Characterization Of Materials Research Articles

Contribution of (Bi0.45Y0.05)Na0.5ZrO3 to induced multiphase coexistence and enhanced piezoelectric properties of K0.5Na0.5NbO3 lead-free ceramics

In this study, new lead-free piezoelectric ceramics (1 − x)(K0.5Na0.5)NbO3 (KNN)–x(Bi0.45Y0.05)Na0.5ZrO3 (BYNZ) were prepared using the conventional solid-state method, and the relation between the doping amount of (Bi0.45Y0.05)Na0.5ZrO3 and its structural and electrical properties was investigated. Material characterization and structural analyses conducted using X-ray diffraction, scanning electron microscopy, and energy-dispersive spectroscopy revealed that KNN–BYNZ ceramics formed single perovskite structures at 0 ≤ x ≤ 0.05 and a polycrystalline phase boundary (PPB) for the coexisting orthorhombic and tetragonal phases at 0.03 ≤ x ≤ 0.05. The ceramics exhibited desirable electrical properties at x = 0.04 (d33 = 305 pC/N, kp = 45.8 %, εr = 1316, tanδ = 2.4 %, Tc = 332 °C, Pr = 28.2 μC/cm2, and Ec = 1.07 kV/mm). These findings reveal that the introduction of BYNZ enables the effective formation of a PPB in the KNN-based ceramics to improve their electrical properties, and they exhibit potential for replacing lead-based piezoelectric ceramics in the market.

Iron-based graphene composite for oxygen reduction reaction in alkaline media: Electrocatalytic activity and lifetime evaluation

The development of electrocatalysts with high catalytic performance and low costs for oxygen reduction reaction (ORR) is still challenging. Herein, an overall solution for ORR in alkaline media, from the catalyst synthesis to catalyst regeneration and to the development of a rapid, reliable and easy approach for electrode surface evaluation, is presented. We focused on the development and characterization of an efficient material for ORR in alkaline media, α-Fe2O3 N-doped graphene (Fe-N-Gr). The associative pathway for the four electron transfer ORR mechanism is sustained by the Raman spectra recorded from the electrode surface. We highlighted the possibility of catalyst regeneration by a simple electrochemical method. After two regeneration rounds and 1500 cycles in O2-saturated 1 M NaOH, the catalyst still retains 40.8 % catalytic activity. Finally, as a part of the overall solution, we demonstrated that a methodology based on Raman spectroscopic measurements and machine learning algorithms can be applied for graphene-based catalysts lifetime investigation.

Micro and macro mechanical characterization of artificial cemented granular materials

Micro and macro mechanical characterization of artificial cemented granular materials

Fabrication of sulfobutylether-β-cyclodextrin/glabridin inclusion complex for promoting bioactivities.

As the main active compound of Glycyrrhiza glabra L., glabridin (GLD) has been shown to have multiple bioactivities, whereas the clinical application of GLD is restricted by its low water solubility. In this study, GLD was encapsulated into a sulfobutylether-β-cyclodextrin (SBE-β-CD)-based inclusion complex (SBE-β-CD/GLD) by the freeze-drying method. The materials characterization, antibacterial activity, stimulated cellular behavior and in vivo full-thickness diabetic wound healing ability of the hydrogels were assessed and analyzed. The successful encapsulation of the inclusion complex was confirmed by ultraviolet (UV) visible spectroscopy, Fourier transform infrared (FT-IR), X-ray diffractometer (XRD), scanning electron microscope (SEM), and nuclear magnetic resonance (NMR). SBE-β-CD as an excipient significantly enhances the water solubility of GLD, and SBE-β-CD/GLD showed excellent biocompatibility on human vascular endothelial cells (HUVECs) and erythrocytes. The SBE-β-CD/GLD inclusion complex exerted a pronounced antibacterial activity on Staphylococcus aureus and Escherichia coli in vitro. The SBE-β-CD/GLD inclusion complex markedly enhanced the antioxidant activity compared with free GLD. The SBE-β-CD/GLD inclusion complex potently accelerates the healing of full-thickness skin defects by inhibiting inflammation. The outcomes suggest that SBE-β-CD could be used as a promising drug delivery system for the clinical application of GLD.

