Machine Learning‐Enabled Smart Gas Sensing Platform for Identification of Industrial Gases-Reference-Cited by-同舟云学术

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Machine Learning‐Enabled Smart Gas Sensing Platform for Identification of Industrial Gases

Published:2022-03-19 Issue:4 Volume:4 Page:2200016
ISSN:2640-4567
Container-title:Advanced Intelligent Systems
language:en
Short-container-title:Advanced Intelligent Systems

Author:

Huang Shirong¹,Croy Alexander²,Panes-Ruiz Luis Antonio¹,Khavrus Vyacheslav³,Bezugly Viktor³,Ibarlucea Bergoi¹⁴^ORCID,Cuniberti Gianaurelio¹⁴⁵

Affiliation:

1. Institute for Materials Science and Max Bergmann Center for Biomaterials TU Dresden 01062 Dresden Germany

2. Institute for Physical Chemistry Friedrich Schiller University Jena Helmholtzweg 4 07743 Jena Germany

3. SmartNanotubes Technologies GmbH Dresdener Str. 172 01705 Freital Germany

4. Center for Advancing Electronics Dresden (CFAED) TU Dresden 01062 Dresden Germany

5. Dresden Center for Computational Materials Science (DCMS) TU Dresden 01062 Dresden Germany

Funder

Bundesministerium für Bildung und Forschung

China Scholarship Council

Horizon 2020 Framework Programme

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202200016

Reference73 articles.

1. Construction of ultrasensitive ammonia sensor using ultrafine Ir decorated hollow graphene nanospheres

2. High sensitivity ammonia detection using metal nanoparticles decorated on graphene macroporous frameworks/polyaniline hybrid

3. Enhanced sensitivity of ammonia sensor using graphene/polyaniline nanocomposite

4. Ammonia sensors and their applications—a review

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