This study describe the effect of temperature on the optical
properties of nickel(ii) phthalocyanine tetrasulfonic acid tetrasodium
salt (NiPcTs) organic thin films which are prepared by spin coating
on indium tin oxide (ITO-glass). The optical absorption spectra of
these thin films are measured. Present studies reveal that the optical
band gap energies of NiPcTs thin films are dependent on the
annealing temperatures. The optical band gap decreases with increase
in annealing temperature, then increased when the temperature rising
to 473K. To enhance the results of Uv-Vis measurements and get
more accurate values of optical energy gaps; the Photoluminescence
spectra of as-deposited and annealed NiPcTs thin fi

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

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

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