Universal nature of particle displacements close to glass and jamming transitions

Decoding biomolecular condensate dynamics: an energy landscape approach

Many eukaryotic proteins and RNAs contain low-complexity domains (LCDs) with a strong propensity for binding and driving phase separation into biomolecular condensates. Mutations in LCDs frequently disrupt condensate dynamics, resulting in pathological transitions to solid-like states. Understanding how the molecular sequence grammar of LCDs governs condensate dynamics is essential for uncovering their biological functions and the evolutionary forces that shape these sequences. To this end, we present an energy landscape framework that operates on a continuous 'stickiness' energy scale rather than relying on an explicit alphabet-based sequence. Sequences are characterized by Wasserstein distance relative to thoroughly shuffled or random counterparts. Armed with an energy landscape framework, map diagrams of material and dynamical properties governed by key energy landscape features modulated by the degree of complexity in LCD arrangements, including the periodicity and local disorder in LCDs. Highly periodic LCD patterns promote elasticity-dominated behavior, while random sequences exhibit viscosity-dominated properties. Our results reveal that minimum sticker periodicity is crucial for maintaining fluidity in condensates, thereby avoiding transitions to glassy or solid-like states. Moreover, we demonstrate that the energy landscape framework explains the recent experimental findings on prion domains and predicts systematic alterations in condensate viscoelasticity. Our work provides a unifying perspective on the sequence-encoded material properties whereby key features of energy landscapes are conserved while sequences are variable.

Diffusion equation and rare fluctuations of the biased aging continuous-time random-walk model

We explore the fractional advection-diffusion equation and rare events associated with the ACTRW model. When waiting times have a finite mean but infinite variance, and the displacements follow a narrow distribution, the fractional operator is defined in terms of space rather than time. The far tail of the positional distribution is governed by rare events, which exhibit a different scaling compared to typical fluctuations. Additionally, we establish a strong relationship between the number of renewals and the positional distribution in the context of large deviations. Throughout the manuscript, the theoretical results are validated through simulations.

Optimizing gelation time for cell shape control through active learning

Hydrogels are popular platforms for cell encapsulation in biomedicine and tissue engineering due to their soft, porous structures, high water content, and excellent tunability. Recent studies highlight that the timing of network formation can be just as important as mechanical properties in influencing cell morphologies. Conventionally, time-dependent properties can be achieved through multi-step processes. In contrast, one-pot synthesis can improve both the efficiency and uniformity of cell encapsulation. Reaction kinetics are sensitive to temperatures and pH conditions, thus, monitoring gelation time across different conditions is essential for formulation. In this work, we choose tetra-poly(ethylene glycol) (TPEG) macromers as a model system to examine the relationship between the rate of polymer network formation and cell morphology. Previous studies of this system focused on reactions at neutral pH and room temperature, leaving much of the formulation space underexplored. We use Gaussian process regression (GPR) to minimize response surface errors by strategically selecting additional investigation points based on prior knowledge. Then we extend the knowledge from pre-trained data at neutral pH to a new surface at physiological pH. We find that the gelation time surface can effectively predict the aspect ratio of the encapsulated cells. Additionally, through focal adhesion kinase inhibition, we show that cell shape is influenced by the properties of the forming network in the initial hours as cells develop connections with the matrix. We demonstrate the utility of a high-throughput microrheology approach in enhancing fabrications of synthetic extracellular matrix and cell assemblies.

Atomistic insights into liquid crystals of board-like molecules via molecular dynamics simulation

As the temperature decreases, rigid anisotropic molecules that usually incorporate polar groups, aromatic rings or multiple bonds, orient along a common direction, eventually forming liquid-crystalline phases under specific thermodynamic conditions. This study explores the phase behavior and dynamics of board-shaped mesogens with a 1,4,5,8-tetraphenyl-anthraquinone core and four lateral arms forming an oligo(phenyleneethynylene) scaffold. These molecules are promising candidates for forming the elusive biaxial nematic phase. Through atomistic molecular dynamics simulations, we observe the formation of nematic and smectic liquid crystals, in qualitative agreement with experimental observations. To characterize the structure, we compute pair correlation functions along relevant symmetry directions and the nematic order parameter, which indicate a dominant uniaxial ordering with very weak biaxiality. In addition, we analyze the dynamics of our board-shaped mesogens along and perpendicular to the nematic director, revealing an intriguing non-Gaussian behavior and dynamical heterogeneities, with coexisting slow and fast molecules. Building on our recent simulations at the colloidal scale, which demonstrated that monodisperse board-like particles are unable to form biaxial nematics while polydisperse particles can, we hypothesize that a similar behavior may occur at the molecular scale in mixtures of molecules. Although pure-component molecular systems reveal weak biaxiality, our findings suggest that investigating mixtures of the most promising candidates, those molecules that form nematic or smectic phases, could uncover conditions conducive to the formation of biaxial nematic liquid crystals.

Characterizing the Slow Dynamics of the Swap Monte Carlo Algorithm

The swap Monte Carlo algorithm introduces nonphysical dynamic rules to accelerate the exploration of the configuration space of supercooled liquids. Its success raises deep questions regarding the nature and physical origin of the slow dynamics of dense liquids and how it is affected by swap moves. We provide a detailed analysis of the slow dynamics generated by the swap Monte Carlo algorithm at very low temperatures in two glass-forming models. We find that the slowing down of the swap dynamics is qualitatively distinct from its local Monte Carlo counterpart, with considerably suppressed dynamic heterogeneity at both single-particle and collective levels. Our results suggest that local kinetic constraints are drastically reduced by swap moves, leading to nearly Gaussian and diffusive dynamics and weakly growing dynamic correlation length scales. The comparison between static and dynamic fluctuations shows that swap Monte Carlo is a nearly optimal local equilibrium algorithm, suggesting that further progress should necessarily involve collective or driven algorithms.

