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.

高级材料对于具有社会福利的许多应用很重要，包括柔性电子，太阳能电池板，可回收塑料，抗菌涂料，净化系统和下一代电池。这些材料的特性在很大程度上取决于它们的结构或如何布置各种原子和较大单元。材料的新工具和数学模型允许科学家测量和预测其中一些特性。但是，这些技术产生了需要复杂分析的大量数据。这项国家科学基金会研究训练（NRT）授予斯托尼·布鲁克大学（NRT）将培训各种各样的研究人员，以分析与复杂的材料结构和财产相关的大型数据集。这些学生将参加科学沟通，工作场所多样性，职业道路和团队建设研讨会，以满足对数据科学培训的劳动力的不断增长的需求。此外，Stony Brook教职员工将与美国化学学会桥梁计划和机构一起工作，这些机构为少数群体和残疾学生提供服务，以促进科学和工程博士学位课程中代表性不足的学生的成功。该项目预计，从化学，材料科学，化学工程，机械工程和地球科学领域的培训培训七十五（75）位博士学位学生，包括38名（38）个资助的学员。 NRT受训人员将为材料社区开发新的软件工具和实验方法，从而改善了高级材料的设计。仪器的进步使探测跨越多个时间和长度尺度的结构动力学的前所未有的能力，数据获取速率非常高。这项研究的变革性方面包括开发新算法，以快速的原位X射线反应光谱；自动筛选复合材料的参数空间，以提高材料发现的速度；以及蒸发组装过程中纳米颗粒运动的首次分辨测量。主要培训要素是（1）有关用于数据分析的软件和/或算法开发的跨学科研究； （2）一所暑期学校将各种各样的学员聚集在一起； （3）有关新兴计算和实验工具的新选修课程和较短的模块； （4）混合小组会议以共同主题为中心； （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（Alan Alda）传播科学中心提供的科学传播培训； （7）由学生驱动的职业阶梯计划，并通过个人发展计划（IDP）和针对学生职业目标量身定制的活动进行指导。为了扩大受训者的收益，该项目将对动态结构进行定量分析，将暑期学校定量分析，向全国学生开放的自我维持计划，并将课程材料分配为开放式视频讲座。NSFResearch Traineeship（NRT）计划旨在鼓励大胆地，新的STEM研究培训培训，以鼓励开发和实施新的型型词士，以培训型模型。该计划致力于通过全面的跨学科或收敛性研究领域的STEM研究生进行有效培训，通过全面的培训模型，这些模型具有创新性，基于循证的，并且与不断变化的劳动力和研究需求保持一致。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务，并被视为值得的支持。

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.

Morphological Control of Nanoporous Copper Formed from Conversion Reaction Synthesis

• DOI: 10.1021/acs.jpcc.2c03544
• 发表时间: 2022-08
• 期刊: The Journal of Physical Chemistry C
• 作者: Shijie Feng;Victoria Petrova;A. Corrao;Shen Wang;Kesong Yang;P. Khalifah;Ping Liu

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