Nanoparticle-protein dispersions constitute complex soft materials in which competing attractive and repulsive interactions can strongly influence their phase behavior, including gelation. In this work, we demonstrate a strategy to utilize interaction between anionic silica nanoparticles and anionic protein bovine serum albumin to achieve heat-induced gels with tunable physical properties. Upon heating, the protein molecules in solution undergo unfolding followed by hydrophobic aggregation, leading to the formation of a three-dimensional gel network. The introduction of negatively charged nanoparticles generates additional electrostatic repulsion that competes with the attractive hydrophobic interactions between partially unfolded proteins. This competition modifies both the structure and mechanical properties of the resulting gels. In particular, nanoparticle-protein gels exhibit markedly enhanced optical transparency ($\sim 90\%$) compared with gels formed from pure protein solutions ($<1\%$). Rheological measurements further show shear-thinning behavior, with the gel strength decreasing systematically with increasing nanoparticle concentration, leading to progressively softer gels. At sufficiently high nanoparticle content, gelation is completely suppressed, thereby stabilizing the protein dispersion against thermal aggregation. The underlying mechanism is elucidated in terms of interaction potentials obtained by modeling small-angle neutron scattering data measured in situ during gel formation. Finally, we demonstrate that nanoparticle concentration and ionic strength serve as effective parameters to control gel opacity and mechanical rigidity, enabling the formation of both soft and rigid gels. These results demonstrate how nanoparticle-mediated interactions can regulate aggregation and gelation in protein-based soft matter systems.

We investigated the local dynamics of polybutadiene over a wide temperature range, from the glassy state to the liquid (rubbery) state, using quasielastic neutron scattering (QENS) combined in a recently developed modified mode distribution analysis. This approach enabled high-precision separation of QENS spectra into vibrational components and multiple relaxation processes without relying on specific model functions. Among these processes, the subpicosecond fast process was analyzed using a local diffusion model, which characterizes the dynamics via two physically meaningful parameters: the local diffusion coefficient and the harmonic potential stiffness. Temperature-dependent analysis revealed a distinct dynamic transition near the glass transition, which is interpreted as the disappearance of cooperative diffusion involving multiple segments. Furthermore, an additional relaxation mode, termed the extra-fast process, was identified and interpreted as a higher-order mode within the local diffusion model. These findings suggest a unified physical framework for the evolution of local motions across the glass transition and establish a robust methodology for quantifying polymer dynamics at subnanometer and subpicosecond scales.

Stochastic gradient descent (SGD) is a fundamental tool in stochastic optimization that is widely used in machine learning. Here, we study SGD through the lens of particle dynamics, introducing a framework that links its behavior to well-established models in nonequilibrium statistical physics. We introduce a minimal model in which SGD is applied to spherical particles in physical space, minimizing the system's energy by stochastically reducing particle overlaps. This process exhibits an absorbing phase transition, prompting us to adopt the framework of biased random organization (BRO), a nonequilibrium absorbing state model, to describe SGD's dynamical behavior. We show that BRO dynamics can be approximated by those of particles with linear repulsive interactions under multiplicative anisotropic noise. Thus, in the limit of small kick sizes (learning rates), we demonstrate that BRO and SGD of linear repulsive particles become equivalent, converging to the same critical packing fraction ${\varphi }_c\approx 0.64$, despite their distinct noise mechanisms. This equivalence is further supported by the observation that both models exhibit behavior consistent with the Manna universality class near criticality. Above the transition, SGD tends to favor flatter regions of the energy landscape, analogous to the solutions it finds during neural network training that are associated with improved generalization.

We introduce a multispecies generalization of the asymmetric simple exclusion process with a “no-passing” constraint, forbidding overtaking, on a one-dimensional reflecting (closed) chain without particle reservoirs. This no-passing rule fragments the Hilbert space into an exponential number of disjoint sectors labeled by the particle species sequence, leading to relaxation dynamics that depend sensitively on the initial ordering. We construct exact matrix-product steady states in every particle species sequence sector and derive closed-form expressions for the particle-number distribution and two-point particle correlation functions. In the two-species case, we identify a parameter regime where some sectors have finite relaxation times while others exhibit metastable relaxation dynamics, revealing the coexistence of fast and slow dynamics and strong dependence on the particle species sequence sector. Our results uncover a novel mechanism for nonequilibrium metastability arising from Hilbert space fragmentation in exclusion processes.