Breaking the Brownian barrier: models and manifestations of molecular diffusion in complex fluids

Over a century ago, Einstein formulated a precise mathematical model for describing Brownian motion. While this model adequately explains the diffusion of micron-sized particles in fluids, its limitations become apparent when applied to molecular self-diffusion in fluids. The foundational principles of Gaussianity and Markovianity, central to the Brownian diffusion paradigm, are insufficient for describing molecular diffusion, particularly in complex fluids characterized by intricate intermolecular interactions and hindered relaxation processes. This perspective delves into the nuanced behavior observed in diverse complex fluids, including molecular self-assembly systems, deep eutectic solvents, and ionic liquids, with a specific focus on modeling self-diffusion within these media. We explore the possibility of extending diffusion models to incorporate non-Gaussian and non-Markovian effects by augmenting the Brownian model using non-local diffusion equations. Furthermore, we validate the applicability of these models by utilizing them to describe results from quasielastic neutron scattering and MD simulations.

Dynamics of switching processes: general results and applications in intermittent active motion

Systems switching between different dynamical phases is a ubiquitous phenomenon. The general understanding of such a process is limited. To this end, we present a general expression that captures fluctuations of a system exhibiting a switching mechanism. Specifically, we obtain an exact expression of the Laplace-transformed characteristic function of the particle's position. Then, the characteristic function is used to compute the effective diffusion coefficient of a system performing intermittent dynamics. Furthermore, we employ two examples: (1) generalized run-and-tumble active particle, and (2) an active particle switching its dynamics between generalized active run-and-tumble motion and passive Brownian motion. In each case, explicit computations of the spatial cumulants are presented. Our findings reveal that the particle's position probability density function exhibit rich behaviours due to intermittent activity. Numerical simulations confirm our findings.

Chlorophyll-Induced Lamellar to Nonlamellar Phase Transitions and Dynamical Heterogeneity in Plant Thylakoid Membranes

Chlorophyll a (CLA) pigments and thylakoid membranes are crucial components of plants for photosynthesis. To understand the effect of CLA on the structure and dynamics of thylakoid membranes, coarse-grained molecular dynamics (CG MD) simulations of thylakoid membranes are performed by varying the numbers of CLA at 293 K using MARTINI-2 force fields. The membrane undergoes a lamellar to nonlamellar phase transition above a critical concentration of CLA. The CLAs dynamically form aggregates of different orders and preferentially fetch the least unsaturated nonbilayer-forming lipids around them, resulting in a nonlamellar phase with fused regions. These fused regions cause a structural arrest of CLA and lipids, inducing dynamic heterogeneity manifested by non-Gaussian parameters and van Hove correlation functions. The lamellar to nonlamellar phase transition of the membrane is associated with a drastic reduction in correlation length of the immobile CLA and lipids governed by the fused topology. Such insights into CLA-induced structural transitions in thylakoid membranes are pertinent for understanding nonphotochemical quenching mechanisms and hold promise for designing future artificial photosynthetic materials and applications in photodynamic therapy.

Decoding Biomolecular Condensate Dynamics: An Energy Landscape Approach

A significant fraction of eukaryotic proteins contain low-complexity sequence elements with unknown functions. Many of these sequences are prone to form biomolecular condensates with unique material and dynamic properties. Mutations in low-complexity regions often result in abnormal phase transitions into pathological solid-like states. Therefore, understanding how the low-complexity sequence patterns encode the material properties of condensates is crucial for uncovering the cellular functions and evolutionary forces behind the emergence of low-complexity regions in proteins. In this work, we employ an alphabet-free energy landscape framework of the stickers and spacers to dissect how the low complexity patterns of proteins encode the material properties of condensates. We find a broad phase diagram of material properties determined by distinct energy landscape features, showing that periodic repeat motifs promote elastic-dominated while random sequences are viscous-dominated properties. We find that a certain degree of sticker periodicity is necessary to maintain the fluidity of condensates, preventing them from forming glassy or solid-like states. Finally, we show that the energy landscape framework captures viscoelastic trends seen in the recent experiments on prion domains and makes predictions for systematic variation of protein condensate viscoelasticity via altering the periodicity and strength of sticker motifs.

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Microscopic theory of the elastic shear modulus and length-scale-dependent dynamic re-entrancy phenomena in very dense sticky particle fluids

We apply the hybrid projectionless dynamic theory (hybrid PDT) formulation of the elastically collective nonlinear Langevin equation (ECNLE) activated dynamics approach to study dense fluids of sticky spheres interacting with short range attractions. Of special interest is the problem of non-monotonic evolution with short range attraction strength of the elastic modulus ("re-entrancy") at very high packing fractions far beyond the ideal mode coupling theory (MCT) nonergodicity boundary. The dynamic force constraints explicitly treat the bare attractive forces that drive transient physical bond formation, while a projection approximation is employed for the singular hard-sphere potential. The resultant interference between repulsive and attractive forces contribution to the dynamic vertex results in the prediction of localization length and elastic modulus re-entrancy, qualitatively consistent with experiments. The non-monotonic evolution of the structural (alpha) relaxation time predicted by the ECNLE theory with the hybrid PDT approach is explored in depth as a function of packing fraction, attraction strength, and attraction range. Isochronal dynamic arrest boundaries based on activated relaxation display the classic non-monotonic glass melting form. Comparisons of these results with the corresponding predictions of ideal MCT, and also the ECNLE and NLE activated theories based on projection, reveal large qualitative differences. The consequences of stochastic trajectory fluctuations on intra-cage single particle dynamics with variable strength of attractions are also studied. Large dynamical heterogeneity effects in attractive glasses are properly captured. These include a rapidly increasing amplitude of the non-Gaussian parameter with packing fraction and a non-monotonic evolution with attraction strength, in qualitative accord with recent simulations. Extension of the microscopic theoretical approach to treat double yielding in attractive glass nonlinear rheology is possible.

Directional change during active diffusion of viral ribonucleoprotein particles through cytoplasm

A mesh of cytoskeletal fibers, consisting of microtubules, intermediate filaments, and fibrous actin, prevents the Brownian diffusion of particles with a diameter larger than 0.10 μm, such as vesicular stomatitis virus ribonucleoprotein (RNP) particles, in mammalian cells. Nevertheless, RNP particles do move in random directions but at a lower rate than Brownian diffusion, which is thermally driven. This nonthermal biological transport process is called "active diffusion" because it is driven by ATP. The ATP powers motor proteins such as myosin II. The motor proteins bend and cross-link actin fibers, causing the mesh to jiggle. Until recently, little was known about how RNP particles get through the mesh. It has been customary to analyze the tracks of particles like RNPs by computing the slope of the ensemble-averaged mean-squared displacement of the particles as a signature of mechanism. Although widely used, this approach "loses information" about the timing of the switches between physical mechanisms. It has been recently shown that machine learning composed of variational Bayesian analysis, Gaussian mixture models, and hidden Markov models can use "all the information" in a single track to reveal that that the positions of RNP particles are spatially clustered. Machine learning assigns a number, called a state, to each cluster. RNP particles remain in one state for 0.2-1.0 s before switching (hopping) to a different state. This earlier work is here extended to analyze the movements of a particle within a state and to determine particle directionality within and between states.

Simulation of the continuous time random walk using subordination schemes

The continuous time random walk model has been widely applied in various fields, including physics, biology, chemistry, finance, social phenomena, etc. In this work, we present an algorithm that utilizes a subordinate formula to generate data of the continuous time random walk in the long time limit. The algorithm has been validated using commonly employed observables, such as typical fluctuations of the positional distribution, rare fluctuations, the mean and the variance of the position, and breakthrough curves with time-dependent bias, demonstrating a perfect match.

Anomalous Fluctuations in a Droplet of Chemically Active Colloids or Enzymes

Chemically active colloids or enzymes cluster into dense droplets driven by their phoretic response to collectively generated chemical gradients. Employing Brownian dynamics simulation techniques, our study of the dynamics of such a chemically active droplet uncovers a rich variety of structures and dynamical properties, including the full range of fluidlike to solidlike behavior, and non-Gaussian positional fluctuations. Our work sheds light on the complex dynamics of the active constituents of metabolic clusters, which are the main drivers of nonequilibrium activity in living systems.

Long-range correlations in elastic moduli and local stresses at the unjamming transition

We explore the behaviour of spatially heterogeneous elastic moduli as well as the correlations between local moduli in model solids with short-range repulsive potentials. We show through numerical simulations that local elastic moduli exhibit long-range correlations, similar to correlations in the local stresses. Specifically, the correlations in local shear moduli exhibit anisotropic behavior at large lengthscales characterized by pinch-point singularities in Fourier space, displaying a structural pattern akin to shear stress correlations. Focussing on two-dimensional jammed solids approaching the unjamming transition, we show that stress correlations exhibit universal properties, characterized by a quadratic p2 dependence of the correlations as the pressure p approaches zero, independent of the details of the model. In contrast, the modulus correlations exhibit a power-law dependence with different exponents depending on the specific interaction potential. Furthermore, we illustrate that while affine responses lack long-range correlations, the total modulus, which encompasses non-affine behavior, exhibits long-range correlations.

Entropic timescales of dynamic heterogeneity in supercooled liquid

Non-Gaussian displacement distributions are universal predictors of dynamic heterogeneity in slowly varying environments. Here, we explore heterogeneous dynamics in supercooled liquid using molecular dynamics simulations and show the efficiency of the relative-entropy based measure, negentropy, in quantifying dynamic heterogeneity over the widely used non-Gaussian parameter. Our analysis shows that the heterogeneity quantified by the negentropy is significantly different from the one obtained using the conventional moment-based definition that considers deviation from Gaussianity up to lower-order moments. We extract the timescales of dynamic heterogeneity using the two methods and show that the differential changes diverge as the system experiences strong intermittency near the glass transition. Further, we interpret the entropic timescales and discuss the general implications of our work.

From creep to flow: Granular materials under cyclic shear

When unperturbed, granular materials form stable structures that resemble the ones of other amorphous solids like metallic or colloidal glasses. Whether or not granular materials under shear have an elastic response is not known, and also the influence of particle surface roughness on the yielding transition has so far remained elusive. Here we use X-ray tomography to determine the three-dimensional microscopic dynamics of two granular systems that have different roughness and that are driven by cyclic shear. Both systems, and for all shear amplitudes Γ considered, show a cross-over from creep to diffusive dynamics, indicating that rough granular materials have no elastic response and always yield, in stark contrast to simple glasses. For the system with small roughness, we observe a clear dynamic change at Γ ≈ 0.1, accompanied by a pronounced slowing down and dynamical heterogeneity. For the large roughness system, the dynamics evolves instead continuously as a function of Γ. We rationalize this roughness dependence using the potential energy landscape of the systems: The roughness induces to this landscape a micro-corrugation with a new length scale, whose ratio over the particle size is the relevant parameter. Our results reveal the unexpected richness in relaxation mechanisms for real granular materials.

Structural and dynamical equilibrium properties of hard board-like particles in parallel confinement

We performed Monte Carlo and dynamic Monte Carlo simulations to model the diffusion of monodispersed suspensions composed of impenetrable cuboidal particles, specifically hard board-like particles (HBPs), in the presence of parallel hard walls. The impact of the walls was investigated by adjusting the size of the simulation box while maintaining constant packing fractions, fixed at η = 0.150, for systems consisting of HBPs with prolate, dual-shaped, and oblate geometries. We observed that increasing the distance between the walls led to the recovery of an isotropic bulk phase, while local particle organization near the walls remained stable. Due to their shape, oblate HBPs exhibit more efficient anchoring at wall surfaces compared to prolate shapes. The formation of nematic-like particle assemblies near the walls, confirmed by theoretical calculations based on density functional theory, significantly influenced local particle dynamics. This effect was particularly pronounced to the extent that a modest portion of cuboids near the walls tended to diffuse exclusively in planes parallel to the confinement, even more efficiently than observed in the bulk regions.

