Modifying the microstructure of algae-based active carbon and modelling supercapacitors using artificial neural...

Area of Science:

• Materials Science
• Electrochemistry
• Biomass Conversion

Background:

• Developing advanced carbon materials for energy storage is crucial.
• Nostoc flagelliforme algae (NF) presents a potential biomass precursor for activated carbon.
• Existing methods for NF-derived carbon may have limitations in pore structure and conductivity.

Purpose of the Study:

• To synthesize an improved activated carbon material from NF using an acid immersion method.
• To enhance the electrochemical performance of NF-derived carbon for supercapacitor applications.
• To establish a predictive model for the relationship between biochar structure and electrochemical properties.

Main Methods:

• Synthesis of activated carbon from NF via hydrofluoric acid immersion.
• Characterization of material properties including porosity and internal resistance.
• Fabrication and testing of symmetric supercapacitors.
• Application of artificial neural network (ANN) with Levenberg-Marquart algorithm for performance prediction.

Main Results:

• Acid treatment significantly increased specific capacitance from 200 to 283 F g^-1.
• The supercapacitor achieved an energy density of 22 W h kg^-1 at 80 W kg^-1 power density.
• Capacitance retention was excellent at 101.7% after 10,000 cycles.
• ANN model accurately predicted energy storage (MSE of 4.39), identifying specific surface area, internal resistance, and pore volume as key factors.

Conclusions:

• Hydrofluoric acid treatment effectively enhances the electrochemical performance of NF-derived activated carbon.
• The developed material is suitable for high-performance supercapacitors with excellent stability.
• ANN modeling provides a valuable tool for predicting and optimizing biomass-derived energy storage materials.