The green, ultrafine cellulose-based porous nanofibrous membranes for efficient heavy metal ion removal through incorporation of chitosan by various electrospinning ways

The rapid global industrialization worsens the contamination of heavy metals in aquatic ecosystems on the earth. In this study, the green, ultrafine cellulose-based porous nanofibrous membranes for efficient heavy metal removal through incorporation of chitosan by the conventional and core-shell electrospinning ways were firstly obtained. The relations among parameters of electrospun solution, micro-morphology and porosity for nanofibers, the variation of chemical active sites and adsorption performance of biocomposite nanofibrous membranes for conventional and core-shell electrospinning as well as the adsorption effect factors of copper ions including initial concentration, pH of solution and interaction time were comprehensively investigated. The results show that the average diameter for conventional and core-shell ultrafine nanofibers at 50% chitosan and 30% chitosan loading can achieve 56.22 nm and 37.28 nm, respectively. The core-shell cellulose acetate/chitosan (CA/CS) biocomposite nanofibrous membranes induced the surface aggregation of copper ions to impede the further adsorption. The more uniform distribution for chemical adsorption sites can be obtained by the conventional single-nozzle electrospinning than by the core-shell one, which promotes the adsorption performance of copper ions and decreases the surface shrinkage of nanofibrous membranes during adsorption. The 30% CS conventional nanofibrous membranes at the pH=5 aqueous solution showed the optimum adsorption capacity of copper ions (86.4 mg/g). The smart combination of renewable biomass with effective chemical adsorptive sites, the electrospinning technology with interwoven porous structure and the adsorption method with low cost and facile operation shows a promising prospect for water treatment.

Competitive Adsorption of Copper, Zinc, and Chromium from Wastewater Using Corn-Cob Ash: Optimization Approach

Corn Cob ash was used in competitive adsorption of copper, zinc, and chromium from wastewater. The central composite design; a sub-set of response surface methodology was used to optimize the adsorption of the heavy metals. The result of the statistical parameters showed the coefficient of determination (R2) of 1.000, 0.999, and 1.000 for copper, zinc, and chromium respectively. The optimal conditions obtained for adsorbent dosage, initial concentration, temperature, contact time, and particle size were 13.20 mg, 79.72 mg/l, 34.95 °C, 40.38 min, and 1400 µm, respectively with the desirability of 1.000. The predicted and the actual values of metal removal obtained were 69.41%, 76.37%, as well as 70.44%, 72.50%, 77.90 % and 71.00% for copper, zinc, and chromium respectively. The ressult indicated a good conformity between the model predicted values and the actual values, thus having small errors of 3.09%, 1.53 % and 0.56 % for copper, zinc, and chromium respectively.

Structural Identification of Metalloproteomes in Marine Diatoms, an Efficient Algae Model in Toxic Metals Bioremediation

The biosorption of pollutants using microbial organisms has received growing interest in the last decades. Diatoms, the most dominant group of phytoplankton in oceans, are (i) pollution tolerant species, (ii) excellent biological indicators of water quality, and (iii) efficient models in assimilation and detoxification of toxic metal ions. Published research articles connecting proteomics with the capacity of diatoms for toxic metal removal are very limited. In this work, we employed a structural based systematic approach to predict and analyze the metalloproteome of six species of marine diatoms: Thalassiosira pseudonana, Phaeodactylum tricornutum, Fragilariopsis cylindrus, Thalassiosira oceanica, Fistulifera solaris, and Pseudo-nitzschia multistriata. The results indicate that the metalloproteome constitutes a significant proportion (~13%) of the total diatom proteome for all species investigated, and the proteins binding non-essential metals (Cd, Hg, Pb, Cr, As, and Ba) are significantly more than those identified for essential metals (Zn, Cu, Fe, Ca, Mg, Mn, Co, and Ni). These findings are most likely related to the well-known toxic metal tolerance of diatoms. In this study, metalloproteomes that may be involved in metabolic processes and in the mechanisms of bioaccumulation and detoxification of toxic metals of diatoms after exposure to toxic metals were identified and described.

A novel ensemble method based on SBLMD-ANN-MOPSO approach for predicting the milling stability regimes

In recent decades, lots of work has been done to mitigate self excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been computed using a modified Local Mean Decomposition (SBLMD) technique in order to cite tool chatter features. Further, three artificial neural network (ANN) training algorithms viz. Resilient Propagation (RP), Conjugate Gradient-Based (CGP) and Levenberg-Marquardt Algorithm (LM) and two activation functions viz. Hyperbolic Tangent Sigmoid (TANSIG) and Log Sigmoid (LOGSIG) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, Multi-Objective Particle Swarm Optimization (MOPSO) has been invoked to optimize developed prediction models for obtaining the most favourable range of input parameters pertaining to stable milling with higher productivity.