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.

Superhydrophobic melamine sponge prepared by radiation-induced grafting technology for efficient oil-water separation

This paper presents a superhydrophobic melamine(ME)sponge(ME-g-PLMA)prepared via high-energy radiation-induced in situ covalent grafting of long-alkyl-chain dodecyl methacrylate(LMA)onto an ME sponge for efficient oil–water separation.The obtained ME-g-PLMA sponge had an excellent pore structure with superhydrophobic(water contact angle of 154°)and superoleophilic properties.It can absorb various types of oils up to 66–168 times its mass.The ME-g-PLMA sponge can continuously separate oil slicks in water by connecting a pump or separating oil underwater with a gravity-driven device.In addition,it maintained its highly hydrophobic properties even after long-term immersion in different corrosive solutions and repeated oil adsorption.The modified ME-g-PLMA sponge exhibited excellent separation properties and potential for oil spill cleanup.

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.

Effects of grain boundaries on irradiation-induced defects in tungsten by molecular dynamics simulations

The effects of two different symmetric tilt grain boundaries （GBs）, ∑13[001]（230） GB and ∑17[001]（140） GB, on displacement cascade processes in tungsten were investigated using molecular dynamics simulations. By quantifying the number of interstitials and vacancies surviving after irradiation with the kinetic energy of primary knock-on atom energies of 1, 3 and 5 keV, respectively, in these simulations, it is found that the GBs have dual nature for radiation-induced defects： They absorb interstitials while leaving more vacancies to survive in the grains. The net effect is that the number of total surviving defects in the GB system is not always less than that in the single crystal. These defect behaviors are understood by quantitatively analyzing the recovery fraction of irradiation-induced defects, the time to reach steady state and the mobility of vacancies and interstitials. It is also found that the ∑17 GB is a more effective sink of radiation-induced point defects than the ∑13 GB. One of the main reasons is that the ∑17 GB has a higher GB energy.

Treatment of radiation-induced brain injury with bisdemethoxycurcumin

Radiation therapy is considered the most effective non-surgical treatment for brain tumors.However,there are no available treatments for radiation-induced brain injury.Bisdemethoxycurcumin(BDMC)is a demethoxy derivative of curcumin that has anti-proliferative,anti-inflammatory,and anti-oxidant properties.To determine whether BDMC has the potential to treat radiation-induced brain injury,in this study,we established a rat model of radiation-induced brain injury by administe ring a single 30-Gy vertical dose of irradiation to the whole brain,followed by intraperitoneal injection of 500μL of a 100 mg/kg BDMC solution every day for 5 successive weeks.Our res ults showed that BDMC increased the body weight of rats with radiation-induced brain injury,improved lea rning and memory,attenuated brain edema,inhibited astrocyte activation,and reduced oxidative stress.These findings suggest that BDMC protects against radiationinduced brain injury.

TLD calibration and absorbed dose measurement in a radiation-induced liver injury model under a linear accelerator

The application of a thermoluminescent detector(TLD) for dose detection at the liver irradiation site in mice under linear accelerator precision radiotherapy and the use of a single high dose to irradiate the mouse liver to construct a biological model of a radiation-induced liver injury(RILD) in mice were to determine the feasibility of constructing a precision radiotherapy model in small animals under a linear accelerator. A 360° arc volumetric rotational intensity-modulated radiotherapy(VMAT) plan with a prescribed dose of 2 Gy was developed for the planned target volume(PTV) at the location of the TLD within solid water to compare the difference between the measured dose of TLD and the assessed parameters in the TPS system. The TLD was implanted in the livers of mice, and VMAT was planned based on TLD to compare the measured and prescribed doses. C57BL/6 J mice were randomly divided into control and 25-Gy radiation groups and were examined daily for changes in body weight. They were euthanized at 3 and 10 weeks after radiation, and the levels of liver serum enzymes such as alanine aminotransferase(ALT), aspartate aminotransferase(AST), and alkaline phosphatase(ALP) were measured to observe any pathological histological changes in the irradiated areas of the mouse liver. The measured values of solid underwater TLD were within ± 3% of the Dmean value of the evaluation parameter in the TPS system. The mice in the 25-Gy radiation group demonstrated pathological signs of radiation-induced liver injury at the site of liver irradiation. The deviation in the measured and prescribed doses of TLD in the mouse liver ranged from-1.5 to 6%;construction of an accurate model of RILD using the VMAT technique under a linear accelerator is feasible.

Decade Milestone Advancement of Defect-Engineered g-C_(3)N_(4) for Solar Catalytic Applications

Over the past decade, graphitic carbon nitride(g-C_(3)N_(4)) has emerged as a universal photocatalyst toward various sustainable carbo-neutral technologies. Despite solar applications discrepancy, g-C_(3)N_(4) is still confronted with a general fatal issue of insufficient supply of thermodynamically active photocarriers due to its inferior solar harvesting ability and sluggish charge transfer dynamics. Fortunately, this could be significantly alleviated by the “all-in-one” defect engineering strategy, which enables a simultaneous amelioration of both textural uniqueness and intrinsic electronic band structures. To this end, we have summarized an unprecedently comprehensive discussion on defect controls including the vacancy/non-metallic dopant creation with optimized electronic band structure and electronic density, metallic doping with ultraactive coordinated environment(M–N_(x), M–C_(2)N_(2), M–O bonding), functional group grafting with optimized band structure, and promoted crystallinity with extended conjugation π system with weakened interlayered van der Waals interaction. Among them, the defect states induced by various defect types such as N vacancy, P/S/halogen dopants, and cyano group in boosting solar harvesting and accelerating photocarrier transfer have also been emphasized. More importantly, the shallow defect traps identified by femtosecond transient absorption spectra(fs-TAS) have also been highlighted. It is believed that this review would pave the way for future readers with a unique insight into a more precise defective g-C_(3)N_(4) “customization”, motivating more profound thinking and flourishing research outputs on g-C_(3)N_(4)-based photocatalysis.

