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Updated: Jun 5, 2025

Facile Synthesis of Worm-like Micelles by Visible Light Mediated Dispersion Polymerization Using Photoredox Catalyst
Published on: June 8, 2016
A fully automated platform for photoinitiated RAFT polymerization
Jules Lee1, Prajakatta Mulay1, Matthew J Tamasi1
1Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
This study introduces a robotic platform for automated Photoinduced Electron/Energy Transfer-Reversible Addition-Fragmentation Chain-Transfer (PET-RAFT) polymerizations. The system enables high-throughput synthesis of polymers with controlled molecular weights and low dispersity.
Area of Science:
- Polymer Chemistry
- Materials Science
- Robotics and Automation
Background:
- Photoinduced Electron/Energy Transfer-Reversible Addition-Fragmentation Chain-Transfer (PET-RAFT) polymerization allows for efficient synthesis of polymers.
- Liquid handling robotics can automate reagent dispensing for combinatorial polymer synthesis.
- Automating photoinitiation and reaction monitoring is crucial for robust high-throughput polymer synthesis.
Purpose of the Study:
- To develop a robotic platform for fully automated PET-RAFT polymerizations.
- To enable individual control of reactions in well plates for high-throughput polymer synthesis.
- To achieve reliable synthesis of polymers with desired molecular weights and low dispersity.
Main Methods:
- Automated reagent dispensing into well plates.
- Custom-designed lightbox for individual well photoinitiation.
- Online reaction monitoring using fluorescence plate reader and robotic arm transfers.
Main Results:
- Robust parallel synthesis of acrylate and acrylamide homopolymers and copolymers.
- Achieved high monomer conversions and low dispersity across synthesized polymers.
- Demonstrated the platform's efficiency for combinatorial polymer chemistry.
Conclusions:
- The developed robotic platform enables fully automated, high-throughput PET-RAFT polymerizations.
- This platform is an efficient tool for combinatorial polymer chemistry, yielding polymers with controlled properties.
- Integration with machine learning can lead to a self-driving lab for polymer discovery.
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