Electroanalysis最新文献

This study evaluates zinc anode substrate materials for zinc–nickel flow batteries, including stainless steel strip, Cu–Ni–Mn alloy, Monel alloy, and Nickel-plated strip. Monel alloy and Nickel-plated steel strip exhibit higher zinc deposition potential, with the Nickel-plated strip showing a low equilibrium potential (E₀ = −1.430 V) and minimal reaction resistance (0.110 Ω), similar to zinc. The Nickel-plated strip also maintains a higher battery capacity after cycling, likely due to the smooth zinc deposition and minimal grain distance, making it the preferred anode substrate.

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.

I have written about different aspects of the responsible and ethical conduct of research (RECR) in the past, also referred to as responsible conduct of research (RCR). I think it is beneficial for us all to be reminded about these important issues and best practices for avoiding pitfalls that might lead to questionable research practices or even research misconduct. We can all agree that RECR is critical for excellence in scholarship and is vital for the public's trust and confidence in science and engineering. The responsible and ethical conduct of research involves not only a responsibility to generate and disseminate knowledge with rigor and integrity, but also a responsibility to (i) conduct peer review with the highest ethical standards, (ii) diligently protect proprietary information and intellectual property from inappropriate disclosure, and (iii) treat students and colleagues fairly and with respect (see https://www.nsf.gov/od/recr.jsp).

Here, I would like to offer some reminders about best practices in authorship (initially published May 2022, https://doi.org/10.1002/elan.202200207). Publishing the product(s) of research work is one of the most important tasks we undertake as scientists. Authorship gives one recognition and credit for work accomplished, necessitates accountability for reported research and scholarship, confers ethical and legal obligations (copyright), and is influential in shaping one's academic career. Electroanalysis seeks to publish original, innovative, and impactful work in the field. For good or bad, we are judged on the number and quality of our published works. The drive to publish work can lead one into making poor decisions regarding the assignment of authorship and or the content presented. Authorship issues remain a concern for editorial teams and publishers.

There are clear guidelines for assigning authorship. These guidelines are generally well accepted as best practices for determining authorship on scholarly work. An individual claiming authorship or being designated as an author on a creative output (e.g., manuscript or book chapter) should meet all the following criteria:

All identified authors are accountable for the study's integrity and the publication's accuracy. Authors should only submit original work. Most journals require that the work not be submitted simultaneously to another journal for consideration. Only when an article has been rejected by or withdrawn from consideration in one journal may it be submitted elsewhere. Authors should avoid fragmentary publication. Dividing research findings into the smallest publishable units might increase an investigator's total number of publications but works against the interests of science. Authors should avoid duplicate publication. Publication of data in more than one journal gives the findings more visibility, but it may also mislead readers into believing that more work has been done in the

This study presents the development of lab-made graphite and silver conductive inks for the fabrication of mask-based printed electrodes. The graphite ink was formulated using glass varnish, graphite powder, acetone, and propylene glycol, whereas the silver ink was composed of silver powder, glass varnish, and acetone. The influence of ink composition, curing temperature, and curing time on the electrical properties of the inks was investigated. The optimized graphite ink containing 6.4% propylene glycol exhibited the best electrochemical performance, with a curing temperature of 40°C for 15 min. Silver ink, used as the pseudo-reference electrode, was cured at 25°C for 5 min. The electrodes were fabricated by printing inks on a polyester substrate, and their electrochemical behavior was evaluated using cyclic voltammetry in a Fe(CN)₆^3−/4− redox probe. Miniaturization of the electrochemical cell was achieved, reducing the working electrode area from 24.54 to 8.35 mm². The electrodes underwent electrochemical pretreatment in an alkaline medium, resulting in improved electron transfer kinetics and increased peak current. Scanning electron microscopy revealed a homogeneous and rough electrode surface with an increased electroactive area after pretreatment. The reproducibility and stability of the electrodes were assessed, and they demonstrated satisfactory performance over multiple cycles and different fabrication batches. The cost analysis showed that lab-made electrodes could be produced at a significantly lower cost compared to commercial electrodes. The graphite and silver inks developed provide a cost-effective and reliable solution for the fabrication of electrodes, offering potential applications in electrochemical sensing and analysis.

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.