Defects In Metals Research Articles

Platinum modification of metallic cobalt defect sites for efficient electrocatalytic oxidation of 5-hydroxymethylfurfural

Co3O4 possesses both direct and indirect oxidation effects and is considered as a promising catalyst for the oxidation of 5-hydroxymethylfurfural (HMF). However, the enrichment and activation effects of Co3O4 on OH− and HMF are weak, which limits its further application. Metal defect engineering can regulate the electronic structure, optimize the adsorption of intermediates, and improve the catalytic activity by breaking the symmetry of the material, which is rarely involved in the upgrading of biomass. In this work, we prepare Co3O4 with metal defects and load the precious metal platinum at the defect sites (Pt-Vco). The results of in-situ characterizations, electrochemical measurements, and theoretical calculations indicate that the reduction of Co–Co coordination number and the formation of Pt–Co bond induce the decrease of electron filling in the antibonding orbitals of Co element. The resulting upward shift of the d-band center of Co combined with the characteristic adsorption of Pt species synergically enhances the enrichment and activation of organic molecules and OH− species, thus exhibiting excellent HMF oxidation activity (including a lower onset potential (1.14 V) and 19 times higher current density than pure Co3O4 at 1.35 V). In summary, this work explores the adsorption enhancement mechanism of metal defect sites modified by precious metal in detail, provides a new option for improving the HMF oxidation activity of cobalt-based materials, broadens the application field of metal defect based materials, and gives an innovative guidance for the functional utilization of metal defect sites in biomass conversion.

Imperfections Immobilization and Regeneration in Perovskite with Redox-Active Supramolecular Assembly for Stable Solar Cells.

Imperfections in metal halide perovskites, such as those induced by light exposure or thermal stress, compromise device performance and stability. A key challenge is immobilizing volatile iodine produced by iodide oxidation and regenerating impurities like elemental lead and iodine. Here, we address this by integrating a redox-active supramolecular assembly of nickel octaethylporphyrin into perovskite film, functioning as both an immobilizer and redox shuttle. Decorated ethyl groups in porphyrin distorts the π ring, increasing the axial ligand adsorption capacity of central metal ion, while reducing intermolecular interactions and promoting iodine adsorption achieving a maximum uptake of 3.83 mg mg-1 to iodine. Adsorbed iodine transfers more electrons to Ni ions, leading to a weakened interaction within I-I bond and facilitating the production of iodide ions. Such a situation further enables selective oxidation of metallic lead defects to Pb2+. Porphyrin supramolecule facilitates hole transport across perovskite grain boundaries, leading to a champion device efficiency of 25.37% for a 0.10 cm2 active area, outperforms the value of control device being 23.96%. Modified devices without encapsulation exhibit significantly enhanced stability, maintaining over 90% of its initial performance after 1,000 hours of continuous 1-sun illumination at maximum power point at 65 oC.

A Redox-Active Ionic Liquid Surface Treatment for Healing CsPbBr3 Nanocrystals.

Additive engineering of lead halide perovskites has been a successful strategy for reducing a variety of deleterious defect types. Ionic liquids (ILs) are a unique group of such additives that have been used to passivate halide vacancies in both bulk lead halide perovskites and their colloidal nanocrystal analogues. Herein, we expand the types of defects that can be addressed through IL treatments in CsPbBr3 nanocrystals with a novel phosphonium tribromide IL that heals metallic lead surface defects through redox chemistry. This new type of surface treatment leads to a significant increase in PLQY and outperforms equivalent treatments with non-redox-active bromide ILs. Such redox-active ligands widen the scope of defect types that can be addressed in semiconductor nanocrystals.

Enhancing Discovery and Understanding of Atomically Dispersed Single Atom Catalysts for CO2 Reduction Reaction with Density Functional Theory and Machine Learning

Atomically dispersed M-N-C catalysts are a promising class of materials for efficiently reducing CO2 to valuable feedstock chemicals such as carbon monoxide and ethylene. However, for such catalysts to achieve commercial viability it is necessary to optimize several properties in tandem such as activity towards the CO2 reduction reaction, Faradaic efficiency towards selected products, and in situ stability. This complex multi-objective optimization problem cannot be effectively tackled with costly trial-and-error experimentation alone. Therefore, computational modeling is necessary to accelerate fundamental understanding and to aid in identifying viable active sites in silico before preparation of down-selected candidate materials is attempted in the laboratory. Density functional theory (DFT) can be utilized to model catalytic surfaces and obtain useful electrochemical properties (e.g., free energies of reaction), but an advanced framework is necessary to effectively explore the large chemical space of possible permutations of different transition metals, proximity of metal centers, defects, and other structural considerations. Furthermore, machine learning may be leveraged to accelerate materials understanding and discovery for CO2RR beyond what is feasible for DFT alone.In this talk, we will discuss our library-based approach for understanding structure-to-function relationships in these materials and identifying novel active sites. This library is built using our high-throughput DFT framework to calculate different properties (e.g., adsorption energies) for a large number of active sites. The structures of the active sites are combinatorically generated based on permutations of transition metals, defects, and other structural considerations. Our automated framework combines DFT and cheminformatics tools to autonomously run DFT calculations as well as to collect, process, and store the results of our simulations and to evaluate different structural descriptors. We will also discuss considerations made for incorporating solvation and electrification effects in a robust manner into our framework.This library is then used as the basis for the development of machine learning models, which learn to map from descriptor-based structural representations of these surfaces (e.g., metal center, type of defect) to target properties, thereby accelerating understanding of structure to function relationships for M-N-C materials. Game theoretical methods are used to provide model interpretability and insight for how individual structural properties contribute to properties of interest, such as activity or overpotential. We discuss the use of our trained model, capable of screening millions of unique chemistries, in discovering novel M-N-C active sites with optimized properties. By thus combining DFT and machine learning, we aim both to provide fundamental understanding of CO2 reduction activity and durability and to discover new viable active sites. Examples of our modeling strategies applied to direct air capture of CO2 utilizing similar workflows will also be discussed.

Design and implementation of a nondestructive testing system for magnetic field imaging based on machine learning

This study presents the design of a nondestructive testing (NDT) system for detecting metal defects using a magnetic field sensor array. The system integrates a hardware-software detection framework that includes parallel computation and processing cores, along with multiple anisotropic magnetoresistance (AMR) sensors. These AMR sensors capture subtle variations in the magnetic field, which serve as the foundation for defect identification. The system enables synchronized data acquisition from multiple AMR sensors, achieving multi-axis data fusion and parallel signal processing. Furthermore, the detection system gathers defect features to train an NDT system based on machine learning (ML). Subsequently, ML-generated defect imaging features are processed through an imaging framework, producing magnetic field imaging (MFI) for precise defect localization. The study tested six different metal samples with various defect sizes and types to validate the system. The results demonstrate the system’s ability to accurately detect and identify different defects, highlighting the high precision of magnetic field detection and the overall effectiveness of the proposed system for NDT.

Self-Etching Synthesis of Superhydrophilic Iron-Rich Defect Heterostructure-Integrated Catalyst with Fast Oxygen Evolution Kinetics for Large-Current Water Splitting.

Developing catalysts with rich metal defects, strong hydrophilicit, and extensive grain boundaries is crucial for enhancing the kinetics of electrocatalytic water oxidation and facilitating large-current water splitting. In this study, we utilized pH-controlled etching and gas-phase phosphating to synthesize a flower-like Ni2P-FeP4-Cu3P modified nickel foam heterostructure catalyst. This catalyst features pronounced hydrophilicity and a high concentration of Fe defects. It exhibits low overpotentials of 156 mV and 210 mV at current densities of 10 and 100 mA cm-2 respectively, and maintains stability for up to 200 h at 100 mA cm-2 with only 7.3% degradation, showcasing outstanding electrocatalytic water oxidation performance. Furthermore, when integrated into a Ni2P-FeP4-Cu3P/NF||Pt-C/NF electrolyzer, it achieves excellent overall water splitting performance, reaching current densities of 10 and 400 mA cm-2 at just 1.47 V and 1.73 V, respectively, and operates stably for 60 h at 500 mA cm-2 with minimal degradation. Analysis indicates that high-valence oxyhydroxides/phosphides of Ni, Fe, and Cu act as the primary active species. The presence of abundant Fe defects enhances electron transfer, strong hydrophilicity improves electrolyte contact, and numerous grain boundaries synergistically modulate the activation energy between active sites and oxygen-containing intermediates, significantly improving the kinetics of water oxidation.

