Application of graphite screen printed electrode modified with dysprosium tungstate nanoparticles in voltammetric determination of epinephrine in the presence of acetylcholine

The current work focuses on the development of a sensitive and selective electrochemical device based on a graphite screen printed electrode modified with Dy_2(WO_4)_3 nanoparticles(DWO/SPE) for the analysis of epinephrine in samples also containing acetylcholine. The study proves that the sensor has excellent electron-mediating behavior in the oxidation of epinephrine in a 0.1 mol/L phosphate buffer solution(PBS)(pH 7.0). The application of the DWO/SPE in differential pulse voltammetry(DPV) is found to lead to distinct response for the oxidation of epinephrine and acetylcholine, with the potentials of the epinephrine and acetylcholine peaks(△E_p) to be 550 mV apart. The detection limits of the method for epinephrine and acetylcholine are 0.5 and 0.7 μmol/L(S/N = 3) and the responses are found to be linear in the concentration ranges of 1.0-900.0 μmol/L and 1.0-1200.0 μmol/L in a PBS buffer(pH = 7.0)respectively. The modified electrode was used for the detection of epinephrine and acetylcholine in real samples and found to produce satisfactory results. These results can be a proof that Dy_2(WO_4)_3 nanoparticles can find promising applications in electrochemical sensors to be used for the analysis of(bio)chemical species.     

Fabrication of platinum coated nanoporous gold film electrode: A nanostructured ultra low-platinum loading electrocatalyst for hydrogen evolution reaction

The electrolytic hydrogen evolution reaction (HER) on platinum coated nanoporous gold film (PtNPGF) electrode is demonstrated. The deposition of platinum occurred as a spontaneous redox process in which a copper layer, obtained by underpotential deposition, was oxidized by platinum ions, which were reduced and simultaneously deposited. The present method could provide a very low Pt-loading electrode and the results demonstrated that ultra thin Pt coating effected efficiently and behaved as the nanostructured Pt for electrocatalytic hydrogen evolution reaction. The loading of Pt was calculated as 4.2 × 10⁻³ μg cm⁻² for PtNPGF electrode. The current density at −0.4 V and −0.8 V vs. Ag/AgCl was as high as 0.66 A μg⁻¹ Pt and 3 A μg⁻¹ Pt, respectively and the j₀ was evaluated as 0.03 mA cm⁻² or 8 mA μg⁻¹ Pt. The results indicated that increasing electrode area had no catalytic effect, but the nanostructure nature of as-fabricated electrode and submonolayer deposition of copper resulted in electrocatalytic activity for PtNPGF electrode.     

Effect of gas diffusion electrode pre-treatment by ultrasonic bath cleaning technique on proton exchange membrane fuel cell performance

Ultrasonic bath cleaning technique was successfully applied for the pre-treatment of the gas diffusion electrode (GDE) before membrane-electrode assembly (MEA). The results show that ultrasonic bath pre-treatment significantly improves MEA performance in all current density regions, especially at the high current density region. This technique also reduces the time of the MEA conditioning, at least 30%. Electrochemical impedance spectroscopy (EIS) results demonstrate that pre-treatment of GDEs by ultrasonic bath technique leads to a decrease in the cell impedance especially in the ohmic resistance, which is very well due to the GDE cleaning process.     

A comparison of artificial neural networks learning algorithms in predicting tendency for suicide

New approaches adopted by behavioral science researchers to use modern modeling and predicting tools such as artificial neural networks have necessitated the study and comparison of the efficiency of different learning algorithms of these networks for various applications. By using well-known and different learning algorithms, this study examines and compares the Perceptron artificial neural network as predicting tendency for suicide based on risk factors within 33 input parameters framework used in neural network. To find the “best” learning algorithm, the algorithms were compared in terms of train and capability. The experimental data were collected through questionnaires distributed among 800 university students. All questionnaires used in this research were standardized with appropriate validity and reliability. The study findings indicated that LM and BFG algorithms had close evaluation in terms of performance index and true acceptance rate (TAR), and they showed higher predictive accuracy than the other algorithms. Furthermore, CFG algorithm had the minimum training time.