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

NSF 融合加速器轨道 L：受自然启发的智能嗅觉传感器，专为嗅探而设计 (iNOSES)

• 批准号: 2344256
• 负责人: Joanna Aizenberg
• 金额: $ 64.99万
• 依托单位: Harvard 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=2344256&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Intelligent

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.

通过最近的事件和发展，包括野火，危险溢出，越来越严格的排放法规以及对空气中的特定挥发物，食物和呼吸呼吸的识别，需要获得有关我们呼吸空气的实时信息的需求。但是，准确地识别气态样品的组成通常依赖于笨重，昂贵且固定的光谱设备。该阶段I项目将引入一个依赖人工智能（AI）的便携式化学气体传感器，以实时提供高度准确的识别。实时化学传感器数据将为跨部门的检测和报告中的标准化铺平道路，这是一项挑战，导致在排放监测，效率低下的定时通风和空气净化过程以及不必要的食物浪费中责任不良，所有这些挑战都造成了巨大的气候，健康和社会经济影响。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潜水员研究团队领导的，他们致力于外展，心态和科学沟通。投资下的概念范围从流体动力学，光学和纳米制作到AI，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，使来自各种背景（科学和人口）的学生能够参加。该项目建立在一个相对简单的传感器上：Bragg堆栈光子晶体，其光学反射光谱在通过挥发为孔中渗透而变化。光谱移位的时间依赖性受化合物或混合物混合产生的独特传输动力学的控制，并用于连续训练机器学习算法，以分类和物理性能预测化合物。在这种方法中，当挥发物被“吸入”并以特定的动态模式“吸入”并“呼气”时，团队介绍并实现了嗅觉启发的“嗅探”序列，从而最大程度地发挥了设备的实时歧视能力。第一阶段的研究将需要1）原型，设备小型化和软件开发，用于检测目标化学物质的子集，2）设备和算法优化，以实时感应和集成到应用程序特定域中，以控制理论，系统设计和机器学习的结合以及3）在现实环境中的促进型型在具有工业级别的partnerperspers offernerial perforts precters precters offerneppers offernection offection Theopical，系统设计和机器学习和3）。多学科的理论，实验和应用团队一起将推动化学敏感性的前沿。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。