show significant promise for industrial methanol synthesis.
  • This approach facilitates targeted catalyst development and accelerates the transition to scalable methane conversion technologies.