ALBERT

Description: Self-assembling peptide hydrogels can be modified regarding their biodegradability, their chemical and mechanical properties and their nanofibrillar structure. Thus, self-assembling peptide hydrogels might be suitable scaffolds for regenerative therapies and tissue engineering. Owing to the use of various peptide concentrations and buffer compositions, the self-assembling peptide hydrogels might be influenced regarding their mechanical characteristics. Therefore, the mechanical properties and stability of a set of self-assembling peptide hydrogels, consisting of 11 amino acids, made from four beta sheet self-assembling peptides in various peptide concentrations and buffer compositions were studied. The formed self-assembling peptide hydrogels exhibited stiffnesses ranging from 0.6 to 205 kPa. The hydrogel stiffness was mostly affected by peptide sequence followed by peptide concentration and buffer composition. All self-assembling peptide hydrogels examined provided a nanofibrillar network formation. A maximum self-assembling peptide hydrogel dissolution of 20% was observed for different buffer solutions after 7 days. The stability regarding enzymatic and bacterial digestion showed less degradation in comparison to the self-assembling peptide hydrogel dissolution rate in buffer. The tested set of self-assembling peptide hydrogels were able to form stable scaffolds and provided a broad spectrum of tissue-specific stiffnesses that are suitable for a regenerative therapy.

Description: The temporal evolution of snowpacks is important for avalanche forecasts and flood predictions. Especially in complex alpine terrain, such as avalanche starting zones, it is until now difficult to derive continuous and non-destructive information on snow parameters. In our previous work, we already demonstrated the feasibility to quantitatively derive snowpack properties and monitor their evolution in time using an upward-looking ground penetrating radar (upGPR) that was buried underneath the snow. To determine some properties, we still needed additional information such as independently measured snow height. To overcome these limitations, we present two promising methods: (1) We combined the upGPR travel-time information with tomographic GPS signal strength losses. This combination allowed determining liquid water content, snow height and snow water equivalent from beneath the snow cover without using external information. The snow parameters derived by combining upGPR and GPS data are in good agreement with conventional sensors as e.g. laser distance gauges or snow pillows. As the GPS sensors are cheap, they can easily be installed in parallel with upGPR systems. (2) To fully exploit the information content of upGPR data, and thus to at least partially compensate for the lack of information, we applied full-waveform inversion (FWI) techniques. We refined the model of the snowpack by repeated forward modeling the waveforms and updating the model parameters to match it with recorded data. This allowed us to determine the density and the liquid water content for each layer in the snowpack. Both approaches show a high potential for alpine management tasks.

Description: The temporal evolution of Alpine snowpacks is important for assessing water supply, hydropower generation, flood predictions and avalanche forecasts. Especially in high mountain regions with an extremely varying topography, it is until now often difficult to derive continuous and non-destructive information on snow parameters. Since autumn 2012, we are running a new low-cost GPS (Global Positioning System) snow measurement experiment at the high alpine study site Weissfluhjoch (2450 m a.s.l.) in Switzerland. The globally and freely broadcasted GPS L1-band (1.57542 GHz) was continuously recorded with GPS antennas, which are installed at the ground surface underneath the snowpack. GPS raw data, containing carrier-to-noise power density ratio (C/N0) as well as elevation and azimuth angle information for each time step of 1 s, was stored and analyzed for all 32 GPS satellites. Since the dielectric permittivity of an overlying wet snowpack influences microwave radiation, the bulk volumetric liquid water content as well as daily melt-freeze cycles can be derived non-destructively from GPS signal strength losses and external snow height information. This liquid water content information is qualitatively in good accordance with meteorological and snow-hydrological data and quantitatively highly agrees with continuous data derived from an upward-looking ground-penetrating radar (upGPR) working in a similar frequency range. As a promising novelty, we combined the GPS signal strength data with upGPR travel-time information of active impulse radar rays to the snow surface and back from underneath the snow cover. This combination allows determining liquid water content, snow height and snow water equivalent from beneath the snow cover without using any other external information. The snow parameters derived by combining upGPR and GPS data are in good agreement with conventional sensors as e.g. laser distance gauges or snow pillows. As the GPS sensors are cheap, they can easily be installed in parallel with further upGPR systems or as sensor networks to monitor the snowpack evolution in avalanche paths or at a larger scale in an entire hydrological basin to derive distributed melt-water runoff information.

Description: Information on snowpack properties is highly relevant for avalanche warning systems, flood predictions and hydropower management within alpine regions influenced by seasonal snow cover. However, snow measurements are often scarce and labour-intense and it is still challenging to monitor snow parameters with sufficient temporal and spatial resolution, especially in remote complex terrain. Since 2012, we are running three low-cost GPS (Global Positioning System) receivers at the high-alpine study site Weissfluhjoch in Switzerland. The sensors record the globally and freely broadcasted GPS L1-band data continuously above and underneath the snow cover. Snow liquid water content and daily melt-freeze cycles were successfully calculated based on GPS signal strength losses, complex permittivity models and external snow height information. The results show high accordance with meteorological and snow-hydrological data. The evolution of liquid water content derived by GPS agrees very well (RMSE: 0.4-0.7 pp) with simultaneous non-destructive upward-looking ground-penetrating radar (upGPR) measurements from below the snow cover. Moreover, we aim to determine further snow properties by analysing temporal changes in the received GPS carrier-to-noise-power-density ratio and carrier phase information for all 32 GPS satellites, whereof preliminary results will be presented, too. Due to its non-destructive setup, low costs and low power consumption, networks of these in-situ GNSS (Global Navigation Satellite System) sensors can be used for a applications in remote areas, for instance to cover a better geographical distribution of snow measurements aiming to support users in the water supply and hazard management sectors.