We present real-time detection measurements of electron tunneling in a graphene quantum dot. By counting single electron charging events on the dot, the tunneling process in a graphene constriction and the role of localized states are studied in detail. In the regime of low charge detector bias we see only a single time-dependent process in the tunneling rate which can be modeled using a Fermi-broadened energy distribution of the carriers in the lead. We find a non-monotonic gate dependence of the tunneling coupling attributed to the formation of localized states in the constriction. Increasing the detector bias above 2 mV results in an increase of the dot-lead transition rate related to back-action of the charge detector current on the dot.Comment: 8 pages, 6 figure

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Snow slab avalanches are caused by cracks forming and propagating in a weak snow layer below a cohesive slab. The gradual damage process leading to the formation of the initial failure within the weak layer (WL) is still not entirely understood. To this end, we designed a novel test apparatus that allows performing loading experiments with large snow samples (0.25 m2) including a WL at different loading rates and simultaneously monitoring the acoustic emissions (AE) response. By analyzing the AE generated by micro-cracking, we studied the evolution of the damage process preceding snow failure. At fast loading rates, the exponent of the AE energy distribution (b-value) gradually changed, and both the energy rate and the inverse waiting time increased exponentially with increasing load. These changes in AE signature indicate a transition from small to large events and an acceleration of the damage processes leading to brittle failure. For the experiments at slow loading rate, these changes in the AE signature were not or only partially present, even if the sample failed, indicating a different evolution of the damage process. The observed characteristics in AE response provide new insights on how to model snow failure as a critical phenomenon.

Snow is a heterogeneous material with strain-and/or load-rate-dependent strength. In particular, a transition from ductile-to-brittle failure behavior with increasing load rate is observed. The rate-dependent behavior can partly be explained with the existence of a unique healing mechanism in snow that stems from its high homologous temperature (temperature close to melting point). As soon as broken elements in the ice matrix get in contact, they start sintering and the structure may regain strength. Moreover, the ice matrix is subjected to viscous deformation, inducing a relaxation of local load concentrations and, therefore, further counteracting the damage process. Ideal tools for studying the failure process of heterogeneous materials are the fiber-bundle models (FBMs), which allow investigating the effects of basic microstructural characteristics on the general macroscopic failure behavior. We present an FBM with two concurrent time-dependent healing mechanisms: sintering of broken fibers and relaxation of load inhomogeneities. Sintering compensates damage by creating additional intact, load-supporting fibers which lead to an increase of the bundle strength. However, the character of the failure is not changed by sintering alone. With combined sintering and load relaxation, load is distributed from old stronger fibers to new fibers that carry fewer load. So as we additionally incorporated load redistribution to the FBM, the failure occurred suddenly without decrease of the order parameter-describing the amount of damage in the bundle-and without divergence of the fiber failure rate. Moreover, the b value, i.e., the power-law exponent of frequency-magnitude statistics of fibers breaking in load redistribution steps, at failure converged to b ≈ 2, a value higher than that of a classical FBM without healing (b = 3 2 ). These results indicate that healing, as the combined effect of sintering and load relaxation, changes the type of the phase transition at failure. This change of the phase transition is important for quantifying or predicting the failure (e.g., by monitoring acoustic emissions) of snow or other materials for which healing plays an important role.

Abstract. Dry-snow slab avalanches start with the formation of a local failure in a highly porous weak layer underlying a cohesive snow slab. If followed by rapid crack propagation within the weak layer and finally a tensile fracture through the slab, a slab avalanche releases. While the basic concepts of avalanche release are relatively well understood, performing fracture experiments in the laboratory or in the field can be difficult due to the fragile nature of weak snow layers. Numerical simulations are a valuable tool for the study of micromechanical processes that lead to failure in snow. We used a three-dimensional discrete element method (3-D DEM) to simulate and analyze failure processes in snow. Cohesive and cohesionless ballistic deposition allowed us to reproduce porous weak layers and dense cohesive snow slabs, respectively. To analyze the micromechanical behavior at the scale of the snowpack (∼1 m), the particle size was chosen as a compromise between low computational costs and detailed representation of important micromechanical processes. The 3-D-DEM snow model allowed reproduction of the macroscopic behavior observed during compression and mixed-mode loading of dry-snow slab and the weak snow layer. To be able to reproduce the range of snow behavior (elastic modulus, strength), relations between DEM particle and contact parameters and macroscopic behavior were established. Numerical load-controlled failure experiments were performed on small samples and compared to results from load-controlled laboratory tests. Overall, our results show that the discrete element method allows us to realistically simulate snow failure processes. Furthermore, the presented snow model seems appropriate for comprehensively studying how the mechanical properties of the slab and weak layer influence crack propagation preceding avalanche release.

Snow failure is the result of gradual damage accumulation culminating in macroscopic cracks. The failure type strongly depends on the rate of the applied load or strain. Our aim was to study the microstructural mechanisms leading to the macroscopic loading rate dependence. We modeled snow failure and the concurrent acoustic emissions for different loading rates with a fiber bundle model and compared the model results to laboratory experiments. The fiber bundle model included two time‐dependent healing mechanisms opposing the loading‐induced damage process: (a) sintering of broken fibers and (b) relaxation of load inhomogeneities due to viscous deformation. The experimental acoustic emissions features could only be reproduced correctly if both healing mechanisms were included in the model. We conclude that both sintering and viscous deformation at a microscopic level essentially contribute to the macroscopic loading‐ and strain‐rate dependent behavior of snow.

Digital cone penetration measurements can be used to infer snow mechanical properties, for instance, to study snow avalanche formation. The standard interpretation of these measurements is based on statistically inferred micromechanical interactions between snow microstructural elements and a well‐calibrated penetrating cone. We propose an alternative continuum model to derive the modulus of elasticity and yield strength of snow based on the widely used cavity expansion model in soils. We compare results from these approaches based on laboratory cone penetration measurements in snow samples of different densities and structural sizes. Results suggest that the micromechanical model underestimates the snow elastic modulus for dense samples by 2 orders of magnitude. By comparison with the cavity expansion‐based model, some of the discrepancy is attributed to low sensitivity of the micromechanical model to the snow elastic modulus. Reasons and implications of this discrepancy are discussed, and possibilities to enhance both methodologies are proposed.

Snow is a highly porous material with properties that may strongly differ depending on the environmental conditions. On slopes, the layered snowpack may fail and avalanches occur. Hence, knowing how snow deforms and fails is essential for understanding and modeling snow avalanche release and flow. The response of snow to imposed load or deformation and the failure behavior depends on the rate of the applied load or of displacement and follows from the complex, foam like, microstructure of snow and the properties of ice. The mechanical response and failure of snow can well be captured with fiber bundle models (FBM). We review the use of FBMs for studying snow failure. In particular, we show how FBMs have been used for studying the micromechanical processes, such as ice sintering and viscous deformation, to reproduce the results of snow failure experiments. Moreover, FBMs can reproduce signatures of acoustic emissions (AE) preceding snow failure, ease the AE interpretation, and shed light on the underlying progressive failure process.