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In this study, we reported the construction of Gold Nanospike (AuNS) structures on the surface of screen-printed carbon electrode (SPCE) used for non-enzymatic electrochemical detection. This modification was prepared with a one-step electrodeposition method by controlling the electrodeposition parameters, such as applied potential and deposition time, via Constant Potential Amperometry (CPA). Those parameters and precursor solution concentration were varied to investigate the optimum electrodeposition configuration. The results confirmed that AuNS were homogenously deposited and well-dispersed on the working electrode surface of SPCE. The AuNS-modified SPCE was implemented as a non-enzymatic sensor toward dopamine and could enhance the electrocatalytic ability compared with the bare SPCE. Further examination shows that the sensing performance of the AuNS-modified SPCE produced an increase in electrochemical surface area (ECSA) at 17.25 times higher than the bare electrode, a sensitivity of 0.056 µA mM−1 cm−2 with a wide linear range of 0.2–50 µM and a detection limit of 0.33 µM. In addition, AuNS-modified SPCE can selectively detect dopamine among other interfering analytes such as ascorbic acid, urea, and uric acid, which commonly coexist in the body fluid. This work demonstrated that AuNS-modified SPCE is a prospective sensing platform for non-enzymatic dopamine detection.

Electrochemical method is often chosen to analyze various bio-chemical reactions due to its operation simplicity with versatile applications. Thus, for this purpose, the procurement of potentiostat, an electrochemical-based analysis instrument, also needs to focus on the low price, but maintaining its rich analysis features. In this paper, we designed a low-cost and portable potentiostat system based on the ESP32 microcontroller. Even with the low-cost system design, it supports various electrochemical measurement methods, i.e. cyclic voltammetry (CV), linear sweep voltammetry (LSV), square wave voltammetry (SWV), differential pulse voltammetry (DPV), normal pulse voltammetry (NPV), and the observation of electrochemical current against time based on chronoamperometry measurement method. The potentiostat performance was assessed by the electrochemical signal accuracy and errors. Furthermore, the measurements can also be carried out semi-parallelly within three measurement channels simultaneously. The results showed that it could produce voltammograms with an average accuracy of more than 90%, compared to the commercial Emstat Pico. Even though having this comprehensive and extensive electrochemical analysis, the cost of the total system realization could be downsized to only USD 21.4.

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In this article, we presented the development of fully modular microfluidic flow cells for an electrochemical using 3D printing. The proposed devices are potential for electrochemical measurements using a small sample volume on a fully portable, reusable, simply fabricated, low-cost, PDMS-free, and leakage-free flow cell. This concept offers a simple, controllable sample over the conventional electrochemical platform with a three-electrode system, which requires a considerable volume of samples or a non-controllable drop cast method for sequential protocols. We demonstrated an easy alignment and lock, namely, click-and-fit modular microfluidics, for quick and easy assembly and disassembly of flow cell modules using magnetic force instead of the screw, polymer glue, or resin. Two microfluidic modules were presented using tube- and syringe-flow cells (TFC and SFC) to integrate the screen-printed carbon electrodes (SPCE) in the electrochemical sensor. The proof-of-concept of the integrated sensor-microfluidic platforms was conducted under cyclic voltammetry using a tiny volume of a ferricyanide redox probe at only ~50 µL, differential pulse voltammetry, and square wave voltammetry. Implementing the proposed click-and-fit microfluidic modules in electrochemical detection achieves higher current peaks than droplet measurements. These flow cell modules are promising for biosensing applications using a small volume of physiological fluid samples.