Arsenite tolerance and removal potential of the indigenous halophilic bacterium, Halomonas elongata SEK2.

The indigenous halophilic arsenite-resistant bacterium Halomonas elongata strain SEK2 isolated from the high saline soil of Malek Mohammad hole, Lut Desert, Iran, could tolerate high concentrations of arsenate (As5+) and arsenite (As3+) up to 800 and 40 mM in the SW-10 agar medium, respectively. The isolated strain was able to tolerate considerable concentrations of other toxic heavy metals and oxyanions, including Cadmium (Cd2+), Chromate (Cr6+), lead (Pb2+), and selenite (Se4+), regarding the high salinity of the culture media (with a total salt concentration of 10% (w/v)), the tolerance potential of the isolate SEK2 was unprecedented. The bioremoval potential of the isolate SEK2 was examined through the Silver diethyldithiocarbamate (SDDC) method and demonstrated that the strain SEK2 could remove 60% of arsenite from arsenite-containing growth medium after 48 h of incubation without converting it to arsenate. The arsenite adsorption or uptake by the halophilic bacterium was investigated and substantiated through Fourier-transform infrared spectroscopy (FTIR), Scanning Electron Microscope (SEM), and Energy Dispersive X-ray (EDX) analyses. Furthermore, Transmission electron microscope (TEM) analysis revealed ultra-structural alterations in the presence of arsenite that could be attributed to intracellular accumulation of arsenite by the bacterial cell. Genome sequencing analysis revealed the presence of arsenite resistance as well as other heavy metals/oxyanion resistance genes in the genome of this bacterial strain. Therefore, Halomonas elongata strain SEK2 was identified as an arsenite-resistant halophilic bacterium for the first time that could be used for arsenite bioremediation in saline arsenite-polluted environments.

Toxic heavy metal/oxyanion tolerance in haloarchaea from some saline and hypersaline ecosystems.

Toxic heavy metal/oxyanion contamination has increased severely through the last decades. In this study, 169 native haloarchaeal strains were isolated from different saline and hypersaline econiches of Iran. After providing pure culture and performing morphological, physiological, and biochemical tests, haloarchaea resistance toward arsenate, selenite, chromate, cadmium, zinc, lead, copper, and mercury were surveyed using an agar dilution method. On the basis of minimum inhibitory concentrations (MICs), the least toxicities were found with selenite and arsenate, while the haloarchaeal strains revealed the highest sensitivity for mercury. On the other hand, the majority of haloarchaeal strains exhibited similar responses to chromate and zinc, whereas the resistance level of the isolates to lead, cadmium, and copper was very heterogeneous. 16 S ribosomal RNA (rRNA) gene sequence analysis revealed that most haloarchaeal strains belong to the Halorubrum and Natrinema genera. The obtained results from this study showed that among the identified isolates, Halococcus morrhuae strain 498 had an exceptional resistance toward selenite and cadmium (64 and 16 mM, respectively). Also, Halovarius luteus strain DA5 exhibited a remarkable tolerance against copper (32 mM). Moreover, strain Salt5, identified as Haloarcula sp., was the only strain that could tolerate all eight tested heavy metals/oxyanions and had a significant tolerance of mercury (1.5 mM).

Modeling of malachite green adsorption onto novel polyurethane/SrFe12O19/clinoptilolite nanocomposite using response surface methodology and biogeography-based optimization-assisted multilayer neural network.

