Cellulose nanofiber-based dual-modal biosensor and machine learning-assisted electrochemical detection of paraoxon in...

The neurotoxic organophosphorus metabolite paraoxon poses severe food safety and environmental risks, underscoring the need for decentralized, field-deployable diagnostics. This study reports a novel portable dual-mode biosensor integrating rapid colorimetric screening with machine learning (ML)-assisted electrochemical analysis for the highly sensitive detection of paraoxon. Central to the platform's architecture is the strategic utilization of cellulose nanofibers (CNFs), which serve a multifunctional macromolecular matrix. The CNF network provides an expansive, highly porous surface area for the stable, biocompatible immobilization of acetylcholinesterase (AChE) while simultaneously driving passive sample capillary absorption. For the electrochemical channel, a conductive graphene/CNF (G/CNF) nanocomposite interface was engineered to accelerate interfacial electron transfer while preserving native enzymatic conformation. Transduction is based on paraoxon-mediated AChE inhibition, which proportionally attenuates both a voltammetric signal and the chromogenic generation of yellow 5-thio-2-nitrobenzoate (TNB^-). To ensure high-fidelity analytical reliability, an ML computational framework was incorporated to classify and deconvolve the electrochemical response profiles. The integrated biosensor features a quantitative linear dynamic range spanning from 0 to 1.0 ppm and incorporates a visual colorimetric screening threshold at 0.5 ppm. Validated in complex agri-food and water matrices, this macromolecular hybrid platform provides a robust, dual-verified approach for point-of-need pesticide monitoring.