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At least 91 records · Page 5

Pore Structure and Wettability of Bossier Shale, East Texas, United States: Insights from Integrated Porosimetry, Scattering, and Imbibition Approaches

In this study, ascertaining the pore geometry and wettability characteristics of tight shales is of great significance for revealing the mechanisms of occurrence, migration, and production of shale resources. Taking the Bossier Shale collected from Well A, East Texas, USA, as an example, the pore geometry and wettability behavior in a broad nm-& μm scale pore spectrum were quantified based on integrated techniques, including water immersion porosimetry (WIP), mercury intrusion porosimetry(MIP), (ultra)small-angle X-ray scattering [(U)SAXS], contact angle, and liquid spontaneous imbibition (SI). Mainly owing to differences in the sample sizes used, data interpretation, as well as the detectable pore type (e.g., connected or not) and pore diameter ranges among different methods, sample porosities derived from WIP, MIP, and (U)SAXS were quite different, which range from 7.70 to 13.34%, 5.12 to 9.52%, and 2.48 to 6.44%, respectively. Therefore, a comparison of the porosities derived from different methods must be handled with utmost care. Additionally, although (U)SAXS is an effective technique for detecting both connected and non-connected pores, the fraction of non-connected pores could be underestimated by directly comparing the pore size distribution derived from (U)SAXS and MIP tests. This is because the sample sizes used for and pore information reflected by these two methods are different. Furthermore, the studied Bossier Shales exhibited various contact angle values and imbibition behaviors when using differently polarized liquids, revealing their mixed-wet characteristics. Comprehensively considering the differences in contact angle, imbibition slope, and imbibed liquid volume, three sub-categories of wettability behavior, with respect to more oil-wet, more water-wet, and intermediate mixed-wet, were qualitatively identified.

(U)SAXS↗

Revealing the Sodium Storage Mechanisms in Hard Carbon Pores

Abstract Hard carbon (HC) is the most promising anode for the commercialization of sodium‐ion batteries (NIBs); however, a general mechanism for sodium storage in HC remains unclear, obstructing the development of highly efficient anodes for NIBs. To elucidate the mechanism of sodium storage in the pores, operando synchrotron small‐angle X‐ray scattering, wide‐angle X‐ray scattering, X‐ray absorption near edge structure, Raman spectroscopy, and galvanostatic measurements are combined. The multimodal approach provides mechanistic insights into the sodium pore‐filling process for different HC microstructures including the pore sizes that are preferentially filled, the extent to which different pore sizes are filled, and how the defect concentration influences pore filling. It is observed that sodium in the larger pores has an increased pseudo‐metallic sodium character consistent with larger sodium clusters. Furthermore, it is shown that the HCs prepared at higher pyrolysis temperatures have a larger capacity from sodium stored in the pores and that sodium intercalation between graphene layers occurs simultaneously with the pore filling in the plateau region. Opportunities are outlined to improve the performance of HC anodes by fully utilizing the pores for sodium storage, helping to pave the way for the commercialization of sodium ion batteries.

Kitsu Iglesias, Luis↗

Evolution of Pore Structure in Organic-Lean and Organic-Rich Mudrocks

The pore structure of mudrocks is a key characteristic to evaluate flow behavior through these rocks. Although significant advances have been made to resolve pore characteristics, porosity evolution, or pore connectivity, there is still insufficient knowledge linking porosity evolution to flow and transport in mudrocks. To better understand these links, we conducted very small-angle (VSANS) and small-angle neutron scattering (SANS) experiments on 13 sets of mudrocks from global locations, characterized by differences in composition, maturity, and depositional environment. Our results indicate that a homogeneous pore structure reflects a stable and low-energy depositional environment. Organic-lean mudrocks mainly contain pores <10 nm, which restricts flow and is beneficial for, e.g., caprock seal integrity. Increasing maturity as a result of increasing burial depth, and therefore, temperature leads to porosity evolution. In organic-rich mudrocks (ORMs), this happens through the generation of organic matter pores and pore preservation around diagenetically formed carbonates. ORMs mainly contain macropores (>50 nm), favoring production from, e.g., unconventional reservoirs. SANS-derived pore size distributions will help to better understand fluid imbibition and flow properties in mudrocks through comprehensive quantitative characterization of the nano- to micron scale pore network conduits and their relationships to burial diagenesis.

