Dendrimers are macromolecules with well-defined three-dimensional structures, sizes and surface charges. In this work, four generations of poly(amidoamine) (PAMAM) dendrimers were investigated at the micro-interface between two immiscible electrolyte solutions (μITIES) to understand their electrochemical responses as simple models of ionised macromolecules. Cyclic voltammetry (CV) across a range of aqueous phase pH revealed that all four generations (G0-G3) presented diffusion-controlled ion-transfer from aqueous to organic phase, while the reverse transfers from organic to aqueous phase varied with both pH and the dendrimer generation. The larger dendrimers (G2 and G3) show an adsorption behaviour at pH ≤ 3.5, but show a diffusional response at pH ≥ 6. On the other hand, the smaller dendrimers (G0 and G1) always show a diffusional response and are not impacted by the pH. This indicates that more highly charged dendrimers condense at the interface. The reverse scan of CVs showed that an increased applied potential was required to remove (desorb) these polycations from the interfaces in comparison to smaller, less charged species. Diffusion coefficients (D) were estimated, showing a decrease with increasing generation. Limits of detection for these dendrimers by CV at the μITIES were 0.4, 0.2, 0.7 and 0.5 μM for G0 to G3, respectively, while differential pulse voltammetry lowered the LODs (0.07, 0.05, 0.09 and 0.08 μM, respectively). These study shows that the μITIES provides a simple way to detect and evaluate the electrochemical behaviour of ionised macromolecules, providing a simple illustration of detection mechanism with diffusion or adsorption processes.

Cancer remains one of the leading causes for death worldwide. Palliative chemotherapy is vital for certain cancer patients, highlighting the critical need for treatment monitoring tools to prevent drug accumulation and mitigate the risk of high toxicity. Therefore, our aim was to evaluate the potential of screen-printed electrodes for the development of sensitive and accurate biosensors for the detection/quantification of antineoplastic drugs. To this purpose, we developed a cisplatin sensor. By functionalizing the gold electrode with human serum albumin and by collecting the electrochemical signal obtained in a H2O2 solution, through voltammetry measurements, we were able to correlate the current measured at 430 mV with the concentration of cisplatin present in human serum samples, with a correlation coefficient of R2 = 0.99. Also, a bleomycin biosensor was developed and proven functional, but further optimization steps were employed in order to improve the accuracy. The developed biosensors have a detection range of 0.0006-43.2 mg/mL for cisplatin and 0.23-7.56 μg/mL for bleomycin in the serum samples. Our preliminary results show that these biosensors can facilitate the real-time monitoring of cisplatin and bleomycin serum levels, allowing healthcare professionals to tailor treatment strategies based on individual patient responses.

In this study, pyridoxine-based polyurethane-modified electrodes were prepared to simultaneously and sensitively measure copper (Cu(II)) and cobalt (Co(II)) ions in complex matrix samples. For the production of the electrodes, firstly, the synthesis of pyridoxine-based polyurethane structures was carried out. In these syntheses, the polymer structure was diversified by using different isocyanates. Polyethyleneglycol-200 (PEG), pyridoxine (B6), and β-cyclodextrin (β-CD) groups were used as the source of polyol. The synthesized polyurethane structures were characterized by different instrumental techniques and used in gold electrode surface modification. Modified sensor surfaces were examined by scanning electron microscopy and atomic force microscopy techniques. The prepared modified sensors were used for the simultaneous detection of Cu(II) and Co(II) ions using the differential pulse voltammetry technique. The limit of detection (LOD), limit of quantitation (LOQ), and R2 values for Cu(II) ions were calculated as 8.81 μM, 29.4 μM, and 0.993, respectively. LOD, LOQ, and R2 values for Co(II) ions were calculated as 9.84 μM, 32.8 μM, and 0.9935, respectively. For repeatability, the relative standard deviation (RSD %) of the prepared simultaneous sensors was determined as 1.54 and 1.71 for Cu(II) and Co(II), respectively. As a result, Cu(II) and Co(II) ions were measured independently and simultaneously with high sensitivity. According to these results, it is predicted that pyridoxine-based polyurethane-modified sensors may be suitable for the simultaneous detection of Cu(II) and Co(II) in medical, food, and agricultural samples.

Electrochemical biosensors have emerged as powerful tools for the ultrasensitive detection of lung cancer biomarkers like carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and alpha fetoprotein (AFP). This review comprehensively discusses the progress and potential of nanocomposite-based electrochemical biosensors for early lung cancer diagnosis and prognosis. By integrating nanomaterials like graphene, metal nanoparticles, and conducting polymers, these sensors have achieved clinically relevant detection limits in the fg/mL to pg/mL range. We highlight the key role of nanomaterial functionalization in enhancing sensitivity, specificity, and antifouling properties. This review also examines challenges related to reproducibility and clinical translation, emphasizing the need for standardization of fabrication protocols and robust validation studies. With the rapid growth in understanding lung cancer biomarkers and innovations in sensor design, nanocomposite electrochemical biosensors hold immense potential for point-of-care lung cancer screening and personalized therapy guidance. Realizing this goal will require strategic collaboration among material scientists, engineers, and clinicians to address technical and practical hurdles. Overall, this work provides valuable insight for developing next-generation smart diagnostic devices to combat the high mortality of lung cancer.

