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The thermal stabilization of polyacrylonitrile fibers (FPAN) is one of the most important steps in the production of carbon fibers (CFs). In this paper, new precursor polymers from PAN have been synthesized with different chemical characteristics using a solution polymerization, and FPAN was obtained using an unconventional wet spinning system in the drying and collapsing steps. The effect of different operation conditions, comonomers, and termonomers on the properties of precursor polymers, polymerization reactions, mechanical properties, structural characteristics, and stabilization of the FPAN was studied and analyzed. FTIR and optical microscopy were used to analyze structural changes of FPAN in the thermal stabilization. The impact of the chemical composition of the precursor polymers on the physicochemical characteristics of FPAN and their behavior in the thermal stabilization process were evaluated. In particular, itaconic acid termonomer improved the tensile strength of the fibers from 8.07 to 16.87 cN/dtex, and the extent stabilization increased from 1.81 to 4.6. FTIR indicated that the reaction of stabilization of the terpolymer developed was initiated at a lower temperature compared to that of a commercial precursor polymer.

This study reports the application of a neural network approach for modeling and analyzing the sorption performance of different lignocellulosic wastes, namely jacaranda fruit, plum kernels, and nutshell, for the removal of heavy metal ions (Pb 2+ , Cd 2+ , Ni 2+ , and Zn 2+ ) from aqueous solutions. This artificial neural networks (ANNs) model was used to determine the relevance and importance of both sorbent and pollutant characteristics on the metal sorption kinetics and isotherms. Results of this study highlighted the role of acidic functional groups, lignin composition of tested biomasses, and the pollutant molecular weight in the sorption of heavy metals. The nutshell biomass showed the best sorption properties for heavy metal removal, where its monolayer sorption capacities ranged from 1.0 to 7.0 mg/g. In summary, this study highlights the capabilities of ANN-based models for analyzing and understanding complex but relevant sorption processes for environmental protection and wastewater treatment.

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