Enzymatic cleaving of entangled DNA rings drives scale-dependent rheological trajectories

DNA, which naturally occurs in linear, ring, and supercoiled topologies, frequently undergoes enzyme-driven topological conversion and fragmentation in vivo, enabling it to perform a variety of functions within the cell. In vitro, highly concentrated DNA polymers form entanglements that yield viscoelastic properties dependent on the topologies and lengths of the DNA. Enzyme-driven alterations of DNA size and shape therefore offer a means of designing active materials with programmable viscoelastic properties. Here, we incorporate multi-site restriction endonucleases into dense DNA solutions to linearize and fragment circular DNA molecules. We pair optical tweezers microrheology with differential dynamic microscopy and single-molecule tracking to measure the linear and nonlinear viscoelastic response and transport properties of entangled DNA solutions over a wide range of spatiotemporal scales throughout the course of enzymatic digestion. We show that, at short timescales, relative to the relaxation timescales of the polymers, digestion of these 'topologically-active' fluids initially causes an increase in elasticity and relaxation times followed by a gradual decrease. Conversely, for long timescales, linear viscoelastic moduli exhibit signatures of increasing elasticity. DNA diffusion, likewise, becomes increasingly slowed, in direct opposition to the short-time behavior. We hypothesize that this scale-dependent rheology arises from the population of small DNA fragments, which increases as digestion proceeds, driving self-association of larger fragments via depletion interactions, giving rise to slow relaxation modes of clusters of entangled chains, interspersed among shorter unentangled fragments. While these slow modes likely dominate at long times, they are presumably frozen out in the short-time limit, which instead probes the faster relaxation modes of the unentangled population.

Pockets, jumps and filaments: classifying ionic motion and determining the role of structure in electrochemical properties of 2Li2S-GeS2superionic glasses

The structural properties of a typical solid electrolyte system (2Li2S-GeS2) is investigated from First principles molecular dynamics simulations. Results reveal that depolymerization of the base GeS2network by alkali additives takes place but appears reduced with respect to the corresponding sodium analog glass. Experimental structure functions are reproduced and reveal that the network is dominated by GeS4/2tetrahedra that are connected by edges (four-membered rings) and corners and disrupted by the addition of lithium, albeit a non-negligible fraction of connecting tetrahedra (Q2units) are still present in the glass structure. Dynamic and electric properties are also studied and emphasize that the size of the migrating cation (Li) is essential for ensuring a good level of ionic conductivity as it displays increased values with respect to the parent Na-bearing system. On the atomic (picosecond) timescale, different typical Li trajectories are identified and their distribution calculated: reduced cage-like motion in pockets constrained by the surrounding (Ge,S) network, back and forth jump motions with short transition states and long-range filamentary motion.

Collective Relaxation Dynamics in a Three-Dimensional Lattice Glass Model

We numerically elucidate the microscopic mechanisms controlling the relaxation dynamics of a three-dimensional lattice glass model that has static properties compatible with the approach to a random first-order transition. At low temperatures, the relaxation is triggered by a small population of particles with low-energy barriers forming mobile clusters. These emerging quasiparticles act as facilitating defects responsible for the spatially heterogeneous dynamics of the system, whose characteristic length scales remain strongly coupled to thermodynamic fluctuations. We compare our findings both with existing theoretical models and atomistic simulations of glass formers.

Nature of Subdiffusion Crossover in Molecular and Polymeric Glassformers

A crossover from a non-Gaussian to Gaussian subdiffusion has been observed ubiquitously in various polymeric and molecular glassformers. We have developed a framework that generalizes the fractional Brownian motion model to incorporate non-Gaussian features by introducing a jump kernel. We illustrate that the non-Gaussian fractional Brownian motion model accurately characterizes the subdiffusion crossover. From the solutions of the non-Gaussian fractional Brownian motion model, we gain insights into the nature of van Hove self-correlation in non-Gaussian subdiffusive regime, which is found to exhibit exponential tails, providing first such experimental evidence in molecular glassformers. The validity of the model is supported by comparison with incoherent quasielastic neutron scattering data obtained from several molecular and polymeric glassformers.

Onset of glassiness in two-dimensional ring polymers: Interplay of stiffness and crowding

The effect of ring stiffness and pressure on the glassy dynamics of a thermal assembly of two-dimensional ring polymers is investigated using extensive coarse-grained molecular dynamics simulations. In all cases, dynamical slowing down is observed with increasing pressure, and thereby, a phase space for equilibrium dynamics is identified in the plane of the obtained monomer density and ring stiffness. When the rings are highly flexible, i.e., have low ring stiffness, glassiness sets in via the crowding of crumpled polymers, which take on a globular form. In contrast, at large ring stiffness, when the rings tend to have large asphericity under compaction, we observe the emergence of local domains having orientational ordering at high pressures. Therefore, our simulations highlight how varying the deformability of rings leads to contrasting mechanisms in driving the system toward the glassy regime.

Anomalous diffusion, non-Gaussianity, nonergodicity, and confinement in stochastic-scaled Brownian motion with diffusing diffusivity dynamics

Scaled Brownian motions (SBMs) with power-law time-dependent diffusivity have been used to describe various types of anomalous diffusion yet Gaussian observed in granular gases kinetics, turbulent diffusion, and molecules mobility in cells, to name a few. However, some of these systems may exhibit non-Gaussian behavior which can be described by SBM with diffusing diffusivity (DD-SBM). Here, we numerically investigate both free and confined DD-SBM models characterized by fixed or stochastic scaling exponent of time-dependent diffusivity. The effects of distributed scaling exponent, random diffusivity, and confinement are considered. Different regimes of ultraslow diffusion, subdiffusion, normal diffusion, and superdiffusion are observed. In addition, weak ergodic and non-Gaussian behaviors are also detected. These results provide insights into diffusion in time-fluctuating diffusivity landscapes with potential applications to time-dependent temperature systems spreading in heterogeneous environments.

