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A two-level factorial design was employed to analyze the influence of agitation, contact time, amount of algae and type of pretreatment on heavy metal removal by Arribadas algae, in a batch system consisting of a synthetic solution simulating a typical effluent from battery manufacturing processes. Dried, ground and sieved 4 g algae samples were able to remove 99 % lead from 100 mL samples of synthetic effluent. Maximum removals for zinc and iron were 37 % and 80 %, respectively. Lead removal using this method is potentially useful for large-scale industrial applications, because Arribadas algae are cheap, abundant, naturally occurring waste materials.

The use of silicone for detection of aromatic hydrocarbons in water using near-infrared spectroscopy is proposed. A sensing phase of poly(dimethylsiloxane) (PDMS) was prepared, and a rod of this material was adapted to a transflectance probe for measurements from 850 to 1800 nm. Deionized water samples contaminated separately with known amounts of benzene, toluene, ethylbenzene, and m-xylene were used for evaluation of the PDMS sensing phase, and measurements were made in a closed reactor with constant stirring. Equilibrium states were obtained after 90, 180, 360, and 405 min for benzene, toluene, ethylbenzene, and m-xylene, respectively. The PDMS sensing phase showed a reversible response, presenting linear response ranges up to 360, 290, 100, and 80 mg [L.sup.-1], with detection limits of 8.0, 7.0, 2.6, and 3.0 mg [L.sup.-1] for benzene, toluene, ethylbenzene, and m-xylene, respectively. Reference spectra obtained with different rods showed a relative standard deviation of 0.5%, indicating repeatability in the sensing phase preparation. A relative standard deviation of 6.7% was obtained for measurements performed with six different rods, using a 52 mg [L.sup.-1] toluene aqueous solution. The sensing phase was evaluated for identification of sources of contamination of water in simulated studies, employing Brazilian gasoline type A (without ethanol), gasoline type C (with 25% of anhydrous ethanol), and diesel fuel. Principal component analysis was able to classify the water in distinct groups, contaminated by gasoline A, gasoline C, or diesel fuel.

Accurate simultaneous analysis of different anionic species using ion-selective electrodes (ISEs) can be achieved even for non-specific sensors by resorting to an ordinary least squares multiple regression in the vicinity of the predicted concentrations. In this work the potentialities of this approach are evidenced by the determination of nitrate and chloride in synthetic and real water samples in which chloride concentration was significantly higher than nitrate. An AgCl/Ag2S electrode based on a homogeneous crystalline membrane together with a PVC electrode based on tert-octylammonium bromide dissolved in dibutylphthalate were used as potentiometric detectors for chloride and nitrate, respectively. For the implementation of the procedure, an automatic system based on sequential injection analysis was used. The results obtained by the standard addition method were biased for low concentrations of nitrate and were dependant on the relative proportion of NO3-/Cl-. The results obtained by the proposed methodology for chloride determination were slightly better when compared to those obtained by the standard addition method. In relation to nitrate determination, the proposed methodology yielded values with a relative root mean square error of prediction (RRMSEP) of 2.8%, while for standard addition calibration, mean error values were approximately 12.1%. [Copyright &y& Elsevier]

This work evaluates the feasibility of using NIR spectroscopy for quantification of three polymorphs of mebendazole (MBZ) in pharmaceutical raw materials. Thirty ternary mixtures of polymorphic forms of MBZ were prepared, varying the content of forms A and C from 0 to 100% (w/w), and for form B from 0 to 30% (w/w). Reflectance NIR spectra were used to develop partial least square (PLS) regression models using all spectral variables and the variables with significant regression coefficients selected by the Jack-Knife algorithm (PLS/JK). MBZ polymorphs were quantified with RMSEP values of 2.37% w/w, 1.23% w/w and 1.48% w/w for polymorphs A, B and C, respectively. This is an easy, fast and feasible method for monitoring the quality of raw pharmaceutical materials of MBZ according to polymorph purity., (Copyright © 2015 Elsevier B.V. All rights reserved.)