Papers

Twist-bend nematics formed by achiral particles support heliconical domains of opposite handedness. Using Monte Carlo and molecular dynamics simulations of repulsive bent particles, we study the interface between two such domains. Rather than a gradually untwisting texture, we find a density-modulated splay-bend-twist structure. The density modulation is along the helix axis with a period of approximately half the bulk pitch, while the twist is locally enhanced in magnitude, alternates in sign, and vanishes only on an undulating surface. An explicit director interpolation shows how gradients along the helix axis and across the interface combine to produce an undulating zero-twist surface.

We develop a multiscale entropy framework based on the wavelet conditional renormalization group that decomposes the total Shannon entropy into contributions from different spatial scales. Generative modeling enables accurate estimation of these entropies from configurations or snapshots, even in nonequilibrium systems whose underlying probability distributions are unknown. Applications to the equilibrium $\varphi^4$ model and nonequilibrium active Model B+ show that multiscale entropies reveal criticality and spatial organization obscured in the total entropy. These entropies serve as scale-dependent order parameters for characterizing phase transitions and pattern formation.

First passage processes in active systems combine stochastic transport with self-propulsion and orientational persistence, making motion along and against an external bias sensitive to the internal active dynamics. For passive biased diffusion, opposite exits can have different splitting probabilities while their conditional first passage time distributions remain identical. We study how this relation changes for an active Brownian particle driven by a constant external force between two absorbing boundaries. Self-propulsion breaks the equality of the uphill and downhill first passage time distributions and modifies the splitting probabilities. Nevertheless, the two directional path ensembles remain exactly related by spatial reflection, which pairs downhill and uphill first passage paths of the same duration while preserving their orientational history. The log-ratio of the probabilities of a path and its reflected partner defines a path-dependent asymmetry functional and provides an exact reweighting between the two ensembles. In the symmetric half-weighted representation, the uphill and downhill first passage time distributions coincide for arbitrary orientational persistence. The same path relation also allows rare uphill statistics to be reconstructed from the more frequently sampled downhill trajectories. Numerical simulations confirm the weighted equality across the explored bias and persistence regimes, while perturbative and asymptotic analyses clarify how orientational persistence produces the directional asymmetry of the unweighted statistics. The exact relation between the two directional path ensembles suggests that similar symmetry-based reconstruction protocols may be found in other nonequilibrium first-passage problems.

The estimation of the breathing mode frequency for viral capsids remains a persistent challenge across literature which spawned various frameworks and methodologies. However, discrepancies were observed between the experimental Low Frequency Raman Scattering (LFRS) and calculated pre-existing values. To solve this discrepancy, we propose a framework which is developed to determine the breathing-mode frequency by modelling the virus as a macroscopic coupled harmonic oscillator. By integrating mass-loading directly into the classical elastodynamic equations, this model yields an analytical expression that couples the system's total inertia with its specific geometry. Moreover, the viruses are segregated according to their geometries into three coordinates to acquire their respective geometric eigen values. Here we illustrate how the proposed Multigeometric Breathing mode Framework for Virus (MBFV) outperforms prior models by yielding closer values than the pre-existing frameworks upon validating against LFRS.

Kinetic models of protein aggregation describe populations by size, not by spatial organization or morphology. We extend Lumry-Eyring nucleated polymerization to a model in which the monomer is a density field and each aggregate is a region bounded by a level set. Growth is a flux condition on the available sites of a surface. Condensation is a reaction between the bonding sites of two surfaces in contact, at a rate set by the bond rate and the contact geometry. The availability of those sites is a field on the interface, and its equilibrium value follows from Wertheim's perturbation theory. The collision efficiency and the Fuchs stability ratio are therefore computed, not fitted. In a well-mixed limit the model's spatial averages satisfy the rate equations term by term; the monomer fraction agrees to eight parts in ten thousand, a difference that arises from equating aggregate size with volume. The condensation kernel's exponent is $0.5806\pm0.0013$ against the $0.600\pm0.010$ fitted to a monoclonal antibody. The computed stability ratio reproduces thirteen of fourteen published conditions at twelve $k_BT$, but only with the bond rate at the top of its range. In a many-body box, aggregates merge at $2.2$ to $3.8$ times the two-body rate.

