Addressing Rigor and Reproducibility in Heterogeneous, Thermal Catalysis

该项目支持一个研讨会，讨论在多相热催化中进行的研究的严谨性和可重复性。 研讨会由西北大学的Neil Schweitzer博士领导，共同组织者Rajamani Gounder（普渡大学）和Robert Rioux（宾夕法尼亚州立大学）。 来自工业界，美国资助机构和国家实验室以及国际催化研究界的专家将在虚拟和面对面的会议上召开会议，以评估催化研究方法的现状，并确定协调更广泛的催化研究界严格和可重复实践的可行机会。 与会者包括一个包容性和多样化的与会者群体，包括学生，早期职业研究人员和高级技术专家。 短期和长期目标将涵盖5个主题领域：1）文献/提案中的标准化报告，2）制定基准标准，3）制作培训媒体和研讨会，4）建立实验数据数据库，5）形成测试实验室网络。 该研讨会的特点是一个新颖的三阶段系列虚拟会议，将围绕在十大（B1 G）会议中心在罗斯蒙特，伊利诺伊州（2022年7月21日和7月22日）举行的约70名参与者的面对面会议。研讨会的产品和成果将首先包括一份研讨会报告，然后是一篇或多篇总结最佳实践的期刊文章，研究人员可以使用这些最佳实践来对热催化的特定子领域进行基准测试，验证和复制数据。 催化剂材料性质、合成方法、表征技术和评估程序的整个范围的复杂性和可变性使人们关注建立社区接受的实践以确保高质量、基准和可再现数据的需要。 此外，近年来，向清洁能源和温室气体减排过渡的紧迫性激励了跨学科，融合和转化的催化研究方法。 研究工程师和科学家具有广泛的材料，化学合成，界面科学，光谱方法以及数据科学和计算模拟方法的专业知识，都为催化研究带来了受欢迎的观点，但通常很少意识到催化系统的复杂性，特别是在工作环境中。 因此，需要机制来提高实验测量的严谨性和再现性（RR），以确保更广泛的研究社区与实现高质量催化研究的共同实践核心保持一致。 类似地，该领域正在迅速转向计算和数据科学驱动的催化剂设计，但这种预测工具的成功实施取决于模型训练和验证，这些模型训练和验证植根于以通用规范为基准的严格获得和可重复的实验数据库。 在现场会议之后，将成立由研讨会负责人和与会者组成的特定小组，以开展其他项目，包括开发反映催化研究最佳实践的在线培训材料和内容，以提高严谨性和可重复性，创建和维护催化数据公共数据库，以及实施测试实验室设施，研究人员可以使用这些设施来对他们的数据进行基准测试或验证。重点将放在让早期职业研究人员参与讲习班的所有阶段，以建立对最佳做法的理解，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.

The project supports a workshop addressing the rigor and reproducibility of research conducted in heterogeneous thermal catalysis. The workshop is led by Dr. Neil Schweitzer of Northwestern University, with co-organizers Rajamani Gounder (Purdue University), and Robert Rioux (Penn State University). Experts from academe, industry, U.S. funding agencies and national laboratories, and the international catalysis research community will convene in both virtual and in-person sessions to assess the state of catalysis research methodology and identify actionable opportunities for coordinating rigorous and reproducible practices across the broader catalysis research community. Participants include an inclusive and diverse group of attendees including students, early-career researchers, and senior-level technical experts. Both short- and long-term objectives will be addressed covering 5 topic areas: 1) Standardized reporting in literature/proposals, 2) Developing benchmarking standards, 3) Producing training media and workshops, 4) Establishing a database of experimental data, and 5) Forming a network of testing labs. The workshop features a novel three-phase series of virtual sessions that will surround an in-person session of roughly 70 participants to be held at the Big Ten (B1G) Conference Center in Rosemont, Illinois (July 21 and July 22, 2022). Products and outcomes of the workshop will first consist of a workshop report followed by one or more journal articles summarizing best practices that researchers can use to benchmark, validate, and reproduce data in specific sub-fields of thermal catalysis.Heterogeneous thermal catalysis has long served as the bedrock of fuels and chemicals manufacturing. Complexity and variability spanning the entire breadth of catalyst materials properties, synthesis methods, characterization techniques, and evaluation procedures, has focused attention on the need to establish community-accepted practices for ensuring high-quality, benchmarked, and reproducible data. In addition, urgency around the transition to clean energy and greenhouse gas reduction has incentivized interdisciplinary, convergent, and translational approaches to catalysis research in recent years. Research engineers and scientists with expertise cutting broadly across materials, chemical synthesis, interfacial science, spectroscopic methods, and methods of data science and computational simulation, all bring welcome perspectives to catalysis research, but often with little awareness of the complexity of catalytic systems, especially in the working environment. Thus, mechanisms are needed to improve rigor and reproducibility (R&R) in experimental measurements to ensure alignment of the broader research community with a common core of practices specific to the realization of high-quality catalysis research. Similarly, the field is moving rapidly toward computational and data-science driven catalyst design, but successful implementation of such predictive tools hinges on model training and validation rooted in rigorously obtained and reproducible experimental data bases benchmarked to common specifications. Following the in-person session, specific sub-groups of workshop leaders and participants will be formed to pursue additional projects, including the development of online training material and content reflecting the best practices in catalysis research to improve rigor and reproducibility, the creation and maintenance of a public database of catalysis data, and the implementation of testing laboratory facilities that can be used by researchers to benchmark or validate their data. Emphasis will be placed on including early-career researchers in all phases of the workshop, to build appreciation of best practices, while also providing recommendations to those engaged in setting standards and practices for review of both grant proposals and journal article submissions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.