Physics-informed Machine Learning approach for a selective, sensitive, and rapid sensor for detecting unsafe levels of carcinogenic/toxic VOCs

项目摘要 每年，34万到90万人的过早死亡可能与排放造成的空气污染有关 挥发性有机化合物(VOCs)，即估计有18亿吨VOCs排放到全球 每一年都要保护环境。此外，一些挥发性有机化合物甚至在痕量水平上也会对健康造成严重的不利影响。 注意力集中，例如癌症、对中枢神经系统和免疫系统的损害。例如，环保局有 已确定188种已知或怀疑会导致癌症或其他严重健康影响的有毒空气污染物，例如 生殖影响、先天残疾或不良环境影响。现有的商用传感器用于 检测VOCs，如光电离检测器，是非选择性的。因此，它们不适合用于检测 同时含有多种致癌/有毒VOCs，例如苯和甲苯，达到不安全水平。另外，当前的 气相色谱-质谱仪(全球色谱)等选择性检测技术 市场规模&GT；到2030年达到150亿美元)庞大(约5磅)、昂贵(约25K-10万美元)、低迷(约2分钟)，并且需要 熟练的/训练有素的操作员。因此，普罗米修斯技术公司正在开发一种专利传感器平台，具有 选择性、低成本、快速、外形小巧的监控解决方案 熟练/训练有素的操作员检测致癌/有毒VOCs的不安全水平。一个重大的技术障碍 开发选择性的VOC传感器是来自一小部分背景混杂的干扰，当 采用特征限单波长解吸曲线进行定量。此应用程序的目标是1) 执行一系列经过验证的基于物理的模型，以低成本生成相当大的光学传感器数据集 考虑到该领域数据的稀缺性，这是至关重要的；2)开发一个基于 在步骤(1)中的数据集上，用于检测具有背景混杂的目标化合物的不安全水平。这 有必要推进我们的专利、选择性和微型化的VOC光学传感器。

Project Summary Each year, between 340,000 and 900,000 premature deaths can be linked to air pollution caused by releasing Volatile Organic Compounds (VOCs), i.e., an estimated 1.8 billion tons of VOCs are emitted to the global environment each year. Also, some VOCs cause serious adverse health effects even at the trace level concentration, e.g., cancer, damage to the central nervous and immune system. For example, the EPA has identified 188 toxic air pollutants known or suspected to cause cancer or other serious health effects, such as reproductive effects, congenital disabilities, or adverse environmental effects. Existing commercial sensors for detecting VOCs, such as photoionization detectors, are non-selective. So, they are unsuitable for detecting unsafe levels of multiple carcinogenic/toxic VOCs simultaneously, e.g., Benzene and Toluene. Also, the current selective detecting technologies such as gas chromatography-mass spectrometry (global chromatography market size >$15B by 2030) are bulky (~5 lbs.), expensive (~$25K - $100K), sluggish (~ 2 minutes), and requires a skilled/trained operator. Therefore, Prometheus Technologies is developing a patented sensor platform with features such as selectivity, low-cost, fast, small form factor monitoring solution that does not require skilled/trained operators to detect unsafe levels of carcinogenic/toxic VOCs. A significant technological hurdle to developing a selective VOC sensor is interference from a small subset of background confounders when a feature-limited single wavelength desorption curve is used for quantification. The goals of this application are 1) to perform a series of verified physics-based models to generate a sizeable optical sensor dataset at a low cost that is essential considering the scarcity of data in this field, and 2) to develop a machine learning model based on the dataset in step (1) for detecting unsafe levels of target compounds with background confounders. This work is necessary to advance our patented selective and miniaturized VOC optical sensor.