Macroscopic Research Articles

Solute transport in unsaturated porous media with spatially correlated disorder

Solute transport in unsaturated porous media is of interest in many engineering and environmental applications. The interplay between small-scale, local forces and the porous microstructure exerts a strong control on the transport of fluids and solutes at the larger, macroscopic scales. Heterogeneity in pore geometry is intrinsic to natural materials across a large range of scales. This multiscale nature, and the intricate links between two-phase flow and solute transport, remain far from well understood, by and large. Here, we use high-resolution direct simulation to quantify solute mixing and dispersion behavior within correlated porous media during drainage under an unfavorable viscosity ratio. Through analysis of flow and transport at multiple realizations, we find that increasing spatial correlations in pore sizes increase the size of the required Representative Elementary Volume (REV). We show that increasing the correlation length enhances solute dispersivity through its impact on the spatial distribution of low-velocity (diffusion-dominated) and high-velocity (advection-dominated) regions. Fluid saturation is shown to directly affect diffusive mass flux among high- and low-velocity zones. Another indirect effect of correlated heterogeneity on solute transport is through its control of the drainage patterns via repeated alteration in the connectivity of flowing pathways. Our findings improve quantitative understanding of solute mixing and dispersion under two-phase conditions, highly relevant to some of our most urgent environmental problems.

Delineation of gastrointestinal tumors biopsies using a fluorescence lifetime imaging optical fiber probe.

Autofluorescence spectroscopy has emerged in recent years as a powerful tool to report label-free contrast between normal and diseased tissues, both in vivo and ex-vivo. We report the application of an instrument employing an optical fiber probe and capable of performing real-time autofluorescence lifetime imaging at a macroscopic scale, under bright background conditions. We validate and demonstrate the practicality of this technology to discriminate healthy against neoplastic tissue in freshly excised tumor biopsies. The capability of delineating tumor margins through processing the fluorescence decays in the phasors domain was demonstrated on four different types of cancer, highlighting the broad range of potential clinical applications for the proposed approach. The presented results suggest that our autofluorescence lifetime imaging probe, together with phasor analysis, can offer a real-time tool to observe lifetime contrast on tissues and, thus, is a suitable candidate for improving in situ tissue diagnostics during surgery.

Experimental and theoretical study of multiple active site-functionalized Spirulina residue-based porous carbon as an economical adsorbent for NH3 and SO2 adsorption: Micro- and macro-mechanistic investigations

Here, we successfully synthesized a cost-effective (7.88 USD/kg) multi-active site (CuCl2, CuO and KCl) functionalized Spirulina residue-based porous carbon (MMBC). This innovative synthesis method utilizes Spirulina residue, a sustainable and low-cost biomass source, making the production of MMBC economically viable and environmentally friendly. Under ambient conditions, MMBC exhibits competitive adsorption capacities for NH3 and SO2 compared to other adsorbents, reaching 3.01 mmol/g (i.e., 51.26 mg/g) and 0.70 mmol/g (i.e., 44.85 mg/g), respectively, and tolerable recoverability. These values are significant improvements over many existing adsorbents, highlighting MMBC's efficiency in removing toxic industrial chemicals (TICs). Additionally, even at low molar fractions of NH3 or SO2, such as 0.001, MMBC demonstrates significant ideal adsorption solution theory (IAST) selectivity for NH3/N2 and SO2/N2, with values of 12.02 and 4.19, respectively. This high selectivity at low concentrations underscores MMBC's potential for applications in environments with trace amounts of pollutants. Furthermore, extensive research has been conducted in this study to elucidate the microscopic and macroscopic adsorption mechanisms of NH3 and SO2 on MMBC. Specifically, investigations in statistical physics models and statistical thermodynamics reveal that the adsorption of NH3 and SO2 on MMBC adhere to a non-aggregative, multilayer, multi-anchorage, and spontaneous exothermic physicochemical behavior. And the adsorption orientation occurs either horizontally or both horizontally and non-horizontally, contingent upon the system and temperature. These findings provide a deeper understanding of the adsorption processes and provide innovative insights into the adsorption mechanisms at the molecular level and at the macroscopic scale. Besides, the material characterization results reveal that the above adsorption process involves both physical interactions (pore diffusion and van der Waals forces) and chemical interactions (CuCl2 with NH3, CuO and KCl with SO2), indicating a physicochemical adsorption mechanism. This dual mechanism of adsorption offers a comprehensive explanation of the high performance of MMBC. In summary, this study developed a cost-effective Spirulina residue-based adsorbent, exhibiting high removal efficiency and selectivity for TICs, and providing deep insights into adsorption mechanisms. The innovative use of Spirulina residue addresses waste management issues and promotes the development of sustainable materials in the field of environmental remediation, contributing to cleaner production.

