Current Analytical Chemistry - Volume 19, Issue 4, 2023

Background: Inexpensive and disposable microfluidic sensing equipment is in strong demand which can detect biomarkers of diseases found in urine or blood. From recent studies, it has been found that multifilament threads can be used for producing low-cost microfluidic devices hence these multifilament threads act as an inexpensive alternative. Thread has various advantages to make it appropriate to be used in microfluidics-based technologies which include its low price, lightweight, easy availability, and hydrophilic nature. The use of any external pumping system is avoided by the presence of capillary channels in threads which allows the easy flow of fluid. Since thread offers more choices of materials over paper and also paper-based microfluidics preparation is expensive therefore thread-based microfluidic sensor has been considered more advantageous over paper-based microfluidic sensors. Methods: Various research reports were collected from search engines like ScienceDirect, Pub-med, ResearchGate, and Google Scholar. Further important outcomes from these reports along with basic experimental setup details have been compiled under different sections of this manuscript. Conclusion: Non-invasive or blood-free diagnosis can reduce the pain and several risk factors compared with the traditional invasive diagnosis so it is gaining more attention regarding health status monitoring. The various applications regarding thread-based devices include the detection of glucose and its determination, diagnosis of diabetes and kidney failure simultaneously, food dyes separation, sweat pH and lactate determination, selective potassium analysis, multiple antibodies detections, an assay of microbes, for acid-base titrations, as ELISA’s platform, diagnosis of infectious diseases, ion sensing, identification of blood types and detection of bio-samples, etc.

In this study, an electrochemical biosensor for the indirect detection of Adenosine triphosphate (ATP) was developed, which was based on the immobilization of the multiwalled carbon nanotubes (MWCNTs) decorated with pyrazole-capped selenium nanoparticles (TRPIDC-CH3 SeNPs) and dual enzyme reaction (hexokinase and glucose oxidase) onto the surface of a bare glassy carbon electrode (GCE) as a working electrode. As confirmed byUltraviolet–visible spectroscopy (UV-Vis), Fourier transform infrared (FTIR) and High-resolution electron microscope (HRTEM), the TRPIDC-CH3 SeNPs successfully green synthesised using Allium sativum cloves and indole pyrazole ligand. The electrochemical study of ATP was performed using cyclic voltammetry (CV) and square wave voltammetry (SWV) techniques on a modified electrode for indirect detection of ATP where the required strong electroactive was [Fe(CN)6]^3-/4-. The phosphate buffer solution (PBS; 0.1 M) was used as a supporting electrolyte at pH 7 containing 1 mM K4[Fe(CN)6]/K3[Fe(CN)6] as the redox probe operated at an average potential of 0.23 V. The electrochemical enzymic biosensor showed outstanding sensitivity, good stability, and satisfactory reproducibility with an average RSD of 2.30%. The ATP was quantifiable in spiked tablets with a limit of detection (LOD) of 0.015 mM and a limit of quantification (LOQ) of 0,050 mM.

Background: In recent years, graphene oxide (GO) and its nanocomposites have shown effective performance in wastewater treatment. Moreover, graphene aerogels (GAs) have excellent properties, such as high surface area, high porosity, low density, high electrical conductivity, and good mechanical properties, due to the combination of three-dimensional porous structures and excellent properties of graphene. Methods: In this study, synthesis of Fe3O4/L-methionine and graphene oxide and graphene aerogel nanocomposites (Fe3O4/L-Met, Fe3O4/L-Met/GO, Fe3O4/L-Met/GA) was performed. Then, the synthesized nanocomposites were confirmed by FT-IR, SEM and BET analyses. The adsorption capacity of cadmium ion by methionine nanocomposites and the effect of various experimental parameters, such as contact time, initial metal ion concentration, and initial pH, on the adsorption process were investigated. Results: The results showed that Fe3O4/L-Met at pH 7 was suitable for Cd (II) removal with 90% removal efficiency. In addition, adsorption capacity experiments at a constant concentration of 50 ppm Cd²⁺ showed that more than 50% of Cd²⁺ ions could be adsorbed by Fe3O4/L-Met and reach equilibrium within 2 hours. Conclusion: Thus, Fe3O4/L-Met/GA showed high adsorption capacity towards Cd²⁺ (212.31 mg/g), which was significantly higher than Fe3O4/L-Met (201.23 mg/g). Finally, adsorption kinetics and isotherm studies were investigated. Adsorption data showed excellent fit with quasi-second order models (R²> 0.99) and Freundlich isotherm models.