This research studied the modeling of malachite green (MG) adsorption onto novel polyurethane/SrFe12O19/clinoptilolite (PU/SrM/CLP) nanocomposite from aqueous solutions by the application of biogeography-based optimization (BBO) algorithm-assisted multilayer neural networks (MNN-BBO) as a new evolutionary algorithm in environmental science. The PU/SrM/CLP nanocomposite was successfully fabricated and characterized by some spectroscopic analyses. Four variables influencing the removal efficiency were modeled by MNN-BBO and response surface methodology (RSM). The MNN-BBO model gave higher percentage removal (99.6%) about 7.6% compared to the RSM technique. Under optimal conditions obtained by MNN-BBO, the four independent variables including pH, shaking rate, initial concentration, and adsorbent dosage were 6.5, 255 rpm, 50 mg.L-1, and 0.08 g, respectively. Under these conditions, the results were fitted well to the Langmuir isotherm with a monolayer maximum amount of sorbate uptake (qmax) of 68.49 mg.g-1 and the pseudo-first-order kinetic pattern with the rate constant (K1) of 0.01 min-1 with the R2 values of 0.9248 and 0.9980, respectively. The results of thermodynamics demonstrated that the MG uptake was not spontaneous due to the positive value of the adsorption ΔG. In addition, the positive values of ΔS (0.079 kJ/mol K) and ΔH (30.816 kJ/mol) indicated the feasible operation and endothermic approach, respectively. Besides, the wastewater investigations showed that the nanocomposite could be used as a new promising sorbent for efficient removal of MG (R% > 72) and magnetically separable from the real samples.

Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid-liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response surface methodology.

A bat inspired algorithm with the aid of artificial neural networks (ANN-BA) has been used for the first time in chemistry and food sciences to optimize solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a green, fast and low cost technique for determination of Cu2+ ions in water and food samples using p-sulfonatocalix (4) arene as a complexing reagent. For this purpose, the influence of four important factors four factors which was influenced on the extraction efficiency such as salt addition, solution pH and disperser and extraction solvent volumes were investigated. Central composite design (CCD) as a comparative technique was employed for optimization of ST-DLLME efficiency. The ANN-BA optimization technique was regarded as a superior model due to its higher value of extraction efficiency (about 7.21%) compared to CCD method. Under ANN-BA optimal conditions, the limit of quantitation (S/N = 10), limit of detection (S/N = 3) and linear range were 0.35, 0.12 and 0.35-1000 µg L-1, respectively. In these circumstances, the percentage recoveries for drinking tea, apple juice, milk, bottled drinking water, river and well water spiked with 0.05, 0.1 and 0.2 mg L-1 of Cu2+ ions were in the acceptable range (91.4-107.1%). In comparison to other methods, the developed ST-DLLME method showed the lowest solvent and sample consumption, shortest value of extraction time, most suitable determination and detection limits and linear range with simple and low cost apparatus. Additionally, the use of bat inspired algorithm as a powerful metaheuristic algorithm with the aid of artificial networks is another advantage of the present work.

Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid-liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition.

Solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a simple, fast, and low-cost technique was developed for simultaneous extraction of Cd2+ and Cu2+ ions in aqueous solutions. Multiobjective evolutionary algorithm based on decomposition with the aid of artificial neural networks (ANN-MOEA/D) was used for the first time in chemistry, environment, and food sciences to optimize several independent variables affecting the extraction efficiency, including disperser volume and extraction solvent volume, pH, and salt addition. To perform the ST-DLLME operations, xylene, methanol, and dithizone were utilized as an extraction solvent, disperser solvent, and chelating agent, respectively. Non-dominated sorting genetic algorithm versions II and III (NSGA II and NSGA III) as multiobjective metaheuristic algorithms and in addition central composite design (CCD) were studied as comparable optimization methods. A comparison of results from these techniques revealed that ANN-MOEA/D model was the best optimization technique owing to its highest efficiency (97.6% for Cd2+ and 98.3% for Cu2+). Under optimal conditions obtained by ANN-MOEAD, the detection limit (S/N = 3), the quantitation limit(S/N = 10), and the linear range for Cu2+ were 0.05, 0.15, and 0.15-1000 µg L-1, respectively, and for Cd2+ were 0.07, 0.21, and 0.21-750 µg L-1, respectively. The real sample recoveries at a spiking level of 0.05, 0.1, and 0.3 mg L-1 of Cu2+ and Cd2+ ions under the optimal conditions obtained by ANN-MOEA/D ranged from 94.8 to 105%.

Rapid Analysis of Acrylamide in Tap and Well Water Samples by Solvent Terminated Dispersive Liquid-Liquid Microextraction Followed by GC-FID.