Understanding how liquid-liquid phase separation impacts the mechanics of filament networks is a fundamental physical problem at the heart of biological cellular processes and soft material design. While a few theoretical mechanisms have been proposed, a clear demonstration of the direct coupling of phase separation to the overall network stiffness is missing. We report experiments that reveal a universal mechanism by which the condensation of macromolecular crowders induces a rigidity transition in a model filament network. We reconstituted stiff sterically interacting helical filaments and polymeric crowders. Initially, the macromolecules were uniformly dissolved and the filaments formed bundles that assembled a rigid entangled network. Once the crowders condensed into droplets, the network structure lost its rigidity and its mechanical response weakened by an order-of-magnitude. The subsequent dissolution of the condensates was accompanied by the re-establishment of rigidity. Our results show that crowder phase separation modulates the mechanics of filament networks by tuning the osmotic pressure holding the network together. This principle may serve as a paradigm for devising dynamically tunable filamentous materials.

Biochemical systems such as protein complexes often occupy configuration spaces whose dimensionality grows with the number of molecular components. Although usually viewed as an obstacle to coherent dynamics, here we show that high dimensionality can enhance topologically protected oscillations. In the topological regime, a stochastic system with a $D$-dimensional configuration space develops a steady-state current confined to a one-dimensional edge cycle. The intervening boundary faces between the $D$-dimensional bulk and the one-dimensional cycle form an interdimensional funnel, sequentially pushing the system towards lower dimensional faces, down to the one-dimensional cycle. As a consequence, increasing $D$ enhances the funnel, leading to improved one-dimensional localization and oscillatory coherence. A quantized biorthogonal Zak phase identifies the transition to the topological regime, which additionally exhibits a highly structured non-Hermitian skin effect. In contrast with previous realizations of topological states in stochastic systems, the phenomena we uncover have no counterpart in quantum condensed matter or active matter systems.

Cavitation, the growth of bubbles in fluids and soft matter, plays a central role in tissue damage, ultrasound therapies, and material failure, yet the influence of viscoelastic dissipation on bubble dynamics remains unclear. Here, we investigate cavitation in polyacrylamide hydrogels with identical elastic moduli but different viscous dissipation. We observe a striking symmetry breaking transition: elastic gels rapidly develop ellipsoidal cavities before rupture, whereas viscoelastic gels sustain large, nearly spherical bubbles. A modified Rayleigh Plesset framework shows that viscous stresses suppress shape instabilities and delay symmetry breaking, whereas elastic stresses accelerate cavity deformation. Finite element simulations independently validate these findings. The Deborah number (De) further captures the competition between material relaxation and cavity growth, with higher De associated with predominantly elastic growth and lower De with greater relaxation. Together, our results establish viscous dissipation as a key stabilizing mechanism that governs bubble morphology and failure in soft materials. These results have direct implications for understanding cavitation-mediated damage in biological tissues, optimizing therapeutic ultrasound, and advancing mechanical characterization of hydrogels for engineering and food processing applications.

Purpose: Pelvic floor muscles undergo large deformations during vaginal delivery, yet the passive tensile behavior of pregnant and non-pregnant pelvic floor muscle and the influence of testing protocols remain incompletely characterized. This study evaluated how pregnancy, loading rate, preconditioning, and storage affect passive tensile mechanics of rat pelvic floor muscle. Methods: Levator ani samples from pregnant and non-pregnant rats were tested in uniaxial tension to failure across multiple preconditioning amplitudes, two loading rates, and fresh or frozen storage conditions. Peak and transition metrics, strain energy density, toe-region stiffness, and high-strain stiffness were extracted using automated region-detection methods. Rat properties were also compared with digitized human cadaver levator ani data. Results: Pregnant rat samples reached greater peak strains than controls, although the difference was not significant. Preconditioning affected peak strain in a loading-rate-dependent manner: responses tapered near 2% preconditioning strain at 0.5% strain/s, whereas no comparable tapering occurred through 15% at 1% strain/s. Compared with human cadaver data, rat samples reached substantially larger peak strains and lower high-strain stiffness. Conclusion: This study provides one of the first direct experimental comparisons of passive pelvic floor muscle mechanics between pregnant and non-pregnant tissue, while systematically evaluating how multiple testing-protocol variables influence the measured response. Distinguishing protocol-dependent variability from biological differences provides a more reproducible basis for cross-study comparison and constitutive model calibration. Rat pelvic floor muscle is a useful model for pregnancy-associated adaptation, but absolute mechanical values should be translated to human tissue cautiously.

