Defects In Metals Research Articles

Elimination of defects in laser metal deposited TiCp/Ti6Al4V composite by synchronous ultrasonic impact treatment

TiCp/Ti6Al4V composites produced by laser metal deposition (LMD) are usually characterized by internal defects and resultant deteriorated mechanical properties. In the present work, a synchronous ultrasonic impact treatment (UIT) was introduced during LMD of TiCp/Ti6Al4V composites. The effect of synchronous UIT on the defects was investigated. The defects in the LMDed composites are sub-spherical pores distributed in the interlayer region. With the assistance of UIT, the maximum surface height difference reduces from 200.2 ± 0.5 μm to 25 ± 0.5 μm and the internal porosity decreases from 1.95% to 0.09%, which is attributed to inhibiting the nucleation of defects and increasing molten pool flow. This work may provide a solution to effectively reduce the defects in additively manufactured parts.

Understanding the Influence of Metal Oxide Layer Thickness and Defects on Resistive Switching Behavior Through Numerical Modeling

Understanding the Influence of Metal Oxide Layer Thickness and Defects on Resistive Switching Behavior Through Numerical Modeling

Resistive Switching: Physics, Devices and Applications

Resistive Switching: Physics, Devices and Applications

Isosymmetric Lattice Collapse in Mixed-Valence Rare-Earth Fullerides at High Pressure─Coupling of Lattice and Electronic Degrees of Freedom

The orthorhombic-structured (Sm1/3Ca2/3)2.75C60 is a member of the family of rare-earth metal intercalated C60 fullerides with stoichiometry, RE2.75C60 (RE = rare-earth metal). At ambient conditions, it crystallizes with the formation of a 2a × 2a × 2a (a ∼ 14 Å) supercell of the face-centered cubic (fcc) A3C60 (A = alkali metal) unit cell. The superstructure is stabilized by the long-range ordering of tetrahedral-site Sm/Ca metal defects (O phase). Synchrotron X-ray powder diffraction measurements at high pressure and ambient temperature reveal that (Sm1/3Ca2/3)2.75C60 is a compressible solid with a bulk modulus, K0 = 24(1) GPa, that transforms to a more densely packed isostructural high-pressure O′ phase above ∼4 GPa. The first-order phase transition retains the formation of the superstructure and is accompanied by a discontinuous lattice size decrease (ΔV/V ∼ 2%). The O′ phase is stabilized by the release of the steric crowding that develops upon compression; the Sm/Ca metal ions, which reside in the tetrahedral and octahedral holes of the fulleride sublattice, shift from their off-centered positions at low pressure (O phase) to nest at the centers of the interstices (O′ phase). The nearly exact coincidence of the pressure response of the reversible hysteretic O ↔ O′ phase transformation with that of the samarium valence transition from +2.33(2) to +2.71(3), as established before by synchrotron X-ray absorption spectroscopy, unambiguously establishes an intimate link between the crystal and electronic structures of the (Sm1/3Ca2/3)2.75C60 fulleride.

Quantification of Linker Defects in UiO-Type Metal–Organic Frameworks

Quantification of Linker Defects in UiO-Type Metal–Organic Frameworks

Defect-tailored MoS2 for superior Fenton-like catalysis: The synergistic reactive sites of 1T phase and sulfur vacancy in electron transfer and radical generation

