Electron Microscopy Image Analysis Research Articles

Measurement of Porosity in Three-way Catalyst Particles Membrane Filter using Electron Microscopy Image Analysis

Measurement of Porosity in Three-way Catalyst Particles Membrane Filter using Electron Microscopy Image Analysis

Deep learning-assisted analysis of HRTEM images of crystalline nanoparticles.

Accurate analysis of high-resolution transmission electron microscopy (HRTEM) images is important for the characterization and design of materials. However, conventional analyses rely mostly on manual procedures, which are time-consuming and lack accuracy, especially when the image contrast is low. Here, we propose an advanced analysis method for extracting crystal features from HRTEM images based on a 2D fast Fourier transform and U-Net based deep learning model. By using HRTEM images of iron oxide nanoparticles as examples, we show that our method is capable of providing information on the crystallinity profile, distribution of crystal planes, phases and defects automatically with high accuracy. In an era of data-driven technological development, we believe that deep learning based analysis tools will facilitate great progress in fundamental research on crystalline materials.

Reuse of tire waste in light concrete

Lightweight concrete is characterized by its low density due to the incorporation of lightweight materials such as aggregate clay or lightweight materials in general. In the city of Aracaju there are companies that promote tire reconditioning generating large amount of waste powder. This study aims to study the reuse of tire rubber waste in lightweight aggregate clay concrete. An experimental program for the analysis of these concretes was developed, varying the percentage of 1, 2.5 and 5% of the tire rubber residue in replacement of the natural fine aggregate and replacing 100% of the natural coarse aggregate with expanded clay aggregate(50% C1506 expanded clay aggregateand 50% expanded clay aggregateC2215). The materials (cement, sand, expanded clays and tire rubber residue) were characterized by granulometric analysis tests and unit mass. The hardened concrete was evaluated by mechanical tests of axial compressive strength, modulus of elasticity and tensile strength by diametral compression, physical tests of water absorption and specific mass, as well as scanning electron microscopy image analysis. The use of expanded clay aggregateand 1% of tire rubber residue ensured better mechanical strength, lower water absorption and higher specific mass than mixtures with 2.5 and 5%, reaching values close to the reference concrete, and the residue be an alternative for reuse avoiding disposal.

Determination of Absorbed Dose for Gafchromic EBT3 Film Using Texture Analysis of Scanning Electron Microscopy Images: A Feasibility Study

Determination of Absorbed Dose for Gafchromic EBT3 Film Using Texture Analysis of Scanning Electron Microscopy Images: A Feasibility Study

A Study on the Deformation Behavior of AA7075 Powder with Three-Dimensional Ball Mill

Aluminum-based composite materials are frequently preferred in many new-generation engineering applications due to their high strength, wear and corrosion resistance, improvement of mechanical properties, machinability, and low density. Mechanical alloying has an important place in the production of composites with high properties in powder metallurgy, which is one of the composite material production methods. In this study, the deformation of AA7075 powder was investigated with a three-dimensional ball mill designed and produced for use in mechanical alloying processes. Three different rotational speeds (150, 200, 250 RPM), three different b all to powder ratios (1:5, 1:10, 1:20) and three different milling times (30, 60, 90 min) in the milling processes. Deformations in the powders were evaluated by particle size analysis and powder structure examination. The obtained results were analyzed with analysis of variance, regression method, three-dimensional graphics, optical microscope and Scanning Electron Microscope images. When the results are examined, the maximum deformation and powder size among the selected experimental parameters were realized at 150 RPM rotational speed, 1:20 ball to powder ratio, and 90 min.

Visualization of styrene-butadiene rubber (SBR) latex and large-scale analysis of the microstructure of lithium-ion battery (LIB) anodes

This study presents a novel method for analyzing binder distribution in a lithium-ion battery (LIB) anode. The developed method is based on scanning electron microscope (SEM) image analysis and can be used to analyze an aqueous styrene-butadiene rubber/carboxymethylated cellulose (SBR/CMC) binder system. The main advantage of this method is that it can provide large-scale cross-sections of multiple samples in a single milling process while maintaining a high-quality cross-section enabling the clear distinction of electrode components. This minimizes the issue of SEM-based analysis in the selection of a representative analysis area. The sample preparation and image acquisition processes were optimized to obtain high-quality images, and image processing procedures were developed for quantitative analysis. Osmium tetroxide (OsO4) staining and resin embedding enabled us to achieve a smooth two-dimensional (2D) cross-section with a clear distinction of the electrode components. The milling process enabled us to produce high-quality large-scale cross-sections similar to those prepared by ion beam milling methods. It was verified that our developed method is reliable in determining the SBR content and porosity. The developed method was used to analyze the SBR distribution and pore structure of anode layers prepared at various drying temperatures.

