Macroscopic Research Articles

Smoothed dissipative particle dynamics (SDPD) is a widely used particle-based method for modeling soft matter systems at mesoscopic and macroscopic scales, offering thermodynamic consistency and direct control over the fluid's transport properties. Here, we present an SDPD model that incorporates the transport of reactants on scales smaller than the discretizing particles, including the evolution of compositional fields. The proposed methodology is well-suited for modeling complex systems governed by advection-diffusion-reaction (ADR) dynamics. Implemented in Large-scale Atomic/Molecular Massively Parallel Simulator, the model is validated using a range of benchmark problems spanning diffusion-dominated, reaction-dominated, and coupled ADR regimes. Our simulation results demonstrate that the implemented SDPD model effectively captures complex behaviors, such as Turing pattern formation. The proposed model holds promise for applications across various fields, including biology, chemistry, materials science, and environmental engineering.

Protein-based biomaterials offer sustainable and biocompatible alternatives to traditional ionic conductors, essential for advancing green energy storage and bioelectronic applications. In this work, a robust, intrinsically self-assembling repeat protein scaffold to enhance ionic conductivity through the selective incorporation of glutamic acids is engineered. These mutations increase the number of available protonation sites and promote the formation of well-defined charge pathways. The self-assembly properties of the system enable the propagation of molecular-level modifications to the macroscopic scale, yielding self-standing protein films with significantly improved ionic conductivity. Specifically, engineered protein-based films exhibit an order of magnitude higher conductivity than their unmodified counterparts, with a further ten-fold enhancement through controlled addition of salt ions. Mechanistic analysis shows that the conductivity enhancement originates from the intertwined contributions of proton transport, hydration, and ion diffusion, all promoted by engineered charged residues. Finally, films of the best-performing variant are integrated, as both separator and electrolyte, into a supercapacitor device with competitive energy storage performance. These findings highlight the potential of rational protein design to create biocompatible, sustainable, and efficient ionic conductors with the stability and processability required to be successfully integrated into the next generation of energy storage and bioelectronic devices.

Modeling bacterial adhesion onto nanostructured silicon carbide using a new physicochemical approach: Statistical physics analysis.

Hard carbon, owing to its tunable pore structure, is emerges as a promising anode material for sodium-ion batteries (SIBs) and holds a great potential to improve low-potential plateau capacityfor boosting the energy density of full cells. However, a key challenge for large-scale SIBs applications is the trade-off between increasing sodium storage pore volume and maintaining high compaction density. Herein, a pre-pore engineering strategy is employed to fabricate high-compaction-density spherical hard carbon with tunable pore structures, realizing simultaneous enhancement of gravimetric and volumetric capacities. Importantly, it is found that pore structure regulation profoundly affects performance across multiple scales. Microscopically, adjusting pore structure alters intrinsic electrochemical properties, with a reversible capacity of 375.40 mAh g-1 and initial Coulombic efficiency of 90.1%. At the mesoscale, monodisperse spheres reduce packing voids and improve compaction. As a result, even under high compaction, the anode maintains a high reversible capacity of 359.49 mAh g-1 and exhibits an excellent volumetric capacity of 390.30 mAh cm-3. The assembly of an Ah-level pouch cell further demonstrates its practical potential. In addition, fabrication methods determine electrode structure and sodium storage at the macroscopic scale, leading to clear differences in low-potential intercalation and pore-filling behaviors between lab-made and practical electrodes.

Quantum mechanics was developed in the early 20th century as a computational algorithm to organize and predict the strange results of a wide variety of experiments involving the microscopic world (molecules, atoms, elementary particles), which appeared random but had a predictable probabilistic distribution. Despite its overwhelming success, the conceptual foundations of this algorithm remain unclear and extremely controversial. How can we connect the microscopic world described by quantum mechanics with the macroscopic world of our daily experience? What is the role of observers in this connection? Why don't strange quantum phenomena occur on macroscopic scales? How can we explain the nonlocal nature of quantum phenomena? In this article I will present some attempts to answer these and other basic questions, which constitute different theories with completely different worldviews, commenting on their strengths and weaknesses. We will conclude that quantum theory is still under construction, as there is no consensual formulation. Its development is a fundamental problem in contemporary physics, and in thought in general.