Unraveling the Glass-like Dynamic Heterogeneity in Ring Polymer Melts: From Semiflexible to Stiff Chain

Ring polymers are an intriguing class of polymers with unique physical properties, and understanding their behavior is important for developing accurate theoretical models. In this study, we investigate the effect of chain stiffness and monomer density on the static and dynamic behaviors of ring polymer melts using molecular dynamics simulations. Our first focus is on the non-Gaussian parameter of center-of-mass displacement as a measure of dynamic heterogeneity, which is commonly observed in glass-forming liquids. We find that the non-Gaussianity in the displacement distribution increases with the monomer density and stiffness of the polymer chains, suggesting that excluded volume interactions between centers of mass have a strong effect on the dynamics of ring polymers. We then analyze the relationship between the radius of gyration and monomer density for semiflexible and stiff ring polymers. Our results indicate that the relationship between the two varies with chain stiffness, which can be attributed to the competition between repulsive forces inside the ring and from adjacent rings. Finally, we study the dynamics of bond-breakage virtually connected between the centers of mass of rings to analyze the exchanges of intermolecular networks of bonds. Our results demonstrate that the dynamic heterogeneity of bond-breakage is coupled with the non-Gaussianity in ring polymer melts, highlighting the importance of the bond-breaking method in determining the intermolecular dynamics of ring polymer melts. Overall, our study sheds light on the factors that govern the dynamic behaviors of ring polymers.

Peptide-Induced Fusion of Dynamic Membrane Nanodomains: Implications in a Viral Entry

Enveloped viruses infect host cells via protein-mediated membrane fusion. However, insights into the microscopic rearrangement induced by the viral proteins and peptides have not yet emerged. Here, we report a new methodology to extract viral fusion peptide (FP)-mediated biomembrane dynamical nanodomain fusion parameter, λ, based on stimulated emission depletion microscopy coupled with fluorescence correlation spectroscopy. We also define another dynamical parameter membrane gradient, defined in terms of the ratio of average lipid diffusion coefficients across dynamic crossover length scales, ξ. Significantly, we observe that λ as well as these mobility gradients are larger in the stiffer liquid-ordered (Lo) phase compared to the liquid-disordered phase and are more effective at the smaller nanodomain interfaces, which are only present in the Lo phase. The results could possibly help to resolve a long-standing puzzle about the enhanced fusogenicity of FP in the Lo phase. Results obtained from the diffusion results have been correlated with the human immunodeficiency virus gp41 FP-induced membrane fusion.

Glassy phases of the Gaussian core model

We present results from molecular dynamics simulations exploring the supercooled dynamics of the Gaussian Core Model in the low- and intermediate-density regimes. In particular, we analyse the transition from the low-density hard-sphere-like glassy dynamics to the high-density one. The dynamics at low densities is well described by the caging mechanism, giving rise to intermittent dynamics. At high densities, the particles undergo a more continuous motion in which the concept of cage loses its meaning. We elaborate on the idea that these different supercooled dynamics are in fact the precursors of two different glass states.

Multilayered noise model for transport in complex environments

Transport in complex fluidic environments often exhibits transient subdiffusive dynamics accompanied by non-Gaussian probability density profiles featuring a nonmonotonic non-Gaussian parameter. Such properties cannot be adequately explained by the original theory of Brownian motion. Based on an extension of kinetic theory, this study introduces a chain of hierarchically coupled random walks approach that effectively captures all these intriguing characteristics. If the environment consists of a series of independent white noise sources, then the problem can be expressed as a system of hierarchically coupled Ornstein-Uhlenbech equations. Due to the linearity of the system, the most essential transport properties have a closed analytical form.

Evidence for Complex Dynamics in Glassy Fast Ion Conductors: The Case of Sodium Thiosilicates

Classical molecular dynamics is used to study the dynamics of alkali ions in a promising fast ion conductor glass system, i.e., Na2S-SiS2. Diffusion in such thiosilicates is found to display various salient features of alkali silicates, i.e., channel-like diffusion with typical length scales emerging as the temperature is decreased to the glassy state, and Arrhenius behavior for both Na ion diffusivity and calculated conductivity. The dynamics appears, however, to be largely heterogeneous as manifested by fast and slow Na ion motion at intermediate times, both in the high-temperature liquid and in the glassy state. In the former, a diffusion-limited regime is found due to the increased motion of the network-forming species that limits the Na ion dynamics, whereas at low temperatures, the typical dynamical heterogeneities are recovered as observed close to the glass transition.

Anomalous lateral diffusion of lipids during the fluid/gel phase transition of a lipid membrane

A lipid membrane undergoes a phase transition from fluid to gel phase upon changing external thermodynamic conditions, such as decreasing temperature and increasing pressure. Extremophilic organisms face the challenge of preventing this deleterious phase transition. The main focus of their adaptive strategy is to facilitate effective temperature sensing through sensor proteins, relying on the drastic changes in packing density and membrane fluidity during the phase transition. Although the changes in packing density parameters due to the fluid/gel phase transition are studied in detail, the impact on membrane fluidity is less explored in the literature. Understanding the lateral diffusive dynamics of lipids in response to temperature, particularly during the fluid/gel phase transition, is albeit crucial. Here we have simulated the phase transition of a single component lipid membrane composed of dipalmitoylphosphatidylcholine (DPPC) lipids using a coarse-grained (CG) model and studied the changes of the structural and dynamical properties. It is observed that near the phase transition point, both fluid and gel phase domains coexist together. The dynamics remains highly non-Gaussian for a long time even when the mean square displacement reaches the Fickian regime at a much earlier time. This Fickian yet non-Gaussian diffusion (FnGD) is a characteristic of a highly heterogeneous system, previously observed for the lateral diffusion of lipids in raft mimetic membranes having liquid-ordered and liquid-disordered phases co-existing together. We have analyzed the molecular trajectories and calculated the jump-diffusion of the lipids, stemming from sudden jump translations, using a translational jump-diffusion (TJD) approach. An overwhelming contribution of the jump-diffusion of the lipids is observed suggesting anomalous diffusion of lipids during fluid/gel phase transition of the membrane. These results are important in unravelling the intricate nature of lipid diffusion during the phase transition of the membrane and open up a new possibility of investigating the most significant change of membrane properties during phase transition, which can be effectively sensed by proteins.

