Electroanalysis最新文献

3D printing, particularly fused deposition modeling, is an important technology applied in the electrochemical field and typically requires surface activation procedures to remove excess of polymeric material and expose the conductive material. The laser ablation method presents advantages, such as low cost, speed, and elimination of chemicals. In this context, this study aims to investigate the modification of graphene/polylactic acid electrode (Gp/PLA) using blue-laser treatment for the improved detection of paracetamol (PAR). 2D Gp/PLA printed layers were deposited on an insulating polycaprolactone substrate to generate a compact three-electrode system in a planar configuration for microliter-drop analysis. The blue-laser-treated electrodes (BL) were obtained using optimized conditions of laser power and speed of 280 mW and 30 mm s⁻¹, respectively. The Gp/PLA-BL electrode was characterized by Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). The SEM images showed the removal of PLA, which was also confirmed by FTIR and XPS spectra. Before the treatment, cyclic voltammograms at 50 mV s⁻¹ of inner-sphere [Fe(CN)₆]^3−/4− redox pair exhibited an ill-defined voltammetric profile (ΔEp = 502 ± 4 mV) while an increase in the reversibility was achieved (ΔEp = 120 ± 1 mV) after the blue-laser ablation. Additionally, the lower charge transfer resistance was measured by electrochemical impedance spectroscopy after the treatment. As a proof-of-concept, analytical curves were constructed for PAR detection in a single drop using both non-treated and treated printed electrodes. An increase in the sensitivity of 2.4-fold was observed after the treatment.

Biofluids that can be noninvasively and frequently collected, such as saliva, have great promise for real-time analyte monitoring at the point of care to inform on patient health. However, analyte quantification in these fluids can be challenging due to their complex composition, that can reduce the signal-to-noise ratio. In the context of electrochemical sensing in saliva, the complexity of saliva can result in signal interference through a high and variable background, such that accurate and reproducible analyte quantification is challenging. Simple analysis algorithms that focus on a single peak feature may work well for analyte quantification in well-defined buffer backgrounds but may not be ideal for analyte quantification in complex biofluids. Motivated by this, for the task of quantifying drug levels in saliva from electrochemical voltammogram measurements, we assessed the performance of five different types of regression models: k-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process (GP), and linear multivariate. We trained and tested the models on hundreds of voltammograms spanning five different analyte concentrations of the antiseizure drug carbamazepine spiked into whole human saliva. For each regression model type, we performed feature selection from nine voltammogram features coupled with hyperparameter tuning, using a performance metric that combined coefficient of determination ($$ and average $$.k For unbiased model assessment, we applied each model to test-set data, using metrics of $$ and $$, and statistically compared model performance using permutation testing. Our analysis (i) identified one critical voltammogram feature associated with the analyte peak that was common across models, but that is not commonly used in voltammogram analysis; (ii) demonstrated that each model's performance was improved by adding between one and two additional voltammogram features; and (iii) indicated that both voltage-based and background current features can improve model accuracy. Test-set results showed that all models produced $$ values above 0.84, but KNN and RF yielded the lowest $$ (19%), significantly better than the linear model (26%). Finally, further model assessment on saliva data from the same individual but collected on a different day (without any additional model training) showed that KNN performed the best with excellent generalizability ($$ of 19%), while RF and the linear model showed substantially degraded performance ($$ values of 25% and 39%, respectively). Overall, our results indicate the high impact potential of machine-learning models to substantially improve accuracy for the quantification of drug levels in saliva over conventional linear regression models.

Titanium dioxide (TiO₂)-based nanocomposites have attracted increasing attention as functional materials for biosensor applications due to their high surface area, biocompatibility, photocatalytic activity, and electron transfer capabilities. These features significantly enhance the sensitivity, specificity, and stability of biosensors across various platforms. This review presents a comprehensive overview of recent advancements in TiO₂-based biosensors, with a focus on three major detection strategies: electrochemical, optical, and electrochemiluminescence (ECL) methods. In the electrochemical domain, TiO₂ nanomaterials have been used to develop sensors capable of detecting analytes such as acrylamide with high sensitivity and fast response times. Optical techniques, including surface plasmon resonance (SPR), have used TiO₂ nanostructures to improve detection of cancer biomarkers such as hepatocellular carcinoma antigens. ECL-based systems utilizing TiO₂ composites show enhanced emission intensity and low detection limits due to improved electron transport properties. Furthermore, the integration of TiO₂ with other nanomaterials—such as silver nanoparticles, graphene quantum dots, and titanium-based hybrids—has led to multifunctional sensing platforms with superior analytical performance. This review also discusses the role of TiO₂ in detecting clinically relevant targets, including carcinoembryonic antigen (CEA), highlighting its utility in early diagnosis, food safety, and environmental monitoring. TiO₂ nanomaterials offer strong potential for next-generation biosensors and point-of-care diagnostic devices due to their versatility, performance, and cost-effectiveness.

