Short-range Interactions Research Articles

Thermo-osmosis of a near-critical binary fluid mixture: A general formulation and universal flow direction.

We consider a binary fluid mixture, which lies in the one-phase region near the demixing critical point, and study its transport through a capillary tube linking two large reservoirs. We assume that short-range interactions cause preferential adsorption of one component onto the tube's wall. The adsorption layer can become much thicker than the molecular size, which enables us to apply hydrodynamics based on a coarse-grained free-energy functional. For transport processes induced by gradients of the pressure, composition, and temperature along a cylindrical tube, we obtain the formulas of the Onsager coefficients to extend our previous results on isothermal transport, assuming the critical composition in the middle of each reservoir in the reference equilibrium state. Among the processes, we focus on thermo-osmosis-mass flow due to a temperature gradient. We explicitly derive a formula for the thermal force density, which is nonvanishing in the adsorption layer and causes thermo-osmosis. This formula for a near-critical binary fluid mixture is an extension of the conventional formula for a one-component fluid, expressed in terms of local excess enthalpy. We predict that the direction of thermo-osmotic flow of a mixture near the upper (lower) consolute point is the same as (opposite to) that of the temperature gradient, irrespective of which component is adsorbed on the wall. Our procedure would also be applied to dynamics of a soft material, whose mesoscopic inhomogeneity can be described by a coarse-grained free-energy functional.

Scale and conformal invariance in rotating interacting few-fermion systems

We show that rotating two-dimensional Fermi gases possess a nonrelativistic scale and conformal invariance at weak but nonzero interactions, where the scale invariance of universal short-range interactions is not yet broken by quantum effects. We demonstrate the symmetry in the excitation spectrum of few-fermion ensembles in a harmonic trap obtained by exact diagonalization. The excitation spectrum is shown to split in a set of primary states and derived excited states that consist of breathing modes as well as two different center-of-mass excitations, which describe cyclotron and guiding-center excitations of the total particle cloud. Furthermore, the conformal symmetry is manifest in the many-body wave function, where it dictates the form of the hyperradial component, which we demonstrate using Monte Carlo sampling of few-body wave functions. Published by the American Physical Society 2024

A novel section–section potential for short-range interactions between plane beams

We derive a novel formulation for the interaction potential between deformable fibers due to short-range fields arising from intermolecular forces. The formulation improves the existing section–section interaction potential law for in-plane beams by considering an offset between interacting cross sections. The new law is asymptotically consistent, which is particularly beneficial for computationally demanding scenarios involving short-range interactions like van der Waals and steric forces. The formulation is implemented within a framework of rotation-free Bernoulli–Euler beams utilizing the isogeometric paradigm. The improved accuracy of the novel law is confirmed through thorough numerical studies. We apply the developed formulation to investigate the complex behavior observed during peeling and pull-off of elastic fibers interacting via the Lennard–Jones potential.

Demonstrating the Connection between the Nonvalence Correlation-Bound Anions of Polyaromatic Hydrocarbons and the Image Potential States of Graphene Using a One-Electron Model Hamiltonian.

The ground and excited state nonvalence correlation-bound (NVCB) anion states of the hexagonal polycylic aromatic hydrocarbons and of hexagonal graphene nanoflakes are characterized using a one-electron model Hamiltonian which incorporates atomic electrostatic moments up to the quadrupole, coupled inducible charges and dipoles, and atom-centered Gaussians to describe the short-range repulsive interactions. Extrapolation of the calculated electron binding energies of the lowest energy symmetric and antisymmetric (with respect to the molecular plane) NVCB anions of both the polycylic aromatic hydrocarbons and the carbon nanoflakes to the n → ∞ limit yields binding energies that are in good agreement with those of the most stable symmetric and antisymmetric image potential states of freestanding graphene as determined from two-photon photoemission spectroscopy (2PPE) experiments.

Molecular-Induced Steric Shape Separation of Nano Geometries

The separation of specific-shaped nanoparticles from particle mixtures is important for the construction of particle domains with shape-specific characteristics. On the other hand, shape-based particle separation techniques have not received as much attention as size-based techniques. The shape separation effect of molecule-coated nanoparticles is primarily dependent on short-range steric interaction. The surfactant’s geometrical arrangement determines the position of the nanoparticle’s various crystallographic facets; this also determines how the various shapes are organized throughout the nanoparticle self-assembly process. In addition to smooth surfaces with a fixed droplet contact line and a stable environment, the self-assembled shape separation effect has been observed on surfaces with wettability stripe patterns and fluctuating environments where the droplet contact line behaves stick–slip-like during the evaporation process. The shape separation of spheres and rods is strongly influenced by steric interactions. Since shape-dependent transport properties exist in such nano-crystalline domains, it is important to understand the shape separation effect.

