Scanning Electron Microscopy Images Research Articles

Synthesis and Characterization of Iron Nanoparticles from a Bioflocculant Produced by Pichia kudriavzevii Isolated from Kombucha Tea SCOBY

The intriguing characteristics of nanoparticles have fueled recent advancement in the field of nanotechnology. In the current study, a microbial-based bioflocculant made from the SCOBY of Kombucha tea broth was purified, profiled, and utilized to biosynthesize iron nanoparticles as a capping and reducing agent. UV–visible absorption spectroscopy, transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDX), and TGA were used to characterize the Fe nanoparticles. The FT-IR spectra showed functional groups such as hydroxyl, a halogen (C-Br), and carbonyl, and the alkane (C-H) functional groups were present in both samples (bioflocculant and FeNPs) with the exception of the Fe-O bond, which represented the successful biosynthesis of FeNPs. The TEM investigation revealed that the sizes of the produced iron nanoparticles were between 2.6 and 6.2 nm. The UV-vis spectra revealed peaks at 230 nm for the bioflocculant and for the as-fabricated FeNPs, peaks were around 210, 265, and 330 nm, which confirms the formation of FeNPs. X-ray diffraction presented planes (012), (104), (110), (113), (024), (116), and (533) and these planes correspond to 17.17, 32.58, 33.75, 38.18, 45.31, 57.40, and 72.4° at 2Ө. The presence of Fe nanoparticles presented with 0.82 wt% from the EDX spectrum of the biosynthesized FeNPs. However, Fe content was not present from the bioflocculant. SEM images reported cumulus-like particles of the bioflocculant, while that of FeNPs were agglomerated and hexagonal with sizes between 18 and 50 nm. The TGA of FeNPs showed thermal stability by retaining above 60% of its weight at high temperatures. It can therefore be deduced that the purified bioflocculant produced by a yeast Pichia kudraivzevii can be utilized to synthesize FeNPs with the current simple and effective method.

Fracture‐mechanical properties of tailored epoxy nanocomposites at elevated temperatures

AbstractAn investigation was conducted to assess the influence of hybridized block‐copolymer (BCP) and core–shell rubber (CSR) particles on the tensile properties, fracture mechanical properties, and toughening mechanisms of a high strength epoxy/anhydride system at elevated temperatures (80°C). Utilizing standard tensile and compact tension (CT) specimens, substantial increases in fracture energy were observed for specimens enhanced with nanoparticles—831% for the 4 wt.% BCP‐toughened system and 600% for the 12 wt.% CSR‐toughened system. Field emission gun–scanning electron microscope imaging indicated that the inclusion of BCP and CSR significantly altered fracture surface morphologies, consistent with the improved mechanical properties. Glass transition temperature (Tg) determined through dynamic mechanical analysis—were not significantly influenced by additive inclusion, presenting a ~1°C decrease for BCP and ~3°C increase for CSR toughened systems relative to the unaltered matrix. Toughening mechanisms induced by the BCP and CSR particles were identified as (a) localized enhanced plastic shear‐band yielding around the particles and (b) cavitation of the particles followed by enhanced plastic void growth in the epoxy matrix. Outcomes from this study highlight the role of BCP and CSR particles in the toughening of epoxy/anhydride systems and provide a comprehensive assessment of their influence on mechanical characteristics at high temperatures.

Detecting and evaluating surface defects in additive manufacturing composite materials via image processing technique

Purpose This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time. Design/methodology/approach This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section. Findings The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time. Originality/value Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

Effect of chemical consolidation on physico-mechanical properties of porous coral reef limestone

Porous coral reef limestone (CRL) is characterized by the low strength and developed pore structure, so improving its physico-mechanical properties is important for marine engineering construction work. Chemically consolidated porous CRL was prepared by taking a non-granular chemical grout (sodium silicate) as the main consolidation material. Then, the chemical consolidation effect was evaluated from two aspects (both the physical and mechanical properties) by conducting computed tomography (CT) scanning and quasi-static uniaxial compression tests. The results indicate that the consolidation material can fill the pores in the CRL, which reduces the porosity of specimens by more than 50%. In addition, the peak strength under quasi-static compression is increased by about 20%. The fragments decrease at failure, and fracture plane slip and crack opening are inhibited. Finally, influences of the special banded shape of consolidation material retained in the specimens on the failure process and failure mode of specimens were investigated. The chemical consolidation mechanism of porous CRL was revealed by combining with the scanning electron microscope (SEM) images. Research results indicate that the non-granular chemical material (sodium silicate) improves the physico-mechanical properties of porous CRL. The research provides reference for the foundation treatment in coral reefs and the design of consolidation schemes for surrounding rocks of underground spaces in marine engineering operations.