Biofluids that can be noninvasively and frequently collected, such as saliva, have great promise for real-time analyte monitoring at the point of care to inform on patient health. However, analyte quantification in these fluids can be challenging due to their complex composition, that can reduce the signal-to-noise ratio. In the context of electrochemical sensing in saliva, the complexity of saliva can result in signal interference through a high and variable background, such that accurate and reproducible analyte quantification is challenging. Simple analysis algorithms that focus on a single peak feature may work well for analyte quantification in well-defined buffer backgrounds but may not be ideal for analyte quantification in complex biofluids. Motivated by this, for the task of quantifying drug levels in saliva from electrochemical voltammogram measurements, we assessed the performance of five different types of regression models: k-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process (GP), and linear multivariate. We trained and tested the models on hundreds of voltammograms spanning five different analyte concentrations of the antiseizure drug carbamazepine spiked into whole human saliva. For each regression model type, we performed feature selection from nine voltammogram features coupled with hyperparameter tuning, using a performance metric that combined coefficient of determination ($$ and average $$.k For unbiased model assessment, we applied each model to test-set data, using metrics of $$ and $$, and statistically compared model performance using permutation testing. Our analysis (i) identified one critical voltammogram feature associated with the analyte peak that was common across models, but that is not commonly used in voltammogram analysis; (ii) demonstrated that each model's performance was improved by adding between one and two additional voltammogram features; and (iii) indicated that both voltage-based and background current features can improve model accuracy. Test-set results showed that all models produced $$ values above 0.84, but KNN and RF yielded the lowest $$ (19%), significantly better than the linear model (26%). Finally, further model assessment on saliva data from the same individual but collected on a different day (without any additional model training) showed that KNN performed the best with excellent generalizability ($$ of 19%), while RF and the linear model showed substantially degraded performance ($$ values of 25% and 39%, respectively). Overall, our results indicate the high impact potential of machine-learning models to substantially improve accuracy for the quantification of drug levels in saliva over conventional linear regression models.

Sapropel and Techirghiol Lake water are an excellent source of organic substances like fulvic acid, which can be extracted and used in the pharmaceutical industry. On-site determination of fulvic acid from lake water and sapropel is valuable for the possibility of exploring the sapropel and water as it is (can serve as daily quality control) for therapeutic purposes, or it can be taken to specialised laboratories for the extraction of fulvic acid, followed by its utilisation in the pharmaceutical industry. An ultrasensitive stochastic sensor based on reduced graphene oxide paste decorated with gold and palladium nanoparticles and modified with quinine was designed, characterised, and validated for the determination of fulvic acid in sapropel and also in the Techirghiol Lake water. The sensor can be used on a wide concentration range, from 5.00 fg mL⁻¹ to 5.00 μg mL⁻¹, with a high sensitivity (1.97 × 10⁸s⁻¹ g⁻¹ mL). High recovery values (>99.00%) were recorded for the determination of fulvic acid in sapropel and in the Techirghiol Lake water. Validation of the proposed sensor and screening method for fulvic acid is done versus an HPLC method. The on-site measurements with the ultrasensitive stochastic sensor will contribute to the reliable determination of the quality of sapropel and water in real time.

