Characterization Of Materials Research Articles

An introduction for non-experts on using X-ray micro computed tomography as a tool for pore scale digital subsurface characterisation of siliciclastic materials

This paper presents an overview of using X-ray micro computed tomography (μCT) as a valuable tool for micro scale investigation of siliciclastic materials. When processed using digital image analysis (DIA), valuable quantitative data can be extracted from μCT 3D images. Subsurface reservoirs are of great importance to society as fluid-bearing formations, but also as storage reservoirs for carbon dioxide. μCT imaging has the capability to perform preliminary, highly detailed investigations of potential reservoirs. This approach has a range of benefits when compared to traditional 2D techniques, such as optical and scanning electron microscopy (SEM). Key advantages include the technique being non-destructive and capable of 3D and 4D visualisation. This facilitates rapid repeated digital measurements and experiments on microstructures. Digital samples can also be readily shared within the scientific community to replicate results and quickly launch new investigations. However, limitations still exist, posing challenges to the wider application of such a methodology. Such limitations include the identification of a representative elementary volume (REV), computational cost, and suitable processing of the output image data. Here, we highlight the value of using μCT and DIA, from our first experiences, to facilitate pore scale siliciclastic reservoir characterisation, but also highlight our perceived limitations and barriers to its much wider application. This paper introduces the key processing stages, opportunities and limitations of these techniques.

Dynamical control of nanoscale electron density in atomically thin n-type semiconductors via nano-electric pulse generator.

Controlling electron density in two-dimensional semiconductors is crucial for both comprehensive understanding of fundamental material properties and their technological applications. However, conventional electrostatic doping methods exhibit limitations, particularly in addressing electric field-induced drift and subsequent diffusion of electrons, which restrict nanoscale doping. Here, we present a tip-induced nanospectroscopic electric pulse modulator to dynamically control nanoscale electron density, thereby facilitating precise measurement of nano-optoelectronic behaviors within a MoS2 monolayer. The tip-induced electric pulse enables nanoscale modulation of electron distribution as a function of electric pulse width. We simultaneously investigate spatially altering photoluminescence quantum yield at the nanoscale region. We model the extent of electron depletion region, confirming a minimum doping region with a radius of ∼265 nanometers for a 30-nanosecond pulse width. Our approach paves the way for engineering local electron density and in situ nano-optical characterization in two-dimensional materials, enabling an in-depth understanding of doping-dependent nano-optoelectronic phenomena.

Model-Fitting Weighted Least Squares as an Alternative to Principal Component Analysis for Analyzing Energy-Dispersive X-ray Spectroscopy Spectrum Images.

Spectrum imaging with energy-dispersive X-ray spectroscopy (EDS) has become ubiquitous in material characterization using electron microscopy. Multivariate statistical methods, commonly principal component analysis (PCA), are often used to aid analysis of the resulting multidimensional datasets; PCA can provide denoising prior to further analysis or grouping of pixels into distinct phases with similar signals. However, it is well known that PCA can introduce artifacts at low signal-to-noise ratios. Unfortunately, when evaluating the benefits and risks with PCA, it is often compared only against raw data, where it tends to shine; alternative data analysis methods providing a fair point of comparison are often lacking. Here, we directly compare PCA with a strategy based on (the conceptually and computationally simpler) weighted least squares (WLS). We show that for four representative cases, model fitting of the sum spectrum followed by WLS (mfWLS) consistently outperforms PCA in terms of finding and accurately describing compositional gradients and inclusions and as a preprocessing step to clustering. Additionally, we demonstrate that some common artifacts and biases displayed by PCA are avoided with the mfWLS approach. In summary, mfWLS can provide a superior option to PCA for analysis of EDS spectrum images as the signal is simply and accurately modeled.

Proton-exchange induced reactivity in layered oxides for lithium-ion batteries.