Heterointerface Engineering-Induced Oxygen Defects for the Manganese Dissolution Inhibition in Aqueous Zinc Ion Batteries

Manganese-based material is a prospective cathode material for aqueous zinc ion batteries(ZIBs)by virtue of its high theoretical capacity,high operating voltage,and low price.However,the manganese dissolution during the electrochemical reaction causes its electrochemical cycling stability to be undesirable.In this work,heterointerface engineering-induced oxygen defects are introduced into heterostructure MnO_(2)(δa-MnO_(2))by in situ electrochemical activation to inhibit manganese dissolution for aqueous zinc ion batteries.Meanwhile,the heterointerface between the disordered amorphous and the crystalline MnO_(2)ofδa-MnO_(2)is decisive for the formation of oxygen defects.And the experimental results indicate that the manganese dissolution ofδa-MnO_(2)is considerably inhibited during the charge/discharge cycle.Theoretical analysis indicates that the oxygen defect regulates the electronic and band structure and the Mn-O bonding state of the electrode material,thereby promoting electron transport kinetics as well as inhibiting Mn dissolution.Consequently,the capacity ofδa-MnO_(2)does not degrade after 100 cycles at a current density of 0.5 Ag^(-1)and also 91%capacity retention after 500cycles at 1 Ag^(-1).This study provides a promising insight into the development of high-performance manganese-based cathode materials through a facile and low-cost strategy.

Trace Cobalt Doping and Defect Engineering of High Surface Area α-Ni(OH)_(2) for Electrocatalytic Urea Oxidation

Owing to the intrinsically sluggish kinetics of urea oxidation reaction(UOR)involving a six-electron transfer process,developing efficient UOR electrocatalyst is a great challenge remained to be overwhelmed.Herein,by taking advantage of 2-Methylimidazole,of which is a kind of alkali in water and owns strong coordination ability to Co^(2+)in methanol,trace Co(1.0 mol%)addition was found to induce defect engineering onα-Ni(OH)_(2)in a dual-solvent system of water and methanol.Physical characterization results revealed that the synthesized electrocatalyst(WM-Ni_(0.99)Co_(0.01)(OH)_(2))was a kind of defective nanosheet with thickness around 5-6 nm,attributing to the synergistic effect of Co doping and defect engineering,its electron structure was finely altered,and its specific surface a rea was tremendously enlarged from 68 to 172.3 m^(2)g^(-1).With all these merits,its overpotential to drive 10 mA cm^(-2)was reduced by 110 mV.Besides,the interfacial behavior of UOR was also well deciphered by operando electrochemical impedance spectroscopy.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

A graphical user interface(GUI)for model-based radiation-induced acoustic computed tomography

Radiation-induced acoustic computed tomography(RACT)is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues.Traditional backprojection(BP)reconstructions carry noisy and limited-view artifacts.Model-based algorithms have been demonstrated to overcome the drawbacks of BPs.However,model-based algorithms are relatively more complex to develop and computationally demanding.Furthermore,while a plethora of novel algorithms has been developed over the past decade,most of these algorithms are either not accessible,readily available,or hard to implement for researchers who are not well versed in programming.We developed a user-friendly MATLAB-based graphical user interface(GUI;RACT2D)that facilitates back-projection and model-based image reconstructions for twodimensional RACT problems.We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI.The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer,thus further accelerating the reconstruction speed.We also share the MATLAB-based codes for evaluating RACT reconstructions,which users with MATLAB programming expertise can further modify to suit their needs.The shared GUI and codes can be of interest to researchers across the globe and assist them in e±cient evaluation of improved RACT reconstructions.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Strip steel surface defect detection algorithm based on improved Faster R-CNN

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.

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.

Applications of Natural Radiation-Induced Paramagnetic Defects in Quartz to Exploration in Sedimentary Basins

Quartz grains in contact with uranium-bearing minerals or fluids are characterized by natural radiation-induced paramagnetic defects （e. g. , oxygen vacancy centers, silicon vacancy centers, and peroxy radicals）, which are amenable to study by electron paramagnetic resonance （EPR） spectroscopy. These natural radiation-induced paramagnetic defects, except for the oxygen vacancy centers, in quartz are concentrated in narrow bands penetrated by α particles： （1） in halos around U- and Th-bearing mineral inclusions and （2） in outer rims or along fractures. The second type of occurrence provides information about uranium mineralization or remobilization （i. e. , sources of uranium, timing of mineralization or remobilization, pathways of uranium-bearing fluids）. It can also be used to evaluate sedimentary basins for potential of uranium mineralization. In particular, the peroxy radicals are stable up to 800℃ and, therefore, are useful for evaluating metasedimentary rocks （e. g. , Paleoproterozoic metasedimentary sequences in the central zone of the North China craton）. EPR study of the Changcheng Series can focus on quartz from the sediment-basement unconformity and faults to determine the presence and types of natural radiation-induced paramagnetic defects, with which to identify and prioritize uranium anomalies. Other potential applications of natural radiation-induced paramagnetic defects in quartz include uranium-bearing hydrocarbon deposits in sedimentary basins. For example, the Junggar, Ordos, and Tarim basins in northwestern China all contain important oil and natural gas fields and are well known for elevated uranium concentrations, including economic sandstone-hosted uranium deposits. Therefore, systematic studies on the distribution of natural radiation-induced paramagnetic defects in quartz from host sedimentary sequences are expected to provide information about the migration of oil and natural gas in those basins.