A fast, green and low cost method for analysis of acrylamide in tap and well water has been presented for the first time using solvent terminated-dispersive liquid liquid microextraction (ST-DLLME) with a simple equipment which does not need centrifugation step followed by GC-FID. The use of one variable at a time optimization method revealed that methanol and octanone were the superior disperser and extraction solvents, respectively. A central composite design (CCD) as a response surface methodology was used for multivariate optimization of five independent factors (volumes of extraction and dispersive solvents, pH, salt addition and extraction time) on the extraction efficiency. Under CCD optimal conditions, the linear range, detection limit (S/N = 3) and quantitation limit (S/N = 10) were 0.1, 0.3 and 0.3-550 ng mL-1, respectively. In these circumstances, the recoveries for real samples (tap and well water) spiked with 0.5, 1 and 10 ng g-1 were in the acceptable range (90.8%-94.1%). In comparison with other methods in the literature, the suggested ST-DLLME approach showed the best analytical performance. The presented green method has potential application as a routine method in the environmental and analytical laboratories for analysis of acrylamide in water samples.

Optimization and modeling of simultaneous ultrasound-assisted adsorption of ternary dyes using copper oxide nanoparticles immobilized on activated carbon using response surface methodology and artificial neural network.

The present study examines simultaneous adsorption of ternary dyes such as rose bengal (RB), safranin O (SO) and malachite green (MG) from aqueous media on copper oxide nanoparticles immobilized on activated carbon (CuO-NPs-AC) in a batch system. To forecast and optimize the adsorption, artificial neural network (ANN) and response surface methodology (RSM) were utilized. The effect of various factors, e.g. dye concentration, sonication time, adsorbent dosage and pH on the adsorption process were evaluated through five level six factor central composite design (CCD) using RSM. Maximum removal efficiency of MG, SO and RB dyes were seen 94.26%, 71% and 76% under optimal operating conditions. The suggested quadratic models revealed good fit with the actual data. To testing the data, the coefficients of determination (R2) of 0.9976, 0.9971 and 0.9952 and Fisher F-values of 2048.92, 1660.95 and 926.84 were obtained for MG, SO and RB dyes, respectively. The same data were utilized to construct the ANN models. The results revealed that both models yielded high R2 values, while the RSM models were slightly more accurate in predictions as compared to ANN models for MG, SO and RB dyes removal. The equilibrium data followed the Langmuir isotherm model, although the rate of the adsorption process well fitted to pseudo-second-order kinetics. The maximum adsorption capacity of the CuO-NPs-AC for MG, SO and RB were found to be 212.79, 149.25 and 172.42 mg/g, respectively.

Optimization of Solvent Terminated Dispersive Liquid-Liquid Microextraction of Copper Ions in Water and Food Samples Using Artificial Neural Networks Coupled Bees Algorithm.

A multivariate method based on solvent terminated dispersive liquid-liquid microextraction was developed for the determination of Cu2+ ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as chelating agent, dispersive and extraction solvents, respectively. The effects of various factors on the extraction efficiency such as extraction and dispersive solvent volumes, salt addition and pH were studied using central composite design (CCD) and artificial neural networks coupled bees algorithm (ANN-BA). Upon comparison of these techniques, ANN-BA model was considered to be better optimization method due to its higher percentage relative recovery (about 5%) as compared to the CCD approach. The linear range and the limits of detection (S/N = 3) and quantitation (S/N = 10) were 0.22-140, 0.08 and 0.22 µg L-1, respectively. Under the optimal conditions, the recoveries for real samples spiked with 0.1 and 0.3 mg L-1 were in the range of 85-98%.

Binary Solvents Dispersive Liquid-Liquid Microextraction (BS-DLLME) Method for Determination of Tramadol in Urine Using High-Performance Liquid Chromatography.