Triggering Gaussian-to-Exponential Transition of Displacement Distribution in Polymer Nanocomposites via Adsorption-Induced Trapping

In many disordered systems, the diffusion of classical particles is described by a displacement distribution P(x, t) that displays exponential tails instead of Gaussian statistics expected for Brownian motion. However, the experimental demonstration of control of this behavior by increasing the disorder strength has remained challenging. In this work, we explore the Gaussian-to-exponential transition by using diffusion of poly(ethylene glycol) (PEG) in attractive nanoparticle-polymer mixtures and controlling the volume fraction of the nanoparticles. In this work, we find "knobs", namely nanoparticle concentration and interaction, which enable the change in the shape of P(x,t) in a well-defined way. The Gaussian-to-exponential transition is consistent with a modified large deviation approach for a continuous time random walk and also with Monte Carlo simulations involving a microscopic model of polymer trapping via reversible adsorption to the nanoparticle surface. Our work bears significance in unraveling the fundamental physics behind the exponential decay of the displacement distribution at the tails, which is commonly observed in soft materials and nanomaterials.

Different glassy characteristics are related to either caging or dynamical heterogeneity

Despite the enormous theoretical and application interests, a fundamental understanding of the glassy dynamics remains elusive. The static properties of glassy and ordinary liquids are similar, but their dynamics are dramatically different. What leads to this difference is the central puzzle of the field. Even the primary defining glassy characteristics, their implications, and if they are related to a single mechanism remain unclear. This lack of clarity is a severe hindrance to theoretical progress. Here, we combine analytical arguments and simulations of various systems in different dimensions and address these questions. Our results suggest that the myriad of glassy features are manifestations of two distinct mechanisms. Particle caging controls the mean, and coexisting slow- and fast-moving regions govern the distribution of particle displacements. All the other glassy characteristics are manifestations of these two mechanisms; thus, the Fickian yet non-Gaussian nature of glassy liquids is not surprising. We discover a crossover, from stretched exponential to a power law, in the behavior of the overlap function. This crossover is prominent in simulation data and forms the basis of our analyses. Our results have crucial implications on how the glassy dynamics data are analyzed, challenge some recent suggestions on the mechanisms governing glassy dynamics, and impose strict constraints that a correct theory of glasses must have.

Noncanonical Relationship between Heterogeneity and the Stokes-Einstein Breakdown in Deep Eutectic Solvents

The relationship between Stokes-Einstein breakdown (SEB) and dynamical heterogeneity (DH) is of paramount importance in the physical chemistry of complex fluids. In this work, we employ neutron scattering to probe the DH and SEB in a series of deep eutectic solvents (DESs) composed of acetamide and lithium salts. Quasielastic neutron scattering experiments reveal SEB in the jump diffusion of acetamide, represented by a fractional Stokes-Einstein relationship. Among these DESs, lithium perchlorate exhibits the most pronounced SEB while lithium bromide displays the weakest. Concurrently, elastic incoherent neutron scans identify that bromide DES is the most heterogeneous and perchlorate is the least. For the first time, our study unveils a counterintuitive incommensurate relationship between DH and SEB. Further, it reveals the intricate contrasting nature of the SEB-DH relationship when investigated in proximity to the glass-transition temperature and further away from it.

Universal to nonuniversal transition of the statistics of rare events during the spread of random walks

Through numerous experiments that analyzed rare event statistics in heterogeneous media, it was discovered that in many cases the probability density function for particle position, P(X,t), exhibits a slower decay rate than the Gaussian function. Typically, the decay behavior is exponential, referred to as Laplace tails. However, many systems exhibit an even slower decay rate, such as power-law, log-normal, or stretched exponential. In this study, we utilize the continuous-time random walk method to investigate the rare events in particle hopping dynamics and find that the properties of the hop size distribution induce a critical transition between the Laplace universality of rare events and a more specific, slower decay of P(X,t). Specifically, when the hop size distribution decays slower than exponential, such as e^{-|x|^{β}} (β>1), the Laplace universality no longer applies, and the decay is specific, influenced by a few large events, rather than by the accumulation of many smaller events that give rise to Laplace tails.

Cooperative Rheological State-Switching of Enzymatically-Driven Composites of Circular DNA And Dextran

Polymer topology, which plays a principal role in the rheology of polymeric fluids, and non-equilibrium materials, which exhibit time-varying rheological properties, are topics of intense investigation. Here, composites of circular DNA and dextran are pushed out-of-equilibrium via enzymatic digestion of DNA rings to linear fragments. These time-resolved rheology measurements reveal discrete state-switching, with composites undergoing abrupt transitions between dissipative and elastic-like states. The gating time and lifetime of the elastic-like states, and the magnitude and sharpness of the transitions, are surprisingly decorrelated from digestion rates and non-monotonically depend on the DNA fraction. These results are modeled using sigmoidal two-state functions to show that bulk state-switching can arise from continuous molecular-level activity due to the necessity for cooperative percolation of entanglements to support macroscopic stresses. This platform, coupling the tunability of topological composites with the power of enzymatic reactions, may be leveraged for diverse material applications from wound-healing to self-repairing infrastructure.

Brownian yet non-Gaussian diffusion of a light particle in heavy gas: Lorentz-gas-based analysis

Non-Gaussian diffusion was recently observed in a gas mixture with mass and fraction contrast [F. Nakai et al., Phys. Rev. E 107, 014605 (2023)2470-004510.1103/PhysRevE.107.014605]. The mean-square displacement of a minor gas particle with a small mass is linear in time, while the displacement distribution deviates from the Gaussian distribution, which is called the Brownian yet non-Gaussian diffusion. In this work, we theoretically analyze this case where the mass contrast is sufficiently large. Major heavy particles can be interpreted as immobile obstacles, and a minor light particle behaves like a Lorentz gas particle within an intermediate timescale. Despite the similarity between the gas mixture and the conventional Lorentz gas system, the Lorentz gas description cannot fully describe the Brownian yet non-Gaussian diffusion. A successful description can be achieved through a canonical ensemble average of the statistical quantities of the Lorentz gas over the initial speed. Furhter, we show that the van Hove correlation function has a nonexponential tail, which is contrary to the exponential tail observed in various systems.

