## 4 Conclusions  
In this study, we developed a novel glass-nanopipet electrode sensor leveraging a non-redox ion-transfer approach via ITIES for the detection of Adr. By utilizing ionophores DB24C8 and DB18C6, we demonstrated that DB24C8 offers superior stability possibly due to its larger ring size and stronger complexation with Adr. Our findings revealed that Adr transfer across the aqueous/DCE interface generates significant peak potential shifts, enabling the construction of a calibration curve with a detection limit as low as 5 PM in Tris buffer, far below physiological Adr concentrations. Notably, the sensor performed robustly across various media, including KCl, DI water, and Tris buffer, and we established that pH variations exerted a more substantial influence on peak potential shifts compared to ionic strength changes. Additionally, our results confirmed that the presence of other cations such as potassium and calcium does not interfere with Adr detection, ensuring reliable performance in complex matrices.  
The practical implications of this work are substantial. The sensor is easy to fabricate, requires no sample pretreatment, and relies on peak potential shifts rather than specific ionophores or current measurements, making it user-friendly and accessible for non-experts in clinical or point-of-care settings. Its robustness and ability to function in real time position this approach as a promising tool for in vivo and in vitro neurochemical studies. However, challenges such as matrix-induced blocking of the interface and the need for recalibration for different biological samples highlight areas for future improvement. Integrating AI-based tools could further enhance the sensor's applicability by eliminating external calibration steps and improving performance in complex biological environments. This work not only advances the understanding of ion transfer for Adr detection but also establishes a framework for future developments in electrochemical sensing. By addressing limitations such as interface stability and expanding the approach to other analytes, this platform can inspire the design of next-generation sensors for diverse applications in health diagnostics and beyond.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15020869/s1](https://www.mdpi.com/article/10.3390/app15020869/s1)]([https://www.mdpi.com/article/10.3390/app15020869/s1](https://www.mdpi.com/article/10.3390/app15020869/s1)), Figure S1: Detection of Adr in KCl with DB18C6; Figure S2: Calibration of Adr in Tris Buffer with DB18C6; Table S1: Tris Buffer Analysis; Sample output of MATLAB (R2022b) code used to generate peak shift.  
Conceptualization, R.J.P. and G.K.; methodology, R.J.P. and G.K.; software, R.J.P. and G.K.; validation, R.J.P. and G.K.; formal analysis, R.J.P., P.P. and G.K.; investigation, R.J.P. and G.K.; resources, P.P.; data curation, R.J.P. and G.K.; writing--original draft preparation, R.J.P. and G.K.; writing--review and editing, R.J.P., G.K., N.M., J.L.S. and P.P.; visualization, R.J.P. and G.K.; supervision, P.P.; project administration, P.P.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All the necessary data are available in the article.  
The authors declare no conflicts of interest.