Marine Algae Bioadsorbents for Adsorptive Removal of Heavy Metals

With the shortage of freshwater resources and as wastewater output of huge industries as well as pollution that might be happening in the ecosystem, wastewater treatment is of utmost importance. Removal of pollutants such as heavy metals from wastewater would provide an exceptional alternative water resource. Extensive research has been done to develop an operative technology to overcome the toxicity and the negative environmental impact of heavy metals and their ionic forms. In this book chapter, biomass bioadsorbents utilizing marine algae for adsorptive removal of heavy metal pollutants from wastewater were discussed. The most common adsorption isotherms and kinetic models, which used to study their nature of adsorption, were also covered

An Overview of Carbon-Based Materials for the Removal of Pharmaceutical Active Compounds

Carbon-based materials, namely activated carbon, carbon nanotube and graphene, are considered as one of the most effective adsorbents for pollutant removal and wastewater treatment. Due to their high surface area and distinct chemical and physical properties of the carbon-based materials, particularly activated carbon and carbon nanotube are rapidly emerging as one of the most effective adsorbents for wastewater treatment. Various studies have reported the applications of activated carbon, carbon nanotubes and graphene as promising adsorbents for removing organic and inorganic pollutants. In this chapter, an introduction about the activated carbon, carbon nanotubes and graphene and their production, prosperities and usage for the removal of pharmaceutical active materials from aqueous media are highlighted and summarized. Challenges and future opportunities for application of these carbon-based materials as adsorbents in wastewater treatment are also addressed in this chapter

The nature and kinetics of the adsorption of dibenzothiophene in model diesel fuel on carbonaceous materials loaded with aluminum oxide particles

The resulted environmental and industrial problems from presence of sulfur compounds such as dibenzothiophene (DBT) in some fuel led to attract greater interest in research on the removal of these compounds. In this study the adsorption isotherms of dibenzothiophene (DBT) in model diesel fuel were obtained and desulfurization kinetics was carried out. The adsorbents used were commercial coconut activated carbon (AC), multiwall carbon nanotubes (CNT) and synthesized graphene oxide (GO) loaded with 5% and 10.9% aluminum (Al) in the form of aluminum oxide (Al2O3) particles to improve the chemical properties of their surface. The physicochemical properties for these adsorbents were characterized using thermal gravimetric analysis (TGA), N2 adsorption–desorption surface area analyzer, scanning electron microscope (SEM), energetic dispersive X-ray diffractogram (EDX), field emission electron microscope (FE-TEM) and X-ray photoelectron spectrometer (XPS). The adsorption capacities for DBT on the aluminum oxide modified adsorbents are improved by about twofold, which is attributable to introduction of Al2O3 Lewis acid as an additional adsorption site. The highest adsorption capacity for DBT (85 ± 1 mg/g) with high selectivity factor relative to naphthalene (54 mg/g) was achieved using loaded activated carbon with 5% Al. The adsorption capacities, removal selectivity and efficiencies with which the other prepared adsorbents remove DBT from model fuel are reported. The adsorption isotherms fitted both the Langmuir and Freundlich models. The adsorption rate for DBT follows pseudo-second order kinetics with correlation coefficients close to 1.00. The adsorbents are stable and reusable for at least 5 times.The authors acknowledge the support for this research by the Chemistry Department and Center for Integrative Petroleum Research (CIPR) in the Research Institute (RI) at King Fahd University of Petroleum & Minerals (KFUPM)

Proton conducting blend membranes: physical, morphological and electronic properties

Blend membranes of sulfonated poly(ether ether ketone) (SPEEK) and sulfonated polyetherimide (SPEI) have been prepared and investigated as a potential polymer electrolyte membrane (PEM) for direct methanol fuel cell (DMFC). Polymers were dissolved in N-methyl-2-pyrrolidone (NMP) in different mixing ratios and membranes were casted using a semi-automatic casting machine on a pre-cleaned glass plate. The influence of SPEI percentage on ion exchange capacity (IEC), water uptake, methanol permeability and proton exchange capacity have been investigated. Blend membranes showed slightly better IEC, water uptake and methanol crossover properties as compare to pure SPEEK; but proton conductivity was slightly lower than that of pure SPEEK membrane. Membrane morphology was investigated by FESEM, TGA and AFM. Overall, a homogeneous surface was observed for most of the blend membranes, with minor phase separation at higher SPEI contents samples. AFM image of the membrane surface shows nanoscale surface roughness.Scopu