58 GEOSCIENCES↗

Solvent Transport in Disordered and Dynamic Membrane Pores: Implications for Reverse Osmosis and Nanofiltration Membranes

Pressure-driven separations with nanoporous membranes, such as reverse osmosis and nanofiltration, play a vital role in addressing water scarcity and enabling resource recovery. Understanding water or solvent transport in membrane pores is essential for advancing membrane separation technologies. A key question in transport modeling is to establish a relationship between solvent permeability and membrane porous structure properties, such as porosity or pore size. The nano- and subnanometer pores in polymeric membranes such as reverse osmosis and nanofiltration membranes are highly tortuous and dynamically connected, which challenges the conventional methods of transport modeling. This study addresses this challenge by developing a theoretical framework to describe solvent transport through membranes with dynamic and disordered porous structure. Specifically, we propose a lattice model to describe the pore network, while preserving the viscous nature of solvent permeation. We further establish a relationship between solvent permeability and membrane porosity or pore size, which is validated by molecular dynamics simulations and experimental data. By integrating this relationship into the solution-friction model, we define pore connectivity and local friction coefficient to quantify the impact of pore structure on solvent permeability. Our analysis highlights the dominant influence of pore connectivity on the permeability of reverse osmosis and nanofiltration membranes, particularly when the pore size approaches the dimensions of solvent molecules. Overall, this study provides critical insights into water and solvent transport mechanisms in nanoporous membranes, opening the door for strategies to substantially enhance membrane performance.

membranes↗

Water Uptake by Silica Nanopores: Impacts of Surface Hydrophilicity and Pore Size

Water uptake at 298 K by two mesoporous silicas with different pore sizes was studied using volumetric vapor sorption. Here, through variation of sample pretreatment temperature and time, the number of surface hydroxyl groups was varied, leading to marked changes in the water sorption behavior. With increasing surface hydroxyl density, the adsorption of water to the pore surfaces increases, leading to formation of a thicker and denser water sorption layer. The BET model was used to measure surface hydroxyl density from low pressure parts of water adsorption isotherms. Adsorption of water molecules scales with the number density of surface hydroxyl groups, and monolayer coverage is reached at reduced pressure of ca. 0.3. The onset of adsorption pore condensation of water shifts to lower reduced pressure with increasing surface hydroxyl density, indicating growing thickness of adsorption layers. However, the water pore condensation step of the desorption branch shifts to smaller reduced pressure with increasing surface hydroxylation, reducing the adsorption-desorption hysteresis width. An adsorbed phase model is utilized to calculate the distribution of pore water molecules between adsorbed and pore condensation phases, and characterize the density and thickness of the water sorption phase. In the smaller pores, the adsorbed phase forms a sparse monolayer, while approximately a bilayer is formed in the wider pores. The previously reported silica nanopore underfilling by water is confirmed and rationalized by a significantly reduced water density in the adsorbed phase with respect to bulk, while the pore core is filled with water of approximately bulk density.

36 MATERIALS SCIENCE↗

Magnetism and interlayer bonding in pores of Bernal-stacked hexagonal boron nitride

When single-layer h-BN is subjected to a high-energy electron beam, triangular pores with nitrogen edges are formed. Because of the broken sp 2 bonds, these pores are known to possess magnetic states. Here, we report on the magnetism and electronic structure of triangular pores as a function of their size. Moreover, in the Bernal-stacked h-BN (AB-h-BN), multilayer pores with parallel edges can be created, which is not possible in the commonly fabricated multilayer AA'-h-BN. Given that these pores can be manufactured in a well-controlled fashion using an electron beam, it is important to understand the interactions of pores in neighboring layers. We find that in certain configurations, the edges of the neighboring pores remain open and retain their magnetism, and in others, they form interlayer bonds. We present a comprehensive report on these configurations for small nanopores. We find that at low temperatures, these pores have near degenerate magnetic configurations, and may be utilized in magnetoresistance and spintronics applications. In the process of forming larger multilayer nanopores, interlayer bonds can form, reducing the magnetization. Yet, unbonded parallel multilayer edges remain available at all sizes. Understanding these pores is also helpful in a multitude of applications such as DNA sequencing and quantum emission.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Pore-Scale Dynamics of Liquid CO2–Water Displacement in 2D Axisymmetric Porous Micromodels Under Strong Drainage and Weak Imbibition Conditions: High-Speed μPIV Measurements

Resolving pore-scale transient flow dynamics is crucial to understanding the physics underlying multiphase flow in porous media and informing large-scale predictive models. Surface properties of the porous matrix play an important role in controlling such physics, yet interfacial mechanisms remain poorly understood, in part due to a lack of direct observations. This study reports on an experimental investigation of the pore-scale flow dynamics of liquid CO 2 and water in two-dimensional (2D) circular porous micromodels with different surface characteristics employing high-speed microscopic particle image velocimetry (μPIV). The design of the micromodel minimized side boundary effects due to the limited size of the domain. The high-speed μPIV technique resolved the spatial and temporal dynamics of multiphase flow of CO 2 and water under reservoir-relevant conditions, for both drainage and imbibition scenarios. When CO 2 displaced water in a hydrophilic micromodel (i.e., drainage), unstable capillary fingering occurred and the pore flow was dominated by successive pore-scale burst events (i.e., Haines jumps). When the same experiment was repeated in a nearly neutral wetting micromodel (i.e., weak imbibition), flow instability and fluctuations were virtually eliminated, leading to a more compact displacement pattern. Energy balance analysis indicates that the conversion efficiency between surface energy and external work is less than 30%, and that kinetic energy is a disproportionately smaller contributor to the energy budget. This is true even during a Haines jump event, which induces velocities typically two orders of magnitude higher than the bulk velocity. These novel measurements further enabled direct observations of the meniscus displacement, revealing a significant alteration of the pore filling mechanisms during drainage and imbibition. While the former typically featured burst events, which often occur only at one of the several throats connecting a pore, the latter is typically dominated by a cooperative filling mechanism involving simultaneous invasion of a pore from multiple throats. This cooperative filling mechanism leads to merging of two interfaces and releases surface energy, causing instantaneous high-speed events that are similar, yet fundamentally different from, burst events. Finally, pore-scale velocity fields were statistically analyzed to provide a quantitative measure of the role of capillary effects in these pore flows.

Li, Yaofa↗

Ethane-CO2 Mixture Adsorption in Silicalite: Influence of Tortuosity and Connectivity of Pores on Selectivity

Selective adsorption using nanoporous materials is an efficient strategy for separating gas mixtures. In a nanoporous material, pores can exist in different shapes and can have different degrees of inter-connectivity. In recent studies, both pore connectivity and tortuosity have been found to affect the adsorption and dynamical properties of ethane and CO2 in silicalite differently. Here, using Monte Carlo simulations, we investigate if these two attributes can affect the selective adsorption of one component from a mixture of ethane and CO2 in silicalite. For this, the adsorption of an equimolar mixture of ethane and CO2 is simulated in 12 models of silicalite—SnZm (n, m = 0, 1, 2, 3 or 4; with n and m denoting, respectively, the fraction (out of 4) of straight and zigzag channels of silicalite that are available for adsorption)—differing in degrees of pore connectivity and tortuosity. The adsorption selectivity in this system is found to exhibit a reversal with the adsorption dominated by ethane at low pressures (below ~1 atm) and by CO2 at higher pressures (above ~10 atm). Pore connectivity is found to suppress the selective adsorption of CO2 at higher pressures and also shifts the selectivity reversal to higher pressures. The selectivity reversal results from a competition between the polarizability-affected adsorption at lower pressures and efficient packing at higher pressures. The efficient packing of CO2 is a compounded effect resulting from the larger effective pore volume available for CO2 due to its stronger interaction with the pore surface and smaller molecular volume. CO2 molecules show a preference to adsorb in non-tortuous pores, and this preference is found to be stronger in the presence of ethane. The effects of pore connectivity and tortuosity elucidated here should be applicable to a wide range of natural and engineered nanoporous materials, and this knowledge could be used to identify materials with better capability for separating and storing CO2 based on their pore attributes.

Gautam, Siddharth (ORCID:0000000314435382)↗

Exploratory Studies of Mechanical Properties, Residual Stress, and Grain Evolution at the Local Regions near Pores in Additively Manufactured Metals

Directed energy deposition (DED) is gaining widespread acceptance in various industrial applications since its unique manufacturing features allow the DED to print metallic parts with very complex geometries. However, DED inevitably generates a lot of internal pores which can limit the widespread applications of the DED technique. The current studies on DED porosity are mostly focused on analyzing pores’ bulk-scale influences on mechanical properties and performances. Since DED pores have a micro-scale existence, with dimensions ranging from a few microns to several hundred microns, it is fundamental to explore the pores’ influences on the micro-scale, including local mechanical properties, residual stress, and grains near pores. However, this important research direction has been neglected. The objective of this work is to fill the above gap in DED porosity research and acquire a fundamental understanding of the role of porosity on a microscopic scale. The authors used nanoindentation approaches to investigate internal pores’ effects on mechanical properties and residual stress in local regions surrounding the pores. In addition, the grains near pores were observed through EBSD, and simulated with the Kinetic Monte Carlo model. The research findings can be provided for DED researchers and industrial practitioners as technical guidance. Most importantly, the research results can work as a good reference for tracing the source of bulk-scale mechanical performances and properties of DED parts with internal pores.

36 MATERIALS SCIENCE↗

Quantitative Insight to Fission Gas Pores Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Machine Learning

Metallic fuels, particularly U-10Zr and its performance in reactor irradiation conditions, have been thoroughly investigated and are a promising candidate for next-generation sodium-cooled fast spectrum nuclear reactors. Irradiation in reactors can lead to the formation of fission gas and increased pore formation which can significantly impact fuel performance. Due to the large number of pores and various phases formed in metallic fuel during irradiation, a quantitative description of fission gas pores as a function of irradiation conditions is not yet available, undermining the fidelity of fuel performance modeling to support fuel qualification. It has been difficult to clearly detect pore boundaries and distinguish matrix phases from fission gas pores using optical microscopy by using simple threshold methods working with low magnification images. The pre-trained deep learning model for fission gas pore detection was applied to ~10,260 high magnification scanning electron microscopy images. The model increased the accuracy of fission gas pore segmentation to obtain statistical features, which cannot be processed manually. A pre-trained decision tree model was used to classify pores as isolated or connected pores, providing new insight into the correlation between the movement of lanthanides, solid fission products, and the radial temperature gradient developed in fuel irradiation conditions. This paper emphasizes the potential that artificial intelligence-based machine learning models have to accelerate qualification and support nuclear fuel development.

36 MATERIALS SCIENCE↗

Pore Structures in Detritusphere of Soils Under Switchgrass and Restored Prairie Vegetation Community

Root detritusphere, that is, the soil in the vicinity of decomposing root residues, plays an important role in soil microbial activity and C sequestration. Pore structure (size distributions and connectivity of soil pores) in the detritusphere serves as a major driver for these processes and, in turn, is influenced by the physical characteristics of both soil and roots. This study compared pore structure characteristics in the root detritusphere of soils of contrasting texture and mineralogy subjected to > 6 years of contrasting vegetation: monoculture switchgrass and polyculture prairie systems. Soil samples were collected from five experimental sites in the US Midwest representing three soil types. Soil texture and mineralogy were measured using a hydrometer and x-ray powder diffraction, respectively. The intact cores were scanned with x-ray computed micro-tomography to identify visible soil pores, biopores, and particulate organic matter (POM). We specifically focused on pore structure within the detritusphere around the POM of root origin. Results showed that the detritusphere of coarser textured soils, characterized by high sand and quartz contents, had lower porosity in the vicinity of POM compared with finer textured soils. POM vicinities in finer textured soils had high proportions of large (> 300 μm diameter) pores, and their pores were better connected than in the coarser soils. Lower porosity in the outer (> 1 mm) parts of the detritusphere of switchgrass than of prairie suggested soil compaction by roots, with the effect especially pronounced in the coarser soils. Here, the results demonstrated that soil texture and mineralogy played a major, while vegetation played a more modest, role in defining the pore structure in the root detritusphere.

