Pore Regions Research Articles

Intrinsically Conductive and Cu-Functionalized Polymer-Composite Membranes as Gas Diffusion Electrodes for CO2 Electroreduction.

We introduced a new class of gas diffusion electrodes (GDEs) with adjustable pore morphology. We fabricated intrinsically conductive polymer-composite membranes containing carbon filler, enabling a pore structure variation through film casting cum phase separation protocols. We further selectively functionalized specific pore regions of the membranes with Cu by a NaBH4-facilitated coating strategy. The as-obtained GDEs can facilitate the electrochemical CO2 reduction reaction (CO2RR) at Cu active sites that are presented inside a defined and electrically conductive pore system. When employing them as free-standing cathodes in a CO2 flow electrolyzer, we achieved >70 % Faradaic efficiencies for CO2RR products at up to 200 mA/cm2. We further demonstrated that deposition of a dense Cu layer on top of the membrane leads to obstruction of the underlying pore openings, inhibiting an excessive wetting of the pore pathways that transport gaseous CO2. However, the presentation of Cu inside the pore system of our novel membrane electrodes increased the C2H4/CO selectivity by a factor of up to 3 compared to Cu presented in the dense layer on top of the membrane. Additionally, we found that gaseous CO2 could still access Cu in macropores after wetting with electrolyte, while CO2RR was completely suppressed in wetted nm-scale pores.

Quantifying the effects of size exclusion on colloidal particle transport in porous media at a pore scale and column scale

AbstractColloid‐facilitated transport has been widely acknowledged as a significant contributor to surface water and groundwater pollution. Compared to water‐dissolved solutes, colloids are subject to a size exclusion effect (SEE) due to their inherent size when transported through porous media. However, only a few studies have quantitatively assessed SEE on colloid transport in porous media. In this study, we conducted dynamic observation experiments (in a quartz sand micro‐model) using a high‐speed camera and directly observed the size exclusion phenomenon of colloid transport in pores. Moreover, we further performed column experiments to quantitatively characterize the breakthrough curves of silica colloids with diameters ranging from 0.1 to 5 µm through different‐size sands. Silica colloids displayed a distinct SEE, as demonstrated by their earlier breakthroughs compared to the NaNO3 tracer. This observation served as a quantitative indicator of the SEE. The breakthrough curves were fitted using the advection‐dispersion equation to quantify this phenomenon, allowing for calculating the water saturation that is inaccessible to mobile colloids (γ). It was determined that γ exhibited a significant negative linear correlation (p < 0.01) with the average grain size (d50) of porous media. Additionally, power–law correlations were identified between γ and the colloid diameter (dp) and dp/d50. When the grain size of porous media remained below 1031 µm, an increase in the colloid diameter (from 0.1 to 5 µm) resulted in a more noticeable SEE, leading to the proportion of inaccessible pore regions to all pores increasing from 0.017 to 0.098, with this change following a power–law trend. These findings provide valuable insights into quantifying the SEE on silica colloids during transport in porous media. Furthermore, they provide theoretical support for future quantitative research on the mechanisms underlying colloid‐facilitated transport.

Enhancing the dipole ring of hexagonal boron nitride nanomesh by surface alloying

Surface templating by electrostatic surface potentials is the least invasive way to design large-scale artificial nanostructures. However, generating sufficiently large potential gradients remains challenging. Here, we lay the groundwork for significantly enhancing local electrostatic fields by chemical modification of the surface. We consider the hexagonal boron nitride (h-BN) nanomesh on Rh(111), which already exhibits small surface potential gradients between its pore and wire regions. Using photoemission spectroscopy, we show that adding Au atoms to the Rh(111) surface layer leads to a local migration of Au atoms below the wire regions of the nanomesh. This significantly increases the local work function difference between the pore and wire regions that can be quantified experimentally by the changes in the h-BN valence band structure. Using density functional theory, we identify an electron transfer from Rh to Au as the microscopic origin for the local enhancement of potential gradients within the h-BN nanomesh.