Pollutants removal by Fenton-like catalysis is of considerable interest, and the solid catalyst play governing roles. Two-dimensional MoS2 can activate peroxymonosulfate (PMS) for radical-mediated catalysis via metallic 1T phase and/or crystal defects (i.e., sulfur vacancy). Herein, we explored the significant synergism between defective sulfur vacancy and metallic 1T rather than semiconducting 2H phase for Fenton-like catalysis on MoS2. This synergism was substrate-dependent during MoS2 preparation. A series of sulfur-defected 1T-MoS2 were prepared by H2O2 oxidation, drastic structural but no morphological changes were measured. The defect-tailored 1T-MoS2 exhibited a superior Fenton-like reactivity toward PMS activation and pollutant degradation in various water matrices. A 10.0 mg·L-1 of aqueous phenol was completely degraded in one hour, and the corresponding pseudo-first order reaction constant increased more than 12 times after defect engineering. No synergistic effect was measured between sulfur vacancy and semiconducting 2H phase on defective 2H-MoS2. Radical-mediated reaction mechanism was identified by ESR, scavenger inhibiting, spectroscopic analysis and electrochemical measurements. Electron transfer was accelerated within metallic 1T phase to favor PMS activation and pollutant degradation on defective sulfur vacancies serving as surface reactive sites. Our findings provided fundamental insights into PMS activation on sulfur vacancy promoted by 1T-MoS2 in Fenton-like catalysis for water purification.

Optimization sulfur doping of electronic structure with metal active center for defect-rich FeCo bimetallic hydroxyl oxide catalysts

Defect engineering is an effective method to tune electronic states, which can provide active sites for electrocatalytic reactions. Herein, a highly efficient sulfur-doped bimetallic hydroxyl oxide (FeCo/Vc-S) has been synthesized by a two-step hydrothermal reaction, which has the rich defective and amorphous phases. The low coordination number of Co in X-ray absorption fine structure (XAFS) analysis further confirms the Co center is the active center, while Fe plays a role in optimizing the electronic structure of active Co species. Meanwhile, abundant oxygen vacancies and metal defects are also found in the material, which greatly enhances the adsorption and desorption efficiency of × OOH. Moreover, density functional theory (DFT) shows that the addition of S atom alters the electron distribution of adjacent metal atoms, thus adjusting the electron distribution of the metal active site. These positive factors endow FeCo/Vc-S with excellent OER electrocatalytic performance, with an overpotential of only 255 mV and Tafel slope is 64.3 mV·dec−1 at a current density of 10 mA cm−2. This study provides a new route for the synthesis of bimetallic hydroxyl oxides, and also provides theoretical design for the identification of active centers and defection-rich catalysts.

In situ growth of Cu2+1O/Cu2(CO3)(OH)2/Co(OH)2 with multi-dimensional hybrid structure for high performance flexible supercapacitors

In situ growth of Cu2+1O/Cu2(CO3)(OH)2/Co(OH)2 on carbon cloth (CC) is achieved via a facile wet chemical oxidation strategy combined with hydrothermal method, and the self-supported Cu2+1O/Cu2(CO3)(OH)2/Co(OH)2 displays a unique multi-dimensional hybrid structure. Cu2+1O (CuO with excess metal defects) electroless plated on the surface of CC can not only retain the nanostructures of Cu2(CO3)(OH)2 and Co(OH)2, but also act as the buffer layer to ease volumetric expansion of the materials during the repeated charge/discharge process. When the CC@Cu2+1O/Cu2(CO3)(OH)2/Co(OH)2 is used in flexible supercapacitors, it exhibits significantly enhanced areal specific capacitance (624.5 mF cm2 at 0.5 mA cm2), rate capability (86.6 %), cyclic stability (100.3 % retention after 1500 cycles) and coulombic efficiency (99.3 % retention after 1500 cycles) due to the synergistic effect of Cu2+1O buffer layer, two-dimensional (2D) Co(OH)2 nanosheets and three-dimensional (3D) Cu2(CO3)(OH)2 nanoblocks.

