Dynamics of Droplets Impacting on Aerogel, Liquid Infused, and Liquid-Like Solid Surfaces

Droplets impacting superhydrophobic surfaces have been extensively studied due to their compelling scientific insights and important industrial applications. In these cases, the commonly reported impact regime was that of complete rebound. This impact regime strongly depends on the nature of the superhydrophobic surface. Here, we report the dynamics of droplets impacting three hydrophobic slippery surfaces, which have fundamental differences in normal liquid adhesion and lateral static and kinetic liquid friction. For an air cushion-like (super)hydrophobic solid surface (Aerogel) with low adhesion and low static and low kinetic friction, complete rebound can start at a very low Weber (We) number (∼1). For slippery liquid-infused porous (SLIP) surfaces with high adhesion and low static and low kinetic friction, complete rebound only occurs at a much higher We number (>5). For a slippery omniphobic covalently attached liquid-like (SOCAL) solid surface, with high adhesion and low static friction similar to SLIPS but higher kinetic friction, complete rebound was not observed, even for a We as high as 200. Furthermore, the droplet ejection volume after impacting the Aerogel surface is 100% across the whole range of We numbers tested compared to other surfaces. In contrast, droplet ejection for SLIPs was only observed consistently when the We was above 5–10. For SOCAL, 100% (or near 100%) ejection volume was not observed even at the highest We number tested here (∼200). This suggests that droplets impacting our (super)hydrophobic Aerogel and SLIPS lose less kinetic energy. These insights into the differences between normal adhesion and lateral friction properties can be used to inform the selection of surface properties to achieve the most desirable droplet impact characteristics to fulfill a wide range of applications, such as deicing, inkjet printing, and microelectronics

Variables influencing the neural correlates of perceived risk of physical harm

Abstract Many human activities involve a risk of physical harm. However, not much is known about the specific brain regions involved in decision making regarding these risks. To explore the neural correlates of risk perception for physical harms, 19 participants took part in an event-related fMRI study while rating risky activities. The scenarios varied in level of potential harm (e.g., paralysis vs. stubbed toe), likelihood of injury (e.g., 1 chance in 100 vs. 1 chance in 1,000), and format (frequency vs. probability). Networks of brain regions were responsive to different aspects of risk information. Cortical language-processing areas, the middle temporal gyrus, and a region around the bed nucleus of stria terminalis responded more strongly to high-harm conditions. Prefrontal areas, along with subcortical ventral striatum, responded preferentially to highlikelihood conditions. Participants rated identical risks to be greater when information was presented in frequency format rather than probability format. These findings indicate that risk assessments for physical harm engage a broad network of brain regions that are sensitive to the severity of harm, the likelihood of risk, and the framing of risk information

On the origins of signal variance in FMRI of the human midbrain at high field.

Functional Magnetic Resonance Imaging (fMRI) in the midbrain at 7 Tesla suffers from unexpectedly low temporal signal to noise ratio (TSNR) compared to other brain regions. Various methodologies were used in this study to quantitatively identify causes of the noise and signal differences in midbrain fMRI data. The influence of physiological noise sources was examined using RETROICOR, phase regression analysis, and power spectral analyses of contributions in the respiratory and cardiac frequency ranges. The impact of between-shot phase shifts in 3-D multi-shot sequences was tested using a one-dimensional (1-D) phase navigator approach. Additionally, the effects of shared noise influences between regions that were temporally, but not functionally, correlated with the midbrain (adjacent white matter and anterior cerebellum) were investigated via analyses with regressors of 'no interest'. These attempts to reduce noise did not improve the overall TSNR in the midbrain. In addition, the steady state signal and noise were measured in the midbrain and the visual cortex for resting state data. We observed comparable steady state signals from both the midbrain and the cortex. However, the noise was 2-3 times higher in the midbrain relative to the cortex, confirming that the low TSNR in the midbrain was not due to low signal but rather a result of large signal variance. These temporal variations did not behave as known physiological or other noise sources, and were not mitigated by conventional strategies. Upon further investigation, resting state functional connectivity analysis in the midbrain showed strong intrinsic fluctuations between homologous midbrain regions. These data suggest that the low TSNR in the midbrain may originate from larger signal fluctuations arising from functional connectivity compared to cortex, rather than simply reflecting physiological noise