Chemical analysis of size-tailored magnetic colloids using slurry nebulization in ICP-OES

In this work, inductively coupled plasma emission spectroscopy was utilized to directly determine the chemical composition of magnetic fluids constituted of size-sorted ferrite nanoparticles in aqueous solution. Nickel and cobalt nanoferrites were chemically synthesized following a bottom-up route and dispersed under various pH conditions. Size and structural characteristics of nanograins were investigated by X-ray diffraction using a synchrotron source. Chemical analysis was then carried out by directly introducing diluted magnetic fluid samples (slurries) into the spectrometer. To achieve reliable measurements, sample conditions and apparatus parameters were carefully investigated. Slurry stability must be optimized in order to obtain reproducible and accurate analysis. The instrument must also be calibrated to minimize the difference between the signal produced by slurries and that of aqueous ordinary solutions. Furthermore, slurry sample introduction offers many advantages over conventional sample digestion, including reduced sample pretreatment time, less possibility of contamination and the use of direct calibration with aqueous solutions.     

Cosmological models in general relativity and brans-dicke theories: a comparison

We review general relativistic and Brans-Dicke cosmological models of the early universe and for the present phase. Both theories render similar results, in general, as far as Mach's principle is concerned. There is some difference in the stability problem for the inflationary phase, and we point out how to test one theory against the other experimentally.     

Density perturbations in a Brans-Dicke cosmological model

A very general flat solution for Brans-Dicke cosmology with a perfect-fluid, Robertson-Walker metric and a perfect gas law of state is examined regarding density perturbations. The model has growing instabilities, but not of exponential character.     

DFT‐GIAO 1H and 13C NMR prediction of chemical shifts for the configurational assignment of 6β‐hydroxyhyoscyamine diastereoisomers

¹H and ¹³C NMR chemical shift calculations using the density functional theory–gauge including/invariant atomic orbitals (DFT–GIAO) approximation at the B3LYP/6‐311G++(d,p) level of theory have been used to assign both natural diastereoisomers of 6β‐hydroxyhyoscyamine. The theoretical chemical shifts of the ¹H and ¹³C atoms in both isomers were calculated using a previously determined conformational distribution, and the theoretical and experimental values were cross‐compared. For protons, the obtained average absolute differences and root mean square (rms) errors for each comparison showed that the experimental chemical shifts of dextrorotatory and levorotatory 6β‐hydroxyhyoscyamines correlated well with the theoretical values calculated for the (3R,6R,2′S) and (3S,6S,2′S) configurations, respectively, whereas for ¹³C atoms the calculations were unable to differentiate between isomers. The nature of the relatively large chemical shift differences observed in nuclei that share similar chemical environments between isomers was asserted from the same calculations. It is shown that the anisotropic effect of the phenyl group in the tropic ester moiety, positioned under the tropane ring, has a larger shielding effect over one ring side than over the other one. Copyright © 2009 John Wiley & Sons, Ltd.     

Wavelength selection framework for classifying food and pharmaceutical samples into multiple classes

Near infrared (NIR) spectroscopy is an efficient, low‐cost analytical technique widely applied to identify the origin of food and pharmaceutical products. NIR spectra‐based classification strategies typically use thousands of equally spaced wavelengths as input information, some of which may not carry relevant information for product classification. When that is the case, the performance of predictive and exploratory multivariate techniques may be undermined by such noisy information. In this paper, we propose an iterative framework for selecting subsets of NIR wavelengths aimed at classifying samples into categories. For that matter, we integrate Principal Components Analysis (PCA) and three classification techniques: k‐Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA). PCA is first applied to NIR data, and a wavelength importance index is derived based on the PCA loadings. Samples are then categorized using the wavelength with the highest index and the classification accuracy is calculated; next, the wavelength with the second highest index is inserted into the dataset and a new classification is performed. This forward‐based iterative procedure is carried out until all original wavelengths are inserted into the dataset used for classification. The subset of wavelengths leading to the maximum accuracy is chosen as the recommended subset. Our propositions performed remarkably well when applied to four datasets related to food and pharmaceutical products. Copyright © 2016 John Wiley & Sons, Ltd.     

Characterization of Gasoline by 1H Nuclear Magnetic Resonance and Chemometrics

Automotive fuel adulteration is an old and significant problem. One common type of fuel adulteration is the addition of diesel to gasoline. Unsupervised models were developed through hierarchical cluster and principal component analysis models. Supervised models through partial least square discriminant analysis using ¹H nuclear magnetic resonance spectra as the input were used to classify samples as adulterated or unadulterated. Quantitative models were developed using partial least squares to determine the gasoline and diesel concentrations in the samples. This set contained samples composed of pure gasoline and anhydrous ethanol reproducing commercial gasoline and other samples treated with diesel. Hierarchical cluster and principal component analysis did not distinguish between adulterated and unadulterated samples except for the most adulterated materials. However, partial least square discriminant analysis classified 100% of the samples correctly. The partial least square algorithm provided excellent regression models for the gasoline and diesel content. The determination coefficient was 0.9920 for both models, whereas the root mean square error of cross-validation and root mean square error of prediction for the diesel model were 2.32 and 1.42%, respectively, and 2.40 and 1.38% for the gasoline model.