CCI Phase I: NSF Center for Adapting Flaws into Features

NSF Center for Adapting Flaws into Features（CAFF）由化学部的化学创新中心（CCI）计划提供支持。这个第一阶段中心由莱斯大学的克里斯蒂·兰德斯领导。赖斯团队的其他成员包括斯蒂芬·林克和彼得·罗斯基。其他团队成员包括来自斯坦福大学的詹妮弗·迪翁、来自伊利诺伊大学厄巴纳分校尚潘的马丁·格鲁贝尔、来自斯托尼布鲁克大学的本·莱文、来自德克萨斯大学奥斯汀分校的肖恩·罗伯茨和来自威斯康星州大学的马丁·赞尼。铁器时代的地理学家们认识到，明智地添加杂质（镍、碳等）可以将材料性能较差的金属（铁）转化为坚固的钢工具。化学杂质，“杂质”，在某些情况下可能是有害的，在其他情况下可能是唯一有价值的，创造了材料的理想“特征”。在现代，硅基电子产品的发展利用了同样的概念。CAFF的首要目标是确定有希望的化学缺陷，了解使这些缺陷具有独特影响力的结构和电子特性，然后演示如何将相同的结构放大到宏观尺度。CAFF将研究原子，纳米和微米尺度上缺陷的类型，位置和稀疏性如何影响，特别是光学材料化学。对CAFF更广泛的影响包括快速跟踪本科教育的民主化，并关注一方面处于公共/K-12外展裂缝之间的化学家群体，另一方面是本科生/研究生培训。关键将是建立一个社区学院合作伙伴网络，包括代表性不足的城市和服务不足的农村社区。具有独特技能的多元化教师和学生将成为帮助形成美国科学未来的合作伙伴。NSF中心将缺陷转化为特征（CAFF）项目旨在改变如何思考，研究和利用缺陷，同时优化新的数据信息成像工具。现实世界中的催化剂、涂层和电池从来都不是理想的，也不能在理想的条件下工作。现实世界中的合成化学家从来没有制造出完全纯的样品。因此，物理化学家必须找到利用而不是避免缺陷的混乱现实的方法，以便将它们转化为特征。CAFF将致力于理解有机-有机界面处的缺陷依赖激子相互转换。该团队还将研究金属-金属纳米颗粒中的异质性驱动的化学动力学。另一个目标是确定在无机-有机混合物中缺陷支持的能量转移的机制。CAFF旨在利用团队的综合专业知识，使用电子，隧道和高光谱显微镜以及超快激光光谱学和理论等方法预测，可视化和解决时间局部化结构和动力学。总体研究和教育目标可以概括为：（i）原子和纳米级可视化和理解化学固有的缺陷;（ii）将局部化学与整体化学行为相关联;（ii）共同开发新的数据科学方法和模块化仪器，以实现对大型数据集的无缝实时，数据无损分析。更广泛的影响包括开发新的数据科学集成工具，这些工具在基础研究中具有潜在的广泛应用。预期的更广泛的科学成果包括开发负担得起的“智能”仪器和模块，包括实验和理论，具有适应时间，空间和光谱分辨率的能力。这些新工具将使化学家、生物制药学家、工程师和材料科学家能够研究在总体平均值中被掩盖的缺陷亚群如何严格控制宏观尺度水平的结果、性质和行为。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智力价值和更广泛的影响审查标准进行评估来支持。

The NSF Center for Adapting Flaws into Features (CAFF) is supported by the Centers for Chemical Innovation (CCI) Program of the Division of Chemistry. This Phase I Center is led by Christy Landes of Rice University. Other Rice team members include Stephan Link and Peter Rossky. Additional team members are Jennifer Dionne from Stanford University, Martin Gruebele from the University of Illinois-Urbana Champaign, Ben Levine from Stony Brook University, Sean Roberts from the University of Texas-Austin, and Martin Zanni from the University of Wisconsin. Iron-age metallurgists learned that judicious addition of impurities (nickel, carbon, etc.) could transform a metal with poor materials properties (iron) into strong steel tools. Chemical impurities, ‘flaws’, can be detrimental in some situations and uniquely valuable in others, creating the desirable ‘features’ of a material. In modern times, the development of silicon-based electronics exploited the same concept. CAFF’s overarching goal is to identify chemical flaws that hold promise, understand the structural and electronic properties that make those flaws uniquely influential, and then to demonstrate how the same structures can be amplified to macroscopic scales. CAFF will examine how the type, location, and sparsity of defects on atomic, nano- and microscales influence, in particular, optical materials chemistry. Broader impacts for CAFF include fast-tracking the democratization of undergraduate education and focusing on a constituency of chemists that have fallen between the cracks of public/K-12 outreach on the one hand, and undergraduate/graduate training on the other. Key will be to build a network of Community College partners that includes underrepresented urban and underserved rural communities. A diverse set of faculty and students with unique skill sets will be partners who can help form the future of American science.The NSF Center for Adapting Flaws into Features (CAFF) project aims to transform how to think about, study, and exploit defects, while optimizing new data-informed imaging tools that are broadly accessible. Real-world catalysts, coatings, and batteries are never ideal nor function in ideal conditions. Real-world synthetic chemists never make completely pure samples. Hence, physical chemists must identify ways to exploit, rather than avoid, the messy reality of flaws, such that they can be turned into features. CAFF will pursue understanding of defect-dependent exciton interconversion at organic-organic interfaces. The team will also study heterogeneity-driven chemical dynamics in metal-metal nanoparticles. Another goal is to identify the mechanisms underlying defect-supported energy-transfer in inorganic-organic hybrids. CAFF aims to leverage the combined expertise of the team in predicting, visualizing, and resolving in time localized structure and dynamics, using methods such as electron, tunneling, and hyperspectral microscopy as well as ultrafast laser spectroscopy and theory. Overall research and educational goals may be summarized as: (i) Atomic and nanoscale visualization and understanding of defects inherent to chemistry; (ii) correlating local chemistry to ensemble chemical behavior; (ii) co-development of novel data science approaches and modular instruments to move toward seamless real-time, data loss-less analysis of large data sets. Broader impacts include the development of new data-science integrated tools with potentially broad applications in basic research. Expected broader scientific outcomes include the development of affordable ‘smart’ instruments and modules, both experimental and theoretical, with the ability to adapt in time, space, and spectral resolution. These new tools will allow chemists, biophysicists, engineers, and materials scientists to study how sub-populations of defects, obscured within the ensemble average, critically control macroscale-level outcomes, properties and behavior.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.