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.

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.

Visualizing extended defects at the atomic level in a Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire

The microstructure significantly influences the superconducting properties.Herein,the defect structures and atomic arrangements in high-temperature Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire are directly characterized via stateof-the-art scanning transmission electron microscopy.Interstitial oxygen atoms are observed in both the charge reservoir layers and grain boundaries in the doped superconductor.Inclusion phases with varied numbers of CuO_(2) layers are found,and twist interfaces with different angles are identified.This study provides insights into the structures of Bi-2212 wire and lays the groundwork for guiding the design of microstructures and optimizing the production methods to enhance superconducting performance.

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).

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.

Interface optimization and defects suppression via Na F introduction enable efficient flexible Sb_(2)Se_(3) thin-film solar cells

Sb_(2)Se_(3) with unique one-dimensional(1D) crystal structure exhibits exceptional deformation tolerance,demonstrating great application potential in flexible devices.However,the power conversion efficiency(PCE) of flexible Sb_(2)Se_(3) photovoltaic devices is temporarily limited by the complicated intrinsic defects and the undesirable contact interfaces.Herein,a high-quality Sb_(2)Se_(3) absorber layer with large crystal grains and benign [hkl] growth orientation can be first prepared on a Mo foil substrate.Then NaF intermediate layer is introduced between Mo and Sb_(2)Se_(3),which can further optimize the growth of Sb_(2)Se_(3)thin film.Moreover,positive Na ion diffusion enables it to dramatically lower barrier height at the back contact interface and passivate harmful defects at both bulk and heterojunction.As a result,the champion substrate structured Mo-foil/Mo/NaF/Sb_(2)Se_(3)/CdS/ITO/Ag flexible thin-film solar cell delivers an obviously higher efficiency of 8.03% and a record open-circuit voltage(V_(OC)) of 0.492 V.This flexible Sb_(2)Se_(3) device also exhibits excellent stability and flexibility to stand large bending radius and multiple bending times,as well as superior weak light photo-response with derived efficiency of 12.60%.This work presents an effective strategy to enhance the flexible Sb_(2)Se_(3) device performance and expand its potential photovoltaic applications.

Theoretical Design of Defects as a Driving Force for Ion Transport in Li_(3)OBr Solid Electrolyte

Due to ever-increasing concerns about safety issues in using Li ionic batteries,solid electrolytes have extensively explored.The Li-rich antiperovskite Li_(3)OBr has been considered as a promising solid electrolyte candidate,but it still suffers challenges to achieve a high ionic conductivity owing to the high intrinsic symmetry of the crystal lattice.Herein,we presented a design strategy that introduces various point defects and grain boundaries to break the high lattice symmetry of Li_(3)OBr crystal,and their effect and microscopic mechanism of promoting the migration of Li-ion were explored theoretically.It has been found that Li_(i)are the dominant defects responsible for the fast Li-ion diffusion in bulk Li_(3)OBr and its surface,but they are easily trapped by the grain boundaries,leading to the annihilating of the Frenkel defect pair V'_(Li)+Li_(i),and thus limits the V'_(Li)diffusion at the grain boundaries.The V_(Br)defect near the grain boundaries can effectively drive V'_(Li)across the grain boundary,thereby converting the carrier of Li^(+)migration from Li,in the bulk and surface to V'_(Li)at the grain boundary,and thus improving the ionic conductivity in the whole Li_(3)OBr crystal.This work provides a comprehensive insight into the Li^(+)transport and conduction mechanism in the Li_(3)OBr electrolyte.It opens a new way of improving the conductivity for all-solid-state Li electrolyte material through the defect design.

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.

In-doping collaboratively controlling back interface and bulk defects to achieve efficient flexible CZTSSe solar cells

Focusing on the low open circuit voltage(V_(OC))and fill factor(FF)in flexible Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)solar cells,indium(In)ions are introduced into the CZTSSe absorbers near Mo foils to modify the back interface and passivate deep level defects in CZTSSe bulk concurrently for improving the performance of flexible device.The results show that In doping effectively inhibits the formation of secondary phase(Cu(S,Se)_(2))and VSndefects.Further studies demonstrate that the barrier height at the back interface is decreased and the deep level defects(Cu_(Sn)defects)in CZTSSe bulk are passivated.Moreover,the carrier concentration is increased and the V_(OC) deficit(V_(OC,def))is decreased significantly due to In doping.Finally,the flexible CZTSSe solar cell with 10.01%power conversion efficiency(PCE)has been obtained.The synergistic strategy of interface modification and bulk defects passivation through In incorporation provides a new thought for the fabrication of efficient flexible kesterite-based solar cells.

Retinal nerve fiber layer defects and chronic kidney disease:the Kailuan Eye Study

AIM:To investigate whether retinal nerve fiber layer defects(RNFLDs)is a potential risk factor for chronic kidney disease(CKD)in Chinese adults.METHODS:The Kailuan Eye Study was a populationbased study that included 14440 participants.All participants underwent detailed assessments,RNFLDs were diagnosed using color fundus photographs.RESULTS:Overall,12507 participants[8533 males(68.23%)]had complete systemic examination data and at least one evaluable fundus photograph.RNFLDs were found in 621 participants[5.0%;95%confidence interval(CI):4.6%-5.34%],and 70 cases of multiple RNFLDs were found(11.27%).After adjusting multiple factors,RNFLDs was significantly associated with CKD severity,the ORs of CKD stage 3,stage 4 and stage 5 were 1.698,4.167,and 9.512,respectively.Multiple RNFLDs were also associated with CKD severity after adjusting multiple factors,the ORs of CKD stage 3 and stage 5 were 4.465 and 11.833 respectively.Furthermore,2294 participants had CKD(18.34%,95%CI:17.68%-18.99%).After adjusting for other factors,CKD presence was significantly correlated with the presence of RNFLDs.CONCLUSION:The strongest risk factors for RNFLDs are CKD and hypertension.Conversely,RNFLDs can be an ocular feature in patients with CKD.Fundoscopy can help detect systemic diseases,and assessment for RNFLDs should be considered in CKD patients.

