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Related Concept Videos

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)00:53

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)

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Acyclic diene metathesis polymerization or ADMET polymerization involves cross-metathesis of terminal dienes, such as 1,8-nonadiene, to give linear unsaturated polymer and ethylene. As ADMET is a reversible process, the formed ethylene gas must be removed from the reaction mixture to complete the polymerization process.
Similar to cross-metathesis, ADMET also involves the formation of metallacyclobutane intermediate by [2+2] cycloaddition of one of the double bonds of a terminal diene with...
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Olefin Metathesis Polymerization: Overview01:13

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Recently, the development of olefin metathesis polymerization advanced the field of polymer synthesis. Simply put, the reorganization of substituents on their double bonds between two olefins in the presence of a catalyst is known as the olefin metathesis reaction. The use of metathesis reaction for polymer synthesis is called olefin metathesis polymerization.
Ruthenium-based Grubbs catalyst is the most commonly used catalyst for olefin metathesis polymerization. Grubbs catalyst consists of a...
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Olefin Metathesis Polymerization: Ring-Opening Metathesis Polymerization (ROMP)01:16

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Ring-opening metathesis polymerization or ROMP involves strained cycloalkenes as starting materials. The mechanism of ROMP proceeds by reacting cycloalkene with Grubbs catalyst to give metallacyclobutane intermediate which undergoes a ring-opening reaction to form new carbene. The new carbene reacts with another molecule of cycloalkene. Repetition of these steps leads to the formation of an unsaturated open-chain polymer product. All these steps are reversible, however, relieving the ring...
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Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes.

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Summary

This study introduces a novel machine-learning algorithm for self-optimization of chemical reactions, reducing experimental costs. It efficiently discovers new process recipes for complex products by minimizing expensive experiments.

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Area of Science:

  • Chemical Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Self-optimization accelerates chemical reaction development and discovery of molecules with desired properties.
  • This technology is now applied to discovering manufacturing process recipes for complex functional products.

Purpose of the Study:

  • To develop and demonstrate a machine-learning algorithm for multiobjective target optimization in chemical processes.
  • To minimize the number of costly experiments required for process discovery.

Main Methods:

  • A novel machine-learning algorithm designed for multiobjective optimization was employed.
  • The algorithm functions as a 'black-box' approach, requiring no prior knowledge of the chemical system.
  • The method was tested on discovering process recipes for semibatch emulsion copolymerization.

Main Results:

  • The algorithm successfully guided the discovery process for new manufacturing recipes.
  • The approach demonstrated effectiveness in targeting specific particle size and achieving full conversion in copolymerization.
  • Reduced experimental effort was achieved through intelligent optimization.

Conclusions:

  • The developed machine-learning algorithm offers an efficient and cost-effective method for self-optimization of chemical reactions.
  • This black-box approach is particularly suitable for the rapid development of processes for specialist, high-value products.
  • The successful demonstration in emulsion copolymerization highlights the algorithm's practical applicability.