Planning: PREC: Developing an Underrepresented Research Student Pipeline Between CSUF and C-CAS

在这个规划补助金，由化学部在NSF，教授迈克尔格罗夫斯和瑞安Cammarota的加州州立大学富勒顿（CSUF），西班牙裔服务和主要的本科院校（PUI）资助，正在与计算机辅助合成中心（C-CAS）开发一个合作伙伴关系，可以形成一个未来提交的基础上的合作伙伴关系，在化学研究和教育（PREC）计划。将建立一个全面的研究生态系统，鼓励CSUF的学生，特别是那些来自代表性不足的群体（URG）的学生，获得化学科学的研究生学位，扩大STEM人才库，解决“失踪的数百万人”问题。这是通过加强在CSUF所有化学专业的顶点项目必须完成，并鼓励学生在他们的职业生涯中完成它。CSUF和C-CAS教师的持续指导将突出研究生研究的价值，因为它涉及到学生的职业规划。建议进行跨学科研究，特别是全氟烷基物质（PFAS）的补救，也被称为“永远的化学品”。实验/计算/数据科学项目的结合将使CSUF学生能够参加C-CAS网站的付费夏季研究体验，由C-CAS学生和教师进行同行指导，并在申请研究生院时接受培训和支持。该项目的预期成果：㈠来自城市大学团体的CSUF学生将在其教育的早期被招募，以有意义地参与研究; ㈡ C-CAS人员的支持将加强CSUF教职员工进行的研究，并通过使他们能够从事更雄心勃勃的项目和更好地竞争资金来建立研究能力; ㈢ CSUF PI的专业知识将加强C-CAS项目，并加强C-CAS机构的努力，通过改善URG学生的招聘来增加多样性;及（iv）C-CAS学员将指导CSUF学生，并与PUI进行有意义的互动，从而为C-CAS学员未来在PUI的职业生涯做好准备。C-CAS内建立的评估基础设施将量化这些成果的进展情况，使CSUF PI和C-CAS能够制定计划，以扩大最有效的干预措施，作为未来PREC提案的一部分。这项计划拨款将与C-CAS建立战略关系，作为建立全面研究生态系统的一部分，鼓励来自代表性不足群体的CSUF学生攻读STEM（科学，技术，工程和数学）领域的研究生学位。使用由计算化学和机器学习指导的实验催化剂评估，学生将研究NHC（N-杂环卡宾）配体和全氟烷基物质中的导向基团如何使金属断裂C-F键，修复这些“永久化学品”。代表性的配体将被选择用于实验评估，通过使用无监督学习工具来映射和聚类计算的化学性质空间。使用贝叶斯算法的反应条件优化将用于鉴定用于评价全套代表性配体的理想溶剂/碱/导向基团组合。最后，实验输出和计算的性能将被整合，以建立一个催化剂的加氢脱氢（HDF）反应的适应度函数。教育目标是试点方法，鼓励CSUF学生在大二或大三进入强制性的顶点研究项目，从而最大限度地提高扩展研究经验的好处。将在CSUF试行的举措包括：（1）建立一个数据库，以便及早招募研究生;（2）建立一个指导程序，帮助学生确定和申请暑期研究机会;（3）举办关于申请研究生院的讲习班;（4）与C-CAS研究生导师建立一个同行导师制度。该项目将建立在CSUF的研究能力，并使C-CAS机构加强努力，以增加化学研究的多样性。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

In this planning grant, funded by the Chemistry Division at NSF, Professors Michael Groves and Ryan Cammarota of California State University-Fullerton (CSUF), a Hispanic-serving and primarily undergraduate institution (PUI), are working with the Center for Computer Assisted Synthesis (C-CAS) to develop a partnership that could form the basis of a future submission to the Partnerships for Research and Education in Chemistry (PREC) program. A comprehensive research ecosystem will be built that encourages CSUF students, especially those from underrepresented groups (URG), to attain graduate degrees in the chemical sciences, expanding the pool of talent in STEM and addressing the “Missing Millions” problem. This is accomplished by enhancing the capstone projects all chemistry majors at CSUF must complete and encouraging students to complete it earlier in their career. Continuous mentoring by CSUF and C-CAS faculty will highlight the value of graduate research as it relates to the students’ career plans. Interdisciplinary research is proposed, particularly remediation of perfluoroalkyl substances (PFAS), also known as “forever chemicals.” The combination of experimental/computational/data science projects will enable CSUF students to participate in paid summer research experiences at C-CAS sites, be peer-mentored by C-CAS students and faculty, and be trained and supported as they apply for graduate school. Expected outcomes from this project: (i) CSUF students from URGs will be recruited earlier in their education to meaningfully participate in research; (ii) support from C-CAS personnel will enhance the research conducted by CSUF faculty members and build research capacity by enabling them to pursue more ambitious projects and better compete for funding; (iii) the expertise of CSUF PIs will enhance C-CAS projects and strengthen efforts at C-CAS institutions to increase diversity by improving recruitment of URG students; and (iv) C-CAS trainees will mentor CSUF students and meaningfully interact with a PUI, thus preparing the C-CAS trainees for future careers at PUIs. Progress toward these outcomes will be quantified by the assessment infrastructure established within C-CAS, allowing the CSUF PIs and C-CAS to formulate a plan for scaling up the most effective interventions as part of a future PREC proposal. This planning grant will form strategic relationships with C-CAS as part of building a comprehensive research ecosystem that encourages CSUF students from underrepresented groups to pursue graduate degrees in STEM (science, technology, engineering and mathematics) fields. Using experimental catalyst evaluation guided by computational chemistry and machine learning, students will study how an NHC (N-heterocyclic carbene) ligand and the directing groups within perfluoroalkyl substances enable a metal to break C–F bonds, remediating these “forever chemicals”. Representative ligands will be selected for experimental evaluation by using unsupervised learning tools to map and cluster computed chemical property space. Reaction condition optimization using a Bayesian algorithm will be used to identify an ideal solvent/base/directing group combination for evaluating the full set of representative ligands. Finally, the experimental outputs and the computed properties will be integrated to build a catalyst fitness function for the hydrodefluorination (HDF) reaction. The education goal is to pilot methods for encouraging CSUF students to enter the mandatory capstone research project in their sophomore or junior year, thus maximizing the benefits of an extended research experience. Initiatives that will be piloted at CSUF include: (1) building a database for earlier recruitment of research students; (2) establishing a guided process for helping students identify and apply for summer research opportunities; (3) standing up workshops on applying to graduate school; and (4) instituting a peer-mentorship system with C-CAS graduate student mentors. The project will build research capacity at CSUF and enable C-CAS institutions to strengthen efforts to increase diversity in chemistry research.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.