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.

液晶是响应式材料，可用于生产低成本和高度选择性化学传感器。液晶为部署数百万可穿戴的化学传感器（例如，在手机或附属于衣服上）提供了一种潜在的可扩展方法，这些传感器收集了有关人类暴露于空气中有毒污染物的高分辨率数据。此信息是了解与空气质量，开发工业实践相关的健康风险的关键，这些危险将工人实践最小化，从而最大程度地减少工人对危险环境的接触，并检测点源（例如，炸药的制造）。当传感器暴露于化学环境时，液晶传感器通过将发生在分子级的事件放大到光学信号中来起作用。放大过程涉及一系列紧密耦合的现象，涵盖了多个长度和时间尺度。这种量表中的跨度超出了目前可以直接从第一原理来表征，建模和预测的东西。该项目旨在结合第一原理和数据驱动方法，以克服这一技术挑战。开发的方法将能够预测液晶设计变量对光学信号信息含量的影响，并将导致对化学传感技术和功能材料设计的革命性影响。该项目的多学科性质将培训新一代工程师将数据科学整合到高级功能材料的设计和分析中。 K-12学生和公众将通过开发动手液晶传感器来参与，这些传感器响应模型的目标化学物质（例如，来自苏打水的二氧化碳）。该项目将研究可扩展的机器学习技术，从而有效利用大量实验性和第一原则模拟数据，以揭示和理解多级液体局部液体晶体的表现。具体而言，项目目标是：i）研究密度功能理论和分子动力学模拟的使用，以确定在液体晶体界面内外发生的基础时空事件的纳米级描述符（例如，结合能）（例如结合能）（例如，结合能），ii）建立提取技术，以确定适当的液体液体液体晶体的晶体和液体晶体的培训（E. e.G）。 iii）开发机器学习技术，以创建能够绘制纳米级和宏观描述符的多尺度模型。这些功能将在增强学习框架中组合在一起，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，臭氧和氮和氧化硫的曝光事件。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

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