Optimasi Screen Printed Carbon Electrode Modifikasi Nikel-Carbon Untuk Analisa Glukosa Menggunakan Pudelstat Secara Cyclic Voltammetry

Abstract

The high cost of commercial potentiostats has encouraged the development of low-cost open-source electrochemical instruments such as PUDELstat. This study aimed to optimize a nickel-carbon ink modified Screen-Printed Carbon Electrode (SPCE) for non-enzymatic glucose detection using Cyclic Voltammetry (CV) with a PUDELstat potentiostat. The effects of electrode surface area (4, 5, and 6 mm²), scan rate (50–250 mV/s), and NaOH electrolyte pH (12.0–13.5) on glucose oxidation were systematically investigated. The analytical performance of the optimized sensor was further evaluated using an artificial sweat matrix. The PUDELstat demonstrated satisfactory calibration performance with coefficients of determination (R²) of 0.96918 and 0.96264 for voltage and current measurements, respectively. The optimum electrode surface area was 6 mm², providing the highest oxidation peak current and good repeatability with %RSD values below 5%. Increasing the scan rate from 50 to 250 mV/s enhanced the oxidation peak current from 0.7125 to 0.9705 µA, indicating a diffusion-controlled electrochemical process consistent with the Randles–Sevcik equation. The optimum electrolyte condition was achieved at pH 13.5, where the highest oxidation current (1.198 µA) was obtained due to the enhanced formation of the electroactive Ni(OH)₂/NiOOH redox couple responsible for glucose oxidation. The optimized sensor exhibited excellent analytical performance with a linear regression equation of y = 0.0621x + 0.024 and a coefficient of determination (R²) of 0.9985. The sensor achieved a limit of detection (LOD) of 11,6 mM and a limit of quantification (LOQ) of 13.2mM. Recovery testing in an artificial sweat matrix yielded a recovery value of 97% with %RSD below 5%, indicating good accuracy and precision. These results demonstrate that the nickel-carbon ink modified SPCE integrated with PUDELstat is a reliable, accurate, and low-cost platform for non-invasive glucose monitoring in sweat matrices.