Association of Congenital Heart Defects (CHD) with Factors Related to Maternal Health and Pregnancy in Newborns in Puerto Rico

Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tional,and gestational maternal diabetes,and their potential impact on the occurrence of congenital heart defects(CHD)during neonatal development.Methods:Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico,we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020.Our assessment encompassed a range of variables,including maternal age,gestational age,BMI,pregestational diabetes,gestational diabetes,hypertension,history of abortion,and presence of preeclampsia.Results:A cohort of 673 patients was included in our study.The average maternal age was 26 years,within a range of 22 to 32 years.The mean gestational age measured 39 weeks,with a median span of 38 to 39 weeks.Of the 673 patients,274(41%)mothers gave birth to neonates diagnosed with CHD.Within this group,22 cases were linked to pre-gestational diabetes,while 202 were not;20 instances were associated with gestational diabetes,compared to 200 without;and 148 cases exhibited an overweight or obese BMI,whereas 126 displayed a normal BMI.Conclusion:We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD.However,our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD.These results may aid in developing effective strategies to prevent and manage CHD in neonates.

Bending Failure Mode and Prediction Method of the Compressive Strain Capacity of A Submarine Pipeline with Dent Defects

A dent is a common type of defects for submarine pipeline.For submarine pipelines,high hydrostatic pressure and internal pressure are the main loads.Once pipelines bend due to complex subsea conditions,the compression strain capacity may be exceeded.Research into the local buckling failure and accurate prediction of the compressive strain capacity are important.A finite element model of a pipeline with a dent is established.Local buckling failure under a bending moment is investigated,and the compressive strain capacity is calculated.The effects of different parameters on pipeline local buckling are analyzed.The results show that the dent depth,external pressure and internal pressure lead to different local buckling failure modes of the pipeline.A higher internal pressure indicates a larger compressive strain capacity,and the opposite is true for external pressure.When the ratio of external pressure to collapse pressure of intact pipeline is greater than 0.1,the deeper the dent,the greater the compressive strain capacity of the pipeline.And as the ratio is less than 0.1,the opposite is true.On the basis of these results,a regression equation for predicting the compressive strain capacity of a dented submarine pipeline is proposed,which can be referred to during the integrity assessment of a submarine pipeline.

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.

YOLO-RLC:An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

Printed circuit boards(PCBs)provide stable connections between electronic components.However,defective printed circuit boards may cause the entire equipment system to malfunction,resulting in incalculable losses.Therefore,it is crucial to detect defective printed circuit boards during the generation process.Traditional detection methods have low accuracy in detecting subtle defects in complex background environments.In order to improve the detection accuracy of surface defects on industrial printed circuit boards,this paper proposes a residual large kernel network based on YOLOv5(You Only Look Once version 5)for PCBs surface defect detection,called YOLO-RLC(You Only Look Once-Residual Large Kernel).Build a deep large kernel backbone to expand the effective field of view,capture global informationmore efficiently,and use 1×1 convolutions to balance the depth of the model,improving feature extraction efficiency through reparameterization methods.The neck network introduces a bidirectional weighted feature fusion network,combined with a brand-new noise filter and feature enhancement extractor,to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep features.Simplify the aspect ratio of the bounding box to alleviate the issue of specificity values.After training and testing on the PCB defect dataset,our method achieved an average accuracy of 97.3%(mAP50)after multiple experiments,which is 4.1%higher than YOLOv5-S,with an average accuracy of 97.6%and an Frames Per Second of 76.7.The comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.

Perfecting HER catalysts via defects:Recent advances and perspectives

Defect engineering has become a promising approach to improve the performance of hydrogen evolution reaction(HER)catalysts.Non-noble transition metal-based catalysts(TMCs)have shown significant promise as effective alternatives to traditional platinum-group catalysts,attracting considerable attention.However,the industrial application of TMCs in electrocatalytic hydrogen production necessitates further optimization to boost both catalytic activity and stability.This review comprehensively examines the types,fabrication methods,and characterization techniques of various defects that enhance catalytic HER activity.Key advancements include optimizing defect concentration and distribution,coupling heteroatoms with vacancies,and leveraging the synergy between bond lengths and defects.In-depth discussions highlight the electronic structure and catalytic mechanisms elucidated through in-situ characterization and density functional theory calculations.Additionally,future directions are identified,exploring novel defect types,emphasizing precision synthesis methods,industrial-scale preparation techniques,and strategies to enhance structural stability and understanding the in-depth catalytic mechanism.This review aims to inspire further research and development in defect-engineered HER catalysts,providing pathways for high efficiency and cost-effectiveness in hydrogen production.

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.

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.

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.

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.