Receptor-mediated endocytosis requires a particle to approach the cell membrane to within a few nanometers before ligand–receptor binding can occur. Existing continuum models often start from an already established contact and do not explicitly describe how crowding particles on the extracellular side influence the distribution of the particle near the membrane. We examine entropic depletion forces as one possible nonspecific contribution to this initial approach. For ideal depletants, the Asakura–Oosawa excluded-volume construction gives an exact depletion potential for the planar geometry before contact. The potential and force vanish continuously at the onset of excluded-volume overlap. This interaction provides a possible contribution to membrane proximity before specific binding, while its extension to curved wrapping geometries requires additional approximation. Within a reduced continuum model, we combine depletion attraction, ligand–receptor binding, membrane deformation, and cytoskeletal viscoelastic dissipation. The viscoelastic contact is formulated through a hereditary integral and a standard linear solid. The kinetic model gives a conditional minimum ligand density for complete engulfment, a finite particle-size window, and a stiffness-dependent upper limit. When the stationary radius lies inside the domain of finite positive wrapping times, the estimated wrapping time has a minimum at a radius that decreases with increasing binding energy density. At fixed viscosity and other independent parameters, the same time approximation predicts slower wrapping as cell stiffness increases. The two positive roots defining the size window merge at a limiting parameter value, which characterizes closure of the admissible size interval. Depletion attraction is interpreted as one possible contribution to particle-membrane association

The rationalization of protein hydrogel design is essential in creating bespoke and functional materials for a range of medical and healthcare applications, such as tissue engineering, wound healing and bio-sensors. The dynamic process of protein unfolding can significantly influence the resultant gel structure and behaviour - conformational changes in individual protein building blocks affect the bulk gel, whilst changes to bulk conditions influence how individual proteins move and respond. In order to characterize and control protein hydrogels, a multi-lengthscale picture must be built so that this complex interplay can be understood. We develop a coarse-grained computational model to investigate fundamental aspects of dynamic unfolding within chemically crosslinked protein hydrogels, focussing on the mesoscale structure during and after gelation. In simplifying protein unfolding to a single dynamic step, we are able to create a heterogeneous network formed of coarse-grained proteins. We observe evolving hydrogels consisting of regions of high-density protein clusters with a low degree of unfolding due to local crowding effects, interconnected by low-density stranded regions with a higher concentration of unfolded proteins. Systematically varying the barrier height for single protein unfolding demonstrates a weak influence on the mesoscale structure as characterised by fractal dimension and correlation length. Conversely, the measured changes in these parameters are greater after the gel experiences sustained conformational relaxation. This work demonstrates the effectiveness of coarse-grained modelling in capturing the heterogeneous mesoscale network of crosslinked unfoldable proteins. A greater predictive understanding of the structure and behaviour of protein hydrogels is gained through this model and will accelerate the design of novel biomaterials.

We present the first systematic investigation of regular tetrahedra densely packed within a circular cylinder. Combining numerical simulations with exact analytical theory, we classify the densest configurations into two continuous, mathematically tractable families: chiral Tetra Helices and achiral Dimer Chains. Crucially, we reveal that the celebrated Boerdijk-Coxeter Helix is not an isolated anomaly, but a natural consequence of this cylindrical confinement. We further identify a striking symmetry-breaking bifurcation at a critical diameter, where the chiral helix coexists with an achiral dimer chain at a strictly identical packing fraction.

Photorenewable polymer adsorbents, aimed at reducing the low density lipoproteins (LDLs) level, are reported experimentally. Previously we developed a mesoscale model for this setup with uniformly grafted chains, and examined the role played by chains length and grafting density [Ilnytskyi, et al., Processes, 2023, 11, 2913]. The analysis is extended here to the cases of bunch-like and grid-like grafting micropatterns. The bunch-like arrangement is the most efficient one displaying low- and high-density peaks for adsorbing effectiveness. The former peak is twice higher compared with uniform grafting, as a consequence of a match between LDLs dimensions and the micropattern pitch. Adsorption isotherms are fitted well by either Langmuir or logistic growth forms, indicating a higher growth rate compared with uniform grafting. The high-density peak exists only for this micropattern and extends the applicability of an adsorbent. At the highest LDL concentration, adsorption is hampered by self-assembly of LDLs into a packed cubic phase.

