In this work, The effect of annealing treatment at different temperatures (373, 423 and 473) K and chemical treatment with talwen at different immersion time (40, 60 and 80) min on structural and optical properties of the bulk heterojunction (BHJ) blend copper phthalocyanine tetrasulfonic acid tetrasodium salt/poly dioxyethylenethienylene doped with polystyrenesulphonic acid (CuPcTs/PEDOT:PSS) thin films were investigated. The films were fabricated using spin coating technique. X-ray diffraction (XRD) measurements displayed only one peak at 2θ =4.5o corresponding to (001) direction which has dhkl larger than for standard CuPcTs. The dhkl increase then decrease with increasing annealing temperature and
the time of chemical treatment w

This work evaluates the influence of combining twisted fins in a triple-tube heat exchanger utilised for latent heat thermal energy storage (LHTES) in three-dimensional numerical simulation and comparing the outcome with the cases of the straight fins and no fins. The phase change material (PCM) is in the annulus between the inner and the outer tube, these tubes include a cold fluid that flows in the counter current path, to solidify the PCM and release the heat storage energy. The performance of the unit was assessed based on the liquid fraction and temperature profiles as well as solidification and the energy storage rate. This study aims to find suitable and efficient fins number and the optimum values of the Re and the inlet tem

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par