Alginate embedded 1-D nanocomposite for environmental remediation: AI modeling, kinetics and thermodynamics

Manganese oxide (α-MnO 2 ) molecular sieves exhibiting promising oxidative capabilities have been embedded into novel alginate (AG) beads. AG (a biopolymer matrix) is known for its effective and practical support adsorber. α-MnO 2 nanorods embedded AG beads (100 mg/g) demonstrated complete catalytic removal of 2,4 dichlorophenol (2,4-DCP) within 30 min at ambient temperature. Further, α-MnO 2 was thoroughly characterized via SEM and XRD. α-MnO 2 has one-dimensional nanorod structure of 200±30 nm (length) x 20±10 nm (diameter) with a high surface-to-volume ratio suitable for catalytic applications. The point-of-zero-charge of the α-MnO 2 embedded alginate beads was determined as pH pzc ≈ 6.8, indicating near-neutral surface charge and explaining the observed optimum removal at pH ≈ 7. The adsorption kinetics were investigated using Pseudo first order and well fitted Runge-Kutta mathematical modeling (for R 2 =0.99). The thermodynamic behavior has been studied via Arrhenius plot based on different temperature experiments. The activation energy (E a ) calculated from the Arrhenius plot was 34.7 kJ mol⁻¹, confirming the endothermic nature of the degradation process. Additionally, machine learning (ML) assisted Adaptive Neuro Fuzzy Interference System (ANFIS) logics were applied to a dataset of 380 experimental points to predict adsorption process accurately along with effects of pH, concentration, catalyst amount, reaction time and effect of temperature. Overall, this study opens a pathway of effective artificial intelligence (AI) and mathematical modeling collaboration to scale up environmental remediation.