Use of reversible hydrides for hydrogen storage

The addition of metals or alloys whose hydrides have a high dissociation pressure allows a considerable increase in the hydrogenation rate of magnesium. The influence of temperature and hydrogen pressure on the reaction rate were studied. Results concerning the hydriding of magnesium rich alloys such as Mg2Ca, La2Mg17 and CeMg12 are presented. The hydriding mechanism of La2Mg17 and CeMg12 alloys is given

First-principles study of iron oxyfluorides and lithiation of FeOF

First-principles studies of iron oxyfluorides in the FeF[subscript 2] rutile framework (FeO[subscript x]F[subscript 2−x], 0≤x≤1) are performed using density functional theory (DFT) in the general gradient approximation (GGA) with a Hubbard U correction. Studies of O/F orderings reveal FeOF to be particularly stable compared to other FeO[subscript x]F[subscript 2−x] (x≠1) structures, where FeF[subscript 2]-FeOF mixing is not energetically favored. The band gap of FeF[subscript 2] is found to decrease as oxygen is substituted into its structure. The GGA + U electronic structure evolves from that of a Mott-Hubbard insulator (x=0) to a charge transfer semiconductor (x=1). Lithiation studies reveal that lithiation sites offering mixed O/F environments are the most stable. An insertion voltage plateau up to Li[subscript 0.5]FeOF on lithiation is found, in agreement with recent Li-ion battery experiments. The energetics of further lithiation with respect to conversion scenarios are discussed.United States. Dept. of Energy. Office of Basic Energy Sciences (Northeastern Center for Chemical Energy Storage Award DE-SC0001294

Impact of water vapor diffusion and latent heat on the effective thermal conductivity of snow

Heat transport in snowpacks is understood to occur through the two processes of heat conduction and latent heat transport carried by water vapor, which are generally treated as decoupled from one another. This paper investigates the coupling between both these processes in snow, with an emphasis on the impacts of the kinetics of the sublimation and deposition of water vapor onto ice. In the case when kinetics is fast, latent heat exchanges at ice surfaces modify their temperature and therefore the thermal gradient within ice crystals and the heat conduction through the entire microstructure. Furthermore, in this case, the effective thermal conductivity of snow can be expressed by a purely conductive term complemented by a term directly proportional to the effective diffusion coefficient of water vapor in snow, which illustrates the inextricable coupling between heat conduction and water vapor transport. Numerical simulations on measured three-dimensional snow microstructures reveal that the effective thermal conductivity of snow can be significantly larger, by up to about 50 % for low-density snow, than if water vapor transport is neglected. A comparison of our numerical simulations with literature data suggests that the fast kinetics hypothesis could be a reasonable assumption for modeling heat and mass transport in snow. Lastly, we demonstrate that under the fast kinetics hypothesis the effective diffusion coefficient of water vapor is related to the effective thermal conductivity by a simple linear relationship. Under such a condition, the effective diffusion coefficient of water vapor is expected to lie in the narrow 100 % to about 80 % range of the value of the diffusion coefficient of water vapor in air for most seasonal snows. This may greatly facilitate the parameterization of water vapor diffusion of snow in models.</p

Combining modelled snowpack stability with machine learning to predict avalanche activity

Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.</p

Structure and Ionic Conductivity in the Mixed-Network Former Chalcogenide Glass System [Na2S]2/3[(B2S3)x(P2S5)1–x]1/3

