Clarification of a wheat straw‐derived medium with ion‐exchange resins for xylitol crystallization

BACKGROUND: Xylitol bioproduction from lignocellulosic residues comprises hydrolysis of the hemicellulose, detoxification of the hydrolysate, bioconversion of the xylose, and recovery of xylitol from the fermented hydrolysate. There are relatively few reports on xylitol recovery from fermented media. In the present study, ion‐exchange resins were used to clarify a fermented wheat straw hemicellulosic hydrolysate, which was then vacuum‐concentrated and submitted to cooling in the presence of ethanol for xylitol crystallization. RESULTS: Sequential adsorption into two anion‐exchange resins (A‐860S and A‐500PS) promoted considerable reductions in the content of soluble by‐products (up to 97.5%) and in medium coloration (99.5%). Vacuum concentration led to a dark‐colored viscous solution that inhibited xylitol crystallization. This inhibition could be overcome by mixing the concentrated medium with a commercial xylitol solution. Such a strategy led to xylitol crystals with up to 95.9% purity. The crystallization yield (43.5%) was close to that observed when using commercial xylitol solution (51.4%). CONCLUSION: The experimental data demonstrate the feasibility of using ion‐exchange resins followed by cooling in the presence of ethanol as a strategy to promote the fast recovery and purification of xylitol from hemicellulose‐derived fermentation media. Copyright © 2008 Society of Chemical Industry     

Interobl: An interactive software tool for displaying and scaling oblique ionograms

By analyzing an oblique ionogram several characteristics, such as LOF, MOF, FMUF, FHLOF, FLLOF, and 2FMOF, can be found. These characteristics are important both for ionospheric studies and for terrestrial communication purposes, as they give information about the sky wave communication conditions in the High Frequency (HF) radio spectrum.A RCS-5B sweeping HF receiver for oblique sounding was installed at Chania (Crete, Greece, 35.7°N, 24.0°E) in April 2005 to perform a radio link with Inskip (UK, 53.5°N, 2.5°W). The receiver, able to record the sounding only as a binary file, was not equipped with a tool to display and scale the recorded trace. This work describes software that is able to do this, consequently increasing the speed of the scaling phase performed by the operator. The usefulness of this software for validating FMUF prediction models is also shown.     

Anodic oxidation of mecoprop herbicide at lead dioxide

The electrochemical oxidation of an aqueous solution containing mecoprop (2-(2-methyl-4-chlorophenoxy)propionic acid) has been studied at PbO₂ anodes by cyclic voltammetry and bulk electrolysis. The influence of current density, hydrodynamic conditions, temperature and pH on the degradation rate and current efficiency is reported. The results obtained show that the use of PbO₂ leads to total mineralization of mecoprop due to the production of oxidant hydroxyl radical electrogenerated from water discharge. The current efficiency for the electro-oxidation of mecoprop is enhanced by low current density, high recycle flow-rates and high temperature. In contrast, the pH effect was not significant. It has also been observed that mecoprop decay kinetics follows a pseudo-first-order reaction and the rate constant increases with rising current density.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Aerobic degradation by white‐rot fungi of trichloroethylene (TCE) and mixtures of TCE and perchloroethylene (PCE)

BACKGROUND: Trichloroethylene (TCE) and perchloroethylene (PCE) are considered among the most important groundwater pollutants around the world. These compounds are usually found together in polluted environments but little is known about the ability of microorganisms to simultaneously degrade TCE and PCE. RESULTS: Data showed that several species of white‐rot fungi, including Trametes versicolor, Ganoderma lucidum, and Irpex lacteus, degrade substantial levels of TCE in pure culture. T. versicolor was chosen for further study since it degraded higher levels of TCE than the other organisms. Initial glucose concentration and reoxygenation of samples increased the amount of TCE dechlorination, but no significant difference in percentage TCE degradation was observed. T. versicolor was able to degrade 34.1 and 47.7% of PCE and TCE added as mixtures (containing 5 and 10 mg L⁻¹, respectively). CONCLUSIONS: The degradation ability of TCE was extended to other species of white‐rot fungi. Percentage degradation as well as chloride release from mixtures of TCE and PCE showed that T. versicolor degrades mixtures of TCE and PCE almost as well as its ability to degrade individually added TCE or PCE. The results suggest the potential promise of T. versicolor for bioremediation of TCE and PCE in the environment. Copyright © 2008 Society of Chemical Industry     

