Integrating automated experimentation with process analytical technology

将自动化实验与过程分析技术相结合

• 批准号: RGPIN-2021-03168
• 负责人: Hein, Jason
• 金额: $ 5.76万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742412
• 关键词: Integrating; automated; experimentation; process; analytical

Over the past 5 years, I have positioned my research program at the forefront of organic chemistry automation. My interdisciplinary team now consists of 25 chemists, engineers, and computer scientists tackling challenges in kinetic analysis, online sampling of complex reaction mixtures, real-time crystallization monitoring, automated synthesis, and the application of artificial intelligence to optimize chemical processes. Our research goals center on developing automated analytical platforms to increase throughput and access data-rich experimentation. These systems can be integrated with robotic tools to execute chemical reactions and artificial intelligence software to "learn" from each iteration, enabling self-optimizing experimentation that accelerates discovery while freeing up researcher time. The labour-intensive process of obtaining analytical data on individual chemical systems is indispensable to fundamental mechanistic elucidation, pharmaceutical development, and process chemistry. Performing these studies manually, however, dramatically slows down development timelines as highly qualified researchers invest time on data collection rather than scientific innovation. My research program develops modular automated tools to gather real-time data across a variety of systems, including batch and continuous processes, air- or water-sensitive chemistry, and heterogeneous reactions that are incompatible with traditional sampling techniques. A key feature to our approach is the integration of process analytical technology (PAT) to provide cross-validated, data-rich information on the processes of interest with the smallest commitment of time and resources. Our suite of PAT includes high performance liquid chromatography, infrared spectroscopy, ion chromatography, nuclear magnetic resonance, advanced microscopy, and even computer vision. My research program aims to continue advancing these research areas to explore chemical synthesis, process optimization, and materials discovery. We will leverage automated online analytics in two main fields: the elucidation of catalytic mechanisms and the development of efficient crystallization procedures. The broader goal of establishing self-driving laboratories will then enable us to optimize chemical transformations and discover new materials at a vastly faster pace than traditional screening and manual experimentation methodologies.

在过去的5年里，我把我的研究项目定位在有机化学自动化的最前沿。我的跨学科团队现在由25名化学家、工程师和计算机科学家组成，致力于解决动力学分析、复杂反应混合物在线采样、实时结晶监测、自动合成以及应用人工智能优化化学过程等方面的挑战。我们的研究目标集中在开发自动化分析平台，以提高吞吐量和访问数据丰富的实验。这些系统可以与机器人工具集成，以执行化学反应，并与人工智能软件集成，以从每次迭代中“学习”，从而实现自我优化实验，加速发现，同时释放研究人员的时间。获得单个化学系统的分析数据的劳动密集型过程对于基本机理阐明、药物开发和过程化学是不可或缺的。然而，手动执行这些研究大大减缓了开发时间，因为高素质的研究人员将时间投入到数据收集而不是科学创新上。我的研究项目开发模块化自动化工具，以收集各种系统的实时数据，包括批量和连续过程，空气或水敏感化学，以及与传统采样技术不兼容的多相反应。我们的方法的一个关键特征是集成了过程分析技术（PAT），以最少的时间和资源提供有关感兴趣过程的交叉验证、数据丰富的信息。我们的PAT套件包括高效液相色谱、红外光谱、离子色谱、核磁共振、高级显微镜甚至计算机视觉。 我的研究计划旨在继续推进这些研究领域，探索化学合成，工艺优化和材料发现。我们将在两个主要领域利用自动化在线分析：催化机制的阐明和高效结晶程序的开发。建立自动驾驶实验室的更广泛目标将使我们能够优化化学转化，并以比传统筛选和手动实验方法快得多的速度发现新材料。