These notes are a written version of lectures given in the 2024 Les Houches Summer School on Large deviations and applications. They are are based on a series of works published over the last 25 years on steady properties of non-equilibrium systems in contact with several heat baths at different temperatures or several reservoirs of particles at different densities. After recalling some classical tools to study non-equilibrium steady states, such as the use of tilted matrices, the Fluctuation theorem, the determination of transport coefficients, the Einstein relations or fluctuating hydrodynamics, they describe some of the basic ideas of the macroscopic fluctuation theory allowing to determine the large deviation functions of the density and of the current of diffusive systems.

We evaluate the exponentially rare fluctuations of the ionic current for a dilute electrolyte by means of macroscopic fluctuation theory. We consider the fluctuating hydrodynamics of a fluid electrolyte described by a stochastic Poisson-Nernst-Planck equation. We derive the Euler-Lagrange equations that dictate the optimal concentration profiles of ions conditioned on exhibiting a given current, whose form determines the likelihood of that current in the long-time limit. For a symmetric electrolyte under small applied voltages, number density fluctuations are small, and ionic current fluctuations are Gaussian with a variance determined by the Nernst-Einstein conductivity. Under large applied potentials, the ionic current distribution is generically non-Gaussian. Its structure is constrained thermodynamically by Gallavotti-Cohen symmetry and the thermodynamic uncertainty principle.

The accurate evaluation of the geometric morphology of rock and rock-like material joint surfaces was considered crucial for studying the mechanical properties of joint surfaces. A method based on grayscale surface and differential box-counting for evaluating the consistency of rock joint surface morphology was proposed in this study. The fractal dimensions of natural red sandstone joint surfaces and 3D printed restored joint surfaces were quantitatively identified. The consistency of the joint surface morphology of the samples was validated on both the image scale and the macroscopic mechanical scale using two approaches: CNN feature extraction and variable-angle shear tests. The validation results demonstrated a high degree of convergence, thereby confirming the accuracy of the proposed method. This study could provide a reference for the determination of the fractal dimension of joint surface morphology and the consistency research.

ABSTRACT Laser Powder Bed Fusion (LPBF) achieves layer-by-layer manufacturing of metal parts by melting metal powder using a high-energy laser beam. In response to the low accuracy and efficiency of the traditional inherent strain method in predicting residual stress and structural deformation during LPBF manufacturing, this study establishes a multiscale parameter transfer model for the inherent strain method. At the same time, to improve the efficiency of the inherent strain calculation, a Gaussian process regression algorithm was introduced for fast prediction. Specific methods: This study proposes a dual-domain heterogeneous inherent strain loading method at the microscopic scale for calculating mesoscopic inherent strain, and uses mesoscopic scale unitisation to handle anisotropic microscopic inherent strains. The macroscopic scale performs partition loading of mesoscopic inherent strain values in different scanning feature regions within a single equivalent layer to achieve the final stress and deformation prediction. The comparison among the multi-scale inherent strain method, commercial simulation software, and actual measurement of machined parts shows that the prediction efficiency has been improved by 30 times at the macro scale, the maximum deformation prediction error was reduced to 3.67%, and the accuracy was improved by 60%.

