Journal Articles Journal of Materials Chemistry A Year : 2024

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

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.
Fichier principal
Vignette du fichier
Boublia-JMaterChemA-PANI-ML-HAL.pdf (19.86 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04897565 , version 1 (20-01-2025)

Identifiers

Cite

Abir Boublia, Zahir Guezzout, Nacerddine Haddaoui, Michael Badawi, Ahmad S Darwish, et al.. 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. Journal of Materials Chemistry A, 2024, 12 (4), pp.2209-2236. ⟨10.1039/d3ta06385b⟩. ⟨hal-04897565⟩
0 View
0 Download

Altmetric

Share

More