Abstract:
Recently, combining the synergistic properties of two-dimensional (2D) and zero-dimensional (0D) materials has attracted significant attention for energy applications. Here, we report the synthesis and characterization of partially oxidised-MXene quantum dots (PO-MXQDs) and reduced graphene oxide composite (PO-MXQDs/rGO) and investigate the resulting composites for energy storage applications towards supercapacitors. Density functional theory (DFT) simulations are used to examine the electrical and structural features of PO-MXQDs/rGO by predicting the chemical interaction involving charge transfer from PO-MXQDs to rGO. The multilayer Artificial Neural Network (ANN) model with hyperparameter tuning was developed at different input parameters to optimize material compositions, concentrations, and types of electrolytes to obtain the suitable conditions for best supercapacitive performance. The optimized PO-MXQDs/rGO showed a specific capacitance value of 1137.8 F.g- 1 and was utilized for the fabrication of flexible micro-supercapacitors (FMSCs) device through the maskassisted vacuum filtration process and asymmetric coin-cell type supercapacitor devices (ACCDs). The FMSCs deliver a higher areal capacitance (5.26 mF.cm- 2), high areal energy density (0.46 mu Wh.cm- 2), high areal power density (210.48 mu W.cm- 2), and the FMSC device has ultra-high cyclic stability up to 30000 cyclic voltammetry (CV) cycles. This method provides an in-depth study on developing 0D-2D hybrid materials with the wisdom of machine learning (ML) and DFT toward energy storage applications.