At fixed composition, the UCx target grain size, porosity and thermal conductivity represent the three main points that affect the target production efficiency. UCx was synthesized using both graphite and graphene as the source of carbon and the target properties in terms of composition, grain size, porosity, thermal diffusivity and thermal conductivity were studied. The main output of this work is related to the remarkable enhancement achieved in thermal conductivity, which can profitably improve thermal dissipation during operational stages of UCx targets.The development of tailored targets for the production of radioactive isotopes represents an active field in nuclear research. Radioactive beams find applications in nuclear medicine, in astrophysics, matter physics and materials science. In this work, we study the use of graphene both as carbon source for UO For this reason, heat dissipation is one of the key issues to be complied for the safety assessment of fuels and development of efficient targets. Consequently targets of actinide carbides are often mixed with an excess of carbon in the form of graphite or pyrolytic carbon in order to tailor their thermal properties. In addition, the isotopic species produced in ISOL targets must be able to escape from the target towards the ion source before they decay. This step of isotope extraction is very problematic, since their decay time may be in the order of few ms.9 that possess enhanced thermal conductivity. ISOL targets are generally produced via carbo-thermal reaction of UO2 with a carbon source. Beside graphite, different carbon source can be used, e.g. multiwall carbon nanotubes (MWCNTs)10 and carbon black11. The presence of a certain amount of porosity within the target and controlled grain size11 must be preserved to allow the produced isotopes to escape from the target via diffusion-effusion processes12.The aim of this work is the production of targets, for second generation ISOL facilities2 in graphene13 with concomitant consequences in terms of their mechanical and thermal properties.Actinide carbides differ from transition metal carbides, due to unfilled 5 f orbital in their electronic structure. For instance this leads to strong chemisorption of UC2 and UC increases with increasing temperature and ranges from 10–11 (W/mK) at room temperature to 20–21 (W/mK) at 2573 K, for UC2 and reaches even higher thermal conductivity values for UC14. Other refractory carbides considered for ISOL facilities include TiC [11] produced as nanostructured target in order to enhance its release efficiency, and lanthanum carbide15 for which a slip casting process was developed to obtain refractory carbide-carbon composites.The thermal conductivity of UC16 have shown how the use of graphene as a carbon source for the carburization of La2O3 to give LaC2 acts as sintering aid. Here the use of graphene as a carbon source for the carburization of UO2 was studied. Thermal properties of the obtained graphene-derived carbides  were compared to graphite derived UC2, to verify if the use of graphene can be beneficial in terms of fundamental properties related to on-line operation of the target materials.This research is the second part of a project titled “Study of the use of graphene as source of carbon for Uranium Carbide-Graphene nanocomposites production” and has been completed in the frame of Infrastructure/user access at the Joint Research Centre Karlsruhe, Directorate G for Nuclear Safety and Security. Previous studiesThe carbothermal reaction to obtain the composite disks has been carried out according to the following reaction:17. For this reason, the characterization results reported here refer only to the sections of the samples that were not in contact with molybdenum: Thus they are fully representative of actual ISOL target materials.Using a molybdenum grid as a crucible, it was observed that the parts directly in contact with the metal showed a higher reactivity than expected, independently on the type of carbon source used for the carbothermal reaction. In commonly adopted procedures to produce ISOL targets, the reaction is performed by heat treatment, with the samples in a vertical position, without interaction with metal crucibles being only in contact with a graphite button or disk at their peripheryx-graphene and UCx-graphite) properties before and after the thermal treatment. UCx-graphene and UCx-graphite have very similar density, and consequently the same level of total porosity, whereas the weight loss observed during synthesis for UCx-graphene is slightly higher than that of UCx-graphite.Table 18, and this can be ascribed to decomposition of the organic binder used in samples’ preparation and the water desorption from the starting powders, which were not taken into account. Higher shrinkage was observed for UCx-graphite.Weight losses are higher than those expected theoretically, as previously observedIn Table x-graphite and UCx-graphene and the difference in distribution of the residual carbon in the structure. While carbon is clearly visible for the UCx-graphite sample, emerging from the surface as described in an earlier study on UCx-graphite16, this is not the case of UCx-graphene. This latter presents a finer distribution of the residual carbon with local dark areas in the image. Higher magnification images reported in Fig. x-graphite and micro-cracks in UCx-graphene with no clear evidence of unreacted carbonaceous phase. The presence of sub-micron sized cracks in UCx-graphene cannot be ruled out because of the limited resolution of SEM. These pores may be responsible of the higher total porosity (lower density) of UCx-graphene with respect to UCx-graphite. The internal microstructure of both samples is revealed in the SEM images of Fig. x-graphene does not show any apparent presence of carbon, indicating also that higher magnification is needed to reveal the residual multilayered graphene.The SEM images reported in Fig. x-graphene particles  and after (solid line) the heat treatment are reported in Figs d in eq. , a certa2 starting ratio for the carbothermal reaction enables this mechanism also in the present case, as predicted by the work of Mukerijee20. The atmosphere used for the carbothermal reduction (Ar or high vacuum) determines the reaction, due to diffusion of CO by-product, whereas in vacuum the extent of interface between reactants is the controlling step21. In previous experiments related to composites derived from carbothermal reduction of lanthanum oxide conducted in vacuum, the use of graphene as carbon precursor led to a high degree of sintering, remarkably higher density and low porosity with respect to graphite based composites14. In the present case, where the reaction is diffusion controlled, the trend in density and total porosity is opposite, although the difference in porosity between graphite and graphene derived composites is not so remarkable. Furthermore, in a previous work18 the total porosity of UCx-graphite composites produced in high vacuum starting from UO2 powders compacted with graphite in conditions similar to those adopted here was remarkably higher than when the carbothermal reduction was performed under Ar.The selected C/UO2 is forming through the highly permeable graphene layers leads to higher porosity than in case of graphite, and is most likely ascribed to carbide grains with size in the nm range, as observed in TEM images. However, further experiments will be performed by conducting the carbothermal reduction of graphene based composites in vacuum, in order to confirm this hypothesis.Therefore, it can be inferred that the environment where the reduction is conducted is crucial in determining the microstructure of the obtained composites, . In the case of a vacuum atmosphere, the presence of graphene is relevant as a sintering aid and the porosity is reduced, as a result of a more efficient surface contact between the reactants. In the case of Ar flow, the fast release of CO while UCIn spite of different microstructure, it is worth noting that the reaction goes in both cases to completion, when either graphene or graphite is used as precursor, as demonstrated by XRD analyses, leading to negligible differences in the structure of the produced carbide.2 in the [112] and [110] directions were calculated and reported in Table As for the structure of residual carbon in the final bodies, the diffraction