Reliable Crack Monitoring Based on Guided Wave Through Periodically Loaded Transmission Line

Cracks are commonly occurring defects in metal. Crack monitoring in large-area infrastructures requires numerous sensors and thus increases the weight, complexity, and integrity problem for wire deployment. The electromagnetic (EM) resonator-based sensor enjoys good sensitivity but suffers limited coverage. The perturbation theory governing crack detection for EM resonator-based sensors indicates that the uneven field distribution inside the resonator is the bottleneck behind the sensitivity and coverage. In this article, the EM energy is confined underneath the resonator but evenly distributed along the transmission line through periodically loading interleaved step impedance resonators (SIRs) on the two sides of a microstrip line. In this way, the sensitivity can be maintained and the coverage can be extended. An averaged attenuation constant is studied and extracted as a feature for crack characterization. The measurement normalized sensitivity is −0.057 N.U./mm2. This article provides a paradigm to bridge the gap between local and global monitoring. The proposed sensor can be used for monitoring potential cracks of in-service complex components.

Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

This paper addresses the detection and identification of flaws in Plug valves. The Plug valve thermal image is acquired using a Fluke thermal camera [TiS20]. Thermal images of the Plug valve are used for the identification of flaws such as Crack, Porosity, Corrosion, and Internal defects. The thermal images detect the surface flaws and never subsurface flaws in Plug valves. The subsurface flaws detection is a challenging problem in valve inspection. In this paper, the thermal images obtained after the dye penetrates the surface valve detect the surface flaws more efficiently after applying the Fuzzy Deep Learning Algorithms. DyePenetrating Test (DPT) combined with Infrared Thermography is proposed to identify heat flux changes and flaws in the faulty metal surface of Plug valves. In DPT, thinned paint is employed on the metal surface that displays metal porosity and even fine cracks. After DPT, thermal images of the Plug valve are processed through the Fuzzy Deep Learning Algorithm to evaluate flaws. The Fuzzy Algorithm is utilized prior to Deep Learning to simplify and speed up the classification task. The flaws are identified using Slicing operation and the following parametric quantities such as Accuracy, Mathew’s Correlation Coefficient (MCC), Local Self-Similarity Descriptor (LSS), Precision/Recall, F-measure, and Jaccard Index. The parametric quantities depict corresponding variations with regard to surface coarseness and metal flaws. The DPT and Fuzzy Deep Learning Algorithm identify metal defects with 80.67% accuracy.

Functionalized Regulation of Metal Defects in ln2S3 of p-n Homojunctions.

The introduction of metal vacancies into n-type semiconductors could efficiently construct intimate contact interface p-n homojunctions to accelerate the separation of photogenerated carriers. In this work, a cationic surfactant occupancy method was developed to synthesize an indium-vacancy (VIn)-enriched p-n amorphous/crystal homojunction of indium sulfide (A/C-IS) for sodium lignosulfonate (SL) degradation. The amount of VIn in the A/C-IS could be regulated by varying the content of added cetyltrimethylammonium bromide (CTAB). Meanwhile, the steric hindrance of CTAB produced mesopores and macropores, providing transfer channels for SL. The degradation rates of A/C-IS to SL were 8.3 and 20.9 times higher than those of crystalline In2S3 and commercial photocatalyst (P25), respectively. The presence of unsaturated dangling bonds formed by VIn reduced the formation energy of superoxide radicals (•O2-). In addition, the inner electric field between the intimate contact interface p-n A/C-IS promoted the migration of electron-hole pairs. A reasonable degradation pathway of SL by A/C-IS was proposed based on the above mechanism. Moreover, the proposed method could also be applicable for the preparation of p-n homojunctions with metal vacancies from other sulfides.

Realizing high-efficient oxygen reduction reaction in alkaline seawater by tailoring defect-rich hierarchical heterogeneous assemblies

The development of efficient and stable oxygen reduction reaction (ORR) electrocatalysts in seawater electrolyte is the key to realizing marine resource utilization and expanding metal-air battery applications. Herein, defect-rich Fe-doped Co nanoparticles encapsulated by N-doped hierarchical carbon (D-FeCo@NHC) are rationally designed and synthesized. It is worth mentioning that the Fe doping helps to promote the generation of metal defect and adjust the electronic structure of D-FeCo@NHC. Density functional theory (DFT) demonstrate that the metal defect and carbon defect jointly optimize the d-band center of D-FeCo@NHC. As a result, in alkaline seawater electrolyte, D-FeCo@NHC display a half-wave potential of 0.874 V, and the assembled liquid Zn-air battery exhibit a high peak power density (173 mW cm−2) and ultra-long cycling stability (over 800 h). This work highlights the defect engineering in the construction of seawater-based oxygen reduction electrocatalysts, which provide a significant and promising approach for seawater-based metal-air cathode materials.

