1,457 research outputs found
Direct 2D measurement of time-averaged forces and pressure amplitudes in acoustophoretic devices using optical trapping
Ultrasonic standing waves are increasingly applied in the manipulation and sorting of micrometer-sized particles in microfluidic cells. To optimize the performance of such devices, it is essential to know the exact forces that the particles experience in the acoustic wave. Although much progress has been made via analytical and numerical modeling, the reliability of these methods relies strongly on the assumptions used, e.g. the boundary conditions. Here, we have combined an acoustic flow cell with an optical laser trap to directly measure the force on a single spherical particle in two dimensions. While performing ultrasonic frequency scans, we measured the time-averaged forces on single particles that were moved with the laser trap through the microfluidic cell. The cell including piezoelectric transducers was modeled with finite element methods. We found that the experimentally obtained forces and the derived pressure fields confirm the predictions from theory and modeling. This novel approach can now be readily expanded to other particle, chamber, and fluid regimes and opens up the possibility of studying the effects of the presence of boundaries, acoustic streaming, and non-linear fluids.ISSN:1473-0197ISSN:1473-018
Contrasting local and long-range-transported warm ice-nucleating particles during an atmospheric river in coastal California, USA
Ice-nucleating particles (INPs) have been found to influence the amount, phase and efficiency of precipitation from winter storms, including atmospheric rivers.Warm INPs, those that initiate freezing at temperatures warmer than -10°C, are thought to be particularly impactful because they can create primary ice in mixed-phase clouds, enhancing precipitation efficiency. The dominant sources of warm INPs during atmospheric rivers, the role of meteorology in modulating transport and injection of warm INPs into atmospheric river clouds, and the impact of warm INPs on mixed-phase cloud properties are not well-understood. In this case study, time-resolved precipitation samples were collected during an atmospheric river in northern California, USA, during winter 2016. Precipitation samples were collected at two sites, one coastal and one inland, which are separated by about 35 km. The sites are sufficiently close that air mass sources during this storm were almost identical, but the inland site was exposed to terrestrial sources of warm INPs while the coastal site was not. Warm INPs were more numerous in precipitation at the inland site by an order of magnitude. Using FLEXPART (FLEXible PARTicle dispersion model) dispersion modeling and radar-derived cloud vertical structure, we detected influence from terrestrial INP sources at the inland site but did not find clear evidence of marine warm INPs at either site.We episodically detected warm INPs from long-range-transported sources at both sites. By extending the FLEXPART modeling using a meteorological reanalysis, we demonstrate that long-range-transported warm INPs were observed only when the upper tropospheric jet provided transport to cloud tops. Using radar-derived hydrometeor classifications, we demonstrate that hydrometeors over the terrestrially influenced inland site were more likely to be in the ice phase for cloud temperatures between 0 and -10°C. We thus conclude that terrestrial and long-rangetransported aerosol were important sources of warm INPs during this atmospheric river. Meteorological details such as transport mechanism and cloud structure were important in determining (i) warm INP source and injection temperature and (ii) ultimately the impact of warm INPs on mixed-phase cloud properties
Propranolol restricts the mobility of single EGF-receptors on the cell surface before their internalization
The epidermal growth factor receptor is involved in morphogenesis, proliferation and cell migration. Its up-regulation during tumorigenesis makes this receptor an interesting therapeutic target. In the absence of the ligand, the inhibition of phosphatidic acid phosphohydrolase activity by propranolol treatment leads to internalization of empty/inactive receptors. The molecular events involved in this endocytosis remain unknown. Here, we quantified the effects of propranolol on the mobility of single quantum-dot labelled receptors before the actual internalization took place. The single receptors showed a clear stop-and-go motion; their diffusive tracks were continuously interrupted by sub-second stalling events, presumably caused by transient clustering. In the presence of propranolol we found that: i) the diffusion rate reduced by 22 %, which indicates an increase in drag of the receptor. Atomic force microscopy measurements did not show an increase of the effective membrane tension, such that clustering of the receptor remains the likely mechanism for its reduced mobility. ii) The receptor got frequently stalled for longer periods of multiple seconds, which may signal the first step of the internalization process
Generation of Vorticity and Velocity Dispersion by Orbit Crossing
We study the generation of vorticity and velocity dispersion by orbit
crossing using cosmological numerical simulations, and calculate the
backreaction of these effects on the evolution of large-scale density and
velocity divergence power spectra. We use Delaunay tessellations to define the
velocity field, showing that the power spectra of velocity divergence and
vorticity measured in this way are unbiased and have better noise properties
than for standard interpolation methods that deal with mass weighted
velocities. We show that high resolution simulations are required to recover
the correct large-scale vorticity power spectrum, while poor resolution can
spuriously amplify its amplitude by more than one order of magnitude. We