Fermi energy level synergistic Bi-based electron bridge construction of Z-type heterojunctions to accelerated photogenerated charge transfer for photostability and contaminant mineralization

Advanced oxidation technologies offer new opportunities for pollutant degradation. Despite the existence of numerous photocatalyst heterojunctions for advanced oxidation, their charge transfer and separation efficiency is low due to inadequate interfacial modulation, especially for Type II heterojunctions. Here, through metal reduction, metal defects with regulating the Fermi level and metal electron bridges with facilitate the separation of photo-generated carriers, facilitating the transition from Type II to Z-type heterojunctions. Herein, we designed a Z-Scheme photocatalyst with Bi-based electron bridges (referred to as ‘fishbone-like’ BiVO4/Bi/CuO) synthesized through in situ reduction, utilized for RhB degradation. Under visible light irradiation, the degradation rate of RhB up to 99 % within 120 min, outperforming most previously reported photocatalysts. Notably, the well-designed BiVO4/Bi/CuO exhibits as reaction rate of 4.13 × 10-2 ·min−1, which is 18 times higher than the pure BiVO4. The BiVO4/Bi/CuO photocatalyst demonstrates exceptional stability over 6 reaction cycles spanning 15 h, due to strengthened contact interface of BiVO4/Bi/CuO. The metal Bi can induce the surface plasmon resonance (SPR) effect, enhancing light absorption. Furthermore, Bi-based electron bridges significantly promote the charge transfer across the Z-type heterojunction interface, which were verified by assorted experimental outcomes and density functional theory (DFT) calculations. This work represents a promising platform for the development of highly efficient and stable photocatalysts that have potential in Pollutant degradation applications.

Transition metal doping for tailoring magnetic properties of graphdiyne

Tailoring the magnetic properties of semiconductors is crucial for realizing spintronic devices with integrated logic and memory functions. Graphdiyne (GDY), a two-dimensional carbon semiconductor, has emerged as a promising material for this purpose. This work demonstrates an effective approach to induce robust room-temperature ferromagnetism in GDY through in-situ transition metal (Fe, Co, Ni) doping via an improved liquid-liquid interface synthesis method. Density functional theory calculations reveal that the ferromagnetism originates from the Kagome lattice formed by the doped transition metal atoms. Remarkably, Fe-doped GDY exhibits a saturation magnetization of 2.86 emu · g−1 at room temperature, substantially higher than Co- and Ni-doped counterparts. Furthermore, post synthesis annealing treatment allows modulating the magnetic properties by introducing hydroxyl groups, with Co-GDY annealed at 500°C showing optimal room-temperature ferromagnetic behavior (0.26 emu · g−1). This work paves the way for tailoring magnetic semiconductors for spintronic applications by leveraging transition metal doping and defect engineering in graphdiyne.

High-throughput magnetic co-doping and design of exchange interactions in topological insulators

Using high-throughput automation of impurity embedding simulations, we create a database of 3d and 4d transition metal defects embedded into the prototypical topological insulators (TIs) Bi2Te3 and Bi2Se3. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of nonmagnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian. We uncover chemical trends in the exchange coupling constants and discuss the impurities' abilities with respect to magnetic order in the fields of quantum anomalous Hall insulators and topological quantum computing. In particular, we confirm that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs. Published by the American Physical Society 2024

Cyclic shift short-distance attention for defect detection of fine etching mesh

Abstract Based on the deficiency of the existing Swin Transformer in the detection of surface defects in metal etched grids, i.e. the isolation of information transmission between different windows, and the limitations of image matrix size and network structure,which make it not suitable for long sequence data processing and dense prediction scenarios, a defect detection method for metal etching nets based on Cyclic shift Long Short Distance Attention (C-LSDA) is proposed. In the proposed method, cross-scale features are used to capture long and short distance relationships,and enhance the feature transfer between windows. Tracking position bias (TPB) is also used to improve relative position bias (RPB), in order to help the model to better understand the spatial relationships in the input data. In addition, the image is downsampled at various scales, so that the proposed method can be suitable for long sequence data processing and dense prediction scenarios. In order to improve the detection accuracy of small targets. a multi-feature fusion pyramid network (MFFPN) for feature fusion is also proposed. The experimental results show that the proposed method is significantly superior to the traditional CNN recognition method and the transformer recognition method. The proposed methodhas good identification accuracy and low computational cost.

Bimetallic metal-organic framework-modified resin for enhanced removal of organophosphorus: Inherent defects and high selectivity

The inherent defects of bimetallic metal–organic frameworks (B-MOFs) play a significant role in enhancing their performance. The combination of resin with MOFs provides a shortcut for optimizing their structure and adsorption performance. This study developed a simple strategy to control the inherent defects of materials by adjusting the Zr/La precursor ratio to enhance the adsorption performance of Zr-La-MOFs@201 for organophosphorus. To achieve this, we prepared a series of X-Zr-La-MOFs@201 and investigated the impact of different ratios on the degree of defects. Among them, 2-Zr-La-MOFs@201 exhibited a maximum adsorption capacity for organophosphorus of 123.6 mg/g, which is 1.25 and 3.08 times compared to UIO-66@201 and D201, respectively. 31P NMR and TGA results indicated that the performance enhancement primarily stemmed from the increased formation of MP bonds, which differs from the commonly mentioned hydroxyl substitution. DFT calculations showed that the presence of defects effectively reduced the reaction activation energy. In actual water treatment, the maximum treatment capacity of this material reached 5000 BV. This impressive performance demonstrates the potential of Zr-La-MOFs@201 for practical applications in other fields, providing a benchmark for the development of new materials.

Unveiling the oxidation stability of 2D gallium-intercalated monolayer epitaxial graphene through correlative microscopy

The oxidation- and air-stability of 2D gallium-intercalated monolayer epitaxial graphene was determined using correlative microscopy. Site-specific studies including AFM, scanning electron microscope, cross section STEM-HAADF, and EELS revealed that the oxygen signal detected by XPS and AES analyses originated from oxidized surface carbon contaminants without the presence of oxygen at the 2D gallium layers. In addition, the air-stability of the 2D gallium was correlated with the presence of intact epitaxial graphene. The absence of graphene leads to oxidation of the 2D gallium in air, consequently losing the crystallinity of the epitaxial gallium layer. This study invokes the importance of correlative microscopy to better understand defects in 2D metals that have been recently recognized through the confinement heteroepitaxy. In addition, this study highlights the advantage of using high spatial resolution STEM techniques in comparison with XPS that has relatively lower resolution.

Light-Induced Metallic and Paramagnetic Defects in Halide Perovskites from Magnetic Resonance.

Halide perovskites are promising next-generation solar cell materials, but their commercialization is hampered by their propensity to degrade under operating conditions, particularly under heat, humidity, and light. Identifying degradation products and linking them to the degradation mechanism at the atomic scale is necessary to design more stable perovskite materials. Here we use magnetic resonance methods to identify and characterize the formation of both metallic lead clusters and Pb3+ defects upon light-induced degradation of methylammonium lead halide perovskite using nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) measurements. Paramagnetic relaxation enhancement (PRE) of the 1H NMR resonances demonstrates the presence of localized paramagnetic Pb3+ defects, a large Knight shift of the 207Pb NMR proves the presence of lead metal, and their relative proportions are determined by the differing temperature dependence in variable-temperature EPR. This work reconciles previous conflicting literature results, enabling the use of EPR spectroscopy to monitor photodegradation of perovskite devices.

Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review

The integration of LPBF printing technologies in various innovative applications relies on the resilience and reliability of parts and their quality. Reducing or eliminating the factors leading to defects in final parts is crucial to producing satisfactory high-quality parts. Extensive efforts have been made to understand the material properties and printing process parameters of LPBF-printed geometries that trigger defects. Studies of interest include the use of various sensing technologies, numerical modeling, and artificial intelligence (AI) to enable a better understanding of the phenomena under investigation. The primary objectives of this article are to introduce the reader to the most widely read published data on (1) the roles of numerical and analytical models in LPBF defect detection; (2) AI algorithms and models applicable to predict LPBF metal defects and causes; and (3) the integration of modeling, AI, and sensing technology, which is commonly used in material characterization and has been proven efficient and applicable to LPBF metal part defect detection over extended periods.

Construction of "Metal Defect/Oxygen Defect Junction" in ZnFe2O4-NiCo2O4 Heterostructures for Enhancing Electrocatalytic Oxygen Evolution.

Defect engineering is a promising approach to improve the conductivity and increase the active sites of transition metal oxides used as catalysts for the oxygen evolution reaction (OER). However, when metal defects and oxygen defects coexist closely within the same crystal, their compensating charges can diminish the benefits of both defect structures on the catalyst's local electronic structure. To address this limitation, a novel strategy that employs the heterostructure interface of ZnFe2O4-NiCo2O4 to spatially separate the metal defects from the oxygen defects is proposed. This configuration positions the two types of defects on opposite sides of the heterojunction interface, creating a unique structure termed the "metal-defect/oxygen-defect junction". Physical characterization and simulations reveal that this configuration enhances electron transfer at the heterostructure interface, increases the oxidation state of Fe on the catalyst surface, and boosts bulk charge carrier concentration. These improvements enhance active site performance, facilitating hydroxyl adsorption and deprotonation, thereby reducing the overpotential required for the OER.

LiFSO-Net: A lightweight feature screening optimization network for complex-scale flat metal defect detection

Defect recognition of flat metals is paramount for ensuring quality control during the production process. However, the diverse origins of metal surface damage, ranging from mechanical impacts to chemical corrosion, and the resulting varied morphology and scale of surface defects, particularly numerous microdefects and elongated defects with high aspect ratios, complicate defect recognition. Existing methods fail to select the most beneficial features during extraction and commonly lose critical feature information during gradient sampling. To overcome these challenges, we propose a lightweight network to optimize feature screening for defect recognition. First, we propose a deformable context–guided block that employs deformable convolution to dynamically adapt the perception of the spatial context, providing precise guidance of relevant semantic information in complex surface textures. Second, we develop a content-aware feature compression block that implements adaptive weighting of features, which significantly reduces information loss during the downsampling stage. Finally, we introduce an intra-scale feature interaction transformer block, which optimizes high-order semantic features to enhance the accuracy and reliability of defect detection. Experimental validation on the NEU-DET, APS-DET, and GC10-DET datasets demonstrated significant improvements in the detection accuracy and parameter efficiency, confirming the proposed method's robust generalizability.

Influence of the Metallic Sublayer on Corrosion Resistance in Hanks' Solution of 316L Stainless Steel Coated with Diamond-like Carbon.

The purpose of the study was to ascertain the corrosion resistance in Hanks' solution of Cr-Ni-Mo stainless steel (AISI 316L) coated with diamond-like carbon (DLC) coatings to establish its suitability for biomedical applications, e.g., as temporary implants. The influence of the carbon coating thickness as well as the correlated effect of the metallic sublayer type and defects present in DLC films on corrosion propagation were discussed. The results obtained were compared with findings on the adhesion of DLC to the steel substrate. The synthesis of carbon thin films with Cr and Ti adhesive sublayers was performed using a combined DC and a high-power-impulse vacuum-arc process. Evaluation of the corrosion resistance was carried out by means of potentiodynamic polarisation tests and scanning electron microscopy. Adhesive properties of the sublayer/DLC coating systems were measured using a scratch tester. It was found that systems with Ti sublayers were less susceptible to the corrosion processes, particularly to pitting. The best anti-corrosion properties were obtained by merging Ti with a DLC coating with a thickness equal to 0.5 μm. The protective properties of the Cr/DLC systems were independent of the carbon coating thickness. On the other hand, the DLC coatings with the Cr sublayer showed better adhesion to the substrate.

