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Hypothesis: Measuring rotational and translational Brownian motion of single spherical particles reveals dissipations due to the interaction between the particle and the environment. Experiments: In this article, we show experiments where the in-plane translational and two rotational drag coefficients of a single spherical Brownian particle can be measured. These particle drags are functions of the particle size and the particle-wall distance, and of the viscous dissipations at play. We measure drag coefficients for Janus particles close to a solid wall and close to a lipid bilayer membrane. Findings: For a particle close to wall, we show that according to hydrodynamic models, particlewall distance and particle size can be determined. For a particle partially wrapped by lipid membranes, in absence of strong binding interactions, translational and rotational drags are significantly larger than the ones of non-wrapped particles. Beside the effect of the membrane viscosity, we show that dissipations in the deformed membrane cap region strongly contribute to the drag coefficients.

Atomistic molecular dynamics simulations have reached a degree of maturity that makes it possible to investigate the lipid polymorphism of model bilayers over a wide range of temperatures. However if both the fluid $L_{\alpha}$ and tilted gel $L_{\beta'}$ states are routinely obtained, the $P_{\beta'}$ ripple phase of phosphatidylcholine lipid bilayers is still unsatifactorily described. Performing simulations of lipid bilayers made of different numbers of DPPC (1,2-dipalmitoylphosphatidylcholine) molecules ranging from 32 to 512, we demonstrate that the tilted gel phase $L_{\beta'}$ expected below the pre-transition cannot be obtained for large systems ($>$ 94 DPPC molecules) through common simulations settings or temperature treatments. Large systems are instead found in a disordered gel phase which display configurations, topography and energies reminiscent from the ripple phase $P_{\beta'}$ observed between the pretransition and the main melting transition. We show how the state of the bilayers below the pretransition can be controlled and depends on thermal history and conditions of preparations. A mechanism for the observed topographic instability is suggested., Comment: 23 pages, 10 figures, Supplementary information file of 32 pages, 2 tables, 21 figures

Health concerns associated with the advent of nanotechnologies have risen sharply when it was found that particles of nanoscopic dimensions reach the cell lumina. Plasma and organelle lipid membranes, which are exposed to both the incoming and the engulfed nanoparticles, are the primary targets of possible disruptions. However, reported adhesion, invagination and embedment of nanoparticles (NPs) do not compromise the membrane integrity, precluding direct bilayer damage as a mechanism for toxicity. Here it is shown that a lipid membrane can be torn by small enough nanoparticles, thus unveiling mechanisms for how lipid membrane can be compromised by tearing from nanoparticles. Surprisingly, visualization by cryo transmission electron microscopy (cryo-TEM) of liposomes exposed to nanoparticles revealed also that liposomal laceration is prevented by particle abundance. Membrane destruction results thus from a subtle particle-membrane interplay that is here elucidated. This brings into a firmer molecular basis the theorized mechanisms of nanoparticle effects on lipid bilayers and paves the way for a better assessment of nanoparticle toxicity.

The $L_\alpha$ phase of lipid bilayers is a fluid self-assembled state, key to the formulation of cosmetics, detergents and pharmaceutics. Despite having been extensively scrutinized in self-assembled phospholipid or surfactant bilayers, the formation of a fluid $L_\alpha$ state has defied understanding in mixtures of fatty alcohols, surfactants and water, where is viewed as the essential step for the preparation of creamy dispersions. Here, atomistic molecular dynamics simulations show the existence of a fluid bilayer in aqueous mixtures of cetyl (C$_{16}$OH) and stearyl (C$_{18}$OH) alcohols, and cetyl-trimethylammonium chloride (CTAC). These simulated bilayer systems display not only a rich temperature phase diagram with many of the features seen in experiments but carry also the unambigous signature of fluid bilayer behavior.

We have adapted a set of classification algorithms, also known as Machine Learning, to the identification of fluid and gel domains close to the main transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers. Using atomistic molecular dynamics conformations in the low and high temperature phases as learning sets, the algorithm was trained to categorize individual lipid configurations as fluid or gel, in relation with the usual two-states phenomenological description of the lipid melting transition. We demonstrate that our machine can learn and sort lipids according to their most likely state without prior assumption regarding the nature of the order parameter of the transition. Results from our machine learning approach provides strong support in favor of a two-states model approach of membrane fluidity.

We study the compression of bundles of aligned macroscopic fibers with intrinsic shape disorder, as found in human hair and in many other natural and man-made systems. We show by a combination of experiments, numerical simulations and theory how the statistical properties of the shapes of the fibers control the collective mechanical behaviour of the bundles. This work paves the way for designing aligned fibrous matter with pre-required properties from large numbers of individual strands of selected geometry and rigidity., Comment: 14 pages, 15 figures