NSF Convergence Accelerator Track L: Intelligent Nature-inspired Olfactory Sensors Engineered to Sniff (iNOSES)

通过最近的事件和发展，包括野火，危险的泄漏，越来越严格的排放法规，以及识别空气中的特定挥发物，变质的食物和患病的呼吸，获取有关我们呼吸的空气的实时信息的需求已经成为人们关注的焦点。然而，准确识别气体样品的组成通常依赖于庞大、昂贵和固定的光谱设备。该项目的第一阶段将引入一种便携式化学气体传感器，该传感器依赖于人工智能（AI）来实时提供高度准确的挥发物识别。实时化学传感数据将为跨部门检测和报告的标准化铺平道路-这是一个有记录的挑战，导致排放监测问责制不佳，通风和空气净化过程效率低下，以及不必要的食物浪费，所有这些都是造成巨大气候，健康和社会经济影响的原因。这项工作将对STEM教育产生重大影响，因为这个高度多学科的项目是由一个多元化的研究团队领导的，该团队致力于推广，指导和科学交流。正在调查的概念范围从流体动力学，光学和纳米纤维到人工智能，建筑模拟和方法标准化。这为不同学科的研究培训创造了许多机会，允许来自各种背景（科学和人口）的学生参与。该项目建立在一个相对简单的传感器上：布拉格堆叠光子晶体，其光学反射光谱在挥发物渗透到其孔隙中时发生变化。光谱位移的时间依赖性由化合物或化合物混合物产生的独特传输动力学控制，并用于连续训练用于化合物分类和物理性质预测的机器学习算法。这种方法的独特之处在于，当挥发物以特定的动态模式“吸入”和“呼出”时，该团队引入并实施了嗅觉启发的“嗅探”序列，从而最大限度地提高了设备的实时辨别能力。第一阶段研究将包括1）原型设计，设备小型化和软件开发，用于检测目标化学品的子集，2）设备和算法优化，用于实时传感和集成到特定应用领域，由控制理论，系统设计和机器学习的组合指导，以及3）与工业专家合作，在真实的环境中进行试点研究。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。

The need to acquire real-time information about the air we breathe has been brought into the spotlight through recent events and developments, including wildfires, hazardous spills, increasingly stringent emissions regulations, and the identification of specific volatiles in air, spoiled food and diseased breath. However, accurately identifying the composition of gaseous samples typically relies on bulky, expensive and stationary spectroscopic equipment. This Phase I project will introduce a portable chemical gas sensor that relies on artificial intelligence (AI) to provide highly accurate identification of volatiles in real-time. The real-time chemical sensing data will pave the way to standardization in detection and reporting across sectors – a documented challenge leading to poor accountability in emission monitoring, inefficiently timed ventilation and air purification processes, and unnecessary food waste, all of which are responsible for immense climate, health, and socio-economic impacts. The work will have a significant impact on STEM education, as this highly multidisciplinary project is led by a diverse research team with a strong commitment to outreach, mentorship, and scientific communication. The concepts under investigation range from fluid dynamics, optics, and nanofabrication to AI, building simulations, and methods standardization. This creates many opportunities for research training in different disciplines, allowing students from all kinds of backgrounds (scientific and demographic) to participate. The project builds upon a relatively simple sensor: a Bragg stack photonic crystal, whose optical reflection spectrum changes upon infiltration by volatiles into its pores. The time-dependence of the spectral shifts is governed by the unique transport dynamics produced by a compound or mixture of compounds, and is used to continuously train a machine learning algorithm for classification and physical property prediction of compounds. Unique to this approach, the team introduces and implements olfactory-inspired ‘sniffing’ sequences when volatiles are ‘inhaled’ and ‘exhaled’ in specific dynamic patterns that maximize the real-time discriminatory power of the device. Phase I research will entail 1) prototyping, device miniaturization, and software development for detection of a subset of the target chemicals, 2) device and algorithm optimization for real-time sensing and integration into application-specific domains, guided by a combination of control theory, systems design, and machine learning, and 3) pilot studies in real environments, in partnership with industrial experts. Together, the multidisciplinary theoretical, experimental, and applied team will push the frontier of chemical sensing.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.