Dysprosium chromite nanoparticles: A promising photocatalyst for the remediation of ciprofloxacin and methylene blue from wastewater

A facile sol-gel synthesis method was employed to synthesize dysprosium chromite (DyCrO3) nanoparticles (NPs), which were subsequently calcined at 750 ∘C to investigate their structural, optical, and photocatalytic properties. Rietveld refinement of powder X-ray diffraction patterns revealed a single-phase orthorhombic structure for the DyCrO3 NPs, exhibiting good crystallinity and belonging to the Pbnm space group. Furthermore, Field Emission Scanning Electron Microscopy and Transmission Electron Microscopy imaging revealed a promising morphology with an average particle size of 30 ± 7 nm. Optical assessments indicated an energy band gap of 2.72 eV, suggesting potential for solar light harvesting as a photocatalyst. Mott-Schottky plots confirmed the n-type semiconductor behavior of the DyCrO3 NPs, and valence band X-ray photoelectron spectroscopy analysis supported these findings. Electronic band structure calculations showed a substantial conduction band minimum and a positive valence band maximum, essential for promoting oxygen reduction and oxidation reactions, respectively, which are crucial for photocatalytic activity. Moreover, the synthesized DyCrO3 NPs demonstrated significant potential as efficient photocatalysts, effectively decomposing the antibiotic ciprofloxacin (CIP) under solar light. Notably, the DyCrO3 NPs achieved 83 % degradation of CIP within 240 min of solar irradiation. Similarly, under identical conditions, the DyCrO3 NPs photocatalyst showed promise in degrading 70 % of the colored organic pollutant methylene blue (MB). Interestingly, during the first 120 min, the degradation of CIP was approximately 25 % greater than that of MB. The ability of DyCrO3 NPs to efficiently degrade both colored and colorless pollutants highlights the catalyst’s inherent photocatalytic nature, rather than merely relying on dye-sensitization effects. Furthermore, the presence of DyCrO3 NPs reduced the activation energy for CIP degradation from 31.657 kJ mol−1 K−1 to 20.846 kJ mol−1 K−1, providing additional evidence of their true catalytic efficacy. Apparent quantum yield values of 40 % for CIP and 35 % for MB degradation further illustrate their superior solar energy harvesting ability. A comprehensive mechanism was developed to explain the impressive photocatalytic performance of the synthesized NPs, highlighting their potential in degrading pharmaceutical antibiotics.

Deep Learning-Based Microscopic Damage Assessment of Fiber-Reinforced Polymer Composites.

Fiber-reinforced polymers (FRPs) are increasingly being used as substitutes for traditional metallic materials across various industries due to their exceptional strength-to-weight ratio. However, their orthotropic properties make them prone to multiple forms of damage, posing significant challenges in their design and application. During the design process, FRPs are subjected to various loading conditions to study their microscopic damage behavior, typically assessed through scanning electron microscopy (SEM). While SEM provides detailed insights into fracture surfaces, the manual analysis of these images is labor-intensive, time-consuming, and subject to variability based on the observer's expertise. To address these limitations, this research proposes a deep learning-based approach for the autonomous microscopic damage assessment of FRPs. Several computationally efficient pre-trained deep learning models, such as DenseNet121, NasNet Mobile, EfficientNet, and MobileNet, were evaluated for their performance in identifying different damage modes autonomously, thus reducing the need for manual interpretation. SEM images of FRPs with five distinct failure modes were used to validate the proposed method. These failure modes include three fiber-based failures such as fiber breakage, fiber pullout, and mixed-mode failure, and two matrix-based failures such as matrix brittle failure and matrix ductile failure. The entire dataset is divided into train, validation, and test sets. Deep learning models were established by training on train and validation sets for five failure modes, while the test set was used as the unseen data to validate the models. The models were assessed using various evaluation metrics on an unseen test dataset. Results indicate that the EfficientNet model achieved the highest accuracy of 97.75% in classifying the failure modes. The findings demonstrate the effectiveness of employing deep learning techniques for microscopic damage assessment, offering a more efficient, consistent, and scalable solution compared to traditional manual analysis.

