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Nonlinearity in mass spectrometry for quantitative multi-component gas analysis in reaction processes
Univ Sci & Technol Beijing, Sch Met & Ecol Engn, Beijing 100083, Peoples R China..
Chinese Acad Sci, Inst Engn Thermophys, Beijing 100190, Peoples R China..
Chinese Acad Sci, Inst Engn Thermophys, Beijing 100190, Peoples R China..
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.ORCID iD: 0000-0003-1102-4342
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2022 (English)In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 1194, p. 339412-, article id 339412Article in journal (Refereed) Published
Abstract [en]

Quantitative mass spectrometry analysis for multi-component gas phase reaction processes is a typical multi-input and multi-output (MIMO) nonlinear problem. Conventional calibration and analytical methods that are based on the common hypothesis of linearity of the detected signal and gas parameters, could result in misjudgment of the reaction mechanism and inaccuracy in the determination of the reaction kinetics. In the present work, theoretical derivations based on equivalent characteristic spectrum analysis (ECSA (R)), discrete mode experiments and continuous mode experiments were performed, and the nonlinearity of mass spectrometry was confirmed. It is only possible to determine the physical parameters such as flow rate and/or concentrations of gases by properly handling the nonlinearity of mass spectrometry. In such case comprehensive reaction mechanisms and even the kinetics of the process can be accurately characterized. Well-handled nonlinear mass spectrometry analysis ensures a reliable and highly accurate identification for the multi-component gas phase reaction processes, and ensures high signal-to-noise ratio for detecting the small-flow gases at a wide range of carrier gas flow.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 1194, p. 339412-, article id 339412
Keywords [en]
Mass spectrometry, Multi-component gases, Reaction, Nonlinearity
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-307274DOI: 10.1016/j.aca.2021.339412ISI: 000742672300007PubMedID: 35063160Scopus ID: 2-s2.0-85122334266OAI: oai:DiVA.org:kth-307274DiVA, id: diva2:1630383
Note

QC 20220120

Available from: 2022-01-20 Created: 2022-01-20 Last updated: 2023-12-07Bibliographically approved

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Hedström, PeterMu, Wangzhong

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