BACKGROUND: Tramadol is an opioid, synthetic analog of codeine and has been used for the treatment of acute or chronic pain may be abused. In this work, a developed Dispersive liquid liquid microextraction (DLLME) as binary solvents-based dispersive liquid-liquid microextraction (BS-DLLME) combined with high performance liquid chromatography (HPLC) with fluorescence detection (FD) was employed for determination of tramadol in the urine samples. This procedure involves the use of an appropriate mixture of binary extraction solvents (70 µL CHCl3 and 30 µL ethyl acetate) and disperser solvent (600 µL acetone) for the formation of cloudy solution in 5 ml urine sample comprising tramadol and NaCl (7.5%, w/v). After centrifuging, the small droplets of extraction solvents were precipitated. In the final step, the HPLC with fluorescence detection was used for determination of tramadol in the precipitated phase. RESULTS: Various factors on the efficiency of the proposed procedure were investigated and optimized. The detection limit (S/N = 3) and quantification limit (S/N = 10) were found 0.2 and 0.9 µg/L, respectively. The relative standard deviations (RSD) for the extraction of 30 µg L of tramadol was found 4.1% (n = 6). The relative recoveries of tramadol from urine samples at spiking levels of 10, 30 and 60 µg/L were in the range of 95.6 - 99.6%. CONCLUSIONS: Compared with other methods, this method provides good figures of merit such as good repeatability, high extraction efficiency, short analysis time, simple procedure and can be used as microextraction technique for routine analysis in clinical laboratories.

Analysis of chlorpheniramine in human urine samples using dispersive liquid-liquid microextraction combined with high-performance liquid chromatography

A simple and environmentally friendly microextraction technique was used for determination of chlorpheniramine (CPM), an antihistamine drug, in human urine samples using dispersive liquid-liquid microextraction (DLLME) followed by high performance liquid chromatography with diode array detection (HPLC-DAD). In this extraction technique, an appropriate mixture of acetonitrile (disperser solvent) and carbon tetrachloride (extraction solvent) was rapidly injected into the urine sample containing the target analyte. Tiny droplets of extractant were formed and dispersed into the sample solution and then sedimented at the bottom of the conical test tube by centrifugation. Under optimal conditions, the calibration curve was linear in the range of 0.055-5.5 µg mL-1, with a detection limit of 16.5 ng mL-1. This proposed method was successfully applied to the analysis of real urine samples. Low consumption of toxic organic solvents, simplicity of operation, low cost and acceptable figures of merit are the main advantages of the proposed technique.

Utilizou-se uma técnica de microextração simples e ambientalmente amigável para a determinação de clorfeniramina (CPM), anti-histamínico, em amostras de urina humana, utilizando a microextração dispersiva líquido-líquido (DLLME), seguida por cromatografia líquida de alta eficiência com detecção por arranjo de diodos (HPLC-DAD). Nesse método de extração, mistura apropriada de acetonitrila (solvente dispersor) e tetracloreto de carbono (solvente de extração) foi injetada rapidamente na amostra de urina contendo o analito alvo. As pequenas gotículas de agente de extração foram formadas e dispersas na solução da amostra e, em seguida, sedimentadas no fundo do tubo cônico de ensaio por centrifugação. Em condições ótimas, a curva de calibração foi linear no intervalo entre 0,055 e 5,5 µg mL-1, com limite de detecção de 16,5 ng mL-1. O método proposto foi aplicado com sucesso na análise de amostras de urina reais. Baixo consumo de solventes orgânicos tóxicos, simplicidade de operação, baixo custo e figuras de mérito aceitáveis são as principais vantagens do método sugerido.

Extraction and Determination of Cyproheptadine in Human Urine by DLLME-HPLC Method.

Novel dispersive liquid-liquid microextraction (DLLME), coupled with high performance liquid chromatography with photodiode array detection (HPLC-DAD) has been applied for the extraction and determination of cyproheptadine (CPH), an antihistamine, in human urine samples. In this method, 0.6 mL of acetonitrile (disperser solvent) containing 30 µL of carbon tetrachloride (extraction solvent) was rapidly injected by a syringe into 5 mL urine sample. After centrifugation, the sedimented phase containing enriched analyte was dissolved in acetonitrile and an aliquot of this solution injected into the HPLC system for analysis. Development of DLLME procedure includes optimization of some important parameters such as kind and volume of extraction and disperser solvent, pH and salt addition. The proposed method has good linearity in the range of 0.02-4.5 µg mL(-1) and low detection limit (13.1 ng mL(-1)). The repeatability of the method, expressed as relative standard deviation was 4.9% (n = 3). This method has also been applied to the analysis of real urine samples with satisfactory relative recoveries in the range of 91.6-101.0%.