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

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被称为“电子鼻”，能够区分混合气体中的几种气体。然而，仍然没有“通用”的气体传感器，也没有一个单一的设备可以在广泛的运行条件下检测到非常广泛的气体光谱。该项目寻求利用计算建模和模拟来开发这样一种装置的初步设计。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。这位研究人员计划为第一个通用气体传感设备制定路线图，这种设备的性能可以与狗的鼻子相媲美，甚至有可能超过狗的鼻子。该方法将使用分子模拟来准确地模拟挥发性有机化合物(VOCs)的行为。此外，还将使用大规模计算优化来搜索可能的传感阵列的广阔组合空间。拟议研究的主要成果将是未来“电子鼻”研究的计算机化路线图。这项拟议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来产生重大的教育影响。研究人员将制作一部涵盖人体排放的VOCs复杂混合物的电影，涉及许多可以从大规模计算/模拟方法中受益的工程应用。拟议研究的总体目标是开发一种指导通用气体传感器实验设计的“计算路线图”。研究表明，经过训练的狗可以通过嗅觉识别各种疾病，这表明通过气体感应检测疾病是可能的。然而，以前的“电子鼻”一直无法与狗的嗅觉能力竞争，因为研究人员一直狭隘地专注于具有20-30个元素(或更少)阵列的气体传感设备。另一方面，狗的鼻子有3亿个嗅觉感受器，相应的感受器类型有数千个。不到30个传感元件的电子鼻不太可能与狗的鼻子匹配，就像不到30像素的数码相机不能与人眼匹配一样。此外，以前的工作完全是由实验驱动的--计算建模仅用于分析事后产生的数据。设计大型阵列(100-1000+个元素)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。研究人员将使用宏正则蒙特卡罗模拟来模拟复杂气体混合物在金属-有机骨架(MOF)阵列中的吸附，金属-有机骨架(MOF)是晶态纳米孔材料。通过计算探索MOF的不同组合，研究人员将在原则上确定哪种阵列可以与生物鼻子的传感性能基准相匹配。该项目将考察基本探索，包括：性能如何随阵列大小进行扩展；在什么大小下，动物的嗅觉性能会交叉基准；哪些材料特性对传感性能有最大影响？该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.