Quantification Of Porosity Research Articles

Pore to core plug scale characterization of porosity and permeability heterogeneities in a Cretaceous carbonate reservoir using laboratory measurements and digital rock physics, Abu Dhabi, United Arab Emirates

Laboratory and digital rock physics study of a Cretaceous limestone reservoir in Abu Dhabi, United Arab Emirates, provides insights into pore connectivity, pore-throat size, and permeability distributions. The procedure involves core analysis at different scales using micro-computed tomography, scanning electron microscopy, and nano-computed tomography; porosity quantification using segmentation techniques; numerical simulation of permeability using lattice Boltzmann method; and upscaling simulation results to core-plug scale using Darcy's Law. The greater proportion of connected versus poorly interconnected pores is attributed to (i) the formation of early diagenetic grain-rimming calcite cement, which reduced the degree of mechanical compaction and pore-throat size reduction, (ii) limited introduction of carbonate mud by bioturbation due to rapid sediment burial, and (iii) dissolution of allochems and partial dolomitization of micrite matrix. Microbial micritization of allochems at the seafloor led to the transformation of grain-supported limestones, dominated by unimodal macropores, into reservoirs with multimodal porosity. Conversely, the mud–supported limestones have depositional unimodal micropore distribution, which is reduced by mechanical compaction and cementation by calcite micro-overgrowths around micrite particles. Good agreement between laboratory and simulated values suggests that lateral facies and related textural variation across different depositional environments have limited implications for digital rock physics techniques in predicting the petrophysical properties of limestone reservoirs. Nevertheless, careful selection of representative elementary volume within a geologic context for a complex, anisotropic limestone reservoir is the key to achieving reliable results.

An evaluation of six techniques for measuring porosity of ribbons produced by roller compaction

Ribbon porosity is a critical parameter to monitor in the roller compaction process. In this study, six techniques for measuring the porosity of solid compacts, i.e., manually by caliper (Caliper), X-ray microtomography (µCT), off-line near-infrared spectroscopy (NIR), laser triangulation (Laser), mercury intrusion porosimetry (MIP), and GeoPyc, were compared using a set of rectangular ribblets of microcrystalline cellulose (MCC). These ribblets, which were compressed at 8–130 MPa on a compaction simulator, exhibited porosities over the range of 0.09 – 0.52. Subsequently, porosities of MCC ribbons made on a roller compactor at specific roll forces of 1.8 kN/cm and 8.8 kN/cm were measured. The Caliper method is convenient for samples with a simple shape but not suitable for real ribbons. The accuracy of GeoPyc measurement relies on accurate conversion factor (unit in cm3/mm), sample shape and size, and sufficient sample volume percentage in the medium. The µCT data is more accurate at lower porosities (< 0.2), while the MIP data is more accurate at higher porosities (> 0.4). The Laser method has good accuracy and is more reproducible compared to other methods in the ribblets measurement. The NIR method is fast, which makes it suitable for in-line monitoring of changes in ribbon quality, but porosity quantification is sensitive to sample presentation, such as surface curvature and roughness. These insights could assist in the choice of the most appropriate method for monitoring ribbon porosity to guide the development and optimization of a roller compaction process for a given formulation.

Multi-scale fatigue damage analysis in filament-wound carbon fiber reinforced epoxy composites for hydrogen storage tanks

This article presents the findings of a multi-scale experimental study on carbon fiber-reinforced epoxy composites (CFRP) used in lightweight hydrogen storage pressure vessels produced via filament winding. The research employs a combination of tension-tension load-controlled fatigue tests and high-resolution physical-chemical characterization and porosity quantification to assess the impact of porosity on mechanical performance. The findings demonstrate that porosity has a detrimental impact on mechanical properties, acting as nucleation sites for damage mechanisms such as crack initiation, fiber-matrix separation and fiber breakage. At the mesoscopic level, microdefects coalesce into transverse cracks and delamination, resulting in complex failure modes under cyclic loading. The results of the tensile tests demonstrated that the orientation of the fibers has a significant impact on the mechanical behavior of the material. The ±15° configuration demonstrated superior tensile strength and modulus, while the ±30° and multilayer configurations exhibited higher ductility. The results of the fatigue testing confirmed that fiber orientation has a significant impact on fatigue life, with the ±15° configuration proving to be the most resistant. Microscopic analysis indicated that pores act as damage initiation points, accelerating failure through matrix cracking, fiber-matrix debonding, and delamination. This study highlights the need for improved porosity control during manufacturing to enhance the durability of hydrogen storage systems. Additionally, it provides valuable insights for optimizing fiber orientation to improve fatigue performance in practical applications.

Modeling porosity and permeability evolution of burrow fillings with packstone fabric in the Upper Jurassic Hanifa Formation, central Saudi Arabia: A digenetic backstripping study

Understanding the development and characteristics of burrow fillings (BF) is crucial for predicting porosity, permeability, and fluid flow behavior in bioturbated carbonate reservoirs. Advanced imaging, digital rock modeling, and backstripping techniques offer valuable insights into this development. In this study, we examined the BF in the bioturbated strata of the Upper Jurassic Hanifa Formation (central Saudi Arabia) to understand how sedimentological and diagenetic processes—such as compaction, dolomitization, and dissolution—influence porosity and permeability over time.Backstripping results provide significant insights into the evolution of porosity and permeability within packstone BF of the studied strata. We found that compacted, grain-supported BF exhibit limited improvement in porosity and permeability post-compaction. In contrast, less compacted intervals with open fabric BF can achieve up to 20% porosity and >5000 mD permeability in their grainstone versions. Notably, even with over 10% mud content, open fabric BF maintained substantial permeability (>1000 mD) due to a connected pore network. Moreover, these BF have the potential to recover porosity and permeability after complete pore network occlusion by the mud matrix through diagenetic processes like preferential dolomitization of the mud matrix and subsequent dissolution.The findings underscore the dynamic evolution of porosity in packstones within BF of bioturbated strata. Initially, mud-rich packstones with low porosity and permeability can transform into more permeable packstones through dolomitization and dissolution. Conversely, initially high-quality, mud-poor packstones with high porosity and permeability may degrade due to compaction. The quantification of porosity and permeability using digital rock modeling and backstripping techniques provides an objective and comprehensive understanding of these evolving properties, highlighting key phases and processes affecting reservoir quality. The approach of integrating digital rock modeling and backstripping technique enhances the ability to predict subsurface storage and flow capacity in bioturbated strata.

Assessing Intra-Bundle Impregnation in Partially Impregnated Glass Fiber-Reinforced Polypropylene Composites Using a 2D Extended-Field and Multimodal Imaging Approach.

This study evaluates multimodal imaging for characterizing microstructures in partially impregnated thermoplastic matrix composites made of woven glass fiber and polypropylene. The research quantifies the impregnation degree of fiber bundles within composite plates manufactured through a simplified compression resin transfer molding process. For comparison, a reference plate was produced using compression molding of film stacks. An original surface polishing procedure was introduced to minimize surface defects while polishing partially impregnated samples. Extended-field 2D imaging techniques, including polarized light, fluorescence, and scanning electron microscopies, were used to generate images of the same microstructure at fiber-scale resolutions throughout the plate. Post-processing workflows at the macro-scale involved stitching, rigid registration, and pixel classification of FM and SEM images. Meso-scale workflows focused on 0°-oriented fiber bundles extracted from extended-field images to conduct quantitative analyses of glass fiber and porosity area fractions. A one-way ANOVA analysis confirmed the reliability of the statistical data within the 95% confidence interval. Porosity quantification based on the conducted multimodal approach indicated the sensitivity of the impregnation degree according to the layer distance from the pool of melted polypropylene in the context of simplified-CRTM. The findings underscore the potential of multimodal imaging for quantitative analysis in composite material production.

Qualification and Quantification of Porosity at the Top of the Fuel Pins in Metallic Fuels Using Image Processing

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, such as fuel axial expansion. While several fuel expansion results obtained from neutron radiography imaging have been published, the analysis of neutron radiography for the purpose of describing statistical properties of porous matter formed on top of the fuel pins, also referred to as fluff in previous publications, is significantly less represented in the literature with just a single paper so far. This study aims to validate and augment results reported in previous publications using automated image processing. The paper describes the statistical properties of the porous matter in terms of nine parameters derived from radiography images and correlates those parameters with such fuel properties as composition, expansion, temperature, and burnup. The reported results are based on 1097 fuel pins of eight different fuel compositions. For three major fuel types, U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr, a clear negative correlation is found between the Pu content and five parameters describing the amount of porous matter generated. The parameters describing granularity properties, however, showed either negative correlation or nonlinear dependency from fuel composition. The parameters describing the amount showed a positive correlation with fuel axial expansion, while granularity parameters showed a negative correlation with axial expansion. The dependency on cladding temperature was found to be weak. A positive correlation is demonstrated for volume parameters and fuel burnup. In general, reported results confirm and validate findings published in previous studies using a much larger number of pins and automated processing techniques, which easily lend themselves to reproducibility, thus avoiding subjective bias.

Characterization of subaerial lava flows of the presalt strata in the Santos Basin based on basic well logs

It is hard to identify volcanic morphologies using well logs to support technical decisions during well drilling and the acquisition of geological information. Therefore, this study was carried out on the volcanic sequence present in the Presalt interval of the Santos Basin, Brazil, which is referred to as the Camboriú Formation. This section was described in a previous study using image well logs as a succession of subaerial lava flows formed mainly by compound pahoehoes, sheet pahoehoes and rubbly pahoehoes. Based on sidewall core samples and petrophysical, geochemical and mineralogical reports, we identified five electrofacies from gamma ray, resistivity, sonic, density and neutron logs, representing patterns for identifying lava flows in subaerial sequences. In addition, we applied a porosity quantification method that resulted in a method for obtaining information from the matrix and presented coherent values in facies that did not present amygdalae or breccias and inferring altered zones from variations in resistivity and density logs or from cross-plots. Patterns of well log responses were defined for each type of lava flow present in the studied well, as was the inference about the most favorable facies for reservoirs. This methodology, which uses basic well logs from basalts, is unprecedented in the Presalt sequence and can be easily used to evaluate this exploratory frontier because of its close association with subaerial volcanism.

Effect of solid solution treatment on the kinetics of hydrogen porosity evolution and mechanical properties in Al–Cu–Li alloys

The evolution kinetics of hydrogen porosity during solid solution treatment (SST) of vacuum-cast Al–Cu–Li alloys and the effect of porosity defects on the mechanical properties of the alloys were investigated. It has been found that porosity at 6h SST was dominated by nucleation and its evoluton of fractions has a linear correlation with time, APS = (0.05 ± 0.06)＋(0.11 ± 0.01) t. However, the growth of porosity at 12 h is evidenced by a decrease in the number of pores and an increase in the size of large pores, expanding inter-porosity distance due to Ostwald ripening. Three dimensional quasi-in situ X-ray CT quantification of porosity demonstrate that the hydrogen concentration at the interface in the early stages of the solid solution was not adequate to balance its surface tension and vacancies were created by cross-diffusion, reducing the interficial energy for porosity nucleation. The effects of porosity on the tensile strength satisfies lnσ-lnσ0 = αlnfp, and those detrimental hydrogen porosities can be predicted by a multi-scale model which take into account hdyrogen diffusion during vacuum solution treatment.

Multimodule imaging of the hierarchical equine hoof wall porosity and structure

The equine hoof wall has a complex, hierarchical structure that can inspire designs of impact-resistant materials. In this study, we utilized micro-computed tomography (μ-CT) and serial block-face scanning electron microscopy (SBF-SEM) to image the microstructure and nanostructure of the hoof wall. We quantified the morphology of tubular medullary cavities by measuring equivalent diameter, surface area, volume, and sphericity. High-resolution μ-CT revealed that tubules are partially or fully filled with tissue near the exterior surface and become progressively empty towards the inner part of the hoof wall. Thin bridges were detected within the medullary cavity, starting in the middle section of the hoof wall and increasing in density and thickness towards the inner part. Porosity was measured using three-dimensional (3D) μ-CT, two-dimensional (2D) μ-CT, and a helium pycnometer. The highest porosity was obtained using the helium pycnometer (8.07%), followed by 3D (3.47%) and 2D (2.98%) μ-CT. SBF-SEM captured the 3D structure of the hoof wall at the nanoscale, showing that the tubule wall is not solid, but has nano-sized pores, which explains the higher porosity obtained using the helium pycnometer. The results of this investigation provide morphological information on the hoof wall for the future development of hoof-inspired materials and offer a novel perspective on how various measurement methods can influence the quantification of porosity.

Quantification of porosity in composite plates using planar X-ray phase contrast imaging

The application of planar Edge-Illumination X-ray Phase-Contrast imaging (EI-XPCi) for the non-destructive quantification of porosity in carbon fiber reinforced polymer (CFRP) specimens, a significant concern in aerospace applications, was investigated. The method enables fast, planar (2D) scans providing access to large samples. A set of woven CFRP plates with porosity content ranging from 0.7% to 10.7% was examined. In addition to standard X-ray attenuation, EI-XPCi provides differential phase and dark-field signals, sensitive to inhomogeneities and interfaces at scales above and below the system spatial resolution, respectively. The correlation with the porosity content from matrix digestion obtained from the dark-field signal was comparable to that from ultrasonic attenuation. The novel analysis of the standard deviation of differential phase (STDP), sensitive to inhomogeneities above the system resolution (approximately 12 μm), resulted in a very high correlation (R2 = 0.995) with the matrix digestion porosity content, outperforming ultrasonic attenuation measurements.

Non-invasive optical characterization and estimation of Zn porosity in gas tungsten arc welding of Fe–Al joints using CR model and OES measurements

In this study, we employed a non-invasive approach based on the collisional radiative (CR) model and optical emission spectroscopy (OES) measurements for the characterization of gas tungsten arc welding (GTAW) discharge and quantification of Zn-induced porosity during the GTAW process of Fe–Al joints. The OES measurements were recorded as a function of weld current, welding speed, and input waveform. The OES measurements revealed significant line emissions from Zn-I in 460–640 nm and Ar-I in 680–800 nm wavelength ranges in all experimental settings. The OES coupled CR model approach for Zn-I line emission enabled the simultaneous determination of both essential discharge parameters i.e. electron temperature and electron density. Further, these predictions were used to estimate the Zn-induced porosity using OES-actinometry on Zn-I emission lines using Ar as actinometer gas. The OES-actinometry results were in good agreement with porosity data derived from an independent approach, i.e. x-ray radiography images. The current study shows that OES-based techniques can provide an efficient route for real-time monitoring of weld quality and estimate porosity during the GTAW process of dissimilar metal joints.

Porosity quantification in pear fruit with X-ray CT and spatially resolved spectroscopy

Hypoxia-related disorders in apple and pears such as browning have been associated with insufficient transport of respiratory gasses inside the fruit during controlled atmosphere (CA) storage. Tissue porosity affects gas exchange and, hence, fruit respiration. It might, therefore, be interesting to adapt the storage conditions to the porosity of the fruit, especially in the case of low-porosity fruit like pears, which are highly susceptible to browning disorders. To improve the current CA storage strategies in that direction, fast, non-destructive measurement of fruit porosity is required. To this end, X-ray CT-based porosity maps and spatially resolved spectroscopy (SRS) were used to characterize respectively the porosity and optical properties of intact pear cultivars (Pyrus communis L.), including ‘Thimo’, ‘Cepuna’, and ‘Conference’. Thereafter, the link between the fruit porosity and light-scattering properties was investigated. The porosity of the tissue region from the fruit surface down to 3 mm deep as well as the spatially averaged porosity of intact pears from 3 cultivars were found to be linearly correlated to the reduced scattering coefficient (µs’) at 760 nm and 835 nm calculated from SRS measurements. Both ‘Cepuna’ and ‘Conference’ pears showed similar porosities and scattering levels, which were slightly higher than those observed for ‘Thimo’ pears. The presented results suggest the possible application of laser scatter imaging at 760 nm and 835 nm for fast, contactless and non-destructive assessment of pear porosity.

A Novel, Image-Based Method for Characterization of the Porosity of Additively Manufactured Bone Scaffolds With Complex Microstructures

Abstract Bone tissue engineering has emerged as a promising strategy for the treatment of osseous fractures, defects, and ultimately diseases caused by, for example, bone tumor resection, accident trauma, and congenital malformation. Additive fabrication of stem cell-seeded, osteoconductive porous scaffolds has been an effective method in clinical practice for the treatment of bone pathologies (such as osteoporosis, osteoarthritis, and rheumatic diseases). Porosity is known to be one of the main morphological characteristics of bone tissues, which affects the functional performance of an implanted bone scaffold. Hence, in situ detection and quantification of scaffold porosity implemented to ensure functional integrity prior to implantation/surgery is an unavoidable need. The objective of this research work is to introduce a robust, image-based method for identification and subsequently characterization of the surface porosity and dimensional accuracy of additively manufactured bone tissue scaffolds, with a focus on pneumatic micro-extrusion (PME) process. It was observed that the presented method would be capable of detecting complex individual pores based on a micrograph. Using the proposed method, not only were scaffold pores detected, but also scaffold porosity was characterized on the basis of various defined quality metrics/traits (such as the relative standard deviation of distance to the nearest pore). The proposed method was validated by contrasting its performance in “surface pore detection” against that of a standard method, tested on a complex benchmark in four different simulated lighting environments. Besides, the performance of the method in terms of “pore filling” was compared to that of a standard method, tested on a real PME-fabricated bone scaffold. It was observed that the proposed method had a better performance in pore filling, detection, and consolidation. Overall, the outcomes of this work pave the way for high-resolution fabrication of patient-specific, structurally complex, and porous bone scaffolds with easily validatable, functional, and medical properties for the treatment of bone pathologies.

Quantitative study on surface porosity and roughness parameters of mineral and organic admixtures based on multi-scale characterisation techniques

Studying surface porosity and surface roughness of mineral and organic admixtures is vital in cement-based applications as these characteristics play a significant role in the behaviour of cement-based composites. Aiming to enhance the characteristics of coconut shell ash (CSA), gum Arabic (GA), cassava starch (CS), rice husk ash (RHA) and burnt clay powder (BCP) including their influence on cement-based composites, the surface porosity and surface roughness characteristics were comprehensively explored using scanning electron microscopy (SEM) under scale of 100 µm and Gwyddion. The surface morphological measurements, surface porosity and surface roughness of the specimens were respectively assessed using parameters such as skewness (Rsk), Kurtosis coefficient (Rku), roughness average (Ra), root mean square roughness (Rq), waviness average (Wa), root mean square waviness (Wq) and plane porosity. The roughness average values were noticed to range from 19 nm to 77 nm whilst the root mean square roughness values were found to vary from 27 nm to 107 nm. The waviness average and root mean square waviness values of specimens ranged from 26–132 nm and 34–157 nm respectively. The skewness values were observed to range from −0.18–0.72 while Kurtosis coefficient values ranged from 3 to 12. The comparisons in plane porosity quantification findings obtained using the implemented algorithms illustrated that the plane porosity values from threshold method were higher than those from segmentation method. The findings from this study extend characterisation of mineral and organic admixtures and subsequently guide management practices of waste materials through utilisation of mineral and organic admixtures in cement-based composites.

Prediction of nuclear magnetic resonance porosity well-logs in a carbonate reservoir using supervised machine learning models

Porosity estimation is a fundamental input for reservoir management and petrophysical characterization, and this feature is usually estimated based on laboratory measurements or through the use of well-logs. As an important resource for porosity quantification, nuclear magnetic resonance (NMR) well-logs are extremely useful; they allow geologists and petrophysicists to rapidly quantify different types of porosities (including total, effective, and free fluid porosity), and to perform a full formation evaluation and a reservoir quality analysis. However, the activation of wireline tools, the signal-to-noise ratio, the environmental conditions, and the characteristics of the formation fluid can create expensive and adverse conditions for subsurface acquisition. This research aims to develop machine learning models for the creation of synthetic NMR well-logs, assisted by auxiliary well-logging features. Four supervised models: multilayer perceptron neural network, AdaBoost, XGBoost, and CatBoost, comparing the adjusted R2 and RMSE. Of these, the CatBoost regressor provided the most highly optimized model. It was able to reduce local dissimilarities with the real dataset, and returned a better global metric score, yielding an adjusted R2 of 0.87 and an RMSE of less than 0.01. Moreover, all of the machine learning models provided substantial improvements in total porosity estimation, particularly compared to conventional empirical calculations based on density and sonic well-logs. An improvement of 0.5520 in the adjusted R2 was achieved for the density porosity, and 0.2 for the sonic porosity. The differences between real NMR well-logs and the machine learning outputs were in general less than 5%, for most of the well-logging interval. In addition, a tree boosted porosity model based on well-logs is presented for the first time, and the contributions and impacts of the input features on the model predictions are explored. Finally, the behaviors of the linear and nonlinear features of the model are examined, which allows us to better understand the complex relationships among the features and the dataset used.

Pore Extractor 2D: An ImageJ toolkit for quantifying cortical pore morphometry on histological bone images, with application to intraskeletal and regional patterning

AbstractObjectivesCortical porosity is used as a proxy of bone quality, fragility, and remodeling activity in anthropological contexts. Histological quantification is limited by time‐intensive manual annotation. Pore Extractor 2D is an ImageJ toolkit developed for computer‐assisted pore identification and automated pore morphometry.Materials and MethodsToolkit components include: (1) Utilities for cortical border clearing, (2) Image Pre‐Processing: Image contrast enhancement and noise reduction, (3) Pore Extractor: User‐directed options for segmenting, closing, and smoothing pore spaces, (4) Pore Modifier: Utilities to expedite manual correction of extracted pore spaces, and (5) Pore Analyzer: Morphometric analyses by pore type and anatomical region. Pore Extractor 2D was validated against manual annotation in a sample of midshaft mid‐thoracic human ribs (n = 30). Intraskeletal and regional analyses were piloted on matched (n = 9) human midshaft femora, tibiae, and sixth ribs.ResultsPore Extractor 2D was statistically consistent with manual annotation of bone area, percent porosity, pore density, and mean pore size. The toolkit significantly (p &lt; .05) reduced the smoothing effect of manual annotation by fitting pore borders to pixel brightness variations. Intraskeletal analyses found that the femur and tibia significantly exceeded the rib in “cortical” type porosity, while the rib predominantly formed “trabecularized” type porosity. Regional analyses determined that pore system expansion was elevated in the anterior femur, the anterior and medial tibia, and the cutaneous cortex of the rib.DiscussionThis toolkit provides expedited, semi‐automated porosity quantification that replicates manual annotation. Intraskeletal and regional variation in pore morphometry reflect localized strain patterning.

Hidden gaps under the canopy: LiDAR-based detection and quantification of porosity in tree belts

Tree belts (TBs) as one of the agroforestry types, provide many ecosystem services (ESs), that reduce the negative environmental impact of agriculture and increase agricultural productivity. Unfortunately, the lack of quantitative and spatial information regarding TB properties, such as the porosity expressed by gap fraction, often leads to insufficient information on the availability of ESs provided by TBs, such as windbreak efficiency, barrier effectiveness for soil erosion and ability to redistribute snow cover. Moreover, image data, which are usually used for the assessment of TB porosity, only provide 2D information. Therefore, we propose the first comprehensive method of TB porosity indicator estimation conducted by gap mapping using 3D LiDAR (Light Detection and Ranging) data. The method allows to diagnose the spatial structure of TBs necessary for reliable evaluation of ES availability. Detailed cross-section diagnostic graphs and TB silhouette maps showed the intra-TB variation of porosity and thus may support the decision making on the crop types and may lead to better predictions of agricultural production. Also, on a testing study area, we revealed that the proposed method is suitable to significantly improve the accuracy of vegetation volume estimation in TB. Such a detailed analysis of the inner TB structure may be used to maximize the positive effect of tree belts on agricultural production and therefore is a way towards the sustainable agriculture as well as better assessment of ES provided by tree belts.

Benefit of coconut-based hair oil via hair porosity quantification.

The present study is intended to characterize the surfactant damage suffered by the hair cortex in routine washing and the mechanistic effect of Coconut Based Hair Oils (CBHO) to mitigate the damage. Surfactants which diffuse into the hair structure solubilize protein moieties, leading to an increase in porosity and internal surface area as well as the pore volume. The changes in hair pores occurring in the hair cortex are measured by nitrogen sorption method in line with the Brunauer-Emmett-Teller (BET) theory. Single fiber tensile parameters were measured using Diastron MTT 175. Color protection was measured quantitatively using spectrophotometer as well as visual rating by trained panelists. The pore surface area data clearly show the benefit of introducing coconut-based hair oils (CBHO) into the hair by preventing increase in hair porosity. A statistically significant decrease in break stress and toughness were observed and the same were reversed by the application of CBHO. A pronounced color protection effect was also recorded with the application of CBHO. The porosity reduction effect seen with the use of CBHO is attributed to the CBHO molecules blocking the diffusion pathways in the endocuticle and the matrix part of the cortical cells, limiting protein surfactant interaction resulting in reduced solubilization and loss. Since, the color molecules are likely to be much smaller than the protein moieties, a pronounced color protection effect suggests that the penetrated CBHO molecules form a dense diffusion barrier in the matrix, cell membrane complex (CMC) and the endocuticle regions of hair - which are the main diffusion pathways out of hair. The study confirms the damage repair potential of CBHO and that it works by increasing the hydrophobicity of hair - both on the hair surface and in the cortex.

Post-acquisition mask misalignment correction for edge illumination x-ray phase contrast imaging.

Edge illumination x-ray phase contrast imaging uses a set of apertured masks to translate phase effects into variation of detected intensity. While the system is relatively robust against misalignment, mask movement during acquisition can lead to gradient artifacts. A method has been developed to correct the images by quantifying the misalignment post-acquisition and implementing correction maps to remove the gradient artifact. Images of a woven carbon fiber composite plate containing porosity were used as examples to demonstrate the image correction process. The gradient formed during image acquisition was removed without affecting the image quality, and results were subsequently used for quantification of porosity, indicating that the gradient correction did not affect the quantitative content of the images.

Hidden Gaps Under the Canopy: Lidar-Based Detection and Quantification of Porosity in Linear Agroforestry Systems

Hidden Gaps Under the Canopy: Lidar-Based Detection and Quantification of Porosity in Linear Agroforestry Systems