Scanning electron microscopy imaging of multilayer-doped GaN: Effects of surface band bending, surface roughness, and contamination layers on doping contrast

Dopant profiling by a scanning electron microscope possesses great potential in the semiconductor industry due to its rapid, contactless, non-destructive, low cost, high spatial resolution, and high accuracy characteristics. Here, the influence of plasma and wet chemical treatments on doping contrast was investigated for a multilayered p-n GaN specimen, which is one of the most promising third-generation wide bandgap semiconductors. Angle-resolved x-ray photoelectron spectroscopy and atomic force microscope were employed to characterize the degree of surface band bending, surface roughness, gallium oxides, and hydrocarbons on the surface of GaN. N2 and air plasmas were unable to remove the surface contamination layers, although the degree of surface band bending was suppressed. In contrast, wet chemical methods offer superior capability in removing contamination layers; however, the surface roughness was increased to varying degrees. Notably, NH4F solution is capable of improving the doping contrast. The underlying mechanism was elucidated from the perspective of surface band bending, surface roughness, and contamination. The findings reported here will provide a feasible solution for effective characterization of semiconductor materials and devices.

A novel murine wire injury based infectious endocarditis model aggravates aortic valve stenosis

Abstract Background With mortality rates up to 40% under optimal medical treatment, infectious endocarditis (IE) remains one of the deadliest infectious diseases worldwide. In the early stages of IE, diagnosis and treatment initiation are often difficult and research is limited due to unreliable models. Most murine IE models rely on permanent prosthetic wire placement and i.v. bacterial injection, thus making it inadequate to study IE pathophysiology with the aim to reflect the course of disease in humans. Purpose To develop a reproducible murine IE model, based on aortic valve injury preceding intravenous S. aureus bacterial challenge. Methods Using a wire-injury model we induced endothelial lesions. 72h after wire injury (WI), we performed intravenous bacterial challenge using S. aureus. Echocardiography was performed to confirm bacterial vegetations and assess valvular function. Cross sections of valvular leaflets were prepared for scanning electron microscopy (SEM). Macrophage, neutrophil and S. aureus-specific immunofluorescence staining was performed and visualized by immunofluorescence microscopy. Bacterial cultivation was performed from peripheral blood and valve tissue. Systemic immune response was analyzed using flow cytometry. Results WI induced endothelial damage in all mice verified using scanning electron microscopy imaging. The combination of wire injury and injection of 10^5 and 10^6 colonies-forming-units (CFU) S. aureus reliably caused IE validated via in vivo echocardiography as well as S.aureus immunofluorescence staining and SEM imaging. Mice undergoing bacterial challenge responded with significant neutrophilia in the blood. Using 10^6 CFU S. aureus was associated with an increased mortality, liver and kidney abscess formation due to systemic bacterial spreading compared to 10^5 CFU. Peak velocity across the aortic valve and aortic valve leaflet thickness as markers for aortic valve stenosis were increased in IE compared to WI only mice. Aortic regurgitation was more prevalent after bacterial challenge mediating increased left ventricular volumes, meanwhile ejection fraction was not altered. Immunofluorescence staining revealed a pro-inflammatory milieu including increased macrophage and neutrophil infiltration in IE compared to WI only mice. Conclusion In vivo echocardiography and ex vivo histological staining revealed reliable IE induction using our new model with S. aureus concentrations of 10^5 CFU. The additional bacterial challenge causes a more severe aortic valve stenosis compared to WI only mice, potentially due to an observed increase in valvular inflammation.Fig 1 Scanning electron microscopy after

Role of Ti3AlC2 MAX phase in regulating biodegradation and improving electrical properties of calcium silicate ceramic for bone repair applications

