Ultra-selective tin oxide-based chemiresistive gas sensor employing signal transform and machine learning techniques.

Gases Machine Learning Oxides Tin Compounds Volatile Organic Compounds / chemistry
Chemiresistive gas sensor Feature extraction Machine learning Selectivity Signal transform Volatile organic compound

Journal

Analytica chimica acta
ISSN: 1873-4324
Titre abrégé: Anal Chim Acta
Pays: Netherlands
ID NLM: 0370534

Informations de publication

Date de publication:
18 Jul 2022
Historique:
received: 17 03 2022
accepted: 24 05 2022
entrez: 11 6 2022
pubmed: 12 6 2022
medline: 15 6 2022
Statut: ppublish

Résumé

Selective detection of gases has been a major concern among metal-oxide based chemiresistive gas sensors due to their intrinsic cross-sensitivity. In this endeavor, we report integration of single metal-oxide based chemiresistive sensor with different soft computing tools to obtain perfect recognition of tested analyte molecules by means of signal processing, feature extraction and machine learning. The fabricated sensor device consists of SnO

Identifiants

DOI: 10.1016/j.aca.2022.339996 PMID: 35690423
pubmed: 35690423
pii: S0003-2670(22)00567-0
doi: 10.1016/j.aca.2022.339996
pii:
doi:

Substances chimiques

Gases 0
Oxides 0
Tin Compounds 0
Volatile Organic Compounds 0
stannic oxide KM7N50LOS6

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

339996

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Auteurs

Snehanjan Acharyya (S)

Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.

Sudip Nag (S)

Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India; Electronics & Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.

Prasanta Kumar Guha (PK)

Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India; Electronics & Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India. Electronic address: pkguha@ece.iitkgp.ac.in.

Articles similaires

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software

Understanding the role of machine learning in predicting progression of osteoarthritis.

Simone Castagno, Benjamin Gompels, Estelle Strangmark et al.
1.00
Humans Disease Progression Machine Learning Osteoarthritis

Editorial: Artificial Intelligence (AI), Digital Image Analysis, and the Future of Cancer Diagnosis and Prognosis.

Dinah V Parums
1.00
Humans Artificial Intelligence Neoplasms Prognosis Image Processing, Computer-Assisted

The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.

Daniele Castellani, Virgilio De Stefano, Carlo Brocca et al.
1.00
Humans Machine Learning Kidney Calculi Sepsis Male

Classifications MeSH

questionsmedicales.fr › Produits chimiques inorganiques › Gaz › Ultra-selective tin oxide-based chemiresistive...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Ultra-selective tin oxide-based chemiresistive...
questionsmedicales.fr › Produits chimiques inorganiques › Composés de l'oxygène › Oxydes › Ultra-selective tin oxide-based chemiresistive...
questionsmedicales.fr › Produits chimiques inorganiques › Composés de l'étain › Ultra-selective tin oxide-based chemiresistive...
questionsmedicales.fr › Composés chimiques organiques › Composés organiques volatils › Ultra-selective tin oxide-based chemiresistive...