Biosensors

Accelerated industrialization has caused complex mixed toxicant pollution, where synergistic or antagonistic interactions render conventional detection methods inadequate. Herein, we develop an integrated framework by pioneering the integration of microbial electrochemical systems (MECs) with machine learning (ML) for quantifying formaldehyde, tetracycline, Ag⁺, and Cu²⁺ in multi-component, multi-ratio, and multi-concentration mixtures. MECs generated dynamic current–time (I–t) signals responsive to toxicant stress, though signal overlap from mixed toxicants hindered direct quantification. Guided by toxicokinetics and electrochemical mechanisms, we developed a novel mechanism-driven feature engineering strategy with exclusively original indicators, which extracted 22 multidimensional features capturing instantaneous characteristics, kinetic patterns, and microbial stress-adaptive responses to resolve signal ambiguity, and provided biologically meaningful, high-information feature inputs that effectively bridge electrochemical response signals and ML modeling. Comparative analysis of four ML models (SVM, KNN, PLS, and RF) showed RF outperformed others, achieving R² > 0.9 for all toxicants (formaldehyde: 0.959; tetracycline: 0.934; Ag⁺: 0.936; Cu²⁺: 0.957) with minimized MAE and RMSE. Microbial community analysis identified Geobacter anodireducens (71.5%, electroactive for heavy metals) and Comamonas testosteroni (12.9%, organic degrader) as key functional taxa, supported by KEGG enzyme abundance data. This work overcomes traditional MEC limitations via innovative feature engineering and pioneering ML integration, providing a rapid, low-cost, and high-accuracy tool for environmental mixed toxicant monitoring.

In this study, an enzyme-free electrochemical sensor based on zinc oxide (ZnO) nanorods synthesized by the thermal decomposition of zinc acetate is presented. The suggested approach ensures simplicity, environmental friendliness, and scalability of the process without the use of an autoclave or high pressure. The morphology and structure of the samples are studied using SEM, TEM, XRD, Raman, FTIR, XPS, PL, and UV-Vis spectroscopy. It is found that heat treatment at 450 °C increases the degree of crystallinity, increases the size of crystallites, and reduces the concentration of surface defects, which leads to improved optical and electrochemical characteristics of the material. Beyond conventional sensitivity metrics, our study demonstrates that the selective detection of ascorbic acid (AA) and uric acid (UA) can be achieved by controlling the applied potential on a single ZnO electrode, an approach that leverages differences in redox energetics and surface interaction dynamics rather than complex surface functionalization. It is shown in this work that the synthesized ZnO samples subjected to heat treatment in air at 450 °C exhibit high sensitivity to ascorbic acid (9951.87 μA·mM⁻¹·cm⁻²; LoD = 1.11 μM) at a potential of 0.2 V and to uric acid (5762.48 μA·mM⁻¹·cm⁻²; LoD = 1.71 μM) in a phosphate buffer solution (pH 7) at a potential of 0.4 V with a linear range of 3 mM, offering a way to create simplified multicomponent electrochemical biosensors based on potential-controlled selectivity.

Monitoring uric acid (UA) concentration is crucial for human health, enabling early detection and prevention of metabolic disorders as well as assessing renal function and overall metabolic balance. Herein, we developed a field-effect transistor (FET)-based UA biosensor using hydrothermally synthesized vertical zinc oxide (ZnO) nanorods (NRs) and uricase. The fabricated FET biosensor was tested in phosphate-buffered saline (PBS) at increasing UA concentrations to evaluate its biosensing performance. The FET biosensor yields a sensitivity of 12.45 μA·mM⁻¹·cm⁻², covering a dynamic range of 0.05–2.75 mM. The calculated detection limit was ~0.0043 mM. The improved sensing performance results in a substantial enhancement of both detection sensitivity and limit of detection compared to the traditional lateral electrode setup. Additionally, selectivity, storage stability, fabrication reproducibility, and applicability for serum UA detection were evaluated. Overall, the vertical electrode configuration of the UA biosensor has the potential to be further extended for the sensitive detection of additional biomarkers.