Quantitative Characterization Research Articles

Promoting Sustainable Coal Mining: Investigating Multifractal Characteristics of Induced Charge Signals in Coal Damage and Failure Process

Monitoring and preventing coal–rock dynamic disasters are essential for ensuring sustainable and safe mining. Induced charge monitoring, as a geophysical method, enables sustainable monitoring of coal–rock deformation and failure. The induced charge signal contains crucial information regarding damage evolution, making it imperative and important to explore its temporal characteristics for effective monitoring and early warnings of dynamic disasters in deep mining. This paper conducted induced charge monitoring tests at different loading rates, investigating the multifractal characteristics of induced charge signals during the early and late stages of loading. It proposed the maximum generalized dimension D(q)max, multifractal spectrum width Δα, and height difference Δf as multifractal parameters for induced charge signals. Additionally, quantitative characterization of coal damage was performed, studying the variation patterns of signal multifractal characteristic parameters with coal damage evolution. This study revealed the induced charge signal of the coal body multifractal characteristics in the whole loading process. In the late loading stage, the double logarithmic curve demonstrated some nonlinearity compared to the previous period, indicating the higher non-uniformity of the induced charge time series. D(q)max and Δα in the late loading stage were higher than those in the early stage and increased with loading rates. As coal damage progressed, there were significant jumps of D(q)max in both the early and late stages of damage, with larger jumps indicating richer fracture events in the coal. The width Δα showed an overall trend of increase–decrease–increase with coal damage evolution, while the height difference Δf fluctuated around zero in the early stage of damage development but increased significantly during severe damage and destruction. By studying the multifractal characteristics of induced charge signals, this study provides insights for the early identification of coal–rock dynamic disasters.

Intraductal magnetic resonance imaging of cholangiocarcinoma -a practical possibility.

Intraductal T2 mapping based on a catheter receiver is proposed as a method of visualizing the extent of intraductal and periductal cholangiocarcinoma (CCA). Compared to external receivers, internal receivers provide locally enhanced signal-to-noise ratios by virtue of their lower field-of-view for body noise, allowing smaller voxels and higher resolution. However, inherent radial sensitivity variation and segmentation for patient safety both distort image brightness. We discuss simulated T2 weighted images and T2 maps, and in vitro images obtained using a thin film catheter receiver of a freshly resected liver specimen containing a polypoid intraductal tumor from a patient with CCA. T2 mapping provides a simple method of compensating non-uniform signal reception patterns of catheter receivers, allowing the visualization of tumor extent without contrast enhancement and potentially quantitative tissue characterization. Potential advantages and disadvantages of in vivo intraductal imaging are considered.

Analysis of aged microplastics: a review

AbstractMicroplastics are emerging contaminants that undergo progressive aging under environmental conditions such as sunlight irradiation, mechanical forces, temperature variations, and the presence of biological organisms. Since aging modifies microplastic properties, such as their own toxicity and the toxicity of trapped pollutants, advanced methods to analyze microplastics are required. Here we review methods to analyze microplastic aging with focus on the aging process, qualitative identification, quantitative characterization, and chemometrics. Qualitative identification is done by mechanical techniques, thermal techniques, e.g., thermal degradation and gas chromatography–mass spectrometry, and spectral techniques, e.g., infrared, Raman, fluorescent, and laser techniques. Quantitative characterization is done by microscopy and mass spectrometry. Microplastic aging results in a series of surface physical changes, biofilm formation, chemical oxidation, thermal alternation, and mechanical deterioration. Changes in mechanical and thermal properties allow to differentiate aged microplastics. Infrared and Raman spectroscopy are rapid and sensitive for chemical identification of microplastics in complex environmental samples. Combining two techniques is preferable for accurate detection and categorization.

A unified photo-excited GaAs model from ab initio simulation in terahertz regime

In this paper, we present a unified model for gallium arsenide (GaAs) based on ab initio simulations which characterizes its terahertz (THz) properties when excited by optical pump. We use density functional perturbation theory to calculate the dielectric properties of GaAs, and investigate the relaxation time of photo-excited GaAs through electron–phonon interactions. In light of the complexities arising from the mixed absorption mechanisms and the sensitivity of GaAs to laser parameters, we have developed a method that leverages time-dependent density functional theory and Boltzmann transport theory. This approach enables us to establish an accurate relationship between the pump laser intensity and the carrier concentration by introducing the percentage of excited electrons, facilitating the quantitative characterization of GaAs’s response under different optical pump intensities. Using the microscopic material parameters solved by first principles, we develop a unified Drude model to describe the macroscopic electromagnetic responses of photo-excited GaAs. We simulate several reported numerical examples of photo-excited GaAs, including a GaAs wafer and GaAs-based THz metamaterial modulators, to validate the proposed unified model as a reliable approach for predicting the THz properties of GaAs. The good agreement between the simulation and measurement results demonstrates that our model successfully captures the dynamic responses of photo-generated carriers and provides guidance for the design of optoelectronic devices based on GaAs. Furthermore, our modeling approach based on ab initio simulations is free from empirical parameters, providing a solid THz modeling method for other photo-excited semiconductor materials.

Elevator music as a tool for the quantitative characterization of reward.

While certain musical genres and songs are widely popular, there is still large variability in the music that individuals find rewarding or emotional, even among those with a similar musical enculturation. Interestingly, there is one Western genre that is intended to attract minimal attention and evoke a mild emotional response: elevator music. In a series of behavioral experiments, we show that elevator music consistently elicits low pleasure and surprise. Participants reported elevator music as being less pleasurable than music from popular genres, even when participants did not regularly listen to the comparison genre. Participants reported elevator music to be familiar even when they had not explicitly heard the presented song before. Computational and behavioral measures of surprisal showed that elevator music was less surprising, and thus more predictable, than other well-known genres. Elevator music covers of popular songs were rated as less pleasurable, surprising, and arousing than their original counterparts. Finally, we used elevator music as a control for self-selected rewarding songs in a proof-of-concept physiological (electrodermal activity and piloerection) experiment. Our results suggest that elevator music elicits low emotional responses consistently across Western music listeners, making it a unique control stimulus for studying musical novelty, pleasure, and surprise.

Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization.

Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leads to different organs/tissues being captured. To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space. Our experiments on 2608 volumetric CT data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas Abdomen Labeling Challenge Beyond the Cranial Vault (BTCV) dataset demonstrate that our model can generate high-quality images that are realistic and similar. We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore longitudinal study of aging dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area. This approach provides a promising direction for mapping slices from different vertebral levels to a target slice and reducing positional variance for single-slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen.

Intelligent Identification and Quantitative Characterization of Pores in Shale SEM Images Based on Pore-Net Deep-Learning Network Model

Among the various shale reservoir evaluation methods, the scanning electron microscope (SEM) image method is widely used. Its image can intuitively reflect the development stage of a shale reservoir and is often used for the qualitative characterization of shale pores. However, manual image processing is inefficient and cannot quantitatively characterize pores. The semantic segmentation method of deep learning greatly improves the efficiency of image analysis and can calculate the face rate of shale SEM images to achieve quantitative characterization. In this paper, the high-maturity shale of the Longmaxi Formation in the Changning area of Yibin City, Sichuan Province, and the low-maturity shale of Beibu Gulf Basin in China are studied. Based on the Pore-net network model, the intelligent identification and quantitative characterization of pores in shale SEM images are realized. The pore-net model is improved from the U-net deep-learning network model, which improves the ability of the network model to identify pores. The results show that the pore-net model performs better than the U-net model, FCN model, DeepLab V3 + model, and traditional binarization method. The problem of low accuracy of the traditional pore identification method is solved. The porosity of SEM images of high-maturity shale calculated by the pore-net network model is between 12 and 19% deviation from the experimental data. The calculated porosity of the SEM image of the low-maturity shale has a large deviation from the experimental data, which is between 14 and 47%. Compared with the porosity results calculated by other methods, the results calculated by pore-net are closer to the true value, which proves that the porosity calculated by the pore-net network model is reliable. The deep-learning semantic image segmentation method is suitable for pore recognition of shale SEM images. The fully convolutional neural network model is used to train the manually labeled shale SEM images, which can realize the intelligent recognition and quantitative characterization of the pores in the shale SEM images. It provides a certain reference value for the evaluation of shale oil and gas reservoirs and the study of other porous materials.

Quantitative characterization of vascular targeted photodynamic therapy efficiency for port-wine stains using image processing techniques

SignificanceVascular targeted photodynamic therapy (V-PDT) is a clinically approved therapeutic approach for treating vascular-related diseases, such as port-wine stains (PWS). For accurate treatment, various light doses are required for different lesions due to the irregularity of vascular size, shape, degree of disease, which commonly alters during different stages of V-PDT, making quantitative analysis of treatment efficiency is highly needed. ApproachLesion images before and after V-PDT treatment of patients with PWS were used to construct a quantitative method to characterize the differences among lesions. Image analysis techniques were applied to calculate Euclidean distances and two-dimensional correlation coefficients for V-PDT efficiency evaluation. ResultsAccording to the image analysis, V-PDT with good treatment efficiency resulted in a larger Euclidean distance and a smaller correlation coefficient, in comparison with the case having lower V-PDT efficiency. ConclusionsThe new method to quantify the Euclidean distances and correlation coefficients is developed, which is promising for the quantitative analysis of V-PDT efficiency for PWS.

Asymmetric optical properties and bandgap shift of pre-strained flexible ZnO films

Strain engineering has been extensively explored to modulate the various intrinsic properties of flexible inorganic semiconductor films. However, experimental characterization of tensile and compressive strain-induced modulation of optoelectronic properties and their differences has not been easily implemented in flexible inorganic semiconductor films. Herein, the strain-dependent structural, optical, and optoelectronic properties of flexible ZnO films under pre-tensile and pre-compressive strains are systemically investigated by a Mueller matrix ellipsometry-based quantitative characterization method combined with x-ray diffraction and first-principle calculation. With extended prestress-driven deposition processing under bi-direction bending modes, pre-tensile and pre-compressive strains with symmetric magnitudes can be achieved in flexible ZnO films, which allows precise observation of the strain-driven asymmetric modulation of optoelectronic properties. When the applied prestrain varies approximately equally from 0% (baseline) to −0.99% (compression) and 1.07% (tensility), respectively, the relative changes for the c-axis lattice constant are 0.0133 and 0.0104 Å, respectively. Meanwhile, the dependence factors of the bandgap energy on the pre-compression and pre-tensile strains were determined as −0.0099 and −0.0156 eV/%, respectively, and the complex refractive index also presents an asymmetric varying trend. With the help of the strain–stress analysis and the first-principle calculation, the intriguing asymmetric strain-optical modulation effect could be attributed to the biaxial strain mechanism and the difference in the deformation potential between the two prestrain modes. These systematic investigation consequences are thus promising as a basis for the booming applications of the flexible inorganic semiconductor ensemble.

Fine quantitative characterization of high-H2S gas reservoirs under the influence of liquid sulfur deposition and adsorption

In order to clarify the influence of liquid sulfur deposition and adsorption to high-H2S gas reservoirs, three types of natural cores with typical carbonate pore structures were selected for high-temperature and high-pressure core displacement experiments. Fine quantitative characterization of the cores in three steady states (original, after sulfur injection, and after gas flooding) was carried out using the nuclear magnetic resonance (NMR) transverse relaxation time spectrum and imaging, X-ray computer tomography (CT) of full-diameter cores, basic physical property testing, and field emission scanning electron microscopy imaging. The loss of pore volume caused by sulfur deposition and adsorption mainly comes from the medium and large pores with sizes bigger than 1 000 μm. Liquid sulfur has a stronger adsorption and deposition ability in smaller pore spaces, and causes greater damage to reservoirs with poor original pore structures. The pore structure of the three types of carbonate reservoirs shows multiple fractal characteristics. The worse the pore structure, the greater the change of internal pore distribution caused by liquid sulfur deposition and adsorption, and the stronger the heterogeneity. Liquid sulfur deposition and adsorption change the pore size distribution, pore connectivity, and heterogeneity of the rock, which further changes the physical properties of the reservoir. After sulfur injection and gas flooding, the permeability of Type I reservoirs with good physical properties decreased by 16%, and that of Types II and III reservoirs with poor physical properties decreased by 90% or more, suggesting an extremely high damage. This indicates that the worse the initial physical properties, the greater the damage of liquid sulfur deposition and adsorption. Liquid sulfur is adsorbed and deposited in different types of pore space in the forms of flocculence, cobweb, or retinitis, causing different changes in the pore structure and physical property of the reservoir.

Quantitative characterization and control mechanism of pore structure in geopolymer foams with addition of various surfactants

Pore structure control mechanism in geopolymer foams (GPFs) remains qualitatively understood, posing great challenges for GPF applications. In this study, by modelling of various pore structures with regular geometric patterns, total porosity, open porosity, pore size distribution, and pore deformation were quantitatively characterized. Then, we focused on slurry viscosity and surface tension as the two crucial parameters, and investigated their impacts on the evolution of pore structures. It was revealed that viscosity primarily influenced foam deformation and coalescence, ultimately affecting foaming height, sample appearance, and the occurrence of foam collapse. Slurry surface tension predominantly affected pores connectivity, low surface tension corresponded to highly interconnected pores, while higher values favored closed pores. Additionally, a simple prediction model that enables the estimation of pore structures and their distributions based on GPFs visual appearance and height was proposed. At last, the relationships between pore structure and macro-performance were analyzed.

A close-packed sphere model for characterising porous networks in atomistic simulations and its application in energy storage and conversion

Hierarchical (micro, meso & macro) porosity in materials plays a crucial role in influencing the movement of ions which governs the energy and power density during energy storage and conversion. The extant available methods to characterise porosity across scales (nano to meso to macro) lacks rigour and accuracy. Having accurate assessment of the porosity in materials can unlock new designs of electrodes for energy efficient energy storage and conversion devices such as batteries, supercapacitors and fuel cells. Through this work, we report the systematic development of a method to fully characterise the carbon porous networks using a molecular dynamics simulation testbed. Our work entails modelling and simulation of porous carbon structures using quenched molecular dynamics (QMD) simulations using Gaussian Approximation potential (GAP) and benchmarking the results with prior literature. This modelling technique can reliably be used for quantitative characterisation of the interconnectivity in porous structures to study ionic movements and charge transfer mechanisms. A new parameter, namely nearest neighbour search (NNS) coefficient was introduced to quantify homogeneity and networking in the porous structures. NNS coefficient increased from 1.62 to 1.92 with decrease of the annealing temperature from 8000 K to 4000 K in carbon. The procedure outlined was although tested on porous carbon networks, but adaptable to study any other material system at multi-length scales.

Quantifying in vitro B. anthracis growth and PA production and decay: a mathematical modelling approach

Protective antigen (PA) is a protein produced by Bacillus anthracis. It forms part of the anthrax toxin and is a key immunogen in US and UK anthrax vaccines. In this study, we have conducted experiments to quantify PA in the supernatants of cultures of B. anthracis Sterne strain, which is the strain used in the manufacture of the UK anthrax vaccine. Then, for the first time, we quantify PA production and degradation via mathematical modelling and Bayesian statistical techniques, making use of this new experimental data as well as two other independent published data sets. We propose a single mathematical model, in terms of delay differential equations (DDEs), which can explain the in vitro dynamics of all three data sets. Since we did not heat activate the B. anthracis spores prior to inoculation, germination occurred much slower in our experiments, allowing us to calibrate two additional parameters with respect to the other data sets. Our model is able to distinguish between natural PA decay and that triggered by bacteria via proteases. There is promising consistency between the different independent data sets for most of the parameter estimates. The quantitative characterisation of B. anthracis PA production and degradation obtained here will contribute towards the ambition to include a realistic description of toxin dynamics, the host immune response, and anti-toxin treatments in future mechanistic models of anthrax infection.

Research on quantitative inversion characterization of high-definition electrical imaging logging in oil-based mud based on backpropagation neural network and multiple population genetic algorithm-Levenberg-Marquardt algorithm

Electrical imaging logging in oil-based mud (OBM) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multifrequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a backpropagation neural network (BPNN) and the multiple population genetic algorithm (MPGA)-Levenberg-Marquardt (LM) algorithm for high-resolution quantitative imaging is developed. First, the numerical simulation is used to calculate the well-logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of more than 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures, etc.

Study of Hydration Repulsion of Zwitterionic Polymer Brushes Resistant to Protein Adhesion through Molecular Simulations.

The fabrication of antifouling zwitterionic polymer brushes represents a leading approach to mitigate nonspecific adhesion on the surfaces of medical devices. This investigation seeks to elucidate the correlation between the material composition and structural attributes of these polymer brushes in preventing protein adhesion. To achieve this goal, we modeled three different zwitterionic brushes, namely, carboxybetaine methacrylate (CBMA), sulfobetaine methacrylate (SBMA), and (2-(methacryloyloxy)ethyl)-phosphorylcholine (MPC). The simulations revealed that elevating the grafting density enhances the structural stability, hydration strength, and resistance to protein adhesion exhibited by the polymer brushes. PCBMA manifests a more robust hydration layer, while PMPC demonstrates the slightest interaction with proteins. In a comprehensive evaluation, PSBMA polymer brushes emerged as the best choice with superior stability, enhanced protein repulsion, and minimally induced protein deformation, resulting in effective resistance to nonspecific adhesion. The high-density SBMA polymer brushes significantly reduce the level of protein adhesion in AFM testing. In addition, we have pioneered the quantitative characterization of hydration repulsion in polymer brushes by analyzing the hydration repulsion characteristics at different materials and graft densities. In summary, our study provides a nuanced understanding of the material and structural determinants influencing the capacity of zwitterionic polymer brushes to thwart protein adhesion. Additionally, it presents a quantitative elucidation of hydration repulsion, contributing to the advancement and application of antifouling polymer brushes.

Blood cell characterization based on deep learning and diffraction phase microscopy

Quantitative characterization of blood cells is very important for medical diagnosis and life science research. This study presents a novel label-free and high-throughput approach for analyzing flowing blood cells in a microfluidic channel by combining deep learning with partially coherent illumination-based diffraction phase microscopy (PCI-DPM). Diluted blood was injected into a microchannel and imaged with PCI-DPM, yielding high-contrast phase images for the blood cells. A modified YOLOv5-s neural network was used to perform automatic identification and analysis of blood cells in the phase image of PCI-DPM, revealing the diameter, dry mass, volumetric density, and speed of blood cells precisely. The proposed approach allows quick and accurate assessment of blood samples, providing valuable insights for patients in different pathological states, such as hematological disorders and infections.

IDENTIFYING THE ROLE OF ELECTROLYTE ADDITIVES FOR LITHIUM PLATING ON GRAPHITE ELECTRODE BY OPERANDO X‐RAY TOMOGRAPHY

The plating of lithium on the graphite electrode is a major degradation mechanism in lithium‐ion batteries (LIBs) and brings safety concerns and leads to short cycle life. Understanding how and when plating occurs is crucial for the development of mitigation strategies, e.g. tuning the electrolyte composition. We present an operando X‐ray tomographic microscopy study to monitor the plating of lithium on a graphite electrode. XTM enables non‐destructive, quantitative operando characterization of lithium deposition and allows us to probe the role of the electrolyte additives vinylene carbonate and lithium bis(fluorosulfonyl)imide in the standard electrolyte LP57. The additives show a better performances in terms of delayed onset of lithium plating, important to utilise the full capacity of the graphite, since once lithium plating is initiated this rapidly becomes dominating and hinders further intercalation. For the base electrolyte a homogeneous and dense morphology of plated lithium is found, whereas a more dendritic morphology is observed in the presence of additives. During delithiation, there is a rapid stripping of some of the plated lithium followed by deintercalation. In addition, our work provides a general methodology to track the morphology of plated lithium, which is crucial for fundamental research about battery safety.

Perspective on quantitative phase imaging to improve precision cancer medicine.

Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states. This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection. We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging. QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research. QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

Bichromatic tetraphasic full-field optical coherence microscopy.

Full-field optical coherence microscopy (FF-OCM) is a prevalent technique for backscattering and phase imaging with epi-detection. Traditional methods have two limitations: suboptimal utilization of functional information about the sample and complicated optical design with several moving parts for phase contrast. We report an OCM setup capable of generating dynamic intensity, phase, and pseudo-spectroscopic contrast with single-shot full-field video-rate imaging called bichromatic tetraphasic (BiTe) full-field OCM with no moving parts. BiTe OCM resourcefully uses the phase-shifting properties of anti-reflection (AR) coatings outside the rated bandwidths to create four unique phase shifts, which are detected with two emission filters for spectroscopic contrast. BiTe OCM overcomes the disadvantages of previous FF-OCM setup techniques by capturing both the intensity and phase profiles without any artifacts or speckle noise for imaging scattering samples in three-dimensional (3D). BiTe OCM also utilizes the raw data effectively to generate three complementary contrasts: intensity, phase, and color. We demonstrate BiTe OCM to observe cellular dynamics, image live, and moving micro-animals in 3D, capture the spectroscopic hemodynamics of scattering tissues along with dynamic intensity and phase profiles, and image the microstructure of fall foliage with two different colors. BiTe OCM can maximize the information efficiency of FF-OCM while maintaining overall simplicity in design for quantitative, dynamic, and spectroscopic characterization of biological samples.

A comparative study of two correlation functions applying to the quantitative characterization of the pore structure within hardened concrete

The paper presents a comparative study between the autocorrelation function (ACF) and the 2-point correlation function (2PCF) for the quantitative analysis of the pore structure within hardened concrete via the image analysis technique. This work was carried out on five cross-sectional images of concrete specimens with varying area porosities ranging from 2.6% to 21.2%. This research indicates that: (i) the ACF provides a comprehensive and effective tool that is capable of capturing the contribution of the area porosity, size, and orientation of the pores in quantifying the pore structure within hardened concrete, while the 2PCF only enables quantifying the size and spacing between the pores; (ii) the ACF requires the cross-sectional images to be square, whereas the 2PCF can analyze images of any size and shape; (iii) for an area porosity below 20%, the characteristic size of the pore structure within hardened concrete, determined by both the ACF and 2PCF approaches, is not significantly different; (iv) on average, the correlation length estimated by 2PCF is more than 1.6 times greater than the corresponding value determined by ACF; and (v) the characteristic size provides a more accurate and comprehensive quantification of the pore structure within hardened concrete specimens than the correlation length.