Transferability of Buckingham Parameters for Short-Range Repulsion between Topological Atoms.

The repulsive part of the Buckingham potential, with parameters A and B, can be used to model deformation energies and steric energies. Both are calculated using the interacting quantum atom energy decomposition scheme where the latter is obtained from the former by a charge-transfer-based energy correction. These energies relate to short-range interactions, specifically the deformation of electron density and steric hindrance, respectively, when topological atoms approach each other. In this work, we calculate and fit the energies of carbonyl carbon, carbonyl oxygen, and, where possible, amine nitrogen atoms to the repulsive part of the Buckingham potential for 26 molecules. We find that while the steric energies of all atom pairs studied display exponential behavior with respect to distance, some deformation energy data do not. The obtained parameters are shown to be transferable by calculating root-mean-square errors of fitted potentials with respect to energy data of the same atom in, as far as possible, all other molecules from our data set. We observed that 36% and 10% of these errors were smaller than 4 kJ mol-1 for steric and deformation energy, respectively. Thus, we find that steric energy parameters are more transferable than deformation energy parameters. Finally, we speculate about the physical meaning of the A and B parameters and the implications of the strong exponential and exponential-linear piecewise relationships that we observe between them.

Counting atypical black hole microstates from entanglement wedges

Disentangled black hole microstates are atypical states in holographic CFTs whose gravity duals do not have smooth horizons. If there exist sufficiently many disentangled microstates to account for the entire black hole entropy, then any black hole microstate can be written as a superposition of states without smooth horizons. We show that there exist sufficiently many disentangled microstates to account for almost the entire black hole entropy of a large AdS black hole at the semiclassical limit GN → 0. In addition, we also argue that in generic quantum many-body systems with short-ranged interactions, there exist sufficiently many area law states in the microcanonical subspace to account for almost the entire thermodynamic entropy in the standard thermodynamic limit. Area law states are atypical since a typical state should contain volume law entanglement. Furthermore, we also present an explicit way to construct such a set of area law states, and argue that the same construction may also be used to construct disentangled states.

Distinct epigenetic modulation of differentially expressed genes in the adult mouse brain following prenatal exposure to low-dose bisphenol A.

Bisphenol A (BPA) is a common component in the manufacture of daily plastic consumer goods. Recent studies have suggested that prenatal exposure to BPA can increase the susceptibility of offspring to mental illness, although the underlying mechanisms remain unclear. In this study, we performed transcriptomic and epigenomic profiling in the adult mouse brain following prenatal exposure to low-dose BPA. We observed a sex-specific transcriptional dysregulation in the cortex, with more significant differentially expressed genes was observed in adult cortex from male offspring. Moreover, the upregulated genes primarily influenced neuronal functions, while the downregulated genes were significantly associated with energy metabolism pathways. More evidence supporting impaired mitochondrial function included a decreased ATP level and a reduced number of mitochondria in the cortical neuron of the BPA group. We further investigated the higher-order chromatin regulatory patterns of DEGs by incorporating published Hi-C data. Interestingly, we found that upregulated genes exhibited more distal interactions with multiple enhancers, while downregulated genes displayed relatively short-range interactions among adjacent genes. Our data further revealed decreased H3K9me3 signal on the distal enhancers of upregulated genes, whereas increased DNA methylation and H3K27me3 signals on the promoters of downregulated genes. In summary, our study provides compelling evidence for the potential health risks associated with prenatal exposure to BPA, and uncovers sex-specific transcriptional changes with a complex interplay of multiple epigenetic mechanisms.

Spatial interactions modulate tumor growth and immune infiltration.

Lenia, a cellular automata framework used in artificial life, provides a natural setting to implement mathematical models of cancer incorporating features such as morphogenesis, homeostasis, motility, reproduction, growth, stimuli response, evolvability, and adaptation. Historically, agent-based models of cancer progression have been constructed with rules that govern birth, death and migration, with attempts to map local rules to emergent global growth dynamics. In contrast, Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining an interaction kernel governing density-dependent growth dynamics. Lenia can recapitulate a range of cancer model classifications including local or global, deterministic or stochastic, non-spatial or spatial, single or multi-population, and off or on-lattice. Lenia is subsequently used to develop data-informed models of 1) single-population growth dynamics, 2) multi-population cell-cell competition models, and 3) cell migration or chemotaxis. Mathematical modeling provides important mechanistic insights. First, short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects. Next, we find that asymmetric interaction tumor-immune kernels lead to poor immune response. Finally, modeling recapitulates immune-ECM interactions where patterns of collagen formation provide immune protection, indicated by an emergent inverse relationship between disease stage and immune coverage.