Abstract Machine learning (ML) has revolutionized energy materials discovery through two key paradigms: ML potentials enabling quantum‐accurate atomistic simulations with 2‐4 orders of magnitude speedup over density functional theory, and ML‐driven screening that efficiently navigates vast chemical spaces for rapid materials optimization. Advanced approaches, including graph neural networks and sparse Gaussian process regression incorporate physical symmetries and conservation laws, going beyond traditional statistical methods. Applications span battery materials, electrocatalysts, solar cells, phase change memory, and hydrogen storage systems, enabling simulations of thousands of atoms over extended timescales beyond the reach of quantum mechanical methods. Together, these complementary ML approaches enable predictive computational models spanning atomic to macroscopic scales. Current challenges include data quality, extrapolation to new chemical spaces, and physical interpretability. Emerging solutions involve equivariant architectures, active learning strategies, and physics‐informed neural networks. The convergence of ML methodologies with experimental workflows can accelerate materials discovery and optimization. This addresses critical sustainable energy challenges in conversion, storage, and utilization while supporting the development of autonomous discovery platforms. In this way, ML helps overcome computational limitations in multiscale energy materials research and supports the efficient design of novel materials with tailored properties.

Although Turing-like ordering is common at macroscopic scales, its realization in metal nanostructures has been hindered by the intrinsic tendency of metals toward symmetric growth and coarsening. Here, we report the first synthesis of a few-nanometer-scale Turing-type pattern in a metal catalyst, achieved through a Nano-Molecular Dual confinement (NanoMolD) strategy. A bilayer hollow silica nanoscaffold with a ∼2 nm internal cavity provides spatial confinement, while in situ self-assembly of a surfactant-palladium ion complex modulates precursor diffusion and surface deposition. This synergistic confinement produces an ultrathin two-dimensional palladium nanomesh with periodic, curved "Turing" stripes encased in silica. The 2D nanomesh features a high density of under-coordinated Pd atoms and twin boundaries, forming a unique catalytic architecture. As a result, this catalyst enables multicomponent carbonylative coupling reactions (e.g., Sonogashira, Suzuki-Miyaura, Buchwald-Hartwig, and alcohol coupling) with exceptional efficiency, affording ≥95-99% yields from aryl iodides and appreciable activity with bromides and chlorides. Furthermore, the confined nanomesh facilitates a one-pot sequential semihydrogenation of the intermediate alkynyl ketone to an α, β-unsaturated ketone (chalcone) by simply introducing H2, with tunable selectivity governed by the nanostructure. The Turing-type nanomesh is stable, fully recyclable, and demonstrates how nanoscale reaction-diffusion structuring can unlock new capabilities in heterogeneous catalysis.

The recently discovered helical polar fluid adopts a spontaneous chiral symmetry breaking (CSB) driven by combination of polarization escape, conformational chirality, and elastic effects. Ferroelectric nematic and ferroelectric smectic phases are intrinsically chiral in the ground state and can be stabilized in an extrinsic twisted configuration through surface anchoring. Herein, the study introduces extrinsic CSB as a novel technique in chiral engineering. To demonstrate this concept, the extrinsic structure of a helielectric conical mesophase (HEC)-3D chiral system-is constructed. Considering the challenges of controlling chirality at the macroscopic scale owing to magnetic fields, light, and fluid vortex motion, the proposed 3D chiral system enables chirality (twist) modulation through an ultralow electric field, thereby controlling unique diffraction pattern and circular polarized light-switching capabilities.

Quantifying and characterizing fluctuations far away from equilibrium is a challenging task. We discuss and experimentally confirm a series expansion for a driven classical system, relating the different nonequilibrium cumulants of the observable conjugate to the driving protocol. This series is valid from micro- to macroscopic length scales, and it encompasses the fluctuation–dissipation theorem (FDT). We apply it in experiments of a Brownian probe particle confined and driven by an optical potential and suspended in a nonlinear and non-Markovian fluid. The expansion states that the form of the FDT remains valid away from equilibrium for Gaussian observables, up to the order presented. We show that this expansion agrees with that of a known fluctuation theorem up to an unresolved difference regarding moments versus cumulants.

