Artificial intelligence application in adsorption of uremic toxins: Towards the eco-friendly design of highly efficient with potential applications as hemodialysis membranes

Abstract

Six Functionalized Activated Carbon Cloths (FACCs) were designed to obtain fundamental information for training a Bayesian Regularized Artificial Neural Network (BRANN) capable of predicting adsorption capacity of the FACCs to synthesize tailor-made materials with potential application as dialysis membranes. Characterization studies showed that FACCs have a high surface area (1354-2073 m² g^-1) associated with increased microporosity (W_{0, average}: 0.57 cm³ g^-1). Materials are carbonaceous, with a carbon content between 69 and 92%. Chemical treatments modify the pH_pzc of materials between 4.1 and 7.8 due to incorporating functional groups on the surface (C=O, -COOH, -OH, -NH, -NH₂). Uremic toxins tests showed a high elimination rate of p-cresol (73 mg g^-1) and creatinine (90 mg g^-1) which is not affected by the matrix (aqueous solution and simulated serum). However, in the case of uric acid, adsorption capacity decreased from 143 mg g^-1 to 71 mg g^-1, respectively. When comparing the kinetic constants of the adsorption studies in simulated serum versus the studies in aqueous solution, it can be seen that this does not undergo significant changes (0.02 min^-1), evidencing the versatility of the material to work in different matrices. The previous studies, in combination with characterization of the materials, allowed to establish the adsorption mechanism. Thus, it permitted to train the BRANN to obtain mathematical models capable to predict the kinetic adsorption of the toxins studied. It is concluded that the predominant adsorption mechanism is due to π-π interactions between the adsorbate unsaturations with the material's pseudo-graphitic planes. Results show that FACCs are promising materials for hemodialysis membranes. Finally, taking into consideration the adsorption capacities and rates, as well as the semiquantitative analysis of the environmental impact associated with the preparation of the adsorbents, the best adsorbent (CC, Eco-Scale = 91.5) was selected. The studies presented show that the material is eco-friendly and highly efficient in the elimination of uremic toxins.

Keywords: Adsorption mechanism; Bayesian regularized artificial neural network; Creatinine; P-cresol; Uric acid.