Molecularly Imprinted Photoelectrochemical Sensor Designed by Molecular Dynamics Simulation Calculations for Highly Selective and Ultrasensitive Detection of Illicit Drugs

As one of the most prevalent drugs, ketamine (KT) presents a significant social risk, making the development of a specific and ultrasensitive detection tool essential. Herein, the molecular dynamics (MD) simulation calculation-based molecularly imprinted polymer (MIP) with excellent recognition capability and the ZnO-CuO p-n heterojunction with efficient charge separation were prepared to fabricate a novel molecularly imprinted photoelectrochemical (MIP-PEC) sensor for the specific and ultrasensitive detection of KT. Impressively, compared with traditional MIP preparation methods that suffer from limited selectivity and inefficient trial-and-error component screening, MD simulation calculations enable the rational design of highly selective MIP, reducing costs and enhancing efficiency. Specifically, the binding energy of KT with functional monomers and cross-linkers, along with the solvation energy of solvents, were analyzed by MD simulation calculations, ultimately identifying the methacrylic acid (MAA), ethylene glycol dimethacrylate (EGDMA), and acetonitrile (ACN) as the optimal components to build MIP with precisely recognized cavities, which effectively improved the detection selectivity of the designed sensor. Furthermore, the ZnO-CuO p-n heterojunction with a precisely matched band effectively reduced the recombination rate of carriers, enhancing photoelectric response and stability. As a result, the constructed MIP-PEC sensor successfully achieved a specific and ultrasensitive assay of KT with a low detection limit down to 0.746 nM, an imprinting factor (IF) of 8.75, and a selectivity factor (SF) of 4.70, surpassing previous reports and enabling successful application in urine, saliva, and wastewater samples. This study provides a distinctly specific and ultrasensitive analytical platform for the assay of illegal drugs in the field of social security.