Non-enzymatic electrochemical glucose sensing using flexible rGO/AgNW@PANI composite films on nylon substrates
Abstract
A flexible non-enzymatic electrochemical glucose sensor was developed using rGO/AgNW@PANI composite films deposited on nylon substrates. The hybrid rGO/AgNW@PANI film serves as both a conductive and sensing layer, in which the rGO and Ag nanowires establish an efficient electron-transfer network, while polyaniline contributes electroactive sites that facilitate glucose oxidation in synergy with the composite architecture. The chemical composition and microstructural features of the composite films were confirmed by FT-IR, FE-SEM, EDS, and XPS analyses. The resulting sensor exhibits a linear electrochemical response to glucose over a concentration range of 0.01-0.1 mM (R 2 = 0.99), with a sensitivity of 2.9 mA mM-1 cm-2, a detection limit of 0.01 mM, and a rapid response time in phosphate-buffered saline (PBS) solution (pH ≈ 6.5). Additionally, the flexible sensor demonstrates excellent mechanical robustness and long-term stability during repeated glucose sensing. These results highlight the potential of flexible rGO/AgNW@PANI composite films on nylon substrates as a promising platform for non-enzymatic electrochemical glucose sensing, with potential relevance to future flexible and wearable sensing applications.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.