10.57647/ijic.2026.1701.03

Enzymatic Urea Biosensor on Screen-Printed Carbon Electrodes Based on a Hierarchical Polypyrrole/Reduced Graphene Oxide Scaffold with Machine Learning- Assisted Analysis

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  • Roozbeh Jamaliyan1,
  • Afshin Farahbakhsh*1,
  • Javad Mohebbi Najm Abad2,
  • Susan Khosroyar1,
  1. Department of Chemical Engineering, Qu.c., Islamic Azad University, Quchan, Iran
  2. Department of Computer Engineering, Qu.c., Islamic Azad University, Quchan, Iran

Received: 2026-06-07

Revised: 2026-02-18

Accepted: 2026-03-15

Published in Issue 2026-03-31

Copyright (c) 2026 Roozbeh Jamaliyan, Afshin Farahbakhsh, Javad Mohebbi Najm Abad, Susan Khosroyar (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Jamaliyan, R., Farahbakhsh, A., Mohebbi Najm Abad, J., & Khosroyar, S. (2026). Enzymatic Urea Biosensor on Screen-Printed Carbon Electrodes Based on a Hierarchical Polypyrrole/Reduced Graphene Oxide Scaffold with Machine Learning- Assisted Analysis. International Journal of Industrial Chemistry, 17(1). https://doi.org/10.57647/ijic.2026.1701.03
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Abstract

This study presents a cost-effective enzymatic urea biosensor developed on a screen-printed carbon electrode (SPCE) using a sequential layer-by-layer modification strategy. A conductive polypyrrole (PPy) scaffold was electrochemically deposited to mitigate the restacking of reduced graphene oxide (rGO) sheets, creating a high-surface-area matrix for the covalent immobilization of urease (Urs). Evaluated in phosphate-buffered saline (PBS), the biosensor exhibited a linear response from PBS blank (0 mM) to 30 mM urea, with a calculated limit of quantification (LOQ) of 12.17 µM, a limit of detection (LOD) of 3.65 µM, and an area-normalized sensitivity of 3.68 µA·mM⁻¹·cm⁻². The device demonstrated high precision with relative standard deviations (RSDs) below 3%, robust storage stability (93% retention after 4 weeks), and good selectivity against physiological interferents. Beyond experimental testing, an explainable machine learning (ML) workflow was used as a supportive post hoc analysis to analyze the influence of fabrication variables and to benchmark urea concentration prediction against conventional electrochemical calibration. Overall, this work presents an engineering-optimized PPy/rGO/Urs modification strategy compatible with disposable SPCE fabrication for urea sensing in PBS and provides a transparent, ML-assisted data-analysis framework for sensor optimization and interpretation under controlled bench-validation conditions.

Keywords

  • Machine learning,
  • Polypyrrole,
  • Reduced graphene oxide,
  • Screen-printed carbon electrode,
  • Urea biosensor,
  • Urease
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