Fabrication of activated graphene based electrodes for ultrasensitive simultaneous electrochemical detection of uric acid and dopamine

• Facile and chemical activation methods employed to prepare for activated graphene-modified electrodes • Selective determination of dopamine and uric acid without interference. • Real sample application. • Sufficient enough peak separation around 0.23 V for DA-UA and 0.25 V for DA-UA in lab and human urine samples. • The electrode possesses good stability, reproducibility, sensitivity, and wide linear range. We describe a facile and efficient chemical activation technique for the synthesis of activated graphene-modified electrodes able to perform simultaneous and specific identification of uric acid (UA) and dopamine (DA) biomolecules. To find out suitable agent for this specific electrocatalytic application, we have used three chemicals, namely potassium hydroxide, zinc chloride, and phosphoric acid that have been tested. Cyclic voltammogram (CV), linear sweep voltammogram (LSV), differential pulse voltammogram (DPV), electrochemical impedance spectroscopy (EIS) measurements, field emission scanning electrochemical microscopy (FESEM), and transmission electron microscopy (TEM) techniques are performed to analyze the activated graphene-modified electrodes being utilized as an electrochemical sensor, it can effectively detect dopamine and uric acid simultaneously in the existence of ascorbic acid. The KOH-activated graphene (AGK) exhibits a high electrocatalytic activity towards selectively detecting dopamine and uric acid. In the DPV and LSV studies, the peak gap between dopamine (DA) and uric acid (UA) reaches a maximum of 0.23 V. The calibration curves were calculated, with detection limits of 1.96 µM for DA and 1.97 µM for UA in LSV, and 0.09 µM for DA and 0.19 µM for UA in DPV, respectively. The ranges of the calibration curves are 2–380 µM for LSV and 0.02–400 µM for DPV. The presented AGK-modified screen-printed carbon electrode has been effectively operated to efficiently detect DA and UA selectively and simultaneously in real-time samples performed with acceptable results.