Fabrication of Co3O4 from Cobalt/2,6-Napthalenedicarboxylic Acid Metal-Organic Framework as Electrode for Supercapacitor Application.

In this study, cobalt-based metal-organic framework (MOF) powder was prepared via the solvothermal method using 2,6-naphthalenedicarboxylic acid (NDC) as the organic linker and N,N-dimethylformamide (DMF) as the solvent. The thermal decomposition of the pristine cobalt-based MOF sample (CN-R) was identified using a thermogravimetric examination (TGA). The morphology and structure of the MOFs were modified during the pyrolysis process at three different temperatures: 300, 400, and 500 °C, which labeled as CN-300, CN-400, and CN-500, respectively. The results were evidenced via field-emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD). The crystallite size of all samples was calculated using Scherrer's equation. The smallest crystallite size of 7.77 nm was calculated for the CN-300 sample. Fourier transform infrared spectroscopy (FTIR) spectra were acquired for all the samples. The graphical study of the cyclic voltammogram (CV) gave the reduction and oxidation peaks. The charge transfer resistance and ionic conductivity were studied using electrical impedance spectroscopy (EIS). The galvanostatic charge-discharge (GCD) responses of all samples were analyzed. The relatively high specific capacitance of 229 F g-1 at 0.5 A g-1 was achieved in the sample CN-300, whereby 110% of capacitance was retained after 5000 cycles. These findings highlighted the durability of the electrode materials at high current densities over a long cycle.

Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte.

A gel polymer electrolyte system based on phthaloylchitosan was prepared. The effects of process variables, such as lithium iodide, caesium iodide, and 1-butyl-3-methylimidazolium iodide were investigated using a distance-based ternary mixture experimental design. A comparative approach was made between response surface methodology (RSM) and artificial neural network (ANN) to predict the ionic conductivity. The predictive capabilities of the two methodologies were compared in terms of coefficient of determination R² based on the validation data set. It was shown that the developed ANN model had better predictive outcome as compared to the RSM model.