Titanium dioxide (TiO₂)-based nanocomposites have attracted increasing attention as functional materials for biosensor applications due to their high surface area, biocompatibility, photocatalytic activity, and electron transfer capabilities. These features significantly enhance the sensitivity, specificity, and stability of biosensors across various platforms. This review presents a comprehensive overview of recent advancements in TiO₂-based biosensors, with a focus on three major detection strategies: electrochemical, optical, and electrochemiluminescence (ECL) methods. In the electrochemical domain, TiO₂ nanomaterials have been used to develop sensors capable of detecting analytes such as acrylamide with high sensitivity and fast response times. Optical techniques, including surface plasmon resonance (SPR), have used TiO₂ nanostructures to improve detection of cancer biomarkers such as hepatocellular carcinoma antigens. ECL-based systems utilizing TiO₂ composites show enhanced emission intensity and low detection limits due to improved electron transport properties. Furthermore, the integration of TiO₂ with other nanomaterials—such as silver nanoparticles, graphene quantum dots, and titanium-based hybrids—has led to multifunctional sensing platforms with superior analytical performance. This review also discusses the role of TiO₂ in detecting clinically relevant targets, including carcinoembryonic antigen (CEA), highlighting its utility in early diagnosis, food safety, and environmental monitoring. TiO₂ nanomaterials offer strong potential for next-generation biosensors and point-of-care diagnostic devices due to their versatility, performance, and cost-effectiveness.

In this study, carbon nitride-multiwalled carbon nanotubes/ MXenes (CN-MWCNTs/Ti₃C₂) nanocomposites with excellent properties were synthesized by a convenient method and characterized using various methods. By modifying the CN-MWCNTs/Ti₃C₂ composites on glassy carbon electrode, an electrochemical sensor capable of sensitive and rapid detection of hesperidin (HPD) was established. Under the optimal conditions, the sensor showed excellent detection performance for HPD (0.1 M PBS (pH 7.0)). The linear range was 0.05–503 μM, and the detection limit (S/N = 3) was 0.017 μM. In addition, the sensor has the advantages of good immunity to interference, stability and reproducibility.

In this study, carbon nitride-multiwalled carbon nanotubes/ MXenes (CN-MWCNTs/Ti₃C₂) nanocomposites with excellent properties were synthesized by a convenient method and characterized using various methods. By modifying the CN-MWCNTs/Ti₃C₂ composites on glassy carbon electrode, an electrochemical sensor capable of sensitive and rapid detection of hesperidin (HPD) was established. Under the optimal conditions, the sensor showed excellent detection performance for HPD (0.1 M PBS (pH 7.0)). The linear range was 0.05–503 μM, and the detection limit (S/N = 3) was 0.017 μM. In addition, the sensor has the advantages of good immunity to interference, stability and reproducibility.

Sapropel and Techirghiol Lake water are an excellent source of organic substances like fulvic acid, which can be extracted and used in the pharmaceutical industry. On-site determination of fulvic acid from lake water and sapropel is valuable for the possibility of exploring the sapropel and water as it is (can serve as daily quality control) for therapeutic purposes, or it can be taken to specialised laboratories for the extraction of fulvic acid, followed by its utilisation in the pharmaceutical industry. An ultrasensitive stochastic sensor based on reduced graphene oxide paste decorated with gold and palladium nanoparticles and modified with quinine was designed, characterised, and validated for the determination of fulvic acid in sapropel and also in the Techirghiol Lake water. The sensor can be used on a wide concentration range, from 5.00 fg mL⁻¹ to 5.00 μg mL⁻¹, with a high sensitivity (1.97 × 10⁸s⁻¹ g⁻¹ mL). High recovery values (>99.00%) were recorded for the determination of fulvic acid in sapropel and in the Techirghiol Lake water. Validation of the proposed sensor and screening method for fulvic acid is done versus an HPLC method. The on-site measurements with the ultrasensitive stochastic sensor will contribute to the reliable determination of the quality of sapropel and water in real time.

Iron-nitrogen-carbon (Fe-NC) catalysts, particularly those with Fe-N₄ coordination moieties, are the most promising alternatives to commercial Pt@C for oxygen reduction reaction (ORR) in green energy conversion. The acid etching strategy is an effective and simple strategy to break the symmetric coordination of Fe-N₄ on the carbon substrate to further enhance the activity. Herein, a superior Fe-NC catalyst with undercoordinated Fe-N₂ moieties was produced through a concentration-controlled acid etching strategy, following an underlying quantitative indicator (I_D/I_G) to regulate its defect degree accurately. Due to the defect-rich and porous carbon structure to accelerate the mass transfer, this Fe-N₂ catalyst exhibited an admirable half-wave potential (E_1/2) of 0.85 V_RHE versus 0.87 V_RHE for commercial Pt@C, and a better stability and a higher limiting current density (−6.3 mA cm⁻²) in alkaline conditions, outperforming the other involved Fe-NCs and the Pt@C. This work provides an acid etching strategy to accurately control the defect degree and break the symmetrical Fe-N₄ coordination structure of Fe-NCs for enhancing the ORR activity.