| description abstract | Lithium-ion batteries (LIBs) are the most reliable energy storage devices nowadays because of their high energy density, long life cycle, and low self-discharge rate. But still, the safety concern is a significant problem in the area. When talking about LIB safety, thermal effects come first; this leads to thermal runaway, fires, and explosions. The critical component of LIB that has a great role in safety is the separator, which serves the purpose of preventing direct contact between the positive and negative electrodes while enabling the movement of lithium ions. This work aimed to find naturally available cellulose material for the LIB separator and to predict the performance of the material by artificial neural network (ANN) for better control of thermal problems that happen with traditional polymer separator materials. The cellulose derived from banana peels is isolated and characterized for its potential use as a separator material. The study conducts the four selected characterization approaches, scanning electronics microscopy (SEM) with three different resolutions to assess the morphology of the extracted cellulose, differential scanning calorimetry (DSC) to measure the heat flow with temperature change on the cellulose and the value obtained 231.22 J/g at a maximum temperature of 323.18 °C, thermogravimetric analysis (TGA) was used to examine the weight loss of the cellulose with respect to temperature variation, which results in a weight loss of 59.37% when the temperature reaches 235 °C, which is considered favorable, and a differential thermal analysis (DTA) was used to know the temperature difference in the banana peel cellulose (BPC), which results in a temperature of 330.23 °C. This morphological and thermal analysis technique for the BPC is used to determine the heat-related properties of the BPC, including phase transitions, thermal stability, and reaction. In addition, these results show BPC as an alternative material for separators in comparison to the existing polymer-based materials. Furthermore, these experimental results are used to train an ANN to predict the performance of BPC material using a binary classification. Because of the training process, 97.58% accuracy was achieved. | |