Changing the order of a dynamical phase transition through fluctuations in a quantum p-spin model

We study the non-equilibrium phase diagram of a fully-connected Ising p-spin model, for generic p > 2, and investigate its robustness with respect to the inclusion of spin-wave fluctuations, resulting from a ferromagnetic, short-range spin interaction. In particular, we investigate the dynamics of the mean-field model after a quantum quench: we observe a new dynamical phase transition which is either first or second order depending on the even or odd parity of p, in stark contrast with its thermal counterpart which is first order for all p. The dynamical phase diagram is qualitatively modified by the fluctuations introduced by a short-range interaction which drive the system always towards various prethermal paramagnetic phases determined by the strength of time dependent fluctuations of the magnetization.

Sm substitution effect on the critical behaviour of Laves phase Gd1−xSmxCo2 intermetallic nearby the ferromagnetic-paramagnetic phase transition

In this work, we present a comparative and detailed study of the critical behavior near ferromagnetic-paramagnetic phase transition in the intermetallic Laves phase compounds Gd1−xSmxCo2, x = 0.5, 0.6 and 0.7. The investigated samples exhibit a second-order phase transition at Curie temperature TC. The critical exponents β, γ, and δ have been estimated through diverse techniques: the Modified Arrott plot, the Kouvel-Fisher plot, the critical isotherm technique, and the magnetocaloric effect method. The critical exponents values that were determined have been validated using the universal scaling hypothesis, wherein the isotherm magnetization curves M(H) converge into two distinct universal branches below and above TC. The substitution of Gd by Sm changes the universality class in the (Gd,Sm)Co2 compounds. For the sample Gd0.5Sm0.5Co2, the critical exponents obtained are closely consistent with the predictions of the 3D-Heisenberg model featuring a magnetic system with a short-range exchange interaction. While, for both samples Gd0.4Sm0.6Co2 and Gd0.3Sm0.7Co2, the values deviate from known theoretical models and might belong to an unconventional universality class with an intermediate-range exchange interaction.

The effects of polyethylene glycol on the nucleation and growth of DNA-functionalized gold nanoparticles crystals

This study presents findings on the crystal growth of DNA-functionalized gold nanoparticles (DNA-AuNPs) in the presence of polyethylene glycol (PEG), serving as an additive. Utilizing optical microscopy, UV–Vis spectroscopy, dynamic light scattering and small angle x-ray scattering, we observed enhanced crystal nucleation and growth facilitated by PEG. This effect is attributed to PEG’s promotion of the overall crystallization process of DNA-AuNPs. Furthermore, we examined the influence of PEG on short-range interactions between DNA-AuNPs, where DNA binding occurs, and long-range interactions in colloidal solutions. The results suggest that PEG plays a crucial role in both promoting crystal nucleation and influencing the interactions between DNA-AuNPs at various distances, shedding light on its potential impact on DNA-AuNP crystal formation.

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22–37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92–0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

Effects of calcium and barium chloride salts on the solubility and dissolution thermodynamics of glycine and DL-alanine from 288.15 K to 328.15 K

This investigation primarily examines the solubility at equilibrium and the dissolution thermodynamics of two crucial amino acids, glycine and DL-alanine, in aqueous solutions containing calcium and barium chloride salts. The solubilities were measured from 288.15 K to 328.15 K using the analytical gravimetric method. The resulting solubilities were then used to deduce the thermodynamics of the solutions. Subsequently, the free energies associated with solvation through transfer were examined.Furthermore, the research delved into various thermophysical characteristics within electrolyte solutions in aqueous media, establishing correlations. In addition, this study elucidated various non-covalent processes, including the solubility and stability of amino acids, by considering short-range interactions such as solute–solvent, solvent–solvent, and acid–base interactions. This analysis primarily provides insights into the involved molecular interactions and energetics that govern the solubility and behavior of amino acids in various solution environments.

Unconventional magnetism mediated by spin-phonon-photon coupling

Magnetic order typically emerges due to the short-range exchange interaction between the constituent electronic spins. Recent discoveries have found a crucial role for spin-phonon coupling in various phenomena from optical ultrafast magnetization switching to dynamical control of the magnetic state. Here, we demonstrate theoretically the emergence of a biquadratic long-range interaction between spins mediated by their coupling to phonons hybridized with vacuum photons into polaritons. The resulting ordered state enabled by the exchange of virtual polaritons between spins is reminiscent of superconductivity mediated by the exchange of virtual phonons. The biquadratic nature of the spin-spin interaction promotes ordering without favoring ferro- or antiferromagnetism. It further makes the phase transition to magnetic order a first-order transition, unlike in conventional magnets. Consequently, a large magnetization develops abruptly on lowering the temperature which could enable magnetic memories admitting ultralow-power thermally-assisted writing while maintaining a high data stability. The role of photons in the phenomenon further enables an in-situ static control over the magnetism. These unique features make our predicted spin-spin interaction and magnetism highly unconventional paving the way for novel scientific and technological opportunities.

