Preparation and properties of gallaborane, GaBH6: structure of the gaseous molecule H2Ga(μ-H)2BH2 as determined by vibrational, electron diffraction, and ab initio studies, and structure of the crystalline solid at 110 K as determined by x-ray diffraction``

Gallaborane (GaBH6, 1), synthesized by the metathesis of LiBH4 with [H2GaCl]n at ca. 250 K, has been characterized by chemical analysis and by its IR and 1H and 11B NMR spectra. The IR spectrum of the vapor at low pressure implies the presence of only one species, viz. H2Ga(μ-H)2BH2, with a diborane-like structure conforming to C2v symmetry. The structure of this molecule has been determined by gas-phase electron diffraction (GED) measurements afforced by the results of ab initio molecular orbital calculations. Hence the principal distances (rα in Å) and angles ( α in deg) are as follows: r(Ga•••B), 2.197(3); r(Ga−Ht), 1.555(6); r(Ga−Hb), 1.800(6); r(B−Ht), 1.189(7); r(B−Hb), 1.286(7); Hb−Ga−Hb, 71.6(4); and Hb−B−Hb, 110.0(5) (t = terminal, b = bridging). Aggregation of the molecules occurs in the condensed phases. X-ray crystallographic studies of a single crystal at 110 K reveal a polymeric network with helical chains made up of alternating pseudotetrahedral GaH4 and BH4 units linked through single hydrogen bridges; the average Ga•••B distance is now 2.473(7) Å. The compound decomposes in the condensed phases at temperatures exceeding ca. 240 K with the formation of elemental Ga and H2 and B2H6. The reactions with NH3, Me3N, and Me3P are also described.

The vibrational spectra of natural and perdeuterated monochlorogallane, H2Ga([mu]-Cl)2GaH2, and an empirical harmonic potential function

The infrared and Raman spectra of monochlorogallane and its fully deuterated isotopomer are recorded and assigned on the basis of the dimeric structures. H2Ga(μ-Cl)2GaH2 and D2Ga(μ-Cl)2GaD2, conforming to D2 symmetry. The observed frequencies are corrected for anharmonicity and fitted to a potential function in which 19 of the 33 independent force constants are refined.

Structure determination, magnetic and optical properties of a new chromium(II) thioantimonate, [Cr((NH2CH2CH2)(3)N)]Sb4S7

The chromium(II) antimony(III) sulphicle, [Cr((NH2CH2CH2)(3)N)]Sb4S7, was synthesised under solvothermal conditions from the reaction of Sb2S3. Cr and S dissolved in tris(2-aminoethyl)amine (tren) at 438 K. The products were characterised by single-crystal X-ray diffraction. elemental analysis, SQUID magnetometry and diffuse reflectance spectroscopy. The compound crystallises in the monoclinic space group P2(1)/n with a = 7.9756(7), b = 10.5191(9), c = 25.880(2) angstrom and beta = 90.864(5)degrees. Alternating SbS33- trigonal pyramids and Sb36 semi-cubes generate Sb4S72- chains which are directly bonded to Cr(tren pendant units. The effective magnetic moment of 4.94(6)mu(B) shows a negligible orbital contribution, in agreement with expectations for Cr(II):d(4) in a (5)A ground state. The measured band gap of 2.14(3) eV is consistent with a correlation between optical band gap and framework density that is established from analysis of a wide range of antimony sulphides. (C) 2007 Elsevier Ltd. All rights reserved.

Acquisition of automatic imitation is sensitive to sensorimotor contingency

The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror system plasticity is sensitive to contingency (i.e., the extent to which activation of one representation predicts activation of another). In Experiment 1, residual automatic imitation was measured following incompatible training in which the action stimulus was a perfect predictor of the response (contingent) or not at all predictive of the response (noncontingent). A contingency effect was observed: There was less automatic imitation indicative of more learning in the contingent group. Experiment 2 replicated this contingency effect and showed that, as predicted by associative learning theory, it can be abolished by signaling trials in which the response occurs in the absence of an action stimulus. These findings support the view that mirror system development depends on associative learning and indicate that this learning is not purely Hebbian. If this is correct, associative learning theory could be used to explain, predict, and intervene in mirror system development.

Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw. Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud

A method for estimating the turbulent kinetic energy dissipation rate from a vertically pointing Doppler lidar, and independent evaluation from balloon-borne in situ measurements

A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude. This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ϵ can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations.