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

基本信息

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

项目摘要

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.

液晶是响应材料，可用于制造低成本和高选择性的化学传感器。液晶为部署数百万个可穿戴化学传感器（例如，在手机中或附着在衣服上）提供了一种潜在的可扩展方法，这些传感器收集有关人类接触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相关的健康风险、制定尽量减少工人接触危险环境的工业实践以及检测点源（例如炸药的制造）至关重要。当传感器暴露在化学环境中时，液晶传感器的工作原理是将分子水平上发生的事件放大为光信号。放大过程涉及一系列跨越多个长度和时间尺度的紧密耦合现象。这种尺度跨度超出了目前可以直接根据第一原理进行表征、建模和预测的范围。该项目旨在结合第一原理和数据驱动的方法来克服这一技术挑战。所开发的方法将能够预测液晶设计变量对光信号信息内容的影响，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训新一代工程师，将数据科学融入先进功能材料的设计和分析中。 K-12 学生和公众将参与开发可动手操作的液晶传感器，这些传感器可响应模型目标化学物质（例如苏打水中的二氧化碳）。该项目将研究可扩展的机器学习技术，以实现大型设备的高效使用实验和第一原理模拟数据，以揭示和理解控制液晶性能的多尺度现象。具体来说，该项目的目标是：i) 研究密度泛函理论和分子动力学模拟的使用，以识别液晶界面内和液晶界面处发生的潜在时空事件的纳米级描述符（例如结合能），ii) 建立特征提取技术识别液晶光信号的合适宏观尺度描述符（例如光学响应时间和纹理场），以及 iii) 开发机器学习技术，能够创建能够映射纳米尺度和宏观尺度描述符。这些功能将被整合到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创新液晶系统设计的识别。该项目的最终工程目标是设计 LC 传感器来推断涉及一氧化碳、臭氧以及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，并通过利用基金会的智力优势和更广泛的评估进行评估，认为值得支持。影响审查标准。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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：
2021-11
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.

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：
2022-12
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
通讯作者：
Bar

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：
2023-06
期刊：
Industrial & Engineering Chemistry Research
影响因子：
4.2
作者：
Zavala; Victor M.
通讯作者：
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：
2022-08
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.

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

使用拓扑描述符从化学响应液晶的时空模式中可扩展地提取信息

DOI：
10.1021/acs.jpcc.3c03076
发表时间：
2023-08
期刊：
The Journal of Physical Chemistry C
影响因子：
0
作者：
Jiang, Shengli;Bao, Nanqi;Smith, Alexander D.;Byndoor, Shraddha;Van Lehn, Reid C.;Mavrikakis, Manos;Abbott, Nicholas L.;Zavala, Victor M.
通讯作者：
Zavala, Victor M.