EAGER: Type I: Data-Driven Analysis of Correlations between Chemical Structure and Electrical

EAGER：I 型：化学结构与电学之间相关性的数据驱动分析

• 批准号: 1842708
• 负责人: David Ginger
• 金额: $ 14.48万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842708&HistoricalAwards=false
• 关键词: EAGER; Type; Data; Driven; Analysis

NONTECHNICAL SUMMARYThis EAGER award supports research and education involving a materials research - data science collaboration kindled at the MATDAT18 Datathon event focused on utilizing data analytics to amplify the capabilities of microscopies for materials. New scientific tools such as new kinds of microscopes are creating ever larger volumes of data, often in new formats. New computer programs are needed to make this data accessible to scientists and to allow researchers to gain the most insight from the acquired data. This project will focus on merging data science tools with new forms of microscopy. As a short, exploratory project, this research will develop open-source data-science-based tools to allow researchers to make better use of data coming from a new infrared microscopy method when applied to soft plastic electronic materials in the presence of electron and ion charges so that these electronic materials can be used as more effective bioelectronic transistors, sensors, and next-generation computing elements. Importantly, the project will provide a graduate student with advanced training at the interface between data science and polymer materials science, while also providing mentoring opportunities for summer undergraduate research students.TECHNICAL SUMMARYThis EAGER award supports research and education involving a materials research - data science collaboration kindled at the MATDAT18 Datathon event. The goal of this project is to merge data-science tools with new nanoscale sub-diffraction infrared imaging capabilities to extract fundamental structure-function relationships in complex polymer semiconductor films and composites, particularly in the context of organic electrochemical transistors (OECTs). OECTs are a promising platform for neural signal transduction and biosensing applications in challenging aqueous environments. They typically comprise a semiconducting polymer in contact with an ion source that serves to modulate charge transport in the polymer. Currently, progress in the field has been held up by a limited understanding of structure-function properties at the nanoscale, particularly by a lack correlated chemical composition with ion and charge transport. This project will use a new chemical mapping tool, photoinduced force microscopy (PiFM), and correlated AFM data as a platform for developing advanced data analysis methods that can quantify relationships among nanoscale properties. The project will develop open source tools for handling the complex hyperspectral data sets generated on polymers. Specifically, the team will: (1) develop new methods for reliably discriminating chemical components in soft conducting polymer systems using hyperspectral PiFM spectra; (2) develop advanced regression beyond common linear methods to extract quantitative structure-function relationships using correlated multimodal AFM data cubes; (3) incorporate the methods developed into open source packages for wider adoption in the AFM community. These results should open new doors to enable broader advances in polymer bioelectronics and electronics.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.

非技术摘要这一急切的奖项支持涉及材料研究的研究和教育 - 在Matdat18 Datathon事件上点燃了数据科学合作，重点是利用数据分析来扩大微观材料的能力。新的科学工具（例如新型显微镜）通常以新格式创建大量数据。需要新的计算机程序，以使科学家可以访问此数据，并允许研究人员从获得的数据中获得最大的见解。该项目将着重于将数据科学工具与新形式的显微镜合并。作为一个简短的探索性项目，这项研究将开发开源数据科学的工具，以使研究人员可以在存在电子和离子电荷的情况下应用于软塑料电子材料时更好地利用来自新的红外显微镜方法的数据，从而可以将这些电子材料用作更有效的生物电子晶体管，传感器，传感器，传感器，以及下一代元素。 重要的是，该项目将在数据科学与聚合物材料科学之间的接口上为研究生提供高级培训，同时还为夏季本科研究专业的学生提供指导机会。技术总结本急切的研究和教育涉及材料研究 - 数据科学合作，在MATDAT18 DATATHON事件上进行了研究。该项目的目的是将数据科学工具与新的纳米级亚曲线红外成像功能合并，以提取复杂聚合物半导体膜和复合材料中的基本结构 - 功能关系，尤其是在有机电力学晶体管（OECT）的背景下。 OECT是在挑战性水环境中进行神经信号转导和生物传感应用的有前途的平台。它们通常包含与离子源接触的半导体聚合物，该聚合物用于调节聚合物中的电荷传输。当前，该领域的进展是通过对纳米级结构功能特性的有限理解，尤其是由于缺乏与离子和电荷转运相关的化学成分。该项目将使用一种新的化学映射工具，光诱导的力显微镜（PIFM），并将AFM数据相关联，作为开发可以量化纳米级属性关系的高级数据分析方法的平台。该项目将开发开源工具来处理聚合物生成的复杂高光谱数据集。 具体而言，团队将：（1）开发使用高光谱PIFM光谱在软导电系统中可靠区分化学成分的新方法； （2）开发超出常见线性方法的高级回归，以使用相关的多模式AFM数据立方提取定量结构 - 功能连接； （3）将开发的方法纳入开源包中，以在AFM社区中更广泛采用。 这些结果应打开新的大门，以使聚合物生物电子学和电子产品更广泛地进展。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来获得支持。