Production of cellulose from Aegagropila Linnaei macro-algae: Chemical modification, characterization and application for the bio-sorptionof cationic and anionic dyes from water.

Marine algae wereprovedto begoodadsorbentsformany pollutants. In this work, Aegagropila Linnaei (Aegagropila L.) was treated with sodium hydroxideand sodium chlorite.

[λ‑Carrageenan‑calcium phosphate] and [sodium alginate‑calcium phosphate] modified with dimethyl diallyl ammonium chloride and diallylamin co-polymer as efficient adsorbents of anionic dyes.

Alginate and carrageenan bio-polymers have been proved to be, only, good sorbents of cationic dyes. In this study, we reported the synthesis of [λ‑carrageenan‑calcium phosphate] and [sodium alginate‑calcium phosphate] modified with dimethyl diallyl ammonium chloride and diallylamin co-polymer, which could be used as effective adsorbents of anionic dyes. Evidence of chemical modification was proved through Fourier Transform Infrared Spectroscopy (FT-IR) band shifting, peaks broadening in X-Ray Diffraction (XRD) and the change in thermal event (TGA).

Populus tremula, Nerium oleander and Pergularia tomentosa seed fibers as sources of cellulose and lignin for the bio-sorption of methylene blue.

Cellulose-based substrates could represent potential funds for the sorption of pollutants. Herein, methylene blue was selected for demonstrating the bio-sorption efficiency of Nerium oleander, Pergularia tomentosa and Populus tremula seed fibers. Their cellulose contents were 45%, 43.

Improved removal of dyes by [sodium alginate/4-methyl-2-(naphthalen-2-yl)-N-propylpentanamide-functionalized ethoxy-silica] composite gel beads.

In this paper, the synthesis of [sodium alginate/4-methyl-2-(naphthalen-2-yl)-N-propylpentanamide-functionalized ethoxy-silica] composite gel beads is described. The characterizations were carried out using Fourier Transform InfraRed, Scanning Electron Microscopy, point zero charge and water content. The morphology of the alginate gel beads are changed after functionalization and are characterized by aggregates of different sizes and irregular shapes.

Convergence analysis of the alternating RGLS algorithm for the identification of the reduced complexity Volterra model.

In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way.