Water's anomalous properties have been hypothesized to originate from a liquid-liquid critical point in the supercooled regime, separating high- and low-density liquid states. Experimental verification remains challenging due to rapid crystallization under these conditions. A recent study reported evidence for such a transition, based primarily on a pronounced increase in the heat capacity of rapidly heated low-density amorphous ice. Here, we show that this heat capacity increase can be explained without invoking a liquid-liquid transition. By combining simulations using a machine-learning potential trained on the state-of-the-art MB-pol water model, combined with the Tool-Narayanaswamy-Moynihan (TNM) model of the glass transition, we demonstrate that the observed signal can arise instead from a dynamical effect induced by the mobilization of rotational and translational molecular degrees of freedom during ultrafast heating. We further show that our findings are fully consistent with recent electron diffraction measurements showing structural arrest of supercooled water close to our predicted glass-transition temperature. These results provide an alternative interpretation of the experimental observations and highlight the importance of nonequilibrium glassy dynamics in the interpretation of the behavior of supercooled water on ultra-short time scales.

Active microrheology is an effective tool to determine the rheological properties of viscous, viscoelastic, or elastic materials on microscopic length scales. The positional response of an embedded probe particle to an externally applied oscillating driving force allows to indirectly characterize the properties of the surrounding media. We aim to explore the linear and nonlinear response of probe particles in a microrheological setup of planar geometry. For this purpose, we extend the computational method of fluid particle dynamics from viscous fluid-like to viscoelastic and elastic media, including nonlinear regimes. We consider a system confined by solid walls. In this case, we validate the approach by quantifying the linear response in terms of a Jeffreys model. Increasing the amplitude of the driving force, we observe distinct nonlinear effects. They include distorted stress-strain curves and a gradual net drift of probe particles initially positioned close to a wall. This drift vanishes in the viscous fluid-like and elastic solid-like limits, but is manifest for intermediate viscoelastic systems. We further address a setup of two probe particles in the absence of walls. They experience reciprocal pairwise oscillatory forcing. Here, nonlinearities in viscoelastic systems induce a net drift gradually moving the particles further apart from each other. Comparing with real setups, our implementation of the driving force is in line with experimental setups of optical tweezers or active magnetic microrheology.

Intrigued by the response of T cell receptors to the presence of a few agonist ligands, we propose a minimal model that can achieve similar performance. The model consists of a small cluster of immobile receptors that bind reversibly to two types (correct/incorrect) of ligands in the environment, with slightly weaker binding strength for the incorrect one. It features binding-state coupling between nearest-neighbor receptors, and receptors in the bound/free states are activated/deactivated by specific enzymes, with rates that allow kinetic proofreading. It is found that, for a range of binding-state coupling strength, incorrect ligands alone cannot activate the receptors, but the binding of merely one correct ligand to a receptor is sufficient to promote the activation of other receptors via induced binding to incorrect ligands. Both response time and signal amplification increase as the receptor binding-state coupling strength increases until it reaches an optimal range to achieve the most rapid and sensitive response. These results suggest a possible mechanism for a speedy and specific response of receptors to very few correct ligands in biological and artificial systems at the subcellular scale.

The motility of microorganisms provides a natural framework for studying active matter and its response to environmental perturbations. We experimentally investigate the trajectories of Paramecium caudatum and Artemia salina under controlled conditions. Using video microscopy, we reconstruct individual trajectories over time. Beyond baseline conditions, we analyze stressed environments where P. caudatum are exposed to a toxic agent and A. salina are placed in distilled water. To characterize the motion, we compute trajectory-based metrics, including mean square displacement, velocity distributions, persistence times, tortuosity, and radius of gyration. These observables quantify changes in motility patterns across species and conditions. To interpret the dynamics, we develop coarse-grained stochastic models parameterized directly from experimental data. P. caudatum is described within a run-and-tumble framework, including reversal events under stress. In contrast, A. salina requires a run-and-tumble-like description with persistent orientational noise, accounting for continuous directional fluctuations during runs. The models reproduce the main trajectory-level observables used for comparison, namely tortuosity and radius of gyration. This combined approach provides insight into how environmental stress modifies locomotion statistics. Our results highlight systematic differences in the statistical signatures of motion, suggesting that trajectory metrics may serve as indicators of physiological stress. This work provides an accessible framework for active matter experiments where individual-level statistics can be directly measured and modeled.

