Ultra-sensitive quantification of copper in food and water samples by electrochemical adsorptive stripping voltammetry

A new electrochemical adsorptive stripping voltammetry method was developed for the determination of trace amounts of copper in food and water samples. The study of electrochemical behavior of Cu ion indicated that Cu(II) and Schiff base formed a complex in H₃BO₄–NaOH buffer solution (pH?=?7.25). An accumulation potential of ?100 mV (vs Ag/AgCl) was applied while the solution was stirred for 60 s. The response curve was recorded by scanning the potential, and the peak current of ?0.31 V (vs Ag/AgCl) was recorded. The peak current and concentration of copper accorded with linear relationship in the range of 0.04–120 ng mL^?1. The relative standard deviation (for 12 ng mL^?1 of copper) was 1.73 %, and the detection limit was 0.007 ng mL^?1. The possible interference of some common ions was studied. The proposed method was applied to the determination of copper in water, rice, wheat, tea, milk, and tomato with satisfactory results.     

Analyzing the role of information and telecommunication technology in human development: panel data analysis

Environmental Science and Pollution Research - Economic growth in modern-day world get attention primarily through innovation and higher productivity, which places technology and knowledge at the...     

Amelioration of toxicopathological effects of cadmium with silymarin and milk thistle in male Japanese quail (Coturnix japonica)

Environmental Science and Pollution Research - Cadmium is an important widely distributed heavy metal in the environment due to its several industrial uses, while milk thistle is an important herb...     

Interaction of ions in water affects water quality of freshwater lake: a case study of major lakes of North India

The influence of ionic composition on the water chemistry and thereby on aquatic flora was investigated. Two major wetlands of North India were taken for the study. Quality parameters of the lakes showed the enrichment of the water especially in the month of September with higher values of P and BOD in almost all of the lakes viz., LSJ, LCH, LSN, and LHT as <5.0, 9.0, 1.0, 1.7 and <7.3, 6.0, 6.3, and 4.0, respectively. Statistically correlated values of the ions in the water quality showed an effect of one on another. An attempt has also been made to investigate if ionic constituent is one of the major causes affecting the aquatic floral diversity.     

CFD-based simulation of dense gas dispersion in presence of obstacles

Quantification of spatial and temporal concentration profiles of vapor clouds resulting from accidental loss of containment of toxic and/or flammable substances is of great importance as correct prediction of spatial and temporal profiles can not only help in designing mitigation/prevention equipment such as gas detection alarms and shutdown procedures but also help decide on modifications that may help prevent any escalation of the event.The most commonly used models - SLAB (Ermak, 1990), HEGADAS (Colenbrander, 1980), DEGADIS (Spicer & Havens, 1989), HGSYSTEM (Witlox & McFarlane, 1994), PHAST (DNV, 2007), ALOHA (EPA & NOAA, 2007), SCIPUFF (Sykes, Parker, Henn, & Chowdhury, 2007), TRACE (SAFER Systems, 2009), etc. - for simulation of dense gas dispersion consider the dispersion over a flat featureless plain and are unable to consider the effect of presence of obstacles in the path of dispersing medium. In this context, computational fluid dynamics (CFD) has been recognized as a potent tool for realistic estimation of consequence of accidental loss of containment because of its ability to take into account the effect of complex terrain and obstacles present in the path of dispersing fluid.The key to a successful application of CFD in dispersion simulation lies in the accuracy with which the effect of turbulence generated due to the presence of obstacles is assessed. Hence a correct choice of the most appropriate turbulence model is crucial to a successful implementation of CFD in the modeling and simulation of dispersion of toxic and/or flammable substances.In this paper an attempt has been made to employ CFD in the assessment of heavy gas dispersion in presence of obstacles. For this purpose several turbulence models were studied for simulating the experiments conducted earlier by Health and Safety Executive, (HSE) U.K. at Thorney Island, USA (Lees, 2005). From the various experiments done at that time, the findings of Trial 26 have been used by us to see which turbulence model enables the best fit of the CFD simulation with the actual findings. It is found that the realizable k-? model was the most apt and enabled the closest prediction of the actual findings in terms of spatial and temporal concentration profiles. It was also able to capture the phenomenon of gravity slumping associated with dense gas dispersion.     

Does energy use and economic growth allow for environmental sustainability? An empirical analysis of Pakistan

Environmental Science and Pollution Research - The rapid growth of environmental pollution and the destruction of eco-systems force every individual economy to focus on environmentally friendly...     

Construction of double tube granular sludge microbial fuel cell and its characteristics and mechanism of azo dye degradation

Environmental Science and Pollution Research - Microbial fuel cells (MFCs) can obtain electrical energy from extensive organic matter and complete wastewater treatment at the same time. The...     

Design and optimization of new La1−xCexNi1−yFeyO3 (x,y = 0–0.4) nano catalysts in dry reforming of methane

The paper reports the production of syngas from dry reforming of methane (DRM) over La_1?xCe_xNi_1?yFe_yO₃ (x, y = 0–0.4) perovskites. A series of La_1?xCe_xNi_1?yFe_yO₃ were designed by central composite design (CCD) and synthesized by a sol–gel auto combustion method. Artificial neural network (ANN) approach was used to determine the relationship between preparation and operational parameters on the performance of the catalysts in the DRM process. Nickel mole fraction, lanthanum mole fraction, calcination temperature, and reaction temperature were considered as input variables, and conversion of methane was considered as the output variable. An ANN model with nine neurons in the hidden layer was the suitable in predicting conversion of methane. The genetic algorithm (GA) was subsequently used to determine the optimal preparation condition for enhancing the conversion of methane. La_0.6Ce_0.4Ni_0.99Fe_0.01O₃ catalyst, calcined at 756°C was obtained to be the most active catalyst owing to the optimal composition of nickel and lanthanum in the catalyst formulation.