A step towards 6D WAXD tensor tomography.

X-ray scattering/diffraction tensor tomography techniques are promising methods to acquire the 3D texture information of heterogeneous biological tissues at micrometre resolution. However, the methods suffer from a long overall acquisition time due to multi-dimensional scanning across real and reciprocal space. Here, a new approach is introduced to obtain 3D reciprocal information of each illuminated scanning volume using mathematic modeling, which is equivalent to a physical scanning procedure for collecting the full reciprocal information required for voxel reconstruction. The virtual reciprocal scanning scheme was validated by a simulated 6D wide-angle X-ray diffraction tomography experiment. The theoretical validation of the method represents an important technological advancement for 6D diffraction tensor tomography and a crucial step towards pervasive applications in the characterization of heterogeneous materials.

Use of Particle Induced X-Ray Emission (PIXE), Particle Induced Gamma–Ray Emission (PIGE), Energy Dispersive X-Ray Analysis (EDXRF) for Detecting Trace Elements in Soil Sample

This review paper provides a comprehensive overview of trace element analysis utilizing ion-atom interaction techniques, focusing on the principles, applications, and recent advancements in the field. Ion- atom interaction techniques, including Particle Induced X- Ray Emission (PIXE), Particle Induced Gamma–Ray Emission (PIGE), Energy Dispersive X-Ray Analysis (EDXRF), Rutherford backscattering spectroscopy (RBS), and ion beam analysis (IBA), offer unique capabilities for trace element analysis with high sensitivity and minimal sample preparation requirements. The paper begins by discussing the fundamental principles underlying ion-atom interaction techniques, such as the interaction of energetic ions with matter and the subsequent detection of emitted X-rays or backscattered particles. It explores the various analytical parameters that influence the sensitivity, resolution, and depth profiling capabilities of these techniques. Furthermore, the review highlights the diverse applications of ion-atom interaction techniques in fields such as environmental monitoring, forensic science, biomedical research, and materials science. It showcases how these techniques have been utilized to address specific analytical challenges, such as the detection of trace contaminants in environmental samples or the characterization of thin films and semiconductor materials.

Furfuryl-pyridinium-functionalization of flaxseed gum for effective methylene blue removal from aqueous solution

This study describes the modification of flaxseed gum (FSG) via furfural to obtain a furfuryl-pyridinium modified adsorbent (Flax-Py) with improved adsorption characteristics towards methylene blue (MB) versus results obtained for unmodified FSG. Materials characterization was achieved via complementary spectral methods (NMR, XPS, FT-IR, Raman, and XRD) as well as thermogravimetric analysis (TGA) and ζ-potential measurements. Synthetic modification of FSG to yield Flax-Py that led to enriched N-content (2.75 atom-% versus 0.76 atom-% for FSG), and the conversion of furfuryl to pyridinium was incomplete (ca. 50 %), where both moieties were identified. The adsorption isotherms at pH 7 showed that FSG possessed favorably high MB adsorption capacity (ca. 540 mg/g); while Flax-Py was notably lower (167 mg/g). Flax-Py facilitated 100% MB removal at low dye concentrations (1-10 mg/L) versus 50 % MB removal for the FSG bioadsorbent. Further, Flax-Py showed moderate loss of adsorption with MB (ca. 12-16% dye removal) after five regeneration cycles. Flax-Py revealed improved settling characteristics (time, density, gelation, and solubility) that facilitate enhanced phase separation of FSG fractions by circumventing the need for centrifugation. The adsorption mechanism was attributed primarily to H-bonding effects with secondary contributions due to electrostatic interactions (negative ζ-potential above pH 4.2). This study successfully modified FSG to obtain an improved adsorbent (Flax-Py) attaining nearly quantitative MB dye removal at environmentally relevant concentrations (< 15 mg/L).