54 ENVIRONMENTAL SCIENCES↗

Pore-Scale Transport Effects in Electrochemical CO 2 Reduction on Gold via Coupled Microkinetic-Transport Modeling

A pore-resolved modeling framework is developed to quantify how pore-scale transport affects the intrinsic microkinetics of CO 2 -to-CO on Au. A DFT-informed microkinetic model is coupled self-consistently to a Generalized-Modified Poisson–Nernst–Planck (GMPNP) transport description in a single, electrolyte-filled cylindrical pore, allowing local concentrations and potential to feed back into site-specific reaction rates. FIB-SEM is used to determine pore sizes within realistic electrode materials. Across pore diameters, d p = 10–6000 nm, the surface-averaged CO 2 reduction rate is systematically reduced relative to the ideal microkinetic baseline where mass transport is not accounted for; the effectiveness factor 𝜂 𝑠,CO 2 , which quantifies this ratio, decreases rapidly at more negative potentials and is about 1% near −1.0 V vs SHE due to reactant depletion. Spatial maps reveal pore-bulk alkalization that emerges at higher cathodic bias, with a small, near-wall pH dip due to electrostatic repulsion of hydroxide at the cathode interface. For a fixed aspect ratio L p /d p , narrower pores exhibit larger 𝜂 𝑠,CO 2 by shortening diffusion paths, whereas variations in the aspect ratio L p /d p play a secondary role. A dimensionless analysis (surface/bulk Damköhler numbers) delineates operating regimes. In conclusion, this work offers a concept for incorporating microkinetic models into homogenized porous-electrode models through effectiveness factors and pore-size distribution.

Au-catalyst↗

Statistical analysis of spatial distribution of pores in metal additive manufacturing

We report mechanical properties of additively manufactured parts are sensitive to the presence of pores form during the manufacturing process. The impact of pores on the mechanical performance has been investigated extensively with respect to different parameters, such as pore volume fraction, shape, and size. However, statistical investigations focusing on the relationships between the spatial distribution of pores and process parameters; and consequently, the performance of the manufactured parts are scattered and limited. Also, these sparse investigations usually suffer from an ambiguous definition of terminologies. For instance, the required criteria to consider a point pattern as complete spatial randomness (CSR) are generally not clarified. Moreover, no numerical formalism is yet developed to show how much the observed spatial results are statistically significant. To address these shortcomings, the statistical definition of CSR in a point pattern and the procedure to quantitatively determine the deviation of a pattern from CSR were discussed. The explained statistical approach was used to investigate the effect of scanning speed parameter on the spatial distribution of gas pores in laser powder bed fusion manufactured stainless steel parts. Furthermore, and to highlight the impact of the spatial distribution of pores on mechanical properties, fatigue performances of parts with clustered and randomly distributed pores were simulated by finite element analysis. It is shown that by reducing the scanning speed, the spatial distribution of gas pores deviated more from CSR, and correspondingly fatigue performance deteriorated.

