| Autor: |
Zalke JB; Department of Electronics Engineering, Ramdeobaba University, Nagpur 440013, MH, India., Bhaiyya ML; Department of Electronics Engineering, Ramdeobaba University, Nagpur 440013, MH, India., Jain PA; Department of Biomedical Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur 440013, MH, India., Sakharkar DN; Department of Biomedical Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur 440013, MH, India., Kalambe J; Department of Electronics Engineering, Ramdeobaba University, Nagpur 440013, MH, India., Narkhede NP; Department of Electronics Engineering, Ramdeobaba University, Nagpur 440013, MH, India., Thakre MB; Department of Chemistry, D.R.B. Sindhu Mahavidhyalaya, Nagpur 440017, MH, India., Rotake DR; Department of Electrical Engineering, Indian Institute of Technology, Hyderabad 502284, TG, India., Kulkarni MB; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA.; Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576104, KA, India., Singh SG; Department of Electrical Engineering, Indian Institute of Technology, Hyderabad 502284, TG, India. |
| Abstrakt: |
Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor's performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5-8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM -1 cm -2 . Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology. |