The effect of span length on the flexural properties of glass and basalt fiber reinforced sandwich structures with balsa wood core for sustainable shipbuilding

The current research aims to analyze the mechanical characterization of sandwich materials through a three-point flexural test. The sandwich structures in question composed of balsa wood as a core and four different types of fiber reinforced vinyl ester composite facesheets, namely Glass, Basalt, Glass/Carbon, and Basalt/Carbon. The sandwich panels were prepared using the vacuum infused processing method. The primary objective of this investigation is to explore the feasibility of utilizing basalt fibers for the production of structural parts in shipbuilding and replacing the already existed glass fibers. The flexural test was carried out with using three-point flexural test with varying the span length from 120 mm, 180 mm, 220 mm. Additionally, an analysis of variance (ANOVA) was then carried out to compare the mean values of properties deduced from these tests. The results showed that using basalt fibers instead of glass fiber reinforced enhanced the flexural stiffness of the sandwich structure. The flexural strength and modulus are shown to depend on span length and fiber type. The flexural modulus increases with an increase in span length. Similarly, flexural strength increases in glass fiber-based structures, while a slight reduction is observed in basalt fiber-reinforced structures the larger span length. The findings of this research suggest that basalt fibers hold potential as a replacement for glass fibers in producing structural components in shipbuilding. These results offer valuable information that can aid in the design and optimization of sandwich materials in shipbuilding.

Machine Learning‐Enhanced Prediction of Inorganic Semiconductor Bandgaps for Advancing Optoelectronic Technologies

AbstractA pivotal challenge in advancing inorganics optoelectronic technologies, is the precise characterization of materials' electronic attributes, with the bandgap being a critical property. Conventional approaches, heavily reliant on time‐intensive and financially demanding experimental and computational methods, such as density functional theory (DFT) calculations, face limitations due to inherent estimation errors. Machine learning methodologies are developed for the prediction of bandgaps of inorganic semiconductors but most of them are employed for datasets created by DFT calculations, hence limiting their performance. Addressing this, the study leverages machine learning methodologies, harnessing both compositional and structural features, to predict the band gaps of inorganic semiconductors with enhanced accuracy. This advancement is reinforced by the employment of an experimental bandgap dataset, which, when integrated with structural descriptors obtained from the Materials Project, significantly improves prediction capabilities. This is evidenced by the model's exceptional performance across two distinct benchmark datasets. Furthermore, the model's adeptness in predicting formation energies underscores its versatility and applicability to a broad spectrum of electronic properties. These findings suggest that the predictive accuracy of this model can be further augmented through the inclusion of additional experimental bandgap measurements and the refinement of structural descriptors. This approach offers a promising and efficient alternative to traditional methodologies, potentially accelerating the development of optoelectronic technologies.

Enhancing Aluminium‐Ion Battery Performance with Carbon Xerogel Cathodes

This study presents groundbreaking results in the field of rechargeable aluminium‐ion batteries, achieving stable capacities exceeding 300 mA h g‐1 for more than 300 cycles. The key to this achievement lies in the utilization of tailor‐made carbon materials and a urea‐AlCl3‐based electrolyte. The article investigates the optimal physicochemical properties of the active material necessary for effective electrodes for these aluminium‐ion batteries. This investigation employs a wide range of materials characterization techniques (XRD, SEM‐EDX, N2 adsorption‐desorption isotherms, Hg porosimetry, XPS, FTIR, Raman and TEM‐EDX) and electrochemical performance analyses to delve into the subject. These findings represent a significant improvement in the capacity of aluminium‐ion batteries, bringing us closer to their implementation and commercialization. This achievement is attributed to the utilization of readily available, cost‐effective, and non‐corrosive materials. The ability to customize carbon xerogels and the use of the urea‐AlCl3 electrolyte offer promising avenues for the practical implementation of these advanced battery technologies, leading to further enhancements in their performance and widespread adoption in various applications.

Material characterization of graphene and boron nitride for composites reinforcement

In this research work, the characterization studies and property evaluation of graphene and boron nitride were performed through various devices/techniques like Particle Size Analyzer, Powder X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), Transmission Electron Microscopy (TEM) and Thermogravimetric Analysis (TGA). This experimental work is focused on validating the suitability of graphene and boron nitride for use as reinforcement materials for fabricating ceramic matrix composites. The parameters influencing the properties of graphene and boron nitride such as phase change, particle size, number of layers, defects, and the presence of oxygen groups were analyzed and presented. This work is also presented through TGA to ascertain the effect of various factors on graphene and boron nitride such as degradation of carbon and nitride region and also maximum mass change rate (Tmax) due to temperature. It also provides an insight into the graphene’s behaviour in a zero-air environment, showing that the breakdown begins at temperatures below 250 °C and peaks at 850 °C. In which a lower quantity of weight loss occurs therefore in a nitrogen atmosphere at 250 °C. Graphene decomposes at a rate of 69.94 wt% in a zero-air environment, but just 8.78 wt% and also In nitrogen, the breakdown weight percentage of boron nitride is 3.54%, while in a zero-air environment, it is 11.99%.

The Potential for Material Property Characterization Using Physics-Informed Neural Networks and Ultrasonic Wave Data

ABSTRACT There is a need for reliable nondestructive test methods that can collect data from structural members and analyze the results in a rapid and efficient manner. Large amounts of test data are needed to achieve such characterization, which provides additional challenges because of their heterogeneity and complexity. Advances in machine learning, in particular physics-informed neural networks (PINN), offer potential to address these problems. PINN is a particular form of artificial neural networks (ANN) and portends notable advantages over traditional measurand analysis or purely data-driven approaches. Here, we explore the potential of heterogeneous material property characterization using PINN and ultrasonic wave data. First, several types of 1-D ultrasonic wave data are numerically simulated for a spatially heterogeneous material, and then PINN is applied to predict wave velocity, defect location, and energy dissipation. Then, three different types of defects are simulated and all defects are detected using the corresponding 2-D ultrasonic wave data and PINN. The presented results demonstrate the promise of PINN to assist with heterogeneous material characterization methods.

Automated Porosity Characterization for Aluminum Die Casting Materials Using X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning.

Detection and characterization of hidden defects, impurities, and damages in homogeneous materials like aluminum die casting materials, as well as composite materials like Fiber-Metal Laminates (FML), is still a challenge. This work discusses methods and challenges in data-driven modeling of automated damage and defect detectors using measured X-ray single- and multi-projection images. Three main issues are identified: Data and feature variance, data feature labeling (for supervised machine learning), and the missing ground truth. It will be shown that simulation of synthetic measuring data can deliver a ground truth dataset and accurate labeling for data-driven modeling, but it cannot be used directly to predict defects in manufacturing processes. Noise has a significant impact on the feature detection and will be discussed. Data-driven feature detectors are implemented with semantic pixel Convolutional Neural Networks. Experimental data are measured with different devices: A low-quality and low-cost (Low-Q) X-ray radiography, a typical industrial mid-quality X-ray radiography and Computed Tomography (CT) system, and a state-of-the-art high-quality μ-CT device. The goals of this work are the training of robust and generalized data-driven ML feature detectors with synthetic data only and the transition from CT to single-projection radiography imaging and analysis. Although, as the title implies, the primary task is pore characterization in aluminum high-pressure die-cast materials, but the methods and results are not limited to this use case.

Mechanistic insight and first principle analysis of cation-inverted zinc ferrite nanostructure: A paradigm for ppb-level room temperature NOx sensor

Herein, we adopted a new paradigm for developing a high-performance gas sensor by leveraging the mixed spinel ZnFe2O4 structure (mZFO) to enhance the adsorption of NOx molecules. Material characterization reveals the formation of the mZFO due to the cation inversion in lattice sites. The estimated value of the inversion degree is observed to shift from 0.78 to 0.39 with an increase in the calcination temperature. The mZFO nanoparticles calcined at 500 °C show exceptional sensing performance due to their suitable grain size (∼2 times Debye length), neck diameter, and surface area. The sensing studies conducted at various NOx concentrations indicate that the sensor can detect ppb level of NOx with a detection limit of about 9 ppb at room temperature. The detailed sensing mechanism is elucidated based on the density functional theory calculations (DFT) and Bader charge analysis. The outstanding sensor performance is attributed to the formation of a mixed spinel structure, wherein the adsorption energy of NOx (∼-0.6 eV) in the presence of surface adsorbed oxygen is higher than that of the normal spinel structure (∼-0.1 eV). Furthermore, the sensor exhibited a fast response and recovery times (7 and 92 s at 800 ppb NO2), excellent stability, and selectivity. The practical suitability of the mZFO sensor was studied by analyzing the vehicle exhaust emissions. We strongly believe this work would pave a novel approach to developing a high-potential gas sensor by modifying the cation distributions in the spinel ferrites.