patterns and Raman spectra of the very same graphite and graphene used in this work (reported in [16]), show a nanocrystallite size of 44.3 nm for graphite and 15.6 nm for graphene. The presence of residual carbon could not be detected by XRD analysis, either because it is present as an amorphous phase and/or because it is below detection limit or due to the presence of high Z uranium atoms. The crystallite size of UCThe thermal diffusivity was measured by the laser flash technique. Thermal diffusivity measurements Fig.  of both λ (in W m−1K−1) was calculated using equation (α (in m2 s−1) and density ρ (in kg m−3) obtained by tomography and corrected for thermal dilatation using data from22, with specific heat Cp (in J kg−1 K−1) data from23.The thermal conductivity equation  from thex-graphene with respect to UCx-graphite derived samples. This is confirmed by the derived thermal conductivity shown in Fig. x-graphene with respect to UCx-graphite throughout the observed temperature region (fill data points). Due to the presence of significant porosity, the absolute values of thermal conductivity of both samples resemble those of 95% dense UO224 more than UC214. For comparison, a projection of thermal conductivity to fully dense UCx-graphite and UCx-graphene samples was calculated using the Maxwell formula25 and is reported (open symbols). It shows, a similar trend to pure, fully dense UC2. The trend obtained for UCx-graphite is in good agreement with that reported by Greene et al.26, in which UC2-C samples with density of 5 g/cm3 showed increasing thermal conductivity with temperature in the range 1873 K–2173 K, with a thermal conductivity of about 5 W/m∙K at 1873 K.If only the data obtained in the heating portion of the measurement (right arrows) is, considered an increase of diffusivity by more than 20% is observed in UCx-graphene versus UCx-graphite is a direct consequence of its intrinsic higher diffusivity, as shown in Fig. The increase of thermal conductivity in UC27. In the case of materials obtained by powder pressing, such as silicon nitride, the dispersion of graphene platelets resulted in an anisotropic thermal response, with thermal conductivity increasing in the direction of alignment of the platelets, perpendicular to the pressing axis29. In this work, however, graphene is not only dispersed in a pressed ceramic matrix but it acts as a carbon source for the reduction of an oxide to a carbide. Considering the hyperstoichiometry of the reaction, the residual graphene in the final samples is either composed of completely unreacted or partially reacted platelets, over which uranium carbide is formed and grown, (cf previously reported carbon nanotubes derived carbides10). In apparent contrast to the recently reported results for lanthanum carbide composites derived from graphene16, there seems to be no sintering aid effect of dispersed graphene in uranium carbide, as confirmed by bulk density measurements. UCx-graphene is indeed characterized by higher porosity. Nitrogen adsorption-desorption analyses could give indications on the presence of micro and mesoporosity, not detectable by SEM, as well as measurement of the true density of sintered UCx could confirm the calculated values of total porosity by eq. From the chemico-physical and functional characterization reported above, it is clear that graphene derived uranium carbides have higher thermal conductivity than those derived from graphite. This phenomenon is to be ascribed to intrinsic properties of graphene, even if its predictability is not straightforward. The addition of well dispersed graphene platelets on ceramic matrices by powder mixing and sintering has been reported as a method to increase thermal conductivity2O < 1 ppm and of O2 < 50 ppm. UO2 sample were prepared by and purchased from the British Nuclear Fuels Ltd. Both Graphite and graphene were used as carbon sources for the synthesis of UC2. Graphite powders of mesh size <325 micron were purchased form Sigma-Aldrich. Elicarb® Graphene was purchased from Thomas Swan&Co. Ltd UK. The Elicarb® Graphene powders are high purity platelets of size ranging from 0.5 nm to 3 nm. The reactants were first individually mixed in a planetary ball-mill (3000 rpm and 2 h), Jar and balls were made of ZrO2, leading to a powder size <5–10 micron.Sample preparation until pressing of the pellet has been done in glove box equipped with a gas MBraun purification system. The atmosphere was kept under nitrogen with a content of HThe powders were first mixed in an agathe mortar using the hyperstoichiometric ratio reported in reaction (1).A solution of 2 wt% of phenolic resin in acetone (15 wt%) was added dropwise as binder. The powders were then further mixed in the ball-mill (during 3 h at 3000 rpm).2-Graphite and UO2-Graphene). The samples were heat threated in one batch under Argon flow in a (Degussa Type VSL) annealing furnace using a Molybdenum crucible at 1963 K for 24 h, heating rate 2 K/min. A Residual Gas Analyzer (Siemens Ultramat 6) was used during the heat treatment in order to monitor the CO evolution and consequently the effect of carbothermal reaction. The weight loss after thermal treatment was measured by an analytical balance Mettler Toledo Mod. SAG204.The powders were then pressed using a uniaxial cold press at 7 ton for 10 min, each. At least five pellets of 13 mm diameter and about 1 mm thickness were prepared for each batch . The total porosity of the samples was calculated by:Samples bulk density ρth is the theoretical density of the final sample, calculated by the mixture rule taking into account the volumetric fractions of UC2 and free carbon present in the final samples , before and after heat treatment.where ρples eq.  and theiScanning electron microscopy was performed on a Philips XL 40 using a tungsten filament (200 V–30 keV).The Transmission Electron Microscope (TEM) study was performed using FEI Tecnai G2 model, equipped with a GATAN Tridiem camera and a GATAN Imaging Filter. The field emission gun was operated during the study at 200 kV.30. The sample was prepared by crushing, as explained in previous work31; the so-prepared sample grids were then introduced in a Plasma cleaner machine in order to get rid of any organic residues, and brought to the TEM for the analysis making use of a La Calhène DPTE® system.The TEM has been adapted for the examination of highly active or irradiated nuclear materials thanks to a flange that has been inserted in the octagon hosting the objective lenses, and a glove box mounted on this flange around the compustageSamples were characterized by powder X-ray diffraction on a Rigaku MiniFlex 600 diffractometer with a θ–2θ configuration using Cu Kα1-α2 radiation. The patterns were measured from 15°−120° in 2Θ angle with a scan/step of 0.02° scan/step and a scan speed of 0.29°/min. The software Topas version 5 was used to refine the XRD data.33 was used for the measurement of the thermal diffusivity α of the samples. The latter are parts of a disk with plane and parallel faces. The thickness of the samples is measured at ambient temperature for different positions at the samples surface in order to verify that the faces are plane and parallel. The effect of thermal dilatation on the thickness is taken into account using the thermal expansion coefficient recommended by Venkata Krishnan et al.34. The samples are heated under vacuum (10−5–10−6 Pa of nitrogen) up to the measurement temperature in a high frequency furnace. As soon as a stable temperature is obtained, a laser pulse of 2 ms is applied to the front surface of the disk. The temperature perturbation on the opposite surface is recorded by a pyrometer and the obtained thermograms are analysed in order to deduce α and the heat loss coefficients by a numerical fitting procedure taking into account explicitly the laser pulse length35. The precision of measurements is better than 5%, and is mainly determined by the samples thickness variations. The experiments were carried out starting at 500 K with the aim of measuring α at increasing temperatures and of examining recovery effects after laboratory thermal annealing above the fuel irradiation temperature. Thermal cycles were applied corresponding to selected sequences of annealing temperatures (Tann) up to 1550 K.A shielded “laser-flash” device designed and constructed at JRC KarlsruheThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.


The original article  containsL1 promoter assayRP-luc) together with empty vector (pcDNA6mycHis) or SAMHD1 expressing vector using calcium phosphate. The reporter plasmid L1RP-luc and the L1 promoter activity assay have been described previously [HEK-293T cells were transfected with the L1 promoter luciferase reporter plasmid (L1


Molecular dynamics simulations of nanolaminated graphene/Cu (NGCu) and pure Cu under compression are conducted to investigate the underlying strengthening mechanism of graphene and the effect of lamella thickness. It is found that the stress-strain curves of NGCu undergo 3 regimes i.e. the elastic regime I, plastic strengthening regime II and plastic flow regime III. Incorporating graphene monolayer is proved to simultaneously contribute to the strength and ductility of the composites and the lamella thickness has a great effect on the mechanical properties of NGCu composites. Different strengthening mechanisms play main role in different regimes, the transition of mechanisms is found to be related to the deformation behavior. Graphene affected zone is developed and integrated with rule of mixtures and confined layer slip model to describe the elastic properties of NGCu and the strengthening effect of the incorporated graphene. However, It is still quite difficult to directly apply graphene as structure materials due to its unique geometric features and the interfacial instability2. Using graphene as constituent phase in composites thus has been considered as a versatile method to make use of the excellent performance of graphene. Effective enhancement in strength and toughness was reported for various graphene-filling composites such as polymer3, ceramic4 and metal matrix composites6.Graphene, as one of the most promising two dimensional material, has shown extraordinary intrinsic electrical, thermal and mechanical properties7. Furthermore, incorporating graphene is proved to improve not only the designed functions but also the mechanical performances of the metal matrix phases, showing great prospects in engineering applications11. Researchers had successfully fabricated graphene/Cu, graphene/Al composites with uniform dispersion of graphene in metal powders and found various degrees of reinforcement in elastic modulus, hardness and tensile strength by no more than 1.0 wt.% graphene addition15. In 2013, nanolaminated graphene/metal composites reinforced by single layer of graphene were synthesized by Kim et al. The nanopillar compression test results showed ultra-high strengths of 1.5 GPa and 4.0 GPa for graphene/Cu and graphene/Ni nanolayered composites respectively16. Uniaxial compression tests were also carried out on graphene/Al nanolaminated micro-pillars17. It was found that the strengthening effects were related to the graphene concentrations and laminate orientation. 137% higher flow stress than pure Al pillars and crack deflection mechanism were observed. In addition, molecular dynamics (MD) simulations were also conducted and revealed that the incorporating graphene can improve the radiation damage resistance18, shock strength21 and ductility of nanolaminated graphene/metal composites23.Graphene/metal composites inspire broad interests in investigating the structure and properties of graphene/metal interface because they are a key to the development of catalysis, sensors, hydrogen storage, and nanoelectronic devices26. Similar mechanisms have been demonstrated in nanolaminated metal composites, in which interface plays an important role in mediating deformation mechanisms and mechanical properties27. In nanolaminated metal composites, three kinds of strengthening mechanisms were proposed to describe the strength variation related to the lamella thickness: the Hall-Patch relationship, the confined layer slip (CLS) model, and the interfacial barrier strength mechanism29. nanolaminated metal composites with a critical layer lamella thickness were found to obtain optimal hardness or strength32. However, for nanolaminated graphene/metal composites, the underlying mechanisms, including the role of graphene/metal interface need to be further explored, especially at the atomic level. Besides whether these models used in nanolaminated metal composites can be applied to graphene/metal composites and the effect of lamella thickness on the properties of nanolaminated graphene/metal composites remains unknown.The load-bearing capacity and blocking dislocation propagation effect of graphene are believed to account for the reinforcement in graphene/metal compositesNanolaminated materials such as multilayered coatings often undergo out-of plane compression loading in actual working condition. Therefore, in this study, we carried out-of plane compression simulations on nanolaminated graphene/Cu (NGCu) composites by MD method to investigate the effects of incorporated graphene and the deformation mechanism. Effects of lamella thickness are also investigated by comparing mechanical performance of models with various lamella thicknesses.34 is proven to accurately depict the many-body atomic interactions in metallic systems and widely used to simulate the deformation behavior under various loading conditions36. Therefore the EAM potential is employed to describe the interaction of copper atoms. Reactive empirical bond order potential is used to depict the interaction between carbon atoms. While the interaction between carbon and copper atoms is described by Lennard-Jones (LJ) potentials which have been proven to successfully investigate the peeling37, thermal conductance38 and shear deformation2 of nanolaminated graphene/metal composites. The LJ parameters with equilibrium separation σ(C-Cu) = 3.0825 Å, potential depth ɛ(C-Cu) = 0.02578 eV and the cutoff rc = 2.5σ(C-Cu) are adopted39. These LJ coefficients have been successfully employed to simulate the interface cracking, radiation damage and high-speed impact processes of graphene (graphite)-copper systems41.The embedded atom method (EAM) potential42. Therefore, in the present work, we consider graphene packed on Cu (111) surface with the zigzag and armchair directions parallel to x and y directions respectively  and (111) single crystals thin films. And the Cu (111) surface is found to grow higher quality monolayer graphene with high area coverage and short growth time00 and  developed by Stukowski47 is employed for post-processing atomistic data obtained from MD simulation. The dislocation extraction algorithm (DXA)48 is used to identify the local environment of particles and assign a structure type  to each particle. This method can be further used to identify dislocations in a crystal and determine their Burgers vectors.All MD simulations are conducted using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.α and β can be observed in each curve. The curves show nonlinear regimes before reaching the first peak α. Then stress drops sharply and gradually grows back to the second peak β before falling to the final plastic flow state. In this case, as shown in Fig. λ and are all higher than that of pure Cu model. In plastic strengthening regime II, A strengthening effect caused by graphene reinforcement can be clearly observed in NGCu while it is absent for Cu model. The graphene exhibits more apparent strengthening effect with the smaller λ, resulting in a higher stress in of NGCu in regime II. In plastic flow regime III, the stress goes to a plastic flow plateau. And with the decrease of λ, the average flow stress rises. Notice that the obtained strength of pure Cu in the simulation is much higher than the experimental value which is usually no more than 2 GPa49. This difference should be ascribed to following reasons: (1) Time-step used in MD simulations is usually a few femtoseconds, leading to materials withstanding much higher strain rate than that in the actual experiments and achieve higher strength to some extent. Meanwhile behaviors such as diffusion process which often occurs with large time scales are ignored in simulations. (2) MD models possess ideal microstructure without pre-exsisting defects30, while experimental samples inevitably contain a variety of interstitials, vacancies, dislocations or grain boundaries, which affect the mechanical properties greatly. (3) The temperature used in the present simulations (10 K) is much lower than that in experiment.Figure 2 at the first peak point α, where the atoms in perfect FCC lattice have been removed, and the rest atoms are colored by their centro-symmetry parameter (CSP) for clarity. It can be seen that dislocation nucleates at point α in both models. Shockley partial dislocations nucleate homogeneously in pure Cu under compression, while dislocations emit from the graphene/Cu interface which in this way acts as a dislocation source. According to Fig. c is the important factor for dislocation nucleation at/near grain boundaries50. According to the work of Armstrong et al.51, τc has athermal and thermal component. And the thermal component is inversely proportional to the activation volume Vc*of thermally activated process by taking τcVc* = 3.1 × 10−20 J. Vc* for graphene/Cu composites at yielding point is only one third of that in the pure Cu through experimental measurement24. Due to the distinct thermal activation behavior of graphene/Cu interface, a higher critical shear stress is needed for graphene/Cu composites to overcome in terms of dislocation nucleation, resulting in higher stress at point α of NGCu models compared with that in pure Cu in Fig. et al.51.Figure 1 and NGCu2 at the second peak point β. It is the fracture of graphene that leads to the sharp drop of the stress at point β. The C-C bonds break along x axis forming an initial crack with a length of 18 Å in NGCu2 leading to a symmetrically distributed shear stress σxy , it is very tricky to impose compression test on graphene and quite challenging to obtain the compressive strain of graphene monolayer. So the Young’s modulus of graphene under compression is rarely obtained in both experiment and simulation. In addition, this equation of ROM treat graphene and Cu matrix in a simplified manner neglecting the interaction between the two phases.From Fig. et al.56, Cu in the region adjacent to the graphene is largely affected by the state of graphene at this scale, but the effect reduces as a function of distance. The deformation and stress state of these Cu atoms are constrained by graphene monolayer, so the mechanical properties of the Cu in region adjacent to the graphene should not be treated in the same way as the Cu far away from the graphene layer. In this case, a model in which a region extending from both sides of the graphene is defined as a graphene-affected zone (GAZ), is developed as illustrated in Fig. VCu* represent the volume of graphene unaffected zone excluded from the GAZ. The thickness hGAZ is decided to include the second nearest layer of Cu atoms, beyond which the interaction between graphene and Cu are comparatively small and are neglected in our model. Thus VGAZ and VCu* can be calculated respectively according to the thickness hGAZ. The value of EGAZ is first calculated by solving the revised rule of mixtures with the parameters of model NGCu7. By substituting the Young’s modulus of pure copper ECu and the model ENGCu7, the corresponding volume fractions of affected zone VGAZ into the equation of revised ROM, EGAZ can be obtained. Then the obtained EGAZ is used to recalculate ENGCu of all other models. The above revised ROM is labeled as ROM* in the present work. Figure E of models with decreasing Cu lamella thickness λ.Inspired by the concept of the approaches proposed by Mayeur 57, we used the ROM* taking the GAZ into account to fit the dislocation nucleation stress in Fig. λ decreasing from 13.8 nm to 1.9 nm, the strain gradually increases. While, the nucleation strain goes to a plateau with λ beneath 1.9 nm, which indicates the strain delay effect of graphene starts to saturate at this concentration.Figure 4 during this regime. It is shown that graphene/Cu interface acts as both the source and barrier of dislocation activity. Dislocations can hardly cross the graphene into adjacent layer due to the high strength of graphene. So the barrier effect of graphene/Cu interface leads to a strengthening in regime II compared with the pure Cu model until the curve reaching the point β. After point β, graphene layers are teared into fragments by the propagation of cracks induced by dislocation motion as discussed in Section 3.2. The CLS model is widely adopted to depict the strength of multilayer films where gliding dislocations are confined by the phase interface or the twin boundary58. Three terms are incorporated in the model to predict the strength increase with decreasing layer thickness, which respectively represent the stress propagating a glide dislocation loop, the contributions of the interfacial stress arose from the elastic deformation of the interfacial region and the interfacial dislocation array on the confined layer slip stress. The effect of interfacial stress may assist or work against the applied stress to cause yielding. To be specific, it would oppose the applied stress under tensile loading59, while for compressive loading, the interface stress would assist the applied stress. At the few to a few tens of nanometers length scales, confined layer slip of single dislocations is regarded as the effective mechanism, so CLS model is available in this case. Once dislocations can be easily transmitted across the interface and thus, CLS model become invalid to predict strength.As mentioned above, a rise of stress in regime II is caused by incorporating graphene monolayers. We average the stress in regime II to represent the strength of the NGCu and try to fit the ROM* model with the calculated average strength. As shown in Fig. M is the Taylor factor, b is the magnitude of the Burgers vector of Cu; h′ is the layer thickness parallel to the glide plane h’ = hCu/sin θ, θ is the angle between the slip plane and the interface; υ represents the Poisson ratio of Cu; μ* = μCu·μgra/(vCu·μCu + vgra·μgra) is the mean shear modulus of NGCu model which can be estimated by the shear modulus of each phases μCu, μgra and their volume fraction vCu, vgra using a relationship of ROM19; α represents the core cut-off parameter, f is the characteristic interface stress of multilayer, L is the mean shear spacing of glide array L = bm/εvGAZ, m is the strain resolution factor. The above revised CLS model is denoted as CLS*. With the parameters M = 3, b = 0.2556 nm, θ = 71.2°, υ = 0.343, μCu = 48.3, μgra = 280, α = 0.5, f = 2 J/m2, ε = 0.2, m = 0.560, the result calculated by CLS* is plotted in Fig. hGAZ. Therefore, it can be concluded that both the load bearing effect of graphene and the barrier effect of graphene/Cu interface synergistic strengthen the nanolaminated composites in the regime II.The GAZ model is also integrated into the CLS relationship as follows:β, the cracks propagate in graphene layers and the stress goes to a plastic flow plateau in plastic flow regime III. The average stress in this regime is calculated and plotted in Fig. λ decreasing. The strengthening effect of graphene monolayer still exists even though the graphene has ruptured. The fragments of graphene can still work as barrier to the dislocation motion, despite that the barrier effect is much weaker than that of the intact monolayer. Figure α where dislocation nucleates, the nucleation strain goes to a plateau when λ further declines from 1.9 nm. It is concluded that the strain delay effect of graphene does not accumulate with greater fraction of graphene with λ beneath 1.9 nm. A critical λ of 1.9 nm is thus obtained to reach both the best strength and ductility of NGCu material.After point The stress-strain curves of NGCu go through 3 regimes: the elastic regime I, and plastic regime II, III, in which different strengthening mechanisms work in corresponding regime. The transition of different mechanisms is related to their deformation behavior in different regimes.It is shown that incorporating graphene monolayer can simultaneously contribute to the strength and ductility of NGCu composites. A critical λ of 1.9 nm is confirmed for the first time to effectively delay both the dislocation nucleation and fracture of NGCu composites in terms of corresponding strain, leading to effective improvement of the strength.* and CLS* model integrated with GAZ are found to well describe the elastic properties of NGCu and the strengthening effect of incorporated graphene.A GAZ model is established to account for the influence between the graphene and adjacent Cu layer. The revised ROMxy.The fracture of graphene which leads to the failure of NGCu composites is caused by the approach of two Shockley partial dislocation lines lying above and beneath the graphene layer. The C-C bonds break along zigzag directions in graphene with a symmetrically distributed shear stress σIn conclusion, we performed the uniaxial compression of nanolaminated NGCu with various Cu lamella thicknesses to investigate the effect of incorporated graphene and the underlying strengthening mechanism using molecular dynamics simulations. The main conclusions are summarized as follows:


