Molecular dynamics study of interactions between edge dislocation and irradiation-induced defects in Fe–10Ni–20Cr alloy

Irradiation-induced defects frequently impede the slip of dislocations, resulting in a sharp decline in the performance of nuclear reactor structural materials, particularly core structural materials. In the present work, molecular dynamics method is used to investigate the interactions between edge dislocations and three typical irradiation-induced defects(void,Frank loop, and stacking fault tetrahedron) with the sizes of 3 nm, 5 nm, and 7 nm at different temperatures in Fe–10Ni–20Cr alloy. The critical resolved shear stresses(CRSSs) are compared among different defect types after interacting with edge dislocations. The results show that the CRSS decreases with temperature increasing and defect size decreasing for each defect type during the interaction with edge dislocations, except for the case of 3-nm Frank loops at 900 K. According to a comparison, the CRSS in Frank loop is significantly higher than that of others of the same size, which is due to the occurrence of unfaulting and formation of superjog or stacking-fault complex during the interaction. The atomic evolution of irradiation-induced defects after interacting with dislocations can provide a novel insight into the design of new structural materials.

Comparison of formation and evolution of radiation-induced defects in pure Ni and Ni-Co-Fe medium-entropy alloy

High-entropy alloys(HEAs)and medium-entropy alloys(MEAs)have attracted a great deal of attention for developing nuclear materials because of their excellent irradiation tolerance.Herein,formation and evolution of radiation-induced defects in Ni Co Fe MEA and pure Ni are investigated and compared using molecular dynamics simulation.It is observed that the defect recombination rate of ternary Ni Co Fe MEA is higher than that of pure Ni,which is mainly because,in the process of cascade collision,the energy dissipated through atom displacement decreases with increasing the chemical disorder.Consequently,the heat peak phase lasts longer,and the recombination time of the radiation defects(interstitial atoms and vacancies)is likewise longer,with fewer deleterious defects.Moreover,by studying the formation and evolution of dislocation loops in Ni-Co-Fe alloys and Ni,it is found that the stacking fault energy in Ni-Co-Fe decreases as the elemental composition increases,facilitating the formation of ideal stacking fault tetrahedron structures.Hence,these findings shed new light on studying the formation and evolution of radiation-induced defects in MEAs.

Influence of helium on the evolution of irradiation-induced defects in tungsten:An object kinetic Monte Carlo simulation

Understanding the evolution of irradiation-induced defects is of critical importance for the performance estimation of nuclear materials under irradiation.Hereby,we systematically investigate the influence of He on the evolution of Frenkel pairs and collision cascades in tungsten(W)via using the object kinetic Monte Carlo(OKMC)method.Our findings suggest that the presence of He has significant effect on the evolution of irradiation-induced defects.On the one hand,the presence of He can facilitate the recombination of vacancies and self-interstitial atoms(SIAs)in W.This can be attributed to the formation of immobile He-SIA complexes,which increases the annihilation probability of vacancies and SIAs.On the other hand,due to the high stability and low mobility of He-vacancy complexes,the growth of large vacancy clusters in W is kinetically suppressed by He addition.Specially,in comparison with the injection of collision cascades and He in sequential way at 1223 K,the average sizes of surviving vacancy clusters in W via simultaneous way are smaller,which is in good agreement with previous experimental observations.These results advocate that the impurity with low concentration has significant effect on the evolution of irradiation-induced defects in materials,and contributes to our understanding of W performance under irradiation.

Photoluminescence Analysis of Injection-Enhanced Annealing of Electron Irradiation-Induced Defects in GaAs Middle Cells for Triple-Junction Solar Cells

Photolumineseenee measurements are carried out to investigate the injection-enhanced annealing behavior of electron radiation-induced defects in a GaAs middle cell for GaInP/GaAs/Ge triple-junction solar cells which are irradiated by 1.8 MeV with a fluence of i ~ 1015 cm-2. Minority-carrier injection under forward bias is observed to enhance the defect annealing in the GaAs middle cell, and the removal rate of the defect is determined with photoluminescenee radiative efficiency recovery. Furthermore, the injection-enhanced defect removal rates obey a simple Arrhenius law. Therefore, the annealing activation energy is acquired and is equal to 0.58eV. Finally, in comparison of the annealing activation energies, the E5 defect is identified as a primary non-radiative recombination center.