The presence of geometric frustration in self-assemblies, stemming from a shape misfit between identical subunits, can lead to the spontaneous formation of finite-sized structures known as self-limitation. Recently proposed models of curved, colloidal shells, dubbed "curvamers", that form one-dimensional stacks have shown curvature-induced frustration to be a promising class of particle designs for engineering self-limiting assembly. However, existing models of curvamer assembly considered only cylindrically curved shells where elastic costs are derived purely from bending. Here, we study the self-limiting behavior of a more general class of curvature-frustrated colloidal shells, extending shapes to include non-Euclidean geometries such as spherical caps and saddles whose deformations can also incur stretching costs. We introduce continuum mechanical and discrete, coarse-grained models of shell stacks to study how qualitatively different intra-assembly strain gradients required for each geometry affect the buildup of super-extensive elastic costs. We find the accumulation of elastic energies in non-Euclidean shell stacks is sensitive to shell thickness via a dimensionless quantity related to the Foppl-von Karman number that characterizes the relative strength of stretching to bending. In particular, thin shells penalize stretching more causing faster elastic energy growth resulting in the suppression of self-limitation with sizes generally largest for cylindrical shells, smallest for spherical caps and intermediate for saddles. We additionally find that while thin spherical caps experience the strongest elastic penalties, the rapidly increasing rate of elastic penalties in stacks of saddles lead to a robust size-selection as inter-particle binding strength grows as well as relatively smaller dispersity in self-limiting stack size in comparison to cylindrical and spherical curvamers.

Iontronic devices that replicate physiological processes will improve energy efficiency to support artificial-intelligence architectures. Energy storage and conversion devices, however, display disordered interfacial and bulk configurations that conventional impedance spectroscopy cannot resolve. Here, we show that the onset of frequency dispersion in the impedance of an electrolytic cell with non-uniformly charged electrodes takes place at approximately twice the resonator natural frequency, independently of whether the cell is driven by oscillating charges or voltage differences, and remains valid for both overlapping and non-overlapping double layers. In addition to characterizing energy storage devices, these universal findings apply to devices interfaced with biomaterials.

Nucleosomes are structures made up of proteins called histones that bind and compact DNA, driving the mesoscale organization of the chromatin polymer. Although histone proteins have diversified over evolutionary time, their contributions to the corresponding diversification of chromatin structure are poorly understood. Here, we mine protein databases for histones and create in silico nucleosomes for 3241 organisms spanning $>$1.5B years of evolution. Using a combination of electrostatic calculations and coarse-grained molecular dynamics simulations, we reveal extensive biophysical diversification of the nucleosome unit. Finally, we perform coarse-grained oligonucleosomal simulations on a subset of evolutionarily and biophysically divergent nucleosomes, demonstrating dramatic differences in bulk phase behavior of chromatin. Taken together, our results suggest a paradigm in which histones may have evolved to facilitate particular types of mesoscale chromatin behavior.

Droplets in complex environments often encounter compositional gradients that drive their propulsion. For example, surfactant gradients induce surface-tension-driven propulsion via the Marangoni effect. Here, we show that such propulsion emerges generically, even in the absence of surfactants. Employing a thin-interface approximation, we derive a compact expression for the droplet velocity in terms of droplet size, viscosity, the sensitivity of surface tension to a regulating component, and the chemical potential gradient of that component. Our theory reveals that droplets move toward regions of lower stability, as encoded in the morphology of the phase diagram, particularly near critical points. Numerical simulations confirm these results, which establish a general route to predicting and designing droplet motility from the phase behavior of complex fluids.