Recycling of mineral wool waste as supplementary cementitious material through thermochemical treatment.

Mineral wool is commonly used in construction as thermal insulation material. After the product's lifetime, it is classified as hazardous waste if no trademark of the European Certification Board for Mineral Wool Products (EUCEB) or the German Institute for Quality Assurance and Labelling (RAL) exists. Mineral Wool Waste (MWW) is typically landfilled in Europe, which is challenging due to its low bulk density and dimensional stability. This circumstance highlights the need for alternative recycling methods that increase the recycling rate of construction and demolition (C&D) waste. This article outlines the recycling opportunities of MWW and focuses on the use of thermochemical treatment of different mixtures of input materials to produce a supplementary cementitious material (SCM). The material characterisation results and investigations on the binder suitability demonstrate that the slag fractions after the thermochemical treatment are well-qualified to be used as reactive binder components. Additionally, a material flow analysis was conducted to estimate the substitution potential of MWW as SCM in the Austrian cement industry.

Systematic evaluation of methods for iron-impregnation of biochar and effects on arsenic in flooded soils.

There is a need for innovative strategies to decrease the mobility of metal(loids) including arsenic (As) and cadmium (Cd) in agricultural soils, including rice paddies, so as to minimize dietary exposure to these toxic elements. Iron (Fe)-modified biochars (FBCs) are used to immobilize As and Cd in soil-water systems, but there is a lack of clarity on optimal methods for preparing FBCs because there are only limited studies that directly compare BCs impregnated with Fe under different conditions. There is also a lack of information on the long-term performance of FBCs in flooded soil environments, where reductive dissolution of Fe (oxy)hydroxide phases loaded onto biochar surfaces may decrease the effectiveness of FBCs. This study uses material characterization methods including FTIR, SEM-EDX, BET, and adsorption isotherm experiments to investigate the effects of Fe-impregnation methods (pH, pyrolysis sequence, and sonication) on the morphology and mineralogy of Fe loaded onto the biochar surface, and to FBC adsorbent properties for arsenate (As(V)), arsenite (As(III)), and Cd. Acidic impregnation conditions favored the adsorption of As(III) onto amorphous Fe phases that were evenly distributed on the biochar surface, including within the biochar pore structure. The combination of sonication with acidic Fe-impregnation conditions led to the best adsorption capacities for As(V) and As(III) (4830 and 11,166 μg As g-1 biochar, respectively). Alkaline Fe-impregnation conditions led to the highest Cd adsorption capacity of 3054 μg Cd g-1 biochar, but had poor effectiveness as an As adsorbent. Amending soil with 5% (w/w) of an acid-impregnated and sonicated FBC was more effective than an alkaline-impregnated FBC or ferrihydrite in decreasing porewater As concentrations. The acid-impregnated FBC also had greater longevity, decreasing As by 54% and 56% in two flooded phases, probably due to the greater stability of Fe(III) within the biochar pore structure that may have a direct chemical bond to the biochar surface. This study demonstrates that FBCs can be designed with selectivity towards different As species or Cd and that they can maintain their effectiveness under anaerobic soil conditions. This is the first study to systematically test how impregnation conditions affect the stability of FBCs in soils under multiple drying-rewetting cycles.