Study on irradiation-induced defects in GaAs/AlGaAs core–shell nanowires via photoluminescence technique

To gain a physical insight into the radiation effect on nanowires（NWs）, the time resolved photoluminescence（TRPL）technique is used to investigate the carrier dynamic behaviors in GaAs/AlGaAs core–shell NWs before and after 1-MeV proton irradiation with fluences ranging from 1.0 × 10^（12） cm^（-2） to 3.0 × 10^（13） cm^（-2）. It is found that the degradations of spectral peak intensity and minority carrier lifetime show similar trends against irradiation fluence, which is closely related to the displacement defects induced by irradiation. We also find that the proton irradiation-induced defects behave as Shockley–Read–Hall（SRH） recombination center trapping free carriers. Finally, the defect concentration could be estimated through measuring the minority carrier lifetime.

High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

Built defects of homogeneous junction to enhance the lithium storage capacity of niobium pentoxide materials

Niobium pentoxide(Nb_(2)O_(5))is deemed one of the promising anode materials for lithium-ion batteries(LIBs)for its outstanding intrinsic fast Li-(de)intercalation kinetics.The specific capacity,however,is still limited,because the(de)intercalation of excessive Li-ions brings the undesired stress to damage Nb_(2)O_(5) crystals.To increase the capacity of Nb_(2)O_(5) and alleviate the lattice distortion caused by stress,numerous homogeneous H-and M-phases junction interfaces were proposed to produce coercive stress within theNb_(2)O_(5)crystals.Such interfaces bring about rich oxygen vacancies with structural shrinkage tendency,which pre-generate coercive stress to resist the expansion stress caused by excessive Li-ions intercalation.Therefore,the synthesized Nb_(2)O_(5) achieves the highest lithium storage capacity of 315 mA h g−1 to date,and exhibits high-rate performance(118 mA h g^(-1) at 20 C)as well as excellent cycling stability(138 mA h g^(-1) at 10 C after 600 cycles).

Magnetic resonance imaging findings of radiation-induced breast angiosarcoma:A case report

BACKGROUND Breast conservation surgery(BCS)with adjuvant radiotherapy has become a gold standard in the treatment of early-stage breast cancer,significantly reducing the risk of tumor recurrence.However,this treatment is associated with adverse effects,including the rare but aggressive radiation-induced angiosarcoma(RIAS).Despite its rarity and nonspecific initial presentation,RIAS presents a challenging diagnosis,emphasizing the importance of imaging techniques for early detection and accurate diagnosis.CASE SUMMARY We present a case of a 48-year-old post-menopausal woman who developed skin ecchymosis on the right breast seven years after receiving BCS and adjuvant radiotherapy for breast cancer.Initial mammography and ultrasound were inconclusive,showing post-treatment changes but failing to identify the underlying angiosarcoma.Contrast-enhanced breast magnetic resonance imaging(MRI)revealed diffuse skin thickening and nodularity with distinctive enhan-cement kinetics,leading to the diagnosis of RIAS.This case highlights the crucial role of MRI in diagnosing and determining the extent of RIAS,facilitating timely and appropriate surgical intervention.CONCLUSION Breast MRI is crucial for detecting RIAS,especially when mammography and ultrasound are inconclusive.

Effect of Vacancy Defects on the Properties of CoS_(2) and FeS_(2)

In order to explore the effect of vacancy defects on the structural,electronic,magnetic and optical properties of CoS_(2) and FeS_(2),first-principles calculation method was used to investigate the alloys.The calculated results of materials without vacancy are consistent with those reported in the literatures,while the results of materials with vacancy defect were different from those of literatures due to the difference vacancy concentration.The Co vacancy defect hardly changes the half-metallic characteristic of CoS_(2).The Fe vacancy defect changes FeS_(2) from semiconductor to half-metal,and the bottom of the spin-down conduction band changes from the p orbital state of S to the d(t_(2g))orbital state of Fe,while the top of the valence band remains the d orbital d(eg)state of Fe.The half-metallic Co vacancy defects of CoS_(2) and Fe vacancy defects of FeS_(2) are expected to be used in spintronic devices.S vacancy defects make both CoS_(2) and FeS_(2) metallic.Both the Co and S vacancy defects lead to the decrease of the magnetic moment of CoS_(2),while both the Fe and S vacancy defects lead to the obvious magnetic property of FeS_(2).Vacancy defects enhance the absorption coefficient of infrared band and long band of visible light obviously,and produce obvious red shift phenomenon,which is expected to be used in photoelectric devices.

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Evaluation of internal void related defects in reinforced concrete slab using electromagnetic wave properties

This study aims to develop a damage-detection algorithm based on the electromagnetic wave properties inside a reinforced concrete structure.The proposed method involves employing two algorithms based on data measured using ground-penetrating radar—a common electromagnetic wave method in civil engineering.The possible defect area was identified based on the energy dissipated by the damage in the frequency-wavenumber domain,with the damage localized using the calculated relative permittivity of the measurements.The proposed method was verified through a finite difference time-domain-based numerical analysis and a testing slab with artificial damage.As a result of verification,the proposed method quickly identified the presence of damage inside the concrete,especially for honeycomb-like defects located at the top of the rebar.This study has practical significance in scanning structures over a large area more quickly than other non-destructive testing methods,such as ultrasonic methods.

Micro defects formation and dynamic response analysis of steel plate of quasi-cracking area subjected to explosive load

As the protective component,steel plate had attracted extensive attention because of frequently threats of explosive loads.In this paper,the evolution of microstructure and the mechanism of damage in the quasi-cracking area of steel plate subjected to explosive load were discussed and the relationships between micro defects and dynamic mechanical response were revealed.After the explosion experiment,five observation points were selected equidistant from the quasi-cracking area of the section of the steel plate along the thickness direction,and the characteristics of micro defects at the observation points were analyzed by optical microscope(OM),scanning electron microscope(SEM) and electron backscattered diffraction(EBSD).The observation result shows that many slip bands(SBs) appeared,and the grain orientation changed obviously in the steel plate,the two were the main damage types of micro defects.In addition,cracks,peeling pits,grooves and other lager micro defects were appeared in the lower area of the plate.The stress parameters of the observation points were obtained through an effective numerical model.The mechanism of damage generation and crack propagation in the quasicracking area were clarified by comparing the specific impulse of each observation point with the corresponding micro defects.The result shows that the generation and expansion of micro defects are related to the stress area(i.e.the upper compression area,the neutral plane area,and the lower tension area).The micro defects gather and expand at the grain boundary,and will become macroscopic damage under the continuous action of tensile stress.Besides,the micro defects at the midpoint of the section of the steel plate in the direction away from the explosion center(i.e.the horizontal direction) were also studied.It was found that the specific impulse at these positions were much smaller than that in the thickness direction,the micro defects were only SBs and a few micro cracks,and the those decreased with the increase of the distance from the explosion center.

Customized scaffolds for large bone defects using 3D‑printed modular blocks from 2D‑medical images

Additive manufacturing(AM)has revolutionized the design and manufacturing of patient-specific,three-dimensional(3D),complex porous structures known as scaffolds for tissue engineering applications.The use of advanced image acquisition techniques,image processing,and computer-aided design methods has enabled the precise design and additive manufacturing of anatomically correct and patient-specific implants and scaffolds.However,these sophisticated techniques can be timeconsuming,labor-intensive,and expensive.Moreover,the necessary imaging and manufacturing equipment may not be readily available when urgent treatment is needed for trauma patients.In this study,a novel design and AM methods are proposed for the development of modular and customizable scaffold blocks that can be adapted to fit the bone defect area of a patient.These modular scaffold blocks can be combined to quickly form any patient-specific scaffold directly from two-dimensional(2D)medical images when the surgeon lacks access to a 3D printer or cannot wait for lengthy 3D imaging,modeling,and 3D printing during surgery.The proposed method begins with developing a bone surface-modeling algorithm that reconstructs a model of the patient’s bone from 2D medical image measurements without the need for expensive 3D medical imaging or segmentation.This algorithm can generate both patient-specific and average bone models.Additionally,a biomimetic continuous path planning method is developed for the additive manufacturing of scaffolds,allowing porous scaffold blocks with the desired biomechanical properties to be manufactured directly from 2D data or images.The algorithms are implemented,and the designed scaffold blocks are 3D printed using an extrusion-based AM process.Guidelines and instructions are also provided to assist surgeons in assembling scaffold blocks for the self-repair of patient-specific large bone defects.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Microscopic defects formation and dynamic mechanical response analysis of Q345 steel plate subjected to explosive load

