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Enhanced aerodynamic reach of vapor and aerosol sampling for real-time mass spectrometric detection using Venturi-assisted entrainment and ionization
Published
Author(s)
Thomas Forbes, Matthew Staymates
Abstract
Venturi-assisted ENTrainment and Ionization (VENTI) was developed herein, demonstrating efficient entrainment, collection, and transport of remotely sampled vapors, aerosols, and dust particulate for real-time mass spectrometry (MS) detection. Integrating the Venturi and Coandă effects and using atmospheric pressure chemical ionization (APCI), VENTI requires no moving parts and only a pressurized gas to generate gas flow and analyte transport, demonstrating the detection of a range of volatile chemical vapors (down to ppb); peroxide- based, nitrate ester, and nitroaromatic explosive vapors (headspace from 40 μg to 5 g samples under various conditions); explosive, narcotic, and mustard gas surrogate aerosols (20 μL plumes); and explosive dust particulate. Transport through remote sampling probes up to 2.5 m in length was achieved with residence times on the order of 10-2 s to 10-1 s and Reynolds numbers on the order of 103 to 104. Venturi-assisted entrainment also successfully enhanced vapor detection by greater than an order of magnitude at 20 cm stand-off (limit of simple suction) as well as the overall aerodynamic reach by approximately 3-fold over simple suction, results that were corroborated and observed with laser-light sheet visualization and schlieren imaging. Continuous real-time Venturi-assisted monitoring of a large room (approximately 90 m2 - 570,000 L) was demonstrated for a 60-minute period without the remote sampling probe, exhibiting detection of chemical vapors (several mL) and the mustard gas surrogate (100 μL) at approximately 3 m stand-off distances within 2 minutes.
Forbes, T.
and Staymates, M.
(2017),
Enhanced aerodynamic reach of vapor and aerosol sampling for real-time mass spectrometric detection using Venturi-assisted entrainment and ionization, Analytica Chimica ACTA, [online], https://doi.org/10.1016/j.aca.2016.12.037, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=921513
(Accessed December 17, 2024)