NSF Convergence Accelerator Track L: Accelerating VOC Sensor Advances and Translation by Machine Learning and Bioinspiration

令人印象深刻的嗅觉感知系统存在于自然出生的生物主体中。例如，宝石甲虫可以探测到50英里外燃烧的树，狗可以嗅出浓度为万亿分之一的物质——比人类鼻子灵敏几个数量级。基于嗅觉的化学传感是最有前途的检测技术之一，具有许多突出的分析属性。它具有无创、高通量、快速、易复用、成本相对较低等特点。过去几十年见证了越来越多的气体探测器（例如电子鼻），然而，工程气体传感器在关键性能属性方面与自然嗅觉系统不匹配：灵敏度和特异性。利用研究人员之前在开发挥发性有机化合物（VOC）传感器方面的经验，该项目团队由工程师，化学家，材料科学家，生物学家，数据科学家以及来自工业和医疗保健的合作伙伴组成，旨在创新和进一步成熟两种微型VOC传感技术，即比色VOC传感器阵列和可穿戴VOC传感器贴片，以达到规模化生产的水平。对于处境不利和资源有限的社区来说，这些具有成本效益、可实地携带和敏感的传感器可能特别有价值，可以通过提高他们在个人健康监测、作物保护和环境检测方面的能力，来应对与全球健康和粮食安全相关的重大挑战。微型传感器工具还为公众宣传和培训下一代劳动力提供了极好的机会。这一融合项目旨在打破嗅觉传感器的转化科学障碍，并加速这种传感器技术的开发和转化为实际产品，以满足人类和植物疾病的非侵入性诊断和环境监测方面的迫切需求。该项目的总体目标是建立一个融合框架，通过应用机器学习和生物启发设计，开发一套价格合理且易于使用的VOC传感器，显著提高分析性能。具体而言，该项目计划包括以下研究任务：1)开发一个机器学习预测模型，用于使用含有98,000种染料及其可扩展制造的Weaver染料库进行比色VOC感测染料筛选；2)通过研究昆虫启发的蜡涂层作为“化学透镜”，将VOC主动“聚焦”到传感器上，设计和优化高灵敏度可穿戴VOC传感器；3)传感器的放大和示范应用，用于人体、植物和环境检测。该项目的融合方法依赖于传统传感器研究（化学、材料和电子学）与另外两个不同学科领域的融合：数据智能和感觉昆虫学。该项目的成果将为嗅觉传感器的设计奠定科学基础，并为传感器的规模扩大建立学术研究团体和工业制造商之间的伙伴关系。该奖项反映了美国国家科学基金会的法定使命，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。

Impressive olfactory sensing systems are present in nature-born biological subjects. For instance, jewel beetles can detect a burning tree 50 miles away, and dogs can sniff out substances at concentrations of one part per trillion – orders of magnitudes more sensitive than human noses. Olfaction-based chemical sensing represents one of the most promising detection technologies that has many outstanding analytical attributes. It is noninvasive, high throughput, fast, easy for multiplexing, and relatively low cost. The past few decades have witnessed a growing amount of gas detectors (e.g., electronic nose), However, the engineered gas sensors do not match natural olfactory systems in terms of key performance attributes: sensitivity and specificity. Leveraging the investigators’ previous experience in developing volatile organic compound (VOC) sensors, this project team, consisting of engineers, chemists, material scientists, biologists, data scientists, and partners from industry and healthcare, aims to innovate and further mature two miniature VOC sensing technologies, namely, colorimetric VOC sensor arrays and wearable VOC sensor patches to the level of scaled manufacturing. These cost-effective, field-portable, and sensitive sensors may prove particularly valuable for disadvantaged and resource-limited communities to address critical challenges associated with global health and food security by improving their capability in personal health monitoring, crop protection, and environmental detection. The miniature sensor tools also provide excellent opportunities for public outreach and training the next-generation workforce.This convergence project seeks to break down the translational science barriers for olfactory sensors and accelerate the development and translation of such sensor technology into real products for addressing urgent needs in noninvasive diagnostics of human and plant diseases and environmental monitoring. The overarching goal of the project is to build a convergence framework for developing a set of affordable and accessible VOC sensors with significantly improved analytical performance by applying machine learning and bio-inspired design. Specifically, the project plan includes the following research tasks: 1) develop a machine learning prediction model for colorimetric VOC sensing dye screening using the Weaver Dye Library with 98,000 dyes and its scalable manufacturing; 2) design and optimize highly sensitive wearable VOC sensors by studying the insect-inspired wax coating as a “chemical lens” for active “focusing” of VOCs onto sensors, and 3) sensor scaling up and demonstration of exemplar applications for human, plant, and environmental detection. The convergence approach of this project relies on the merging of conventional sensor research (chemistry, materials, and electronics) with two other distinct disciplinary areas: data intelligence and sensory entomology. The project results will establish a scientific foundation in olfactory sensor design and partnership between academic research groups and industry manufacturers for sensor scaling up.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.