measure the scalar and vector modes of the stress tensor induced by orbit
crossing using an adaptive technique, showing that its vector modes lead, when
input into the vorticity evolution equation, to the same vorticity power
spectrum obtained from the Delaunay method. We incorporate orbit crossing
corrections to the evolution of large scale density and velocity fields in
perturbation theory by using the measured stress tensor modes. We find that at
large scales (k~0.1 h/Mpc) vector modes have very little effect in the density
power spectrum, while scalar modes (velocity dispersion) can induce percent
level corrections at z=0, particularly in the velocity divergence power
spectrum. In addition, we show that the velocity power spectrum is smaller than
predicted by linear theory until well into the nonlinear regime, with little
contribution from virial velocities.Comment: 27 pages, 14 figures. v2: reorganization of the material, new
appendix. Accepted by PR
Spin alignment of dark matter haloes in filaments and walls
The MMF technique is used to segment the cosmic web as seen in a cosmological
N-body simulation into wall-like and filament-like structures. We find that the
spins and shapes of dark matter haloes are significantly correlated with each
other and with the orientation of their host structures. The shape orientation
is such that the halo minor axes tend to lie perpendicular to the host
structure, be it a wall or filament. The orientation of the halo spin vector is
mass dependent. Low mass haloes in walls and filaments have a tendency to have
their spins oriented within the parent structure, while higher mass haloes in
filaments have spins that tend to lie perpendicular to the parent structure.Comment: 4 pages, 2 figure
Nitrogen deposition shows no consistent negative nor positive effect on the response of forest productivity to drought across European FLUXNET forest sites
Atmospheric reactive nitrogen (N) deposition is an important driver of carbon (C) sequestration in forest ecosystems. Previous studies have focused on N-C interactions in various ecosystems; however, relatively little is known about the impact of N deposition on ecosystem C cycling during climate extremes such as droughts. With the occurrence and severity of droughts likely to be exacerbated by climate change, N deposition—drought interactions remain one of the key uncertainties in process-based models to date. This study aims to contribute to the understanding of N deposition-drought dynamics on gross primary production (GPP) in European forest ecosystems. To do so, different soil water availability indicators (Standardized Precipitation Evapotranspiration Index (SPEI), soil volumetric water) and GPP measurements from European FLUXNET forest sites were used to quantify the response of forest GPP to drought. The computed drought responses of the forest GPP to drought were linked to modelled N deposition estimates for varying edaphic, physiological, and climatic conditions. Our result showed a differential response of forest ecosystems to the drought indicators. Although all FLUXNET forest sites showed a coherent dependence of GPP on N deposition, no consistent or significant N deposition effect on the response of forest GPP to drought could be isolated. The mean response of forest GPP to drought could be predicted for forests with Pinus trees as dominant species (R2 = 0.85, RMSE = 8.1). After extracting the influence of the most prominent parameters (mean annual temperature and precipitation, forest age), however, the variability remained too large to significantly substantiate hypothesized N deposition effects. These results suggest that, while N deposition clearly affects forest productivity, N deposition is not a major nor consistent driver of forest productivity responses to drought in European forest ecosystems
Statistical methods used to combine the effective reproduction number, R(t), and other related measures of COVID-19 in the UK
In the COVID-19 pandemic, a range of epidemiological models have been used to
predict the number of new daily infections, , daily rate of exponential
growth, , and effective reproduction number, . These models differ in
their approaches (e.g. mechanistic or empirical) and/or assumptions about
spatial or age mixing, and some capture uncertainty in scientific understanding
of disease dynamics, and/or have different simplifying assumptions. Combining
estimates from multiple models to better understand the variation of these
outcome measures is important to help inform decision making. We incorporate
estimates of these outcome measures from a number of candidate models for
specific UK nations/regions using meta analysis. Random effects models have
been implemented to accommodate differing modelling approaches and assumptions
between candidate models. Restricted maximum likelihood (REML) is used to
estimate the heterogeneity variance parameter, with two approaches to calculate
the confidence interval for the combined effect: standard Wald-type intervals
and the Knapp and Hartung (KNHA) method. Approaches using REML alone and
REML+KNHA provided similar ranges of variation for and . However,
differences were observed when combining estimates on , with the REML+KNHA
approach providing more conservative confidence intervals. This is likely due
to the limited number of candidate models contributing estimates for this
outcome measure, coupled with the large variability observed between model
estimates. Utilising these meta-analysis techniques has allowed for
statistically robust combined estimates to be calculated for key COVID-19
outcome measures, allowing an overall assessment of the current response
measures with associated uncertainty. This in turn allows timely and informed
decision making based on all available information
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