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On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS

This paper reports on-line mixture quantification with FAIMS. Ternary gas mixtures composed of acetone, ethanol, and diethyl ether were used for quantification. We succeeded in an on-line quantification of ppm-level concentration and even sub-ppm-level gases using the gradient descent method. It too...

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Detalles Bibliográficos
Autores principales: Yokoshiki, Yasufumi, Nakamoto, Takamichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960543/
https://www.ncbi.nlm.nih.gov/pubmed/31835545
http://dx.doi.org/10.3390/s19245442
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author Yokoshiki, Yasufumi
Nakamoto, Takamichi
author_facet Yokoshiki, Yasufumi
Nakamoto, Takamichi
author_sort Yokoshiki, Yasufumi
collection PubMed
description This paper reports on-line mixture quantification with FAIMS. Ternary gas mixtures composed of acetone, ethanol, and diethyl ether were used for quantification. We succeeded in an on-line quantification of ppm-level concentration and even sub-ppm-level gases using the gradient descent method. It took 10 minutes to quantify the ternary mixture. However, it was too long, because we aim to track the temporal change of each component concentration in the mixture. Then, an algorithm based on feedback control was introduced to reduce the quantification time. The feedback method successfully tracked concentrations in three cases. The simulation result shows that the proposed method can reduce the quantification time.
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spelling pubmed-69605432020-01-23 On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS Yokoshiki, Yasufumi Nakamoto, Takamichi Sensors (Basel) Article This paper reports on-line mixture quantification with FAIMS. Ternary gas mixtures composed of acetone, ethanol, and diethyl ether were used for quantification. We succeeded in an on-line quantification of ppm-level concentration and even sub-ppm-level gases using the gradient descent method. It took 10 minutes to quantify the ternary mixture. However, it was too long, because we aim to track the temporal change of each component concentration in the mixture. Then, an algorithm based on feedback control was introduced to reduce the quantification time. The feedback method successfully tracked concentrations in three cases. The simulation result shows that the proposed method can reduce the quantification time. MDPI 2019-12-10 /pmc/articles/PMC6960543/ /pubmed/31835545 http://dx.doi.org/10.3390/s19245442 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yokoshiki, Yasufumi
Nakamoto, Takamichi
On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS
title On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS
title_full On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS
title_fullStr On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS
title_full_unstemmed On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS
title_short On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS
title_sort on-line mixture quantification to track temporal change of composition using faims
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960543/
https://www.ncbi.nlm.nih.gov/pubmed/31835545
http://dx.doi.org/10.3390/s19245442
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