NRT-HDR: Quantitative Analysis of Dynamic Structures

NRT-HDR：动态结构的定量分析

• 批准号: 1922639
• 负责人: Surita Bhatia
• 金额: $ 300万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922639&HistoricalAwards=false
• 关键词: NRT; HDR; Quantitative; Analysis; Dynamic

Advanced materials are important for many applications with societal benefits, including flexible electronics, solar panels, recyclable plastics, antibacterial coatings, water purification systems, and next-generation batteries. Properties of these materials depend heavily upon their structure or how various atoms and larger units are arranged. New instruments and mathematical models of materials allow scientists to measure and predict some of these properties. However, these techniques yield large quantities of data requiring complex analyses. This National Science Foundation Research Traineeship (NRT) award to Stony Brook University will train a diverse population of researchers with the skills to analyze the large data sets associated with complex material structures and properties. These students will participate in scientific communication, workplace diversity, career paths, and team-building workshops to meet the growing needs for a workforce trained in data science. Additionally, Stony Brook faculty will work together with the American Chemical Society Bridge Program and institutions that serve minority and disabled students to promote success of underrepresented students in science and engineering PhD programs. The project anticipates training seventy-five (75) PhD students, including thirty-eight (38) funded trainees, from the fields of chemistry, materials science, chemical engineering, mechanical engineering, and geosciences. NRT trainees will develop new software tools and experimental methods for the materials community, contributing to improved design of advanced materials. Advances in instrumentation have enabled an unprecedented ability to probe structural dynamics that span many time and length scales, with incredibly high rates of data acquisition. Transformative aspects of the research include the development of new algorithms for rapid in situ X-ray spectroscopy of reactions; automated screening of parameter space of composites to advance the pace of materials discovery; and the first time-resolved measurements of nanoparticle motion during evaporative assembly. The main training elements are (1) interdisciplinary research on development of software and/or algorithms for data analysis; (2) a summer school that brings cohorts of diverse trainees together; (3) new elective courses and shorter modules on emerging computational and experimental tools; (4) hybrid group meetings centered around a common theme; (5) scalable responsible conduct of research, professional development, and career paths modules; (6) science communication training provided by the Alan Alda Center for Communicating Science; and (7) a student-driven career ladder program and guided mentorship through Individual Development Plans (IDPs) and activities tailored to students' career goals. To extend benefits beyond trainees, the project will implement a new graduate certificate on Quantitative Analysis of Dynamic Structures, transition the summer school to a self-sustaining program open to students nationwide, and distribute curriculum materials as open-access video lectures.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

先进材料对许多具有社会效益的应用非常重要，包括柔性电子产品、太阳能电池板、可回收塑料、抗菌涂料、净水系统和下一代电池。这些材料的性质在很大程度上取决于它们的结构或各种原子和较大单元的排列方式。新的仪器和材料的数学模型使科学家能够测量和预测其中的一些性质。然而，这些技术产生了大量需要复杂分析的数据。这项授予石溪大学的国家科学基金会研究培训(NRT)奖将培训一批具有分析与复杂材料结构和性质相关的大型数据集的技能的研究人员。这些学生将参加科学交流、工作场所多样性、职业道路和团队建设研讨会，以满足对受过数据科学培训的劳动力日益增长的需求。此外，石溪学院的教职员工将与美国化学学会桥梁计划以及为少数族裔和残疾学生服务的机构合作，促进未被充分代表的学生在科学和工程博士项目中取得成功。该项目预计将培训75名博士生，其中包括来自化学、材料科学、化学工程、机械工程和地球科学领域的38名受训人员。NRT学员将为材料界开发新的软件工具和实验方法，为改进先进材料的设计做出贡献。仪器设备的进步使探测跨越许多时间和长度尺度的结构动力学具有前所未有的能力，具有令人难以置信的高数据采集率。这项研究的变革性方面包括开发快速原位反应X射线光谱分析的新算法；自动筛选复合材料的参数空间以加快材料发现的步伐；以及首次对蒸发组装过程中的纳米颗粒运动进行时间分辨测量。主要培训内容包括：(1)数据分析软件和/或算法开发的跨学科研究；(2)将不同受训人员聚集在一起的暑期学校；(3)新的选修课程和关于新兴计算和实验工具的较短模块；(4)以共同主题为中心的混合小组会议；(5)可扩展的负责任的研究、专业发展和职业道路模块；(6)艾伦·阿尔达科学传播中心提供的科学交流培训；以及(7)以学生为导向的职业阶梯计划和指导，通过根据学生的职业目标量身定做的个人发展计划和活动。为了将利益扩展到学员之外，该项目将实施新的动态结构定量分析研究生证书，将暑期班转变为对全国学生开放的自给自足的项目，并以开放获取视频讲座的形式分发课程材料。NSF研究培训(NRT)计划旨在鼓励为STEM研究生教育培训开发和实施大胆的、具有潜在变革意义的新模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Collective Nanoparticle Dynamics Associated with Bridging Network Formation in Model Polymer Nanocomposites

与模型聚合物纳米复合材料中桥接网络形成相关的集体纳米粒子动力学

• DOI: 10.1021/acsnano.1c01283
• 发表时间: 2021
• 期刊: ACS Nano
• 影响因子: 17.1
• 作者: Yavitt, Benjamin M.;Salatto, Daniel;Zhou, Yuxing;Huang, Zhixing;Endoh, Maya;Wiegart, Lutz;Bocharova, Vera;Ribbe, Alexander E.;Sokolov, Alexei P.;Schweizer, Kenneth S.
• 通讯作者: Schweizer, Kenneth S.

Thermodynamic and Kinetic Barriers Limiting Solid-State Reactions Resolved through In Situ Synchrotron Studies of Lithium Halide Salts

• DOI: 10.1021/acs.chemmater.2c02543
• 发表时间: 2023-01
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: Monty R. Cosby;Christopher J. Bartel;A. Corrao;A. Yakovenko;L. Gallington;G. Ceder;P. Khalifah

Dynamics and microrheology of colloidal clay-polymer glasses and gels: Size-dependent phenomena and re-entrant behavior at early aging times

胶体粘土聚合物玻璃和凝胶的动力学和微流变学：早期老化时的尺寸依赖性现象和重入行为

• DOI: 10.1063/5.0130816
• 发表时间: 2023
• 期刊: The Journal of Chemical Physics
• 作者: Shen, Jiachun;Bhatia, Surita R.
• 通讯作者: Bhatia, Surita R.

Spatial-Temporal Dynamics at the Interface of 3D-Printed Photocurable Thermoset Resin Layers

3D 打印光固化热固性树脂层界面的时空动力学

• DOI: 10.1021/acsaenm.2c00248
• 发表时间: 2023
• 期刊: ACS Applied Engineering Materials
• 作者: Yavitt, Benjamin M.;Wiegart, Lutz;Salatto, Daniel;Huang, Zhixing;Tsapatsaris, Leonidas;Endoh, Maya K.;Poeller, Sascha;Schiel, Manuel;Petrash, Stanislas;Koga, Tadanori
• 通讯作者: Koga, Tadanori

Structural properties of optically clear bacterial cellulose produced by Komagataeibacter hansenii using arabitol.

• DOI: 10.1016/j.bioadv.2023.213345
• 发表时间: 2023-02
• 期刊: Biomaterials advances
• 作者: Elizabeth M. van Zyl;M. Kennedy;Wendy Nason;Sawyer J. Fenlon;E. Young;Luis J. Smith;S. Bhatia;J. Coburn