Effect of hydraulic retention time on performance of autotrophic, heterotrophic, and split-mixotrophic denitrification systems supported by polycaprolactone/pyrite: Difference and potential explanation.

Biological denitrification is still the most important pathway to purifying nitrate-containing wastewater. In this study, pyrite (FeS2 ) and polycaprolactone (PCL) were used as electron donors to construct sole or combined denitrification systems, that is, pyrite-based autotrophic denitrification (PAD) system, PCL-supported heterotrophic denitrification (PHD) system, and split-mixotrophic denitrification system (combined PAD + PHD), all of which were operated under five different hydraulic retention times (HRTs) for 150 days. The results showed that the removal rates (RE) of nitrate (NO3 - -N) and inorganic phosphorus (PO4 3- -P) by PAD were 91% and 94%, respectively, but the effluent sulfate (SO4 2- ) concentration was as high as 168.2mg/L; the removal rate of NO3 - -N by PHD was higher than 99%, but the PO4 3- -P could not be removed ideally; the removal rates of NO3 - -N and PO4 3- -P by PAD + PHD were higher than 95% and 99%, respectively, and the effluent SO4 2- concentration was only 7.2mg/L. Through the analysis of the surface scanning electron microscope (SEM) images of the two kinds of media before and after use, it was found that the coupled mode of PAD + PHD was more favorable for biofilm formation than the sole PAD or PHD process, and the microorganisms in the PAD + PHD mode made more full use of electron donors. Moreover, the biomass of the PAD + PHD mode was lower than that of the PAD or PHD process, but the denitrification efficiency of the coupled mode was more efficient, indicating that the functional microorganisms in the PAD + PHD mode might have a certain synergistic effect. PRACTITIONER POINTS: Removal rates of NO3 -, PO4 3 -, and SO4 2 - by PAD were 91%, 94%, and -233%, respectively. Removal rate of NO3 - by PHD exceeded 99%, but PO4 3 - could not be removed ideally. Removal rates of NO3 -, PO4 3 -, and SO4 2 - by PAD + PHD were 95%, 99%, and 86%, respectively. The coupled mode was more favorable for biofilm formation than the sole PAD or PHD. The coupled mode had lower biomass but got more excellent denitrification efficiency.

Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images

BackgroundSynapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to another with high spatial precision. Synaptic analysis, based on synapse numbers and fine morphology, is the basis for understanding neurological functions and diseases. Manual analysis of synaptic structures in electron microscopy (EM) images is often limited by low efficiency and subjective bias. New methodWe developed a multifunctional synaptic analysis system based on several advanced deep learning (DL) models. The system achieved synapse counting in low-magnification EM images and synaptic ultrastructure analysis in high-magnification EM images. ResultsThe synapse counting system based on ResNet18 and a Faster R-CNN model had a mean average precision (mAP) of 92.55%. For synaptic ultrastructure analysis, the Faster R-CNN model based on ResNet50 achieved a mAP of 91.60%, the DeepLab v3 + model based on ResNet50 enabled high performance in presynaptic and postsynaptic membrane segmentation with a global accuracy of 0.9811, and the Faster R-CNN model based on ResNet18 achieved a mAP of 91.41% for synaptic vesicle detection. ConclusionsThe proposed multifunctional synaptic analysis system may help to overcome the experimental bias inherent in manual analysis, thereby facilitating EM image–based synaptic function studies.

Binary and analog variation of synapses between cortical pyramidal neurons.

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.

3D modeling of PVDF membrane aging using scanning electron microscope and OpenCV image analysis

The aging of membranes compromises their integrity and reduces the quality of filtered water. In the present study, we constructed three-dimensional (3D) models of polyvinylidene fluoride (PVDF) membranes to evaluate the effects of membrane aging. Virgin and used PVDF membrane images were obtained by field emission scanning electron microscopy (FE-SEM), and the size distribution of the PVDF spherulites was analyzed using a Python-OpenCV program. Assuming a random distribution, the 3D membrane models were assessed in hydraulic and particle tracing simulations using COMSOL Multiphysics®. Compared to virgin membranes, the PVDF spherulites in the 10-year-old used membranes were enlarged and sloughed off owing to repeated filtration and backwashing. The porosity, filtration flux, and particle retention of the used membranes varied significantly more than those of the virgin membranes. Although our results were constrained by the size of the 3D membrane models and overall small sample size, our study provides an empirical basis for the 3D modeling of membranes from SEM images and our method could be applied for the optimization of membrane structure and integrity for water treatment and purification.