Calcium silicate ceramic is a promising bioceramic for various biomedical applications, but its high biodegradation rate and low strength restrict its clinical utility. As a result, the study devised an innovative solution to address these issues by utilizing the titanium aluminum carbide phase, potentially for the first time in biological applications, in conjugation with hydroxyapatite. Then, using powder metallurgy technology, they added these phases to calcium silicate to create nanocomposites. After soaking in simulated body fluid for ten days, the produced nanocomposites were assessed for bioactivity and biodegradability using scanning electron microscopy, inductively coupled plasma-atomic emission spectroscopy, and weight loss assays. Their electrical and dielectric properties were also measured before and after soaking in the simulated body fluid solution. Furthermore, the tribo-mechanical properties of all sintered samples were measured. Interestingly, adding 40% hydroxyapatite nanoparticles to calcium silicate reduced the porosity from 12 to 6%. However, adding five vol% of the titanium aluminum carbide phase to the same sample increased the porosity to 8%. Importantly, these recorded percentages of porosity were comparable to those of compact bone porosity, which range from 5 to 13%. The addition of hydroxyapatite and titanium aluminum carbide phase significantly improved the rapid biodegradation of calcium silicate, albeit with a slight decrease in its bioactive properties, as evidenced by the incomplete surface coverage of the samples with the hydroxyapatite layer in the scanning electron microscopy images. The electrical properties of the nanocomposites were better with the addition of hydroxyapatite and titanium aluminum carbide phase, which helped the bone heal faster. The addition of a titanium aluminum carbide phase significantly improved the mechanical properties of the resulting nanocomposites. For example, the calculated values for compressive strength of all examined samples were 131, 115, 105, 147, and 135 MPa. Based on the results, the prepared samples can be used in orthopaedic and dental applications.

Microstructure evolution of AlSi10Mg alloy in RAP process

Abstract With its computer-aided layer-by-layer production approach, additive manufacturing (AM) and powder bed laser fusion (PBLF) paved the way to produce metallic parts more precisely than any other manufacturing technique. However, the combinability and the interaction of this relatively new manufacturing technique with the other near-net shape production techniques is still a mystery. In this study, the recrystallization and partial melting (RAP) behavior, which is a feedstock production approach for semisolid forming methods, investigated on AlSi10Mg parts produced by PBLF and conventional casting were compared in terms of microstructural and hardness evaluations. After the reheating process, the globalization of Si particles and the breakdown of the Si network around the melt pools were displayed with light microscope and scanning electron microscope (SEM) images. The hardness values of the PBLF as-fabricated specimens were found to be significantly higher than the as-cast specimens; however, the values were almost equaled after the RAP treatment and even got lower on the bottom and top regions of the PBLF samples after 20 min of reheating because of the enlarged shrinkage porosities and the coarsened morphology.

Natural Kapok fiber-derived two-dimensional carbonized sheets as sustainable electrode material

Natural biomass-derived carbon nanostructures have attracted research interest because of their unique surface and electrochemical properties. The present study embodied the carbonized micro-nano sheets derived from the low-cost natural source Kapok silk fiber. The material was obtained via a facile thermal pyrolysis process. Diffraction analysis showed a broad graphene structure-like peak, indicating the formation of graphene-like carbon nanosheets. Field emission scanning electron microscope (FESEM) and transmission electron microscopic (TEM) images confirmed the presence of fragmented carbon sheets, some of which were folded to form a tube-like structure. Raman study showed the presence of D and G band with the Id/Ig ratio of 0.96 which indicated the formation of few-layered carbon nanosheets. Furthermore, the electrochemical performance was evaluated for lithium-ion battery as well as supercapacitor. A specific capacity of 465 mAh.g-1 at 0.1 C rate for Li-ion battery and a specific capacitance of 473.61 F.g-1 for supercapacitor have been obtained with the capacitance retention of 95 %. This study provides insights into a strategy for the sustainable production and utilization of natural fiber-based carbon in energy storage systems.

Synthesis of Porous Carbons from Candlenut Shells Using Various Types of Activators for Environmentally Friendly Supercapacitors