Multi‐Interfacial Heterostructure Design of Carbon Fiber/Silicone Rubber‐Oriented Composites for Microwave Absorption and Thermal Management

AbstractIn order to ensure the operation and longevity of electronic devices, the design of multifunctional composites integrating microwave absorption (MA) and thermal conduction has become the key to solving the problem. However, the superior MA properties and thermal conductivity are usually incompatible in the blend system. In this study, a modified carbon fiber/silicone rubber‐oriented structure is designed based on the multiscale design concept of heterostructures with multiple interfaces. The forward deposition‐reverse growth mechanism is utilized at the microscopic level to construct multi‐interfacial heterogeneous structures on the surface of 1D carbon fibers (CFs). The magneto‐electric coupling network of the multi‐interfacial heterostructure induces interfacial polarization and enhances MA properties and heat transfer. Subsequently, the structural design of the modified CFs is carried out on a macroscopic scale using the ice template method. The directionally aligned modified CFs/silicone rubber aerogel composites are obtained by backfilling with silicone rubber (SR). The samples achieved an effective absorption bandwidth of 4.41 GHz and a maximum reflection loss of −42.29 dB with ultra‐thin thickness (1.3 mm). The thermal conductivity of the sample is improved to 200% compared to pure silicone rubber. The composites with directional alignment have promising applications in lightweight and flexible electronic packaging.

Modal spectrum analysis for an imperfect beam model with random vibration

The cross-Spectral Density (SD) is a method of analyzing the relationship between randomly-input forces and their responses in the frequency domain in spectrum analysis. It is essential for analyzing antiresonant eigenfrequencies that are difficult to understand using only direct-SD. Additionally, the mass in a beam is often analyzed in modern engineering to control the vibration characteristics of a simple beam or as a major model for error. Especially, the scalability can be expected in various fields such as nanosensors as well as macroscopic scale by studying imperfect mass. Firstly, the correlation function and SD are defined for an arbitrary vibration input in a beam. Then, the response function is established according to a harmonic input of some unit force amplitude. Moreover, the direct- and cross-SDs for the response are calculated and mathematically compared with both results. Further analysis is performed by comparing the variation of the SD with response position, the standard deviation (StDev) of the mean of maximum responses, and the bandwidth. The developed cross-SD of the imperfect beam extends the previously developed SD models of the perfect beam and can provide ideas for new types of eigenfrequency analysis, vibration isolation, etc.

Understanding and Harnessing Nanoscale Immiscibility in Ru-In Alloys for Selective CO2 Hydrogenation.

Bimetallic alloys made from immiscible elements are characterized by their tendency to segregate on the macroscopic scale, but their behavior is known to change at the nanoscale. Here, we demonstrate that in the Ru-In system, In atoms preferentially decorate the surface of 6 nm Ru nanoparticles, forming Ru-In superficial immiscible alloys. This surface decoration dramatically affects the catalytic performance of the system, even at small atomic fractions of In added to Ru. The interfaces between Ru and In enabled unexplored methanol productivity from CO2 hydrogenation, which outperformed not only the individual constituents but also ordered RuIn3 intermetallic alloys. Our work highlights that the formation of superficial immiscible alloys could offer new insights into the understanding and design of heterogeneous catalysts.

Predicting the wash behaviour of filter cakes with inhomogeneous cake geometry on a macroscopic scale

Displacement washing of isotropic filter cakes with inhomogeneous cake geometry is investigated theoretically. Fluid flow is described by Darcy’s law, while the advection–dispersion equation is used to describe solute transport within the cake. An additional mass transfer term is taken into account to describe mass transfer from non-permeated to permeated domains of the filter cake. The equations are solved numerically in three spatial dimensions. Model predictions are compared to experimental data from preceding publications of the authors and show good agreement. An explorative case study demonstrates the negative impact of typical inhomogeneous cake geometries on displacement washing.

Benchmarking Physical Model Experiments with Numerical Simulations for the Wangjiashan Landslide-Induced Surge Waves in the Baihetan Reservoir Area

The Baihetan Hydropower Station reservoir area began impoundment in 2021, triggering the reactivation of ancient landslides and the formation of new ones. This not only caused direct landslide disasters but also significantly increased the likelihood of secondary surge wave disasters. This study takes the Wangjiashan (WJS) landslide in the Baihetan reservoir area as an example and conducts large-scale three-dimensional physical model experiments. Based on the results of the physical model experiments, numerical simulation is used as a comparative verification tool. The results show that the numerical simulation method effectively reproduces the formation and propagation process of the WJS landslide-induced surge waves observed in the physical experiments. At the impoundment water level of 825 m, the surge waves generated by the WJS landslide pose potential threats to the Xiangbiling (XBL) residential area. In this study, the numerical simulation based on computational fluid dynamics confirmed the actual propagation forms of the surge waves, aligning well with the results of the physical experiments at a microscopic scale. However, at a macroscopic scale, there is some discrepancy between the numerical simulation results and the physical experiment outcomes, with a maximum error of 25%, primarily stemming from the three-dimensional numerical source model. This study emphasizes the critical role of physical model experiments in understanding and mitigating surge wave disasters in China. Furthermore, physical experiments remain crucial for accurate disaster prediction and mitigation strategies. The theories and methods used in this study will provide important references for future research related to landslide disasters in reservoir areas.

Artificial Ground Freezing—On the Soil Deformations during Freeze–Thaw Cycles

Artificial ground freezing (AGF) has emerged as a prominent treatment method due to its ability to mechanically strengthen the soil while reducing its permeability. However, its implementation has raised concerns about its impact, particularly with respect to frost heave and subsequent thaw-induced displacements. These soil movements can cause subsidence and pose a significant threat to the integrity of surface structures. Overburden pressure plays a crucial role in AGF and determines the amount of heave generated. This paper presents an analysis of the existing literature about soil freezing and thawing. The aim is to offer an understanding of these processes, specifically with regard to their application in AGF. This paper explains the behavior of soil during freezing, with particular emphasis on the influence of overburden pressure. It also investigates frozen soils’ thawing and freeze–thaw (FT) cycles’ long-term effects on soil properties. AGF offers improved soil strength and reduced water permeability, enhancing construction project stability. However, the interplay between the temperature, soil composition, and initial ground conditions during freezing is complex. This thermo-hydro-chemo-mechanical process strengthens the soil and reduces its permeability, but it can also induce frost heave due to water expansion and ice lens formation. Overburden pressure from the overlying soil limits ice lens growth. FT cycles significantly impact soil properties. In fine-grained soils, FT cycles can lead to over-consolidation, while rapid thawing can generate high pore pressures and compromise stability. Importantly, FT acts as a weathering mechanism, influencing soil properties at both the microscopic and macroscopic scales. These cycles can loosen over-consolidated soil, densify normally consolidated soil, and increase overall hydraulic conductivity due to structural changes. They can also weaken the soil’s structure and deteriorate its mechanical performance.

Exact solutions to macroscopic fluctuation theory through classical integrable systems

We give a short overview of recent developments in exact solutions for macroscopic fluctuation theory by using connections to classical integrable systems. A calculation of the cumulant generating function for a tagged particle is also given, agreeing with a previous result obtained from a microscopic analysis.

Multi-Scale Study of a Phase Change Material on a Tropical Island for Evaluating Its Impact on Human Comfort in the Building Sector.

Our study explores the utilization of a phase change material (PCM) to optimize energy efficiency and thermal comfort in buildings in tropical climates. Employing a comprehensive multi-scale approach, this research encompasses both microscopic and macroscopic analyses to rigorously evaluate the PCM's performance under various environmental conditions. It evaluates the effect of PCMs on ambient conditions in the face of temperature variations and high humidity, utilizing experimental methods at different scales (microscopic and macroscopic). Microscopic analyses reveal the composite structure of the PCM, consisting of microencapsulated paraffin within a cellulose fiber matrix. At a macroscopic scale, experiments using two real-scale test cells evaluated thermal performance and its influence on thermal comfort. Temperature and humidity data were meticulously collected over an extended period to assess the PCM's impact on indoor regulation. We employed type T thermocouples and flux meters to monitor thermal dynamics and energy flux across the building walls. This setup facilitated a detailed comparison of temperature variations and thermal comfort metrics between the PCM-equipped test cell and a control cell. The results indicate a seasonal duality of the PCM: beneficial in winter for thermal regulation but problematic in summer due to excessive heat retention. The conclusions highlight the importance of carefully selecting and adapting PCMs for tropical climates, thus providing valuable insights for designing sustainable buildings in regions facing similar climatic challenges.

Rough neutron fields and nuclear reactor noise.

