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Related Concept Videos

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

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...
Reduction of Alkenes: Catalytic Hydrogenation02:13

Reduction of Alkenes: Catalytic Hydrogenation

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

Reduction of Alkynes to cis-Alkenes: Catalytic Hydrogenation

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.
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.
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...

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Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation.

Pengfei Du1, Abdellah Ait El Fakir1, Shinya Mine2

  • 1Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan.

Journal of the American Chemical Society
|July 14, 2026
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Summary

Machine learning accelerates catalyst discovery by exploring novel elements for efficient CO2 hydrogenation to ethanol. This data-driven approach identified over 50 superior catalysts, including a highly effective multielemental Pd-Au catalyst.

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Area of Science:

  • Catalysis
  • Materials Science
  • Computational Chemistry

Background:

  • High-performance catalyst discovery is vital but traditionally slow.
  • Machine learning (ML) shows potential for accelerating catalyst development.
  • Previous ML applications in catalysis have had limited success in discovering truly novel catalysts.

Purpose of the Study:

  • To develop an ML approach for discovering novel, high-performance catalysts for CO2 hydrogenation to ethanol.
  • To incorporate previously unstudied elements into the catalyst design pool.
  • To demonstrate the efficacy of a closed-loop ML-driven discovery system.

Main Methods:

  • Utilized a closed-loop system with 24 iterations of ML predictions and experimental validation.
  • Tested a total of 555 catalysts, building a large experimental dataset.
  • Employed advanced characterization techniques including in situ/operando X-ray absorption spectroscopy (XAS), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), and DRIFTS.

Main Results:

  • Discovered over 50 catalysts with superior activity for ethanol synthesis.
  • Identified a highly effective multielemental Pd-Au/K-Sr-Fe-Zn-Cd-Yb-Re/CeO2-ZrO2 catalyst.
  • Achieved an ethanol space-time yield of 8.2 mmol gcat−1 h−1 with 57.6% CO2 conversion and 23.2% ethanol selectivity.

Conclusions:

  • The ML approach successfully identified novel and highly efficient catalysts beyond traditional element pools.
  • The study highlights the critical roles of individual elements in the optimized multielemental catalyst.
  • This data-driven methodology significantly advances the discovery of catalysts for CO2 utilization.