Porous carbon is one of the promising electrode materials for supercapacitors due to its unique and engineerable microstructural properties. The study of the synthesis of porous carbon from waste biomass is very important due to the abundance of natural resources, low cost production and contribute to solving environmental problems. In this study, porous carbons derived from candlnut shell with various type of activator was studied the chemical structural, morphological and electrochemical properties then evaluated as electrodes for supercapacitor. We have been successfully synthesized of porous carbon from candlenut shells using three steps of the process, i.e.: carboni-zation, activation and calcination. Carbonization was carried out at 700°C in a furnace using a closed crucible to minimize the oxygen. The chemical activation conducted using three types of activators, i.e. ZnCl2, H3PO4 and KOH then calcination process by heated at 800°C for 1 h under Ar flow. The results of the Fourier-transform infrared (FTIR) analysis show that the carbonization process increases the content of aromatic C=C functional groups and reduce the OH, C-H, C-O and C=O functional groups. The carbonization process has also increased the electrical conductivity of the sample around 0.8525 S/m. The results of Scanning Electron Microscope (SEM) images can be observed that the activation process of carbon has formed which was indicated by the appearance of many pores on the surface area of carbon. N2 adsorption/desorption isotherms (Brunauer–Emmett–Teller (BET)) characterization was indicated that the porous carbon is dominated by mesoporous with a pore size around 2-50 nm. BET characterization also can be determined the surface area of porous carbon around 477 m2/g for ZnCl2, 636 m2/g for H3PO4, and 681 m2/g for KOH. This synthesized materials are further employed in a symmetric supercapacitor using simple glass cell. The best performance of supercapacitor achieved by KOH porous carbons with 16.30 F/g of specific capacitance, 2.26 Wh/kg of energy density and 1038 W/kg of power density.

Supercapacitor performance of pure and Ni-doped CuO electrodes prepared by USP

Abstract In this study, the ultrasonic spray pyrolysis (USP) technique was employed to fabricate pure and 3% Ni-doped copper oxide (CuO) thin films (TFs) on In2O3/SnO2 (ITO) coated glass substrates. X-ray diffraction (XRD) analysis revealed that both pure and 3% Ni-doped CuO TFs exhibit a polycrystalline structure with the tenorite phase oriented preferentially along the (111) plane. The morphological properties of these TFs were characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM) imaging. Energy dispersive x-ray spectroscopy (EDX) confirmed that the deposited TFs achieved the desired stoichiometry. The electrochemical properties of both pure and 3% Ni-doped CuO TFs were evaluated using cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), and electrochemical impedance spectroscopy (EIS). At a current density of 1 A g−1, the specific capacitance of pure CuO TFs was found to be 78 F g−1, decreasing to 2 F g−1 at 10 A g−1. In contrast, Ni-doped CuO TFs exhibited a specific capacitance of 722 F g−1 at 1 A g−1 and 20 F g−1 at 10 A g−1. These results indicate that Ni-doping significantly enhances the specific capacitance values. The superior performance of Ni-doped CuO electrodes is attributed to their uniformly high porosity and improved electrical conductivity, demonstrating Ni-doped CuO as an optimal electrode material for pseudocapacitors.

Synthesis mechanism of TiO2 nanoparticles via high frequency electrical arc discharge

Abstract The rapid advancement of nanofabrication techniques has significantly increased the utilization of nanoparticles in recent years. This study investigates the synthesis of titanium dioxide (TiO₂) nanoparticles, highlighting their unique properties and diverse applications across scientific and industrial fields. These nanoparticles are valued for their biocompatibility and advantageous optical, electrical, and physical properties. Various synthesis methods—chemical, physical, and biological—are reviewed, with a particular focus on the electric arc discharge method. This method is distinguished by its efficiency and environmental friendliness, enabling the production of highly pure nanoparticles. Utilizing continuous and alternating sparks between two electrodes, the technique generates spherical nanoparticles with adjustable sizes, controlled by the energy of each spark. An RC circuit-based device was designed for this electrical discharge process. Dynamic light scattering (DLS) measurements revealed an average particle size of 164.51 nm with a standard deviation of 44.08 nm. Field emission scanning electron microscopy (FESEM) images showed both solid and hollow spherical TiO2 nanoparticles. Energy dispersive spectroscopy (EDS) confirmed that the TiO2 particles contained only titanium and oxygen, with no other elements detected. X-ray diffraction (XRD) analysis verified the crystal structure, predominantly identifying the anatase phase of the synthesized nanoparticles. This research enhances the understanding of TiO2 nanoparticle synthesis and characterization, providing a foundation for future innovations in their extensive applications.