Condensation Characterization for PV Soiling

Condensation (dew) on PV modules greatly affects their soiling. In this study, we compared several techniques for measuring and characterization of condensation on soiled glass samples in the desert environment of Doha, Qatar. The techniques were: two types of moisture sensor; optical microscopy; scanning electron microscopy image analysis; and dew-point/surface temperatures. It was found that moisture sensors are practical and useful for measuring condensation on PV modules. As condensation increased, dust particles in the soiling layer became more tightly clustered together by the droplets, and more cementation crystal needles were generally observed. Importantly, it was found that condensation commenced when the surface temperature was several °C above the dew point, indicating it is necessary to directly measure condensation on soiled surfaces rather than estimate it from environmental conditions.

Morphological controls on flow conductivity and viscosity of bentonite–polymer composites

In this study, the authors investigate the morphological organisation of polymers in bentonite–polymer composites (BPCs) used in geosynthetic clay liners and controls on hydraulic conductivity and viscosity. A limited understanding of the relationship between the microstructure of BPCs and their macroscopic engineering behaviour motivates this study. The polymer resides in the inter-granular pores of bentonite based on synchrotron-based small-angle X-ray scattering measurements, scanning electron microscopy imaging and pore size analyses. On wetting, the polymer swells to produce a swollen hydrogel residing in the inter-granular pores. Enhanced tortuosity arising from hydrogel formation reduces the hydraulic conductivity of BPCs. The higher viscosity of the BPC slurry is attributed to the higher shear resistance emerging from swelling of the polymer fraction on wetting. These findings link the morphological features of BPCs to the observed hydraulic conductivity and viscosity of these materials.

Porous Carbon-Carbon Composite Materials Obtained by Alkaline Dehydrochlorination of Polyvinyl Chloride.

Porous carbon-carbon composite materials (PCCCM) were synthesized by the alkaline dehydrochlorination of polyvinyl chloride solutions in dimethyl sulfoxide containing the modifying additives of a nanostructured component (NC): graphite oxide (GO), reduced graphite oxide (RGO) or nanoglobular carbon (NGC), with subsequent two-step thermal treatment of the obtained polyvinylene-NC composites (carbonization at 400 °C and carbon dioxide activation at 900 °C). The focus of the study was on the analysis and digital processing of transmission electron microscopy images to study local areas of carbon composite materials, as well as to determine the distances between graphene layers. TEM and low-temperature nitrogen adsorption studies revealed that the structure of the synthesized PCCCM can be considered as a porous carbon matrix in which either carbon nanoglobules (in the case of NGC) or carbon particles with the "crumpled sheet" morphology (in the case of GO or RGO used as the modifying additives) are distributed. Depending on the features of the introduced 5-7 wt.% nanostructured component, the fraction of mesopores was shown to vary from 11% to 46%, and SBET-from 791 to 1115 m2 g-1. The synthesis of PCCNC using graphite oxide and reduced graphite oxide as the modifying additives can be considered as a method for synthesizing a porous carbon material with the hierarchical structure containing both the micro- and meso/macropores. Such materials are widely applied and can serve as adsorbents, catalyst supports, elements of power storage systems, etc.

A Tool for Rapid Analysis Using Image Processing and Artificial Intelligence: Automated Interoperable Characterization Data of Metal Powder for Additive Manufacturing with SEM Case

A methodology for the automated analysis of metal powder scanning electron microscope (SEM) images towards material characterization is developed and presented. This software-based tool takes advantage of a combination of recent artificial intelligence advances (mask R-CNN), conventional image processing techniques, and SEM characterization domain knowledge to assess metal powder quality for additive manufacturing applications. SEM is being used for characterizing metal powder alloys, specifically by quantifying the diameter and number of spherical particles, which are key characteristics for assessing the quality of the analyzed powder. Usually, SEM images are manually analyzed using third-party analysis software, which can be time-consuming and often introduces user bias into the measurements. In addition, only a few non-statistically significant samples are taken into consideration for the material characterization. Thus, a method that can overcome the above challenges utilizing state-of-the-art instance segmentation models is introduced. The final proposed model achieved a total mask average precision (mAP50) 67.2 at an intersection over union of 0.5 and with prediction confidence threshold of 0.4. Finally, the predicted instance masks are further used to provide a statistical analysis that includes important metrics such as the particle size distinction.

