Electrochemical Detection of Cortisol by Silver Nanoparticle-Modified Molecularly Imprinted Polymer-Coated Pencil Graphite Electrodes.

The sensitive cortisol detection by an electrochemical sensor based on silver nanoparticle-doped molecularly imprinted polymer was successfully improved. This study describes the method development for cortisol detection in both aqueous solution and biological samples using molecularly imprinted poly(hydroxyethyl methacrylate-N-methacryloyl-(l)-histidine methyl ester)-coated pencil graphite electrodes modified with silver nanoparticles (AgNPs) by differential pulse voltammetry (DPV). The cortisol-imprinted pencil graphite electrode (PGE) has a large surface area because of doped AgNPs with enhanced electroactivity. The prepared molecularly imprinted polymer was characterized by scanning electron microscopy. The DPV response of the synthesized electrode with outstanding electrical conductivity was clarified. Cortisol-imprinted polymer-coated PGEs (MIP), cortisol-imprinted polymer-coated PGEs with AgNPs (MIP@AgNPs), and nonimprinted polymer-coated PGEs with AgNPs (NIP@AgNPs) were evaluated for sensitive and selective detection of cortisol in aqueous solution. Five different cortisol concentrations (0.395, 0.791, 1.32, 2.64, and 3.96 nM) were applied to the MIP@AgNPs, and signal responses were detected by the DPV with a regression coefficient (R2) value of 0.9951. The modified electrode showed good electrocatalytic activity toward cortisol for the linear concentration range from 0.395 to 3.96 nM, and a low limit of detection was recorded as 0.214 nM. The results indicate that the MIP@AgNPs sensor has great potential for sensitive and selective cortisol determination in biological samples.

Machine learning-based models for the qualitative classification of potassium ferrocyanide using electrochemical methods.

Iron is one of the trace elements that plays a vital role in the human immune system, especially against variants of SARS-CoV-2 virus. Electrochemical methods are convenient for the detection due to the simplicity of instrumentation available for different analyses. The square wave voltammetry (SQWV) and differential pulse voltammetry (DPV) are useful electrochemical voltammetric techniques for diverse types of compounds such as heavy metals. The basic reason is the increased sensitivity by lowering the capacitive current. In this study, machine learning models were improved to classify concentrations of an analyte depending on the voltammograms obtained alone. SQWV and DPV were used to quantify the concentrations of ferrous ions (Fe+2) in potassium ferrocyanide (K4Fe(CN)6), validated by machine learning models for the data classifications. The greatest classifier algorithms models Backpropagation Neural Networks, Gaussian Naive Bayes, Logistic Regression, K-Nearest Neighbors Algorithm, K-Means clustering, and Random Forest were used as data classifiers, based on the data sets obtained from the measured chemical. Once competed to other algorithms models used previously for the data classification, ours get greater accuracy, maximum accuracy of 100% was obtained for each analyte in 25 s for the datasets.