Green synthesis of silver nanoparticles containing Cichorium intybus to treat the sepsis-induced DNA damage in the liver of Wistar albino rats

Abstract Sepsis is a severe reaction of the body to an infection, presenting a critical medical crisis. It represents an imbalance between the body’s anti- and pro-inflammatory reactions. The occurrence of sepsis, which leads to multiple-organ failure and increased mortality, is marked by dysfunction in the lungs, heart, kidneys, and liver. The involvement of reactive oxygen species is believed to contribute to the progression of sepsis. Data suggest potential advantages of phenolic compounds derived from plants in combating sepsis. Plant polyphenols can be antioxidants by scavenging free radicals, chelating metals, and binding to proteins. In this research, silver nanoparticles (AgNPs) were produced by the aqueous extract of Cichorium intybus leaf for the purpose of treating sepsis-induced DNA harm. The recent study focused on the biological aspect including the cytotoxicity properties on normal (HUVEC) cell line. The AgNPs were analyzed by transmission electron microscopy (TEM), scanning electron microscopy (SEM), fourier-transform infrared spectroscopy, X-ray powder diffraction, and UV-Vis. The TEM and SEM images of AgNPs exhibited the average size of 35.29 nm with spherical morphology. In the in vivo study, the animals were categorized into four groups: sepsis-induced, sham, AgNPs-20, and AgNPs-100. AgNPs treatment resulted in a significant decrease in tissues damage (p < 0.01). The sepsis-induced group showed a significantly elevated malondialdehyde (MDA) level in comparison to the sham group (p < 0.01). Nevertheless, the groups that received AgNPs experienced a decrease in MDA levels and an increase in glutathione and superoxide dismutases levels (p < 0.01). Additionally, the rats treated with AgNPs exhibited a reduction in the IL-1β mRNA expression levels (p < 0.01).

Enabling Electric Field Model of Microscopically Realistic Brain.

Modeling brain stimulation at the microscopic scale may reveal new paradigms for a variety of stimulation modalities. We present the largest map of distributions of the extracellular electric field to date within a layer L2/L3 mouse primary visual cortex brain sample, which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250×140×90 μm 3 volume). We used the map to identify microscopic perturbations of the extracellular electric field and their effect on the activating thresholds of individual neurons. Previous relevant studies modeled a macroscopically homogeneous cortical volume. Result: Our immediate result is a reduction of the predicted stimulation field strength necessary for neuronal activation by a factor of approximately 0.7 (or by 30%) on average, due to microscopic perturbations of the extracellular electric field-an electric field "spatial noise" with a mean value of zero. Although this result is largely sample-specific, it aligns with experimental data indicating that existing macroscopic theories substantially overestimate the electric fields necessary for brain stimulation. Currently, there is a discrepancy between macroscopic volumetric brain modeling for brain stimulation and experimental results: experiments typically reveal lower electric intensities required for brain stimulation. This study is arguably the first attempt to model brain stimulation at the microscopic scale, enabled by automated analysis of modern scanning electron microscopy images of the brain. The immediate result is a prediction of lower electric field intensities necessary for brain stimulation, with an average reduction factor of 0.7.

Impact of Biofilms on Surface Properties of Polymethyl Methacrylate (PMMA) Resins.

Poly(methyl methacrylate) (PMMA) resins are widely used in medical and dental applications. Their susceptibility to bacterial biofilm formation poses significant challenges related to material degradation and infection risk. This study investigated the effects of Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) biofilms on PMMA resin surface properties over a 45-day period at 35°C. The study examined various parameters including biofilm adhesion, morphology, surface roughness, hydrophobicity, solid fraction, and zeta potential. PMMA resin specimens were inoculated with bacteria and incubated for 45 days. Biofilm adhesion was visually assessed, while surface characterization was conducted using scanning electron microscopy (SEM), atomic force microscopy (AFM), roughness analysis, contact angle measurements, solid fraction determination, and zeta potential analysis. The P. aeruginosa and S. aureus isolates were selected based on their biofilm-positive characteristics, which were further confirmed using Congo red and biofilm formation assays through crystal violet staining and spectrophotometric analysis. The results demonstrated robust biofilm adhesion on PMMA surfaces. SEM and AFM imaging revealed textured surfaces with elevated structures and depressions within the biofilm matrix. Biofilm-exposed resins exhibited significantly increased roughness (Ra = 164.5 nm, Rq = 169.5 nm) and hydrophobicity (mean angle = 85.5°-90.5°) compared to control samples (Ra = 38-50 nm, angle = 55°). Solid fraction measurements indicated a denser biofilm matrix on exposed resins (0.908) compared to controls (0.65). Additionally, zeta potential values were more negative for biofilm-exposed resins (mean = -84.2 mV) than controls (-45.0 mV). These findings underscore the substantial alterations in PMMA resin surface properties induced by bacterial biofilms, emphasizing the critical need for strategies to prevent biofilm formation and mitigate associated risks in healthcare settings. Future research should focus on developing anti-biofilm coatings or treatments to preserve the integrity and functionality of PMMA materials.