Graphene foam with highly enriched incommensurately-stacked layers was grown and applied as an active electrode in rechargeable batteries. A 93% incommensurate graphene foam demonstrated a reversible specific capacity of 1,540 mAh g−1 with a 75% coulombic efficiency, and an 86% incommensurate sample achieves above 99% coulombic efficiency exhibiting 930 mAh g−1 specific capacity. The structural and binding analysis of graphene show that lithium atoms highly intercalate within weakly interacting incommensurately-stacked graphene network, followed by a further flexible rearrangement of layers for a long-term stable cycling. We consider lithium intercalation model for multilayer graphene where capacity varies with N number of layers resulting LiN+1C2N stoichiometry. The effective capacity of commonly used carbon-based rechargeable batteries can be significantly improved using incommensurate graphene as an anode material.Graphite’s capacity of intercalating lithium in rechargeable batteries is limited (theoretically, 372 mAh g Lithium-ion batteries (LIBs) as high energy- and power-density rechargeable batteries are in high demand for energy storage systems. Development of high capacity LIBs anode materials will have a profound and direct impact on current commercial and emerging markets such as portable electronics, electric vehicles and electric grid storage.−1. However, Si anodes exhibit significant volume change during the initial charge/discharge cycles that results in significant chemical and mechanical degradation in anode and consequent rapid capacity fading24Lithium intercalation into graphitic materials has been studied since the 1950’s−1), consisting of commensurately-stacked layers of graphene, can be achieved by transferring one lithium atom per six carbon atoms resulting LiC6 stoichiometryAB stacking) layers due to the strength of repulsive interactions that arise due to the orthogonality of interplanar π orbitals of sp2 carbon. Numerous classes of carbonaceous materials were synthesized over the past 30 years and higher capacities have been reported, but no significant gain in effective capacity has been achievedet al. suggested that single layer graphene (SLG) possesses a capacity of 744 mAh g−1 on the basis of double-site insertion of lithium as Li2C6et al. proposed Li2 covalent arrangements2 with 1,116 mAh g−1 capacity. Later, Binda et al. succeeded in preparing a LiC2 compound1213−1) was measured for chemically-derived defective graphene−1), but these cells were unstable for high discharge rates and for long-term cycling. Recently, the defective porous graphene network demonstrated a tendency to increase capacity during cycling, achieving up to 900 mAh g−1 which remained stable over hundreds of cycles18202223The maximum capacity of crystalline graphite 372 mAh g2 mAh g−1−1) than theoretical capacity of graphite which is stable throughout 100 cycles. Testing for 100 charge/discharge cycles 86% sample indicates excellent reversibility with over 99% coulombic efficiency with 930 mAh g−1 capacity. To explain exceptionally high capacities, we investigate lithium intercalated graphene structures and propose a model in which the finite layers of graphene can demonstrate higher capacity, achieving a maximum of 1,674 mAh g−1 in bilayer configuration.In this study we demonstrate a novel incommensurately-stacked graphene 3D network and its use as a high-capacity lithium intercalation anode in rechargeable batteries. We found that specific capacity is enhanced when commensurately stacking order of graphene layers was reduced in an anode material. As-grown highly (up to 93%) incommensurate graphene demonstrates four times higher reversible capacity  peak at 54.73° (etc. indicates the absence of commensurate stacking within layers. The (100) and (110) peaks at 42.63° and 77.57°, respectively, originates from in-plane crystallinity of graphene. The Gaussian fit of the (002) peak reveals few peaks with d varies from 3.34 to 3.45 Å as an evident of various number of layers (inset- 2g−1 as measured by Bruner-Emmett-Teller (BET) method (see 2g−1) assuming these graphene samples consists of 26–30 layers on average. Similarity of d-spacing suggests that no Nitrogen gas absorbs between layers, and an increased SSA is the result of thinner graphene sheets with pores.In recent years, copper and nickel have been found to be an excellent catalytical substrates to develop two26272930rbon see  and 6. Xt 54.73° . These ps inset-  similar thod see , which iHigh-Resolution Transmission Electron Microscopy (HRTEM) confirms that the sheets contain 2–30 layers see , and mucI2D/IG  of forty-five typical Raman spectra from various regions of samples were carefully analyzed 39La = 467.8–568 nm which confirms high crystallinity in-plane  is similar for all seven graphene samples which allowed us to neglect any impact of original defects on the battery performance.High crystallinity determines the charge collection in electrodes since the electrical conductivity arises in part from the hopping of carriers between crystallites. in-plane . Raman min-plane  revealed−1 (Sample 7) up to 1,542 mAh g−1 (Sample 1) by an increase in graphene incommensurateness from 19% up to 93%  coulombic efficiency (as Sample 1 in −1) than for Sample 2  even though there is no significant difference in incommensurateness, defect’s concentration and BET results between these two samples (100.8 m2g−1 SSA for Sample 1 and 95.7 m2g−1 SSA for Sample 2). Therefore, we consider an additional factor, the number of graphene layers, to explain this variance in capacity. Recombination of layers during the second cycle may govern further reversible capacity and coulombic efficiency as well on subsequent cycles. The incommensurately-stacking feature of pristine layers such as rotation angle and interaction with commensurate layers can play significant role in the initial intercalation stage, and lead to further reconstruction of layers.Graphene foams (~1 mg) with 19–93% incommensurateness (Samples 1–7) were prepared as thin electrodes and tested over 80–100 cycles in electrochemical cells (EC) . Voltagep to 93% . Commercss inset- with the+potential were characterized by cyclic voltammetry  formation. In this regard, we prepared electrodes and investigated the structural, binding and electronic changes of graphene after five cycles of battery tests.As the measured reversible capacities are considerably high, at that point we consider a lithium insertion model other than a model of LiCth cycle (−1 specific capacity as Sample 3. Note that this peak was decreased (as Sample 2) with increased capacity and it disappeared from its nominal position (2θ = 26.45°) when capacity was greater of 930 mAh g−1 (as Sample 1). De-inserted electrodes exhibited similar tendency with the same capacities. When lithium intercalates within commensurately-stacked graphene layers