Defects In Materials Research Articles

Functionalization Strategies of Iron Sulfides for High-Performance Supercapacitors

AbstractSupercapacitors have emerged as a promising class of energy storage technologies, renowned for their impressive specific capacities and reliable cycling performance. These attributes are increasingly significant amid the growing environmental challenges stemming from rapid global economic growth and increased fossil fuel consumption. The electrochemical performance of supercapacitors largely depends on the properties of the electrode materials used. Among these, iron-based sulfide (IBS) materials have attracted significant attention for use as anode materials owing to their high specific capacity, eco-friendliness, and cost-effectiveness. Despite these advantages, IBS electrode materials often face challenges such as poor electrical conductivity, compromised chemical stability, and large volume changes during charge–discharge cycles. This review article comprehensively examines recent research efforts aiming at improving the performance of IBS materials, focusing on three main approaches: nanostructure design (including 0D nanoparticles, 1D nanowires, 2D nanosheets, and 3D structures), composite development (including carbonaceous materials, metal compounds, and polymers), and material defect engineering (through doping and vacancy introduction). The article sheds light on novel concepts and methodologies designed to address the inherent limitations of IBS electrode materials in supercapacitors. These conceptual frameworks and strategic interventions are expected to be applied to other nanomaterials, driving advancements in electrochemical energy conversion.

Unveiling sulfur vacancy pairs as bright and stable color centers in monolayer WS2

Color centers, arising from zero-dimensional defects, exploit quantum confinement to access internal electron quantum degrees of freedom, holding potential for quantum technologies. Despite intensive research, the structural origin of many color centers remains elusive. In this study, we employ in-situ cathodoluminescence scanning transmission electron microscopy combined with integrated differential phase contrast imaging to examine how defect configuration in tungsten sulfide determines color-center emission. Using an 80-kV accelerated electron beam, defects were deliberately produced, visualized, excited in situ and characterized in real time in monolayer WS2 within hBN|WS2 | hBN heterostructures at 100 K. These color centers were simultaneously measured by cathodoluminescence microscopy and differentiated by machine learning. Supported by DFT calculations, our results identified a crucial sulfur vacancy configuration organized into featured vacancy pairs, generating stable and bright luminescence at 660 nm. These findings elucidate the atomic-level structure-exciton relationship of color centers, advancing our understanding and quantum applications of defects in 2D materials.

Achieving Over 10% Efficiency in Kesterite Solar Cells via Selenium-Free Annealing

The unsatisfactory crystalline quality of the absorber layer has seriously impeded the development of kesterite photovoltaic devices, including a small grain size, a typical multi-layer structure, and inherent defects and defect clusters. Herein, we simultaneously modify the nature of the absorber bulk and the heterojunction interface by sputtering quaternary alloy targets and subsequent selenium-free annealing to improve the photovoltaic performance of Cu2ZnSnSe4 (CZTSe) solar cells. By adjusting the sputtering power, the elemental content and distribution in the precursor film are effectively controlled, which helps to obtain a single-layer large grain CZTSe absorber layer with better homogenization and compactness. The harmful inherent defects are significantly suppressed by such a modification. Simultaneously, the widened depletion width and enhanced built-in electric field, as well as improved band alignment at the heterojunction, enhance carrier collection. As a consequence, the efficiency of CZTSe solar cells has improved greatly from 6.91% to 10.12%. The single-target preparation strategy without selenization provides a simple and novel perspective for simultaneously adjusting the morphology and defects of kesterite photovoltaic materials, paving the way for promoting innovative industrial applications.

Trace benzene capture by decoration of structural defects in metal-organic framework materials.

Capture of trace benzene is an important and challenging task. Metal-organic framework materials are promising sorbents for a variety of gases, but their limited capacity towards benzene at low concentration remains unresolved. Here we report the adsorption of trace benzene by decorating a structural defect in MIL-125-defect with single-atom metal centres to afford MIL-125-X (X = Mn, Fe, Co, Ni, Cu, Zn; MIL-125, Ti8O8(OH)4(BDC)6 where H2BDC is 1,4-benzenedicarboxylic acid). At 298 K, MIL-125-Zn exhibits a benzene uptake of 7.63 mmol g-1 at 1.2 mbar and 5.33 mmol g-1 at 0.12 mbar, and breakthrough experiments confirm the removal of trace benzene (from 5 to <0.5 ppm) from air (up to 111,000 min g-1 of metal-organic framework), even after exposure to moisture. The binding of benzene to the defect and open Zn(II) sites at low pressure has been visualized by diffraction, scattering and spectroscopy. This work highlights the importance of fine-tuning pore chemistry for designing adsorbents for the removal of air pollutants.

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.

Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations

In situ TEM is by far the most commonly used microscopy method for imaging dislocations, i.e., line-like defects in crystalline materials. However, quantitative image analysis so far was not possible, implying that also statistical analyses were strongly limited. In this work, we created a deep learning-based digital twin of an in situ TEM straining experiment, additionally allowing to perform matching simulations. As application we extract spatio-temporal information of moving dislocations from experiments carried out on a Cantor high entropy alloy and investigate the universality class of plastic strain avalanches. We can directly observe “stick–slip motion” of single dislocations and compute the corresponding avalanche statistics. The distributions turn out to be scale-free, and the exponent of the power law distribution exhibits independence on the driving stress. The introduced methodology is entirely generic and has the potential to turn meso-scale TEM microscopy into a truly quantitative and reproducible approach.

The Impact of Biowaste Composition and Activated Carbon Structure on the Electrochemical Performance of Supercapacitors

The increasing demand for sustainable and efficient energy storage materials has led to significant research into utilizing waste biomass for producing activated carbons. This study investigates the impact of the structural properties of activated carbons derived from various lignocellulosic biomasses—barley straw, wheat straw, and wheat bran—on the electrochemical performance of supercapacitors. The Fourier Transform Infrared (FTIR) spectroscopy analysis reveals the presence of key functional groups and their transformations during carbonization and activation processes. The Raman spectra provide detailed insights into the structural features and defects in the carbon materials. The electrochemical tests indicate that the activated carbon’s specific capacitance and energy density are influenced by the biomass source. It is shown that the wheat-bran-based electrodes exhibit the highest performance. This research demonstrates the potential of waste-biomass-derived activated carbons as high-performance materials for energy storage applications, contributing to sustainable and efficient supercapacitor development.

Experimental investigations of chemo-mechanical magneto-rheological finishing of Al-6061 via composite magnetic abrasive

ABSTRACT To achieve flawless soft material surfaces, defects and irregularities must be eliminated. Traditional abrasive-mixing techniques frequently produce surface flaws as a result of abrasive embrittlement, resulting in poor surface-quality. As a result, ensuring an immaculate finish is critical for these surfaces. In contrast, the chemo-mechanical magneto-rheological finishing (CMMRF) process, which employs a composite magnetic abrasive (CMA)-based smart fluid, has proven to be extremely effective at producing nano-finished surfaces. This study used a central-composite-rotatable design (CCRD) based on response surface methodology (RSM) to analyze how CMAs variables, slurry-based parameters, and polishing time affect final surface roughness (R a ) and percentage change in roughness (%∆R a ) for Al-6061. The CMMRF process resulted in a superior Al-surface finish (R a ) of 29 nm and a %∆R a of 91.23%. Statistical models predicted R a and %∆R a values, which matched experimental results. Characterization tests on the final Al-surface revealed significant reduction in initial machining scratches and valleys, resulting in a superior mirror-polished surface via CMA-based polishing.

Effect of graphene on absorption spectrum of Si/SiO2 multilayer

In recent decades, photonic crystals have emerged as a key technology in several fields, including light management, photovoltaics, detection, and lasing. This success can be attributed to the ability to regulate electromagnetic waves, which can be achieved by, through a variety of techniques, disruption the periodicity of crystals. In this study, the introduction of graphene as a material defect in a one-dimensional photonic crystal consisting of six Si/SiO2 patterns is subjected to investigation. The calculations are performed using the transfer matrix method, with the experimental data serving as the input, following a protocol that involves exploring the variation of the absorption spectra as a function of the parameters of the material system and the incident electromagnetic wave. The findings illustrate that the absorption peak exhibits a pronounced dependency on the positioning of the material defect, the graphene layer thickness, and the incident angle. This paves the way for a more detailed inquiry into its potential utility in the domain of detection.

Narrowband Electroluminescence from Color Centers in Hexagonal Boron Nitride.

Defects in wide bandgap materials have emerged as promising candidates for solid-state quantum optical technologies. Electrical excitation of single emitters may lead to scalable on-chip devices and therefore is highly sought after. However, most wide bandgap materials are not amenable to efficient doping, posing challenges for electrical excitation and on-chip integration. Here, we demonstrate narrowband electroluminescence from visible and near-infrared color centers in hexagonal boron nitride (hBN). We harness van der Waals tunnel junctions of graphene-hBN-graphene. Charge carriers are electrically injected into hBN, exciting localized defects that emit nonclassical light, as characterized by the second order correlation measurement. Remarkably, the devices operate at room temperature and produce robust, narrowband emission spanning from visible to the near-infrared. Our work marks an important milestone in vdW materials and their promising attributes for integrated quantum technologies and on-chip photonic circuits.

Study on the mechanism of laser-induced breakdown and damage performance of internal defects in transparent materials through local optical field modulation

This paper establishes a photothermal damage model for bubble impurities affecting laser optical field modulation based on Mie scattering theory and incorporates the effects of optical field modulation. This model elucidates the evolution mechanism of synergistic damage in fused silica, with simulation results validated through experimental verification. A novel characterization of optical breakdown due to bubble impurities is proposed, occurring on a millisecond timescale through the dynamic evolution of combustion waves. The model delineates the influence of bubble size and spacing on optical field distribution, temperature, stress distribution, and their evolutionary behaviors. The modulation of the optical field due to double bubble impurities creates a localized “hot spot,” resulting in a differential transverse contraction stress at the edges of the bubble impurities, thereby reducing the damage threshold of fused silica. The spacing of 1.1 λ represents the enhancement node for optical field modulation by double bubble impurities. Furthermore, localized oscillations in the optical field arise when the spacing between the double bubbles exceeds 1.1 λ, attributed to changes in the refractive index at the bubble defects and resonance oscillations generated by optical field modulation. This study not only enhances our understanding of the optical field modulation processes occurring at 1064 nm in the presence of bubble defects but also establishes a theoretical foundation for detecting internal defects at this wavelength without inducing surface damage.

Positron annihilation spectroscopy guided by two-component density functional theory calculations distinguishes irradiation-induced vacancy type in 4H-SiC

Based on two-component density functional theory integrated with the projector augmented-wave basis and incorporating both calculated and experimental data from Positron Annihilation Spectroscopy (PAS), this study introduces a novel method for identifying and analyzing specific types of vacancies when multiple types of vacancies are coexisting. This method was then tested on 4H-SiC irradiated by 300 keV C4+ ion beams. By calculating charge density to analyze positron annihilation lifetime spectroscopy and calculating wave functions to analyze slow positron-beam Doppler broadening spectroscopy, for the first time, silicon monovacancies (VSi) and carbon monovacancies (VC) in irradiated 4H-SiC were quantitatively detected separately, allowing them to be distinguished with high accuracy. In addition, a decreasing trend in the relative percentage of VC with increasing irradiation dose, consistent with that expected when irradiating with carbon ions, was also observed, illustrating both the effectiveness and potential of this method for broader applications in material defect analysis. This study not only addresses the challenges of identifying multiple coexisting vacancy types using PAS but also extends the applicability and depth of PAS in fields such as nuclear energy, aerospace, and semiconductors.

Enhancing insulating properties of glass-fiber reinforced polymers using plasma fluorination-modified boron nitride nanosheets

The insulation failure of glass fiber-reinforced polymers (GFRP) limits their use in high-voltage insulation fields. In this study, boron nitride nanosheets (BNNS) were prepared using ball-milling and further fluorinated via dielectric barrier discharge (DBD) plasma treatment. The GFRP nanocomposites were fabricated using different filler types and doping concentrations. The test results indicated that fluorinated BNNS (F-BNNS) displays good dispersion and interfacial compatibility, enhances the interfacial bonding strength of the “fiber-matrix-filler” ternary system in GFRP, and reduces the internal defects of materials. In addition, the insulating properties of the composites were related to the filler concentration. Adding a trace amount of F-BNNSs increased the flashover and breakdown voltages of the GFRP by up to 28.77% and 21.62%, respectively. The results of molecular dynamics simulations and trap distribution calculations showed that plasma fluorination of BNNS increases the bandgap and deep trap energy level at the interface. The extremely strong charge-binding ability of the deep traps inhibits continuous charge injection and regulates carrier migration, which improves the insulating properties of the composites. The results of this study provide a novel, environmentally friendly, and efficient solution for improving the insulating properties of GFRP.

Determination of the geometric parameters of the defects based on the tomographically obtained data and their influence on the fatigue behavior of the S960 with laser cladded protective layers

This article deals with identifying defects of layered high strength steel materials with the help of tomography measurement. Test samples were made of S960 High Strength Steel to which layers of either aluminium bronze, hardchrome, cobalt alloy or stainless steel were applied by a laser cladding technology. The experimental campaign included a study of morphometric parameters of internal defects in and near the bi-material interface region using X-ray micro-tomography and their potential influence on the fatigue behavior during a three-point bending test.

Effects of triflute pin geometry on defect formation and material flow in FSW using CEL approach

Complicated tool pin designs in Friction Stir Welding (FSW) need to be considered in terms of material flow and defect formation. This study investigates the effects of the triflute tool's geometrical parameters on temperature, strain, void formation, and material mixing using a numerical method. The numerical model employs a coupled Eulerian-Lagrangian (CEL) formulation and successfully predicts void formation and material mixing during friction stir welding (FSW). Four tool pin designs are considered for material flow, including one cylindrical pin and three triflute pins with flute radii of 1 mm, 1.5 mm, and 2 mm. The findings indicate that the stir zone is divided into shoulder-driven and pin-driven zones, each exhibiting distinct material flow patterns. In the shoulder-driven zone, material flow toward the advancing side is dominant, while in the pin-driven zone, it flows toward the retreating side. Flutes on the FSW pin tool increase the sweeping rate, strain, and material movement in the stir zone. However, flutes with a larger radius sweep a greater amount of material and thus require more softened material to facilitate movement. Therefore, for defect-free joint formation, a higher rotational speed of the tool will be required, which may adversely affect tool lifespan and joint mechanical properties. The effectiveness of flutes with a smaller radius of 1 mm is significantly greater than that of those with a larger radius (1.5 or 2 mm) in enhancing material flow and achieving defect-free welding.

The phonon-modulated Jahn–Teller distortion of the nitrogen vacancy center in diamond

The negatively charged nitrogen vacancy (NV) center in diamond is an optically accessible material defect with a unique combination of spin and optical properties that has attracted interest in quantum-information sciences and as a design candidate for nanoscale quantum sensors. Here, we present time-resolved nonlinear optical spectroscopy measurements, conducted with ultrabroadband laser pulses, that reveal strong modulation of the excited-state by the longitudinal optical (LO) phonon of the diamond lattice. The LO phonon and its overtones geometrically distort neighboring NV centers, driving long lived (3.5 ps) excited state relaxation of coupled NV centers after the initial excitation and ultrafast (<150 fs) decay of the Jahn–Teller distortion. These observations elevate the LO phonon to an important tuning mode of the Jahn–Teller conical intersection and help resolve previous spectroscopy experiments that noted longer-lived excited-state dynamics.

Ultrasonic characterization of defects in polycrystalline materials based on TFM image reconstruction using denoising diffusion probabilistic models

