Ref HAL: hal-04279663_v1
Ref Arxiv: 2310.16770
Ref. & Cit.: NASA ADS
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Large groups of active cilia collectively beat in a fluid medium as metachronal waves, essential for some microorganisms motility and for flow generation in mucociliary clearance. Several models can predict the emergence of metachronal waves, but what controls the properties of metachronal waves is still unclear. Here, we investigate numerically a simple model for cilia in the presence of noise on regular lattices in one- and two-dimensions. We characterize the wave using spatial correlation and the frequency of collective beating. Our results clearly show that the viscosity of the fluid medium does not affect the wavelength; the activity of the cilia does. These numerical results are supported by a dimensional analysis, which is expected to be robust against the model for active force generation, unless surrounding fluid influences the cilia activity. Interestingly, enhancement of cilia activity increases the wavelength and decreases the beating frequency, keeping the wave velocity almost unchanged. These results might have significance in understanding paramecium locomotion and mucociliary clearance diseases.

Commentaires: 6 pages, 5 figures

Ref HAL: hal-04260533_v1
DOI: 10.1088/2515-7639/ad06cc
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Soft materials are usually defined as materials made of mesoscopic entities, often self-organized, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterize them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g., tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This roadmap paper intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterization, instrumental, simulation and theoretical methods as well as general concepts.

Ref HAL: hal-04242193_v1
DOI: 10.1103/PhysRevX.13.021030
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The fracture of materials can take place below the critical failure condition via the slow accumulation ofinternal damage followed by fast crack propagation. While failure due to subcritical fracture accounts formost of the structural failures in use, it is theoretically challenging to bridge the gap between moleculardamage and fracture mechanics, not to mention predicting the occurrence of sudden fracture, due to thelack of current nondestructive detection methods with suitable resolution. Here, we investigate the fractureof elastomers by using simultaneously space- and time-resolved multispeckle diffusing wave spectroscopy(MSDWS) and molecular damage mapping by mechanophore. We identify a fracture precursor thataccelerates the strain-rate field over a large area (cm2 scale), at considerably long times (up to thousands ofseconds) before macroscopic fracture occurs. By combining deformation or damage mapping and finiteelementsimulations of the crack-tip strain field, we unambiguously attribute the macroscopic responsein elastic deformation to highly localized molecular damage that occurs over a sample area of about0.01 mm2. By unveiling this mechanism of interaction between the microscopic molecular damage and theminute but long-ranged elastic deformation field, we are able to develop MSDWS as a flexible, wellcontrolledtool to characterize and predict microscopic damage well before it becomes critical. Tested usingordinary imaging and simple image processing, MSDWS predictions are proven applicable for unlabeledand even opaque samples under different fracture conditions.

Ref HAL: hal-04228262_v1
Ref Arxiv: 2210.07119
DOI: 10.1103/PhysRevLett.131.119801
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Ref HAL: hal-04228257_v1
Ref Arxiv: 2304.12039
DOI: 10.1063/5.0168506
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Upon heating, ultrastable glassy films transform into liquids via a propagating equilibration front, resembling the heterogeneous melting of crystals. A microscopic understanding of this robust phenomenology is, however, lacking because experimental resolution is limited. We simulate the heterogeneous transformation kinetics of ultrastable configurations prepared using the swap Monte Carlo algorithm, thus allowing a direct comparison with experiments. We resolve the liquid–glass interface both in space and in time as well as the underlying particle motion responsible for its propagation. We perform a detailed statistical analysis of the interface geometry and kinetics over a broad range of temperatures. We show that the dynamic heterogeneity of the bulk liquid is passed on to the front that propagates heterogeneously in space and intermittently in time. This observation allows us to relate the averaged front velocity to the equilibrium diffusion coefficient of the liquid. We suggest that an experimental characterization of the interface geometry during the heterogeneous devitrification of ultrastable glassy films could provide direct experimental access to the long-sought characteristic length scale of dynamic heterogeneity in bulk supercooled liquids.

Ref HAL: hal-04301565_v1
Ref Arxiv: 2307.08815
DOI: 10.1039/D3SM00625E
Ref. & Cit.: NASA ADS
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The evaporation of drops of colloidal suspensions plays an important role in numerous contexts, such as the production of powdered dairies, the synthesis of functional supraparticles, and virus and bacteria survival in aerosols or drops on surfaces. The presence of colloidal particles in the evaporating drop eventually leads to the formation of a dense shell that may undergo a shape instability. Previousworks propose that, for drops evaporating very fast, the instability occurs when the particles form a rigid porous solid, constituted of permanently aggregated particles at random close packing. To date, however, no measurements could directly test this scenario and assess whether it also applies todrops drying at lower evaporation rates, severely limiting our understanding of this phenomenon and the possibility of harnessing it in applications. Here, we combine macroscopic imaging and spaceand time-resolved measurements of the microscopic dynamics of colloidal nanoparticles in drying drops sitting on a hydrophobic surface, measuring the evolution of the thickness of the shell and the spatial distribution and mobility of the nanoparticles. We find that, above a threshold evaporationrate, the drop undergoes successively two distinct shape instabilities, invagination and cracking. While permanent aggregation of nanoparticles accompanies the second instability, as hypothesized in previous works on fast-evaporating drops, we show that the first one results from a reversible glass transition of the shell, unreported so far. We rationalize our findings and discuss their implicationsin the framework of a unified state diagram for the drying of colloidal drops sitting on a hydrophobic surface.

Ref HAL: hal-04216928_v1
DOI: 10.1016/j.rinp.2023.106782
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Particle interactions play a fundamental role in condensed matter physics because they determine both the dynamical behavior and the equilibrium physical properties of a given system at temperature and density . However, these interactions are not always precisely known in experiments, or in simulations of coarse-grained systems. A direct determination of the pair interaction potential in a given system could help understand observed behaviors and make further predictions. Given a number of equilibrium configurations of a system, it would be desirable to find a method to directly determine the pair potential by only using these snapshots. We propose two simple methods towards this goal for the specific case of the systems in 3 dimensional space with the Mie potential, which includes two exponents as and . Well-equilibrated system configurations are produced by molecular dynamical simulations using the Mie potential with different exponent combinations . In the first method, we construct a correspondence between the value and location of the first peak of the radial distribution function and the couple , which allows us to determine the potential with an accuracy of 100% when given a set of equilibrium configurations for an unknown potential. In the second method, we train an artificial neural network to learn this correspondence. We find that all combinations are correctly predicted. Both methods support the idea that the pairwise interaction can often easily be inferred by using equilibrium snapshots.