The increasing prevalence of diabetes mellitus necessitates the development of advanced glucose-monitoring systems that are non-invasive, reliable, and capable of real-time analysis. Wearable electrochemical biosensors have emerged as promising tools for continuous glucose monitoring (CGM), particularly through sweat-based platforms. This review highlights recent advancements in enzymatic and non-enzymatic wearable biosensors, with a specific focus on the pivotal role of nanomaterials in enhancing sensor performance. In enzymatic sensors, nanomaterials serve as high-surface-area supports for glucose oxidase (GOx) immobilization and facilitate direct electron transfer (DET), thereby improving sensitivity, selectivity, and miniaturization. Meanwhile, non-enzymatic sensors leverage metal and metal oxide nanostructures as catalytic sites to mimic enzymatic activity, offering improved stability and durability. Both categories benefit from the integration of carbon-based materials, metal nanoparticles, conductive polymers, and hybrid composites, enabling the development of flexible, skin-compatible biosensing systems with wireless communication capabilities. The review critically evaluates sensor performance parameters, including sensitivity, limit of detection, and linear range. Finally, current limitations and future perspectives are discussed. These include the development of multifunctional sensors, closed-loop therapeutic systems, and strategies for enhancing the stability and cost-efficiency of biosensors for broader clinical adoption.

Rapid and accurate bacteria identification, particularly Escherichia coli (E. coli), is essential in the monitoring of health, environment, and food safety. E. coli, a prevalent pathogenic bacterium, serves as a key indicator of food and water contamination. Carbon quantum dots (CQDs) have appeared as promising fluorescent probes because of their small size, ease of synthesis, low toxicity, and tunable fluorescence using different carbon-rich precursors. Advances in both bottom-up and top-down synthesis procedures have enabled precise control over CQD properties and surface functionalities, enhancing their capabilities in biosensing. Among the critical factors influencing CQD performance is the strategic selection of precursors, which determines the surface chemistry and recognition potential of the resulting nanodots. The integration with other nanomaterials and the surface modification of CQDs with specific functional groups or recognition elements further improves their sensitivity and selectivity toward E. coli. This review summarizes recent progress in the modification of CQDs for the fluorescent detection of E. coli, highlighting relevant sensing mechanisms and the influence of different precursors, such as antibiotics and sugars, as well as various functionalization and surface modification strategies. The aim is to provide insight into the rational design of efficient, selective, and cost-effective CQD-based biosensors for bacterial detection.

Controlling water and food contamination by pathogenic organisms requires quick, simple, and low-cost methods. using affinity between mannose and type I fimbriae in the cell wall of Escherichia coli bacteria as evaluation elements compared to the conventional plate counting technique enables a reliable sensing platform for the detection of bacteria. In this study, a simple new sensor based on electrochemical impedance spectroscopy (EIS) for rapid and sensitive detection of Escherichia coli was developed. Biorecognition layer of the sensor was formed by covalent attachment of p-carboxyphenylamino mannose (PCAM) to gold nanoparticles (AuNPs) electrodeposited on the surface of glassy carbon electrode (GCE). The resultant structure of PCAM was characterized and confirmed using FTIR. The developed biosensor demonstrated a linear response with a logarithm of bacterial concentration (R2 = 0.998) in the range of 1.3 × 10 1~ 1.3 × 106 CFU.mL−1with the limit of detection of 2 CFU.mL−1 within 60 min. The sensor did not generate any significant signals with two non-target strains, demonstrating high selectivity of the developed biorecognition chemistry. Selectivity of the sensor and its applicability to analysis of the real samples was investigated in samples of tap water. Overall, the developed sensor has shown to be promising for the detection of E. coli pathogen in water due to its high sensitivity, short detection time, low cost, high specificity, and user-friendliness.