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Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.
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Activation of homogenous polyolefin catalysis with a machine-assisted reactor laboratory-in-a-box (μAIR-LAB)
Traditionally catalysis research and development has been limited to large purpose-built labs, requiring years of planning and implementation before the first molecules were even examined. However, recent developments in microfluidics, robotics, system miniaturization and machine intelligence allow the decoupling of research from multi-million dollar purpose-built facilities. Additionally this scaling-down of research has significant benefits for the environment, development timelines and researcher workload. In this publication we demonstrate the construction of a microfluidic catalysis research platform contained within a standard hard-sided case measuring just 0.73 m 2 , consuming under 100 W of power, and generating 66.7 μL of chemical waste per min. The system integrates a purpose-built microreactor with hot-swappable chuck, vacuum enclosure, manifolds, pumps, robotic autosampling, open-source controls and thermographic performance analysis. The system was used to investigate nine chemically different activators for a zirconocene-catalyzed α-olefin polymerization through efficient experimentation and automated transfer learning ML-based data interpretation. The contributions of different chemical structures to catalytic productivity were analyzed. Conclusions made include those regarding co-catalyst chemistry and probable operating conditions. This work demonstrates that a compact flow-based microfluidic platform can screen exothermic catalytic reactions and interpret the results using machine intelligence.
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- Award ID(s):
- 1701393
- PAR ID:
- 10190516
- Date Published:
- Journal Name:
- Reaction Chemistry & Engineering
- Volume:
- 5
- Issue:
- 8
- ISSN:
- 2058-9883
- Page Range / eLocation ID:
- 1450 to 1460
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D0RE00139B