Investigating the role of network former interactions on charge carrier diffusivity in glasses

Ionic transport is a critical property for the glass industry, since emerging applications such as sensors, batteries, and electric melting are based on the phenomenon. Short-range interactions (anion-charge carrier) have not been able to explain the total activation barrier observed experimentally, and, as such, it is critical to understand the larger role of all ions in a glass, not just the carrier and the ‘site’ ions. This research focuses on the role of network formers and their impact on diffusion in glasses, something that current models lack an explicit explanation of. Atomistic simulations with randomly generated parameters for the cation potentials and classical simulations were used to determine the diffusion coefficients and activation energies for synthetic network formers. Using this database, explainable machine learning algorithms were employed to explore network former interactions and determine which parameters are the most influential for ion diffusion. Results suggest that the bond length of the cations changes the geometry of the structure contributing the greatest to cation-modifier interactions.

Neural-network quantum states for ultra-cold Fermi gases

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases.

Multiparticle collision dynamics simulations of hydrodynamic interactions in colloidal suspensions: How well does the discrete particle approach do at short range?

The multiparticle collision dynamics (MPCD) simulation method is an attractive technique for studying the effects of hydrodynamic interactions in colloidal suspensions because of its flexibility, computational efficiency, and ease of implementation. Here, we analyze an extension of the basic MPCD method in which colloidal particles are discretized with a surface mesh of sensor nodes/particles that interact with solvent particles (MPCD + Discrete Particle or MPCD + DP). We use several situations that have been described analytically to probe the impact of colloidal particle mesh resolution on the ability of the MPCD + DP method to resolve short-ranged hydrodynamic interactions, which are important in crowded suspensions and especially in self-assembling systems that create high volume fraction phases. Specifically, we consider (A) hard-sphere diffusion near a wall, (B) two-particle diffusion, (C) hard-sphere diffusion in crowded suspensions, and (D) the dynamics of aggregation in an attractive colloidal suspension. We show that in each case, the density of sensor nodes plays a significant role in the accuracy of the simulation and that a surprisingly high number of surface nodes are needed to fully capture hydrodynamic interactions.

Sliding dynamics for bubble phases on periodic modulated substrates

We analyze a bubble-forming system composed of particles with competing long-range repulsive and short-range attractive interactions driven over a quasi-one-dimensional periodic substrate. We find various pinned and sliding phases as a function of substrate strength and drive amplitude. When the substrate is weak, a pinned bubble phase appears that depins elastically into a sliding bubble lattice. For stronger substrates, we find anisotropic bubbles, disordered bubbles, and stripe phases. Plastic depinning occurs via the hopping of individual particles from one bubble to the next in a pinned bubble lattice, and as the drive increases, there is a transition to a state where all of the bubbles are moving but are continuously shedding and absorbing individual particles. This is followed at high drives by a moving bubble lattice in which the particles can no longer escape their individual bubbles. The transition between the plastic and elastic sliding phases can be detected via signatures in the velocity-force curves, differential conductivity, and noise. When the bubbles shrink due to an increase in the attractive interaction term, they fit better inside the pinning troughs and become more strongly pinned, leading to a reentrant pinning phase. For weaker attractive terms, the size of the bubbles becomes greater than the width of the pinning troughs and the depinning becomes elastic with a reduced depinning threshold. Published by the American Physical Society 2024

MAFormer: A transformer network with multi-scale attention fusion for visual recognition

Vision Transformer and its variants have demonstrated great potential in various computer vision tasks. However conventional vision transformers often focus on global dependency at a coarse level, which results in a learning challenge on global relationships and fine-grained representation at a token level. In this paper, we introduce Multi-scale Attention Fusion into transformer (MAFormer), which explores local aggregation and global feature extraction in a dual-stream framework for visual recognition. We develop a simple but effective module to explore the full potential of transformers for visual representation by learning fine-grained and coarse-grained features at a token level and dynamically fusing them. Our Multi-scale Attention Fusion (MAF) block consists of: i) a local window attention branch that learns short-range interactions within windows, aggregating fine-grained local features; ii) global feature extraction through a novel Global Learning with Down-sampling (GLD) operation to efficiently capture long-range context information within the whole image; iii) a fusion module that self-explores the integration of both features via attention. Our MAFormer achieves state-of-the-art results on several common vision tasks. In particular, MAFormer-L achieves 85.9% Top-1 accuracy on ImageNet, surpassing CSWin-B and LV-ViT-L by 1.7% and 0.6% respectively. On MSCOCO, MAFormer outperforms the prior art CSWin by 1.7% mAPs on object detection and 1.4% on instance segmentation with similar-sized parameters. With the performance, MAFormer demonstrates the ability to generalize across various visual benchmarks and prospects as a general backbone for different self-supervised pre-training tasks in the future.