Abstract The microwave dielectric properties of CaMoO 4 were significantly enhanced by partial strontium substitution using a solid‐state synthesis method. Sr substitution effectively reduced the sintering temperature. The lattice mismatch‐induced local stress fields and distortions may extend to the macroscopic scale, ultimately resulting in residual strain. The temperature coefficient of the resonant frequency can be enhanced by moderate strain fields and lattice distortions. Ionic transport was suppressed, and dielectric loss was further reduced owing to the mitigation of oxygen vacancies, blockage of diffusion pathways, and the increase in the migration energy barrier induced by Sr incorporation. Ultimately, (Ca 0.5 Sr 0.5 )MoO 4 ceramics sintered at 950°C exhibited excellent dielectric performance ( ε r = 10.15, Q × f = 99 928 GHz, τ f = ‒8.55 ppm/°C).

The temporomandibular joint (TMJ) features unique tissue structures that support its complex functional demands. Alterations in these structures are often linked to jaw dysfunction, with pain being one of the most prevalent symptoms. However, the mechanisms underlying TMJ pain and its relationship with structural deterioration or functional impairment remain poorly understood. A comprehensive understanding of the interplay among TMJ structure, function, and pain is essential for uncovering disease mechanisms and developing effective therapies. To date, TMJ research in humans and animal models has been predominantly conducted in separate domains of structure, function, and pain, limiting integrative insights. Clinical studies also show inconsistent correlations among joint structural changes, jaw dysfunctions, and craniofacial pain, complicating diagnosis and treatments. This review aims to bridge these traditionally fragmented areas by synthesizing current knowledge across macroscopic and microscopic scales in human and animal models. TMJ diseases involve spatially proximate cellular, extracellular, and neural components that undergo multiscale spatiotemporal changes. These components experience complex mechanical loading during joint movement, triggering mechanical, neural, and immune responses that interact bidirectionally to influence TMJ integrity and pain. In turn, the brain modulates motor output and autonomic function, further affecting joint mechanics and cellular and nociceptive responses. To holistically and quantitatively assess these spatiotemporal dynamic processes, we propose a multiscale and multiphysics framework that integrates joint and tissue biomechanics, biochemical signals, cellular responses, nociception, and psychosocial influences. Realizing this vision requires a transdisciplinary effort and the development and adaptation of advanced methods to study TMJ at unprecedented resolution and details. By unifying structural, functional, and pain-related data, this integrated multiscale approach holds promise for elucidating new mechanisms of TMJ development, disease onset and progression, and pain chronicity. Ultimately, it may guide more effective diagnostics and treatments, including the combined use of physical therapy, neuromodulation, and biologically targeted interventions.

Objective.DNA damage, particularly double-strand break (DSB), is the primary mechanism for cell death in radiation therapy. High-linear energy transfer particles, like protons and helium ions, induce more complex DSB than photons, increasing their biological effectiveness. Simulating particle transport at the DNA level with Monte Carlo (MC) codes is computationally intensive, often limiting studies to single cells. This study presents an efficient method using the microdosimetric gamma model (MGM) to estimate DSB numbers and complexity in macroscopic setups.Approach.The MGM analytically predicts the number of DSBs and their complexity induced by protons orα-particles. We integrated it into the TOPAS MC toolkit (TOPAS-MGM), enabling the calculation of DNA damage at macroscale scenarios. We have calculated DNA damage distributions inin-vitro-like geometries and water phantoms with proton and helium beams.Results.Cross-comparisons with TOPAS-nBio show that the DNA damage outputs from macroscopic simulations are consistent and 100 000 times faster than DNA scale simulations. We tested DNA damage induction with proton and helium ion beams and alpha-emitting radiopharmaceuticals. For clinical beams, the DNA along the beam path showed a significant increase in the number of induced DSB and their complexity at the Bragg peak, especially with helium ions. Radiopharmaceuticals induced a markedly heterogeneous number of damages compared to beams.Significance. This work offers a method to simulate DNA damage and its complexity in macroscale scenarios for protons andα-particles. The output could potentially be used to predict cell killing based on DNA repair models or to assess the biological effectiveness of particle therapy using DNA damage and complexity as key metrics.