Hernandezine induces autophagic cell death in human pancreatic cancer cells via activation of the ROS/AMPK signaling pathway

Hernandezine (Her) is a bisbenzylisoquinoline alkaloid extracted from the traditional Chinese herbal medicine Thalictrum glandulosissimum. Evidence shows that Her is a natural agonist of adenosine monophosphate (AMP)-activated protein kinase (AMPK) and induces apoptosis and autophagy in tumor cells. In this study, we investigated the role of autophagy in Her-induced cell death in human pancreatic cancer cell lines. We showed that Her dose-dependently suppressed cell proliferation, promoted autophagy and induced autophagic death in pancreatic ductal adenocarcinoma (PDAC) cell lines Capan-1 and SW1990. The IC50 values of Her in inhibition of Capan-1 and SW1990 cells were 47.7 μM and 40.1 μM, respectively. Immunoblotting showed that Her (1−40 μM) promoted the conversion of LC3-I to LC3-II, and Her exerted concentration-dependent and time-dependent effects on autophagy activation in PDAC cells. In transmission electron microscopy and fluorescence image analysis, we found that autophagic vacuoles were significantly increased in Her-treated cells. Knockdown of ATG5, a key gene in the autophagy pathway, alleviated the activation of autophagy by Her. These results demonstrated that Her induced autophagy in PDAC cells. Intensely activated autophagy could promote cell death. The autophagy inhibitors, BafA1 and HCQ significantly inhibited Her-induced cell death, implying that Her induced autophagic cell death in PDAC cells. Moreover, we showed that Her activated autophagy by increasing the phosphorylation of AMPK and decreasing the phosphorylation of mTOR/p70S6K. Knockdown of AMPKα relieves the autophagic cell death induced by Her. Furthermore, Her concentration-dependently enhanced reactive oxygen species (ROS) generation in PDAC cells. Antioxidants could reduce the phosphorylation of AMPK and suppress autophagic cell death induced by Her. Our study provides evidence for the development of Her as a therapeutic agent for the treatment of pancreatic cancer.

Solvothermal growth of moiré superlattices in antimony telluride spiral-type nanoplates

The study of moiré superlattices (MSLs) has surged in recent years, as they have been found to host several emergent quantum phenomena associated with high electron correlations. In this study, we put forward a viable chemical route to the formation of moiré patterns in antimony telluride (Sb2Te3) nanoplates by leveraging screw dislocation-driven growth. These Sb2Te3 MSLs are synthesized using a scalable, one-pot, colloidal solution-liquid-solid (SLS) procedure. We report twist angles up to 0.52° (estimated using a combination of transmission electron microscope (TEM) image analysis and computer simulation). We investigate the early growth stages of Sb2Te3 spiral-type nanoplates under TEM, and we detail the growth of Sb2Te3 spiral-type nanoplates that feature a central nanopore, caused by tandem growth with nanorods. We hope that these methods will facilitate future investigation into the electronic properties of Sb2Te3 MSLs.

Laser diagnostics to characterize the in-flame growth of platinum nanoparticles manufactured by the reactive spray deposition technology

Reactive Spray Deposition Technology (RSDT) is an atmospheric pressure, flame-based advanced manufacturing method used to fabricate Membrane Electrode Assemblies (MEAs) for proton exchange fuel cells and water electrolyzers. RSDT combines the flame synthesis of catalyst nanoparticles and their deposition onto the membrane in one step. The properties of the synthesized nanoparticles, such as their Size Distribution Function (SDF), determine the manufactured electrodes’ performance, which can be evaluated when the deposition is complete via ex-situ characterization of the products. Efforts to improve RSDT for manufacturing state-of-the-art MEAs can be significantly enhanced with integrated laser diagnostics that enable in situ measurement of the synthesized catalyst nanoparticles. This paper reports the implementation of laser diagnostics in an RSDT facility and evaluates their potential to assist the manufacturing process. Laser Light Scattering (LS) and Laser-Induced Incandescence (LII) measurements are performed in the oxygen-rich zone of the flame at various distances from the flame fuel jet nozzle downstream of its luminous region. The measurements quantitatively track the evolution in size and volume fraction of platinum-based nanoparticles in two flames that yield different catalytic properties in the manufactured electrodes. The profiles of the measured nanoparticles’ volume fraction along the flame axis can be estimated a priori so that the average nanoparticle sizes can be measured in quasi-real time via LS. Nanoparticles experience an extremely slow growth rate while being convected at distances from 150 nm to 300 nm from the fuel nozzle. Complementary characterizations of the synthesized nanoparticles are performed ex-situ via High-Angle Annular Darkfield Scanning Transmission Electron Microscopy (HAADF-STEM) image analyses of samples collected on grids at a fixed distance from the fuel jet nozzle. Comparing the results from laser and microscopy techniques not only cross-validates the findings but also provides the parameters to infer the bimodal SDF in-situ and yields evidence that the coagulation efficiency of the synthesized nanoparticles has extremely low values in the investigated zone of the flames.

