New Express Method for Measuring the Thermal Diffusivity of Non-Metallic Materials on an Example of Zirconium Ceramics

Russian Physics Journal -     

Millimeter-wave Hilbert-transform spectrum analyzer based on Josephson junction

Conclusions A prototype of the Hilbert-transform spectrum analyzer based on the Josephson junctions is described and first spectral measurements are demonstrated in millimeter-wave band. The measurements in the whole millimeter-wave band may be carried out by this spectrum analyzer with resolution as low as 2 GHz and during a time interval as low as a few milliseconds. According to this set of parameters, this Josephson-effect spectrum analyzer may be used for microwave diagnostics of the thermonuclear plasma.     

The first three anisotropy constants of nickel

The technique of ferromagnetic resonance at 23 GHz has been used to determine the first three anisotropy constants of pure Ni down to 4.2K. A temperature and orientation dependent linewidth has also been observed.     

Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions

Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Ultrafast relaxation of excited Dirac fermions in epitaxial graphene using optical differential transmission spectroscopy

We investigate the ultrafast relaxation dynamics of hot Dirac fermionic quasiparticles in multilayer epitaxial graphene using ultrafast optical differential transmission spectroscopy. We observe differential transmission spectra which are well described by interband transitions with no electron-hole interaction. Following the initial thermalization and emission of high-energy phonons, the electron cooling is determined by electron-acoustic phonon scattering, found to occur on the time scale of 1 ps for highly doped layers, and 4-11 ps in undoped layers. The spectra also provide strong evidence for the multilayer structure and doping profile of thermally grown epitaxial graphene on SiC.