LiNixCoyMn1-x-yO2 (0 < x, y < 1, NCM) is the dominant positive material for the state-of-the-art lithium-ion batteries. However, the sensitivity of NCM materials to moisture makes their manufacturing, storage, transportation, electrode processing and recycling complicated. Although it is recognized that protons play a critical role in their structure stability and performance, proton exchange with Li+ in NCM materials has not been well understood. Here, we employ advanced characterizations and computational studies to elucidate how protons intercalate into the layered structure of NCM, leading to the leaching of Li+ and the formation of protonated NCM. It is found that protonation facilitates cation rearrangement and formation of impurity phases in NCM, significantly deteriorating structural stability. The adverse effects induced by protons become increasingly pronounced with a higher Ni content in NCM. Through a comprehensive investigation into the thermodynamics and kinetics of protonation, we discover that Li deficiencies in NCM materials can be resolved via solution process in the presence of Li+ ions and controlled proton concentration. The underlying mechanism of relithiation is further explored through materials characterizations and kinetics modeling. This work provides crucial insights into controlling structural and compositional defects of Li-ion battery positive material in complicated processing environment.

Characterization and Tribology Performance of Refractory Materials Coatings with Blast Furnace Slag (BFS) Fabricated by Thermal Spraying

Characterization and Tribology Performance of Refractory Materials Coatings with Blast Furnace Slag (BFS) Fabricated by Thermal Spraying

Focus on synthesis, characterization and applications of low dimensional nanomaterials

Abstract This Editorial summarizes the content of a focus collection on the synthesis, characterization and applications of low dimensional materials. The collection groups original research and review articles providing recent results and prospects in the field, with particular attention on optoelectronic applications, dielectric and magnetic properties, and electrochemical and biological performances.

Machine learning enabled fast optical identification and characterization of 2D materials.

Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models - SegNet, 1D U-Net, and 2D U-Net- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective and accessible method for identifying layer thickness in this class of materials.

Fracture mode classification of UHPC based on deep learning and wavelet time–frequency spectrum derived from acoustic emission waveform

Fracture mode categorization is essential in identifying the failure stage, evaluating the damage characteristics, and providing references for structural maintenance. Acoustic emission (AE), as a passive nondestructive technique, presents great potential in damage characterization of cementitious materials and structures. In this study, a convolutional neural network (CNN) and gated recurrent unit (GRU) integrated model for tensile, shear, and mixed cracking mode classification of ultra-high-performance concrete (UHPC) was proposed and verified based on AE waveform features. First, four-point bending and direct shear tests for UHPC specimens were carried out, respectively, to generate AE waveform signals corresponding to different cracking modes (tensile, shear, and mixed ones). Subsequently, the collected one-dimensional AE signals were converted into two-dimensional time–frequency spectrum by continuous wavelet transform. And an AE dataset was constructed after manual labeling based on the corresponding relationships between cracking modes and wavelet time–frequency spectra. Then, an automatic CNN-GRU automatic classification model was established using wavelet time–frequency spectrum images as input variables, where the CNN extracts spatial image features, and the GRU screens time dependencies and realizes final classification. The proposed CNN-GRU model achieves an accuracy of 96.17% for cracking mode classification of UHPC, which is superior both in computational accuracy and efficiency to lightweight CNN and AlexNet. Classification outcomes for UHPC specimen under four-point bending and direct shear tests revealed the proportion of tensile cracks progressively decreased with the increase of damage, while shear cracks exhibited the opposite trend. The percentage of mixed mode cracks maintained relatively stable throughout the failure stages. The model proposed in this study presents favorable performance to reveal the underlying damage evolution mechanism during UHPC fracture, which can also provide references for the cracking mode classification of other cementitious materials and structures.

Ground-Based Remote Standoff Laser Spectroscopies and Reflectance Spectral Imaging for Multimodal Analysis of Wall Painting Stratigraphy.

This paper presents a novel multimodal remote sensing setup to analyze the complex stratigraphy of historical wall paintings at distances of order 10 m. The proposed method enables comprehensive investigation of the chemical composition of multilayer paint stratigraphy by combining standoff laser-induced breakdown spectroscopy for elemental profiling with noninvasive standoff Raman spectroscopy and visible and near-infrared (400-900 nm) reflectance spectral imaging for depth-resolved complementary material characterization from a range of distances with instruments and operators located on stable ground. Following proof-of-concept laboratory tests, the feasibility and effectiveness of this standoff analytical approach is demonstrated through field analysis of a whitewashed historical wall painting, successfully identifying at least seven distinct layers from a distance of 7 m. The remote sensing method presented here can also be applied to other scientific and industrial domains to characterize the chemical composition of layered materials at a distance.

MXene/Bacterial Cellulose Hybrid Materials for Sustainable Soft Electronics

This work evaluated bacterial cellulose (BC) as a possible biodegradable soft electronics substrate in comparison to polyethylene terephthalate (PET), while also focusing on evaluating hybrid MXene/BC material as potential flexible electronic sensor. Material characterization studies revealed that the BC material structure consists of nanofibers with diameters ranging from 70 to 140 nm, stacked layer-by-layer. BC samples produced are sensitive to post-treatment with isopropanol resulting in a change of structural and mechanical properties. The viscoelastic properties of the BC substrates have been studied experimentally in comparison with the PET film. Aged BC substrate showcased similar viscoelastic properties stability, while exhibiting better properties above 70 °C, with total storage modulus change of −15% and loss modulus change of 21%. MXenes prepared using the Minimally Intensive Layer Delamination (MILD) method were screen-printed onto BC substrates and PET films to form MXene/BC (MX/BC) and MXene/PET (MX/PET) devices. The electrical properties results showcased different resistive behavior on both BC and PET substrate samples with different impedance moduli. MX/PET presented lower sheet resistance of around 156 Ω·sq−1, while MX/BC was 2733 Ω·sq−1. Finally, the MX/BC and MX/PET devices were subjected to repeatable quasi-static load tests and the piezoresistive sensing behavior of the devices has been reported.

Performance of superconducting resonators suspended on SiN membranes

Suspending devices on thin SiN membranes can limit their interaction with the bulk substrate and reduce parasitic capacitance to ground. While suspending devices on membranes are used in many fields including radiation detection using superconducting circuits, there has been less investigation into maximum membrane aspect ratios and achievable suspended device quality, metrics important to establish the applicable scope of the technique. Here, we investigate these metrics by fabricating superconducting coplanar waveguide resonators entirely atop thin (∼110 nm) SiN membranes, where the membrane's shortest length to thickness yields an aspect ratio of approximately 7.4×103. We compare these membrane resonators to on-substrate resonators on the same chip, finding similar internal quality factors ∼105 at single photon levels. Furthermore, we confirm that these membranes do not adversely affect resonator thermalization and conduct further materials characterization. By achieving high quality superconducting circuit devices fully suspended on thin SiN membranes, our results help expand the technique's scope to potential uses including incorporating higher aspect ratio membranes for device suspension and creating larger footprint, high impedance, and high quality devices.

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Abstract Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

Design and Experimental Demonstration of Wavelength‐Selective Metamirrors on Sapphire Substrates

The increasing demand for novel mirror coating designs for new generation of gravitational wave detectors is stimulating significant research interest in investigations of reflective properties of metasurfaces. Given this strong interest, this article details a systematic methodology for fabricating reflecting metasurfaces (metamirrors) designed to operate at target wavelengths of 1064 or 1550 nm. The proposed metasurfaces consist of silicon cylindrical nanoparticles placed on a sapphire substrate. First, the dimensional parameters of the structures are thoroughly selected through numerical simulations combined with material characterization. The configurations are subsequently analyzed analytically to reveal the mirror effect, which arises from the excitation of electric and magnetic dipole moments. Following this, the metasurfaces are fabricated and experimentally characterized, demonstrating reflectivity exceeding 95% around the design wavelengths, which is in good agreement with theoretical predictions. Overall, the work demonstrates the feasibility and detailed methodology for the fabrication of thin, lightweight metamirrors capable of achieving near‐perfect reflectivity at the specified target wavelengths.

Smart piezoelectric composite: impact of piezoelectric ceramic microparticles embedded in heat-treated 7075-T651 aluminium alloy

AbstractSignificant advances have been made in material synthesis in the last two decades, with a focus on polymers, ceramics, metals, and smart materials. Piezoelectric-based smart materials generate an electric voltage in response to loads, enabling distributed monitoring in critical structural parts. Friction stir processing (FSP) is a versatile approach that can enhance material performance in various engineering fields. The primary objective of the current research is to examine the sensorial properties of heat-treated AA7075-T651 aluminium plates that have been included with Lead Zirconate Titanate (PZT) and Barium Titanate (BT) particles via FSP. This study includes a comparative analysis of sensitivities with AA5083-H111 self-sensing material, metallographic and physicochemical characterization, and an assessment of the mechanical properties impacted by the incorporation of piezoelectric particles. The sensitivity of AA7075-PZT was found to be significantly higher than that of AA7075-BT. AA7075-PZT achieved a maximum sensitivity of 15.27 × 10−4 μV/MPa while AA7075-BT had a sensitivity of only 7.28 × 10−4 μV/MPa, which is 52% lower. Microhardness and uniaxial tensile tests demonstrated that the presence of particles has an influence on both mechanical strength and electrical conductivity of aluminium components, as opposed to those that do not have particles. The complete investigation intends to give significant insights into the performance and prospective uses of these innovative smart materials, therefore advancing materials science and engineering. Graphical abstract

CeO2-doped silicate glass: material characterization and protective properties for γ-ray shielding applications

CeO2-doped silicate glass: material characterization and protective properties for γ-ray shielding applications

3D Printed Stent from Graphene-Polyethylene Glycol Diacrylate Using Digital Light Processing Technique

Abstract This paper presents the development of the photocurable resin material based on the graphene reinforced polyethylene glycol diacrylate (gPEGDA) for vascular stent fabrication using a commercial 3D printer. 3D printing with digital light processing (DLP) technique is an attractive alternative for low-cost fast fabrication with high accuracy. Four photocurable resin compositions were prepared by mixing PEGDA and varied composition of graphene and the photoinitiator according to the design of experiment of 22 full factorial design. The diphenyl (2,4,6-trimethylbenzoyl) phosphine oxide (TPO) photoinitiator was adopted to meet the required 405 nm UV-light source wavelength of the 3D printer for stent fabrication. Material characterization of the UV-absorbance and viscosity tests were conducted and optimized to obtain resin printability. Mechanical characteristics tests were conducted to obtain the best resin composition for stent application. For this purpose, the tensile tests were conducted according to the ASTM D638 standard using the type-V specimen size. The test specimens were 3D printed with varied UV exposure time 20 and 30 seconds. Finally, the stents were successfully fabricated using a commercial 3D printer DLP with the bottom parameter time setting of 60 seconds, and the UV exposure time of 30 seconds. The resin material was applicable for 3D printing of the stent. The result has shown that 3D printer with DLP technique is suitable for stent fabrication with excellent surface quality. Moreover, the innovative bioresorbable stent materials and fabrication approach could open up new possibilities in the development of medical devices, particularly in the treatment of vascular diseases.

Amorphous silica production from Colombian rice husk: demonstration in scaled-up process Products

Introduction: the agroindustry generates significant waste, posing environmental, health, and economic challenges. Among these, rice husk, a byproduct of the food industry, stands out due to its potential as a source of silicon. Due to its silicon content, rice husk offers a unique opportunity for sustainable energy production and the extraction of high-value products, such as amorphous silicon dioxide (SiO2). However, optimizing processes for its efficient conversion remains a challenge.Objective: the aim of this study was to optimize the nitric acid concentration for the pretreatment of Colombian rice husk in order to produce high-purity amorphous SiO2 and demonstrate the feasibility of scaling up the process.Methods: a two-stage process was developed, which involved treating rice husk with nitric acid, followed by calcination at 620 °C. The nitric acid concentration was optimized to achieve the highest SiO2 purity. Material characterization was performed using thermogravimetric analysis (TGA), X-ray diffraction (XRD), X-ray fluorescence (XRF), and nitrogen adsorption-desorption. To assess the scalability of the process, the treatment was replicated on a larger scale using the optimized acid concentration.Results: the optimized process using a nitric acid concentration of 0.2 M yielded amorphous SiO2 with a purity of 94.9% and a surface area of 298 m²/g. When scaled up, the process achieved SiO2 with a purity of 95.5%, confirming the feasibility of the methodology for industrial applications. Conclusions: the treatment of rice husk with nitric acid followed by calcination proves to be an effective and scalable approach for producing high-purity amorphous SiO2. This process not only holds potential for industrial applications but also provides a sustainable solution for valorizing agroindustrial waste, contributing to the circular economy.