Polyaniline impact on graphitic C3N4’s structural and physicochemical properties for high stability energy storage systems: Practical and theoretical studies

Polyaniline (PANI), and graphitic C3N4 (GCU) have a significant research consideration owing to their properties. Accordingly, in the current study, PANI, GCU, and their composite (PANI@GCU) are synthesized through aniline chemical polymerization in the presence of C3N4. Materials characterizations are done using X-ray diffraction (XRD), Scanning electron microscopy (SEM), and Fourier transform infrared (FT-IR). SEM images demonstrate that the PANI layers cover the surface of the GCU. Samples are studied through dielectric and electrochemical analysis. The electrical conductivity value shows a conductivity increment in the order: PANI@GCU > PANI > GCU. The PANI@GCU composite sample exhibits a bulk conductivity of 23 S/m at 10 MHz frequency. It also has a 584 F/g specific capacitance with 96.7 % stability after 1000 cycles at 1 A/g. The results of PANI@GCU supercapcitance performance are supported with molecular modelling that shows the interaction between GCU and PANI through the elongation of C-N bond of PANI beside of GCU layer bending to create arc and arrow structure. The structure of PANI@GCU has a lower band gap (ΔEgap) than PANI, and GCU separately which agrees with the enhancement of the conductivity of the composite than its constituent.

Dynamic Behavior of Twisted UHMWPE Yarns

Twisting is an effective method to reduce defects during fabric weaving and improve the impact resistance of the armor material. This study examined the dynamic behavior of twisted ultra-high molecular weight polyethylene (UHMWPE) yarns. Material characterization was conducted using three-dimensional X-ray tomography and scanning electron microscopy (SEM). Dynamic mechanical experiments were carried out on the twisted yarns using a Kolsky bar, with the yarn's failure process captured in real time through high-speed imaging. Additionally, the digital imaging correlation (DIC) technique was used to calibrate the strain measured by the Kolsky bar. Post-fracture analysis was performed using SEM. The effects of twisting extent, strain rate, and gauge length on the dynamic behavior of twisted yarns were studied. It was observed that all twisted yarns underwent elongation, shrinkage, fiber breakage, untwisting, and yarn rupture. Furthermore, as the twisting degree and strain rate increased, and the gauge length decreased, the fracture area became more concentrated. The tensile strength peaked at a twisting degree of 250 t/m and decreased thereafter. Moreover, twisting was found to alter the sensitivity of the yarns' stress–strain curves and tensile properties to the strain rate and gauge length.

Thermal characterization of ceramic pills at millimeter waves with a TE01δ cavity resonator

The progress in 5G/6G technology prompts a demand for new materials operating at millimeter waves that can be exploited as substrates for electronic devices or antennas. In particular, a rapid development of millimeter wave band communication technology demands ceramic materials, which could be applied in low temperature cofired ceramic (LTCC) techniques. This kind of ceramics should possess a low dielectric constant (εr), a low loss tangent (tanδ) and a near-zero temperature coefficient. In the view of above, there is an urgent need for accurate and reliable material microwave characterization techniques that can operate at frequencies well above 15 GHz. Due to the fact that the majority of LTCC ceramic materials can be produced in a cylindrical shape, a suitable characterization method exploits a cylindrical resonator operating at a TE01δ mode, which is dedicated to the characterization of low-loss materials also as a function of temperature.

Amorphous RhOx decorated black indium oxide for rapid and flexible NO2 detection at room temperature

This paper focuses on the preparation and characterization of a composite material made from rhodium oxide amorphous-decorated black indium oxide (RhOx/B-In2O3) for use in MEMS gas sensors. The sensors were utilized for the detection of NO2 gas at room temperature, and the results demonstrate that the RhOx/B-In2O3 sensor exhibits excellent gas selectivity, high response, and extremely short response/recovery times (8s/17s) to 5 ppm NO2 gas at room temperature. Additionally, a flexible NO2 gas sensor based on the optimized RhOx/B-In2O3 composite was fabricated to expand its application. One of the unique aspects of the RhOx/B-In2O3 material is the existence of defects, which allows for greater adsorption of NO2 gas. This feature contributes to the increased gas selectivity seen in the B-In2O3 material as compared with common In2O3. The chemical sensitization and electron-rich properties of amorphous RhOx further enhance the gas-sensing performance of the material by reducing the reaction activation energy and increasing surface-adsorbed oxygen. Overall, the results demonstrate the potential of the RhOx/B-In2O3 composite material for use in advanced gas-sensing technologies.