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Related Concept Videos

Catalysis02:50

Catalysis

30.1K
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.
30.1K
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 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
Oxidation of Alkenes: Syn Dihydroxylation with Potassium Permanganate02:21

Oxidation of Alkenes: Syn Dihydroxylation with Potassium Permanganate

16.3K
Alkenes can be dihydroxylated using potassium permanganate.  The method encompasses the reaction of an alkene with a cold, dilute solution of potassium permanganate under basic conditions to form a cis-diol along with a brown precipitate of manganese dioxide.
16.3K
Radical Autoxidation01:20

Radical Autoxidation

3.1K
The oxidation of an organic compound in the presence of air or oxygen is called autoxidation. For example, cumene reacts with oxygen to form hydroperoxide. Autoxidation involves initiation, propagation, and termination steps. Many organic compounds are susceptible to autoxidation—especially ethers in the presence of oxygen, which form hydroperoxides. Even though this reaction is slow, old ether bottles contain small amounts of peroxide, which leads to laboratory explosions during ether...
3.1K
Oxidation of Alkenes: Syn Dihydroxylation with Osmium Tetraoxide02:44

Oxidation of Alkenes: Syn Dihydroxylation with Osmium Tetraoxide

12.6K
Alkenes are converted to 1,2-diols or glycols through a process called dihydroxylation. It involves the addition of two hydroxyl groups across the double bond with two different stereochemical approaches, namely anti and syn. Dihydroxylation using osmium tetroxide progresses with syn stereochemistry.
12.6K

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Temperature-programmed Deoxygenation of Acetic Acid on Molybdenum Carbide Catalysts
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Machine Learning-Driven Global Optimization of Single-Atom Catalyst-Mediated Advanced Oxidation Processes.

Wenjie Gao1, Yongsheng Xu2, Xianglin Chang1

  • 1School of Environmental Science and Engineering/Tianjin Research Center for Safe Disposal of Organic Solid Waste and Energy Utilization Engineering, Tianjin University, Tianjin 300072, China.

Environmental Science & Technology
|October 18, 2025
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Summary

Machine learning accurately predicts single-atom catalyst performance for water purification via advanced oxidation processes. Optimal catalysts pair specific metal electron counts with low electronegativity coordination, enhancing pollutant degradation.

Keywords:
AOPscontaminant propertiesd electronsmachine learningsingle-atom catalysts

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Area of Science:

  • Environmental Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Single-atom catalysts (SACs) are crucial for advanced oxidation processes (AOPs) in water purification.
  • Understanding combined effects of catalyst properties and contaminant characteristics on AOPs is limited.
  • Predicting SAC performance for pollutant degradation requires integrated analysis.

Purpose of the Study:

  • To develop a machine learning model for predicting SAC performance in AOPs.
  • To identify key descriptors influencing AOP kinetics and thermodynamics.
  • To guide the intelligent design of SACs for water purification.

Main Methods:

  • Utilized a random forest model with a global optimization strategy.
  • Identified key descriptors: central metal d electron number and coordination environment electronegativity.
  • Performed theoretical calculations (charge density, adsorption energy, DOS, COHP) to analyze reaction mechanisms.

Main Results:

  • The ML model accurately predicted pollutant degradation performance.
  • d electron number (5-7) and average electronegativity (<3.04) are critical for optimal SACs.
  • Contaminant properties (energy gap <3.92 eV, dipole moment >7 D) also significantly impact degradation.

Conclusions:

  • Machine learning provides an effective pathway for designing high-performance SACs for AOPs.
  • Optimized SACs can be designed by tuning metal and coordination environment properties.
  • This approach facilitates the development of advanced water purification systems.