Quantification of trace amounts of iron is of great importance in various fields. In the industrial sector, it is crucial to monitor the release of iron out of corrosion, pickling treatment, and steel manufacturing to address potential environmental and economic challenges. In biological systems, despite its indispensability, it is essential to maintain iron concentration below a specific threshold. Electrochemical (EC) methods provide significant analytical capabilities due to their simplicity, ease of use, and cost-effectiveness. This review focuses on the fundamental principles of EC methods for iron detection, including potentiometry, amperometry, coulometry, voltammetry, and electrochemical impedance spectroscopy (EIS). It further explains the process of obtaining calibration curves, and subsequently, determining the concentration of unknown ions. Additionally, technical notes are presented on selecting the initial signal value, reducing the duration of tests, excluding non-faradaic signals, and extending the linear region with the lowest detection limit. These notes are supported by key findings from relevant case studies.

In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

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.

Pregabalin (PGB) is a γ-aminobutyric acid (GABA) alkylated analog prescribed to treat neuropathic pain, fibromyalgia, and postherpetic neuralgia. Using analytical, spectroscopic methods and molecular docking and molecular dynamics (MD) simulations, a detailed experimental and theoretical investigation was conducted into the binding process and interactions between PGB and double-stranded fish sperm deoxyribonucleic acid (dsDNA). It was evident from the collected experimental results that PGB binds with ds-DNA. PGB attaches to dsDNA via minor groove binding, as demonstrated by the results of electrochemical studies, UV-Vis absorption spectroscopy, and replacement study with ethidium bromide and Hoechst-32588. PGB\'s binding constant (Kb) with dsDNA, as determined by the Benesi-Hildebrand plot, is 2.41×104 ± 0.30 at 298 K. The fluorescence investigation indicates that PGB and dsDNA have a binding stoichiometry (n) of 1.21 ± 0.09. Molecular docking simulations were used in the research to computational determination of the interactions between PGB and dsDNA. The findings demonstrated that minor groove binding was the mechanism by which PGB interacted with dsDNA. Based on the electrochemically responsive PGB-dsDNA biosensor, we developed a technique for low-concentration detection of PGB utilizing differential pulse voltammetry (DPV). The voltammetric analysis of the peak current decrease in the deoxyadenosine oxidation signals resulting from the association between PGB and dsDNA enabled a sensitive estimation of PGB in pH 4.80 acetate buffer. The deoxyguanosine oxidation signals exhibited a linear relationship between 2 and 16 μM PGB. The values for the limit of detection (LOD) and limit of quantitation (LOQ) were 0.57 μM and 1.91 μM, respectively.

Amylin, a pancreatic hormone that is cosecreted with insulin, has been highlighted as a potential treatment target for obesity. Amylin receptors are distributed widely throughout the brain and are coexpressed on mesolimbic dopamine neurons. Activation of amylin receptors is known to reduce food intake, but the neurochemical mechanisms behind this remain to be elucidated. Amylin receptor activation in the ventral tegmental area (VTA), a key dopaminergic nucleus in the mesolimbic reward system, has a potent ability to suppress intake of palatable fat and sugar solutions. Although previous work has demonstrated that VTA amylin receptor activation can dampen mesolimbic dopamine signaling elicited by random delivery of sucrose, whether this is also the case for fat remains unknown. Herein we tested the hypothesis that amylin receptor activation in the VTA of male rats would attenuate dopamine signaling in the nucleus accumbens core in response to random intraoral delivery of either fat or sugar solutions. Results show that fat solution produces a greater potentiation of accumbens dopamine than an isocaloric sucrose solution. Moreover, activation of VTA amylin receptors elicits a more robust suppression of accumbens dopamine signaling in response to fat solution than to sucrose. Taken together these results shed new light on the amylin system as a therapeutic target for obesity and emphasize the reinforcing nature of high-fat/high-sugar diets.

Two analytical methods, inductively coupled plasma mass spectrometry (ICP-MS) combined with high-performance liquid chromatography (HPLC) and voltammetry (VM), for three chemical species of dissolved iodine (iodide, iodate, and total dissolved iodine: TDI) were compared for dozens of coastal seawater samples owing to the compatibility of data between both methods. The median differences in the measured concentrations of TDI, total inorganic dissolved iodine (TII, the sum of iodide and iodate), and iodate between ICP-MS and VM were equivalent to 9.2, 13, and 14%, respectively. These differences were within the ranges that could be explained by the repeated-measurement precision of each measurement method for TDI, TII, and iodate. The difference for iodide was 19%, which was larger than the value based on the repeated-measurement precision for both methods. This is considered to be caused by the chemical instability and lower concentrations of iodide compared to other iodine species in seawater, in addition to the heterogeneity of natural samples. Finally, both methods provided reasonable measurement values for the iodine concentration in natural seawater samples.