SiC Electrochemical Sensor Validation for Alzheimer Aß42 Antigen Detection.

Alzheimer's disease (AD) is a neurodegenerative disease with only late-stage detection; thus, diagnosis is made when it is no longer possible to treat the disease, only its symptoms. Consequently, this often leads to caregivers who are the patient's relatives, which adversely impacts the workforce along with severely diminishing the quality of life for all involved. It is, therefore, highly desirable to develop a fast, effective and reliable sensor to enable early-stage detection in an attempt to reverse disease progression. This research validates the detection of amyloid-beta 42 (Aß42) using a Silicon Carbide (SiC) electrode, a fact that is unprecedented in the literature. Aß42 is considered a reliable biomarker for AD detection, as reported in previous studies. To validate the detection with a SiC-based electrochemical sensor, a gold (Au) electrode-based electrochemical sensor was used as a control. The same cleaning, functionalization and Aß1-28 antibody immobilization steps were used on both electrodes. Sensor validation was carried out by means of Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) aiming to detect an 0.5 µg·mL-1 Aß42 concentration in 0.1 M buffer solution as a proof of concept. A repeatable peak directly related to the presence of Aß42 was observed, indicating that a fast SiC-based electrochemical sensor was constructed and may prove to be a useful approach for the early detection of AD.

Wearable electrochemical biosensors to measure biomarkers with complex blood-to-sweat partition such as proteins and hormones.

Smart electronic devices based on micro-controllers, also referred to as fashion electronics, have raised wearable technology. These devices may process physiological information to facilitate the wearer's immediate biofeedback in close contact with the body surface. Standard market wearable devices detect observable features as gestures or skin conductivity. In contrast, the technology based on electrochemical biosensors requires a biomarker in close contact with both a biorecognition element and an electrode surface, where electron transfer phenomena occur. The noninvasiveness is pivotal for wearable technology; thus, one of the most common target tissues for real-time monitoring is the skin. Noninvasive biosensors formats may not be available for all analytes, such as several proteins and hormones, especially when devices are installed cutaneously to measure in the sweat. Processes like cutaneous transcytosis, the paracellular cell-cell unions, or even reuptake highly regulate the solutes content of the sweat. This review discusses recent advances on wearable devices based on electrochemical biosensors for biomarkers with a complex blood-to-sweat partition like proteins and some hormones, considering the commented release regulation mechanisms to the sweat. It highlights the challenges of wearable epidermal biosensors (WEBs) design and the possible solutions. Finally, it charts the path of future developments in the WEBs arena in converging/emerging digital technologies.

Electrochemical monitoring of Acidithiobacillus thiooxidans biofilm formation on graphite surface with elemental sulfur.

Inorganic wastewaters and sediments from the mining industry and mineral bioleaching processes have not been fully explored in bioelectrochemical systems (BES). Knowledge of interfacial changes due to biofilm evolution under acidic conditions may improve applications in electrochemical processes, specifically those related to sulfur compounds. Biofilm evolution of Acidithiobacillus thiooxidans on a graphite plate was monitored by electrochemical techniques, using the graphite plate as biofilm support and elemental sulfur as the only energy source. Even though the elemental sulfur was in suspension, S0 particles adhered to the graphite surface favoring biofilm development. The biofilms grown at different incubation times (without electric perturbation) were characterized in a classical three electrode electrochemical cell (sulfur and bacteria free culture medium) by non-invasive electrochemical impedance spectroscopy (EIS) and cyclic voltammetry. The biofilm structure was confirmed by Environmental Scanning Electrode Microscopy, while the relative fractions of exopolysaccharides and extracellular hydrophobic compounds at different incubation times were evaluated by Confocal Laser Scanning Microscopy. The experimental conditions chosen in this work allowed the EIS monitoring of the biofilm growth as well as the modification of Extracellular Polymeric Substances (EPS) composition (hydrophobic/ exopolysaccharides EPS ratio). This strategy could be useful to control biofilms for BES operation under acidic conditions.

Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN).

In this work, three models based on Artificial Neural Network (ANN) were developed to describe the behavior for the inhibition corrosion of bronze in 3.5% NaCl + 0.1 M Na2SO4, using the experimental data of Electrochemical Impedance Spectroscopy (EIS). The database was divided into training, validation, and test sets randomly. The parameters process used as the inputs of the ANN models were frequency, temperature, and inhibitor concentration. The outputs for each ANN model and the components in the EIS spectrum (Zre, Zim, and Zmod) were predicted. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and linear in the output layer, while the Levenberg-Marquardt algorithm was applied to determine the optimum values of the weights and biases. The statistical analysis of the results revealed that ANN models for Zre, Zim, and Zmod can successfully predict the inhibition corrosion behavior of bronze in different conditions, where what was considered included variability in temperature, frequency, and inhibitor concentration. In addition, these three input parameters were keys to describe the behavior according to a sensitivity analysis.