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

Ullah, Mohammad and Hasan, Md. Munirul and Rasidi, Roslan and Jose, Rajan and Izan Izwan, Misnon (2024) Sustainable graphitic carbon derived from oil palm frond biomass for supercapacitor application: Effect of redox additive and artificial neural network based modeling approach. Journal of Electroanalytical Chemistry, 971 (118570). pp. 1-34. ISSN 1572-6657. (Published)

Preview	Pdf Sustainable graphitic carbon derived from oil palm frond biomass_ABST.pdf Download (115kB) | Preview
	Pdf Sustainable graphitic carbon derived from oil palm frond biomass.pdf Restricted to Repository staff only Download (6MB) | Request a copy

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.

Item Type:	Article
Uncontrolled Keywords:	Electrochemical capacitor; Activated carbon; Machine learning; Artificial intelligent; Electrification
Subjects:	Q Science > Q Science (General) Q Science > QD Chemistry
Faculty/Division:	Faculty of Industrial Sciences And Technology Institute of Postgraduate Studies Centre for Advanced Intelligent Materials Faculty of Computing
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	20 Sep 2024 07:22
Last Modified:	20 Sep 2024 07:22
URI:	http://umpir.ump.edu.my/id/eprint/42349
Download Statistic:	View Download Statistics

Actions (login required)

View Item