Graphene Quantum Dot Integrated Polyaniline Nanofibers for Applications in Cortisol Biosensors

Cortisol, a key hormone involved in stress response, metabolism, and immune function, is a vital biomarker for diagnosing various health conditions. Rapid and cost-effective cortisol detection is important for clinical and medical applications, but traditional methods can be slow and expensive. In this study, we developed a novel cortisol biosensor using porous polyaniline (PANI) nanofibers integrated with nitrogen-doped graphene quantum dots (N-GQDs), offering a highly sensitive, flexible, and low-cost solution for cortisol monitoring.<br/><br/>Polyaniline is a conductive polymer with excellent electrical properties, making it suitable for biosensor development. To synthesize high-molecular-weight PANI, the reaction mixture was cooled to -40°C for an extended period, which enhanced the conductive properties of the polymer. The resulting PANI was then dissolved in dimethyl sulfoxide (DMSO) and mixed with polyethylene oxide (PEO). This composite was processed via electrospinning, a technique that allows for the production of continuous nanofibers with controlled diameter and morphology.<br/><br/>The electrospun PANI-PEO nanofibers were then washed in water to dissolve the PEO, leaving behind a porous network of PANI fibers. This porous structure is critical, as it increases the surface area available for further modification and enhances sensor performance. To improve conductivity and create more binding sites for the anticortisol enzyme, nitrogen-doped graphene quantum dots (N-GQDs) were incorporated into the pores of the PANI nanofibers. N-GQDs are highly conductive, biocompatible, and have a large surface area, making them ideal for boosting the sensor’s sensitivity.<br/><br/>The integration of N-GQDs into the porous PANI fibers served multiple functions: it enhanced the electrical properties of the nanofibers, allowing for more efficient signal transduction, and it provided a larger surface area for the immobilization of the anticortisol enzyme. This combination of enhanced conductivity and surface area greatly improved the sensor's ability to detect cortisol.<br/><br/>The composite PANI-N-GQD nanofibers were fabricated into biosensor electrodes and tested in a dilute solution of cortisol in water. The sensor’s performance was evaluated based on its response to varying cortisol concentrations, and it showed excellent sensitivity. Compared to control sensors made from plain PANI fibers, the N-GQD-integrated fibers demonstrated significantly improved detection capabilities. This improvement can be attributed to the synergistic effects of the porous structure and the N-GQDs, which together enhanced the conductivity and enzyme attachment, leading to a more sensitive biosensor.<br/><br/>In addition to its application in cortisol detection, the PANI-N-GQD composite offers several advantages for a wide range of biosensing technologies. The nanofibers are highly conductive, flexible, and cost-effective, making them ideal for wearable and portable biosensors. Moreover, the electrospinning process used to fabricate these nanofibers is scalable, enabling large-scale production of sensor components. These characteristics make the PANI-N-GQD platform a versatile and attractive solution for various biosensor applications.<br/><br/>Future work will focus on further optimizing the integration of N-GQDs into the PANI fibers and improving the sensor’s long-term stability and reproducibility. Additionally, the potential to use these fibers for detecting other biomarkers and chemicals will be explored, as their versatility and ease of fabrication make them a promising platform for a wide range of sensing applications.<br/><br/>In conclusion, the integration of nitrogen-doped graphene quantum dots into porous PANI nanofibers provides a powerful approach for developing highly sensitive, flexible, and low-cost cortisol biosensors. This innovative platform has the potential to be adapted for other biosensing applications, offering a scalable, efficient, and cost-effective solution for future sensor technologies.