Quantitative Characterization Of Microstructure Research Articles

Accelerating precipitation hardening by natural aging in a 6082 Al-Mg-Si alloy

It is well known that long time natural aging (NA) after quenching from solution treatment will significantly reduce the precipitation hardening kinetics and peak hardness of most 6xxx aluminum alloys during later artificial aging (AA). Here we demonstrate an effective strategy to accelerate precipitation hardening, taking advantage of NA. It is found that by a short time pre-aging (PA) at AA temperature, NA for up to 1 year can reduce the time to peak strength in a 6082 alloy during later AA. A simultaneous increase in yield strength and uniform elongation at peak-aged condition can be achieved as a result of finer and denser age-hardening precipitates than those formed by direct AA treatment. Quantitative characterization of the precipitate microstructure by annular dark field scanning transmission electron microscopy (ADF-STEM) and atom probe tomography (APT) reveals that PA generates a small fraction of fine β″ needle precipitates composed of 6–9 β″-eyes and a substantially high density of GP-zones composed of 3–5 β″-eyes, which are stable at room temperature and can grow easily into β″ upon AA. During NA after PA, more GP-zones with at least 2 β″-eyes form while the larger GP-zones inherited from PA grow further, both of which can act as the precursors of β″ precipitates during later AA.

Quantitative microstructure characterization for a red-bed soft rock in the Three Gorges Reservoir area

Quantitative microstructure characterization for a red-bed soft rock in the Three Gorges Reservoir area

Scanning electron microscopy-based quantification of keratin and hyaluronic acid microstructure in electrospun scaffolds

BackgroundThe extracellular matrix (ECM) structural deficiencies in chronic wounds prevent the wounds from healing through natural physiological processes. Electrospun biocompatible polymers offer a platform to produce microstructure wound dressing materials that mimic the ECM containing various bioactives to address the deficiencies in the chronic wound healing process. Quantitative characterization of the electrospun fiber microstructure could provide valuable information on using fiber constructs to facilitate wound healing. This work employed a validated image analysis tool to quantitatively explain various parameters for the microstructure of six electrospun fiber constructs, D1{Polycaprolactone (PCL), Polyvinyl alcohol (PVA), Keratin}, D2{PCL, PVA, keratin, Co-enzyme Q10 (CoQ10)}, D3 (PCL, PVA, keratin, mupirocin), D4 (PCL, PVA, keratin, CoQ10, mupirocin, valsartan), D5 {PVA, Hyaluronic acid (HA)}, and D6 (PVA), using scanning electron microscopy imaging modality.ResultsThe fiber intersection density (FID) parameter was quantified in the formulations, e.g., 0.272% for D5 and 0.416% for D4. Orientation histograms for D1 and D6 are characteristic of isotropic materials, while orientations for D2 and D3 indicate anisotropy with 2 preferred orientations in each formulation. D4 and D5 present orientations characteristic of transversely isotropic materials. The tortuosity for D2 and D4 indicates almost straight fiber segments, in contrast with undulated fiber segments in all other formulations. Furthermore, the mean fiber diameter was quantified, e.g., 1.414 and 1.630 mm for D3 and D4, respectively.ConclusionCo-electrospun PVA/PCL microfibers offer great potential for controlled delivery of bioactives needed to accelerate the healing of chronic wounds. This image-based analysis technology quantitatively characterized different formulations of electrospun fiber scaffolds. This analysis sets the stage for future study that utilizes microstructural information in finite element biomechanical modeling, to investigate possible influence of structure-based mechanical factors on the ECM restorative potential of wound dressings. Adjustment of electrospinning conditions could produce fabricated constructs like the native ECM structural components with a functional role in wound healing.

Digital reconstruction of three‐dimensional contours and its application to microstructural evaluation of postblast rock fissure surfaces

AbstractFracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks. Based on the improved cube covering method and the 3D contour digital reconstruction model, this study proposes a quantitative microstructure characterization method combining the roughness evaluation index and the 3D fractal dimension to study the change rule of the fracture surface morphology after blasting. This method was applied and validated in the study of the fracture microstructure of the rock after blasting. The results show that the fracture morphology characteristics of the 3D contour digital reconstruction model have good correlation with the changes of the blasting action. The undulation rate of the three‐dimensional surface profile of the rock is more prone to dramatic rise and dramatic fall morphology. In terms of tilting trend, the tilting direction also shows gradual disorder, with the tilting angle increasing correspondingly. All the roughness evaluation indexes of the rock fissure surface after blasting show a linear and gradually increasing trend as the distance to the bursting center increases; the difference between the two‐dimensional roughness evaluation indexes and the three‐dimensional ones of the same micro‐area rock samples also becomes increasingly larger, among which the three‐dimensional fissure roughness coefficient JRC and the surface roughness ratio Rs display better correlation. Compared with the linear fitting formula of the power function relationship, the three‐dimensional fractal dimension of the postblast fissure surface is fitted with the values of JRC and Rs, which renders higher correlation coefficients, and the degree of linear fitting of JRC to the three‐dimensional fractal dimension is higher. The fractal characteristics of the blast‐affected region form a unity with the three‐dimensional roughness evaluation of the fissure surface.

Quantitative characterization of microstructure and research on spatial variation characteristics of loess of different strata in Luochuan, Shaanxi, China.

The complete sequence of loess strata in Luochuan has become a typical section in loess strata, and is the main focus of research for many scholars studying loess. We were based on the theory of aeolian loess and established a set of quantitative index parameters for loess microstructure through our previous research, such as equivalent diameter, sphericity, morphology ratio, orientation angle Phi, orientation angle Theta, pore Eq-Radius, throat Eq-Radius and throat channelLength. Through the quantitative characterization of various index parameters of the Luochuan loess, we found that the probability density of each index parameter meets a specific distribution well, and in terms of spatial dimension, it shows that as the depth of the strata increases, the average particle size and the mode of pore Eq-Radius, throat Eq-Radius and throat channelLength generally increase, while the mode of particle morphology ratio generally decreases. In addition, loess particles in deeper strata are less prone to vertical sedimentation and tend to deposit gently or horizontally. Most particles in different strata are distributed in a northwest or southwest direction. During the formation period of strata, the main cause for spatial differences is the material carrying force. We conducted a statistical analysis on the correlation between the macroscopic physical properties of loess and its microstructure index parameters. Specifically, we found a positive correlation between loess density and the average particle size and the mode of particle equivalent diameter, Additionally, we found a negative correlation between loess liquid limit and plastic limit, and the mode of particle morphology ratio. Furthermore, there was a negative correlation between permeability coefficient and the mode of pore Eq-Radius, throat Eq-Radius, and throat channelLength.

Quantitative characterization of the carbonate rock microstructure considering topological features: a case study from the Gaoshiti–Moxi block of the Sichuan Basin

The precise characterization of the rock microstructure is crucial for predicting the physical characteristics, flow behavior, and mechanical properties of rocks. This is particularly important for carbonate rocks, which depict a complex microstructure with multimodal pore radius distribution and natural fractures. Here, topological features that are typically ignored are taken into account to quantify the carbonate microstructure. Carbonate samples used are obtained from the Gaoshiti–Moxi block of the Sichuan Basin, which showed remarkable potential for oil and gas. Specifically, nuclear magnetic resonance (NMR), X-ray micro-computed tomography (micro-CT), and mercury injection capillary pressure (MICP) techniques are performed to describe the topological and geometric characteristics. The results indicate that NMR and MICP techniques can describe more rock pores than micro-CT. However, due to the presence of pore shielding in MICP tests, the pore radius obtained by MICP is smaller than that obtained by micro-CT and NMR. Furthermore, the effective method used for characterizing the pore structure is NMR technology. The hardest part is that the coefficient between the pore radius and T2 relaxation time is difficult to calculate. Therefore, a better calculation method must be found. In addition, micro-CT is an irreplaceable technique for obtaining a large number of topological and geometric features, and multi-phase or single-phase flow simulations can be conducted via digital rock models. However, for carbonates, micro-CT is not sufficient to describe the complete pore systems because macropores cannot be fully represented and sub-resolution micropores cannot be described. Those macropores and micropores have a very important effect on their seepage properties. Therefore, multi-scale digital rock modeling involving small and large pores is essential for complex rocks, which is of great significance for the analysis of pore systems and the simulation of rock physical properties.

Evolution of microstructure and deformation mechanisms in a metastable Fe42Mn28Co10Cr15Si5 high entropy alloy: A combined in-situ synchrotron X-ray diffraction and EBSD analysis

In this work, a combination of in-situ high synchrotron X-ray diffraction and electron backscattered diffraction were used to systematically investigate the activation and evolution of the deformation mechanisms in an as-cast Fe42Mn28Co10Cr15Si5 metastable high entropy alloy deformed until fracture at room temperature. This work unveils the critical role of the dual-phase γ-f.c.c. / ε-h.c.p. microstructure on the deformation response of the alloy. The different deformation modes, i.e., slip, transformation induced plasticity (TRIP) and transformation induced twinning (TWIP), were seen to initiate at different loading stresses and then to overlap. Quantitative microstructural characterization, which included the evolution of the phase fraction, stress partitioning, dislocation density, c/a ratio and lattice strain for different planes, was performed to elucidate the role of each phase on the macroscopic mechanical response of the metastable high entropy alloy. Furthermore, the magnitude of the different strengthening contributions has been quantified for the first time.

Efficient and accurate segmentation of adhesive lamellae in tri-modal microstructure of titanium alloy based on OpenCV

The adhesive characteristics of the tri-modal microstructure in titanium alloys, particularly the adhesion of the lamellar phase that constitutes over 50% of the microstructure, significantly hinder its quantitative characterization, further impeding the establishment of the quantitative relationships between the microstructure and mechanical properties. Addressing this challenge, this study utilized the OpenCV computer vision library to accomplish efficient and precise automated segmentation of the adhesive lamellae in tri-modal microstructure by enhancing the existing segmentation algorithm based on concave point matching. Specifically, by taking the number of the adhesive target after segmentation and Harris corner detection threshold as the optimization goal and variable respectively, and employing the genetic algorithm, the threshold, accounting for the segmentation effect, was adaptively determined to accurately identify the corner points of the adhesive lamellae. And then quick determination of the concave points within these corner points was achieved by substituting conventional pixel circle with equidistant pixels distributed along the circumference. Upon identifying the concave points, classification of the points was performed based on their spacing and location, and corresponding matching criteria were established and applied. Eventually, by introducing segmentation lines between the matched points, adhesive lamellae were segmented with an accuracy exceeding 85%, which will lay a robust foundation for the rapid and automated quantitative characterization of the tri-modal microstructure in titanium alloys.

A novel microstructure-informed machine learning framework for mechanical property evaluation of SiCf/Ti composites

In this work, a novel microstructure-informed machine learning (MIML) framework is proposed for the longitudinal ultimate tensile strength (UTS) evaluation and the construction of microstructure-property linkages of titanium matrix composites (TMCs). The main components of the MIML framework include (i) high-fidelity simulation and dataset generation using finite element method (FEM), (ii) the quantitative characterization of microstructures obtained by 2-point cross-correlation analysis and principal component analysis, (iii) the extraction of structure-property linkages based on support vector regression model and (iv) the validation of MIML by experimental data. The fiber breakage observed by advanced microstructure characterization is considered as the dominant failure mode during mechanical tests of TMCs, which is embedded in the FEM simulations. The MIML approach reaches capable accuracy in UTS prediction of TMCs using FEM-generated datasets and trained MIML is validated by experimental data. In addition, the permutation importance-based method is utilized to accurately identify the key feature parameters related to UTS. The first two principal components of microstructural reduced-order representation are recognized as highly relevant features for UTS. The well-trained MIML model is a promising tool in the evaluation of the mechanical property, and further provides a technical chain for the establishment of microstructure-property linkages of multi-phase composites.

Quantitative characterization of microstructures and elastic properties in plasma sprayed coatings based on “Ultrasonic – Elasticity – Microstructure” interaction mechanism

In plasma sprayed coatings, pores are the primary microstructure, and their features such as porosity, shape, and orientation significantly influence the thermal insulation and elastic properties of the coatings. Therefore, the ultrasonic characterization of pore feature parameters plays a crucial role in guiding the processing and predicting the lifespan of coatings. However, the coupling of multiple pore feature parameters leads to ill-posed problem and nonlinearity between these parameters and the ultrasonic feature signals, making it challenging to identify multiple pore feature parameters accurately. In this study, based on the “ultrasonic - elasticity - structure” interaction mechanism, a novel strategy for the ultrasonic quantitative identification of pore feature parameters and elastic constants in plasma sprayed coatings is proposed.Firstly, a random pore model (RPM) was developed to reveal the complex morphology of pores in plasma sprayed coatings. The porosity, average aspect ratio, orientation factor, and contour roughness factor were statistically analyzed for 15 sets of constructed RPMs, followed by ultrasonic finite element simulations. Then, a sensitivity matrix (SM) inversion method is developed for the “ultrasonic - elasticity” relationship by accurately measuring the elastic constants of the coatings through ultrasonic wave velocity inversion at multiple angles, yielding a relative error of only 0.57%. Lastly, a Multi-output Support Vector Regression machine learning model optimized by genetic algorithm (GA-MSVR) was proposed to establish the “elasticity - microstructure” relationship and identify multiple pore feature parameters based on elastic constants. The maximum relative errors for the four pore feature parameters are found to be 12.28%, 6.35%, 18.87%, and 7.62%, respectively, demonstrating the feasibility of the proposed strategy. Furthermore, the sensitivity matrix was employed to explain the cause for the larger relative error of the orientation factor.In summary, a challenging task of quantitatively identifying pore feature parameters in plasma sprayed coatings is addressed by proposing a novel solving strategy based on the “ultrasonic - elasticity - microstructure” interaction mechanism. The developed RPM, SM inversion method, and GA-MSVR model contribute to the accurate and comprehensive identification of pore feature parameters. The results obtained validate the feasibility of the proposed strategy.

The First Discovery of a Microstructure in Black Plaster and Its Performance Characterization.

Black plaster is one of the classic dosage forms of traditional Chinese medicine for external use and has been widely utilized since the Tang and Song Dynasties. In this paper, we take Goupi Gao as the research object and discuss the scientific characteristics of the black plaster dosage form. Goupi Gao ointment is a plaster for external use of traditional Chinese medicine. Methods for the morphological and quantitative characterization of black plaster's microstructure, based on FESEM-IPP (Field Emission Scanning Electron Microscope IPP Image Processing) technology, were established. According to the actual operating temperature of Goupi Gao, three temperatures were selected: 28°C, 35°C, and 45°C. A UPLC analysis method was applied to the cinnamaldehyde and eugenol in Goupi Gao, and the release behavior of Goupi Gao from three samples at three temperatures was investigated using the paddle over disk method. Preparation of rabbit model of knee osteoarthritis of cold blood stasis type by cold stimulation combined with drug induction. In terms of morphology, Goupi Gao and the blank black plaster matrix both formed a double continuous phase system with a thicker vegetable oil phase and crossed "branched" soap crystal fibers. Based on the IPP image quantification parameters, the pore area (A) was highly positively correlated with temperature. After the 28 °C treatment, A1 = (216.8±59.5) μm2; after the 35 °C treatment, A2 = (259.7±52.8) μm2; after the 45 °C treatment, A3 = (408.0±57.7) μm2, and there were no significant differences in other pore parameters. The black plaster matrix's unique structure makes it highly applicable in numerous medications; it exhibits slow-release and performs well in extreme temperatures, with good adhesion and peeling properties.

Machine learning based quantitative characterization of microstructures

Identifying structure–property relationships of polycrystalline microstructures demands an accurate and precise quantification of their features. Measuring grain sizes is tedious and creates a non-transparent bias when being performed by different individuals, impairing comparability with references. Here, we present a novel use of region-based convolutional neural networks (R-CNNs) to quantify several microstructural characteristics and their distributions: Feret diameter, axis length, area, circumference, dihedral angle and coordination number. We utilize a two-step approach: (i) a semi-automatic annotation tool to generate training data for (ii) a fully automated R-CNN, quantitatively evaluating images. Using Al-doped ZnO as a model system, we trained two R-CNNs, one for ZnO and one for precipitated ZnAl2O4. The R-CNN performs well in evaluating grain size characteristics from images with low contrast and in differentiating uni-, and bimodal grain size distributions on the sub-micron, and nanoscale. An extended statistical analysis of the distributions is performed to extract microstructural parameters quantitatively. This innovative solution makes grain size measuring amenable, time-effective, less biased, consistent, and statistically more precise.

Model-based quantitative characterization of anode microstructure and its effect on the performance of molten carbonate fuel cell

This paper presents research on the impact of the material microstructure on the performance of Molten Carbonate Fuel Cells (MCFC). The study is motivated by the need to increase the operation time of MCFC stacks. As the MCFC stacks are mainly composed of porous materials, the paper shows the impact of the porosity on both fuel cell performance and degradation rate. The study is based on the mathematical model, validated through experiments with varying microstructure parameters, achieving an overall relative error of 8%. The study evaluated the performance of MCFC using anodes with different porosity and found that increasing anode porosity from 40% to 70% led to a nearly two-fold increase in maximum power density. However, from both the degradation test and polarization curves, the optimal level of porosity for the anode is around 55%. The porosity level at 55% ensures the highest catalytic surface of the electrodes and is a compromise between the mechanical properties of the electrode and its electrochemical properties.

Quantified effect of quench rate on the microstructures and mechanical properties of an Al–Mg–Si alloy

It is well known that lower quench rate leads to the formation of precipitate free zones (PFZs) adjacent to grain boundaries in aging hardening materials such as Al–Mg–Si alloys. However, the combined effect of PFZs and intragranular precipitates on the yield strength of Al–Mg–Si alloys is not systematically understood. To clarify this, an Al–Mg–Si alloy was water/oil/air quenched after solid solution treatment, then aged at 180 °C for 6 h. It was found that a lower quench rate led to coarser precipitates and wider PFZs. The effects of PFZs and precipitates on the yield strength of the Al–Mg–Si alloy were separated by quantitative characterization of multi-scale microstructures and mechanical simulations. Wide PFZs were found to have a notable effect on yield strength, amounting over half the strengthening effect of intragranular precipitates. This effect increases as the width of PFZ increases, due to more serious strain localization caused by PFZs. A corresponding equation has thus been proposed to describe the deleterious effects of PFZs on yield strength of Al–Mg–Si alloys.

A viscoplastic-coupled phase field modelling for mechanical behaviors of thermal barrier coating system with randomly microporous structures

In this study, we have introduced a novel viscoplastic-coupled phase field approach to simulate the deformation and failure progression of thermal barrier coating systems (TBCs) with disordered microporous structures. The theory has been incorporated in the finite element method, for the first time, to characterize crack initiation and propagation in TBCs, considering both time-dependent deformations and tension–compression asymmetry mechanical behaviors. Furthermore, we have proposed a numerical framework for failure modeling of porous solids, which includes the quantitative characterization of microstructures, the parametric finite element modeling, and the visualization of calculation outcomes. We have performed several simulations of different microstructures, which have been compared against experimental data, demonstrating the predictive capabilities of our method in intricate structures. Our findings have revealed that cracks tend to emerge from nearby micropores, and an increase in porosity can lead to earlier crack initiation and a decrease in TBCs strength. Furthermore, micropores in the same horizontal line tend to cause cracks to propagate more easily, resulting in a substantial reduction in strength. For TBCs exposed to cyclic loading and thermal growth oxidation (TGO) growth stress, interfacial cracks are primarily caused by the deflection of cracks in the top coat (TC) layer. The method presented in this study offers a fresh perspective on the failure mechanism of solids with complex microstructures and contributes to the mechanical improvement of TBCs based on structural considerations.

A Technique for the Quantitative Characterization of Weld Microstructure and Application to Mo Welds

A Technique for the Quantitative Characterization of Weld Microstructure and Application to Mo Welds

Correlation of image textures of a polarization feature parameter and the microstructures of liver fibrosis tissues

Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures. Liver fibrosis is a characteristic of many types of chronic liver diseases. The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures. The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis. Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues. In this study, a fiber-sensitive polarization feature parameter (PFP) was derived through the forward sequential feature selection (SFS) and linear discriminant analysis (LDA) to target on the identification of fibrous structures. Then, the Pearson correlation coefficients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated. The results show the gray level run length matrix (GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919. The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures. This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis.

Quantitative Characterization of Tight Rock Microstructure of Digital Core

The fluid flow is closely related to the reservoir microstructure. And the pore throat is small, and the pore structure is complex of tight reservoirs, so the fluid flow mechanism is different from the conventional sandstones. In this paper, the sample size and scanning accuracy are determined by mercury-pressure experiments, and the gray-scale images of core samples are enhanced, filtered, and noise reduced, segmented by Avizo software, and finally 3D digital cores are constructed to realize quantitative characterization of pore throat parameters. The results show the following: (i) It is highly accurate to determine 3D digital core by comparing porosity measurement and calculating porosity; (ii) the average pore radius of the three samples is more than 7 μm, the pore number is less than 651, the average throat length is greater than 159 μm, and the percentage of connected pore volume is above 95%; (iii) large pores are mainly developed in the reservoir, while a certain number of isolated pores exist, and the connected pores are distributed in sheets and strips; (iv) the pores of tight reservoir are mainly micron pores, and the distribution frequency histogram of pore radius is single peak; (v) porosity is related to connectivity, pore radius, and pore number; and (vi) the influence of throat on porosity and permeability is greater than that of pore. This paper is helpful for quantitative evaluation of reservoir microscopic parameters and provides technical support for visualization and quantitative characterization of rock microstructure.

Quantitative Microstructural Characterization of Precipitates and Oxide Inclusions in Inconel 625 Superalloy Additively Manufactured by L-PBF Method

In this study, we perform quantitative characterization of precipitates and oxide inclusions in Inconel 625 additively manufactured by the laser powder-bed fusion (L-PBF) process. The application of different microscopy techniques allowed us to characterize the microstructure at micro- and nano-scale in the as-built and stress-relieved condition and correlate the features of grains and cellular substructure with parameters of particles along the planes parallel and perpendicular to the build direction. The optimized imaging conditions and image analysis procedure allowed easily distinguishing precipitates and oxide inclusions and performing their quantitative analysis. The results showed that intercellular areas are the preferential sites of precipitation of the Laves phase and NbC carbides with diameters in the range of 10 to 440 nm. Moreover, aluminum oxide inclusions with diameters in the range of 30 to 300 nm are randomly distributed. Regardless of the processing conditions of the examined samples, the influence of the stress-relief annealing on the secondary phases was not observed. In both the as-built and stress-relieved samples, the size of precipitates is in submicrometer scale. The analysis provided detailed information about the parameters of particles depending on the orientation versus the build direction. It was demonstrated that despite the tendency for columnar grain morphology and the anisotropy of the cellular substructure, the particle distribution is almost uniform throughout the volume of the additively manufactured L-PBF Inconel 625.Graphical

Polarization microwave-induced thermoacoustic imaging for quantitative characterization of deep biological tissue microstructures

Polarization optical imaging can be used to characterize anisotropy in biological tissue microstructures and has been demonstrated to be a powerful tool for clinical diagnosis. However, the approach is limited by an inability to image targets deeper than ∼ 1 mm due to strong optical scattering in biological tissues. As such, we propose a novel polarization microwave-induced thermoacoustic imaging (P-MTAI) technique to noninvasively detect variations in deep tissue by exploiting the thermoacoustic signals induced by four pulsed microwaves of varying polarization orientations. The proposed P-MTAI method overcomes the penetration limits of conventional polarization optical imaging and provides submillimeter resolution over depths of several centimeters. As part of the paper, the structural characteristics of tissues were quantified using a new parameter, the degree of microwave absorption anisotropy. P-MTAI was also applied to the noninvasive detection of morphological changes in cardiomyocytes as they transitioned from ordered to disordered states, providing a potential indication of myocardial infarction.