Active materials represent a new class of condensed matter in which motile elements may collectively form dynamic, global structures out of equilibrium. Here, we present a general strategy to reconfigure active particles into various collective states by introducing imbalanced interactions. We demonstrate the concept with computer simulations of self-propelled colloidal spheres, and experimentally validate it in a two-dimensional (2D) system of metal-dielectric Janus colloids subjected to perpendicular a.c. electric fields. The mismatched, frequency-dependent dielectric responses of the two hemispheres of the colloids allow simultaneous control of particle motility and colloidal interactions. We realized swarms, chains, clusters and isotropic gases from the same precursor particle by changing the electric-field frequency. Large-scale polar waves, vortices and jammed domains are also observed, with the persistent time-dependent evolution of their collective structure evoking that of classical materials. This strategy of asymmetry-driven active self-organization should generalize rationally to other active 2D and three-dimensional (3D) materials.

Soft structures in nature such as protein assemblies can organize reversibly into functional and often hierarchical architectures through noncovalent interactions. Molecularly encoding this dynamic capability in synthetic materials has remained an elusive goal. We report on hydrogels of peptide-DNA conjugates and peptides that organize into superstructures of intertwined filaments that disassemble upon the addition of molecules or changes in charge density. Experiments and simulations demonstrate that this response requires large scale spatial redistribution of molecules directed by strong noncovalent interactions among them. Simulations also suggest that the chemically reversible structures can only occur within a limited range of supramolecular cohesive energies. Storage moduli of the hydrogels change reversibly as superstructures form and disappear, as does the phenotype of neural cells in contact with these materials.

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Thermal energy agitates all matter, and its competition with ordering tendencies is a fundamental organizing principle in the physical world; this observation suggests that an effective temperature might emerge when external energy input enhances agitation. However, despite the repeated proposal of this concept based on kinetics for various nonequilibrium systems, the value of an effective temperature as a thermodynamic control parameter has been unclear. Here, we introduce a two-component system of driven Janus colloids, such that collisions induced by external energy sources agitate the system, and we demonstrate quantitative agreement with hallmarks of statistical thermodynamics for binary phase behavior: the archetypal phase diagram with equilibrium critical exponents, Gaussian displacement distributions, and even capillarity. The significance is to demonstrate a class of dynamical conditions under which thermodynamic analysis extends quantitatively to systems that are decidedly nonequilibrium except that the effective temperature differs from the physical temperature.active matter | colloid | temperature | thermodynamics | nonequilibrium

Water-in-salt electrolytes (WiSE) are concentrated aqueous electrolytes recently developed that are of great interest because of their possible relevance for batteries. The origin for their promising application has been ascribed to the formation of percolating nanodomains in the bulk. However, the interfacial structure of WiSE still remains to be understood. In this paper, we characterize the potential-dependent double layer of a LiTFSIbased electrolyte on a charged electrode surface. Ultramicroelectrode (UME) measurements reveal a surface-confinement effect for a ferricyanide redox species at the electrode/WiSE interface. Potential-dependent atomic force microscopy (AFM) shows the presence of layers, the structure of which changes with the applied potential. Thicker layers (6.4 and 6.7 Å) are observed at positive potentials, associated with [Li(H 2 O) x ] + ([TFSI] − ) y ion pairs, while thinner layers (2.8 and 3.3 Å) are found at negative potentials and associated with [Li(H 2 O) x ] + alone. Vibrational spectroscopy shows that the composition of the double layer also changes with potential, where [TFSI] − is enriched at positive and [Li(H 2 O) x ] + enriched at negative potentials.

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.

We present two recent applications of lattice-gas modeling techniques to electrochemical adsorption on catalytically active metal substrates: urea on Pt (100) and (bi)sulfate on Rh(111). Both systems involve the speci c adsorption of small molecules or ions on well-characterized single-crystal electrodes, and they provide a particularly good t between the adsorbate geometry and the substrate structure. The close geometric t facilitates the formation of ordered submonolayer adsorbate phases in a range of electrode potential positive of the range in which an adsorbed monolayer of hydrogen is stable. In both systems the ordered-phase region is separated from the adsorbed-hydrogen region by a phase transition, signi ed in cyclic voltammograms by a sharp current peak. Based on data from in situ radiochemical surface concentration measurements, cyclic voltammetry, and scanning tunneling microscopy, and ex situ Auger electron spectroscopy and low-energy electron di raction, we have developed speci c lattice-gas models for the two systems. These models were studied by group-theoretical ground-state calculations and numerical Monte Carlo simulations, and e ective lattice-gas interaction parameters were determined so as to provide agreement with the experimental results.

Although many works have confirmed the layering structure of nanoconfined ionic liquids (ILs), fundamental studies of their dynamic properties are less abundant. This work evaluates the resistance of molecularly thin ion layers to flow out of films confined between atomically flat surfaces. We measure the time-dependent drainage of six different ionic liquids from large surface separations down to ∼3 nm with angstrom resolution in dynamic force measurements with a surface forces apparatus and determine the effective viscosity of the films of thickness smaller than ∼10 nm by numerically solving the equation of motion. The latter requires appropriate models for the equilibrium surface forces (electrostatic, solvation, and van der Waals forces) and the Reynolds theory of lubrication to describe the hydrodynamic drag induced by the motion of the solid surface in the viscous liquid. We show that the effective viscosity of the six ILs becomes up to 2 orders of magnitude larger than the viscosity of the unconfined liquids and is quantized as a function of the number of confined ion layers. When the effective viscosity is normalized by the bulk viscosity, the data of the six ionic liquids collapse into an exponential function of the ratio between screening length and the film thickness. Given the ionic nature of the liquids and the collapse, we propose that the increase in viscosity is partially due to an electroviscous retardation. This work thus describes an electrokinetic feature of thin ionic liquid films confined in a narrow space, like a porous medium or a lubricated contact between solids, and therefore it has practical important implications in areas from energy storage and nanofluidics to tribology.