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A fully automated platform for photoinitiated RAFT polymerizations
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Lee, Jules. A fully automated platform for photoinitiated RAFT polymerizations. Retrieved from https://doi.org/doi:10.7282/t3-vsyj-7073

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TitleA fully automated platform for photoinitiated RAFT polymerizations
NameLee, Jules (author); Gormley, Adam (chair); Roth, Charles (member); Pierce, Mark (member); Rutgers University; School of Graduate Studies
Date Created2022
Other Date2022-10 (degree)
SubjectBiomedical engineering, 96-well plates, Automation, Python, RAFT polymers
Extent1 online resource (89 pages) : illustrations
DescriptionPhotoinduced Electron/energy Transfer-Reversible Addition-Fragmentation Chain Transfer (PET-RAFT) polymerization allows for high-throughput synthesis of numerous polymer architectures on the benchtop in parallel. Recent developments have further increased throughput by incorporating the use of liquid handling robotics to automate PET-RAFT reagent handling and dispensing into 96-well plates. This allows for the synthesis of up to 96 different polymer compositions in parallel which enables the combinatorial synthesis of large polymer libraries. Although the use of liquid handling robotics can enable automated polymer reagent dispenses in 96-well plates, the photoinitiation/polymerization step still requires advancement to provide a fully automated platform that closes the gap between reagent dispensing and polymerization screening. Here, we describe the development of a robotic platform using Python, instrumentation, a custom-designed “lightbox”, and an online fluorescence monitoring to fully automate the entire PET-RAFT process in synthesizing multiple different polymer architectures in parallel. On our platform, reagents are automatically dispensed in a 96 well plate, individual wells are automatically exposed to light through our custom-built lightbox until the polymerizations are complete, and reactions are screened using fluorescence monitoring on a UV-Vis spectrophotometer, with the transfers between instruments occurring via a robotic arm. We found that this platform could enable robust parallel polymer synthesis with spatial and temporal control of both acrylate/acrylamide homopolymers, random heteropolymers, and block copolymers with high conversions and low polydispersity. With these results, this platform is a tool that allows for efficient combinatorial chemistry. In addition, with the inclusion of machine learning protocols to help navigate the polymer space towards specific properties of interest, this robotic platform can ultimately become a self-driving lab that can dispense, synthesize, and screen polymer libraries.
NoteM.S.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-vsyj-7073
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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