TY - JOUR A1 - Drescher, Daniela A1 - Orts Gil, Guillermo A1 - Laube, G. A1 - Natte, Kishore A1 - Veh, R.W. A1 - Österle, Werner A1 - Kneipp, Janina T1 - Toxicity of amorphous silica nanoparticles on eukaryotic cell model is determined by particle agglomeration and serum protein adsorption effects N2 - Cell cultures form the basis of most biological assays conducted to assess the cytotoxicity of nanomaterials. Since the molecular environment of nanoparticles exerts influence on their physicochemical properties, it can have an impact on nanotoxicity. Here, toxicity of silica nanoparticles upon delivery by fluid-phase uptake is studied in a 3T3 fibroblast cell line. Based on XTT viability assay, cytotoxicity is shown to be a function of (1) particle concentration and (2) of fetal calf serum (FCS) content in the cell culture medium. Application of dynamic light scattering shows that both parameters affect particle agglomeration. The DLS Experiments verify the stability of the nanoparticles in culture medium without FCS over a wide range of particle concentrations. The related toxicity can be mainly accounted for by single silica nanoparticles and small agglomerates. In contrast, agglomeration of silica nanoparticles in all FCS-containing media is observed, resulting in a decrease of the associated toxicity. This result has implications for the evaluation of the cytotoxic potential of silica nanoparticles and possibly also other nanomaterials in standard cell culture. KW - Agglomeration KW - Cytotoxicity KW - Fibroblast cells KW - Serum proteins KW - Silica nanoparticles PY - 2011 DO - https://doi.org/10.1007/s00216-011-4893-7 SN - 1618-2642 SN - 1618-2650 VL - 400 IS - 5 SP - 1367 EP - 1373 PB - Springer CY - Berlin AN - OPUS4-23678 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Veh, G. A1 - Lützow, N. A1 - Tamm, J. A1 - Luna, L.V. A1 - Hugonnet, R. A1 - Vogel, Kristin A1 - Geertsema, M. A1 - Clague, J.J. A1 - Korup, O. T1 - Less extreme and earlier outbursts of ice-dammed lakes since 1900 N2 - Episodic failures of ice-dammed lakes have produced some of the largest floods in history, with disastrous consequences for communities in high mountains. Yet, estimating changes in the activity of ice-dam failures through time remains controversial because of inconsistent regional flood databases. Here, by collating 1,569 ice-dam failures in six major mountain regions, we systematically assess trends in peak discharge, volume, annual timing and source elevation between 1900 and 2021. We show that extreme peak flows and volumes (10 per cent highest) have declined by about an order of magnitude over this period in five of the six regions, whereas median flood discharges have fallen less or have remained unchanged. Ice-dam floods worldwide today originate at higher elevations and happen about six weeks earlier in the year than in 1900. Individual ice-dammed lakes with repeated outbursts show similar negative trends in magnitude and earlier occurrence, although with only moderate correlation to glacier thinning8. We anticipate that ice dams will continue to fail in the near future, even as glaciers thin and recede. Yet widespread deglaciation, projected for nearly all regions by the end of the twenty-first century9, may bring most outburst activity to a halt. KW - Outburst floods KW - Bayesian hierachical modelling KW - Trends in extreme events PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-570529 DO - https://doi.org/10.1038/s41586-022-05642-9 VL - 614 IS - 7949 SP - 701 EP - 707 AN - OPUS4-57052 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Vogel, K. A1 - Sieg, T. A1 - Veh, G. A1 - Fiedler, B. A1 - Moran, T. A1 - Peter, M. A1 - Rottler, E. A1 - Bronstert, A. T1 - Natural Hazards in a Changing World: Methods for Analyzing Trends and Non‐Linear Changes N2 - Estimating the frequency and magnitude of natural hazards largely hinges on stationary models, which do not account for changes in the climatological, hydrological, and geophysical baseline conditions. Using five diverse case studies encompassing various natural hazard types, we present advanced statistical and machine learning methods to analyze and model transient states from long‐term inventory data. A novel storminess metric reveals increasing European winter windstorm severity from 1950 to 2010. Non‐stationary extreme value models quantify trends, seasonal shifts, and regional differences in extreme precipitation for Germany between 1941 and 2021. Utilizing quantile sampling and empirical mode decomposition on 148 years of daily weather and discharge data in the European Alps, we assess the impacts of changing snow cover, precipitation, and anthropogenic river network modifications on river runoff. Moreover, a probabilistic framework estimates return periods of glacier lake outburst floods in the Himalayas, demonstrating large differences in 100‐year flood levels. Utilizing a Bayesian change point algorithm, we track the onset of increased seismicity in the southern central United States and find correlation with wastewater injections into deep wells. In conclusion, data science reveals transient states for very different natural hazard types, characterized by diverse forms of change, ranging from gradual trends to sudden change points and from altered seasonality to overall intensity variations. In synergy with the physical understanding of Earth science, we gain important new insights into the dynamics of the studied hazards and their possible mechanisms. KW - Natural hazards KW - Time dependent modeling KW - Transient state KW - Data science KW - Climate change PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-601540 DO - https://doi.org/10.1029/2023EF003553 VL - 12 IS - 5 SP - 1 EP - 29 PB - American Geophysical Union (AGU) AN - OPUS4-60154 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -