Search Results (418)

Drying in granular porous media is governed by coupled thermal and hydraulic processes that can be substantially modified by biological activity. This proof-of-concept study investigated how surface heating and fungal colonization influence the evolution of thermal conductivity (λ) and matric suction (ψ) as functions of volumetric water content ${\theta }_v$ in Ottawa 20/30 sand. Four treatments were examined: sterile sand at 22 °C (T1), sterile sand at 28 °C (T2), fungal-amended sand with 10% biomass and 9-day incubation (T3), and fungal-amended sand with 15% biomass and 30-day incubation (T4). Samples were instrumented to monitor ${\theta }_v$, λ, and ψ during controlled evaporation using synchronized HYPROP and VARIOS measurements on the same specimen. Across all treatments, λ increased with ${\theta }_v$ (that is, λ declined as drying progressed), and ψ reflected the transition from hydraulically connected to disconnected pore water. Heating shortened the drying time but did not materially change the form of the λ–${\theta }_v$ relationship or generate strong matric gradients in sterile sand. Low biomass (T3) produced thermal and hydraulic responses comparable to the heated sterile control (T2), indicating limited pore-scale modification at early colonization. In contrast, high biomass (T4) widened the effective saturation range, maintained low and nearly uniform ψ across depth, and exhibited the steepest mid-range λ–${\theta }_v$ slope with a higher peak λ (~4 Wm⁻¹K⁻¹), consistent with hyphae and extracellular polymers stabilizing thin water films. A soil water retention curve (SWRC) analysis using the van Genuchten model further indicated increased water retention and delayed air entry with an increasing fungal biomass, with approximate air-entry values increasing from ~1.8 kPa (T3) to ~3.0 kPa (T4). Tests were terminated upon tensiometer cavitation rather than complete gravimetric dryness, constraining observations at very low ${\theta }_v$. These results indicate that heating primarily affects the rate of drying, whereas fungal networks alter the pathway by preserving hydraulic and thermal continuity at relatively high ${\theta }_v$. This behavior suggests a potential role of bio-mediated structuring in influencing near-surface thermo-hydraulic processes relevant to energy foundations, soil covers, and desiccation management in biologically active or bio-engineered soils. Full article

Pore structure characterization is crucial for understanding fluid flow in porous media, including oil and gas reservoirs, aquifers, and geologic formations. Carbonate formations, prevalent in oil and gas reservoirs, pose challenges due to their complex pore structure. This review summarizes diverse methods reported in the literature to characterize the pore structure of carbonate formations. Emphasizing the need for multiple methods and scales, the paper discusses pore-throat classification, imaging techniques (X-ray CT, SEM, and NMR), and pore network modeling. These methods help identify typical pore-throat types and structural features, which commonly exhibit irregular shapes and poor connectivity, thus influencing fluid flow behavior. Pore network modeling elucidates structural heterogeneity and its impact on fluid flow. These insights are vital for oil and gas production and groundwater management, providing a comprehensive understanding of fluid flow in porous media. Full article

This study develops a ternary Fe-based geopolymer system composed of metakaolin (MK), red mud (RM), and fly ash (FA) for the preparation of sustainable water-retaining subgrade materials for sponge-city roadbed applications. Unlike conventional formulations primarily designed for structural strength or rapid permeability, the proposed MK–FA–RM system was designed to improve water-storage capacity while maintaining adequate mechanical support and environmental compatibility. In this ternary system, MK provides highly reactive aluminosilicate species for geopolymer network formation, RM introduces Fe-bearing phases and enhances industrial solid-waste utilization, and FA contributes to particle packing, workability, and resource efficiency. A constrained ternary mixture design implemented using Design-Expert software was adopted to optimize precursor proportions. Within the investigated compositional range, the fitted first-order mixture model showed acceptable statistical adequacy for preliminary composition screening (R² = 0.86). The optimal blend (60% MK, 30% RM, and 10% FA) achieved a 7-day compressive strength of 8.37 MPa and a water retention rate of 35.3% under ambient curing conditions, satisfying the strength requirement considered for the target subgrade/base-layer application. Microstructural and phase analyses suggest that the synergistic interaction of the three precursors promoted Fe-modified aluminosilicate gel formation together with conventional geopolymer gel products, while improving matrix continuity and preserving interconnected pore space for water storage. This multiscale structural effect helps explain how the material achieved a balance between water retention capacity and mechanical support. Under the tested conditions, the material maintained acceptable residual strength after short-term exposure to water, acid, and sulfate-containing solutions. Life-cycle assessment indicated a 70% reduction in CO₂ emissions compared with ordinary Portland cement, while pilot-scale cost analysis showed a 39% lower production cost than MetaMax-based geopolymer materials. Pilot-scale application further demonstrated the constructability and water-regulation potential of the material in practical environments. Overall, the proposed ternary Fe-based geopolymer demonstrates that Fe-rich industrial wastes can be engineered into low-carbon and economically viable water-retaining subgrade materials that balance hydraulic regulation, structural adequacy, and sustainability. Nevertheless, long-term durability, cyclic loading performance, and direct nanoscale characterization of Fe-bearing gel evolution still require further investigation. Full article

Geothermal energy extraction, hydrocarbon recovery, and CO₂ geological sequestration are frequently hindered by interfacial barriers and slow mass transfer. While high-power ultrasound offers a sustainable, purely physical method for reservoir stimulation, its field effectiveness remains debated because traditional macroscopic experiments fail to isolate mechanisms like acoustic streaming and cavitation. This review systematically examines acoustic mechanisms in porous media via microfluidic visualization, focusing on pore-scale fluid dynamics during enhanced oil recovery, hydrate dissociation, and CO₂ sequestration. Microscopic evidence reveals that fluid transport mechanisms depend heavily on pore geometry and local acoustic intensity. In wider channels, nonlinear acoustic flow provides sustained, directed convection to strip away concentration boundary layers; in narrow throats, microjets and pulsed stresses generated by transient cavitation are responsible for physically breaking capillary barriers. The spatiotemporal synergy of these mechanisms is critical for multiphase fluid transport in tight porous networks. Pore geometry serves not only as the application context but also as a core physical variable. To translate microfluidic results into reservoir-scale applications, future research must address two-dimensional simplifications, thermodynamic discrepancies under high-temperature and high-pressure conditions, and bubble cluster interactions, alongside the development of adaptive frequency-modulated control and multiscale computational models. Full article

Proton exchange membrane water electrolysis is a promising technology for large-scale green hydrogen production due to its high efficiency, compact design, and rapid dynamic response. However, its commercialization is strictly limited by high material costs, durability issues, and complex multiphysics coupling within the membrane electrode assembly. This work provides a comprehensive and critical review of key physicochemical processes and advanced predictive modeling approaches for PEMWEs. To capture recent paradigm shifts, we introduce an innovative multi-dimensional classification framework—incorporating spatial resolution, temporal dynamics, and methodological paradigms—to critically evaluate lumped-parameter, continuum, microscale, and multiscale models, explicitly defining their applicability bounds and inherent limitations. The fundamental mechanisms governing electrode kinetics, membrane water transport, and gas–liquid two-phase flow are analyzed, establishing state-of-the-art quantitative benchmarks for microstructural parameters and advanced 3D flow field topologies under high-current-density and high-pressure regimes. Furthermore, we systematically examine model validation rigor, typical prediction errors, and the critical failure of static models in capturing dynamic property shifts during extreme bubble breakthrough. Recent breakthroughs integrating in situ diagnostics, pore-scale simulations, density functional theory, and Physics-Informed Neural Networks are extensively discussed. Future efforts must prioritize mechanical–electrochemical–thermal coupling, transient degradation prognostics, and machine learning-driven predictive digital twin technologies to overcome current empirical limitations and accelerate the gigawatt-scale deployment of PEMWE systems. Full article

Understanding the pore structure of high-rank coals is essential in evaluating gas storage and transport. Here, twelve semianthracite samples from the early Permian Shanxi Formation were investigated by proximate analysis, optical microscopy, low-temperature N₂ adsorption, and fractal analysis, coupled with diffusion coefficient modeling. The coals exhibit diverse pore types (plant-cellular, interparticle, and dissolution pores) shaped by coalification and minerals and show Type IV (a) isotherms with H4 hysteresis loops, indicating complex pore networks. Pore-size partitioning reveals that mesopores and macropores dominate total pore volume, whereas mesopores contribute most of the specific surface area. The pore structure exhibits strong fractal characteristics with an average comprehensive fractal dimension (F_c) of 2.628. The calculated gas diffusion coefficient decreases monotonically with increasing pressure from 1 MPa to 5.8 MPa, with a more pronounced decline at low pressure, indicating a clear pressure-dependent attenuation effect. Diffusion capacity is weakly related to average pore diameter but shows positive correlations with total pore volume and, particularly, macropore volume. Multiple linear regression further demonstrates that pore volume structure is the dominant control on diffusion under both low- and high-pressure conditions, with the relative importance ranked as macropores > mesopores > micropores. Macropores provide the main low-resistance transport framework, mesopores serve as transitional pathways linking storage and transport domains, whereas micropores mainly contribute to gas storage and may even suppress apparent diffusion when overly developed. These results reveal a clear functional differentiation of multiscale pore systems and highlight that gas migration in semianthracite is jointly governed by pore size distribution, connectivity, tortuosity, and fractal network topology. Full article

Pore-throat structures in a carbonate reservoir were classified into ten petrophysical facies representing coarse, medium, or fine throat types based on Mercury Injection Capillary Pressure (MICP) data from 77 core samples, directly reflecting distinct flow capacities. Using Nuclear Magnetic Resonance (NMR) data from 20 samples, an artificial neural network (ANN) model was developed with four conventional logs, namely Gamma Ray (GR), Deep Laterolog Resistivity (RD), Density (DEN), and Compensated Neutron Log (CNL), as inputs to predict the T₂ spectrum continuously. A cumulative pore-throat size distribution matching method was then used to transform predicted T₂ spectra into capillary pressure curves. The resulting pore-throat parameters show excellent agreement with core measurements, with relative errors for key parameters—such as median pore-throat radius (R₅₀) and sorting coefficient (Sp)—below 15%. This approach extends discrete core data to continuous wellbore profiles, enabling pore-throat prediction and facies classification in intervals lacking MICP data. It effectively identifies dominant flow channels and tight interlayers, with facies validated by thin-section petrography, providing a robust basis for evaluating highly heterogeneous carbonate reservoirs. Full article

Binder jetting relies on the infiltration of binder droplets into a porous powder bed, where the spatial arrangement of droplets critically influences feature formation and structural integrity. In particular, the role of droplet spacing in regulating infiltration behavior remains insufficiently understood. In this study, droplet infiltration is investigated using a reconstructed three-dimensional powder bed combined with a Volume of Fluid (VOF) model. Both single- and dual-droplet configurations are examined to isolate the effect of droplet spacing on spreading, merging, and capillary-driven penetration. The results show that droplet spacing governs the redistribution of liquid flow between lateral spreading and vertical infiltration. Three distinct regimes are identified as spacing decreases: independent infiltration at large spacing, cooperative merging at intermediate spacing, and over-penetration at small spacing. These regimes reflect a transition from isolated droplet behavior to strongly coupled infiltration within the pore network. An optimal spacing of approximately 150 μm is found to balance spreading and penetration, enabling continuous deposition with controlled infiltration depth. Experimental measurements show good agreement with numerical predictions, with an average deviation of 8.66%. The present study clarifies the mechanism by which droplet spacing controls infiltration behavior and provides practical guidance for parameter selection in binder jetting processes. Full article

Granite residual soil slopes are highly water-sensitive and prone to rapid collapse, strength degradation, and rainfall-induced erosion. This study investigates the improvement effects and underlying mechanisms of carboxymethyl cellulose (CMC) on the water stability, mechanical properties, and rainfall erosion resistance of granite residual soil from Fuzhou, Fujian Province, China. Laboratory tests, including unconfined compressive strength (UCS) tests, direct shear tests, disintegration tests, slope rainfall scouring model experiments, X-ray diffraction test (XRD) and scanning electron microscopy (SEM) observations, were conducted to evaluate the performance and microstructural behavior of CMC-stabilized soils. The results indicate that the addition of CMC significantly enhances soil resistance to disintegration: the 24 h disintegration ratio decreased to 0.5% at 0.5% CMC content. The incorporation of CMC can significantly enhance the unconfined compressive strength (UCS) of the soil and lead to an increase in cohesion, while its effect on the internal friction angle is limited. Under simulated rainfall conditions (30° slope, 120 mm·h⁻¹ rainfall intensity, 60 min duration), slopes stabilized with 0.5% CMC exhibited suppressed rill formation and a 47.5% reduction in sediment yield, accompanied by delayed moisture increase at different depths and reduced infiltration rates. Microstructural analyses reveal that CMC hydration forms gel-like films and filamentous bridges, promoting particle aggregation and pore filling, thereby constructing a denser particle network without generating new chemical compounds. This microstructure collectively enhances soil disintegration resistance, mechanical strength, and slope erosion resistance. Full article

Silica fume (SF) is a valuable industrial by-product for low-carbon cementitious systems, but it weakens early-age strength due to slow pozzolanic activation. To overcome this limitation and, crucially, to elucidate the influence of pore system geometry on macroscopic performance, graphene oxide (GO) was introduced as a modifying agent. Concurrently, the fractal dimension (D) of the pore network was adopted as a pivotal descriptor linking microstructure to macroscopic strength. Results show that GO compensates for the early strength loss caused by SF and further amplifies long-term gains by accelerating hydration and promoting gel continuity. SF reduces total porosity through filler and pozzolanic reactions, while GO dramatically increases geometric complexity of pores, producing the highest fractal dimension and the most refined pore structure in the matrix. Critically, the proposed log–log interaction model demonstrates that compressive strength is jointly controlled by porosity and fractal dimension, rather than porosity alone. Higher fractal dimension intensifies strength gains in low-porosity matrices by reflecting improved pore connectivity control and energy-dissipation pathways. This establishes fractal dimension as a powerful, mechanistically interpretable index for predicting performance and guiding structural design in SF–GO modified cementitious composites. Full article

Background: Nanoporous anodic alumina (NAA) has emerged as a promising platform for localized drug delivery in biomedical implants owing to its tunable nanoscale pore structure and biocompatibility. However, achieving the desired pore characteristics currently relies on time-consuming trial-and-error adjustments of anodization parameters. Methods: We developed a comprehensive data-driven machine learning framework using a feed-forward artificial neural network (ANN) with three hidden layers (64-32-16 neurons) trained on 77 samples from a compiled dataset of 99 anodization experiments spanning 1995–2025. The model predicts the NAA pore diameter based on anodization conditions (electrolyte type, concentration, voltage, temperature, and time). Results: The ANN achieved R² = 0.803, root mean square error (RMSE) = 25.83 nm, and mean absolute error (MAE) = 17.05 nm on training data; however, 5-fold cross-validation revealed moderate generalization (CV R² = 0.471 ± 0.078). Multiple linear regression showed comparable training performance (R² = 0.804) but superior cross-validation (CV R² = 0.729 ± 0.083). Feature importance analysis identified anodization voltage (29.15% ANN importance) and electrolyte type (30.23%) as the most influential factors. Coupling ANN-predicted pore dimensions with Higuchi diffusion modeling demonstrated that the pore diameter increased from 50 to 100 nm, nearly doubling the initial release rates (8 to 11 h⁻¹) and reducing the time to 50% release from 39.1 to 20.7 h. Conclusions: This data-driven approach offers a powerful tool to reduce experimental iteration and accelerate the development of advanced drug-delivery implants by enabling the rational design of NAA pore structures for optimized drug loading and release kinetics. Full article

Conductive core–shell superabsorbent polymers (SAPs) are emerging as multifunctional additives for cementitious materials, combining moisture management with electrical functionality. In cement-based systems, a swellable polymeric core enables internal curing and crack-sealing through controlled water uptake and release, while a conductive shell introduces ionic and/or electronic charge transport, addressing key limitations of conventional non-conductive SAPs. This dual functionality provides a pathway toward smart cementitious composites with enhanced durability, self-sensing capability, and moisture-responsive behavior. This review focuses on the physical chemistry mechanisms governing conductive core–shell SAPs in cementitious environments, with emphasis on swelling thermodynamics, water transport kinetics, interfacial phenomena, and charge transport mechanisms. The roles of osmotic pressure, elastic network constraints, ionic effects, and pore solution chemistry are critically discussed, together with their impact on conductivity, hydration processes, microstructure development, and long-term performance. The relative contributions of ionic and electronic conduction are examined in relation to hydration state, shell morphology, and percolation of conductive networks. In addition, the relevance of core–shell SAP architectures to sustainable packaging is briefly discussed as a secondary application, illustrating how similar physicochemical principles—such as moisture buffering and functional coatings—apply beyond construction materials. Finally, key knowledge gaps are identified, including long-term stability in highly alkaline environments, trade-offs between swelling capacity and conductivity, environmental impacts of conductive phases, and the need for integrated experimental and modeling approaches. Addressing these challenges is essential for the rational design and practical implementation of conductive core–shell SAPs in next-generation cementitious materials. Full article

In porous ignimbrites, porosity defines the first-order control on elastic trend, but rocks with similar porosity can still behave differently because their pore networks are arranged differently. We analyzed 50 specimens from seven ignimbrite units in Mexico using density and porosity measurements, permeability tests, mercury intrusion porosimetry, image-based pore descriptors, and ultrasonic P- and S-wave velocities. At the unit scale averages, total porosity ranges from 31.4% in Tl to 42.9%, but elastic properties and permeability vary widely, showing that porosity alone does not define a unique physical state. Two end-member pore-network tendencies can be recognized: crack-linked, throat-restricted systems and more equant or intergranular systems. At similar porosity, crack-dominated networks are generally less stiff and less permeable, whereas more equant networks show higher permeability and stiffer behavior under dry conditions. Effective-medium models indicate that most samples are consistent with KT aspect ratios of 0.15–0.20 and a critical-porosity range of 40–60%. Overall, porosity defines the first-order elastic trend, whereas pore-network architecture explains much of the remaining hydraulic variability and part of the residual elastic spread. Full article

The automated production of large-scale labeled datasets from concrete X-ray computed tomography (CT) images is a fundamental prerequisite for training and validating deep learning-based segmentation models. However, existing methods either require extensive manual annotation or rely on domain-specific deep learning models that themselves demand labeled data—a circular dependency. This paper presents a parameter-driven three-class segmentation framework that automatically classifies each pixel in a concrete CT slice into one of three material phases: void (air pores and cracks), coarse aggregate, and mortar matrix, generating annotation masks suitable for large-scale dataset production without manual labeling. The proposed method combines: (1) fixed-threshold void detection calibrated to concrete CT grayscale characteristics; (2) adaptive percentile-based initial segmentation responsive to image-specific statistics; (3) multi-criteria connected component scoring based on area, shape descriptors (circularity, solidity, compactness, extent, aspect ratio), intensity distribution, and boundary gradient; (4) material science-informed size constraints aligned with concrete phase volume fractions; and (5) a material continuity enforcement module that applies topological hole-filling and conditional convex-hull consolidation to eliminate internal contamination within accepted aggregate regions, reducing boundary roughness by 7.6% and recovering misclassified boundary pixels. All parameters are centralized in a configuration file, enabling reproducible batch processing of 224 × 224 pixel CT slices at 0.07–1.12 s per image. Evaluated on 1007 224 × 224 concrete CT patches cropped from 200 representative scan frames, the framework produces three-class segmentation masks with physically consistent void fractions (mean 3.2%), aggregate fractions (mean 32.4%), and mortar fractions (mean 64.4%), all within ranges reported in the concrete CT literature (used as a dataset-scale QC screen, not a validation metric). Primary outputs and the archived image–mask pairs for this work are provided as an 8-bit patch archive. For pixel-wise validation, we report IoU, Dice, and pixel accuracy on an independently labeled subset that can be unambiguously paired with the released predictions: averaged over 57 matched patches, mean pixel accuracy is 88.6%, macro-mean IoU is 74.7%, and macro-mean Dice is 84.9%. The framework provides a fully automated annotation pipeline for dataset production, eliminating manual labeling costs for concrete CT image collections. The generated datasets are suitable for training semantic segmentation networks such as U-Net and its variants. Full article

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs. Full article