Autonomous noisy oscillations in biochemical and mesoscopic systems require nonequilibrium driving and therefore dissipation. A striking conjecture by Oberreiter, Barato, and Seifert (OBS) proposes a universal lower bound on the entropy produced per oscillation period in terms of the coherence number of the slowest oscillatory mode L. Oberreiter et al. [Phys. Rev. E 106, 014106 (2022)]. Here we derive a weaker but rigorous lower bound that preserves the OBS structure while introducing a mode-uniformity factor that quantifies how evenly the oscillatory eigenmode is distributed across states in the steady-state inner product. The result makes explicit that an eigenvalue-only prefactor can fail when the dominant oscillatory mode is localized. We also outline a proof-of-principle route for estimating this factor from low-dimensional data under single-mode dominance and sufficiently informative measurements, and derive an eigenvector-free corollary using only the smallest stationary probability. Translation-invariant Markov jump processes on a ring provide a symmetry-protected class with $\eta =1$, so the refinement reduces to the OBS form; the drift-diffusion limit on a circle saturates the bound.

We examine the impact of spatial fractality on universal quantum transport in mesoscopic systems featuring Sierpiński carpet scattering geometries. Transport calculations are performed using the nonequilibrium Green's function formalism within the Landauer-Büttiker framework, allowing for a detailed statistical characterization of conductance and shot noise power fluctuations. Our analysis reveals that the fractal dimension of these fluctuations increases systematically with the iteration depth of the structure, reflecting enhanced complexity in quantum interference patterns. A direct and robust correlation is established between the Hausdorff dimension of the scattering region and the effective fractal dimension of transport observables, independent of the Wigner-Dyson universal symmetry class (GOE, GUE, and GSE). This provides compelling evidence that scattering across internal lacunae constitutes the dominant mechanism shaping transport behavior in the universal regime. Moreover, we find that the density of local extrema increases while the correlation length decreases with increasing fractality; however, the number of extrema per correlation length remains remarkably with negligible variation. Notably, the autocorrelation functions progressively deviate from the standard Lorentzian form, indicating that geometric self-similarity introduces nontrivial modifications to the spectral correlations predicted by Random Matrix Theory.

Markedly increased computational power and data acquisition have led to growing interest in data-driven inverse dynamics problems. These seek to answer a fundamental question: What can we learn from time-series measurements of a complex dynamical system? For small systems interacting with external environments, the effective dynamics are inherently stochastic, making it crucial to properly manage noise in data. Here we explore this for systems obeying Langevin dynamics and, using currents, we construct a learning framework for stochastic modeling. Currents have recently gained attention for their role in bounding entropy production from thermodynamic uncertainty relations (TURs). We introduce a fundamental relationship between the cumulant currents there and standard machine-learning loss functions. Using this, we derive loss functions for several key thermodynamic functions directly from the system dynamics without the (common) intermediate step of deriving a TUR. These loss functions reproduce results derived both from TURs and other methods. More significantly, they open a path to discover new loss functions for previously inaccessible quantities. Notably, this includes access to per-trajectory entropy production, even if the observed system is driven far from its steady state. We also consider higher-order estimation. Our method is straightforward and unifies dynamic inference with recent approaches to entropy production estimation. Taken altogether, this reveals a deep connection between diffusion models in machine learning and entropy production estimation in stochastic thermodynamics.

In any ecosystem, the conditions of the environment and the characteristics of the species that inhabit it are entangled, coevolving in space and time. We introduce a model that couples active agents with a dynamic environment, interpreted as a nutrient source. Agents are persistent random walkers that gather food from the environment and store it in an inner energy depot. This energy is used for self-propulsion, metabolic expenses, and reproduction. The environment is a two-dimensional surface divided into patches, each of them producing food. Thus, population size and resource distribution become emergent properties of the system. Combining simulations and an analytical framework to analyze limiting cases, we show that the system exhibits distinct phases separating quasistatic and highly motile regimes. We observe that, in general, population sizes are inversely proportional to the average energy per agent. Furthermore, we find that, counterintuitively, reduced access to resources or increased metabolic expenditure can lead to a larger population size. The proposed theoretical framework provides a link between active matter and movement ecology, allowing to investigate short versus long-term strategies to resource exploitation and rationing, as well as sedentary versus wandering strategy. The introduced approach may serve as a tool to describe real-world ecological systems and to test environmental strategies to prevent species extinction.

Designing a miniature microscale engine that can override the role of thermal fluctuations has remained elusive and is an important open challenge. Here, we provide the design and theoretical framework for a unique information-based engine—a work-to-work converter—comprising a submicron-sized bead and motor protein microtubule (MT) complex in an optical trap setup. We demonstrate how, by implementing a simple motor protein state-dependent feedback protocol of the optical trap stiffness, this engine is able to harness and convert the movement of a motor protein into work output. Unlike other conventional microengines, the fidelity and performance of this engine are determined by the stochasticity of motor (un)binding characteristics. We obtain an analytical form of the work distribution function, average work output, and average power output, providing quantitative predictions for engine performance which are validated by stochastic simulations. Remarkably, the average work output per cycle is at least an order of magnitude higher than the thermal fluctuations and supersedes the performance of other microscale engines realized so far.

