Appraisal of Cu(ii) adsorption by graphene oxide and its modelling artificial neural network.

Graphene oxide (GO), as an emerging material, exhibits extraordinary performance in terms of water treatment. Adsorption is a process that is influenced by multiple factors and is difficult to simulate by traditional statistical models. Artificial neural networks (ANNs) can establish highly accurate nonlinear functional relationships between multiple variables; hence, we constructed a three-layered ANN model to predict the removal performance of Cu(ii) metal ions by the prepared GO.

Preparation of new adsorbent-supported Fe/Ni particles for the removal of crystal violet and methylene blue by a heterogeneous Fenton-like reaction.

Prepared material-supported Fe/Ni particles (PM-Fe/Ni) were produced and applied as an adsorbent, reductant and Fenton-like catalyst for removing methylene blue (MB) and crystal violet (CV) from aqueous solutions. Fe/Ni particles were prepared by reducing ferric chloride with sodium borohydride and supported on the produced porous material. Various techniques including X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy analysis (SEM) were employed to characterize the crystal phase, surface area, surface morphology and functional groups.