Nuclear reactor cores achieve sustained fission chain reactions through the so-called "critical state"-a subtle equilibrium between their material properties and their geometries. Observed at macroscopic scales during operations, the resulting stationary neutron field is tainted by a noise term that hinders various fluctuations occurring at smaller scales. These fluctuations are either of a stochastic nature (whenever the core is operated at low power) or related to various perturbations and vibrations within the core, even operated in its power regime. For reasons that are only partially understood using linear noise theory, incidental events have been reported, characterized by an increase of the power noise. Such events of power noise growth, sometimes up to seemingly unbounded levels, have already led in the past to voluntary scramming of reactors. In this paper, we will use a statistical field theory of critical processes to model the effects of neutron power noise. We will show that the evolution of the neutron field in a reactor is intimately connected to the dynamic of surface growths given by the Kardar-Parisi-Zhang equation. Recent numerical results emerging from renormalization-group approaches will be used to calculate a threshold in the amplitude of the reactor noise above which the core enters a new criticality state, and to estimate the critical exponents characterizing this phase transition to rough neutron fields. The theoretical model of nonlinear noise built in this paper from ab initio statistical mechanics principles will be correlated and compared to data of misunderstood reactor noise levels and reactor instabilities and will be shown to provide both qualitative and quantitative insights into this long-standing issue of reactor physics.

A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1% of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts. Moreover, the simulation performance shows a slight improvement (a 6% average decrease in RMSE and an average increase of 1% in R2) even with reduced factor data. Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), and 5-day maximum precipitation (RX5day), were the main driving factors controlling flood disasters. This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity, offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.

Noise intensity of a Markov chain.

Stochastic transitions between discrete microscopic states play an important role in many physical and biological systems. Often these transitions lead to fluctuations on a macroscopic scale. A classic example from neuroscience is the stochastic opening and closing of ion channels and the resulting fluctuations in membrane current. When the microscopic transitions are fast, the macroscopic fluctuations are nearly uncorrelated and can be fully characterized by their mean and noise intensity. We show how, for an arbitrary Markov chain, the noise intensity can be determined from an algebraic equation, based on the transition rate matrix; these results are in agreement with earlier results from the theory of zero-frequency noise in quantum mechanical and classical systems. We demonstrate the validity of the theory using an analytically tractable two-state Markovian dichotomous noise, an eight-state model for a calcium channel subunit (De Young-Keizer model), and Markov models of the voltage-gated sodium and potassium channels as they appear in a stochastic version of the Hodgkin-Huxley model.

The Origin of the Slow-to-Alfvén Wave Cascade Power Ratio and Its Implications for Particle Heating in Accretion Flows

The partition of turbulent heating between ions and electrons in radiatively inefficient accretion flows plays a crucial role in determining the observational appearance of accreting black holes. Modeling this partition is, however, a challenging problem because of the large scale-separation between the macroscopic scales at which energy is injected by turbulence and the microscopic ones at which it is dissipated into heat. Recent studies of particle heating from collisionless damping of turbulent energy have shown that the partition of energy between ions and electrons is dictated by the ratio of the energy injected into the slow and Alfvén wave cascades as well as the plasma β parameter. In this paper, we study the mechanism of the injection of turbulent energy into slow- and Alfvén-wave cascades in magnetized shear flows. We show that this ratio depends on the particular (r ϕ) components of the Maxwell and Reynolds stress tensors that cause the transport of angular momentum, the shearing rate, and the orientation of the mean magnetic field with respect to the shear. We then use numerical magnetohydrodynamic shearing-box simulations with background conditions relevant to black hole accretion disks to compute the magnitudes of the stress tensors for turbulence driven by the magneto-rotational instability and derive the injection power ratio between slow and Alfvén wave cascades. We use these results to formulate a local subgrid model for the ion-to-electron heating ratio that depends on the macroscopic characteristics of the accretion flow.

Complete integrability of the problem of full statistics of nonstationary mass transfer in the simple inclusion process.

The simple inclusion process (SIP) interpolates between two well-known lattice gas models: the independent random walkers and the Kipnis-Marchioro-Presutti model. Here we study large deviations of nonstationary mass transfer in the SIP at long times in one dimension. We suppose that N≫1 particles start from a single lattice site at the origin, and we are interested in the probability P(M,N,T) of observing M of the particles, 0≤M≤N, to the right of the origin at a specified time T≫1. At large times, the corresponding full probability distribution has a large-deviation behavior, -lnP(M,N,T)≃sqrt[T]s(M/N,N/sqrt[T]). We determine the rate function s exactly by uncovering and utilizing complete integrability, by the inverse scattering method, of the underlying equationsof the macroscopic fluctuation theory. We also analyze different asymptotic limits of the rate function s.

Pipeline for Multi-Scale Three-Dimensional Anatomic Study of the Human Heart.

