Municipal wastewater sludge/laser-induced graphene nanozyme for machine-learning-assisted smartphone colorimetric tetracycline sensing
- 1VNU-HCM, Key Laboratory of Chemical Engineering and Petroleum Processing (Key CEPP Lab), Ho Chi Minh City University of Technology (HCMUT) 268 Ly Thuong Kiet Street, Dien Hong Ward Ho Chi Minh City Vietnam [email protected].
- 2Faculty of Chemical Engineering, Ho Chi Minh City University of Technology (HCMUT) 268 Ly Thuong Kiet Street, Dien Hong Ward Ho Chi Minh City Vietnam.
- 3Vietnam National University Ho Chi Minh City (VNU-HCM) Linh Xuan Ward Ho Chi Minh City Vietnam.
Abstract
Municipal wastewater sludge (MWS) is an abundant Fe-containing waste stream with significant potential for valorization as a functional catalytic material. Herein, an Fe-rich MWS-derived phase was integrated with polyimide-derived laser-induced graphene (LIG) to construct a multifunctional LIG/MWS composite nanozyme for tetracycline (TC) sensing. LIG was fabricated directly using a low-cost 450 nm diode laser, while MWS was recovered through coagulation-flocculation, calcination, acid washing, and drying. The optimized LIG/MWS composite exhibited enhanced peroxidase-like activity toward the TMB-H₂O₂ system, where TC inhibited color development through interaction with Fe-containing catalytic sites. The proposed colorimetric assay enabled TC determination over 0-50 µM with a detection limit of 0.70 µM and R ² = 0.9866. The sensing strategy was further translated into a smartphone-readable paper-based analytical device (PAD), where RGB-based calibration provided portable TC screening capability. In parallel, a separate 96-well smartphone-imaging dataset was analyzed using principal component analysis (PCA) and supervised machine learning to evaluate multivariate concentration prediction and acquisition-condition variability. PCA revealed that the first two principal components explained 97.10% of RGB variance, while illumination was identified as the dominant source of systematic variation. Among PLSR, SVR, random forest, XGBoost, and ANN models, ANN achieved the best nested grouped-cross-validation performance (R ² = 0.987, RMSE = 6.95 µM), whereas leave-one-condition-out validation highlighted outdoor illumination as the major limitation for model generalization. The LIG/MWS composite also demonstrated secondary methylene blue adsorption, highlighting its multifunctional potential. This work provides a sustainable nanozyme platform integrating waste valorization, smartphone-assisted sensing, and machine-learning-enabled colorimetric analysis.
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BibTeX
@article{nghia2026municipal,
title = {{Municipal wastewater sludge/laser-induced graphene nanozyme for machine-learning-assisted smartphone colorimetric tetracycline sensing}},
author = {Nguyen Ngoc Nghia and Phung Thanh An and Nguyen Huu Hieu},
journal = {RSC advances},
year = {2026},
month = oct,
doi = {10.1039/d6ra09624g},
url = {https://europepmc.org/article/PMC/PMC13719271},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Municipal wastewater sludge/laser-induced graphene nanozyme for machine-learning-assisted smartphone colorimetric tetracycline sensing
AU - Nguyen Ngoc Nghia
AU - Phung Thanh An
AU - Nguyen Huu Hieu
PY - 2026
DA - 2026/10/30
JO - RSC advances
DO - 10.1039/d6ra09624g
UR - https://europepmc.org/article/PMC/PMC13719271
AB - Municipal wastewater sludge (MWS) is an abundant Fe-containing waste stream with significant potential for valorization as a functional catalytic material. Herein, an Fe-rich MWS-derived phase was integrated with polyimide-derived laser-induced graphene (LIG) to construct a multifunctional LIG/MWS composite nanozyme for tetracycline (TC) sensing. LIG was fabricated directly using a low-cost 450 nm diode laser, while MWS was recovered through coagulation-flocculation, calcination, acid washing, and drying. The optimized LIG/MWS composite exhibited enhanced peroxidase-like activity toward the TMB-H₂O₂ system, where TC inhibited color development through interaction with Fe-containing catalytic sites. The proposed colorimetric assay enabled TC determination over 0-50 µM with a detection limit of 0.70 µM and R ² = 0.9866. The sensing strategy was further translated into a smartphone-readable paper-based analytical device (PAD), where RGB-based calibration provided portable TC screening capability. In parallel, a separate 96-well smartphone-imaging dataset was analyzed using principal component analysis (PCA) and supervised machine learning to evaluate multivariate concentration prediction and acquisition-condition variability. PCA revealed that the first two principal components explained 97.10% of RGB variance, while illumination was identified as the dominant source of systematic variation. Among PLSR, SVR, random forest, XGBoost, and ANN models, ANN achieved the best nested grouped-cross-validation performance (R ² = 0.987, RMSE = 6.95 µM), whereas leave-one-condition-out validation highlighted outdoor illumination as the major limitation for model generalization. The LIG/MWS composite also demonstrated secondary methylene blue adsorption, highlighting its multifunctional potential. This work provides a sustainable nanozyme platform integrating waste valorization, smartphone-assisted sensing, and machine-learning-enabled colorimetric analysis.
SN - 2046-2069
ER -