Standoff chemical identification of harmful aerosols
Abstract
Aerosols - solid or liquid particles suspended in air - are an effective means of dispersing chemical warfare agents and toxic pharmaceuticals, posing acute risks to public safety and security. Exposure to aerosol particulate matter in the environment and in industrial settings has also been linked to respiratory and cardiovascular disease. In both cases, rapid chemical identification is key to generating early warnings and attributing sources. Established methods chemically identify aerosols only after extracting a sample, while standoff techniques such as lidar classify aerosols only into broad categories. Chemical identification at a distance is hindered by aerosol extinction signatures that are weak, spectrally broad, and obscured by far stronger absorption from ambient gases. Here, we demonstrate time-resolved, standoff chemical differentiation of aerosols with dual-comb spectroscopy and machine learning identification algorithms. The high spectral resolution of dual-comb spectroscopy allows ambient gas absorption to be removed, and the algorithms identify chemicals without knowledge of aerosol size and morphology. We demonstrate identification of aerosolized benzyl fentanyl (a fentanyl simulant) in mixtures with other aerosols in a standoff measurement through ambient H2O, CO2, and CH4. Despite the aerosol producing only subtle, <1% changes in the extinction spectrum across hundreds of wavenumbers, we find the limit of identification to be an order of magnitude below lethal concentrations for a 1 m aerosol cloud. This technique provides a new paradigm for performing rapid chemical speciation of aerosols using their infrared absorption signatures without extractive sampling. Together with advances in chip-based mid-infrared dual-comb spectrometry, we anticipate this technique will enable real-time networks for standoff detection of aerosol emissions and aerosolized chemical threats.
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BibTeX
@misc{mathews2026standoff,
title = {{Standoff chemical identification of harmful aerosols}},
author = {Garrett C. Mathews and Alyssa Lalko and Anna C. Ziola and Masayuki Takeuchi and Leo C. Bloxham and Douglas A. Day and Anne V. Handschy and Amanda Makowiecki and Kevin Williamson and Nazanin Hoghooghi and Scott Egbert and Peter Chang and Tsung-Han Wu and Anthony Harness and Robert Wright and Jiang Li and Tin Komljenovic and John Bowers and Satoshi Takahama and Daven Henze and Kerry Vahala and Scott A. Diddams and Jose L. Jimenez and Gregory B. Rieker},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.09999},
eprint = {2611.09999},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.09999},
}
RIS
TY - UNPB
TI - Standoff chemical identification of harmful aerosols
AU - Garrett C. Mathews
AU - Alyssa Lalko
AU - Anna C. Ziola
AU - Masayuki Takeuchi
AU - Leo C. Bloxham
AU - Douglas A. Day
AU - Anne V. Handschy
AU - Amanda Makowiecki
AU - Kevin Williamson
AU - Nazanin Hoghooghi
AU - Scott Egbert
AU - Peter Chang
AU - Tsung-Han Wu
AU - Anthony Harness
AU - Robert Wright
AU - Jiang Li
AU - Tin Komljenovic
AU - John Bowers
AU - Satoshi Takahama
AU - Daven Henze
AU - Kerry Vahala
AU - Scott A. Diddams
AU - Jose L. Jimenez
AU - Gregory B. Rieker
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.09999
UR - https://arxiv.org/abs/2611.09999
AB - Aerosols - solid or liquid particles suspended in air - are an effective means of dispersing chemical warfare agents and toxic pharmaceuticals, posing acute risks to public safety and security. Exposure to aerosol particulate matter in the environment and in industrial settings has also been linked to respiratory and cardiovascular disease. In both cases, rapid chemical identification is key to generating early warnings and attributing sources. Established methods chemically identify aerosols only after extracting a sample, while standoff techniques such as lidar classify aerosols only into broad categories. Chemical identification at a distance is hindered by aerosol extinction signatures that are weak, spectrally broad, and obscured by far stronger absorption from ambient gases. Here, we demonstrate time-resolved, standoff chemical differentiation of aerosols with dual-comb spectroscopy and machine learning identification algorithms. The high spectral resolution of dual-comb spectroscopy allows ambient gas absorption to be removed, and the algorithms identify chemicals without knowledge of aerosol size and morphology. We demonstrate identification of aerosolized benzyl fentanyl (a fentanyl simulant) in mixtures with other aerosols in a standoff measurement through ambient H2O, CO2, and CH4. Despite the aerosol producing only subtle, <1% changes in the extinction spectrum across hundreds of wavenumbers, we find the limit of identification to be an order of magnitude below lethal concentrations for a 1 m aerosol cloud. This technique provides a new paradigm for performing rapid chemical speciation of aerosols using their infrared absorption signatures without extractive sampling. Together with advances in chip-based mid-infrared dual-comb spectrometry, we anticipate this technique will enable real-time networks for standoff detection of aerosol emissions and aerosolized chemical threats.
ER -