Measuring the randomness of micro- and nanostructure spatial distributions: Effects of scanning electron microscope image processing and analysis.

The quantitative characterisation of the degree of randomness and aggregation of surface micro- and nanostructures is critical to evaluate their effects on targeted functionalities. To this end, the methods of point pattern analysis (PPA), largely used in ecology and medical imaging, seem to provide a powerful toolset. However, the application of these techniques requires the extraction of the point pattern of nanostructures from their microscope images. In this work, we address the issue of the impact that Scanning Electron Microscope (SEM) image processing may have on the fundamental metric of PPA, that is, the Nearest Neighbour Index (NNI). Using typical SEM images of polymer micro- and nanostructures taken from secondary and backscattered electrons, we report the effects of the (a) noise filtering and (b) binarisation threshold on the value of NNI as well as the impact of the image finite size effects. Based on these results, we draw conclusions for the safe choice of SEM settings to provide accurate measurement of nanostructure randomness through NNI estimation.

Macroscopic Mechanical Properties and Microscopic Bonding Mechanisms of Glass Fiber-Modified Loess

Focusing on the performance improvement of glass fiber-modified loess, a series of laboratory tests were carried out, and the macroscopic mechanical properties of glass fiber-modified loess were studied by conventional triaxial shear tests. With the help of scanning electron microscopy (SEM), the pores and cracks image analysis system (PCAS) and nuclear magnetic resonance (NMR) tests, the microscopic properties of the improved loess were studied qualitatively and quantitatively from three aspects: mode of structural contact, pore morphological characteristics and pore structure arrangement characteristics. Finally, the interface zone bond-slip model was used to analyze the internal mechanism of glass fiber and loess interface zone slip. The macroscopic test results show with the same glass fiber length, the shear strength, cohesion and internal friction angle of the modified loess all increased first and then decreased with increasing incorporation of glass fibers. And the optimal ratio of glass fibers is 0.6%. Under the same fiber ratio, the shear strength and cohesion of the improved loess continued to increase with increasing fiber length. The optimal fiber length used in the project was 9 mm. Qualitative analysis of the SEM images show that with the increase in the glass fiber incorporation ratio, the fiber increasingly sutures the cracks in the soil and tightens the soil at both ends of the cracks. Quantitative analysis shows that the proportion of pore area is the largest at the 0.8% mixing ratio, followed by the 0% mixing ratio, and the smallest at the 0.6% mixing ratio. It can be seen from the NMR test results that the T2 spectral distribution curve under the fiber ratios of 0.2%–0.6% is shifted to the left compared with the ratios of 0% and 0.8%. The peak-top signal intensity of the main spectrum peak when the fiber ratio is 0.6% is the weakest.

Statistical Assessment of High-Performance Scaled Double-Gate Transistors from Monolayer WS2.

Scaling of monolayer transition metal dichalcogenide (TMD) field-effect transistors (FETs) is an important step toward evaluating the application space of TMD materials. Although some work on ultrashort channel monolayer (ML) TMD FETs has been published, there exist no comprehensive studies that assess their performance in a statistically relevant manner, providing critical insights into the impact of the device geometry. Part of the reason for the absence of such a study is the substantial variability of TMD devices when processes are not carefully controlled. In this work, we show a statistical study of ultrashort channel double-gated ML WS2 FETs exhibiting excellent device performance and limited device-to-device variations. From a detailed analysis of cross-sectional scanning transmission electron microscopy (STEM) images and careful technology computer aided design (TCAD) simulations, we evaluated, in particular, an unexpected deterioration of the subthreshold characteristics for our shortest devices. Two potential candidates for the observed behavior were identified, i.e., buckling of the TMD on the substrate and loss of gate control due to the source geometry and the high-k dielectric between the metal gate and the metal source electrode.