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

Physics-informed Machine Learning approach for a selective, sensitive, and rapid sensor for detecting unsafe levels of carcinogenic/toxic VOCs
基于物理的机器学习方法,用于选择性、灵敏且快速的传感器,用于检测致癌/有毒 VOC 的不安全水平
批准号:
10600819
负责人:
Hamed Attariani
金额:
$27.56万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
已结题
起止时间:
2023-01-01 至 2024-12-31

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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.
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湍流和化学交互作用对H2-Air-H2O微混燃烧中NO生成的影响研究
  • 批准号:
    51976048
  • 项目类别:
    面上项目
  • 资助金额:
    61.0万元
  • 批准年份:
    2019
  • 负责人:
    邱朋华
  • 依托单位: