Characterization Of Porous Materials Research Articles

Porous material characterization with Bayesian and machine learning method

Two main classes of characterization methods are usually employed for acoustic parameters of porous media, namely the analytical inversion method or the numerical fitting procedure. While analytical inverse methods suffer from a high sensivity to the operator, the numerical fitting methods can lead to non physical sets of parameters. The fitting procedure can be bounded by the analytical inversion. The Bayesian method, which is presented in this work, is the appropriate tool to couple these methods. It enables to carry out a numerical fitting procedure using the analytical inversion as prior estimation while increasing the confidence in the resulting uncertainties. In a second part, machine learning method and data mining are used as an alternative characterization method which could be a good prior estimation for the Bayesian method. Results and performances will be shown for various porous media.

3D microstructure reconstruction and characterization of porous materials using a cross-sectional SEM image and deep learning

Accurate assessment of the three-dimensional (3D) pore characteristics within porous materials and devices holds significant importance. Compared to high-cost experimental approaches, this study introduces an alternative method: utilizing a generative adversarial network (GAN) to reconstruct a 3D pore microstructure. Unlike some existing GAN models that require 3D images as training data, the proposed model only requires a single cross-sectional image for 3D reconstruction. Using porous ceramic electrode materials as a case study, a comparison between the GAN-generated microstructures and those reconstructed through focused ion beam-scanning electron microscopy (FIB-SEM) reveals promising consistency. The GAN-based reconstruction technique demonstrates its effectiveness by successfully characterizing pore attributes in porous ceramics, with measurements of porosity, pore size, and tortuosity factor exhibiting notable agreement with the results obtained from mercury intrusion porosimetry.

Synthesis and Characterization of Porous Materials from Waste Wheat Bran

The purpose of this study was to investigate how the amount of ZnCl2 and temperature affect the process of converting waste wheat bran, known for its hemicellulose struc-ture, into porous material. The characterization of the wheat bran was done using proximate and primary component analysis, and Thermogravimetric analysis (TG) test, Fourier Transform Infrared Spectroscopy (FT-IR) spectra, Energy-dispersive X-ray spectroscopy (EDS) results, and Scanning electron microscopy (SEM) images. The influence of temperature on the surface areas of activated carbons is more significant than the impact of varying the amount of ZnCl2. When the carbonization temperature reached 500 °C, porous structures developed, and the highest surface areas achieved for all impregnation ratios (1:1, 2:1, and 3:1) were 1234, 1478, and 1422 m2/g, respectively. Activated carbon was found to have acidic (0.88 mmol/g) and basic (0.54 mmol/g) functional groups on its surface, after being synthesized through carbonization at 500 °C using ZnCl2 at a 2:1 impregnation ratio in accordance with Boehm titration. This promising activared carbon made from wheat bran, activated by ZnCl2, is efficient and environmentally friendly, and it is a potential solution for water pollution treatment.

Gas Sorption Characterization of Porous Materials Employing a Statistical Theory for Bethe Lattices.

In the present work, a recently developed statistical theory for adsorption and desorption processes in mesoporous solids, modeled by random Bethe lattices, has been applied to obtain pore size distributions and interpore connectivity from sorption isotherms in real random porous materials, employing a robust and validated methodology. Using the experimental adsorption-desorption N2 isotherms at 77.4 K on Vycor glass, a porous material with random pore structure, we demonstrate the solution of the inverse problem resulting in extracted pore size distribution and interpore connectivity, notably different from the predictions of earlier theories. The results presented are corroborated by the analysis of 3D digital images of reconstructed Vycor porous glass, showing excellent agreement between the predictions of geometric analysis and the new statistical theory.

Co-Adsorption of Alcohols and Water in JUK-8 Studied Using Quasi-Equilibrated Thermodesorption.

JUK-8 ([Zn(oba)(pip)]n, oba2- = 4,4'-oxybis(benzenedicarboxylate), pip = 4-pyridyl-functionalized benzene-1,3-dicarbohydrazide) is a hydrolytically stable flexible metal-organic framework. Owing to its unusual adsorptive properties, JUK-8 can be considered as a promising sensing material for construction of detectors of volatile organic compounds (VOCs) in air. Quasi-equilibrated temperature-programmed desorption and adsorption (QE-TPDA) is a versatile method dedicated to characterization of porous materials. In this work, QE-TPDA was employed to study co-adsorption of water and selected alcohols in JUK-8. For the first time an infrared detector sensitive to organic compounds was used in the QE-TPDA measurements, allowing the study of the influence of water vapor on sorption of VOCs. The QE-TPDA profiles of the studied alcohols, exhibiting two desorption maxima and two adsorption minima, are consistent with the standard sorption isotherms, revealing a two-step adsorption-desorption mechanism. The profiles recorded in the presence of water are noticeably changed in different ways for different alcohols. While at low relative humidity (RH) (ca. 20%) the low temperature adsorption states of ethanol and 1-propanol were only slightly destabilized, for 2-propanol almost complete suppression of adsorption was observed. The results found for moderate RH levels (ca. 50%) indicated that the opening of the JUK-8 structure, responsible for its breathing behavior, was followed by the filling of the just generated pores with a water-alcohol mixture.

On the use of neural networks for the structural characterization of polymeric porous materials

The structural characterization is an essential task in the study of porous materials. To achieve reliable results, it requires to evaluate images with hundreds of pores. Current methods require large time amounts and are subjected to human errors and subjectivity. A completely automatic tool would not only speed up the process but also enhance its reliability and reproducibility. Therefore, the main objective of this article is the study of a deep-learning-based technique for the structural characterization of porous materials, through the use of a convolutional neural network. Several fine-tuned Mask R–CNN models are evaluated using different training configurations in four separate datasets each composed of numerous SEM images of diverse polymeric porous materials: closed-pore extruded polystyrene (XPS), polyurethane (PU), and poly(methyl methacrylate) (PMMA), and open-pore PU. Results prove the tool capable of providing very accurate results, equivalent to those achieved by time-consuming manual methods, in a matter of seconds.

Non-Contact and Self-Calibrated Photopyroelectric Method for Complete Thermal Characterization of Porous Materials.

A general theory of a photopyroelectric (PPE) configuration, based on an opaque sample and transparent pyroelectric sensor, backing and coupling fluids is developed. A combined back-front detection investigation, based on a frequency scan of the phase of the PPE signals, followed by a self-normalization of the phases' behavior, leads to the possibility of simultaneously measuring both thermal effusivity and diffusivity of a solid sample. A particular case of this configuration, with no coupling fluid at the sample/backing interface and air instead of coupling fluid at the sample/sensor interface (non-contact method) is suitable for simultaneous measurement ofboth thermal diffusivity and effusivity (in fact complete thermal characterization) of porous solids. Compared with the already proposed configurations for investigations of porous materials, this novel configuration makes use of a fitting procedure with only one fitting parameter, in order to guarantee the uniqueness of the solution. The porous solids belong to a class of materials which are by far not easy to be investigated using PPE. To the best of our knowledge, porous materials represent the only type of compounds, belonging to condensed matter, which were not taken into consideration (until recently) as potential samples for PPE calorimetric investigations. Consequently, the method proposed in this paper complete the area of applications of the PPE method. Applications on some porous building materials and cellulose-based samples validate the theory.

John - Sivanandan Achari Isotherm Method for the Determination of Surface Area of Porous Materials: Analysis of Granular Activated Carbons

John –Sivanandan Achari isotherm equationlog log Ce= C+n log qe,has many advantages for the structural characterization of porous materials. Linear plot of log logCe versus log qe conform to a straight line and subsequent extrapolation of amount adsorbed (qe), to the xaxis. This way one can directly determine the monolayerqm(J-SA) and hence the specific surface area (SA m2 /g) of the material.This can be analytically proved by applying the method in any type of solid-liquid equilibrium adsorption data. In this study, adsorption of methylene blue (MB) dye molecules on modified granular activated carbon (GAC) has been attempted to determine the porosity, and surface area by the new method. The solids are GAC 383 (native carbon- coconut shell based), GACO383 (native carbon oxidized) and a new carbon (GACOLaO1273). This carbon is prepared in the laboratory by steam activation after incorporating GAC383with Lanthanum(La 3+) ions. Carbons are tested in batch reactors with fixed MB concentrations by adsorptive techniques. The isotherm model behavior of the carbons are hereby reported using John - Sivanandan Achari isotherm (J-SA) equation using specific adsorption data. Porous material category as IUPAC type, constants and parameters are further evaluated with Langmuir, Freundlich, Dubinin - Radushkevich isotherms. It is seen that the isotherm followed the features of micro porous materials on using John – Sivanandan Achari (J-SA) isotherm. Porosity and surface area determined are found to give agreeing results on comparing with other isotherm models. This research is intended to highlight the significance of John and John-Sivanandan Achari (J-SA) isotherms as a tool for material characterization studies- a model originated as Indian analytical scientific skill.

Reliable Surface Area Assessment of Wet and Dry Nonporous and Nanoporous Particles: Nuclear Magnetic Resonance Relaxometry and Gas Physisorption

The reliable assessment of surface area is extremely important for many applications, e.g., catalysis, separation, and energy storage/conversion. Within this context, major progress has been made concerning the textural characterization of porous materials in the gas/dry state, e.g., gas physisorption and mercury porosimetry. However, these methods are not sufficient for the characterization of wet materials utilized in liquid-phase processes. For this, the application of nuclear magnetic resonance (NMR) relaxometry has been considered, but a systematic and rigorous assessment of the applicability of NMR relaxometry for reliable surface and pore size characterization of nanoporous materials is missing. Hence, we present a systematic study in which we assess the applicability of NMR relaxometry for reliable surface area assessment utilizing for the first time true surface area benchmark data based on argon 87 K adsorption on nonporous particles (silica and carbon black) coupled with the development of an advanced methodology including the investigation of the choice of the probe molecule and the effect of its accessibility to the pore network. Our results show that the method provides a fast (a few minutes per measurement) and reliable surface area of silica and carbon black model materials immersed in a liquid phase. In addition, our work clearly demonstrates the potential of NMR relaxometry for the targeted surface area assessment of defined pore classes (here ultramicropores) and suggests a new methodology for the characterization of pore entrances (pore window size). Furthermore, we investigate the effect of wettability and suggest that NMR relaxometry could be developed into a unique tool for assessing the wetting characteristics of adsorbate phases on pore surfaces. This fundamental study can be considered a first major step in enabling NMR relaxometry for reliable surface area assessment for wet materials, particularly relevant for materials used in processes occurring in a liquid phase.

Study of the correlation between magnetic susceptibility and NMR relaxation using T2-filtered and T1-weighted CPMG

NMR is widely applied in Materials Science for the characterization of porous materials. The advantage of this technique is the high correlation between the NMR measurements and the physical-chemical features of the material. Magnetic susceptibility is one of many other properties that have been investigated by time domain NMR. In this work, the study of the influence of magnetic susceptibility on the relaxation process of micro and macroporosity is carried out, which is obtained using the T2-filtered and T1-weighted techniques combined with the CPMG experiment. This approach seeks to simplify the problem by treating microporosity and macro porosity conditions separately. The obtained data show the strong influence of the magnetic susceptibility on the relaxation times as expected, but showing more influence for the shorter ones, related to small pores.

Test-bench for the experimental characterization of porous material used in thermoacoustic refrigerators.

The design of thermoacoustic coolers involves an adequate modeling of the thermoacoustic core's performance, which requires, in particular, a precise knowledge of their thermo-physical properties. Materials such as wire mesh stacks, foams, or compressed fibrous media are hard to describe, and their thermo-physical properties are rarely well enough quantified. Moreover, the classical linear thermoacoustic theory is not sufficient to accurately describe the performance of these materials. This paper deals with the experimental performance characterization of various materials for thermoacoustic heat pumping. A dedicated experimental test-bench has been specially developed, which is composed of two loudspeakers placed at opposite ends of a waveguide containing the porous material and a feedback loop to control the acoustic field in the porous material. Its originality is attributable to the possibility of identifying the optimal acoustic field, specific to each material, that maximizes the temperature difference at the ends of the material. Moreover, a specific protocol is implemented to access and compare the thermoacoustic heat flux through various materials at these optimal acoustic fields. Comparison of the experimental and theoretical results shows a reasonable agreement.

Preparation and Characterization of Porous Materials from Pineapple Peel at Elevated Pyrolysis Temperatures.

In this work, pineapple peel (PP) was reused as a precursor in biochar (BC) production at elevated temperatures (i.e., 500–900 °C) for residence times of 0–60 min. The findings showed that pyrolysis temperature and residence time played a vital role in pore development. As pyrolysis temperature increased from 800 to 900 °C for residence times of 20 and 60 min, the data on the Brunauer–Emmett–Teller (BET) surface area of the resulting biochar products significantly jumped from 11.98–32.34 to 119.43–133.40 m2/g. In addition, there was a significant increase in the BET surface area from 1.02 to 133.40 m2/g with the residence time of 0 to 20 min at 900 °C. From the data of the nitrogen adsorption–desorption isotherms and the pore size distribution, both micropores (pore diameters of <2.0 nm) and mesopores (pore diameters of 2.0–50.0 nm) are present in the PP-based biochar products. Due to its good fittings in the pseudo-second-order model and its hydrophilic nature, as seen in the Fourier transform infrared spectroscopy (FTIR), the resulting biochar could be a porous material to be used for the effective removal of cationic compounds (i.e., methylene blue (MB)) from liquid phases.

Physical parameters influence on the transmitted signal through a rigid porous medium in the low-frequency range of ultrasound

The acoustic characterization of porous materials with rigid structure plays an important role in the prediction of the acoustic behavior of these media. In order to obtain an accurate prediction of its response as a function of frequency, several parametric models have been proposed. However, semi-phenomenological models are the most used to describe the acoustic behavior of porous materials with rigid or flexible structures according to a certain number of macroscopic physical properties related to the internal structure of the material and essential for their valuation. Among these models, the one proposed by Johnson et al. to describes the complex density of an acoustic porous material with an immobile skeleton having arbitrary pore shapes. The objective of this work is to study the effect of these parameters involved in Johnson's model as well as other new parameters, recently introduced by Sadouki [ Phys. Fluids 33, (2021)), on the transmitted signal in the low-frequency regime of ultrasound.

Indirect determination of airflow resistance of textiles with reference samples

Airflow resistance is a non-acoustic parameter of great relevance in the acoustic characterization of porous materials. It is used in several sound absorbing material prediction models and it is also a control parameter for acoustic conditioning and insulation in different building solutions. The ISO 9053 standard defines several methods to obtain it, using both direct measurements and indirect techniques. However, both procedures may involve problems related to the placement of the textile samples in the tube or to the stability of the samples during testing. In this work, the use of reference materials to stabilize the measurement of thin materials is proposed. Airflow resistance results obtained for different materials in an impedance tube are presented. The tests have been carried out by following the Ingard &amp; Dear method, as an indirect technique accepted by the standard. Several material compositions with a wide range of airflow resistance values have been analyzed with different reference materials.

Predicting through-Plane Porosity Profiles of Fibrous Porous Media from 2D Images with a Single-Input Multi-Output Convolutional Neural Network

While significant advances have been made in polymer electrolyte membrane fuel cells (PEMFC), water management in the gas diffusion layer (GDL) continues to be an important avenue for increasing overall cell efficiency (1). Efforts have been made to better understand how the structure of the GDL impacts water management through advanced microstructure characterization techniques such as synchrotron x-ray and laboratory-based computed tomography imaging, as well as neutron imaging (2-4).Deep learning tools like convolutional neural networks (CNNs) are openly available resources and have been employed for image classification and object detection since the late 1990s (5). While CNNs are less often used for real number regression tasks, they present a unique opportunity to gain meaningful insight into the GDL microstructure from lower quality data sources. Recently deep learning has been implemented in geological porous media applications to predict morphological, hydraulic, and mechanical properties with good success, and should be further investigated for use in the fuel cell community (6).In this study, a database containing over 2200 3D fibrous porous materials was created. The materials exhibited porosities ranging from 40% to 95% and were designed to represent GDLs in a PEMFC. The materials were used to create 2D images for training CNNs to predict average porosities and through plane porosity profiles. The CNNs, based on the popular ResNet50 and Xception network architectures, were adapted for real number regression rather than for traditional classification tasks. Both architectures accurately predicted average porosities with an R2 of 0.98 (ResNet50) and 0.99 (Xception). Xception was then further adapted into a single-input multi-output convolutional neural network (SiMo CNN) and trained to predict through-plane porosity profiles using only 2D images. We achieved good results with the SiMo CNN for predicting porosity profiles with an R2 of 0.91 and a mean absolute error of 1.7%. This study illustrates the usefulness of CNNs in image analysis of fibrous porous materials like the GDL and highlights the potential for CNNs to be further applied in the design and characterization of materials for electrochemical energy conversion.References Ijaodola OS, El-Hassan Z, Ogungbemi E, Khatib FN, Wilberforce T, Thompson J, et al. Energy efficiency improvements by investigating the water flooding management on proton exchange membrane fuel cell (PEMFC). Energy. 2019;179:246-67.Ince UU, Markötter H, George MG, Liu H, Ge N, Lee J, et al. Effects of compression on water distribution in gas diffusion layer materials of PEMFC in a point injection device by means of synchrotron X-ray imaging. Int J Hydrogen Energy. 2018;43(1):391-406.Battrell L, Patel V, Zhu N, Zhang L, Anderson R. Imaging of the desaturation of gas diffusion layers by synchrotron computed tomography. J Power Sources. 2019;416:155-62.Siegwart M, Harti RP, Manzi-Orezzoli V, Valsecchi J, Strobl M, Grünzweig C, et al. Selective visualization of water in fuel cell gas diffusion layers with neutron dark-field imaging. J Electrochem Soc. 2019;166(2):F149.LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278-324.Rabbani A, Babaei M, Shams R, Da Wang Y, Chung T. DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials. Adv Water Resour. 2020;146:103787.

Visualization of mercury percolation in porous hardened cement paste by means of X-ray computed tomography

Mercury intrusion porosimetry (MIP) has been extensively used for the pore structure characterization of porous materials, but the percolation of mercury in porous cement-based materials during MIP has never been comprehensively investigated before. Here we visually probe the mercury percolation in a porous hardened cement paste (HCP) using X-ray computed tomography (X-CT). Novel double surface coatings on the HCP samples before and after MIP tests by an epoxy resin are specifically designed. The first coating enables the one-directional penetration of mercury in the HCP samples during MIP, while the second coating can partially seal the release of the entrapped mercury. MIP tests are operated under different maximum applied pressures (10,000 and 30,000 psi) and equilibrium time (5, 10, and 30 s). The high X-ray attenuation of mercury enables the visual trace of the intrusion mercury percolation pathways and the entrapped mercury clusters in the HCP samples by X-CT. The position-dependent entrapped mercury clusters in the pores account for the gray value changes and gradients in the HCP samples after MIP. The MIP characteristics, such as, total intrusion volume, volume size distribution and mercury entrapment, change with the maximum applied pressure and equilibrium time, but the percolation pore size shows the consistent values. The findings of this work deepen the understandings of MIP for the microstructure characterization of porous materials.

Solid State NMR for Nonexperts: An Overview of Simple but General Practical Methods

There are varieties of methods available for the exploration of solids using nuclear magnetic resonance (NMR) spectroscopy. Some of these methods are quite sophisticated, others require specialized equipment. This review is addressed to those for whom NMR is not the main research method. It discusses simple methods that can be applied to solids with little or no adaptation to a specific system. Despite their technical simplicity and ease of use, these methods are powerful analytical tools that provide unique insights into the structure, dynamics, and noncovalent interactions in homo- and heterogeneous systems. Particular attention is paid to the characterization of porous materials and solids containing phosphorus. 31P NMR of organometallic compounds has been used as an example of how theoretical calculations can help in deeper analysis of experimental data.

Characterization of Nanoporous Materials.

Detailed analysis of textural properties, e.g., pore size and connectivity, of nanoporous materials is essential to identify correlations of these properties with the performance of gas storage, separation, and catalysis processes. The advances in developing nanoporous materials with uniform, tailor-made pore structures, including the introduction of hierarchical pore systems, offer huge potential for these applications. Within this context, major progress has been made in understanding the adsorption and phase behavior of confined fluids and consequently in physisorption characterization. This enables reliable pore size, volume, and network connectivity analysis using advanced, high-resolution experimental protocols coupled with advanced methods based on statistical mechanics, such as methods based on density functional theory and molecular simulation. If macro-pores are present, a combination of adsorption and mercury porosimetry can be useful. Hence, some important recent advances in understanding the mercury intrusion/extrusion mechanism are discussed. Additionally, some promising complementary techniques for characterization of porous materials immersed in a liquid phase are introduced.

Impact of Geometrical Disorder on Phase Equilibria of Fluids and Solids Confined in Mesoporous Materials.

Porous solids used in practical applications often possess structural disorder over broad length scales. This disorder strongly affects different properties of the substances confined in their pore spaces. Quantifying structural disorder and correlating it with the physical properties of confined matter is thus a necessary step toward the rational use of porous solids in practical applications and process optimization. The present work focuses on recent advances made in the understanding of correlations between the phase state and geometric disorder in nanoporous solids. We overview the recently developed statistical theory for phase transitions in a minimalistic model of disordered pore networks: linear chains of pores with statistical disorder. By correlating its predictions with various experimental observations, we show that this model gives notable insight into collective phenomena in phase-transition processes in disordered materials and is capable of explaining self-consistently the majority of the experimental results obtained for gas-liquid and solid-liquid equilibria in mesoporous solids. The potentials of the theory for improving the gas sorption and thermoporometry characterization of porous materials are discussed.

DeePore: A deep learning workflow for rapid and comprehensive characterization of porous materials

DeePore22GitHub Repository: https://github.com/ArashRabbani/DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro–tomography images. By combining naturally occurring porous textures we generated 17,700 semi–real 3–D micro–structures of porous geo–materials with size of 2563 voxels and 30 physical properties of each sample are calculated using physical simulations on the corresponding pore network models. Next, a designed feed–forward convolutional neural network (CNN) is trained based on the dataset to estimate several morphological, hydraulic, electrical, and mechanical characteristics of the porous material in a fraction of a second. In order to fine–tune the CNN design, we tested 9 different training scenarios and selected the one with the highest average coefficient of determination (R2) equal to 0.885 for 1418 testing samples. Additionally, 3 independent synthetic images as well as 3 realistic tomography images have been tested using the proposed method and results are compared with pore network modelling and experimental data, respectively. Tested absolute permeabilities had around 13% relative error compared to the experimental data which is noticeable considering the accuracy of the direct numerical simulation methods such as Lattice Boltzmann and Finite Volume. The workflow is compatible with any physical size of the images due to its dimensionless approach and can be used to characterize large–scale 3–D images by averaging the model outputs for a sliding window that scans the whole geometry.