Front propagation in ultrastable glasses is dynamically heterogeneous

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.

Method to probe the pronounced growth of correlation lengths in active glass-forming liquids using an elongated probe

The growth of correlation lengths in equilibrium glass-forming liquids near the glass transition is considered a critical finding in the quest to understand the physics of glass formation. These understandings helped us understand various dynamical phenomena observed in supercooled liquids. It is known that at least two different length scales exist; one is of thermodynamic origin, while the other is dynamical in nature. Recent observations of glassy dynamics in biological and synthetic systems where the external or internal driving source controls the dynamics, apart from the usual thermal noise, lead to the emergence of the field of active glassy matter. A question of whether the physics of glass formation in these active systems is also accompanied by growing dynamic and static lengths is indeed timely. In this article, we probe the growth of dynamic and static lengths in a model active glass system using rod-like elongated probe particles, an experimentally viable method. We show that the dynamic and static lengths in these nonequilibrium systems grow much more rapidly than their passive counterparts. We then offer an understanding of the violation of the Stokes-Einstein relation and Stokes-Einstein-Debye relation using these lengths via a scaling theory.

Anomalous diffusion of nanoparticles in the spatially heterogeneous biofilm environment

Biofilms contain extracellular polymeric substances (EPS) that provide structural support and restrict penetration of antimicrobial treatment. To overcome limited penetration, functionalized nanoparticles (NPs) have been suggested as carriers for antimicrobial delivery. Using microscopy, we evaluate the diffusion of nanoparticles in function of the structure of Salmonella biofilms. We observe anomalous diffusion and heterogeneous mobility of NPs resulting in distinct NPs distribution that depended on biofilm structure. Through Brownian dynamics modeling with spatially varying viscosity around bacteria, we demonstrated that spatial gradients in diffusivity generate viscous sinks that trap NPs near bacteria. This model replicates the characteristic diffusion signature and vertical distribution of NPs in the biofilm. From a treatment perspective, our work indicates that both biofilm structure and the level of EPS can impact NP drug delivery, where low levels of EPS might benefit delivery by immobilizing NPs closer to bacteria and higher levels hamper delivery due to shielding effects.

Universal Evolution of Fickian Non-Gaussian Diffusion in Two- and Three-Dimensional Glass-Forming Liquids

Recent works show that glass-forming liquids display Fickian non-Gaussian Diffusion, with non-Gaussian displacement distributions persisting even at very long times, when linearity in the mean square displacement (Fickianity) has already been attained. Such non-Gaussian deviations temporarily exhibit distinctive exponential tails, with a decay length λ growing in time as a power-law. We herein carefully examine data from four different glass-forming systems with isotropic interactions, both in two and three dimensions, namely, three numerical models of molecular liquids and one experimentally investigated colloidal suspension. Drawing on the identification of a proper time range for reliable exponential fits, we find that a scaling law λ(t)∝tα, with α≃1/3, holds for all considered systems, independently from dimensionality. We further show that, for each system, data at different temperatures/concentration can be collapsed onto a master-curve, identifying a characteristic time for the disappearance of exponential tails and the recovery of Gaussianity. We find that such characteristic time is always related through a power-law to the onset time of Fickianity. The present findings suggest that FnGD in glass-formers may be characterized by a "universal" evolution of the distribution tails, independent from system dimensionality, at least for liquids with isotropic potential.

Extending the analogy between intracellular motion in mammalian cells and glassy dynamics

How molecules, organelles, and foreign objects move within living cells has been studied in organisms ranging from bacteria to human cells. In mammalian cells, in particular, cellular vesicles move across the cell using motor proteins that carry the vesicle down the cytoskeleton to their destination. We have recently noted several similarities between the motion of such vesicles and that in disordered, "glassy", systems, but the generality of this observation remains unclear. Here we follow the motion of mitochondria, the organelles responsible for cell energy production, in mammalian cells over timescales from 50 ms to 70 s. Qualitative observations show that single mitochondria remain within a spatially limited region for extended periods of time, before moving longer distances relatively quickly. The displacement distribution is roughly Gaussian for shorter distances (≲0.05 μm) but exhibits exponentially decaying tails at longer distances (up to 0.40 μm). This behaviour is well-described by a model developed to describe the motion in glassy systems. These observations are extended to in total 3 different objects (mitochondria, lysosomes and nano-sized beads enclosed in vesicles), 3 different mammalian cell types (HEK 293, HeLa, and HT22), from 2 different organisms (human and mouse). Further evidence that supports glass-like characteristics of the motion is a difference between the time it takes to move a longer distance for the first time and subsequent times, as well as a weak ergodicity breaking of the motion. Overall, we demonstrate the ubiquity of glass-like motion in mammalian cells, providing a different perspective on intracellular motion.

Non-Gaussian Diffusion Near Surfaces

We study the diffusion of particles confined close to a single wall and in double-wall planar channel geometries where the local diffusivities depend on the distance to the boundaries. Displacement parallel to the walls is Brownian as characterized by its variance, but it is non-Gaussian having a nonzero fourth cumulant. Establishing a link with Taylor dispersion, we calculate the fourth cumulant and the tails of the displacement distribution for general diffusivity tensors along with potentials generated by either the walls or externally, for instance, gravity. Experimental and numerical studies of the motion of a colloid in the direction parallel to the wall give measured fourth cumulants which are correctly predicted by our theory. Interestingly, contrary to models of Brownian-yet-non-Gaussian diffusion, the tails of the displacement distribution are shown to be Gaussian rather than exponential. All together, our results provide additional tests and constraints for the inference of force maps and local transport properties near surfaces.

