Dendritic Fe₂O₃-rGO nanoarchitectures for electrochemical uric acid sensing coupled with explainable machine learning-driven sensor analytics.
9 affiliations
- 1Visvesvaraya Technological University Belagavi 590018 India.
- 2Department of Chemistry, Centre of Excellence in Nanomaterials and Devices (CND), RV College of Engineering Bengaluru 560059 India [email protected] [email protected].
- 3ESSNANO Materials Lab Pvt. Ltd Bengaluru 560092 India https://essnano.com/.
- 4Department of Physics, Jnana Bharathi Campus, Bangalore University Bengaluru 560056 India.
- 5Department of Physics, Centre of Excellence in Nanomaterials and Devices (CND), RV College of Engineering Bengaluru 560059 India.
- 6Department of Computer Science and Engineering, RV College of Engineering Bengaluru 560059 India.
- 7Centre for Theoretical Physics and Natural Philosophy, Studiorum for Advanced Studies, Mahidol University Nakhon Sawan 60130 Thailand.
- 8Department of Engineering Science, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University Nerul Navi Mumbai 400706 India.
- 9School of Engineering and Computer Science, Washington State University Vancouver Vancouver WA 98686 USA [email protected].
Abstract
Uric acid (UA) is an important biomarker that is associated with metabolic and renal disorders and demands sensitive detection methods. In this work, a dendritic Fe₂O₃-reduced graphene oxide (FOG) nanocomposite was prepared by a one-pot hydrothermal process and used to modify a glassy carbon electrode (GCE) for UA sensing. The synthesized composites were characterized by XRD, Raman spectroscopy, FESEM, HRTEM, and XPS to study their morphology, structure and properties as well as the chemical interactions between Fe₂O₃ and rGO. The synergistic effect of dendritic Fe₂O₃ and conductive rGO enhanced the accessibility of electrolyte, electron transfer and active sites. Electrochemical studies confirmed a well-defined oxidation peak at 0.304 V for UA, demonstrating excellent electrocatalytic activity. The sensor offered a linear range of 11-189 µM, a detection limit of 3.07 µM, and a sensitivity of 471.1 µA µM⁻¹ cm⁻². High stability, repeatability, and reproducibility were achieved, establishing FOG@GCE as a cost-effective platform for clinical UA detection. Beyond conventional peak-current calibration, a leakage-aware explainable ML framework interrogates multidimensional DPV information. Gaussian process regression achieved R ² = 0.996, RMSE = 3.6 µM, and MAE = 2.9 µM under nested validation, while SHapley Additive exPlanations (SHAP) enabled interpretation links predictive performance to physically meaningful electrochemical descriptors. This integration of heterointerface engineering, electrochemical sensing, uncertainty-aware prediction, and explainable AI establishes a compelling framework for next-generation intelligent chemical sensing.
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BibTeX
@article{subash2026dendritic,
title = {{Dendritic Fe₂O₃-rGO nanoarchitectures for electrochemical uric acid sensing coupled with explainable machine learning-driven sensor analytics.}},
author = {Anil Subash S and Manjunatha C and Nagendra Babu A P and Radha Nagaraj and Shwetha K P and Bahumanya R G and Vishal Chaudhary and Chandresh Kumar R and Praveen Sekhar},
journal = {Nanoscale advances},
year = {2026},
month = oct,
doi = {10.1039/d6na00705h},
url = {https://europepmc.org/article/MED/42903696},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Dendritic Fe₂O₃-rGO nanoarchitectures for electrochemical uric acid sensing coupled with explainable machine learning-driven sensor analytics.
AU - Anil Subash S
AU - Manjunatha C
AU - Nagendra Babu A P
AU - Radha Nagaraj
AU - Shwetha K P
AU - Bahumanya R G
AU - Vishal Chaudhary
AU - Chandresh Kumar R
AU - Praveen Sekhar
PY - 2026
DA - 2026/10/20
JO - Nanoscale advances
DO - 10.1039/d6na00705h
UR - https://europepmc.org/article/MED/42903696
AB - Uric acid (UA) is an important biomarker that is associated with metabolic and renal disorders and demands sensitive detection methods. In this work, a dendritic Fe₂O₃-reduced graphene oxide (FOG) nanocomposite was prepared by a one-pot hydrothermal process and used to modify a glassy carbon electrode (GCE) for UA sensing. The synthesized composites were characterized by XRD, Raman spectroscopy, FESEM, HRTEM, and XPS to study their morphology, structure and properties as well as the chemical interactions between Fe₂O₃ and rGO. The synergistic effect of dendritic Fe₂O₃ and conductive rGO enhanced the accessibility of electrolyte, electron transfer and active sites. Electrochemical studies confirmed a well-defined oxidation peak at 0.304 V for UA, demonstrating excellent electrocatalytic activity. The sensor offered a linear range of 11-189 µM, a detection limit of 3.07 µM, and a sensitivity of 471.1 µA µM⁻¹ cm⁻². High stability, repeatability, and reproducibility were achieved, establishing FOG@GCE as a cost-effective platform for clinical UA detection. Beyond conventional peak-current calibration, a leakage-aware explainable ML framework interrogates multidimensional DPV information. Gaussian process regression achieved R ² = 0.996, RMSE = 3.6 µM, and MAE = 2.9 µM under nested validation, while SHapley Additive exPlanations (SHAP) enabled interpretation links predictive performance to physically meaningful electrochemical descriptors. This integration of heterointerface engineering, electrochemical sensing, uncertainty-aware prediction, and explainable AI establishes a compelling framework for next-generation intelligent chemical sensing.
SN - 2516-0230
ER -