This paper presents a refined ultrasonic total focusing method (TFM) for accurately characterizing key defect parameters including size, orientation, and location in complex materials. The proposed approach aims to accurately reconstruct the defect shape in images compromised by grain interference. First, a TFM image database was generated with varying defect parameters and grain noise levels by adopting a custom forward modeling process based on superposition of defect and grain scattering data. Second, a new architecture called TFM-DDPM was then proposed which incorporates TFM and the conditional denoising diffusion probabilistic model (DDPM). Third, pixel-level uncertainty can be quantified through repeated sampling of noise which follows a standard normal distribution. An acceptance criterion for the reconstruction was further established by analyzing the quantified uncertainty. In simulation, the median Dice coefficient between the de-noised TFMs of 30° cracks and their corresponding ground truth images increased from 0.5585 to 0.9137 using the proposed approach, and the median IoU metric rose from 0.3875 to 0.8408. By applying the 6-dB drop approach to the reconstructed images, the root mean squared errors (RMSEs) of size, angle and location were decreased by 93.59%, 86.90% and 86.25%, respectively. It is also demonstrated that our method can reject 69.19% of incorrect characterization results caused by high levels of noise and/or images with steeply inclined (e.g., 45°) cracks, while accepting 97.29% accurate results. Experimental results obtained on nickel-based alloy K403 specimens also exhibited significant improvements. For 2–3 mm cracks, the average sizing error reduced from 0.46 mm to 0.13 mm, and the error in crack angle decreased from 28.63° to 1.50°. Comparable performance enhancements were observed for 4–5 mm cracks using the proposed approach.

In-flight pixel degradation of the Sentinel 5 Precursor TROPOMI-SWIR HgCdTe detector

Abstract The TROPOMI-SWIR HgCdTe detector on the Sentinel-5 Precursor mission has been performing in-orbit measurements of molecular absorption in Earth's atmosphere since its launch in October 2017. In its polar orbit the detector is continuously exposed to potentially harmful energetic particles. Calibration measurements taken during the eclipse are used to inspect the performance of this detector. This paper explores the in-orbit degradation of the HgCdTe detector.&amp;#xD;After five years, the detector is still performing within specifications, even though pixels are continuously hit by cosmic radiation. The bulk of the impacts have no lasting effects, and most of the damaged pixels (95%) appear to recover on the order of a few days to several months, attributed to a slow spontaneous recovery of defects in the HgCdTe detector material. This is observed at the operational temperature of 140 K. The distribution of the observed recovery times has a mean around nine days with a significant tail towards several months. Pixels that have degraded have a significant probability to degrade again. The location of faulty pixels follows a Poissonian distribution across the detector. No new clusters have appeared, revealing that impacts are dominated by relatively low energetic protons and electrons. Due to the observed spontaneous recovery of pixels, the fraction of pixels meeting all quality requirements in the nominal operations phase has always been over 98.7%. &amp;#xD;The observed performance of the TROPOMI-SWIR detector in-flight impacts selection criteria of HgCdTe detectors for future space instrumentation.

Digital Engineering in Photonics: Optimizing Laser Processing

This article explores the transformative impact of digital engineering on photonic technologies, emphasizing advancements in laser processing through digital models, artificial intelligence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material deformation and defects. The integration of AI further refines these models, improving productivity and quality in applications such as micromachining and cladding. Additionally, the combination of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manufacturing, offering significant improvements in process optimization, defect reduction, and system efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solutions for complex production environments.

Diagnosis and intervention in civil construction pathologies

Civil engineering consistently encounters challenges related to the durability and safety of buildings, making the study of pathological manifestations a critical area of inquiry. These defects not only undermine the functionality, structural integrity, and aesthetic quality of constructions but also pose significant risks to occupant safety. It is imperative to understand the origins and progression of these pathologies to mitigate structural and functional damage. The application of technical standards, while essential, is often inadequate, thereby exacerbating the occurrence of these problems. This research seeks to identify the underlying causes and origins of building pathologies and to propose effective strategies for their prevention and remediation. Through an interdisciplinary methodology, incorporating diverse sources of information such as bibliographic surveys, technical site visits, and data analysis, the study identifies key pathological manifestations in buildings, including fissures, cracks, concrete deterioration, stains, and efflorescence. The research highlights critical contributing factors such as design errors, material defects, execution failures, and insufficient maintenance. The primary aim of this study is to investigate the principal causes of pathologies and pathological manifestations in a building located in the municipality of Nanuque, and to propose preventative and corrective measures. The research methodology is divided into two distinct phases: an extensive literature review to provide a theoretical foundation, enabling the analysis of prior studies on the subject, followed by a case study involving direct on-site assessments to document the identified pathologies. The findings reveal a range of issues, including reinforcement corrosion, concrete segregation, cracks, fissures, and efflorescence. Informed by the literature, the study proposes appropriate interventions to enhance the safety, stability, aesthetics, and comfort of the building's occupants.