TiO₂-Graphite Supported Hybrid MIP Sensor for Sensitive Vitamin C Detection in Cosmetics

In this study, an ascorbic acid-selective polymer composite—molecularly imprinted poly(4-vinylpyridine) integrated with titanium dioxide nanomaterials on graphite (AA-MI-P(4-VP)@TiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>-G) was synthesized using a hybrid polymerization technique, combining sol-gel and thermal polymerization processes. The material served as a biosensor based on molecular imprinting for the highly sensitive detection of ascorbic acid (AA) via cyclic voltammetry and differential pulse voltammetry techniques. The imprinted polymer’s morphology, elemental composition, structure, and imprinting endorsement were investigated by Scanning Electron Microscopy (FESEM), Fourier Transform Infrared Spectroscopy (FTIR), UV-Visible Spectroscopy (UV-Vis), Brunauer-Emmett-Teller analysis (BET), and Energy-Dispersive X-Ray Spectroscopy (EDX). Electrochemical impedance spectroscopy showed that TiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> modification reduced charge transfer resistance in AA solution, enhancing current sensitivity by approximately 1.5 times compared to the bare MIP sensor. Under optimized conditions, the DPV peak currents of the biosensor showed a wide linear response over the concentration range of 10–4000 μM with a detection limit of 0.059 μM (S/N=3) and quantification limit of 0.18 μM. Experimental results demonstrate that the AA-MI-P(4-VP)@TiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>-G biosensor exhibits excellent ascorbic acid sensing performance, with high repeatability, selectivity, and stability, even in the presence of interfering compounds like dopamine and uric acid. The proposed biosensor was successfully used for quantification of AA in cosmetics with the help of a robust statistical model developed by partial least squares regression (PLSR) technique. The model showed a high prediction of 95.27% for the detection of AA in cosmetic samples. The results were in agreement with those obtained by high-performance liquid chromatography (HPLC).