Work

Predicting Carbon Nanotube Capacitance in H ₂ SO ₄ Electrolytes Through Machine Learning and Data Augmentation Techniques

Year: 2025

Type: article

Abstract: Carbon nanotube (CNT)-based supercapacitors inH2SO4electrolytes face design optimization challenges due to limited experimental datasets for machine learning model development. This study introduces an integrated framework combining physics-informed data augmentation with advanced machine learning to predict CNT capacitance from structural and electrochemical features. Three augmentation methods—bootstrap, PCA-based, and perturbation—were systematically evaluated across six machine learning algorithms using 69 experimental samples. Bootstrap augmentation with XGBoost achieved superior performance (R² = 0.9518, RMSE = 65.48 F/g), representing a significant improvement over traditional approaches. SHAP analysis identified voltage window and structural defect density as the most critical predictive features, providing actionable insights for CNT electrode design. This framework effectively addresses data scarcity in materials research while reducing experimental costs and accelerating supercapacitor development.

Source: IEEE Access

DOI: 10.1109/access.2025.3618751

Authors: Hatem M. Noaman, Adel Assiri, Fahad Kamal Alsheref, Mohamed Shaban, Wael Zakaria Tawfik, Rabab Hamed M. Aly

Institutions: Beni-Suef University, King Khalid University, Islamic University of Madinah

Language: English

Topic: Supercapacitor Materials and Fabrication

Subfield: Electronic, Optical and Magnetic Materials

Field: Materials Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075