Evidence map›Paper›PMID 39771700›Full record

ArticleSensors (Basel, Switzerland)2024

Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO

Emilie Bialic, Jimmy Leblet, Aymen Sendi, Paul Gersberg, Axel Maupoux, Nicolas Lassabe, Philippe Menini

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Emilie BialicCapgemini Engineering Research and Development, 31000 Toulouse, France.
Jimmy LebletUR Magellan, Iaelyon School of Management, University Jean Moulin Lyon 3, 1 Avenue des Frères Lumière, 69008 Lyon, France.
Aymen SendiLaboratoire d'Analyse et d'Architecture des Systèmes (LAAS), Université de Toulouse, CNRS, UPS, 7 Avenue du Colonel Roche, 31031 Toulouse, France.
Paul GersbergCapgemini Engineering Research and Development, 31000 Toulouse, France.
Axel MaupouxCapgemini Engineering Research and Development, 31000 Toulouse, France.
Nicolas LassabeCapgemini Engineering Research and Development, 31000 Toulouse, France.
Philippe MeniniLaboratoire d'Analyse et d'Architecture des Systèmes (LAAS), Université de Toulouse, CNRS, UPS, 7 Avenue du Colonel Roche, 31031 Toulouse, France.ORCID 0000-0002-7532-9148

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare…) has increased in the last decade. Multisensor modules, such as electronic noses, seem to be a promising and inexpensive alternative to traditional sensors that were only sensitive to one gas at a time. However, the selectivity, the non-repetitiveness of their manufacture, and their drift remain major obstacles to the use of electronic noses. In this first work, we show how the mathematical modeling of the sensor response can be used to find new selectivity characteristics, different from those classically used in the literature. We identified new specific characteristics that have no physical meaning that can be used to find criteria for the presence of formaldehyde and nitrogen dioxyde alone or in a mixture. We discuss the limitations of the methodology presented and suggest avenues for improvement, with more precise modeling techniques involving symbolic regression.

Indexed as

data analysiselectronic nosemathematical modelingmetal oxide gas sensorsnanomaterialsselectivitytemperature modulation

Identifiers

PMID39771700
PMCPMC11679981

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.