In this work, the cytotoxicity and toxicity of phosphonium-based deep eutectic solvents (DESs) with three hydrogen bond donors, namely glycerine, ethylene glycol, and triethylene glycol were investigated. The cytotoxicity effect was tested using brine shrimp (Artemia salina). The toxicity was investigated using the two Gram positive bacteria Bacillus subtilis and Staphylococcus aureus, and two Gram negative bacteria Escherichia coli and Pseudomonas aeruginosa. The cytotoxicity of tested DESs was much higher than that of their individual components, indicating their toxicological behavior was different. It was also found that there was toxic effect on the studied bacteria, indicating their potential application as anti-bacterial agents. To the best of our knowledge, this is the first time the cytotoxicity and toxicity of phosphonium-based DESs were studied.
An industrial grade acidic crude palm oil (ACPO) pre-treatment process was carried out using ethanesulfonic acid (ESA) as a catalyst in the esterification reaction. ESA was used in different dosages to reduce free fatty acid (FFA) to a minimum level for the second stage of biodiesel production via alkaline transesterification reaction. Different process operating conditions were optimized such as ESA dosage (0.25-3.5% wt/wt), methanol to ACPO molar ratio (1:1-20:1), reaction temperature (40-70 °C), and reaction time (3-150 min). This study revealed the potential use of abundant quantities of ACPO from oil palm mills for biodiesel production. The lab scale results showed the effectiveness of the pre-treatment process using ESA catalyst. Three consecutive catalyst recycling runs were achieved without significant degradation in its performance. Second and third reuse runs needed more reaction time to achieve the target level of FFA content. Esterification and transesterification using ESA and KOH respectively is proposed for biodiesel industrial scale production. The produced biodiesel meets the international standards specifications for biodiesel fuel (EN 14214 and ASTM D6751).
In continuation of investigation for environmentally benign protocol for new solvents termed deep eutectic solvents (DESs), it is herein reported results concerning the toxicity and cytotoxicity of choline chloride (ChCl) based DESs with four hydrogen bond donors including glycerine, ethylene glycol, triethylene glycol and urea. The toxicity was investigated using two Gram positive bacteria Bacillus subtilis and Staphylococcus aureus, and two Gram negative bacteria Escherichia coli and Pseudomonas aeruginosa. The cytotoxicity effect was tested using the Artemia salina leach. It was found that there was no toxic effect for the tested DESs on all of the studied bacteria confirming their benign effects on these bacteria. Nevertheless, it was found that the cytotoxicity of DESs was much higher than their individual components (e.g. glycerine, ChCl) indicating that their toxicological behavior is different. For our best knowledge this is the first time that toxicity and cytotoxicity of DESs were studied. The toxicity and cytotoxicity of DESs varied depending on the structure of components. Careful usage of the terms non-toxicity and biodegradability must be considered. More investigation on this matter is required.
Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.