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

Jiashuai Wang; Zhe Li; Shaocun Yan; Xue Yu; Yanqing Ma; Lei Ma

doi:10.1039/c9ra01255a

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

RSC Adv. 2019 May 14;9(26):14797-14808. doi: 10.1039/c9ra01255a. eCollection 2019 May 9.

Authors

Jiashuai Wang¹, Zhe Li^{1

2}, Shaocun Yan^{1

2}, Xue Yu¹, Yanqing Ma^{1

3}, Lei Ma¹

Affiliations

¹ Tianjin International Center for Nanoparticles and Nanosystems, Tianjin University 300072 P. R. China mayanqing@tju.edu.cn.
² School of Chemistry and Chemical Engineering, Shihezi University 832003 P. R. China.
³ State Laboratory of Precision Measuring Technology and Instruments, Tianjin University 300072 P. R. China.

Abstract

An improved activated carbon material is synthesized from nostoc flagelliforme algae (NF) using an acid immersing method. The material has more pores and lower internal resistance compared with raw NF. Hydrofluoric acid can effectively decompose cellulose fibers and remove inorganic impurities, giving the carbon materials high mesopore volumes, which makes electrolyte ions rapidly transfer to the active site on the electrode surface. The specific capacitance of the sample was increased from 200 to 283 F g^-1 after immersing in hydrofluoric acid. In addition, the symmetric supercapacitor shows an excellent energy density of 22 W h kg^-1 at a power density of 80 W kg^-1. The capacitance remains at 101.7% after 10 000 cycles. Furthermore, in order to find the relationship between the biochar structure and electrochemical performance in supercapacitors, an artificial neural network (ANN) method is used for studying the complex synergy mechanism. The specific capacitance is modelled using various input factors like aspect ratio (r _L/D), cellulose ratio (CL(%)), specific surface area (S _BET), pore volume (V _tot), internal resistance (R _s) and so on. The Levenberg-Marquart back propagation algorithm with sigmoid and ReLu active function is adopted to train the model. Random forest is used to analyse the relative importance of every input factor on specific capacitance. Results show that the model can estimate the energy storage with a mean squared error of 4.39 for materials with specific structure. Importance analyses indicate the first three significant variables are S _BET, R _s and V _por. The ANN model can accurately predict the electrical properties of biomass-based carbon materials, and also provide guidance for the selection of energy storage materials in the future.