As the basic protective element, steel plate had attracted world-wide attention because of frequent threats of explosive loads. This paper reports the relationships between microscopic defects of Q345 steel plate under the explosive load and its macroscopic dynamics simulation. Firstly, the defect characteristics of the steel plate were investigated by stereoscopic microscope(SM) and scanning electron microscope(SEM). At the macroscopic level, the defect was the formation of cave which was concentrated in the range of 0-3.0 cm from the explosion center, while at the microscopic level, the cavity and void formation were the typical damage characteristics. It also explains that the difference in defect morphology at different positions was the combining results of high temperature and high pressure. Secondly, the variation rules of mechanical properties of steel plate under explosive load were studied. The Arbitrary Lagrange-Euler(ALE) algorithm and multi-material fluid-structure coupling method were used to simulate the explosion process of steel plate. The accuracy of the method was verified by comparing the deformation of the simulation results with the experimental results, the pressure and stress at different positions on the surface of the steel plate were obtained. The simulation results indicated that the critical pressure causing the plate defects may be approximately 2.01 GPa. On this basis, it was found that the variation rules of surface pressure and microscopic defect area of the Q345 steel plate were strikingly similar, and the corresponding mathematical relationship between them was established. Compared with Monomolecular growth fitting models(MGFM) and Logistic fitting models(LFM), the relationship can be better expressed by cubic polynomial fitting model(CPFM). This paper illustrated that the explosive defect characteristics of metal plate at the microscopic level can be explored by analyzing its macroscopic dynamic mechanical response.

Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).

Defects evolution in n-type 4H-SiC induced by electron irradiation and annealing

Radiation damage produced in 4H-SiC by electrons of different doses is presented by using multiple characterization techniques. Raman spectra results indicate that SiC crystal structures are essentially impervious to 10 Me V electron irradiation with doses up to 3000 kGy. However, irradiation indeed leads to the generation of various defects, which are evaluated through photoluminescence(PL) and deep level transient spectroscopy(DLTS). The PL spectra feature a prominent broad band centered at 500 nm, accompanied by several smaller peaks ranging from 660 to 808 nm. The intensity of each PL peak demonstrates a linear correlation with the irradiation dose, indicating a proportional increase in defect concentration during irradiation. The DLTS spectra reveal several thermally unstable and stable defects that exhibit similarities at low irradiation doses.Notably, after irradiating at the higher dose of 1000 kGy, a new stable defect labeled as R_(2)(Ec-0.51 eV) appeared after annealing at 800 K. Furthermore, the impact of irradiation-induced defects on SiC junction barrier Schottky diodes is discussed. It is observed that high-dose electron irradiation converts SiC n-epilayers to semi-insulating layers. However, subjecting the samples to a temperature of only 800 K results in a significant reduction in resistance due to the annealing out of unstable defects.

Molecular Dynamics Simulation of Shock Response of CL-20 Co-crystals Containing Void Defects

To investigate the effect of void defects on the shock response of hexanitrohexaazaisowurtzitane(CL-20)co-crystals,shock responses of CL-20 co-crystals with energetic materials ligands trinitrotoluene(TNT),1,3-dinitrobenzene(DNB),solvents ligands dimethyl carbonate(DMC) and gamma-butyrolactone(GBL)with void were simulated,using molecular dynamics method and reactive force field.It is found that the CL-20 co-crystals with void defects will form hot spots when impacted,significantly affecting the decomposition of molecules around the void.The degree of molecular fragmentation is relatively low under the reflection velocity of 2 km/s,and the main reactions are the formation of dimer and the shedding of nitro groups.The existence of voids reduces the safety of CL-20 co-crystals,which induced the sensitivity of energetic co-crystals CL-20/TNT and CL-20/DNB to increase more significantly.Detonation has occurred under the reflection velocity of 4 km/s,energetic co-crystals are easier to polymerize than solvent co-crystals,and are not obviously affected by voids.The results show that the energy of the wave decreases after sweeping over the void,which reduces the chemical reaction frequency downstream of the void and affects the detonation performance,especially the solvent co-crystals.