We study a two-dimensional Gaussian Rouse chain divided into two contiguous blocks with different local friction coefficients but identical Hookean elasticity. In quiescent conditions, friction heterogeneity leaves the continuous-time equilibrium Gaussian conformation unchanged while producing distinct block mobilities and a modified generalized relaxation spectrum. The exact friction-weighted translational diffusion coefficient retains the scaling $D_\gamma\propto N^{-1}$, with a friction-dependent prefactor; the corresponding diffusion law and generalized-mode formulation extend beyond the diblock to more general local friction profiles. We then place the chain in an incompressible Batchelor-Kraichnan random flow. Under a common external flow, the high-friction block becomes more extended than the low-friction block. The deformation asymmetry strengthens with friction contrast and develops across the weak- to moderate-flow regime, remaining clearly visible at the strongest finite-time conditions. It also persists when the chain-specific longest-mode Weissenberg number is matched across friction ratios, showing that, within the same finite-time protocol, the effect is not accounted for solely by a shift of the global relaxation timescale. In generalized-mode coordinates, the smooth random strain does not directly mix mode indices and preferentially amplifies the slowest mode. At larger flow strengths, extension distributions broaden markedly and exhibit pronounced temporal drift over the simulated time window. Since all simulated chain-specific longest-mode Weissenberg numbers remain below unity, this drift is interpreted as slow convergence toward the broad, power-law-tailed stationary Hookean regime near the coil-stretch threshold. Overall, friction heterogeneity alone is sufficient to redistribute deformation along an otherwise mechanically uniform polymer.

The paper studies the optical properties of assemblies of metallic prolate and oblate nanospheroids. The frequency dependences of averaged absorption and scattering cross-sections, absorption, scattering and reflection coefficients are obtained. The calculations were performed for assemblies of spheroidal particles of various metals. The difference in the results of calculating the frequency characteristics of the absorption and scattering cross-sections for assemblies of prolate and oblate spheroids manifested itself in different location and value of the maxima. A qualitative similarity of the frequency dependences of the absorption and scattering coefficients was established. It is shown that the frequency curves of assemblies of spheroids of different metals differ significantly in the location of the extrema within the range from the visible to the ultraviolet region of the spectrum

We investigate square-lattice skyrmion crystal states in a centrosymmetric spin model with momentum-dependent magnetic interactions, including a uniform ferromagnetic interaction, easy-axis anisotropic interactions at two orthogonal finite wave vectors, higher-harmonic wave-vector interactions, and an external magnetic field. Using simulated annealing calculations, we determine the low-temperature magnetic phase diagram as functions of magnetic field and uniform ferromagnetic interaction strength. We find that the skyrmion crystal phase is stabilized at intermediate fields when the uniform ferromagnetic interaction is weak, exhibiting a noncoplanar double-$Q$ spin texture, finite scalar spin chirality, and higher-harmonic components. Increasing the uniform ferromagnetic interaction suppresses and eventually eliminates the skyrmion crystal phase through direct first-order transitions to the fully polarized state, whereas its weakening expands the skyrmion stability region and stabilizes a single-$Q$ conical spiral phase at high fields. These results clarify the mechanisms governing the square-lattice skyrmion formation in centrosymmetric magnets.

The effect of B homopolymer adding on the aggregation behavior of amphiphilic coil/rod/coil BAB triblock copolymers was studied using lattice self-consistent field theory. It depends on the length of the hydrophobic rod block and copolymer concentration. Compared with the solutions, at low concentrations, homopolymer addition is favorable to the emergence of cubic and large lamellar micelles. Although it enriches the structural behavior, the rearrangement is suppressed. As rod blocks increase, the rearrangement is related to the orientation-dependent diffusion and inverse diffusion processes. At relatively high concentrations, adding homopolymers promotes rearrangement. For long rod block case, the arrangements related to inverse laminarization and the cooperative growth emerges. At high concentrations, for short rod block system, homopolymer addition is advantageous to micelle rearrangement concerned with order/disorder transition, and the rearrangements in the system of intermediate length and long rod blocks are suppressed. This work aids in understanding of the growth mechanism of micelles with rigid block cores.

Exponential potentials were used since Kramers, Hellman, Glauberman, Yukhnovskii and Kelbg for solving problems of quantum chemistry, ionic solutions and plasmas. Here we develop the theory further and add new results, in particular with respect to the quantum-statistical theory. In particular, we derive the Kelbg quantum potential for the exponential interactions and discuss screening as well as the problem of thermodynamic stability. Further we give new applications to a qualitative theory of alkali plasmas and fusion plasmas. We show that the exponential potential and the early analytical results of Yukhnovskii and Kelbg allow a qualtative analytical treatment of such difficult problems as the phase transitions in electrolytes and alkali plasmas.