Glasses in the system [Na2S]2/3[(B2S3)x(P2S5)1–x]1/3 (0.0 ≤ x ≤ 1.0) were prepared by the melt quenching technique, and their properties were characterized by thermal analysis and impedance spectroscopy. Their atomic-level structures were comprehensively characterized by Raman spectroscopy and 11B, 31P, and 23Na high resolution solid state magic-angle spinning (MAS) NMR techniques. 31P MAS NMR peak assignments were made by the presence or absence of homonuclear indirect 31P–31P spin–spin interactions as detected using homonuclear J-resolved and refocused INADEQUATE techniques. The extent of B–S–P connectivity in the glassy network was quantified by 31P{11B} and 11B{31P} rotational echo double resonance spectroscopy. The results clearly illustrate that the network modifier alkali sulfide, Na2S, is not proportionally shared between the two network former components, B and P. Rather, the thiophosphate (P) component tends to attract a larger concentration of network modifier species than predicted by the bulk composition, and this results in the conversion of P2S74–, pyrothiophosphate, Na/P = 2:1, units into PS43–, orthothiophosphate, Na/P = 3:1, groups. Charge balance is maintained by increasing the net degree of polymerization of the thioborate (B) units through the formation of covalent bridging sulfur (BS) units, B–S–B. Detailed inspection of the 11B MAS NMR spectra reveals that multiple thioborate units are formed, ranging from neutral BS3/2 groups all the way to the fully depolymerized orthothioborate (BS33–) species. On the basis of these results, a comprehensive and quantitative structural model is developed for these glasses, on the basis of which the compositional trends in the glass transition temperatures (Tg) and ionic conductivities can be rationalized. Up to x = 0.4, the dominant process can be described in a simplified way by the net reaction equation P1 + B1 P0 + B4, where the superscripts denote the number of BS atoms for the respective network former species. Above x = 0.4, all of the thiophosphate units are of the P0 type and both pyro- (B1) and orthothioborate (B0) species make increasing contributions to the network structure with increasing x. In sharp contrast to the situation in sodium borophosphate glasses, four-coordinated thioborate species are generally less abundant and heteroatomic B–S–P linkages appear to not exist. On the basis of this structural information, compositional trends in the ionic conductivities are discussed in relation to the nature of the charge-compensating anionic species and the spatial distribution of the charge carriers

INFLUENCE DE LA SUBSTITUTION DE L'OXYGÈNE PAR LE FLUOR SUR LES PROPRIÉTÉS MAGNÉTIQUES ET LA CONDUCTIVITÉ ÉLECTRIQUE DE QUELQUES FERRITES OXYFLUORÉS A STRUCTURE SPINELLE

Les phases oxyflurées ZnxMFe2-xO4-xFx (M = Fe, Co, Ni ; 0 ⩽ x ⩽ 0,50 pour Fe ; 0 ⩽ x ⩽ 0,80 pour Co et Ni) et NxFe3-xO4-x (N = Fe, Ni et 0 ⩽ x ⩽ 0,50) ont été préparées par substitutions de l'oxygène par le fluor dans les ferrites Fe3O4, CoFe2O4 et NiFe2O4. Alors que l'arrangement ferromagnétique des spins des cations B n'est pas modifié par la substitution Zn2±Fe3+ dans les spinelles ZnxFe3-xO4-xFx, la même substitution conduit à un "spin canting" dans ZnxCoFe2-xO4-xFx et ZnxNiFe2-xO4-xFx. La conductivité électrique montre que la conduction est due à un mécanisme de hopping entre cations B ; le sous réseau anionique et le sous réseau cationique A ne participant pas à la conduction.The oxyfluoride phases ZnxMFe2-xO4-xFx (M = Fe, Co, Ni ; 0 ⩽ x ⩽ 0.50 for Fe ; 0 ⩽ x ⩽ 0.80 for Co and Ni) and NxFe3-xO4-xFx phases (N = Fe, Ni and 0 ⩽ x ⩽ 0.50) have been prepared by substitution in the Fe3O4, CoFe2O4 and NiFe2O4 ferrites of oxygen by fluorine. Whereas the ferromagnetic spin arrangement of the B cations is not modified by the Zn2±Fe3+ substitution in the ZnxFe3-xO4-xFx spinels, the same substitution leads to a "sping canting" in the ZnxCoFe2-xO4-xFx and Znx NiFe2-xO4-xFx spinels. Electrical conductivity shows that the conduction is due to an electronic hopping mechanism between the B cations ; the anionic sublattice and the cationic A sublattice do not participate in the conduction