Detailed study of non-failing human hearts rejected for transplantation provides a unique opportunity to perform structural analyses across microscopic and macroscopic scales. These techniques include tissue clearing (modified immunolabeling-enabled three-dimensional (3D) imaging of solvent-cleared organs) and immunohistochemical staining. Mesoscopic examination procedures include stereoscopic dissection and micro-computed tomographic (CT) scanning. Macroscopic examination procedures include gross dissection, photography (including anaglyphs and photogrammetry), CT, and 3D printing of the physically or virtually dissected or whole heart. Before macroscopic examination, pressure-perfusion fixation may be performed to maintain the 3D architecture and physiologically relevant morphology of the heart. The application of these techniques in combination to study the human heart is unique and crucial in understanding the relationship between distinct anatomic features such as coronary vasculature and myocardial innervation in the context of the 3D architecture of the heart. This protocol describes the methodologies in detail and includes representative resultsto illustrate progress in the research of human cardiac anatomy.

Investigation of the fission gas release and grain boundary percolation in oxide fuels: A COMSOL multiphysics-based study

In this study, we explore fission gas release (FGR) behaviour in oxide fuels using the COMSOL Multiphysics framework. Building upon the conventional Booth model, our model incorporates the influence of grain boundary network percolation on FGR. The microstructure of the fuel pellet is represented by a 2D axi-symmetric geometry. We investigate various factors affecting FGR, mainly includes bubble contact angle, resolution rate, radial position. This study aims to capture the physical phenomena observed in FGR experiments and assess the sensitivity of FGR to these parameters at both microscopic and macroscopic scales. Moreover, our model accounts for long-range percolation on the networked grain boundaries, a factor overlooked by the Booth model. The gas resolution of grain boundaries is also considered. Finally, we compare and analyze the results obtained from our model with those from the Booth model and other relevant models, aiming at enhancing our understanding of FGR behavior in oxide fuels.

Optimizing gate control coordination signal for urban traffic network boundaries using multi-agent deep reinforcement learning

The macro-aggregated dynamic characteristics of urban network traffic flow encapsulated embodied in macroscopic fundamental diagram theory provide a concise perspective for network-level traffic control. However, accurately translating inter-regional macro-traffic flow control into fine-grained control at each intersection along the boundary remains challenging. To address this issue, a regional boundary intersections collaborative control method is proposed, leveraging the model-free data-driven advantages of multi-agent deep reinforcement learning. First, the proposed model develops a real-time communication module using a bi-directional long short-term memory model and an attention mechanism, enabling intersections to capture spatiotemporal features of the boundary traffic flow. Second, the feedback reward mechanism of the boundary intersection agent is meticulously constructed by considering the traffic state of the protected region, the boundary control pressure, and the traffic pressure of the intersection. The proposed model fosters cooperation at boundary intersections and delicately balances their macro-micro control contradictions based on mastering the boundary state information of the road network. Data-driven boundary gating control can fine-regulate the traffic flow distribution of the network from the perspective of macro-micro to promote its operation efficiency. The simulation experiments verify the effectiveness of the proposed multi-agent signal collaborative control method.

Numerical Study of Concrete: A Mesoscopic Scale Simulation Methodology

This study aims to understand and simulate the mechanical properties of concrete, focusing specifically on the mesoscopic scale and its relation to the macro scale. Investigating concrete at this level involves examining its composition as a heterogeneous amalgamation of mortar, aggregates, and the Interfacial Transition Zone (ITZ). Numerical models, utilizing the finite element method (FEM), are employed to thoroughly examine the structural behavior of concrete. The study uses MATLAB (2023a) programming to develop three-dimensional models, which are then subjected to FEM analysis. Various mesoscopic Representative Volume Elements (RVEs) are formulated, considering spherical aggregates with different locations and dimensions to capture the complex nature of concrete. MATLAB is used to generate files containing comprehensive information about the RVEs, which are then processed with FEM to simulate compression strength tests. As the complexity increases with the inclusion of the ITZ, prismatic RVEs are developed to better represent real-world conditions. The proposed mesoscopic model establishes a foundational framework for a numerical simulation methodology tailored to laboratory compression tests, bridging the gap between mesoscopic and macroscopic scales. This approach provides detailed insights into concrete behavior, elucidating deformation and fracture mechanisms. Although not a complete substitute for experimental methods, these models offer a cost-effective and efficient alternative, identifying vulnerable areas and exploring the effects of additional materials on concrete behavior. The progressive replacement of laboratory tests with numerical simulations using RVEs of specific compositions will make the study of concrete behavior at the mesoscopic scale increasingly sustainable, paving the way for more efficient and environmentally friendly research practices in the field.