Bona fide stochastic resonance under nonGaussian active fluctuations

We report on the experimental observation of stochastic resonance (SR) in a nonGaussian active bath without any periodic modulation. A Brownian particle hopping in a nanoscale double-well potential under the influence of nonGaussian correlated noise, with mean interval τ_P and correlation time τ_c, shows a series of equally-spaced peaks in the residence time distribution at integral multiples of τ_P. The strength of the first peak is found to be maximum when the mean residence time _d matches the double condition, 4τ_c ≈ τ_P ≈ _d/2, demonstrating a new type of bona fide SR. The experimental findings agree with a simple model that explains the emergence of SR without periodic modulation of the double-well potential. Additionally, we show that generic SR under periodic modulation, known to degrade in strongly correlated continuous noise, is recovered by the discrete nonGaussian kicks.

Fluctuating diffusivity emerges even in binary gas mixtures

Diffusivity in some soft matter and biological systems changes with time, called the fluctuating diffusivity. In this work, we propose a novel origin for fluctuating diffusivity based on stochastic simulations of binary gas mixtures. In this system, the fraction of one component is significantly small, and the mass of the minor component molecule is different from that of the major component. The minor component exhibits fluctuating diffusivity when its mass is sufficiently smaller than that of the major component. We elucidate that this fluctuating diffusivity is caused by the time scale separation between the relaxation of the velocity direction and the speed of the minor component molecule.

Distributed medium viscosity yields quasi-exponential step-size probability distributions in heterogeneous media

The analysis of the statistics of random walks undertaken by passive particles in complex media has important implications in a number of areas including pathogen transport and drug delivery. In several systems in which heterogeneity is important, the distribution of particle step-sizes has been found to be exponential in nature, as opposed to the Gaussian distribution associated with Brownian motion. Here, we first develop a theoretical framework to study a simplified version of this problem: the motion of passive tracers in a range of sub-environments with different viscosity. We show that in the limit of a large number of equi-distributed sub-environments spanning a broad viscosity range, an exact analytical expression for the underlying particle step-size distribution can be derived, which approaches an exponential distribution when step sizes are small. We then validate this using a simple experimental system of glycerol-water mixtures, in which the volume fraction of glycerol is systematically varied. Overall, the assumption of exponentially distributed step sizes may substantially over-estimate the incidence of large steps in heterogeneous systems, with important implications in the analysis of various biophysical processes.

Fingerprinting Brownian Motions of Polymers under Flow

We present a quantitative approach to the self-dynamics of polymers under steady flow by employing a set of complementary reference frames and extending the spherical harmonic expansion technique to dynamic density correlations. Application of this method to nonequilibrium molecular dynamics simulations of polymer melts reveals a number of universal features. For both unentangled and entangled melts, the center-of-mass motions in the flow frame are described by superdiffusive, anisotropic Gaussian distributions, whereas the isotropic component of monomer self-dynamics in the center-of-mass frame is strongly suppressed. Spatial correlation analysis shows that the heterogeneity of monomer self-dynamics increases significantly under flow.

Disordered Collective Motion in Dense Assemblies of Persistent Particles

We explore the emergence of nonequilibrium collective motion in disordered nonthermal active matter when persistent motion and crowding effects compete, using simulations of a two-dimensional model of size polydisperse self-propelled particles. In stark contrast with monodisperse systems, we find that polydispersity stabilizes a homogeneous active liquid at arbitrary large persistence times, characterized by remarkable velocity correlations and irregular turbulent flows. For all persistence values, the active fluid undergoes a nonequilibrium glass transition at large density. This is accompanied by collective motion, whose nature evolves from near-equilibrium spatially heterogeneous dynamics at small persistence, to a qualitatively different intermittent dynamics when persistence is large. This latter regime involves a complex time evolution of the correlated displacement field.

Crucial Role of S8-Rings in Structural, Relaxation, Vibrational and Electronic Properties of LiquidSulfur close to the λ Transition

Liquid sulfur has been studied by density-functional based molecular-dynamics simulations at different temperatures ranging from 400 K up to 700 K across the well-documented λ-transition. Structure models containing either a majority of Sn chains or S8 rings are considered and compared to experimental data from X-ray scattering. The comparison suggests a liquid structure of a majority of 2-fold sulfur at low temperature, dominated by S8 rings that open progressively upon temperature increase. Typical features associated with such rings are analyzed and indicate that they contribute to a specific third correlating distance in the pair correlation function and to a contribution at low wavevector k in reciprocal space. The vibrational properties of liquid sulfur are also considered and indicate a contribution at 60 meV that is associated with both chains and rings, albeit the latter lead to a more intense peak at this wavenumber. The underlying network structure also impacts the dynamic properties of the melts which display enhanced dynamic heterogeneities when S8 rings are present. The analysis of the electronic Kohn-Sham energies shows insulating character with a gap of about ≃2.0 eV, albeit the presence of localized mid-gap states is acknowledged that can be associated, in part, with the presence of S6rings.

Decoding polymer self-dynamics using a two-step approach

The self-correlation function and corresponding self-intermediate scattering function in Fourier space are important quantities for describing the molecular motions of liquids. This work draws attention to a largely overlooked issue concerning the analysis of these space-time density-density correlation functions of polymers. We show that the interpretation of non-Gaussian behavior of polymers is generally complicated by intrachain averaging of distinct self-dynamics of different segments. By the very nature of the mathematics involved, the averaging process not only conceals critical dynamical information, but also contributes to the observed non-Gaussian dynamics. To fully expose this issue and provide a thorough benchmark of polymer self-dynamics, we perform analyses of coarse-grained molecular dynamics simulations of linear and ring polymer melts as well as several theoretical models using a "two-step" approach, where interchain and intrachain averagings of segmental self-dynamics are separated. While past investigations primarily focused on the average behavior, our results indicate that a more nuanced approach to polymer self-dynamics is clearly required.