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Structures with artificially engineered mechanical properties, often called mechanical metamaterials, are interesting for their tunable functionality. Various types of mechanical metamaterials have been proposed in the literature, designed to harness light or magnetic interactions, structural instabilities in slender or hollow structures, and contact friction. However, most of the designs are ideally engineered without any imperfections, in order to perform deterministically as programmed. Here, we study the mechanical performance of randomly stacked cylindrical shells, which act as a disordered mechanical metamaterial. Combining experiments and simulations, we demonstrate that the stacked shells can absorb and store mechanical energy upon compression by exploiting large deformation and relocation of shells, snap-fits, and friction. Although shells are oriented randomly, the system exhibits statistically robust mechanical performance controlled by friction and geometry. Our results demonstrate that the rearrangement of flexible components could yield versatile and predictive mechanical responses. Mechanical metamaterials are artificially designed structures with tunable behavior, typically obeying precisely programmed dynamics. Here, a metamaterial based on randomly stacked flexible cylindrical shells provides a disordered yet statistically robust and controllable structure for mechanical energy dissipation and storage.

A confined drop flowing against a rectangular obstacle placed off-center in a microfluidic conduct may break into two daughter droplets of different volumes when the capillary number at play C , the ratio between viscous and capillary effects, exceeds a threshold C b . We study the influence of the viscosity ratio p between dispersed and continuous phases on that process by discussing the experimental variations of the volume fraction of the daughter droplets with C and p . A single free parameter model that yields an analytical formula for the volume of the daughter droplets as a function of the variables at play is introduced. Using this model that well describes our experiments, we accurately determine C b for different p . Our findings underline the key role of confinement on drop breakup showing that C b is three orders of magnitude smaller than the value found in bulk experiments under shear flow and that C b decreases with p in our study.

Among the properties of light that dictate its mechanical effects, polarization has held a special place since the mechanical identification of the photon spin1. Nowadays, little surprise might be expected from the mechanical action of linearly polarized weakly focused (paraxial) beams on transparent and homogeneous dielectrics. Still, here we unveil vectorial optomechanical effects mediated by the material anisotropy and the longitudinal field component inherent to real-world beams2,3. Experimentally, this is demonstrated by using an elastic anisotropic medium prone to exhibit a sensitive and reversible effect, that is, a nematic liquid crystal, and our results are generalized to vector beams4. This represents an alternative to irreversible damaging approaches restricted to strongly non-paraxial fields5. The reported creation of multiple self-induced lenses from a single beam also open up topology assisted all-optical information routing strategies. Moreover, our findings point out the transverse internal optical energy flows (spin and orbital)6 as novel triggers to tailor structured optical nonlinearities.Researchers demonstrate vectorial optomechanical effects using a nematic liquid crystal and report creation of multiple self-induced lenses from a single beam.

Gel layers bound to a rigid substrate are used in cell culture to control differentiation and migration and to lower the friction and tailor the wetting of solids. Their thickness, often considered a negligible parameter, affects cell mechanosensing or the shape of sessile droplets. Here, we show that the adjustment of coating thickness provides control over energy dissipation during the spreading of flowing matter on a gel layer. We combine experiments and theory to provide an analytical description of both the statics and the dynamics of the contact line between the gel, the liquid, and the surrounding atmosphere. We extract from this analysis a hitherto-unknown scaling law that predicts the dynamic contact angle between the three phases as a function of the properties of the coating and the velocity of the contact line. Finally, we show that droplets moving on vertical substrates coated with gel layers having linear thickness gradients drift toward regions of higher energy dissipation. Thus, thickness control opens the opportunity to design a priori the path followed by large droplets moving on gel-coated substrates. Our study shows that thickness is another parameter, besides surface energy and substrate mechanics, to tune the dynamics of liquid spreading and wetting on a compliant coating, with potential applications in dew collection and free-surface flow control.

The development of self-propelled particles at the micro- and the nanoscale has sparked a huge potential for future applications in active matter physics, microsurgery, and targeted drug delivery. However, while the latter applications provoke the quest on how to optimally navigate towards a target, such as e.g. a cancer cell, there is still no simple way known to determine the optimal route in sufficiently complex environments. Here we develop a machine learning-based approach that allows us, for the first time, to determine the asymptotically optimal path of a self-propelled agent which can freely steer in complex environments. Our method hinges on policy gradient-based deep reinforcement learning techniques and, crucially, does not require any reward shaping or heuristics. The presented method provides a powerful alternative to current analytical methods to calculate optimal trajectories and opens a route towards a universal path planner for future intelligent active particles.

A classic paradigm of soft and extensible polymer materials is the difficulty of combining reversible elasticity with high fracture toughness, in particular for moduli above 1 MPa. Our recent discovery of multiple network acrylic elastomers opened a pathway to obtain precisely such a combination. We show here that they can be seen as true molecular composites with a well–cross-linked network acting as a percolating filler embedded in an extensible matrix, so that the stress–strain curves of a family of molecular composite materials made with different volume fractions of the same cross-linked network can be renormalized into a master curve. For low volume fractions (<3%) of cross-linked network, we demonstrate with mechanoluminescence experiments that the elastomer undergoes a strong localized softening due to scission of covalent bonds followed by a stable necking process, a phenomenon never observed before in elastomers. The quantification of the emitted luminescence shows that the damage in the material occurs in two steps, with a first step where random bond breakage occurs in the material accompanied by a moderate level of dissipated energy and a second step where a moderate level of more localized bond scission leads to a much larger level of dissipated energy. This combined use of mechanical macroscopic testing and molecular bond scission data provides unprecedented insight on how tough soft materials can damage and fail.

Merging two periodic droplet trains at a T-junction, we investigate the production of one-dimensional trains of drops of alternating composition. The structure of these trains consists of a succession of well-defined patterns and defects. A discrete model recently introduced to describe the structure of double emulsions made with two-step microfluidic dripping techniques predicts the nature of these patterns and their scheme of arrangement in a train as functions of the rates at which the two droplet trains reach the junction. Millifluidic experiments validate these predictions.

Modern theories of melting of two-dimensional systems are discussed that are mainly based on the concepts of the Berezinskii–Kosterlitz–Thouless (BKT) theory of phase transitions in two-dimensional systems with continuous symmetry. Today there exist three basic scenarios of melting of two-dimensional crystals. First of all, this is the Berezinskii–Kosterlitz–Thouless–Halperin–Nelson–Young (BKTHNY) theory, in which two-dimensional crystals are melted through two BKT-type continuous transitions with an intermediate hexatic phase. In this case a first-order phase transition can also occur. The third scenario has recently been proposed by Bernard and Krauth (BK), in which melting can occur through a BKT-type transition; in this case the hexatic phase–isotropic fluid transition is a first-order transition. The review presents a critical analysis of the approaches used to determine the parameters and the type of transition by computer simulation methods.

Mutually interacting components form complex systems and these components usually have long- range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders （MF-X-WL）. We test the validity of tile MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions （bFBMs） with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets （returns and volatilities） and online worlds （online numbers of different genders and different societies） and determine intriguing joint multifractal behavior.