Calculation Of Porosity Research Articles

Fabrication of PVA/SiO2 (Nanofiber) Membranes Prepared Using Electrospinning Method for Lithium Battery Separator

Separators play an important role in ensuring the safety of Lithium-Ion Batteries (LIB). This study uses PVA and SiO2 as membrane separators with the aim of increasing membrane porosity, absorption of liquid electrolyte, ionic conductivity and reducing interface resistance, then using the electrospinning method with variations in the composition of PVA/SiO2 (0.5%), PVA/SiO2(1%), PVA/SiO2(1.5%) and PVA/SiO2(2%). The FTIR analysis results contained-OH groups in the PVA material, while bond formation between PVA-SiO2 was indicated by the presence of Si-O-C peaks. The analysis results of the SEM showed that the mean nanofiber diameters for the PVA and PVA-SiO2 membranes (1%, 2%) were 259±18 nm, 227±16 nm, and 200±11 nm. The results of the calculation of porosity and electrolyte absorption: the higher the SiO2 concentration, the higher the porosity and electrolyte absorption value on the membrane. Therefore, the results of the membrane wettability analysis contain PVA and PVA-SiO2 to almost 0° electrolyte. We can say that the membrane is hydrophilic. Electrical Conductivity and Impedance (EIS) are interconnected, and according to theory, the higher the SiO2 concentration, the higher the electrical conductivity value and the lower the interfacial resistance. Based on the above criteria, this material can be classified as a good separator material for Li-ion batteries.

ANALISIS TINGKAT POROSITAS BATUPASIR KARBONATAN KEREK SEBAGAI RESERVOIR DI DAERAH SIDOLAJU DAN SEKITARNYA KECAMATAN WIDODAREN KABUPATEN NGAWI PROVINSI JAWA TIMUR

The research area is located in Sidolaju and its surrounding, Widodaren District, Ngawi Regency, East Java Province. Distribution of Kerek’s carbonated sandstone in the study area is the focus of this research, where the Kerek’s carbonated sandstone is not yet known for its porosity ability to become a reservoar. In general Kerek’s Formation arranged by clastic deep sea sedimentary rock that layered and has a good potential become reservoar. The purpose of this research was to determine the porosity level of Kerek’s carbonated sandstone as a reservoar. The research method used in surface geological mapping, taking 3 samples of Kerek’s carbonated sandstone, laboratory analysis, studio analysis and knowing the reservoar potential based on the level of rock porosity according to Koesoemadinata, 1980. The results of the calculation of porosity obtained values ranging from 15%-28% and classified as good-special. Sample number I has porosity percentage of 15.571%, sample number II has porosity percentage of 23.974% and sample number III has porosity percentage of 28.108%. The difference in the percentage of porosity is caused by secondary factors such as loading, cementation and fracture so as to increase and decrease the percentage of rock porosity.

Comparison of 2D and 3D Plasma Electrolytic Oxidation (PEO)-Based Coating Porosity Data Obtained by X-ray Tomography Rendering and a Classical Metallographic Approach.

In this work, the porosity of plasma electrolytic oxidation (PEO)-based coatings on Al- and Mg-based substrates was studied by two imaging techniques—namely, SEM and computer microtomography. Two approaches for porosity determination were chosen; relatively simple and fast SEM surface and cross-sectional imaging was compared with X-ray micro computed tomography (microCT) rendering. Differences between 2D and 3D porosity were demonstrated and explained. A more compact PEO coating was found on the Al substrate, with a lower porosity compared to Mg substrates under the same processing parameters. Furthermore, huge pore clusters were detected with microCT. Overall, 2D surface porosity calculations did not show sufficient accuracy for them to become the recommended method for the exact evaluation of the porosity of PEO coatings; microCT is a more appropriate method for porosity evaluation compared to SEM imaging. Moreover, the advantage of 3D microCT images clearly lies in the detection of closed and open porosity, which are important for coating properties.

Novel preparation of lithium‐ion battery wet‐processed separator based on the synergistic effect of porous skeleton nano‐Al2O3in situ blending and synchro‐draw

AbstractRoutine lithium‐ion battery separators with uneven micropores and poor electrolyte affinity raise ion transport barriers and become the battery‐performance‐limiting factors. A wet‐processed separator with homogeneous porous structure and porous skeleton nano‐Al2O3 in situ blending is readily prepared by thermally induced phase separation of paraffin, nano‐Al2O3 and ultra‐high molecular weight polyethylene (UHMWPE) in this work. SEM, ImageJ statistical analysis, porosity and Gurley calculation show that a separator that has undergone asynchronous drawing exhibits ample sturdy fibrils, heterogeneous pore size dispersion, poor permeability and strong anisotropy. However, UHMWPE deforms much more uniformly under a synchro‐draw, which distinctly lessens coarse fibrils, centralizes porous construction after stretching and brings better isotropy for the separator. Additionally, nano‐Al2O3 scattered in the cast film further weakens the heterogeneity of micropores stemming from the uneven thermally induced phase separation. Wettability tests and thermal diagnoses also show that nano‐Al2O3 on the porous skeleton strengthens the thermal stability and electrolyte affinity of the separator. Consequently, batteries containing nano‐Al2O3 composite separators show much higher electrochemical stability, ionic conductivity and Li+ transport number because of the synergistic effect of the even microvoids and nano‐Al2O3 on the porous skeleton, which expedites Li+ transport and endows superior lithium‐ion battery performance. © 2022 Society of Industrial Chemistry.

The feasibility study of XACT imaging for characterizing osteoporosis.

Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and the deterioration in bone microarchitecture, which might be related to age and space travel. An unmet need exists for the development of novel imaging technologies to characterize osteoporosis. The purpose of our study is to investigate the feasibility of X-ray-induced acoustic computed tomography (XACT) imaging for osteoporosis detection. An in-house simulation workflow was developed to assess the ability of XACT for osteoporosis detection. To evaluate this simulation workflow, a three-dimensional digital bone phantom for XACT imaging was created by a series of two-dimensional micro-computed tomography (micro-CT) slices of normal and osteoporotic bones in mice. In XACT imaging, the initial acoustic pressure rise caused by the X-ray induce acoustic (XA) effect is proportional to bone density. First, region growing was deployed for image segmentation of different materials inside the bone. Then k-wave simulations were deployed to model XA wave propagation, attenuation, and detection. Finally, the time-varying pressure signals detected at each transducer location were used to reconstruct the XACT image with a time-reversal reconstruction algorithm. Through the simulated XACT images, cortical porosity has been calculated, and XA signal spectra slopes have been analyzed for the detection of osteoporosis. The results have demonstrated that osteoporotic bones have lower bone mineral density and higher spectra slopes. These findings from XACT images were in good agreement with porosity calculation from micro-CT images. This work explores the feasibility of using XACT imaging as a new imaging tool for Osteoporosis detection. Considering that acoustic signals are generated by X-ray absorption, XACT imaging can be combined with traditional X-ray imaging that holds potential for clinical management of osteoporosis and other bone diseases.

Paw-Net: Stacking ensemble deep learning for segmenting scanning electron microscopy images of fine-grained shale samples

Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to differentiate the boundaries for different mineral objects to facilitate subsequent analyses, such as porosity calculation. Recently, various machine learning methods, especially convolutional neural networks (CNNs), have been explored to segment SEM images of fine-grained shale samples. However, we found that general CNNs do not yield optimal performance due to insufficient training data and imbalanced objects in SEM images. This work has revised the U-Net architecture, a popular approach for biomedical image analyses, by incorporating a loss function that addresses the imbalance issue. Furthermore, we used the ensemble learning method to train multiple models and combined the results to improve the overall performance of segmentation. We prepared 2162 sub-images from raw SEM images in our experiments and divided them into training, validation, and testing datasets. The overall results show that our method improves the average Intersection over Union (IOU) of mineral objects from 0.49 to 0.58, compared to the original U-Net model. Our method can clearly distinguish each object from others with boundaries, even in highly imbalanced images. Training our models takes less than 3 mins using a single GPU, while manual labeling can take up to 3 hrs for each image. Therefore, the method helps geoscientists gain insights quickly and effectively by building neural network models from a small dataset of SEM images.

Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution (GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory (LSTM) combined with the Discrete Element Method (DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in three-dimensional (3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.

Effect of the heating rate on the thermal explosion behavior and oxidation resistance of 3D-structure porous NiAl intermetallic

Porous NiAl intermetallic compounds demonstrate great potential in various applications by their high porosity and excellent oxidation resistance. However, to obtain a controllable NiAl intermetallic structure by tuning different process parameters remains unclear. In this work, porous NiAl intermetallic compounds were fabricated by economic and energy-saving thermal explosion (TE) reaction. The relationship between microstructure and process parameters was revealed using three-dimensional X-ray microscopy (3D-XRM) with high resolution and non-destructive characteristics. The geometrical features and quantitative statistics of the porous NiAl obtained at different heating rates (2, 10, 20 °C min−1) were compared. The result of the closed porosity calculation showed that a lower heating rate (2 °C min−1) promoted the Kirkendall reaction between Ni and Al, resulting in a high closed porosity (5.25%). However, at a higher heating rate (20 °C min−1), a homogeneous NiAl phase was observed using the threshold segmentation method, indicating uniform and complete TE reaction can be achieved at a high heating rate. The result of the 3D fluid simulation showed that the sample heated at 10 °C min−1 had the highest permeability (2434.6 md). In this study, we systematically investigated the relationship between the heating rates and properties of the porous NiAl intermetallic, including the phase composition, porosity, exothermic mechanism, oxidation resistance, and compression resistance. Our work provides constructive directions for designing and tailoring the performance of porous NiAl intermetallic compounds.

Simulation of intra-saccular devices for pre-operative device size selection: Method and validation for sizing and porosity simulation

Intra-saccular devices (ID) are novel braided devices used for complex intracranial aneurysms treatment. Treatment success is associated with correct device size selection. A technique that predicts the ID size within the aneurysm before intervention will provide a powerful computational tool to aid the interventionist during device selection. We present a method to calculate the device’s final height, radial expansion and porosity within the patient’s anatomy, which allows assessing different device sizes before treatment takes place. The proposed sizing technique was tested in-vitro and in real patient’s geometries obtained from 3DRA angiographic images of 8 unruptured aneurysms previously treated with IDs. The obtained simulated height was compared to the real height, with a mean error of less than 0.28 mm (±0.44). The porosity calculation method was tested in-vitro with an error of 0.02 (±0.022). The results of both sizing and porosity experiments resemble well measures from real patients. This methodology could be used before treatment to provide the interventionist with additional information that allows selecting the device that best fits the patient’s aneurysm to be treated.

Three-dimensional modeling and porosity calculation of coal rock pore structure

Three-dimensional modeling and porosity calculation of coal rock pore structure

Characterization of optimal healing efficiency based on a new nonlinear ultrasonic method for the process of gradient heating healing in asphalt mixture

In this research, we proposed a new nonlinear ultrasonic method, second harmonic generation (SHG) technique, to investigate the gradient heating-healing properties and determine the optimal healing efficiency in asphalt mixture based on the principle of ultrasonic Rayleigh surface wave. The energy approach is used to characterize the released energy based on the laws of Newton's convection. The released energy can be used to determine the optimum healing period according to law of conservation of energy. Three healing indices, absorbed energy, effective healing depth and nonlinear ultrasonic parameter, are respectively defined to characterize the healing efficiency of asphalt mixture for different healing periods. The healing gradient direction is verified by the X-ray computed tomography (CT) scanning images and corresponding porosity calculation. The results indicate the existence of an optimal healing period and healing efficiency in the healing process of asphalt mixture, and the calculation method proposed here is suitable to predict the optimal healing efficiency. While the nonlinear parameter is considerably prominent due to its sensitivity and high resolution for each healing period. Finally, through the correlation analysis of three healing indices, it is found that the nonlinear ultrasonic parameter is an ideal alternative of two other healing indices in terms of characterizing the gradient heating-healing properties of asphalt mixture specimens.

Spherical nanoparticles can be used as non-penetrating tracers to determine the extra-particle void volume in packed-bed chromatography columns

The optimization of downstream processing in silico can accelerate bioprocess development by limiting experiments to the most promising separation conditions that have been identified using chromatography models. Such models describe protein binding and mass transport in packed-bed columns and thus require precise knowledge about the columns and the resins they contain. One important set of properties is the resin porosities, often determined using combinations of penetrating and non-penetrating tracers. However, the former can be disproportionately small, providing data of limited practical relevance, and the latter can undergo unwanted interactions with the resin, interfering with porosity calculations. Here we characterize and minimize the interactions of three novel hard-sphere non-penetrating tracers with the model resin Q Sepharose HP under various conditions and determine the corresponding inter-particle porosities. We found that conductivities > 100 mS cm−1 were necessary to suppress tracer–resin interactions despite them sharing the same surface charge. We combined these data with those from proteins studied under non-binding conditions, which can be used as authentic penetrating tracers, to determine both the intra-particle and total porosities. Furthermore, we found that the inter-particle porosity was below the theoretical limit of dense sphere packing (25.95%) and provide experimental data showing that the discrepancy is caused by resin particle deformation during the packing of columns under pressure.

Effect of Recycled Aggregate Content on Water Permeability and Pore Structure of Pervious Concrete Pavement

The shortage of natural aggregate (NA) and a large amount of construction waste generation bring opportunities for the application of recycled aggregate (RA). To investigate the mechanical properties and permeability of Recycled Aggregate Pervious Concrete (RAPC), this study tested the compressive strength, tensile strength, flexural strength, porosity, permeability coefficient, and water retention of RAPC with different RA contents (0, 25%, 50%, 75%, and 100%) and analyzed the changes of these parameters with RA content. Among them, two methods were used for the calculation of porosity. The results showed that the porosity, permeability, and water retention of RAPC increased with the increase in RA content, but the compressive strength, splitting tensile strength, flexural strength, and uniaxial tensile strength decreased. According to the demand of pavement engineering, RAPC should have good water permeability while meeting the strength requirements, so the optimal RA content is 50%.

Experimental Study on Microstructure Evolution and Fractal Features of Expansive Soil Improved by MICP Method

Experimental study on one-dimensional consolidation and scanning electron microscope imaging of expansive soil improved by MICP method has been carried out, by using WG type consolidator and electron scanning microscope. Theoretical analysis on microstructure evolution process of improved expansive soil has been carried out based on fractal theory and damage theory. Through the research, the influence mechanism of cementation and filling effect of calcium carbonate precipitation on the microstructure of improved soil samples such as particle size and pore characteristics is revealed. Based on fractal theory, a porosity calculation model of improved expansive soil has been established considering microstructure damage of soil. Furthermore, a fractal calculation theory of consolidation deformation of improved expansive soil has been proposed. The relevant calculation parameters have also been determined. The rationality of this calculation theory is verified by comparing the calculated results with the tested results. With these research results, a theoretical foundation for further research on microstructure evolution of expansive soil improved by MICP method has been laid. A new train of thought for quantitative research on the water stability and swell–shrink characteristics as well as strength characteristics of improved expansive soil has been provided.

Towards Predicting the Sequential Appearance of Zeolitic Imidazolate Frameworks Synthesized by Mechanochemistry.

We use computational materials methods to study the sequential appearance of zinc-based zeolitic imidazolate frameworks (ZIFs) generated in the mechanochemical conversion process. We consider nine ZIF topologies, namely RHO, ANA, QTZ, SOD, KAT, DIA, NEB, CAG and GIS, combined with the two ligands 2-methylimidazolate and 2-ethylimidazolate. Of the 18 combinations obtained, only six (three for each ligand) were actually observed during the mechanosynthesis process. Energy and porosity calculations based on density functional theory, in combination with the Ostwald rule of stages, were found to be insufficient to distinguish the experimentally observed ZIFs. We then show, using classical molecular dynamics, that only ZIFs withstanding quasi-hydrostatic pressure P ≥ 0.3 GPa without being destroyed were observed in the laboratory. This finding, along with the requirement that successive ZIFs be generated with decreasing porosity and/or energy, provides heuristic rules for predicting the sequences of mechanically generated ZIFs for the two ligands considered.

The Effect of Different Modifying Methods on Physical, Mechanical and Thermal Performance of Cellular Geopolymers as Thermal Insulation Materials for Building Structures

Geopolymers represent a new class of inorganic materials that have great potential for practical application due to the properties of used raw materials, as well as the peculiarities of the cementitious matrix structure formed during the geopolymerization process. Cellular geopolymer specimens were produced in this study using class F fly ash product, which is characterized by low reactivity during geopolymerization. Several standard methods, as well as microstructural studies were applied to evaluate the effect of the following factors on the physical-mechanical and thermophysical characteristics of cellular geopolymers: the use of various mineral modifying components for synthesis of geopolymer systems; high-temperature treatment; the introduction method of alkaline activator. It was observed that “ageing” an aqueous alkali solution for 24 h before mixing with fly ash and foam agent was able to provide a boost of compressive strength of cellular geopolymer specimens up to about 2.5 times, while decreasing the average density by about 28% for all experimental mixes, except for PC-modified mixes. Additionally, high-temperature treatment at 600 °C enables an enhanced strengthening effect of pore structure in cellular geopolymer matrix up to 1.5 times. This phenomenon is especially pronounced for the mixes with 24 h “aged” alkaline solution with exception for PC-modified mixes; for those, high-temperature treatment at 600 °C leads to strength decrease up to 40%. The introduction method of alkaline activator and high-temperature treatment showed a controversial effect on thermal conductivity coefficient depending on the mineral modifying component used for the synthesis of cellular geopolymers. The proposed method for calculation of total porosity of cellular structure of geopolymers as a polycomponent material demonstrated a high degree of correlation with the R2 value of at least 0.96 between the average density and the calculated total porosity. However, a low degree of correlation with R2 not exceeding 0.29 was observed for the measured nanoporosity, regardless of the introduction method of alkaline activator and high-temperature treatment.

Application of unlabelled big data and deep semi-supervised learning to significantly improve the logging interpretation accuracy for deep-sea gas hydrate-bearing sediment reservoirs

Due to the extremely complex reservoirs and strong heterogeneity, deep-sea gas hydrate logging porosity calculations still have problems, which further leads to insufficient resource calculation accuracy. Logging reservoir evaluation methods based on intelligence may be able to provide more reliable prediction results, especially the logging evaluation model based on deep learning with great potential. This paper proposes a new method to form unlabelled logging big data, and based on this, establishes a semi-supervised deep learning method suitable for deep-sea gas hydrate-bearing sediments porosity calculation, forming a porosity evaluation model. The method of forming logging big data expands 380 original data samples into 2280 labelled samples and 60050 unlabelled samples, which reduces the sampling requirements for deep-sea sediment formations with high sampling costs. The evaluation results show that the model not only obtains better results than other methods in the inspection wells corresponding to the areas where the training wells are located, but also obtains very good results in other wells that are not involved in the modelling. Compared with traditional prediction methods, the average relative error of porosity prediction is less than 4%. As far as we know, this is the first time that deep learning has been successfully applied to deep-sea hydrate sediment reservoir logging evaluation. It provides a new idea for intelligent logging evaluation of deep-sea hydrate sediment reservoirs.

COMPARATIVE STUDY OF POROSITY DETERMINATION METHODS FOR OGIP IN FRACTURED BASEMENT RESERVOIR

Determination of porosity in fractured basement reservoirs has always been a challenge due to thecomplexity of processes involved in the generating of pore structure as well as the rock heterogeneity. As aresult, estimate of original gas in place (OGIP) is subject to substantial uncertainty. Intention of this studyis to evaluate the most effective method to determine fracture porosity and hence reducing uncertainty ofOGIP volume in the fractured basement reservoir. The evaluation is based on comparison to the core derivedporosity as the point of reference. Included in this evaluation are the techniques of secondary porosity index(SPI), dipole share imager (DSI), dual laterolog (DL), and formation micro imager (FMI). The SPI andDSI logs derived fracture porosities are found over optimistic to the core reference. The FMI determinedfracture porosities are considered in fair agreement with core data. Results from the DL technique comparevery favorably with core data and thought to be the best calculation of porosity in the fractured basementgas reservoir investigated in this study. Those results supported by data that have been collected from thepublished literatures. Found that typical value of fracture porosities in the fractured basement rocks isless than 1%. The comparative study presented here helps in reducing uncertainty related to the fracturedbasement reservoir development.

Recent Progress in Biopolymer-Based Hydrogel Materials for Biomedical Applications.

Hydrogels from biopolymers are readily synthesized, can possess various characteristics for different applications, and have been widely used in biomedicine to help with patient treatments and outcomes. Polysaccharides, polypeptides, and nucleic acids can be produced into hydrogels, each for unique purposes depending on their qualities. Examples of polypeptide hydrogels include collagen, gelatin, and elastin, and polysaccharide hydrogels include alginate, cellulose, and glycosaminoglycan. Many different theories have been formulated to research hydrogels, which include Flory-Rehner theory, Rubber Elasticity Theory, and the calculation of porosity and pore size. All these theories take into consideration enthalpy, entropy, and other thermodynamic variables so that the structure and pore sizes of hydrogels can be formulated. Hydrogels can be fabricated in a straightforward process using a homogeneous mixture of different chemicals, depending on the intended purpose of the gel. Different types of hydrogels exist which include pH-sensitive gels, thermogels, electro-sensitive gels, and light-sensitive gels and each has its unique biomedical applications including structural capabilities, regenerative repair, or drug delivery. Major biopolymer-based hydrogels used for cell delivery include encapsulated skeletal muscle cells, osteochondral muscle cells, and stem cells being delivered to desired locations for tissue regeneration. Some examples of hydrogels used for drug and biomolecule delivery include insulin encapsulated hydrogels and hydrogels that encompass cancer drugs for desired controlled release. This review summarizes these newly developed biopolymer-based hydrogel materials that have been mainly made since 2015 and have shown to work and present more avenues for advanced medical applications.

The effect of polyvinylpirrolidone on the performance of polyvinylidene fluoride membranes

In this investigation, polyvinylidene fluoride membranes were resulted by a phase inversion technique with polyvinylpyrrolidone (PVP) as an agent to form pores, as well as n-methyl pyrrolidone as a solvent. In addition, the effect of PVP concentration (1-4%) was investigated to prepare membranes with better membrane antifouling performance and characteristics. Furthermore, functional groups, morphological structures, and membrane porosity were analysed by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and membrane porosity calculation. The surface SEM images revealed that the size of the modified membrane pores increased. The increase of the PVP concentration added, resulted in the number of modified membrane pores. FTIR spectra confirmed that PVP functional groups were dispersed in the PVDF membrane matrix. Optimum pure water permeability (PWP) of 60 L/(m2?h?bar) was achieved using 3% PVP, resulting in a humic acid rejection percentage of 80% and a water flux recovery ratio (FRR) of 85%. These findings indicate that the utilization of PVP as a pre-forming agent resulted in higher PWP, lower humic acid rejection, and good antifouling properties.