Construction of more oxygen vacancies of BaCo0.4Fe0.4Zr0.2O3-δ for high-performance proton-conducting solid oxide fuel cell cathodes

The cathodic catalytic activity for the oxygen reduction reaction (ORR) plays a critical role in determining the performance of proton-conducting solid oxide fuel cells (P-SOFCs). BaCo0.4Fe0.4Zr0.2O3-δ (BCFZ) has emerged as a promising cathode for P-SOFCs due to its triple-phase conductivity. Nevertheless, its suboptimal proton conductivity and hydration ability at intermediate temperatures prevent it from achieving the anticipated ORR catalytic activity. To address these limitations, this study explores the enhancement of electrocatalytic activity in BCFZ through alkali metal doping and A-site defect construction. The effects of such modifications on oxygen surface exchange kinetics and ORR catalytic activity are systematically investigated. The findings reveal that BCFZ exhibits relatively low parameters in terms of electronic conductivity, oxygen ion conductivity, oxygen vacancy concentration, kchem, and Dchem. Conversely, these parameters are markedly improved in A-site deficient BCFZ (D-BCFZ) and Na/K-doped BCFZ. Consequently, the enhancement of these properties yields a significant increase in ORR catalytic activity, with D-BCFZ demonstrating the best electrochemical performance. Specifically, P-SOFC with D-BCFZ cathode achieves an Rp value of just 0.073 Ω cm2 and a Pmax value of 0.928 W cm−2 at 650 °C. This study provides theoretical insight into the mechanisms by how alkali metal doping and defect construction enhance the ORR catalytic activity of BCFZ. These findings offer valuable guidance for the development and optimization of high-performance P-SOFC cathodes.

Effect of Interface Defects on the Electric–Thermal–Stress Coupling Field Distribution of Cable Accessory Insulation

The combined insulation interface of a high-voltage cable and accessories is the weakest part of a cable system. In this paper, the parameters of the dielectric constant, thermal conductivity, and elastic modulus of cross-linked polyethylene (XLPE) and silicone rubber (SIR) are obtained experimentally. On this basis, the model of a specific type of 110 kV cable and prefabricated insulation joint is established. A simulation of the electric–thermal–stress coupling field in the presence of typical defects in the main insulation–inner semi-conductive (SEMI) shielding layer (XLPE/SEMI interface) and the main insulation–silicone rubber insulation layer (XLPE/SIR interface) is studied. The simulation results show that at the XLPE/SIR interface, the electric field distortion caused by bubble defects reached 20.17 kV/mm, and the temperature rose to 56.15 °C. The effect of air-gap defects on the interface is similar to that of bubble defects. In addition, the semi-conductive impurity defects induced an increase in temperature to 56.82 °C and an increase in stress to 0.32 MPa. At the XLPE/SEMI interface, the electric field distortion induced by bubble defects was 19.98 kV/mm, and the temperature rose to 61.72 °C. The electric field distortion caused by metallic and semi-conductive defects was 8.44 kV/mm and 8.64 kV/mm, respectively. This study serves as a reference for the fault analysis and the operation and maintenance of cable accessories.

Boosting spatial and energy resolution in STM with a double-functionalized probe.

The scattering of superconducting pairs by magnetic impurities on a superconducting surface leads to pairs of sharp in-gap resonances known as Yu-Shiba-Rusinov (YSR) bound states. Similar to the interference of itinerant electrons scattered by defects in normal metals, these resonances reveal a periodic texture around the magnetic impurity. The wavelength of these resonances is, however, often too short to be resolved even by methods capable of atomic resolution, i.e., scanning tunneling microscopy (STM). We combine a CO molecule with a superconducting cluster pre-attached to an STM tip to maximize both spatial and energy resolution, thus demonstrating the superior properties of such double-functionalized probes by imaging the spatial distribution of YSR states. Our approach reveals rich interference patterns of the hybridized YSR states of two Fe atoms on Nb(110), previously inaccessible with conventional STM probes. This advancement extends the capabilities of STM techniques, providing insights into superconducting phenomena at the atomic scale.