Kinetic modelling of textural changes in ready-to-eat breakfast cereals during soaking in semi-skimmed milk

Summary Ready‐to‐eat breakfast cereals immersed in milk undergo undesirable changes in texture because of sudden moisture uptake. The textural changes are ascribable to a plasticizing effect of water, which modifies the mechanical strength of products by softening the starch/protein matrix. In this work, some textural parameters of different cereal flakes were derived from the force–displacement curves monitored during 300 s of immersion in milk. Hardness loss, deformability increment and changes in the force–displacement curve profile were calculated and plotted against soaking time. The application of a sugar coating process to a model cereal flake increased the initial product hardness and improved the preservation of the textural parameters during immersion. The Peleg model closely fitted the experimental data, with regression coefficients from 0.967 to 0.999.     

Shear mode coupling and tilted grain growth of A1N thin films in BAW resonators.

Polycrystalline A1N thin films were deposited by RF reactive magnetron sputtering on Pt(111)/Ti electrode films. The substrates were tilted by an angle ranging from 40 degrees to 70 degrees with respect to the target normal. A low deposition temperature and a high sputter gas pressure were found ideal for tilted growth. The resulting grain tilt angle amounts to about half the substrate tilt angle. For coupling evaluation, 5 GHz solidly mounted resonator structures have been realized. The tilted grain A1N films exhibited a permittivity in the 9.5-10.5 range and loss tangent of 0.3%. Two shear modes as well as the longitudinal mode could be clearly identified. The coupling coefficient k2(eff) of the fundamental thickness shear mode (TS0) was found to be about 0.5%, which is compatible with a c-axis tilt of about 6 degrees.     

Cubic polynomial patches through geodesics

We consider patches that contain any given 3D polynomial curve as a pregeodesic (i.e. geodesic up to reparametrization). A curve is a pregeodesic if and only if its rectifying plane coincides with the tangent plane to the surface, we use this fact to construct ruled cubic patches through pregeodesics and bicubic patches through pairs of pregeodesics. We also discuss the G¹ connection of (1,k) patches with abutting pregeodesics.     

Combined analytical solution of overland flow and sediment transport

With reference to the kinematic wave theory coupled with the hypothesis of constant linear velocity for the rating curve, rising limb analytical solutions have been calculated for overland flow, over an Hortonian-infiltrating surface, and sediment discharge. These analytical solutions are certainly easier to use than the numerical integration of the basic equations and they may be used to obtain an initial evaluation of the parameters of more complex models generally devised for complicated cases.Notation a exponent of the Horton law [T^–1] - b exponent of the rill erosion equation - B inter-rill erosion coefficient [ML^–m–2T ^m–1] - c sediment concentration [ML^–3] - c _o reference sediment concentration [ML^–3] - E _I inter-rill erosion [ML^–2T^–1] - E _R rill erosion [ML^–2T^–1] - f _c final infiltration rate of the soil [LT^–1] - f _o initial infiltration rate of the soil [LT^–1] - h flow depth [L] - h _o reference flow depth [L] - i infiltration rate [LT^–1] - k rill erosion coefficient [ML^–1–b T^–1] - K integration constant - L() Laplace transformation - m exponent of the inter-rill erosion equation - n Manning's coefficient [L^–1/3T] - p rainfall intensity [LT^–1] - q water discharge per unit width [L²T^–1] - q _s sediment discharge per unit width [ML^–1T^–1] - t time [T] - t _p ponding time [T] - x distance along the flow direction [L] Greek Letters coefficient of the stage-discharge equation [L^2–T^–1] - exponent of the stage-discharge equation - rill erosion coefficient [L^–1]