Design and fabrication of machine learning trained silver nanoparticles-infused multi-walled carbon nanotube-based sensor for antiviral drug monitoring

Acyclovir (ACY), an antiviral drug, has been predominantly used to treat herpes simplex and zoster infections. Despite its therapeutic benefits, its precise monitoring is very vital because its usage may lead to a wide range of adverse effects, including neurotoxicity, urticaria, malaise, and nephrotoxicity. To fulfil this demand, herein we reported the fabrication of silver (Ag) nanoparticles infused multi-walled carbon nanotubes (MWCNTs) based on a novel sensor for the precise monitoring of ACY. Ag nanoparticles were uniformly infused over the surface of MWCNTs through polydopamine (PDA) functionalization. Additionally, we have trained ML algorithms on our experimental dataset to optimize, predict and validate ACY voltametric current response as function of material concentration, drying time and pH. ML has simplified the experimental procedures and improved the electrooxidation of ACY by optimized experimental parameters. Briefly, our results demonstrated that the designed Ag/ PDA-MWCNTs-based sensor shows efficient electrooxidation of ACY with reliable linear response (5-290 nM), high sensitivity (2.8796 mu A nM-1), low detection limit (2.7408 nM) and high selectivity even in the presence of co-existing interferences. The efficient electrocatalytic activity of the designed sensor could be attributed to the high surface area of the MWCNTs along with homogeneous distribution of Ag nanoparticles through PDA functionalization. The designed electrode further displayed reliable reproducibility and reusability against ACY even in real urine samples. To the best of our knowledge, this is the first study of using ML optimized Ag/PDAMWCNTs based sensor for electrochemical monitoring of ACY.