Mild sulfonated polyether ketone ether ketone ketone incorporated polysulfone membranes for microbial fuel cell application

To address the impediments of low power generation of Nafion, which is the main hurdle in the commercialization of microbial fuel cells (MFC), the current study focuses on developing a new PEM for MFC from mild sulfonation of PEKEKK with relatively improved physiochemical properties. In this study, mild post sulfonation of a polyether ketone ether ketone ketone (PEKEKK) has been successfully achieved using 98% H2SO4 at 90°C under reflux. 5%–30% (wt%) of sulfonated PEKEKK (SPEKEKK) loaded polysulfone (PSU) composite membranes were fabricated via a solution casting method. Ingeminating evidence of the sulfonation and structure of sulfonated polymer was proved by 1H NMR peaks integration data and FTIR, respectively. The addition of SPEKEKK to PSU showed significant improvement in conductivity owing to the availability of more protonated sites (-SO3H) and water mediated pathways for the conduction of protons. The composite membrane containing 30 wt% SPEKEKK exhibits the highest conductivity of 0.12 S/cm at 90°C. The water uptakes and swelling ratio of the composite membranes are all higher than that of the pristine PSU membrane and show an increasing trend with increasing SPEKEKK content, thus validating the availability of water domains. Meanwhile, the lowest initial decomposition temperatures assigned to sulfonic acid groups and main chain degradation of the polysulfone/polyether ketone ether ketone ketone (PSU/SPEKEKK) composite membranes occurred at ~300°C and ~500°C respectively, which reflects an excellent thermal stability property. The experimental results indicate that the PSU/SPEKEKK membrane has the potential to greatly enhance the efficiency of MFCs

Characterisation and microbial community analysis of lipid utilising microorganisms for biogas formation.

In the anaerobic process, fat-oil-grease (FOG) is hydrolysed to long-chain fatty acids (LCFAs) and glycerol (GLYC), which are then used as substrates to produce biogas. The increase in FOG and LCFAs inhibits methanogenesis, and so far, most work investigating this inhibition has been carried out when FOG or LCFAs were used as co-substrates. In the current work, the inhibition of methanogenesis by FOG, LCFAs and GLYC was investigated when used as sole substrates. To gain more insight on the dynamics of this process, the change of microbial community was analysed using 16S rRNA gene amplicon sequencing. The results indicate that, as the concentrations of cooking olive oil (CO, which represents FOG) and LCFAs increase, methanogenesis is inhibited. For instance, at 0.01 g. L-1 of FOG, the rate of biogas formation was around 8 ml.L-1.day-1, and this decreased to = 45°C and NaCl > 3% led to a significant decrease in the rate of biogas formation. Microbial community analyses were carried out from samples from 3 different bioreactors (CO, OLEI and GLYC), on day 1, 5 and 15. In each bioreactor, microbial communities were dominated by Proteobacteria, Firmicutes and Bacteroidetes phyla. The most important families were Enterobacteriaceae, Pseudomonadaceae and Shewanellaceae (Proteobacteria phylum), Clostridiacea and Ruminococcaceae (Firmicutes) and Porphyromonadaceae and Bacteroidaceae (Bacteroidetes). In CO bioreactor, Proteobacteria bacteria decreased over time, while those of OLEI and GLYC bioreactors increased. A more pronounced increase in Bacteroidetes and Firmicutes were observed in CO bioreactor. The methanogenic archaea Methanobacteriaceae and Methanocorpusculaceae were identified. This analysis has shown that a set of microbial population is selected as a function of the substrate

A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water

It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization