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.

非技术总结EAGER奖支持涉及材料研究的研究和教育-在MATDAT 18 Datasheet活动中点燃的数据科学合作，专注于利用数据分析来放大材料显微镜的能力。新的科学工具，如新型显微镜，正在创造越来越大的数据量，通常是以新的格式。需要新的计算机程序来使科学家能够访问这些数据，并使研究人员能够从所获得的数据中获得最深入的见解。该项目将专注于将数据科学工具与新形式的显微镜相结合。作为一个简短的探索性项目，这项研究将开发基于开源数据科学的工具，使研究人员能够更好地利用来自新的红外显微镜方法的数据，当应用于存在电子和离子电荷的软塑料电子材料时，这些电子材料可以用作更有效的生物电子晶体管，传感器和下一代计算元件。 重要的是，该项目将为研究生提供数据科学和高分子材料科学之间的高级培训，同时也为暑期本科研究生提供指导机会。技术总结EAGER奖项支持材料研究-数据科学合作的研究和教育，该合作在MATDAT 18 Datasheet事件中点燃。该项目的目标是将数据科学工具与新的纳米级亚衍射红外成像功能相结合，以提取复杂聚合物半导体薄膜和复合材料中的基本结构-功能关系，特别是在有机电化学晶体管（OECTs）的背景下。OECTs是一个有前途的平台，神经信号转导和生物传感应用在具有挑战性的水环境。它们通常包括与离子源接触的半导体聚合物，所述离子源用于调节聚合物中的电荷传输。目前，在该领域的进展已举行了有限的理解在纳米尺度上的结构-功能特性，特别是缺乏相关的化学成分与离子和电荷传输。该项目将使用一种新的化学绘图工具，光诱导力显微镜（PiFM），和相关的AFM数据作为开发先进的数据分析方法，可以量化纳米级属性之间的关系的平台。该项目将开发开源工具，用于处理聚合物上生成的复杂高光谱数据集。 具体而言，该小组将：（1）开发新的方法，使用高光谱PiFM光谱可靠地鉴别软导电聚合物系统中的化学成分;（2）开发超越常见线性方法的高级回归，使用相关的多模态AFM数据立方体提取定量结构-功能关系;（3）将开发的方法纳入开源软件包，以便在AFM社区中更广泛地采用。 这些成果将为聚合物生物电子学和电子学的更广泛发展打开新的大门。该奖项反映了NSF的法定使命，并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，被认为值得支持。