Researchers from the Nara Institute of Science and Technology in Japan, led by Professor Mikiya Fujii, have used machine learning to optimize the polymerization process for producing styrene-methyl methacrylate co-polymers. By employing a flow synthesis system, which ensures efficient mixing and precise control over reaction conditions, they modeled the effect of variables such as initiator concentration, solvent-to-monomer ratio, temperature, and reaction time. Machine learning revealed that lower temperatures and longer reaction times were key to achieving a 50% styrene composition, along with the importance of solvent concentration. This approach reduced experimentation time and offered new insights into polymer chemistry, showcasing the potential for smarter and greener manufacturing processes. Findings were published in Science and Technology of Advanced Materials: Methods.
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