BIGDATA: IA: Collaborative Research: Data-Driven, Multi-Scale Design of Liquid-Crystals for Wearable Sensors for Monitoring Human Exposure and Air Quality

大数据：IA：协作研究：用于监测人体暴露和空气质量的可穿戴传感器的数据驱动、多尺度液晶设计

• 批准号: 1837812
• 负责人: Victor Zavala Tejeda
• 金额: $ 124.35万
• 依托单位: University of Wisconsin-Madison
• 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: 2018-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837812&HistoricalAwards=false
• 关键词: BIGDATA; IA; Collaborative; Research; Data

Liquid crystals are responsive materials that can be used to manufacture low-cost and highly selective chemical sensors. Liquid crystals provide a potentially scalable approach toward deploying millions of wearable chemical sensors (e.g., in mobile phones or attached to clothing) that collect high-resolution data on human exposure to toxic contaminants in the air. This information is key to understanding health-risks associated with air quality, developing industrial practices that minimize workers' exposure to hazardous environments, and detecting point sources (e.g., fabrication of explosives). Liquid crystal sensors work by amplifying events that occur at the molecular-level into an optical signal when the sensor is exposed to a chemical environment. The amplification process involves a sequence of tightly coupled phenomena spanning multiple length and time scales. This span in scales lies beyond what is currently possible to characterize, model, and predict directly from first principles. This project seeks to combine first-principles and data-driven methodologies to overcome this technical challenge. The methods developed will enable the prediction of the influence of liquid crystal design variables on the information content of optical signals and will lead to a revolutionary impact on chemical sensing technologies and on the design of functional materials. The multidisciplinary nature of this project will train a new generation of engineers in the integration of data science into the design and analysis of advanced functional materials. K-12 students and the public will be engaged through development of hands-on liquid crystal sensors that respond to model target chemicals (e.g., carbon dioxide from sodas).The project will investigate scalable machine learning techniques that enable the efficient use of large sets of experimental and first-principles simulation data to uncover and understand multi-scale phenomena that govern the performance of liquid crystals. Specifically, the project goals are to: i) Investigate the use of density functional theory and molecular dynamics simulations to identify nanoscale descriptors of the underlying spatiotemporal events occurring within and at liquid crystal interfaces (e.g., binding energies), ii) Establish feature extraction techniques to identify suitable macroscale descriptors of liquid crystal optical signals (e.g., optical response times and texture fields), and iii) Develop machine learning techniques that enable the creation of multi-scale models capable of mapping nanoscale and macroscale descriptors. These capabilities will be combined in a reinforcement learning framework that will help guide experimental data collection and identification of innovative liquid crystal system designs. The ultimate engineering goal of the project is to design LC sensors to infer exposure events involving carbon monoxide, ozone, and nitrogen and sulfur oxide.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.

液晶是一种响应性材料，可用于制造低成本和高选择性的化学传感器。液晶提供了一种潜在的可扩展的方法来部署数百万可穿戴的化学传感器(例如，在移动电话或连接到衣服上)，这些传感器收集关于人类暴露于空气中有毒污染物的高分辨率数据。这些信息对于了解与空气质量有关的健康风险、制定最大限度地减少工人暴露在危险环境中的工业做法以及检测点源(如制造炸药)至关重要。液晶传感器的工作原理是，当传感器暴露在化学环境中时，将分子水平上发生的事件放大为光学信号。放大过程涉及跨越多个长度和时间尺度的一系列紧密耦合现象。这种规模的跨度超出了目前直接根据基本原理进行表征、建模和预测的可能。该项目寻求将第一原则和数据驱动方法结合起来，以克服这一技术挑战。所开发的方法将能够预测液晶设计变量对光信号信息量的影响，并将对化学传感技术和功能材料的设计产生革命性的影响。该项目的多学科性质将培养新一代工程师，将数据科学整合到先进功能材料的设计和分析中。K-12的学生和公众将通过开发响应模型目标化学物质(例如来自汽水的二氧化碳)的动手液晶传感器来参与。该项目将研究可扩展的机器学习技术，使大量实验和第一原理模拟数据能够有效地使用，以揭示和理解支配液晶性能的多尺度现象。具体地说，该项目的目标是：i)研究使用密度泛函理论和分子动力学模拟来识别在液晶界面内和在液晶界面处发生的潜在时空事件的纳米尺度描述符(例如，结合能)，ii)建立特征提取技术以识别液晶光学信号的合适的宏观尺度描述符(例如，光学响应时间和织构场)，以及iii)开发能够创建能够映射纳米尺度和宏观尺度描述符的多尺度模型的机器学习技术。这些能力将被结合在一个强化学习框架中，该框架将有助于指导实验数据收集和识别创新的液晶系统设计。该项目的最终工程目标是设计LC传感器，以推断涉及一氧化碳、臭氧、氮氧化物和硫氧化物的暴露事件。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

The Euler characteristic: A general topological descriptor for complex data

欧拉特性：复杂数据的通用拓扑描述符

• DOI: 10.1016/j.compchemeng.2021.107463
• 发表时间: 2021
• 期刊: Computers & Chemical Engineering
• 影响因子: 4.3
• 作者: Smith, Alexander;Zavala, Victor M.
• 通讯作者: Zavala, Victor M.

Topological data analysis: Concepts, computation, and applications in chemical engineering

拓扑数据分析：化学工程中的概念、计算和应用

• DOI: 10.1016/j.compchemeng.2020.107202
• 发表时间: 2021
• 期刊: Computers & Chemical Engineering
• 影响因子: 4.3
• 作者: Smith, Alexander D.;Dłotko, Paweł;Zavala, Victor M.
• 通讯作者: Zavala, Victor M.

Outlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering)

Outlook：我如何学会热爱机器学习（过程系统工程中机器学习的个人观点）

• DOI: 10.1021/acs.iecr.3c01565
• 发表时间: 2023
• 期刊: Industrial & Engineering Chemistry Research
• 影响因子: 4.2
• 作者: Zavala, Victor M.

Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals Using 3D Convolutional Neural Networks

使用 3D 卷积神经网络分析液晶的时空光学响应来传感气体混合物

• DOI: 10.1021/acssensors.2c00362
• 发表时间: 2022
• 期刊: ACS Sensors
• 影响因子: 8.9
• 作者: Bao, Nanqi;Jiang, Shengli;Smith, Alexander;Schauer, James J.;Mavrikakis, Manos;Van Lehn, Reid C.;Zavala, Victor M.;Abbott, Nicholas L.
• 通讯作者: Abbott, Nicholas L.

Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy

使用机器学习和中红外光谱技术在线表征混合塑料废物

• DOI: 10.1021/acssuschemeng.2c06052
• 发表时间: 2022
• 期刊: ACS Sustainable Chemistry & Engineering
• 影响因子: 8.4
• 作者: Long, Fei;Jiang, Shengli;Adekunle, Adeyinka Gbenga;M Zavala, Victor;Bar-Ziv, Ezra
• 通讯作者: Bar-Ziv, Ezra