Regulating droplet-surface contact time is a core demand for optimizing performance in inkjet printing, spray cooling, agricultural spraying, and other industrial fields. Conventional strategies relying on surface microstructure modification suffer from high fabrication costs and compromised surface properties. This study investigates the regulation of droplet-surface contact time via a predeposited microparticle in two configurations, while also presenting a numerical coupling of the three-dimensional pseudopotential lattice Boltzmann (LB) method and discrete element method (DEM) to resolve fluid–particle-wall interactions. Results demonstrate that a microparticle predeposited on a superhydrophobic surface extends contact time primarily by expanding the droplet spreading area, achieving a maximum extension of up to ∼40% (depending on the Weber number and particle volume fraction). This effect is enhanced with higher particle volume fraction $\phi$ and lower contact angle, as the liquid-particle contact area $A_{l-p}^{*}$ exhibits a linear relationship with spreading time across different Weber numbers. For a particle predeposited within a droplet, the formed particle-laden droplet exhibits a characteristic droplet spreading-particle rebound motion pattern. The inertial stretching effect induced by particle rebound suppresses droplet spreading and accelerates retraction, leading to a significant reduction in contact time (up to ∼20%). This shortening effect is intensified by higher particle volume fraction, density, restitution coefficient, and impact Weber number. Additionally, microparticle predeposition modulates droplet dynamics and deformation, leading to energy conversion and dissipation, thereby reducing the rebound stretching height and preventing droplet breakup. This surface-friendly method provides a high-efficiency and feasible pathway for precise droplet-surface contact time control and offering important theoretical support for the optimization of droplet-related industrial technologies.

Spanwise Wall Oscillation is a promising active flow control technique for turbulent skin-friction drag reduction (DR). While the effectiveness of the method in incompressible flows is well documented, its performance in compressible regimes, and the underlying physical mechanisms, particularly those related to thermal fields, remain relatively unexplored. The present study employs Direct Numerical Simulation to investigate the effects of different oscillation amplitudes on compressible channel turbulent flow, with particular emphasis on wall heat transfer. A detailed analysis is performed on turbulent statistics, including mean velocity and temperature profiles, Reynolds stresses, decompositions of the skin friction and heat transfer coefficient, and the behavior of near-wall coherent structures. This analysis aims to elucidate the differences arising from varying oscillation amplitudes. The results indicate that both DR and heat transfer rate are enhanced with increasing oscillation amplitude. Furthermore, higher amplitudes promote the breakup of velocity and temperature streaks within the viscous sublayer, while exerting negligible influence on the buffer layer. Analysis of the decomposed skin-friction and heat-transfer coefficients further reveals that wall oscillations amplify the dominant role of Reynolds shear stress in DR and of turbulent viscous dissipation in heat transfer enhancement.

We present an isothermal global Buckley-Leverett framework for multicomponent, multiphase flow in porous and fractured media that retains the interpretability of classical Buckley-Leverett while incorporating essential physics: equation-of-state-based phase behavior, multicomponent Maxwell-Stefan diffusion, dynamic capillarity, stress-sensitive permeability, and non-Darcy fracture flow. The formulation yields a single global-pressure equation driving the total Darcy flux and an exact fractional-flow decomposition of phase velocities with buoyancy and capillary drifts; inertial effects enter as per-phase damping that renormalizes mobilities. Crucially, the combination of Maxwell-Stefan diffusion and dynamic capillarity renders transport pseudoparabolic, resolving the loss of strict hyperbolicity that plagues three-phase Buckley-Leverett and ensuring a well-posed initial-value problem. In practice, each time step solves the scalar global-pressure equation, reconstructs phase fluxes via the split, and advances strictly conservative component balances; axisymmetric (cylindrical) forms for radial injection with vertical buoyancy are provided. The model reduces exactly to classical Buckley-Leverett when added physics are disabled, making it a practical backbone for carbon storage, geothermal exchange, and contaminant transport in fractured, compositionally complex reservoirs.