such as graphite, the sheets rotate from AB (d = 3.35 Å) into AA stacking (d = 3.6 Å) bringing carbon honeycombs directly above and below one anotherθ position. On the contrary, no noticeable Bragg peak was observed within 38° and 9° (inset- d-spacing (d ≥ 3.6 Å)The XRD (002) peak of intercalated electrodes (unexposed) is lower and broader than pristine samples as measured after 5th cycle . This ty° inset-  for highd-spacing peaks at dLi-inserted = 3.9–4.06 Å and dde-inserted = 3.65–3.8 Å  which correlates with the theoretically calculated values of lithium hosting distance from graphene1.84–2.02 ÅAA stacking during lithium intercalation, they stayed in that position after de-insertion governing further reversible cycling. This reorganization is different from the intercalation of graphite, where the sheets return back to AB initial stacking during de-insertion.SAED was measured for several sheets which indicates that the stacking geometry of incommensurate graphene sheets changes after the insertion/de-insertion of lithium. The revealed 65–3.8 Å  indicate1- related to interior carbon layer with no lithium intercalated and G2- related to bonded carbon layer with lithium451 to G2 peaks was 0.08 averaged  Bragg peak is rather caused by delamination of multilayer graphene into single sheets than in-plane structural damage.s electron with carbon 2p orbitals. When transferred lithium atoms are not fully involved in charge transfer, the battery lifetime reduces due to metallic lithium plating the anodes.Usually, when lithium atoms adsorbs onto a carbon structure, it stabilizes by weakly binding its 2 2 and it tends to the decrease towards to the metallic Lithium (55.2 eV) with increase of Li concentration2−x (0 ≤ x ≤ 2). Electron Energy Loss Spectrometry (EELS) analysis of Li-inserted nanosheets, under TEM, revealed Li-K edge at 55.8 eV (6 (57.5 eV)2O, Li2O2) were revealed by either XPS (~56.5 eV) or EELS and most lithium atoms transferred to graphene participated in charge transfer. Note that samples were briefly exposed to air during transfer to the vacuum chambers. The peak at 58.3 eV on EELS is assigned to LiOH as a result of reaction between lithium atoms and H2O.The Li 1 s has been registered at 56 eV by XPS for LiC 55.8 eV , which isp2), O-C-O and C = O bonds (sp2 peak by 0.5 eV (compared to pristine) confirms the charge transfer to carbon. The C = O peak associated with Li2CO3 can arise from the reaction of Li + with carbonates of the electrolyte or with the reaction in air. Sufficient decrease of C = O peak for de-inserted samples indicates that most lithium was de-bonded during the de-insertion as shown in  + ions and insertion mechanism occurs other than reported so far. Sato’s mechanism considered maximal capacity of 1,116 mAh g−1 according to LiC2 formation when Lithium atoms can form Li-Li dimer molecule due to weakly binding of Li 2 s electrons on carbon site. Here, XPS analysis revealed that electrodes demonstrated large amount of Li 1 s electrons interacted to Carbon, and no metallic Lithium was registered either XPS or EELS which allow us to consider ionic lithium (Li + ) model.The XPS spectrum of C 1 s shows multiple splitting in Gaussian fit by forming C-C ( O bonds . The shis and C 1 s peaks (dividing by sensitivity factors). An averaged LiC1.83  was acquired from various spots of the sample which demonstrated a capacity of 1,269 mAh g−1 at the 5th cycle of EC measurement.To confirm consistency of our EC measurements, we estimated Li:C ratio by appropriate interpretation of Li 1 AA stacking. Moiré patterns are observable for small regions as an evidence of uncompleted AA stacking  when bilayer participates in charge transfer as Li3C4 while an infinite number of N would approach to LiC2   .d-spacing position and graphene interplanar interaction became even weaker than in pristine layers (absence of XRD 002 peak). Then, during the re-intercalation at the second cycle, the recombination of layers can happen differently by coupling of various number of graphene layers  and 930 mAh g−1 (over 99% coulombic efficiency) applied as an anode in lithium battery cells which was stable throughout hundred cycles at the high current density. We propose a lithium insertion model for multilayer configuration where all carbon hexagons are occupied by lithium atoms and the specific capacity increases as the number of graphene layers decreases. We found that high crystallinity and high incommensurateness within layers are key factors for exceptionally increased capacity. In fact, the weakened Van der Waals interaction of graphene layers enables easy and full penetration of lithium atoms, followed by flexible adjustment of the layers for stable cycling. An effective capacity increase over six times compared to traditional commercial graphite cells promises the feasibility of the rapid development of lightweight, cost-efficient, high-capacity rechargeable batteries based on the work presented here.We successfully developed graphene foam with up to 93% of incommensurately-stacked multilayers that shows four times higher capacity of storing lithium than theoretical capacity of graphite. Large in-plane crystallites were produced over well-interconnected curved graphene sheets with separation of few-micron size pores. This graphene network demonstrates a reversible capacity of 1,540 mAh g2 (3:2) (Research grade) gas mixture ~80 sccm rate as a carrier gas. A relatively low rate of methane gas, e.g. 5–8 sccm, was applied as a carbon source at 1,000–1,050 °C, and then the furnace was cooled down to room temperature at 100 °C/min rate. The nickel was then separated from the graphene by etching of Ni/graphene template directly in acids (1 M HCl + 1 M HNO3) followed by washing of the residue in DI-water several times. Large black pieces flooded the water surface once the Ni metal template was removed completely. To avoid agglomeration in the graphene network, a drying process was then carried out using a Critical Point Dryer (CPD) (Samdri® PVT-3D). The graphene network was rinsed with pure ethylene alcohol several times by replacing the water from network and was kept in the alcohol before starting the drying process. The samples were placed into a drying small chamber, covered by high purity ethanol which was slowly replaced by high purity (minimum 99.8%) liquid CO2 with pressure 800 psi (±5%) (bone- dry LCO2) followed by cooling the sample to below 0 °C where they were completely covered by LCO2. The chamber was then slowly heated up to 40 °C at a pressure of ~1,200 psi. After heating, the chamber was cooled down to room temperature and pressure slowly dropped to ~400 psi. At that point, the chamber was opened and the dried graphene pieces were acquired. The final dried graphene foam was yielded 0.1–0.15% of the initial mass of Ni metal.Commercially-available Ni powder with 1–15 μm size of particles  was used as a catalyst and substrate to grow graphene. The catalyst particles were placed into a 1.5″ diameter quartz tube and heated up to 1,000 °C ~50 mTorr of pressure using Ar/H2. The electrodes then were dried at 160 °C for 5 hours in a vacuum oven. All cells were assembled in Argon filled dry glove box. The active graphene material was used as a working electrode and Li foil as a counter electrode separated from each other by glass fiber  using 2032 Coin-type cell. 1 M solution of LiPF6 was dissolved in a 1:2 ratio (by volume) of ethylene carbonate and dimethyl carbonate, and used as an electrolyte. The galvanostatic charge-discharge measurements were carried out using the “Arbin” instrument. Discharge and charge measurements were carried out at a voltage range of 3.0–0.005 V with different current densities of 0.1–5.0 A g−1. The cyclic voltammetry (CV) measurements were performed at the voltage range of 3.0-0.005 V with scan speed of 1 mV s−1 using Bio-Logic USA Science Instruments.The active material of graphene was weighed accurately in the range of 1 mg and it first was mixed with teflonized acetylene black (TAB-2) binder ratio (1:3 in mg) in agate