Sustainable graphitic carbon derived from oil palm frond biomass for supercapacitor application: Effect of redox additive and artificial neural network‑based modeling approach

Abstract

One-step pyrolyzed graphitic carbon (GC) derived from oil palm frond biomass was synthesized at different durations (1, 3, and 5 h) without utilizing of activating agent. The optimum GC-5 h exhibited a honeycomb-like structure (1.9 nm), high carbon content (84 %), graphitic peak at 2θ ∼ 24.2°, and wide pore size (2.17 nm) suitable to accommodate solvated electrolyte ions. Symmetric supercapacitor (SSC) cells with three redox additives (hydroquinone (HQ), ammonium monovanadate (AM), and potassium ferrocyanide (PF)) in H2SO4 electrolyte are tested. The GC-5 h SSC shows a CS of 595F/g in 0.01 M HQ/H2SO4 electrolyte at a current density of 3 A/g. The cell exhibits an energy density (ED) of 22 Wh kg−1 and a power density (PD) of 2,400 W kg−1. It demonstrates a capacitance retention of 93 % after 10,000 cycles. To develop the intricate interactions between the electrode structure, active operating circumstances, and electrochemical performance of the SSC, an artificial neural network (ANN) approach was applied herein. The model uses the Levenberg-Marquart training method, incorporating the Tanh and Purelin activation functions. The data demonstrate that the constructed ANN model can predict the SSC with values that nearly match the experimental data with an MSE of 6.8122 × 10−5 and R of 0.9989.