Metal intrusion enhanced deep learning-based high temperature deterioration analysis of rock materials

The deep learning-based method demonstrates superior capability in high-temperature deterioration analysis of rock materials through scanning electron microscopy (SEM) characterization. However, the blurred boundaries between rock particles and pore structure presented in SEM images always affect the training efficiency. Hence, in this study, the metal intrusion technology and backscattered electron (BSE) observations are applied to assist a deep learning-based model to analyze the deterioration of the rock materials exposed to different levels of high temperatures. >2000 micro images for each level were inputted and trained to distinguish the features of rock after 5 temperature levels deterioration. The results reveal that the classification accuracy on the heat deterioration of the rock materials achieved 94.3%. The classification accuracy under the optimized observation area (from 240 pixels × 240 pixels to 280 pixels × 280 pixels) is stable over 90%, balancing the high precision and training efficiency. Moreover, the interpretable high-temperature degradation characteristics of the micro-damage on rock particles and pore regions can be extracted, enabling further analysis of the degradation process of high-temperature treated rocks. We expect this work will inspire a high-precision, wide-application and cost-efficient method for the deterioration analysis of rock materials in underground projects.

Correlation between clinical phenotypes and genotypes among 46 children with SCN1A-related developmental epileptic encephalopathy

To explore the correlation between clinical phenotypes and genotypes among 46 children with SCN1A-related developmental epileptic encephalopathy (DEE). Clinical data of 46 children with DEE and SCN1A variants identified at the Guangzhou Women and Children's Medical Center between January 2018 and June 2022 were collected. The children were grouped based on their age of onset, clinical manifestations, neurodevelopmental status, and results of genetic testing. The correlation between SCN1A genotypes and clinical phenotypes was analyzed. Among the 46 patients, 2 children (4.35%) had developed the symptoms before 3 months of age, 42 (91.30%) were between 3 to 9 months, and 2 cases (4.35%) were after 10 months. Two cases (4.35%) presented with epilepsy of infancy with migrating focal seizures (EIMFS), while 44 (95.7%) had presented with Dravet syndrome (DS), including 28 cases (63.6%) with focal onset (DS-F), 13 cases (29.5%) with myoclonic type (DS-M), 1 case (2.27%) with generalized type (DS-G), and 2 cases (4.55%) with status epilepticus type (DS-SE). Both of the two EIMFS children had severe developmental delay, and among the DS patients, 7 cases had normal development, while the remaining had developmental delay. A total of 44 variants were identified through genetic sequencing, which included 16 missense variants and 28 truncating variants. All EIMFS children had carried the c.677C>T (p.Thr226Met) missense variant. In the DS group, there was a significant difference in the age of onset between the missense variants group and the truncating variants group (P < 0.05). Missense variants were more common in D1 (7/15, 46.7%) and pore regions (8/15, 53.3%), while truncating variants were more common in D1 (12/28, 42.9%). Children with variants outside the pore region were more likely to develop myoclonic seizures. The clinical phenotypes of DEE are diverse. There is a difference in the age of onset between individuals with truncating and missense variants in the SCN1A gene. Missense variants outside the pore region are associated with a higher incidence of myoclonic seizures.

Dynamic deformation and meso-structure of coarse-grained saline soil under cyclic loading with freeze-thaw cycles

The cumulative deformation properties of subgrade soil under cyclic traffic loads are critical for optimizing pavement structure design and ensuring long-term highway structural performance. This study aims to investigate the coupling effect of freeze-thaw cycles and cyclic loads on the cumulative deformation behaviors and meso-structure of coarse-grained saline soil (CGSS) subgrade filling in high-cold areas. Dynamic triaxial tests and computed tomography (CT) scanning were conducted to analyze the CGSS under different working conditions. The research focused on the dynamic deformation development and damage evolution under varying freeze-thaw cycles and load amplitudes. The research results show that the cumulative deformation behavior of CGSS under cyclic loading is relatively sensitive to the freeze-thaw process. The cumulative dynamic strain increases as the freeze-thaw cycles, with a critical freeze-thaw cycle number of five. The stable cumulative dynamic strain curve exhibits clear three-stage characteristics when plotted in semi-log coordination, with critical loading cycles at 20 and 1,000. After 10–100 loading cycles, the cumulative strain curve quickly shows failure. The CGSS’s low density and pore regions greatly increase after a freeze-thaw cycle. The rise in dynamic stress amplitude notably affects the bonding between soil particles and crystalline salts. The coupling effect of the freeze-thaw cycle and dynamic activity exacerbates the deterioration of soil structure, resulting in variations in CT values within the scanning layer in the final state.

Photoluminescence spectroscopy to detect microregion stress distribution in glass‐to‐metal seals

AbstractThe stress distribution in sealing glass is one of the most concerning issues for glass‐to‐metal (GtM) seals. In this study, photoluminescence spectroscopy was applied by exploiting the piezospectroscopic effect of Cr‐doped α‐Al2O3 (ruby), and the overall stress distribution along the radius of a concentric seal was detected and analyzed. It was discovered that the compressive stress first increased and then decreased at the edge of the seal. The maximum compressive stress of 91 MPa was detected at ∼20 µm from the interface and was ascribed to the interaction between the glass and surface oxide film of the metal. Stress distributions in pore regions were also investigated and obvious stress fluctuations were observed clustering around pores. A circle of compressive stress was observed around the pores in a single glass without a metal housing; for the pores in the center of the concentric seal, the stress distribution was dominated by the inner contraction of the metal housing; and for the pores near the edge of the seal, the concentration of tensile stress located perpendicular to that of the compressive stress. This study demonstrates a high‐resolved and nondestructive method to detect the stress field and explores the potential for future tailorability of stress distribution in GtM seals.

Understanding Degradation Behavior of Direct-Ammonia Solid Oxide Fuel Cells Using Nanoscale Analyses

Direct-ammonia solid oxide fuel cells (SOFCs) offer a cost-effective and efficient avenue for accelerating the use of hydrogen as a major energy vector. The effect of directly feeding ammonia as a fuel into SOFCs and their associated degradation behavior, however, needs to be fully understood in order to design robust cells capable of operating with good performance and durability at the required high temperatures for extended periods of time. In this study, we performed detailed analyses using advanced techniques such as high-resolution secondary ion mass spectrometry (SIMS) imaging and microscopy methods to examine the microstructural evolution and factors affecting nickel nitriding behavior of model Ni metal surfaces as well as Ni/yttria-stabilized zirconia (YSZ) cermet anodes directly exposed to ammonia as a function of temperature. The results show preferential formation of nickel nitride and presence of OH- species on Ni metal surfaces directly exposed to ammonia, as well as grain boundaries in the bulk which were not directly exposed to ammonia, suggesting enhanced ionic diffusion along these regions. Nickel nitriding is also accompanied by volume expansion which is considered to result in additional mechanical stress. Ni/YSZ cermet anodes, on the other hand, exhibit a significantly higher amount of nickel nitride formed preferentially in pore regions at lower temperatures, suggesting the possibility of nickel nitride decomposition occurring at higher temperatures. The results further suggest a correlation between nickel nitride formation and microstructural features of Ni/YSZ cermet anodes which influence gas diffusion properties. Lastly, a model for the degradation behavior of Ni/YSZ cermet anodes on the basis these results will be proposed and discussed.

Mixture Adsorption in Nanoporous Zeolite and at Its External Surface: In-Pore and Surface Selectivity.

