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A mass spectrometric study of pyridine N-oxide and pyridine-d5 N-oxide was carried out with a hybrid quadrupole/time-of-flight (TOF) mass spectrometer coupled with an electrospray (ES) source. In addition to the observation of protonated, sodiated, and proton-bound dimers of pyridine N-oxide and pyridine-d5 N-oxide, mass scans revealed the presence of several doubly-charged ion species. Doubly-charged ions of m/z 191 were identified as diprotonated tetramers of pyridine N-oxide; a structure has been proposed for the diprotonated tetramer and its energy relative to that of protonated pyridine N-oxide has been obtained from geometry optimizations. The principal ion species observed were subjected to collision-induced dissociation; accurate mass measurements were made of each fragment ion so as to determine its elemental composition. On the basis of mass spectrometric evidence, it is suggested that dissociation of pyridine N-oxide may occur during the ES process and the resulting fragments become embedded in doubly-charged ions. The proton affinity for both pyridine N-oxide and pyridine-d5 N-oxide was calculated; the difference between these proton affinities was compared with an experimentally determined difference between the proton affinities of pyridine N-oxide and pyridine-d5 N-oxide. 相似文献
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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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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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