Comprehensive nanostructural study of SSRM nanocontact on silicon

Scanning Spreading Resistance Microscopy electro-mechanical nanocontacts are nowadays well understood and numerous influent parameters have been identified (bias, load, surface state sample, radius of curvature of the tip). Despite several simulation and modelization possibilities, calibration curves are required to ensure reliable electrical characterizations. In this paper, we bring, through nanostructural studies (Scanning Transmission Electron Microscopy) of surface state of both SSRM tips and doped silicon surface a new understanding of tip-sample interaction during SSRM measurements. As a result of load, a nanometric residual amorphous silicon layer was observed which thickness depends on applied force and might be due to as well to the plastic transformation (Si to β-tin phase) as to plough-effect residues resulting from the tip indentation into the sample. It appears thus important in a failure analysis process to find the best compromise between stable electrical SSRM response and sample/tip surface degradation.     

Approximate chemical analysis of volcanic glasses using Raman spectroscopy

The effect of chemical composition on the Raman spectra of a series of natural calcalkaline silicate glasses has been quantified by performing electron microprobe analyses and obtaining Raman spectra on glassy filaments (~450 µm) derived from a magma mingling experiment. The results provide a robust compositionally‐dependent database for the Raman spectra of natural silicate glasses along the calcalkaline series. An empirical model based on both the acquired Raman spectra and an ideal mixing equation between calcalkaline basaltic and rhyolitic end‐members is constructed enabling the estimation of the chemical composition and degree of polymerization of silicate glasses using Raman spectra. The model is relatively insensitive to acquisition conditions and has been validated using the MPI‐DING geochemical standard glasses 1 as well as further samples. The methods and model developed here offer several advantages compared with other analytical and spectroscopic methods such as infrared spectroscopy, X‐ray fluorescence spectroscopy, electron and ion microprobe analyses, inasmuch as Raman spectroscopy can be performed with a high spatial resolution (1 µm²) without the need for any sample preparation as a nondestructive technique. This study represents an advance in efforts to provide the first database of Raman spectra for natural silicate glasses and yields a new approach for the treatment of Raman spectra, which allows us to extract approximate information about the chemical composition of natural silicate glasses using Raman spectroscopy. We anticipate its application in handheld in situ terrestrial field studies of silicate glasses under extreme conditions (e.g. extraterrestrial and submarine environments). © 2015 The Authors Journal of Raman Spectroscopy Published by John Wiley & Sons Ltd     

On the divisor function in short intervals

Let d(n) denote the number of positive divisors of the natural number n. The aim of this paper is to investigate the validity of the asymptotic formula

$\begin{array}{lll}\sum \limits_{x < n \leq x+h(x)}d(n)\sim h(x)\log x\end{array}$

for \({x \to + \infty,}\) assuming a hypothetical estimate on the mean

$\begin{array}{lll} \int \limits_X^{X+Y}(\Delta(x+h(x))-\Delta (x))^2\,{d}x, \end{array}$

which is a weakened form of a conjecture of M. Jutila.

     

Approximation theorems for a family of multivariate neural network operators in Orlicz-type spaces

In this paper, we study the theory of a Kantorovich version of the multivariate neural network operators. Such operators, are activated by suitable kernels generated by sigmoidal functions. In particular, the main result here proved is a modular convergence theorem in Orlicz spaces. As special cases, convergence theorem in \(L^p\)-spaces, interpolation spaces, and exponential-type spaces can be deduced. In general, multivariate approximations by constructive neural network algorithms are useful for applications to neurocomputing processes involving high dimensional data. At the end of the paper, several examples of activation functions of sigmoidal-type for which the above theory holds have been described.     

Thorough evaluation of the validity of conventional enantio-gas chromatography in the analysis of volatile chiral compounds in mandarin essential oil: A comparative investigation with multidimensional gas chromatography

The present research is focused on the determination of the enantiomeric distribution of chiral compounds, contained in mandarin essential oils, by means of conventional chiral gas chromatography with flame ionization detection (enantio-GC-FID); the results attained were compared with those derived from heart-cutting multidimensional GC-mass spectrometry (MDGC/MS), to evaluate the reliability of the monodimensional technique as a tool for quality control. The Deans-switch MDGC system was equipped with two GC ovens, which were connected via a heated transfer line, a flame ionization detector (FID1) in the first dimension and a quadrupole MS as second-dimension detector. The a priori knowledge of potential co-elutions concerning target compounds (an enantiomer and an interfering compound), when using enantio-GC-FID, could enable the use of corrected enantiomer excess values. Correction factors could be calculated through a preliminary GC-FID analysis (using an apolar column), considering the peak areas of the known interferences. The method used for the calculation of a so-called “coelution correction factor” is described, along with some examples.     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.