Organisms that locomote by propagating approximately sinusoidal waves of body bending maintain performance across different environmental substrates by modifying the frequency $\omega$ or wave number $k$ of their gait. We identify a unifying relationship between these parameters for overdamped undulatory swimmers (including nematodes, spermatozoa, and mm-scale fish) moving in diverse environmental rheologies, in the form of an active “dispersion relation” $\omega \propto k^{\pm 2}$. A model treating the organisms as actively driven viscoelastic beams in a surrounding fluid reproduces the experimentally observed scaling. The relative strength of rate-dependent dissipation in the body and in the environment determines whether $k^{-2}$ or $k^2$ scaling is observed. The existence of these scaling regimes reflects the $k$ and $\omega$ dependence of the various underlying force terms and how their relative importance changes with the composition of the external environment and the parameters of neuronally commanded gait. In the regime where the body dissipation dominates, the application of boundary conditions does not introduce an explicit dependence on the body length, as would be expected in other wave systems in physics. Hence, mechanics constrains the relationship between the gait parameters but allows for their continuous variation along the dispersion curve.

The complex and interrelated ultrafast phenomena of intense secondary-ion (SI) emission triggered by nanoscale energy deposition by energetic impacts of large-cluster ions of buckminsterfullerene (${\mathrm{C}}_{60}$) on solid targets are characterized through a statistical analysis of their mass-analyzed SI counting data. In this study, substituted benzylpyridinium ions, which are important “thermometer” ions, are used as a target to examine energy deposition phenomena at the point and moment that SIs are emitted from the target surface upon each impact. In addition, time-of-flight mass-analyzed SIs emitted under single-impact conditions are counted using an event-by-event counting system to obtain the flight-time data for each impact event. Subsequently, the counting data are analyzed using a newly developed analytical model, based on our previous finding that the probability distribution for the number of emitted SIs induced by each ${\mathrm{C}}_{60}$ ion impact follows a Poisson distribution, to statistically derive underlying information on the phenomena involved. Based on the information obtained by the statistical analysis, we finally present a detailed picture of SI emission phenomena that have been difficult to elucidate using conventional experimental and analytical methods.

Despite heat flux's role in regulating energy conversion in collisionless plasmas, its properties and evolution in the magnetosheath downstream of the Earth's bow shock are scarcely explored. We use Magnetospheric Multiscale in situ measurements to quantify and characterize the electron heat flux in the magnetosheath. We find that the heat flux is shaped by the magnetosheath magnetic field as it drapes around the magnetosphere. While it is affected by solar wind upstream conditions and increases with magnetic field strength, it is not substantially changed by local magnetosheath processes. Also, the heat flux is limited by whistler instability thresholds.

Variable speed generalized Lévy walks (VGLWs) are a class of spatiotemporally coupled stochastic processes that unify a broad range of previously studied models within a single parametrized framework. Their dynamics consist of discrete random steps, or flights, during which the walker's speed varies deterministically with both the elapsed time and the total duration of the flight. We investigate the anomalous diffusive behavior of VGLWs and analyze it through decomposition into the three fundamental constitutive effects that capture violations of the Central Limit Theorem (CLT): the Joseph effect, reflecting long-range increment correlations, the Noah effect, arising from heavy-tailed step-size distributions with infinite variance, and the Moses effect, associated with statistical aging and nonstationarity. Our results show that anomalous diffusion in VGLWs is typically generated by a nontrivial combination of all three effects, rather than being attributable to a single mechanism. Strikingly, we find that within the VGLW framework the Noah exponent $L$, which quantifies the strength of the Noah effect, is unbounded from above, revealing a richer and more extreme landscape of anomalous diffusion than in previously studied Lévy-walk-type models.

We investigate the spectral properties of non-Hermitian real random matrices whose entries exhibit long-range correlations decaying as $|r-r^{\prime }{|}^{-\alpha }$. We find a progressive breakdown of the circular law, controlled by the decrease of $\alpha$. In all cases, the radial eigenvalue density decreases away from the origin. At $\alpha >1$, an effective radius, reminiscent of the circular law, is retrieved, while instead, for $\alpha <1$, the eigenvalue distribution broadens with matrix size, and its spectral radius grows like a power law, with exponents numerically consistent with the predictions of the extended central limit theorem for the magnitude of fluctuations. The case $\alpha =1$ appears as a case with self-similar eigenvalue density and a slowly growing spectral radius. Long-range correlations also enhance clustering of real eigenvalues and slow the resorption of the Saturn effect. These results reveal a correlation-driven transition and suggest the emergence of a new universality class for correlated non-Hermitian random matrices.

We investigate the time-averaged displacement correlation function in single-particle tracking within the generalized Langevin equation framework, reformulated in terms of the velocity correlation function. We identify scaling laws for the normalized displacement correlation function applicable to both nonstationary and aging dynamics. This correlation decays more rapidly with lag time in aging dynamics than in nonaging but nonstationary process. We further examine the underlying ergodic properties of the process and introduce a kinetic energy-based metric to define the effective friction. As an application, we analyze a multiexponential memory kernel commonly used in the protein-folding studies and show that the effective friction increases when components with a shorter decay time are incorporated.