Interfacial microstructure and mechanical properties of WC–Co/40Cr joints brazed at low–temperature with Ag–Cu–Ti + Sn novel filler

It is crucial to design a novel active filler metal for brazing WC–Co cemented carbide–steel joints at low temperatures. In this study, we designed a low-melting-point multi-component active filler metal (Ag–Cu–Sn–Ti) based on Ag–Cu–Ti filler metal to achieve perfect combination of WC–Co/40Cr steel joints. The interfacial microstructure of the WC–Co/40Cr steel joint was comprehensively characterized, and the joint-bonding mechanism, mechanical properties, and fracture behavior after brazing at 700–780 °C were systematically analyzed. On both sides of the filler metal, WC and α-Fe combined with the TiC layer, enhancing the bonding of the filler metal to base materials. The typical structure of the joint is WC–Co/TiC layer/Co2SnTi + Cu–doped Cu81Sn22 + Ag [s, s] + Ti–Cu granules/TiC layer/40Cr. The maximum shear strength of 241 MPa was obtained when the joint was brazed at 740 °C for 10 min, which is approximately 50% higher than those of previous low-temperature vacuum-brazed WC–Co/steel joints. The main factors leading to premature failure of joint include the distribution of phases in filler metal layer and defects of WC–Co caused by Co diffusion.

Linker Defects in Metal–Organic Frameworks for the Construction of Interfacial Dual Metal Sites with High Oxygen Evolution Activity

AbstractDesigning efficient electrocatalysts based on metal–organic framework (MOF) nanosheet arrays (MOFNAs) with controlled active heterointerface for the oxygen evolution reaction (OER) is greatly desired yet challenging. Herein, a facile strategy for the synthesis of MOF‐based nanosheet arrays (γ‐FeOOH/Ni‐MOFNA) is developed with abundant heterointerfaces between Ni‐MOF and γ‐FeOOH nanosheets by introducing linker defects to the former. The experimental and theoretical results show the key role of linker defects in inducing the growth of secondary γ‐FeOOH nanosheets onto the surface of Ni‐MOFNAs, which further leads to the formation of interfacial Ni/Fe dual sites with high oxygen evolution activity. Notably, the resulting γ‐FeOOH/Ni‐MOFNA exhibits excellent OER performance with low overpotentials of 193 and 222 mV at 10 and 100 mA cm−2, respectively. Furthermore, the study of the structure–performance relationship of MOF‐based heterostructures reveals that Ni sites at the interface of the γ‐FeOOH/Ni‐MOFNA have higher activity than those at the interface of NiFe layered double hydroxide and Ni‐MOFNA. This study provides a new prospect on heterostructured electrocatalysts with highly active sites for enhanced OER.

Influence of transition metal defects on electronic and magnetic properties of bulk silicon: Ab-initio simulation

Influence of transition metal defects on electronic and magnetic properties of bulk silicon: Ab-initio simulation

Kinetic Impacts of Defect Sites in Metal–Organic Framework Catalysts under Varied Driving Forces

Defects in metal–organic frameworks (MOFs) primarily manifest as missing linkers or metal nodes induced through synthesis, post-synthetic modification, and/or exposure to reaction conditions. By changing the nature of active site(s) and perturbing crystalline frameworks, defects confer physicochemical alterations to MOF catalysts that may promote or inhibit intrinsic reactivity, electron transfer and excitation, and mass transport. However, the complexity and dynamic character of defects often obfuscate the structure–function relations needed to permit rational catalyst design. Here, highlights of recent studies examining the impact of MOF defects in thermo-, photo-, and electrocatalytic systems pertinent to energy applications demonstrate progress toward identifying defect impacts on MOF catalysis, particularly for widely studied zirconium-based frameworks. Moreover, the combination of ex situ and operando/in situ defect identification and quantification will be paramount in future research to improve the mechanistic understanding of MOF-catalyzed systems for energy conversion but also extends to MOF energy storage, photovoltaics, and gas separations/storage applications.