mortar. The electrodes were pressed on the stainless steel collector having an area of ~2 cmth cycle when the electrode was fully discharged (Li-inserted) and another one was stopped when the electrode was charged (de-inserted). The coin cells were unsealed inside of the glove box filled with Argon gas, the electrolyte was rinsed away as much as possible using dimethyl carbonate solvent following a drying process under Argon flow. Dried graphene electrodes were sealed and packed in plastic boxes and kept in glove boxes before measurements. We referred to those electrodes or sheets as “Li-inserted” or “de-inserted” in the main text.The pairs of graphene cells were assembled as described in the previous section, and discharge/charge capacity measurements were performed in 5 cycles by applying 5 mV voltage. One measurement was stopped after 5ZEISS) microscope using 5–15 KV accelerated voltage. Elemental analysis was performed using EDAX LN2 cooling detector. Scanning Transmission Electron Microscope (STEM) analysis (30 KV accelerated Voltage) was also performed for analyzing the inner morphology of graphene sheets.SEM imaging was done by SUPRA 35 VP (BRUKER D8HR  diffractometer with CuKα irradiation (λ = 1.54 Å) was used for XRD characterization of pristine graphene foam and graphene-based electrodes. The data were analyzed using X’Pert High Score software. The background of the patterns was subtracted automatically in a similar manner for the all curves.HORIBA Scientific) was used for Raman characterization of all samples. Two different wavelength of excitation Laser λ = 638 nm and λ = 532 nm were applied for the characterization of pristine graphene and graphene-based electrodes. Raman mapping was performed by 5 μm (x and y axis) step under ×40 Optical lenses. LabSpec5 software was used for subtracting the background in a similar manner for each Raman spectra. The fitting of peaks and their detailed analysis was done using Origin 8.5 software.XploRA Confocal Raman Microscope system (−9 Torr range and non-monochromatized Mg Kα X-ray (hν ≈ 1253.6 eV) was used for the excitation radiation. XPSPEAK41 analyzer software was used for interpreting the data. The shift of peaks has been calculated by interpretation of C, F and O 1 s peaks together using the data link: http://srdata.nist.gov/xps/.XPS spectra were acquired using a VG Scientific MultiLab 3000 Ultra-High Vacuum (UHV) surface analysis system, equipped with a dual anode (Mg/Al) X-ray source, a VG-CLAM4 mispherical electron energy analyzer, and a 9-channel array detector. The measurements were performed at the base chamber pressure in the 10A FEI Tecani F20 transmission electron microscope, operating at 200 kV and equipped with a 1024 channel Gatan Image Filter (GIF) spectrometer was used for HRTEM imaging and EELS measurements. For this study, the spectrometer was fully aligned and the full width at half maximum (FWHM) of the zero-loss peak was measured without the sample to be about 0.85 eV. High-resolution EELS spectra, acquired using an energy dispersion of 0.1 eV per channel. The carbon K-, lithium K- and oxygen K-ionization edges were collected in the diffraction mode from several areas of the sample. The pre-edge background of raw data was fitted using an exponential model and subtracted from the all data.How to cite this article: Paronyan, T. M. et al. Incommensurate Graphene Foam as a High Capacity Lithium Intercalation Anode. Sci. Rep.7, 39944; doi: 10.1038/srep39944 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate. Target tracking has been discussed in many articles due to its military and civil applications, which range from threat warnings, to intelligent surveillance and situational awareness ,6,7,8,9.As mentioned above, the existing methods for maneuvering target tracking apply the IMM approach, or the switching multiple models technique, to the tracking filter. In these methods, a finite set of dynamic models are used each time. Because the motion mode space of a target is continuous, a sufficiently large set of dynamic models is usually required to cover the range of possible motion modes of the target. Such a large set is impractical because an increase in the number of dynamic models, also leads to an increase in the computational load. Additionally, it is worth noting that the actual motion mode of a target at any given time, may be different from all of the dynamic models, even if a sufficiently large set of dynamic models are used by the filter.When tracking the target that maneuvers at a variable turn rate, a limited set of dynamic models with different turn rates are usually used by the filter ,16. SincTo track the target that maneuvers at a random turn rate, we establish a formula for computing the turn rate of the target. This formula solves the estimation issue of the turn rate by using the state vector of a target at a previous time, and its measurement at the current time. Applying the estimation method of the turn rate to the MB filter, we present the MB filter with an adaptive estimation of the turn rate. Its performance is demonstrated by the simulation results.Pasha’s Gaussian mixture PHD filter is applied to the tracking of multiple maneuvering targets in systems with linear Gaussian jump Markov system models, and is used as the comparison object in this paper. We will first give a brief description of this filter. A simplified version of Pasha’s Gaussian mixture PHD filter is composed of the following four steps:Let If the predicted posterior intensity is given by Equation (1), then the updated posterior intensity is given by:The birth intensity is generated from the measurements at time The posterior intensity at time After this combination, the Gaussian terms whose weight is less than threshold In this Section, we will estimate the turn rate of a maneuvering target by using its state vector at time We introduce an Based on Equation (18), the position coordinates of point We assume that a sensor observes the position of the target and we use  and (38) indicate that a marginal distribution Let We then use the distribution, existence probability, and turn rate with index In this step, we use the measurement at time We then combine the marginal distributions of the existing targets in Equation (46), with those of new targets in Equation (49), to form the marginal distributions of individual targets at time After this combination, we prune the targets whose existence probability In this section, we use an example to reveal the tracking performance of the MB filter with an adaptive estimation of the turn rate for multiple maneuvering targets. In this example, Pasha’s PHD filter  is used Three coordinated turn models with different turn rates are used in Pasha’s PHD filter. The state transition and covariance matrices for models Example 1.Five targets are considered in this example. Targets 1, 2, 3, and 4 appear at We use parameters tance in , the OSPTo reveal the effect of the clutter rate and detection probability on the tracking performance of the proposed filter, we use different clutter rates and detection probabilities to generate simulation measurements, and use the proposed filter and Pasha’s filter to address the simulation measurements, respectively, for 150 trials. The performance time is also an important measure for the performance of a filter. In this study, the formula for calculating the turn rate of a maneuvering target is derived. Based on this formula, we may estimate the turn rate of a target by using its state vector at a previous time, and its measurement at the current time. Applying the estimation method of the turn rate to the MB filter, we present a MB filter with an adaptive estimation of the turn rate, to track multiple targets maneuvering at a random turn rate. Based on simulation experimental data, we test the performance of the proposed filter, by comparing it with Pasha’s filter. The experimental results suggest that the proposed filter is better than Pasha’s filter at tracking the targets that maneuver at a variable turn rate.


