Anionic Polymerization Using Flow Microreactors

In this work, the combination of different modeling approaches with in‐line proton nuclear magnetic resonance (1H‐NMR) spectroscopy is used to assist the transfer of a reversible addition‐fragmentation chain transfer (RAFT) polymerization of methyl methacrylate to a micro‐scale reactor. This approach is then applied to find the optimal process parameters like temperature or residence time as well as the best composition of the reaction mixture in order to optimize the conversion and molecular characteristics of the synthesized polymer. A kinetic model based on ordinary differential equations implemented in the program Predici is first validated based on experimental data of reactions performed at various temperatures. Further on, two glass chip reactors and a coil reactor are used and combined in different ways to investigate the influence of the reactor geometry on the polymerization process. This optimization step is assisted by multiphysics modeling that focuses on the heat transfer properties of specific areas inside the reactors. This experimental setup is used successfully to carry out a stationary polymerization. This study shows that instationary experiments in a micro‐fluidic reactor system equipped with in‐line analytics allow for the fast development of a kinetic model for RAFT polymerizations.

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“…[ 31 ] In the previous two decades, it has increasingly become a focus of science and chemical reaction technology. [ 32–34 ]…”

Section: Introductionmentioning

confidence: 99%

Model‐Assisted Optimization of RAFT Polymerization in Micro‐Scale Reactors—A Fast Screening Approach

Kandelhard

Schuldt

Schymura

et al. 2021

Macro Reaction Engineering

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“…[56][57][58] Anionic polymerization in a microreactor has achieved great advances and some examples were nicely mentioned in the previous reviews. [59][60][61][62][63][64] More and more microreactors with varied mixers and reactors were applied to anionic polymerization. This review does not comprehensively summarize the progress of anionic polymerization in a microreactor but attempts to discuss the effect of the structure of a microreactor on anionic polymerization.…”

Section: Introductionmentioning

confidence: 99%

Anionic polymerizations in a microreactor

Zhao

Wang

et al. 2022

React. Chem. Eng.

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“…Without going into details about the catalytic reaction mechanism already established, this minireview highlights the role of microfluidics in developing knowledge about polyolefin catalysis quickly and efficiently. Though microfluidics has been implemented for several types of homogeneous polymerization, − the reports on the use of microfluidics for heterogeneous polyolefin catalysis are few. This minireview highlights the actual examples of the implementation of microfluidics for polyolefin catalysis and the knowledge generated from the continuous-flow experiments, real-time reaction monitoring techniques, and machine learning applied for the understanding.…”

Section: Introductionmentioning

confidence: 99%

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

Sharma

Hartman

2022

Energy Fuels

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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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Anionic Polymerization Using Flow Microreactors

Cited by 21 publications

References 125 publications

Model‐Assisted Optimization of RAFT Polymerization in Micro‐Scale Reactors—A Fast Screening Approach

Model‐Assisted Optimization of RAFT Polymerization in Micro‐Scale Reactors—A Fast Screening Approach

Anionic polymerizations in a microreactor

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

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