Performance evaluation of free hole-transport layer CsPbI3 perovskite solar cells

Stable inorganic cesium lead iodide (CsPbI3)-based perovskite solar cells received intensive research in the last 2 decades. Several experimental and simulation studies have been performed on CsPbI3 solar cells to achieve high efficiency. However, the usage of the hole-transport layer (HTL) has a good impact on the solar cell’s power conversion efficiency (PCE); HTL layer usage increases the cost of the cell. In this work, a SCAPS-1D simulation study is performed on HTL-free CsPbI3 perovskite solar cells to enhance the device performance. The optical refractive index and reflectivity of CsPbI3 are calculated with density functional theory. The solar cell device structure is investigated and optimized by changing parameters like metal back contact work function, absorber thickness, acceptor density, and defect density. It is found that the optimized solar cell device achieved a PCE of 17.2% with Selenium (Se) as a back metal contact. The optimized absorber thickness for the best PCE is 2000 nm, doping density concentration of 1 × 1019 cm−3, and defect density concentration of 2 × 1012 cm−3. A PCE of 24.5% is achieved using these parameters of the CsPbI3 absorber. The simulation study may guide the fabrication of low-cost and highly efficient HTL-free inorganic CsPbI3 solar cells.

Understanding the Influence of Metal Oxide Layer Thickness and Defects on Resistive Switching Behavior Through Numerical Modeling

The resistive switching dynamics in metal–insulator–metal (MIM) structures may be explained through the formation and rupture of metallic filaments in the dielectric layer. Considerable research has been performed as an attempt to explain the microscopic origins of how regions that are originally insulating can change into conductors. Looking at how these conducting local regions are interconnected, the simulation of these localized switches can help understand how material properties such as thickness and defects influence the switching behavior and parameters. Here, the random circuit breaker (RCB) model is implemented and the dependence of the forming, Set, and Reset voltages on the oxide thickness and defect percentage is compared with experimental data. Only the forming voltage increases with the thickness, showing that, after the first step, the conductive filament formation and rupture occur only at a thinner thickness. It is observed that a higher percentage of initial conductive defects in the oxide layer promotes the forming step.

HDR image-based deep learning approach for automatic detection of split defects on sheet metal stamping parts

Sheet metal stamping is widely used for high-volume production. Despite the wide adoption, it can lead to defects in the manufactured components, making their quality unacceptable. Because of the variety of defects that can occur on the final product, human inspectors are frequently employed to detect them. However, they can be unreliable and costly, particularly at speeds that match the stamping rate. In this paper, we propose an automatic inspection framework for the stamping process that is based on computer vision and deep learning techniques. The low cost, remote sensing capability and simple implementation mean that it can be easily deployed in an industrial setting. A particular focus of this research is to account for the harsh lighting conditions and the highly reflective nature of products found in manufacturing environments that affect optical sensing techniques by making it difficult to capture the details of a scene. High dynamic range images can capture details of an environment in harsh lighting conditions, and in the context of this work, can capture highly reflective metals found in sheet metal stamping manufacturing. Building on this imaging technique, we propose a framework including a deep learning model to detect defects in sheet metal stamping parts. To test the framework, sheet metal ‘Nakajima’ samples were pressed with an industrial stamping press. Then optimally exposed, sequence of exposures, tone-mapped and high dynamic range images of the samples were used to train convolutional neural network-based detectors. Analysis of the resulting models showed that high dynamic range image-based models achieved substantially higher accuracy and minimal false-positive predictions.