Using toluene, ethylene, and water as gas compounds with different representative molecular interactions, we perform atom-scale simulations for their mixtures to investigate the selectivity of the core nanoporosity and external surface in a prototypical zeolite. As expected, the overall behavior suggests that increasing the pressure of a given component promotes the desorption of the coadsorbing species. However, for water-toluene mixtures, we identify that the pseudohydrogen bonding between water and toluene leads to beneficial coadsorption as toluene adsorption in the low-pressure range promotes water adsorption. Moreover, when the zeolite is completely filled with water, toluene adsorption does not occur due to steric repulsion, and ethylene shows oversolubility as the amount of ethylene per water molecule is significantly larger than in bulk water. The underlying oversolubility mechanism is found to be due to localized ethylene adsorption in the density minima arising from the layering of water in nanoconfinement. Despite these specific effects, the relatively weak coadsorption effects in the zeolite nanoporosity, which are found to be reasonably captured using the ideal adsorbed solution theory, arise from the fact that adsorption of these gases having different molecular sizes occurs in distinct pore regions (channel type, channel intersection). Finally, in contrast to confinement in the nanoporosity, mixture adsorption at the external surface does not show coadsorption effects as it mostly follows the Henry regime. These results show that selectivity is mostly governed by the confinement effects as the external surface leads to a selectivity loss.

Auto-pore segmentation of digital microscopic leather images for species identification

In leather images, precise pore segmentation is a necessary medium for accurate species prediction. However, due to foreground color, texture, size, and boundary variability, traditional methods encounter false segmentation. This paper proposes a deep learning-based novel auto-pore segmentation network (ApSnet). It aims to automatically segment the species-definite pores from novel digital microscopic leather image data. The ApSnet learns the pore regions from raw and the corresponding ground-truth images. The images undergo patch-based image augmentation to fasten the learning process. The depth and the concatenations of the existing Unet are reduced to design the simple and shallow ApSnet. The weighted average loss function strengthens the image understanding to segment the accurate pore pixels. The experimental analysis affirms the superior performance of the proposed ApSnet against Unet, Unet++, and I-Unet. The study also validates ApSnet with KDSB18 and KVasir-SEG datasets for medical image segmentation. It also tests the robustness of ApSnet on the leather images with fuzzy textures. Finally, the k-nearest neighbor (KNN) model learns and classifies the features from the ApSnet segmented images with 97.4% accuracy. Thus, the analysis proves that precise pore segmentation is indispensable in interpreting species-distinct pore features. Therefore, the present work develops a human–computer interactive platform for accurate leather species prediction by designing a computationally efficient ApSnet-based segmentation model.

A mathematical model-based investigation of liquid film dewetting over porous solid substrates

Thin liquid films resting on solid surfaces are susceptible to dewetting when physical inhomogeneities like surface roughness, textured patterns, and porosities are present on the substrate surface. These inhomogeneities affect liquid transport phenomena through dynamic wetting behavior and imbibition. They can render the liquid film unstable, thereby resulting in rupture and dewetting. Here, we mathematically model and simulate the dynamics of a thin liquid film with passive air above it, dewetting a porous solid substrate that bounds the liquid from below. The solid is modeled as a surface with regions of equally spaced pores, with a partial slip condition for the lateral velocity component of the liquid film, a spatially varying long-range attractive force parameter, as well as short-range repulsive interaction force parameters between the liquid–air interface and liquid–solid interface. Our results explain how the size and spacing of pore regions on the substrate, slip length of the liquid, and intermolecular force potentials influence the formation of morphological patterns and dewetting time scales of the liquid film.

Segmentation of backscattered electron images of cement-based materials using lightweight U-Net with attention mechanism (LWAU-Net)

Backscattered electron (BSE) image segmentation is used to characterise the microstructure of cement-based materials to deepen the understanding of macroscopic properties. However, processing bottlenecks have been encountered when using conventional segregation methods for cement-based materials with complex imaging conditions, and it is often necessary to manually adjust the parameters in image processing. This paper used a lightweight U-shaped convolutional neural network with an attention mechanism (LWAU-Net) to segment the backscattered electron images of cement-based materials, and the results of different attention modules were investigated. An augmentation method combining jigsaw transform and piecewise affine is also proposed. The presented segmentation method can more completely segment the hydrated cement, unhydrated cement, and pore regions under different sampling conditions. It achieves high-precision recognition by using lightweight models. The paper finally quantifies the degree of cement hydration and proves the feasibility of the proposed method.

Variational quantitative phase-field modeling of nonisothermal sintering process.

Phase-field modeling has become a powerful tool in describing the complex pore-structure evolution and the intricate multiphysics in nonisothermal sintering processes. However, the quantitative validity of conventional variational phase-field models involving diffusive processes is a challenge. Artificial interface effects, like the trapping effects, may originate at the interface when the kinetic properties of two opposing phases are different. On the other hand, models with prescribed antitrapping terms do not necessarily guarantee the thermodynamics variational nature of the model. This issue has been solved for liquid-solid interfaces via the development of the variational quantitative solidification phase-field model. However, there is no related work addressing the interfaces in nonisothermal sintering, where the free surfaces between the solid phase and surrounding pore regions exhibit strong asymmetry of mass and thermal properties. Also, additional challenges arise due to the conserved order parameter describing the free surfaces. In this work, we present a variational and quantitative phase-field model for nonisothermal sintering processes. The model is derived via an extended nondiagonal phase-field model. The model evolution equationshave naturally cross-coupling terms between the conserved kinetics (i.e., mass and thermal transfer) and the nonconserved one (grain growth). These terms are shown via asymptotic analysis to be instrumental in ensuring the elimination of interface artifacts, while also examined to not modify the thermodynamic equilibrium condition (characterized by a dihedral angle). Moreover, we demonstrate that the trapping effects and the existence of surface diffusion in conservation laws are direction-dependent. An anisotropic interpolation scheme of the kinetic mobilities that differentiates between the normal and tangential directions along the interface is discussed. Numerically, we demonstrate the importance of the cross-couplings and the anisotropic interpolation by presenting thermal-microstructural evolutions.

Bioceramic scaffolds with triply periodic minimal surface architectures guide early-stage bone regeneration.

The pore architecture of porous scaffolds is a critical factor in osteogenesis, but it is a challenge to precisely configure strut-based scaffolds because of the inevitable filament corner and pore geometry deformation. This study provides a pore architecture tailoring strategy in which a series of Mg-doped wollastonite scaffolds with fully interconnected pore networks and curved pore architectures called triply periodic minimal surfaces (TPMS), which are similar to cancellous bone, are fabricated by a digital light processing technique. The sheet-TPMS pore geometries (s-Diamond, s-Gyroid) contribute to a 3‒4-fold higher initial compressive strength and 20%-40% faster Mg-ion-release rate compared to the other-TPMS scaffolds, including Diamond, Gyroid, and the Schoen's I-graph-Wrapped Package (IWP) in vitro. However, we found that Gyroid and Diamond pore scaffolds can significantly induce osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Analyses of rabbit experiments in vivo show that the regeneration of bone tissue in the sheet-TPMS pore geometry is delayed; on the other hand, Diamond and Gyroid pore scaffolds show notable neo-bone tissue in the center pore regions during the early stages (3-5 weeks) and the bone tissue uniformly fills the whole porous network after 7 weeks. Collectively, the design methods in this study provide an important perspective for optimizing the pore architecture design of bioceramic scaffolds to accelerate the rate of osteogenesis and promote the clinical translation of bioceramic scaffolds in the repair of bone defects.

Multi-Scale and Multi-Region Pore Structure Analysis on Sandy Conglomerate Whole Core With Digital Rock Model

Abstract Based on industrial computed tomography (CT), the whole core sandy conglomerate is scanned with a resolution of 0.5 mm/voxel, and the representative debris region and filling region subsample is selected to be scanned with a resolution of 15 µm/voxel using micro-CT. Then, four regions of the whole core sandy conglomerate image are segmented with the multi-threshold segmentation algorithm including macro pore, debris, filling, and gravel regions, while binary segmentation is performed on the debris and filling subsamples to segment the debris pores and filling pores respectively. Finally, the multi-scale and multi-region pore network model of the sandy conglomerate was constructed by the integration method to analyze the different types of pore characteristics. It can be found that the integrated sandy conglomerate model can reflect the structural characteristics of macro pore, debris pore, and filling pore at the same time. Meanwhile, the porosity and permeability of the integrated sandy conglomerate model are calculated and they are basically consistent with that of lab test results, which greatly increase the accuracy of the multi-scale multi-region pore network model.

Ion dynamics into different pore size distributions in supercapacitors under compression

Compressing supercapacitor (SCs) electrode is essential for improving the energy storage characteristics and minimizing ions’ distance travel, faradaic reactions, and overall ohmic resistance. Studies comprising the ion dynamics in SC electrodes under compression are still rare. So, the ionic dynamics of five aqueous electrolytes in electrodes under compression were studied in this work for tracking electrochemical and structural changes under mechanical stress. A superionic state is formed when the electrode is compressed until the micropores match the dimensions with the electrolyte’s hydrated ion sizes, which increases the capacitance. If excessive compression is applied, the accessible pore regions decrease, and the capacitance drops. Hence, as the studied hydrated ions have different dimensions, the match between ion/pore sizes differs. To the LiOH and NaClO4 electrolytes, increasing the pressure from 60 to 120 and 100 PSI raised the capacitance from 13.5 to 35.2 F g−1 and 30.9 to 39.0 F g−1, respectively. So, the KOH electrolyte with the lowest and LiCl with the biggest combination of hydrated ion size have their point of maximum capacitance (39.5 and 36.7F g−1) achieved at 140 and 80 PSI, respectively. To LiCl and KCl electrolytes, overcompression causes a drop in capacitance higher than 23%.

Correlating nanostructure features to transport properties of polymer electrolyte membrane electrolyzer anode catalyst layers

In this work, polymer electrolyte membrane water electrolyzer catalyst layers are stochastically generated with α pore regions (defined as regions with pores <250 nm in size), β pore regions (defined as regions with pores >250 nm in size) and an ionomer layer of uniform thickness. Pore network modelling simulations are performed to correlate structural characteristics (porosity, mean pore diameter, and β pore volume fraction) to single and two-phase mass transport properties. Porosity and two-phase mass permeability are well correlated, but order of magnitude variations are observed due to inhomogeneous pore size distributions at the same porosity. The effect of varying ionomer and iridium oxide material mass fractions on ion and electron transport properties is also explored. Electrical conductivity is well correlated with catalyst volume fraction, while ionic conductivity is less correlated, likely due to changes in connectivity resulting from the inhomogeneous structure of the porous phase. Due to the non-homogeneity of the pore sizes in the catalyst layer, existing correlations for porous media are insufficient predictors. As such, we recommend that both porosity and mean pore diameter are considered when estimating transport properties. It is additionally recommended that both α pore and β pore regions must be incorporated into catalyst layer designs to allow both enhanced connectivity and mass transport characteristics.

Are Natural Fractures in Sandstone Reservoir: Water Wet – Mixed Wet – Or Oil Wet?

This study accurately measures the wettability contact angle of native Kuwaiti sandstone reservoir that hosts mixed pore size distributions in both the tight sandstone matrix as well as the natural fracture (NF) embedded in it. Also, this study, effectively, investigates the geometrical size and shape of natural available voids whether matrix voids or NF voids captured in the rock 2D image frame system. Correspondingly, this study is, successfully, measure tight matrix, NF Pore wall, and NF pore opening wettability performance and recovery efficiency contributions inside the sandstone reservoir. A model pore/ grain contact angle wettability is generated. Therefore, this study thrives to enhance new physics that will advance reservoir characterization and production improvement through modeled and measured wettability contact angle. The prepared fresh tight sandstone rock sample in the form of rock fragment is imaged and characterized for porosity, permeability, and wettability contact angle in 2D format utilizing SEM-BSE imaging techniques. The generated images will be quantified using pre-defined logic for wettability contact angle measurement. The data generated will be used to estimate the wettability distribution. Each image captured will be investigated for a magnification of X51 (1 mm Scale). This magnification scale will ensure measurement of all possible pore/ grain petrophysical porosity &amp; permeability features, as well as wettability contact angle of 3 region representations for the tight matrix, the natural fracture pore wall, and the inside fracture void. From measured data and computed logics, the majority portions of natural pore voids and pore-walls are medium-water-wet; however, some fracture-pore-walls show mixed and strongly oil wetting preference. The main factors in the understanding the fracture wettability are pore size distribution and pore morphology that suggests the wettability affinity likelihood. This study shows 3 natural pore regions: tight matrix, natural fracture pore wall, and inside the natural void space. These regions are necessary to characterize wettability behavior for oil production and crude oil reservoir recovery schemes, especially in EOR schemes such as water production and/ or water injection operations. Also, the fracture-to-matrix ratio shows some new interesting features characterizations.

Peat macropore networks – new insights into episodic and hotspot methane emission

Abstract. Peatlands are important natural sources of atmospheric methane (CH4) emissions. The production and emission of CH4 are strongly influenced by the diffusion of oxygen into the soil and of CH4 from the soil to the atmosphere, respectively. This diffusion, in turn, is controlled by the structure of macropore networks. The characterization of peat pore structure and connectivity through complex network theory approaches can give conceptual insight into how the relationship between the microscale pore space properties and CH4 emissions on a macroscopic scale is shaped. The evolution of the pore space that is connected to the atmosphere can also be conceptualized through a pore network modeling approach. Pore regions isolated from the atmosphere may further develop into anaerobic pockets, which are local hotspots of CH4 production in unsaturated peat. In this study, we extracted interconnecting macropore networks from three-dimensional X-ray micro-computed tomography (µCT) images of peat samples and evaluated local and global connectivity metrics for the networks. We also simulated the water retention characteristics of the peat samples using a pore network modeling approach and compared the simulation results with measured water retention characteristics. The results showed large differences in peat macropore structure and pore network connectivity between vertical soil layers. The macropore space was more connected and the flow paths through the peat matrix were less tortuous near the soil surface than at deeper depths. In addition, macroporosity, structural anisotropy, and average pore throat diameter decreased with depth. Narrower and more winding air-filled diffusion channels may reduce the rate of gas transport as the distance from the peat layer to the soil–air interface increases. The network analysis also suggests that both local and global network connectivity metrics, such as the network average clustering coefficient and closeness centrality, might serve as proxies for assessing the efficiency of gas diffusion in air-filled pore networks. However, the applicability of the network metrics was restricted to the high-porosity near-surface layer. The spatial extent and continuity of the pore network and the spatial distribution of the pores may be reflected in different network metrics in contrasting ways. The hysteresis of peat water content between wetting and drying was found to affect the evolution of the volume of connected air-filled pore space in unsaturated peat. Thus, the formation of anaerobic pockets may occur in a smaller soil volume and methanogenesis may be slower when the peat is wetting compared to in drying conditions. This hysteretic behavior might explain the hotspots and episodic spikes of CH4 emissions, and therefore, it should be taken into account in biogeochemical models.

A Micro-Scale Non-Linear Finite Element Model to Optimize the Mechanical Behavior of Bioprinted Constructs.

Extrusion-based bioprinting is an enabling biofabrication technique that is used to create heterogeneous tissue constructs according to patient-specific geometries and compositions. The optimization of bioinks as per requirements for specific tissue applications is an essential exercise in ensuring clinical translation of the bioprinting technologies. Most notably, optimum hydrogel polymer concentrations are required to ensure adequate mechanical properties of bioprinted constructs without causing significant shear stresses on cells. However, experimental iterations are often tedious for optimizing the bioink properties. In this work, a nonlinear finite element modeling approach has been undertaken to determine the effect of different bioink parameters such as composition, concentration on the range of stresses being experienced by the cells in the bioprinted construct. The stress distribution of the cells at different parts of the constructs has also been modeled. It is found that both bioink chemical compositions and concentrations can substantially alter the stress effects experienced by the cells. Concentrated regions of softer cells near pore regions were found to increase stress concentrations by almost three times compared with stress generated in cells away from the pores. The study provides a method for rapid optimization of bioinks, design of bioprinted constructs, as well as toolpath plans for fabricating constructs with homogenous properties.