Enhanced carbon dioxide capture through composite of polyamide 6 nanofibers and zeolites: Preparation and performance evaluation

The increasing concentration of atmospheric carbon dioxide (CO2) demands innovative solutions for its capture and sequestration. Composite and nanocomposite materials have gained attention for their high surface area, adjustable properties, and improved adsorption capabilities. This manuscript explores the preparation, characterization, and performance assessment of a novel composite material consisting of polyamide 6 nanofibers combined with two types of zeolites, designed for CO2 capture. We detail the fabrication process, as well as the structural, chemical, and morphological characterization of the composites. The results confirmed the successful modification of polyamide 6 nanofibers with both types of zeolites. CO2 sorption measurements revealed that the composite with zeolite Y exhibited a notably high sorption capacity, with a maximum of 35.8 cm³/g, significantly surpassing the performance of amine-modified nanofibers, which demonstrated sorption capacities approximately three times lower. These findings highlight the potential of this composite for efficient CO2 capture, providing valuable insights into its practical application for reducing greenhouse gas emissions. While the CO2 sorption capacities of the composite are lower than those of pure zeolites, the composite offers practical advantages, such as easier handling and simplified application.

Comparative analysis of traditional and transfer learning algorithms for coal spoil classification via close-range imagery

ABSTRACT The characterisation of materials is a prerequisite for evaluating and predicting the stability of mining waste dumps. Over the past three decades, the BHP Mitsubishi Alliance Coal framework has been a cornerstone in Australian coal mines for characterising waste dump materials. However, its reliance on subjective human observations has introduced potential inaccuracies and subjectivity into the process. In response to these limitations, this study proposes an innovative approach to classify coal spoil attributes by remotely acquiring images through phones/tablets. Automated image-based classification relies on feature extraction and a substantial amount of data. Nevertheless, the inherent complexity of geological factors contributing to the formation of both rare and dominant materials leads to imbalanced data. Recognising the need for classification mechanisms to overcome these challenges in spoil classification, the study explores and compares the use of convolutional neural networks, hybrid deep learning, and traditional techniques. Among the sixteen models evaluated in this study, the ResNet18-support vector machine model emerges as a powerful tool in geotechnical characterisation. However, it is essential to address issues of interpretability and adaptability to diverse datasets. As this study evolves, the field of geotechnical characterisation of spoil can anticipate the development of more robust methods in the future.

Enhancing performance through friction welding of dissimilar aluminium 2014 and 7075 hybrid metal-matrix composite: insights into material characterization, crystallographic texture, and mechanical properties

Abstract Welding high-strength, age-hardenable aluminium alloys from the 2XXX and 7XXX series poses challenges when using fusion-based welding processes. Common issues such as hydrogen porosity, hot cracking, stress corrosion cracking, and solidification cracking can lead to a reduction in mechanical properties due to the loss of strengthening precipitates and alloying elements. Consequently, friction stir welding (FSW) has emerged as a promising solid-state welding technique for joining dissimilar aluminium alloys and composites, particularly in aerospace applications where a combination of strength, corrosion resistance, and other desirable properties is required. This study investigates the FSW of dissimilar aluminium alloys, specifically AA2014 and a hybrid metal-matrix composite of AA7075 reinforced with silicon carbide and graphite particles. The microstructural and mechanical properties of the welded joints were characterized, revealing an average grain size of 2.39 μm ± 1.2 in the stir zone (SZ). A high angle grain boundary volume fraction of 47.25% was observed in the SZ, compared to 2.01% in the AA7075 composite and 15.03% in the AA2014 base metal. The presence of shear textures and the distribution of cube, S, and H textures indicate a homogeneous mixing of the base materials and a high shear strain rate during welding. The hardness of the SZ increased by 13%, and the ultimate tensile strength, yield strength, and elongation of the joint were measured at 251 MPa, 183 MPa, and 6.44%, respectively. The flexural strength of the joints improved significantly, with the CW13 sample showing a 67% increase compared to CW1. The dissimilar joint CW13, welded at 12.5 mm min−1, achieved a maximum joint efficiency of 95%.