Microstructural Insights and Composition Analysis of a Fractured Brass Alloy of Unknown Composition Using Advanced Characterization Techniques

This study aims to investigate the microstructural and compositional characteristics of a fractured brass alloy using energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The brass alloy, initially of unknown composition, was determined to consist of 63.65 at% Cu, and 36.35 at% Zn through EDS analysis. Microstructural analysis via SEM revealed a typical ductile fracture pattern with necking and dimple formation, indicative of significant plastic deformation prior to failure. The alloy exhibited a grain size ranging from 10 µm to 80 µm and displayed both α-phase (FCC structure) and possible traces of β-phase, though XRD detected only the α-phase due to the low concentration of β-phase (<5%). A comparison between EDS and XRD results for analyzing chemical composition showed that good assumptions could be made from the lattice parameters of the binary alloy following Nelson-Relay function to predict an approximate composition in binary alloy systems, which was found to be 34 at% Zn in our analysis. Furthermore, the sample’s crystal structure was confirmed as FCC, which supported its observed ductility and plastic deformation prior to fracture. The SEM analysis also confirmed that the material went through an annealing process. The fracture morphology further showed that the FCC structure and the annealing process both contributed to the material’s increased ductility. The analysis further confirms that EDS is more reliable for chemical composition determination in an alloy system. Evidence of annealing twinning in the SEM images suggests the sample underwent an annealing process, contributing to its enhanced ductility.

A 23-million-year record of morphological evolution within Neotropical grass pollen.

Grass-dominated biomes in South America comprise c. 20 million years of history, yet their evolution and underlying drivers remain poorly understood. Here we apply a novel approach that combines scanning electron microscopy imaging with computational analysis to quantify the morphometrics of grass (Poaceae) pollen micro-ornamentation from the Neotropics since the Early Miocene (23 million years ago). Three spatial-temporal pollen sets were assembled to further elucidate the variation and evolutionary traits of grasses through space and time. Our results reveals that three spatial-temporal pollen groups occupy unique, partially overlapping regions of their exine morphospace. The direction of this shift is consistent over time, progressing towards less dense ornamentation. Interestingly, the extent of the occupied morphospace did not vary significantly. This is the first time that the true morphological variation in Poaceae pollen micro-ornamentation becomes apparent through time. We hypothesize that changes in grass pollen exine since the Early Miocene were driven by evolutionary processes (evolutionary drift and/or directional selection), and potentially migration at the continental scale. The high diversity in pollen micro-ornamentation is likely related to their evolutionary success in the Neogene.

Segmentation and Metallographic Evaluation of Aluminium Slurry Coatings Using Machine Learning Techniques

Analysis of scanning electron microscope (SEM) images is crucial for characterising aluminide diffusion coatings deposited via the slurry route on steels, yet challenging due to various factors like imaging artefacts, noise, and overlapping features such as resin, precipitates, cracks, and pores. This study focuses on determining the thicknesses of the coating layers Fe2Al5 and, if present, FeAl, pore characteristics, and chromium precipitate fractions after the heat treatment that forms the diffusion coating. A deep learning SEM image segmentation model utilising U-Net architecture is proposed. Ground truth data were generated using the trainable Weka segmentation plugin in ImageJ, manually refined for accuracy, and supplemented with synthetic data from Blender 3D software for data augmentation of a limited number of SEM label images. The deep learning model trained on a combination of synthetic and real SEM data achieved mean dice scores of 98.7% ± 0.2 for the Fe2Al5 layer, 82.6% ± 8.1 for pores, and 81.48% ± 3.6 for precipitates when evaluated on manually labelled SEM data. The deep learning procedure was applied to evaluate a series of SEM images of diffusion coatings obtained with three different slurry compositions. The evaluation revealed that using a slurry without a rheology modifier may lead to a thicker partial Fe2Al5 layer that is formed by inward diffusion. The relation between the outward and inward diffusion Fe2Al5 layers was not affected by the coating thickness. The thinner diffusion coating presents lower pores and chromium precipitate fractions independently of the slurry selected.