Nanogreen is the new future: the conversion of lignin and lignocellulosic wastes into nanomaterials

Adsorption of contaminants by nanomaterials synthesized by green and conventional routes: a critical review

Nanomaterials, due to their large surface area and selectivity, have stood out as an alternative for the adsorption of contaminants from water and effluents. Synthesized from green or traditional protocols, the main advantages and disadvantages of green nanomaterials are the elimination of the use of toxic chemicals and difficulty of reproducing the preparation of nanomaterials, respectively, while traditional nanomaterials have the main advantage of being able to prepare nanomaterials with well-defined morphological properties and the disadvantage of using potentially toxic chemicals. Thus, based on the particularities of green and conventional nanomaterials, this review aims to fill a gap in the literature on the comparison of the synthesis, morphology, and application of these nanomaterials in the adsorption of contaminants in water. Focusing on the adsorption of heavy metals, pesticides, pharmaceuticals, dyes, polyaromatic hydrocarbons, and phenol derivatives in water, for the first time, a review article explored and compared how chemical and morphological changes in nanoadsorbents synthesized by green and conventional protocols affect performance in the adsorption of contaminants in water. Despite advances in the area, there is still a lack of review articles on the topic.

Optimization of biomethane production from sweet sorghum bagasse using artificial neural networks combined with particle swarm algorithm

In the face of international movement away from fossil fuels caused pollution menace, many research labs are rushing towards next big breakthrough via effective biorefinery development employing non-edible agro-residues as feedstock. This work aims to evaluate and optimize the methane potential of underutilized full strength sweet sorghum bagasse (SSB) via anaerobic digestion (AD). Biochemical methane potential assays are set up for SSB AD under mesophilic and thermophilic conditions at four substrate-o-inoculum ratios (SIR) 3, 5, 7, and 9 with pH 6.5, 7.5, and 8.5 and with 80, 90, and 100 rpm mixing speed over 50 days. SIR 5 produced the highest cumulative biomethane yield of 4.25 L methane g-1 VS with a shorter lag time of 7.5 days and technical digestion time of 24 days. The influence of physio-chemical parameters on AD process dynamics is supported with 16s rRNA metagenomic sequencing. Based on complete experimental data sets, two artificial neural network (ANN) models are developed to identify the relevant significance of process parameters and to predict bagasse methane potential. Further, the developed ANN model is integrated with particle swarm optimization algorithm to create ideal AD process operating conditions which maximize the target variable, biomethane. The trained and cross-validated ANN-PSO model showed good-fit-accuracy with R2 > 0.995 and demonstrated satisfactory performance in the biomethane yield prediction from AD of non-edible agri-residues.