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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. 相似文献
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Scarborough TD Strohaber J Foote DB McAcy CJ Uiterwaal CJ 《Physical chemistry chemical physics : PCCP》2011,13(30):13783-13790
We report on the photoionization and photofragmentation of benzene (C(6)H(6)) and of the monohalobenzenes C(6)H(5)-X (X = F, Cl, Br, I) under intense-field, single-molecule conditions. We focus 50-fs, 804-nm pulses from a Ti:sapphire laser source, and record ion mass spectra as a function of intensity in the range ~10(13) W/cm(2) to ~10(15) W/cm(2). We count ions that were created in the central, most intense part of the focal area; ions from other regions are rejected. For all targets, stable parent ions (C(6)H(5)X(+)) are observed. Our data is consistent with resonance-enhanced multiphoton ionization (REMPI) involving the neutral (1)ππ* excited state (primarily a phenyl excitation): all of our plots of parent ion yield versus intensity display a kink when this excitation saturates. From the intensity dependence of the ion yield we infer that both the HOMO and the HOMO-1 contribute to ionization in C(6)H(5)F and C(6)H(5)Cl. The proportion of phenyl (C(6)H(5)) fragments in the mass spectra increases in the order X = F, Cl, Br, I. We ascribe these substituent-dependent observations to the different lifetimes of the C(6)H(5)X (1)ππ* states. In X = I the heavy-atom effect leads to ultrafast intersystem crossing to a dissociative (3)nσ* state. This breaks the C-I bond in an early stage of the ultrashort pulse, which explains the abundance of fragments that we find in the iodobenzene mass spectrum. For the lighter X = F, Cl, and Br this dissociation is much slower, which explains the lesser degree of fragmentation observed for these three molecules. 相似文献
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Thomas Boucher CJ Carey Melinda Darby Dyar Sridhar Mahadevan Samuel Clegg Roger Wiens 《Journal of Chemometrics》2015,29(9):484-491
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. 相似文献
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