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EAGER: CDS&E: A Computational Roadmap for a Universal Gas Sensor

渴望：CDS

基本信息

批准号：
1937179
负责人：
Christopher Wilmer
金额：
$ 10万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937179&HistoricalAwards=false
关键词：
EAGER CDS&amp Computational Roadmap Universal

项目摘要

Most gas sensors can only detect a single gas species (e.g., a smoke detector). Some more advanced gas sensors called "electronic noses" are able to distinguish among a few gas species in a gas mixture. However, there is still no "universal" gas sensor, or a single device that could detect a very broad spectrum of gases under a wide range of operating conditions. This project seeks develop a preliminary design of a such a device using computational modeling and simulations. A portable universal gas sensor would have enormous socioeconomic impact, similar in magnitude to the digital camera, and would revolutionize health diagnostics. The investigator plans to create a roadmap for the first universal gas sensing device whose capabilities could match, and potentially surpass, those of a dog's nose. The approach will use molecular simulations to accurately model the behavior of volatile organic compounds (VOCs). Also, large-scale computational optimization will be used to search the vast combinatorial space of possible sensing arrays. The primary outcome of the proposed research will be a computationally-derived roadmap for future "electronic nose" research. The proposed research is expected to have a significant educational impact via the production of high-quality scientific movies for both undergraduates and the broader public. The investigator will produce a movie that will cover the complex mixtures of VOCs emitted from the human body, touching on the many engineering applications that can benefit from large-scale computational/simulation approaches.The overall objective of the proposed research is to develop a "computational road map" guiding experimental design of a universal gas sensor. It has been shown that trained dogs can identify various illnesses through smell along, indicating that detection of diseases via gas sensing is possible. However, prior "electronic noses" have been unable to compete with canine olfactory abilities because researchers have been narrowly focused on gas sensing devices with arrays of 20-30 elements (or less). A dog's nose, on the other hand, has 300 million olfactory receptors, with corresponding receptor types numbering in the thousands. It is unlikely that an electronic nose with less than 30 sensing elements can match a dog's nose any more than a digital camera with less than 30 pixels can match a human eye. Moreover, prior work has been entirely experimentally driven - computational modeling has only been used to analyze the data generated after the fact. Designing large arrays (100-1000+ elements) presents a combinatorial challenge and hence is a classic "big data" problem, which can only be solved using computational data science and engineering methods. The investigator will use grand canonical Monte Carlo simulations to model complex gas mixture adsorption in arrays of metal-organic frameworks (MOFs), which are crystalline nanoporous materials. By computationally exploring different combinations of MOFs, the investigator will then determine, in principle, what kind of array could match the sensing performance benchmarks of a biological nose. The project will examine fundamental questing including: how does performance scale with array size; at what sizes does one cross benchmark olfactory performance of animals; and what material characteristics have the greatest influence on sensing performance?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.

大多数气体传感器只能检测单一气体种类（例如，烟雾探测器）。一些更先进的气体传感器称为“电子鼻”，能够区分气体混合物中的几种气体。然而，仍然没有“通用”气体传感器，或者可以在广泛的操作条件下检测非常广谱的气体的单一设备。该项目旨在使用计算建模和模拟来开发这种设备的初步设计。便携式通用气体传感器将产生巨大的社会经济影响，其规模与数码相机相似，并将彻底改变健康诊断。研究人员计划为第一个通用气体传感设备创建一个路线图，该设备的功能可以与狗的鼻子相匹配，甚至可能超过狗的鼻子。该方法将使用分子模拟来准确模拟挥发性有机化合物（VOC）的行为。此外，大规模的计算优化将用于搜索可能的传感阵列的巨大组合空间。拟议研究的主要成果将是未来“电子鼻”研究的计算衍生路线图。这项拟议中的研究预计将通过为本科生和更广泛的公众制作高质量的科学电影产生重大的教育影响。研究人员将制作一部电影，涵盖人体排放的复杂挥发性有机化合物混合物，触及可以从大规模计算/模拟方法中受益的许多工程应用。拟议研究的总体目标是开发一个“计算路线图”，指导通用气体传感器的实验设计。已经表明，经过训练的狗可以通过气味沿着识别各种疾病，这表明通过气体传感检测疾病是可能的。然而，先前的“电子鼻”无法与犬类的嗅觉能力竞争，因为研究人员一直狭隘地关注具有20-30个元件（或更少）的阵列的气体传感设备。另一方面，狗的鼻子有3亿个嗅觉受体，相应的受体类型有数千种。一个传感元件少于30个的电子鼻不可能比得上狗的鼻子，就像一个像素少于30个的数码相机不可能比得上人眼一样。此外，以前的工作完全是实验驱动的-计算建模仅用于分析事后生成的数据。设计大型数组（100-1000+元素）是一个组合挑战，因此是一个经典的“大数据”问题，只能使用计算数据科学和工程方法来解决。研究人员将使用巨正则蒙特卡罗模拟来模拟金属有机框架（MOFs）阵列中复杂的气体混合物吸附，MOFs是结晶纳米多孔材料。通过计算探索MOF的不同组合，研究人员将在原则上确定什么样的阵列可以匹配生物鼻子的传感性能基准。该项目将研究基本的问题，包括：性能如何与阵列大小的规模;在什么规模的一个交叉基准动物的嗅觉性能;和什么材料特性对传感性能的影响最大？该奖项反映了NSF的法定使命，并被认为是值得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的支持。