The reinforcing and characteristics of interphase as the polymer chains adsorbed on the nanoparticles in polymer nanocomposites

Novel hybrid neuro-fuzzy model to anticipate the heat transfer in a heat exchanger equipped with a new type of self-rotating tube insert

In this investigation, a combination of the wingsuit flying search (WFS) and teaching-learning-based optimization (TLBO) algorithms is developed as a new combinatorial optimization algorithm. The proposed combinatorial algorithm is tested over some well-known benchmark functions and then integrated with the artificial neural network (ANN) to construct a novel hybrid model. After that, the obtained hybrid model is employed to anticipate the experimentally obtained values of the average Nusselt number (Nu), average friction coefficient (f) as well as thermal-hydraulic performance ratio (η), in a heat exchanger equipped with a new type of self-rotating tube insert, against governing parameters. The insert is placed in the tube side of the water heater to heat natural gas. The proposed insert consists of various numbers of self-rotating modules. Indeed, the rotating insert is introduced to create effective secondary sweeping flow on the inner side of the tube. Since this type of tube insert simultaneously provides heat transfer enhancement and undesired pressure drop, a thermal-hydraulic performance ratio is defined to consider both of them. The governing parameters are the number of inserts (0 ≤ N ≤ 30), reservoir's temperature (40 °C  ≤  T_R ≤ 50 °C) as well as Reynolds number (6 × 10³ ≤ Re ≤ 18 × 10³). It was found that the WFS-TLBO enhances the effectiveness of the main ANN in anticipating the Nusselt number (Nu), average friction coefficient (f) as well as performance ratio (η). Moreover, introducing the WFS-TLBO algorithm into the neural network provides an enhancement in the effectiveness of the hybrid models based on the single WFS and TLBO algorithms in anticipating the same parameters.

Potential Use of Chitosan-TiO2 Nanocomposites for the Electroanalytical Detection of Imidacloprid

The detection of toxic insecticides is a major scientific and technological challenge. In this regard, imidacloprid is a neonicotinoid that is a systemic insecticide that can accumulate in agricultural products and affect human health. This work aims to study the properties of chitosan–TiO₂ nanocomposites in which nanoparticles with high surface area serve as molecular recognition sites for electroanalytical imidacloprid detection. We show that the best sensitivity to imidacloprid was obtained using a modified electrode with a chitosan–TiO₂ nanocomposite with a 40 wt.% of TiO₂ nanoparticles. By using a three-phase effective permittivity model which includes chitosan, TiO₂, an interface layer between nanoparticles and a matrix, we showed that nanocomposites with 40 wt.% of TiO₂ the interface volume fraction reaches a maximum. At higher nanoparticle concentration, the sensitivity of the sensor decreases due to the decreasing of the interface volume fraction, agglomeration of nanoparticles and a decrease in their effective surface area. The methodology presented can be helpful in the design and optimization of polymer-based nanocomposites for a variety of applications.