activity
20162021
most citedMechanical stability of particle-stabilized droplets under micropipette aspiration

14 citations · 15 across the 3 of their papers we have counts for

collaborators

6 papers

physics.data-an2021

Unravelling the origins of anomalous diffusion: from molecules to migrating storks

Ohad Vilk, Erez Aghion, Tal Avgar +10

Anomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has bee…

physics.bio-ph2020★ 1 cited

Hydrodynamics of a dense flock of sheep: edge motion and long-range correlations

Marine de Marcken, Raphael Sarfati

Sheep are gregarious animals, and they often aggregate into dense, cohesive flocks, especially under stress. In this paper, we use image processing tools to analyze a publicly avai…

cond-mat.soft2020

Temporally Anticorrelated Subdiffusion in Water Nanofilms on Silica Suggests Near-Surface Viscoelasticity

Raphael Sarfati, Daniel K. Schwartz

We used single-molecule tracking to probe the local rheology of interfacial water. Fluorescent rhodamine molecules were tracked on silica surfaces as a function of ambient relative…

cond-mat.mtrl-sci2017

Direct Measurement of Strain-dependent Solid Surface Stress

Qin Xu, Katharine E. Jensen, Rostislav Boltyanskiy +3

Surface stress, also known as surface tension, is a fundamental material property of any interface. However, measurements of solid surface stress in traditional engineering materia…

cond-mat.soft2016★ 14 cited

Mechanical stability of particle-stabilized droplets under micropipette aspiration

Niveditha Samudrala, Jin Nam, Raphael Sarfati +2

We investigate the mechanical behavior of particle-stabilized droplets using micropipette aspiration. We observe that droplets stabilized with amphiphilic dumbbell-shaped particles…

cond-mat.soft2016

Maximum likelihood estimations of force and mobility from short single Brownian trajectories

Raphael Sarfati, Jerzy Blawzdziewicz, Eric R. Dufresne

We describe a method to extract force and diffusion parameters from single trajectories of Brownian particles based on the principle of maximum likelihood. The analysis is well-sui…