We explore composition dependence of the principal properties of liquid dimethylsulfoxide (DMSO)-methanol (MeOH) liquid mixtures by using molecular dynamics computer simulations. A set of non-polarizable semi-flexible models for the DMSO molecule combined with methanol models is investigated. Composition trends of density, excess mixing volume and excess mixing enthalpy are evaluated. Besides, we study the composition dependence of self-diffusion of species and shear viscosity ofthe static dielectric constant and the surface tension.Certain aspects of the microscopic structure are analyzedin terms of radial distribution functions and of the average number of hydrogen-bonded molecules. The quality of several combinations of the models is illustrated and critically evaluated by comparisons with experimental data.

Polymer properties emerge from interactions across scales, yet existing polymer models typically preserve either detailed monomer chemistry without an explicit polymer graph or polymer connectivity with simplified monomer representations, due to computational constraints, as polymers typically contain tens of thousands of atoms. We present Uni-Macro-FRPN (FRPN), a Full-Resolution Polymer Network that retains both detailed atom-level and monomer-level features and explicit polymer structure information within a unified framework. Two Transformers jointly learn atom-informed monomer semantics, sequence order, and chain topology from BigSMILES-derived representations. On the Block Copolymer Database (BCDB) lamellar-versus-non-lamellar classification task, FRPN achieves 86.4% accuracy and 90.6% ROC-AUC, establishing state-of-the-art performance. Ablation results indicate that the gain is not explained solely by increased parameter count. On a linear homopolymer benchmark, monomer-centric learning remains competitive, highlighting a boundary case where polymer-scale organization is simple. To test generalization beyond linear polymers, we further construct an all-atom molecular-dynamics benchmark of 1640 datapoints spanning diverse monomer chemistries, sequence orderings, chain topologies, and physical properties. FRPN achieves the strongest overall performance on this topology-rich benchmark, with diagnostics supporting the benefit of jointly modeling monomer chemistry and polymer structure. Taken together, FRPN provides a practical route for moving polymer representation learning beyond monomer-centric representations. The leading performance of FRPN also suggests a promising direction for polymer informatics: future polymer prediction models should treat polymers not only as collections of monomer descriptors, but as complete multiscale chemical and topological objects.

Casimir forces, arising from quantum and thermal fluctuations, depend strongly on the electromagnetic susceptibilities of the interacting materials. Here, we theoretically investigate the Casimir force between two graphene sheets separated by a nanometer-thin layer of liquid water. We find that deviations from the bulk case can be as large as 24 %, mostly due to the static susceptibility components. This effect does not decrease monotonically as a function of the layer thickness: it increases up to a maximum at 70 nm and it slowly declines to the bulk limit only at 1 micron. This is due to two competing effects: on one hand, the anisotropy decreases with the layer thickness; on the other hand, the relative contribution of the most anisotropic, static susceptibility components increases.

In many soft-matter and biological systems, task completion relies on the cumulative arrival of multiple searchers rather than the speed of a single pioneer. The completion kinetics are therefore set not only by the first arrival, but by the full ordered sequence of first-passage times. Here, we determine how stochastic resetting optimizes these ordered arrivals for all arrival ranks. We construct an exact finite-$N$ reference for non-interacting Brownian searchers and obtain the mean ordered first-passage time $\langle T_{(k)} \rangle$ and its optimal resetting rate $r_k^*$. For searchers with identical initial conditions, $r_k^*$ increases monotonically with arrival rank and, with increasing population size, approaches the known large-$N$ quantile limit where a finite optimum appears only above a critical rank fraction $\phi_c \simeq 0.412$. Spatial heterogeneity qualitatively reorganizes this sequence, shifting its maximum from late to early ranks even without particle interactions. We then compare this baseline with Brownian colloid experiments, interacting active Brownian particles, and a collective autochemotactic search with persistent environmental memory. Across these systems, sensitivity to resetting increases strongly with arrival rank, while deviations from appropriate non-interacting references reveal the influence of direct interactions, finite return overhead, and environmental memory. Our results show that optimal resetting in multiparticle search is governed by the required completion rank and must be evaluated relative to protocol- and geometry-matched baselines.