Abstract The electrodeposition method is among the various methods to produce metal foams by coating open-cell polymeric foams with a metallic layer. This process is governed by strong mechanical and electrical interactions which arise due to different factors such as presence of ions in the electrolyte, applied external current, charged solid surface and ionic concentration gradient. Hence, the related physical effects result in a nonlinear coupled process at the macroscale, which introduces a complex challenge for modelling and computational treatment. This work proposes a model to describe the electrocoating of polyurethane foams with nickel ions at macroscale, in an isothermal process and under the simplifying assumptions such as rigidity of the foam and incompressibility of the electrolyte. To do so, the multi-phase flow through the porous medium has to be modelled on a macroscopic scale. The governing equations describing the coating process are developed from the fundamental balance equations of mixture theory. By reasonable physical assumptions, different processes contributing to ionic transport, i.e. diffusion, convection and migration, are considered, and finally, the influence of different parameters in each transport mechanism is investigated. First 1D simulations show that the presented model is able to describe the experimentally observed effects, at least in a qualitative way.

Moiré superlattices, engineered through precise stacking of van der Waals (vdW) layers, hold immense promise for exploring strongly correlated and topological phenomena. However, these applications have been held back by the common preparation method: tear-and-stack of Scotch tape exfoliated monolayers, which suffer from low efficiency and reproducibility, twist angle inhomogeneity, interfacial contamination, and micrometer sizes. Here, we report an effective strategy to construct highly consistent mixed-dimensional and twisted bilayer vdW moiré structures with high production throughput, near-unity yield, pristine interfaces, precisely controlled twist angles, and macroscopic scale (up to centimeters) with enhanced thermal stability. We demonstrate the versatility across various vdW materials, including transition metal dichalcogenides, graphene, and hBN. The expansive size and high quality of moiré structures enable reciprocal-space high-resolution mapping of the superlattices and back-folded moiré mini band structures with low energy electron diffraction (LEED) and angle-resolved photoemission spectroscopy (ARPES). In particular, we identify the backfolded bands at the K point of twisted transition metal dichalcogenide moiré structures. This technique will have broad applications in both fundamental studies and the mass production of twistronic devices.

The small mass limit is derived for a McKean–Vlasov equation subject to environmental noise with state-dependent friction. By applying the averaging approach to a non-autonomous stochastic slow-fast system with the microscopic and macroscopic scales, the convergence in distribution is obtained.

Summary To enable the quantitative evaluation of oil recovery and mobilization across different pore types using the nuclear magnetic resonance (NMR) transverse relaxation time (T2) technique, high-concentration manganese(II) chloride (MnCl2) brines are frequently used to suppress the hydrogen signal from water. However, the potential effects of MnCl2 on the interfacial properties of the crude oil/brine/rock system remain largely uncharacterized. In this study, we combine contact angle measurements with molecular dynamics (MD) simulations to investigate the impact of high-concentration MnCl2 on wettability at both macroscopic and molecular scales. Our findings demonstrate that the influence of high-concentration MnCl2 is minimal, as indicated by the contact angle change obtained from both the contact angle measurements and molecular simulations. High-concentration MnCl2 reduces the density of water molecules near the quartz surface while simultaneously increasing the local concentration of crude oil, particularly its acidic components, which results in a slight increase in the contact angle across different brine compositions, suggesting a trend toward reduced water affinity. However, this change is insufficient to induce a wettability alteration. Compared with the systems without MnCl2, manganese(II) (Mn2+) ions exhibit a negligible impact on the ionic distribution near mineral surfaces. Moreover, the strong hydration energy of Mn2+ disrupts hydrogen bonding, both among water molecules and between water molecules and surface hydroxyl groups on quartz, thereby contributing to the observed slight increase in contact angle. Overall, the findings suggest that high-concentration MnCl2 exerts only a minor effect on wettability and is unlikely to significantly influence imbibition-driven oil recovery.