TY - JOUR
T1 - Activation of homogenous polyolefin catalysis with a machine-assisted reactor laboratory-in-a-box (μAIR-LAB)
AU - Rizkin, Benjamin A.
AU - Hartman, Ryan L.
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Number CBET-1701393. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© The Royal Society of Chemistry.
PY - 2020/8
Y1 - 2020/8
N2 - 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 m2, 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.
AB - 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 m2, 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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U2 - 10.1039/d0re00139b
DO - 10.1039/d0re00139b
M3 - Article
AN - SCOPUS:85093672504
SN - 2058-9883
VL - 5
SP - 1450
EP - 1460
JO - Reaction Chemistry and Engineering
JF - Reaction Chemistry and Engineering
IS - 8
ER -