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
Abir Boublia, Zahir Guezzout, N. Haddaoui, Michaël Badawi, Ahmad S. Darwish, Tarek Lemaoui, Fawzi Banat, Krishna Kumar Yadav, Byong‐Hun Jeon, Noureddine Elboughdiri, Yacine Benguerba, Inas M. AlNashef
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.
Topics & Concepts
PolyanilineNanocompositeGrapheneOutlierResilience (materials science)Anomaly detectionConductivityMaterials scienceElectrical resistivity and conductivityComputer scienceArtificial intelligenceNanotechnologyComposite materialPhysicsElectrical engineeringEngineeringPolymerQuantum mechanicsPolymerizationConducting polymers and applicationsAdvanced Sensor and Energy Harvesting MaterialsGas Sensing Nanomaterials and Sensors