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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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Oxidative Cleavage of Alkenes: Ozonolysis01:46

Oxidative Cleavage of Alkenes: Ozonolysis

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In ozonolysis, ozone is used to cleave a carbon–carbon double bond to form aldehydes and ketones, or carboxylic acids, depending on the work-up.
Ozone is a symmetrical bent molecule stabilized by a resonance structure.
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Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
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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.
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The hydrogenation process takes place on the...
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Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

3.4K
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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Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
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Updated: Sep 10, 2025

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Knowledge discovery and performance-energy optimization in heterogeneous catalytic ozonation via adaptive multi-task

Wei Zhuang1, Qianqian Luo1, Qingyang Jiang1

  • 1State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China.

Water Research
|August 20, 2025
PubMed
Summary

This study introduces an adaptive multi-task learning framework to optimize heterogeneous catalytic ozonation for water treatment, balancing performance and energy use effectively. It identifies key factors like ozone dosage and catalyst composition for improved efficiency.

Keywords:
Energy consumptionHeterogeneous catalytic ozonationMachine learningMulti-task learningPareto optimizationPerformance

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Area of Science:

  • Environmental Science
  • Chemical Engineering
  • Water Treatment Technologies

Background:

  • Heterogeneous catalytic ozonation (HCO) shows promise for water treatment but faces challenges in optimizing performance and energy consumption.
  • Balancing degradation efficiency with energy demands is crucial for sustainable application of HCO.

Purpose of the Study:

  • To develop an innovative adaptive multi-task learning (MTL) framework for optimizing performance (PO), energy consumption (EC), and performance-energy balance (PEB) in HCO.
  • To achieve a multi-task balance between the pseudo-first-order rate constant (k) and Electrical Energy per Order (EE/O) using implicit functions and Pareto optimization.

Main Methods:

  • Constructed a Performance-Energy Balance Multi-Task Learning (PEB-MTL) framework incorporating implicit functions and Pareto optimization.
  • Employed adaptive task weighting and a random perturbation-enhanced Particle Swarm Optimization (PSO) algorithm for simultaneous optimization.
  • Conducted feature importance analysis using molecular descriptors and performed reverse experiments for validation.

Main Results:

  • The PEB-MTL framework successfully balanced degradation rate and energy consumption, with prediction errors below 10% in validation.
  • Ozone dosage was identified as the most critical factor influencing HCO performance.
  • Catalyst composition significantly impacted the degradation of ozone-resistant pollutants and had a notable effect on energy consumption.

Conclusions:

  • The developed adaptive MTL framework offers a robust approach for optimizing HCO processes, enhancing both efficiency and energy sustainability in water treatment.
  • This work advances the field by providing a systematic method to address the trade-offs between performance and energy consumption in catalytic ozonation.
  • Findings highlight the importance of catalyst design and operational parameters like ozone dosage for effective and energy-efficient water purification using HCO.