Background: Inclusion of multiwall-carbon nanotubes (MWCNTs) as ion-to-electron transducers within the ionophore-doped PVC membrane has a great impact on the stability and robustness of the produced sensor performance compared to conventional liquid-based ISEs. Objective: Solid-contact ion selective electrodes (SC-ISEs) were fabricated and optimized for the assay of sulfacetamide sodium in both ophthalmic eye drops and in rabbit aqueous humor. Methods: 2-hydroxypropyl-β-cyclodextrin (2HP-β-CD) was selected as an ionophore to dope the ionselective membrane to enhance its selectivity towards sulfacetamide. The performance of multiwall-CNTs as ion-to-electron transducer was evaluated by comparing MWCNT-based SC-ISE with control sensor which does not include the MWCNTs. The electrochemical performance characteristics of the proposed sensors were assessed in accordance with the IUPAC recommendations. A green profile assessment of the proposed method was performed using Eco-Scale and AGREES metrics. Results: Inclusion of MWCNT into the sensing membrane improved the performance of the developed sensor. The linearity range was (2.5 x 10^-4 M - 1.0 x 10^-2 M) for both sensors and the LOD was estimated to be 5.6 x 10^-5 M for GCE/ISM(CNT) and 1.5x 10^-4 M for control sensor GCE/ISM. The results of green assessment for both the developed and the official method showed an excellent greenness of the proposed method. Conclusion: The proposed sensor can be applied successfully for the determination of sulfacetamide in eye drops and rabbit aqueous humour.

Background: The spectrophotometric detection of glucose usually requires the use of glucose oxidase (GOD) and horseradish peroxidase (HRP). These natural enzymes have specificity and can react with substrates efficiently and quickly, but their performance is easily influenced by external factors, such as humidity, temperature, and solution pH. In this study, no enzyme method was developed for the detection of glucose. Objective: In this work, gold nanoparticles (AuNPs) and BiVO4 were calcined onto the glass surface, offering excellent glucose oxidase-like activity under light irradiation. Coupled with silver nanoparticles (AgNPs), it can be applied to the colorimetric detection of glucose without the use of any natural enzyme. Methods: The heterostructure of AuNPs and BiVO4 onto glass substrate (G/AuNPs/BiVO4) was synthesized by deposition and calcination at 500°C. It exhibited oxidase-like activity towards glucose oxidation in the presence of oxygen (O2) under light irradiation and then generated gluconic acid and hydrogen peroxide (H2O2). The production of H2O2 could etch AgNPs, resulting in a clear color change of the solution. Results: A decrease in the absorbance showed a good linear relationship with glucose concentration in the range of 20-400 μM, with a detection limit of 5 μM. Conclusion: An enzyme-free method is proposed for the colorimetric detection of glucose. The photoactivated enzyme mimic of G/AuNPs/BiVO4 exhibited good recyclability with water rinsing. This is promising for wide applications in various fields.

In the present investigation, an electrochemical sensing approach based on the electropolymerization modulation of brilliant green on a glassy carbon electrode has been introduced for rapid and sensitive identification of serotonin (SE) and epinephrine (EP) by cyclic and differential pulse voltammetric procedures. Under adequate circumstances, the analytical variable like the pH of the supporting solution, was maintained between the range of 6.2-8.0. Furthermore, the electro-kinetic parameter was surveyed, and the electrode depicted the proportionality between the current intensities with the concentration of analytes with a low detection limit (0.74×10^-6 M for SE and 0.58×10^-6 M for EP). The modulated sensor portrayed the supreme electrocatalytic characteristics toward the simultaneous quantification of SE and EP in a sample mixture.

Background: For many years, seawater desalination technique has been operational to deal with water scarcity. In Boujdour region, located near the Atlantic Ocean southwest of Morocco, most water drinking is produced by a reverse osmosis seawater desalination plant. The permeate conductivity prediction is used to evaluate the performance of desalination plants. Objective: The present paper focuses on the modeling and comparison of the Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) for the prediction of permeate conductivity for a one-year period. Methods: Six input variables are considered, including turbidity, temperature, pH, feed conductivity, feed flow, and transmembrane pressure (TMP). Firstly, the MLR identifies the most important variables influencing the permeate conductivity with the aim of developing a regression model for the dependent and independent parameters. Secondly, the ANN method is examined to analyze the performance of desalination plant. A study of the effect of the number of neurons and the number of hidden layers on the efficiency of the neural network has been made. Results and Conclusion: Results confirm that the MLR and ANN models forecast the permeate conductivity with a suitable coordination coefficient of the real and predicted values. ANN model has been successfully tested for reliability with a correlation coefficient R² of 99.097% and a mean square error (MSE) of 0.002607.