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Fenton-RAFT Polymerization: An "On-Demand" Chain-Growth Method.

Amin ReyhaniThomas G McKenzieHadi Ranji-BurachalooQiang FuGreg G Qiao
Published in: Chemistry (Weinheim an der Bergstrasse, Germany) (2017)
Fine control over the architecture and/or microstructure of synthetic polymers is fast becoming a reality owing to the development of efficient and versatile polymerization techniques and conjugation reactions. However, the transition of these syntheses to automated, programmable, and high-throughput operating systems is a challenging step needed to translate the vast potential of precision polymers into machine-programmable polymers for biological and functional applications. Chain-growth polymerizations are particularly appealing for their ability to form structurally and chemically well-defined macromolecules through living/controlled polymerization techniques. Even using the latest polymerization technologies, the macromolecular engineering of complex functional materials often requires multi-step syntheses and purification of intermediates, and results in sub-optimal yields. To develop a proof-of-concept of a framework polymerization technique that is readily amenable to automation requires several key characteristics. In this study, a new approach is described that is believed to meet these requirements, thus opening avenues toward automated polymer synthesis.
Keyphrases
  • high throughput
  • deep learning
  • machine learning
  • white matter
  • single cell
  • air pollution
  • hydrogen peroxide
  • wastewater treatment
  • nitric oxide