36 MATERIALS SCIENCE↗

Factors controlling pore network development of thermally mature Early Palaeozoic mudstones from the Baltic Basin (N Poland)

Understanding the formation of pore space, especially in low porosity shales (as source rocks and as unconventional resources), is critical to the oil and gas industry, since pores control the space available for hydrocarbon and participate in hydrocarbon transport. We examined 87 Ordovician and Silurian mudstone samples collected from four wells located in the Pomeranian part of the Baltic Basin (northern Poland), one of the primary Polish targets for hydrocarbon exploration. These samples represent the Pelplin, the Paslek, the Jantar, the Prabuty, and the Sasino Formations, which still requires more detailed porosity studies. Our study aimed to identify factors controlling porosity development, by applying bulk techniques (organic petrology and TOC analyses, quantitative mineralogy, and porosimetry) as well as nano- to microscale techniques (thin section petrography, electron microscopy). The studied samples are mainly argillaceous mudstones. The results of porosimetry measurements, combined with image analysis, indicate that the pores of all studied rocks are dominated by micropores (pores < 2 nm in diameter), mesopores (2-50 nm in diameter) and small macropores. The SEM images showed three main pore types: a) voids related to clay mineral aggregates, b) pores inside organic matter particles, and c) pores between other mineral grains. In the Jantar and Sasino mudstones, the organic matter content and its thermal maturity control porosity. The occurrence of solid bitumen in the rocks from these formations reduces samples’ mesoporosity because of the pore-clogging effect. In contrast, in the Paslek and Prabuty Formations, there is low organic matter content and specific surface area and the volume of mesopores increase with clay minerals content. In the Pelplin mudstones, there are no prevailing factors controlling porosity. As a result, we suggest that a combination of SEM image analysis and dual liquid porosity (DLP) measurements is a powerful method to assess porosity available for petroleum flow in mudstones.

03 NATURAL GAS↗

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗

Synthesis and characterization of porous polydimethylsiloxane structures with adjustable porosity and pore morphology using emulsion templating technique

Abstract Porous polydimethylsiloxane (PDMS) framework with adjustable pore structures has been fabricated by polymerization of the continuous phase in the emulsion templates. Different types of surfactants, including nonionic (Triton X‐100), cationic (Benzalkonium chloride), anionic (sodium dodecyl sulfate), and silicone‐based block copolymer were used to stabilize the water‐in‐oil emulsion system. Deionized water with a wide range of internal phases varying from 10% to 60% by weight was employed to make the low internal phase emulsion and medium internal phase emulsion. The effect of surfactant type, surfactant concentration, and the internal phase volume on the stability of the emulsion, pore morphology, and pore size distribution was explored. The stability of the emulsion was investigated by comparing the pore morphology of the cured sample at different set times, such as 0, 6, and 72 h. Scanning electron microscopy was employed for the characterization of the porous structures. The image analysis was conducted, and the pore size distribution, porosity, and open‐cell ratio of each sample were calculated. Interconnected pores have been seen in the porous PDMS made from emulsions with an internal phase larger than 30%. The results demonstrated that the frequency of open‐cell pores and the pore size is dependent on the surfactant types.

Abshirini, Mohammad↗

Hydrogen Diffusion in Slit Pores: Role of Temperature, Pressure, Confinement, and Roughness

Diffusion of hydrogen (H 2 ) is important to understand the leakage risk and transport behavior for H 2 geologic storage. We applied molecular dynamics simulations to investigate the influencing factors of H2 diffusion in the slit pores of calcite, hematite, and quartz, owing to their abundance. It is revealed that the H2 self-diffusion coefficient increases with the temperature, regardless of the type of pore minerals. The diffusion of H 2 in the 20 nm slit pores falls into the bulk diffusion regime when the pressure is 10 MPa. The self-diffusion of H 2 decreases with pressure in all three types of slit pores, following a power law model with the exponents ranging from -0.825 to -0.964. Furthermore, the impact of confinement on H 2 diffusion is more pronounced for the slit pores with stronger interactions with H 2 -like calcite. The role of surface roughness in H 2 diffusion depends on the slit aperture. The rough surface enhances H 2 diffusion in the larger slit pores due to the enlarged effective pore space, whereas it weakens H 2 diffusion in the small slit pores due to stronger adsorption. These findings will fill the knowledge gap on the coupling effect of different factors influencing H 2 diffusion.

08 HYDROGEN↗