Artificially Intelligent, Autonomous Microreactors for the Discovery of Polyolefin Catalysis

实验室规模反应系统目前的技术差距限制了以下领域的商业应用：1)提供第一原理动力学的自动化微反应器平台，2)用于催化剂活性筛选的自动化微反应器平台，3)捕获实时浓度-响应曲线的在线分析，以及4)可分析、报告和推荐的计算数据分析。工艺开发和反应工程师可能会实施这样的反应系统，以加快他们费力的研究和开发活动。该研究项目旨在缩小实验室规模催化剂筛选和表征方面的这些技术差距，以广泛加快材料开发和聚合物商业化时间表。人工智能自主微反应器(MicroAIR)的研究是为了解决目前限制下一代催化剂发现研究需求的技术挑战。这项研究的主导假设是，在迭代发现下一代烯烃催化剂系统期间，采用在线分析技术的MicroAIR可以加快速度、提高精度并将对能源和环境的影响降至最低。这一假设的成功检验将满足将实时、多相微流体跟踪和反馈算法与可直接测量反应参数的非侵入性分析方法相结合的主要需求。反应器系统破译相行为、分析反应过程、确定哪种催化剂最活跃、确定准确的动力学表达式的能力是microAIR可以解决的巨大挑战，以便更有效地筛选催化剂和发现动力学。存在这样的机会：i)提高过程动力学的准确性，ii)建立输入/输出响应以完全识别业务组合中的催化剂，以及iii)改进催化剂发现中的实时分析和加速决策的呈现。对最新技术的回顾表明，实验室规模的均相聚烯烃催化系统目前面临的技术挑战包括：1)完全指纹催化剂性能的组合挑战，2)大型商用催化剂系统库的评估，3)流动中化学运输挑战的控制工程，4)对催化剂活性的自适应响应传感，5)在流动中与独特的反应器/混合器设计相结合的芯片分析，以及6)与适应性实验设计和执行相结合的实时数据分析。这项拟议的研究如果成功，将对聚合物制造产生广泛影响，它还将引入新的实验室技术，以发现新的科学。该项目还将包括课程开发活动，并通过纽约大学的孵化器计划与社区进行接触。

The current technology gaps of laboratory-scale reaction systems have limited the commercial availability of: 1) automated microreactor platforms that deliver first principles kinetics, 2) automated microreactor platforms for catalyst activity screening, 3) online analytics that capture real-time concentration-response profiles, and 4) computational data analytics able to analyze, report, and recommend. Process development and reaction engineers could potentially implement such reaction systems to expedite their laborious research and development activities. The research project aims at closing these technology gaps in laboratory-scale catalyst screening and characterization to broadly accelerate materials development and polymer commercialization timelines. The study of artificially intelligent, autonomous microreactors (microAIRs) is proposed to address the technical challenges that currently limit the next-generation needs in catalyst discovery research. The governing hypothesis for the study is that microAIRs engineered with online analytics can accelerate, improve accuracy, and minimize the energy and environmental impacts during the iterative discovery of a next-generation olefin catalyst system. Successful testing of this hypothesis will address the principal need to combine real-time, multiphase microfluidics tracking and feedback algorithms with a non-invasive analytical method that can directly measure a reaction parameter. The ability of a reactor system to decipher phase behaviors, analyze the reaction progress, decide which catalyst is the most active, and identify accurate kinetic expressions are tremendous challenges that microAIRs can solve in order to more efficiently screen catalyst and discover kinetics. Opportunities exist to i) improve the accuracy of process kinetics, ii) establish input/output responses that fully fingerprint a catalyst in a business portfolio, and iii) improve the presentation of real-time analytics and accelerated decision making in catalyst discovery. Review of the state-of-the-art reveals that current technical challenges for laboratory-scale, homogeneous polyolefin catalytic systems include: 1) combinatorial challenge to fully fingerprint catalyst performance, 2) evaluation of large commercial libraries of catalyst systems, 3) engineering for control of chemical transport challenges in flow, 4) sensing with adaptive response to catalyst activity, 5) on-chip analytics coupled with unique reactor/mixer designs in flow, and 6) real-time data analysis with adaptive experimental design and execution. The proposed research, if successful, will broadly impact polymers manufacturing, and it will also introduce novel laboratory techniques for the discovery of new science. The project will also involve curriculum development activities and outreach to the community through NYU's incubator program.