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灵敏度系统存在于自然生物学主体中。例如，珠宝甲虫可以检测到50英里外的一棵燃烧的树，狗可以以每万亿美元浓度的浓度嗅出物质 - 比人鼻子更敏感的放大点。基于嗅觉的化学敏感性是具有许多出色的分析属性的最有前途的检测技术之一。它是无创，高通量，快速，易于多重且成本相对较低的。在过去的几十年中，就关键性能属性而言，工程气体传感器的气体探测器越来越多（例如电子鼻）与天然嗅觉系统不匹配：灵敏度和特异性。 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 innovative and further mature two miniature VOC sensor technologies, namely, colorimetric VOC sensor arrays and wearable VOC sensor patches to the level of scaled manufacturing.这些具有成本效益的，现场和敏感的传感器可能对处境不利和资源有限的社区特别有价值，可以通过提高其个人健康监测，农作物保护和环境检测的能力来应对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外展和培训下一代劳动力提供了绝佳的机会。本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，以解决人类和植物疾病和环境监测的无创诊断中紧急需求的真实产品。该项目的总体目标是建立一个融合框架，以通过应用机器学习和生物启发的设计来开发一组负担得起且可访问的VOC传感器，并通过应用机器学习和生物启发的设计可显着提高分析性能。具体而言，项目计划包括以下研究任务：1）使用具有98,000染料及其可扩展制造的Weaver Dye库中的Coloriemetric VOC传感器开发机器学习预测模型； 2）通过研究昆虫启发的蜡涂层作为一种“化学镜头”来设计和优化高度敏感的可穿戴VOC传感器，用于主动将VOC的主动“聚焦”到传感器上，3）传感器对人，植物和环境检测的示例应用进行扩展和演示。该项目的收敛方法取决于传统传感器研究（化学，材料和电子产品）与其他两个不同的学科领域的合并：数据智能和感觉昆虫学。该项目的结果将在嗅觉传感器设计和学术研究小组与行业制造商之间的合作伙伴关系方面建立科学基础，以扩大传感器的扩展。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的优点和更广泛的影响来评估NSF的法定任务。