Size-Dependent Electrochemistry of Laser-Induced Graphene Electrodes.

Laser-induced graphene (LIG) electrodes have become popular for electrochemical sensor fabrication due to their simplicity for batch production without the use of reagents. The high surface area and favorable electrocatalytic properties also enable the design of small electrochemical devices while retaining the desired electrochemical performance. In this work, we systematically investigated the effect of LIG working electrode size, from 0.8 mm to 4.0 mm diameter, on their electrochemical properties, since it has been widely assumed that the electrochemistry of LIG electrodes is independent of size above the microelectrode size regime. The background and faradaic current from cyclic voltammetry (CV) of an outer-sphere redox probe [Ru(NH3)6]3+ showed that smaller LIG electrodes had a higher electrode roughness factor and electroactive surface ratio than those of the larger electrodes. Moreover, CV of the surface-sensitive redox probes [Fe(CN)6]3- and dopamine revealed that smaller electrodes exhibited better electrocatalytic properties, with enhanced electron transfer kinetics. Scanning electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy showed that the physical and chemical surface structure were different at the electrode center versus the edges, so the electrochemical properties of the smaller electrodes were improved by having rougher surface and more density of the graphitic edge planes, and more oxide-containing groups, leading to better electrochemistry. The difference could be explained by the different photothermal reaction time from the laser scribing process that causes different stable carbon morphology to form on the polymer surface. Our results give a new insight on relationships between surface structure and electrochemistry of LIG electrodes and are useful for designing miniaturized electrochemical devices.

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry.

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.

A colorimetric paper-based optode sensor for highly sensitive and selective determination of thiocyanate in urine sample using cobalt porphyrin derivative.

In this work, a highly sensitive colorimetric paper-based optode for the determination of thiocyanate in urine samples was developed for the first time. The cocktail solution of the optode was composed of 5,10,15,20-tetrakis(4-octyloxyphenyl)porphyrin cobalt(II) complex (L), tridodecylmethylammonium chloride (TDMACl), 2-nitrophenyl octyl ether, and polyvinyl chloride as an ionophore, an ion exchanger, a plasticizer, and a polymer, respectively. The paper-based optode responded to thiocyanate by increasing the blue component in the RGB index and a visible change, with the naked-eye, of the optode color from pink to green was observed. From the central composite design, the optimized conditions that yielded the highest sensitivity were 4.70 mmol/kg TDMACl and 13.75 mmol/kg L. The developed optode sensor was highly selective and responded to thiocyanate over other anions, with a working range of 0.001-5 mM and with a coefficient of determination (R2) of 0.9915. The limits of detection using naked-eye and camera were determined to be 50.0 µM and 1.26 µM, respectively. In addition, the LOD and LOQ estimated from the standard deviation of the blank were 0.65 and 1.87 µM, respectively. Furthermore, this sensor was successfully applied to the detection of thiocyanate in urine samples from non-smokers and smokers. The results were in good agreement with the standard ion chromatography (IC) technique. This developed paper-based optode sensor was simple, low-cost, portable, and easy to use as a sensing device without any complicated instrument.