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

NSF 融合加速器轨道 L：通过机器学习和生物灵感加速 VOC 传感器的进步和转化

• 批准号: 2344423
• 负责人: Qingshan Wei
• 金额: $ 65万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344423&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Accelerating

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.

令人印象深刻的嗅觉传感系统存在于自然出生的生物体中。例如，金龟子可以探测到50英里外燃烧的树木，狗可以嗅出浓度为万亿分之一的物质--比人类的鼻子灵敏几个数量级。基于嗅觉的化学传感代表了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、易于多路复用和相对低的成本。在过去的几十年中，气体检测器的数量不断增加（例如，然而，工程气体传感器在关键性能属性（灵敏度和特异性）方面与天然嗅觉系统不匹配。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，旨在创新并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，以达到规模化制造的水平。这些具有成本效益的，现场便携式和敏感的传感器可能被证明对弱势和资源有限的社区特别有价值，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解决与全球健康和粮食安全相关的关键挑战。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。该融合项目旨在打破嗅觉传感器的转化科学障碍，加速将此类传感器技术开发和转化为真实的产品，以满足人类和植物疾病的非侵入性诊断以及环境监测的迫切需求。该项目的总体目标是建立一个融合框架，用于开发一套经济实惠且易于使用的VOC传感器，通过应用机器学习和生物灵感设计，显着提高分析性能。具体而言，该项目计划包括以下研究任务：1）利用拥有98，000种染料的Weaver Dye Library及其可规模化制造，开发用于比色VOC传感染料筛选的机器学习预测模型; 2）通过研究昆虫启发的蜡涂层作为用于将VOC主动“聚焦”到传感器上的“化学透镜”，设计和优化高灵敏度的可穿戴VOC传感器，以及3）传感器放大和用于人类、植物和环境检测的示范应用的演示。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）与其他两个不同学科领域的融合：数据智能和感官昆虫学。该项目的成果将为嗅觉传感器设计奠定科学基础，并为学术研究团体和工业制造商之间的合作关系奠定基础，以扩大传感器的规模。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。