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Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview


Volume 37, Issue 1, Pages 1-18


DOI: 10.1021/acs.energyfuels.2c02365



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Polyolefins are the most produced plastics in the world, and the properties of polyolefins are strongly affected by reactor conditions. Microfluidics combined with machine-learning algorithms can play a significant role in designing and optimizing polymer properties.
Polyolefins are the largest produced plastics in the world, wherein continuous stirred tank reactors and fluidized bed reactors are traditionally employed for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require a stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles, and effect of reactor conditions is essential for designing specific polymer properties, namely, the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single-particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High-throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents, generating less waste. When supplemented with efficient machine-learning algorithms, automated high-throughput microfluidics has the potential to rapidly optimize the process and facilitate the development of new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine-learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. The rise in the utilization of microfluidics, with the advancement of machine-learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding the reaction pathway for producing polymers with specific properties for specialized applications.


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