We have developed a highly sensitive sensor of ZnFe₂O₄/reduced graphene oxide (ZnFe₂O₄/RGO) nanocomposite for electrochemical detection of hydrazine, fabricated by a simple hydrothermal protocol. Subsequently, a screen-printed electrode (SPE) surface was modified with the proposed nanocomposite (ZnFe₂O₄/RGO/SPE), and revealed an admirable electrocatalytic capacity for hydrazine oxidation. The ZnFe₂O₄/RGO/SPE sensor could selectively determine micromolar hydrazine concentrations. The as-produced sensor demonstrated excellent ability to detect hydrazine due to the synergistic impacts of the unique electrocatalytic capacity of ZnFe₂O₄ plus the potent physicochemical features of RGO such as manifold catalytic sites, great area-normalized edge-plane structures, high conductivity, and large surface area. The hydrazine detection using differential pulse voltammetry exhibited a broad linear dynamic range (0.03–610.0 µM) with a low limit of detection (0.01 µM).

This work develops a two-step model for the conductivity of polymer carbon nanotube (CNT) nanocomposites (PCNT) assuming the properties of tunneling and interphase regions.

An extensive variety of chemical engineering processes include the transfer of heat energy. Since increasing the effective contact surface is known as one of the popular manners to improve the efficiency of heat transfer, the attention to the nanofluids has been attracted. Due to the difficulty and high cost of an experimental study, researchers have been attracted to fast computational methods. In this work, Adaptive neuro-fuzzy inference system and least square support vector machine algorithms have been applied as a comprehensive predictive tool to forecast the nanofluids thermal conductivity in terms of diameter, temperature, the thermal conductivity of the base fluid, the thermal conductivity of nanoparticle and volume fraction. To this end, a large and comprehensive experimental databank contains 1109 data points have been collected from reliable sources. The particle swarm optimization is utilized to reach the best structures of the proposed algorithms. A comprehensive statistical and graphical investigations are carried out to prove the accuracy and ability of proposed models. In addition, the comparisons outputs indicate that the least square support vector machine algorithm has the best performance among the existing correlations and Adaptive neuro-fuzzy inference system algorithms for forecasting thermal conductivity of different nanofluids.