Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical conductivity and gas sensing performance-Reference-Cited by-同舟云学术

Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical conductivity and gas sensing performance

Published:2024 Issue:4 Volume:12 Page:2209-2236
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Boublia Abir¹,Guezzout Zahir²,Haddaoui Nacerddine¹,Badawi Michael³^ORCID,Darwish Ahmad S.⁴⁵,Lemaoui Tarek⁴⁵^ORCID,Banat Fawzi⁴⁶,Yadav Krishna Kumar⁷⁸^ORCID,Jeon Byong-Hun⁹^ORCID,Elboughdiri Noureddine¹⁰¹¹^ORCID,Benguerba Yacine¹²¹⁰^ORCID,AlNashef Inas M.⁴⁵⁶^ORCID

Affiliation:

1. Laboratoire de Physico-Chimie des Hauts Polymères (LPCHP), Département de Génie des Procédés, Faculté de Technologie, Université Ferhat ABBAS Sétif-1, Sétif, 19000, Algeria

2. Unité de Recherche sur les Matériaux Emergents –Sétif-; URMES, Equipe de Valorisation Des Polymères, Université Ferhat ABBAS Sétif-1, Sétif, 19000, Algeria

3. Université de Lorraine, CNRS, L2CM, F-57000 Metz, France

4. Department of Chemical Engineering, Khalifa University of Science and Technology, Al Saada St., Abu Dhabi, 127788, United Arab Emirates

5. Research & Innovation Center for Graphene and 2D Materials (RIC-2D), Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates

6. Center for Membrane and Advanced Water Technology (CMAT), Khalifa University, Al Saada St., Abu Dhabi, 127788, United Arab Emirates

7. Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal, 462044, India

8. Environmental and Atmospheric Sciences Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq

9. Department of Earth Resources & Environmental Engineering, Hanyang University, 222-Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

10. Chemical Engineering Department, College of Engineering, University of Ha'il, P.O. Box 2440, Ha'il 81441, Saudi Arabia

11. Chemical Engineering Process Department, National School of Engineers Gabes, University of Gabes, Gabes 6029, Tunisia

12. Laboratoire de Biopharmacie Et Pharmacotechnie (LBPT), Université Ferhat ABBAS Sétif-1, Sétif, Algeria

Abstract

This study employs various machine learning algorithms to model the electrical conductivity and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a comprehensive dataset gathered from over 100 references.

Funder

Ministry of Higher Education and Scientific Research

Direction Générale de la Recherche Scientifique et du Développement Technologique

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D3TA06385B

Reference128 articles.

1. A comprehensive overview of common conducting polymer-based nanocomposites; Recent advances in design and applications

2. Polymer nanotechnology: Nanocomposites