The role of trap characteristics in modulating charge transport properties is attracting much attentions in electrical and electronic engineering, which has an important effect on the electrical properties of dielectrics. This paper focuses on the electrical properties of Low-density Polyethylene (LDPE)/graphene nanocomposites (NCs), as well as the corresponding trap level characteristics. The dc conductivity, breakdown strength and space charge behaviors of NCs with the filler content of 0 wt%, 0.005 wt%, 0.01 wt%, 0.1 wt% and 0.5 wt% are studied, and their trap level distributions are characterized by isothermal discharge current (IDC) tests. The experimental results show that the 0.005 wt% LDPE/graphene NCs have a lower dc conductivity, a higher breakdown strength and a much smaller amount of space charge accumulation than the neat LDPE. It is indicated that the graphene addition with a filler content of 0.005 wt% introduces large quantities of deep carrier traps that reduce charge carrier mobility and result in the homocharge accumulation near the electrodes. The deep trap modulated charge carrier transport attributes to reduce the dc conductivity, suppress the injection of space charges into polymer bulks and enhance the breakdown strength, which is of great significance in improving electrical properties of polymer dielectrics. During the past two decades, plenty of research has been done on preparation, evaluation, and characterization of novel nanocomposites (NCs), and most of the results presented that the NCs showed better electrical, thermal and mechanical performances than the original polymers and microcomposites8. Especially, their excellent electrical properties, including low permittivity, low conductivity, high breakdown strength as well as enhanced partial discharge (PD) and tracking resistance, make them the third generation of insulating materials11. The novel properties of NCs are thought to be ascribed to the small-scale effect, boundary effect and quantum size effect of nanoparticles that involve their high specific surface area12. In 2004 and 2005, Lewis and Tanaka published two research papers, giving deep insight into the microscopic interfaces between nanofillers and polymer matrix and trying to reveal the mechanism of the polymer-nanofiller interaction zones on the electrical performance14. However, this mechanism is still incomplete now. Especially, the understanding of the correlativity among the microscopic interaction zones, the charge carrier transport in the mesoscopic view and the macroscopic electrical properties is not fully clear. Further investigation should be carried out to clarify the relationship among the microscopic, mesoscopic and macroscopic properties of NCs.Nanodielectric materials have been one of the research hotspots in electrical and electronic engineering, since its concept was first proposed by Lewis in 199419. Additionally, with the development of organic chemistry synthesis, the surface modification of nanofillers, such as surface grafting, were employed to achieve a better dispersion in polymer matrix, thus enhancing the effect of polymer-filler interaction zones21. Nevertheless, it is difficult for the common nanofillers mentioned above to achieve a specific surface area of 1000 m2/g in theory23. Monolayer graphene, as a unique nano-scaled filler with a thickness of only an atomic layer, has an enormous specific surface area up to ~2000 m2/g, which may significantly increase the polymer-filler interaction zones and advantage to exploit the further potentials of nanodielectrics27.In previous investigations, various nanofillers, including oxides , nitrides , montmorillonite (MMT) clay and so on, were doped into polymers to prepare NCs28. Its high mechanical strength, excellent flexibility, unusual optical properties as well as superior electrical and thermal conductivity make it promising for applications in infrared detectors, solar cells, light-emitting diodes (LED), quantum devices and so on30. In the research field of insulation dielectrics, previous researches mainly focused on the electrical, thermal and mechanical properties of various polymer/graphene NCs in the near-range or above the percolation threshold. Gaska found that the LDPE composites containing 1 wt% graphene nanosheets showed non-linear electrical conductivities and enhanced mechanical properties27. Fim reported that the PE/graphene composites became more thermally stable, and semiconductive when the fill content was above a critical percolation threshold of ~3.8 vol%31. However, few study on the electrical properties of NCs with the very low filler content (far below the percolation threshold) has been carried out until now, and the mechanism of the graphene addition on the electrical properties of composites is still unknown.Graphene was first discovered and characterized in 2004In this research, Low-density Polyethylene (LDPE) is employed as polymer matrix, which is widely used as insulating material in electrical engineering. LDPE/graphene NCs with the filler content of 0 wt%, 0.005 wt%, 0.01 wt%, 0.1 wt% and 0.5 wt% are prepared by melt blending. The electrical properties of NCs, including the dc conductivity, breakdown strength and space charge behaviors, are measured. Meanwhile, their trap level distributions are characterized by isothermal discharge current (IDC) tests. Based on the results, a schematic model is proposed for illustrating the trap modulated charge carrier transport in LDPE/graphene NCs, thereby further revealing the relationship between the trap level distributions and the electrical properties.Figure X) can be calculated by the formula as follows32:H0 is the melting enthalpy of LDPE fully crystallized (100%) and generally ∆H0 = 293 J/g33. ∆Hm is the melting enthalpy of LDPE NCs investigated. The melting temperature and melting enthalpy are shown in Table Figure 34. However, for the 0.5 wt% LDPE/graphene composites, the charging current decays slowly during the measuring time and still does not reach a steady state up to 2000 s, which is ascribed to the injection and accumulation of space charge in the bulk rather to a slow polarization process35.Figure −15 S/m, 1.98 × 10−16 S/m, 7.57 × 10−16 S/m, 2.02 × 10−14 S/m and 4.87 × 10−13 S/m for 0 wt%, 0.005 wt%, 0.01 wt%, 0.1 wt% and 0.5 wt% LDPE/graphene composites, respectively. The 0.005 wt% NCs has a significantly lower dc conductivity than the neat LDPE. It is known that as a zero-gap semiconductor, the graphene has a high conductivity under electric stress. Based on the percolation theory, when the filler content is 0.005 wt%, the fillers are well-dispersed in the polymer matrix and the NDNF extends to tens micrometers as shown in Fig. 36. Therefore, it is speculated that no conducting path of charge carriers forms throughout the 0.005 wt% LDPE/graphene NCs even under high electric field. On the other hand, there are a large amount of interaction zones at the microcosmic interfaces between graphene fillers and LDPE matrix, attributed to the large specific surface area of graphene (1217 m2/g in this study). It is believed that the polymer-filler interaction zones can capture charge carriers, thus suppressing the transport of charge carriers. Adding up these microscopic effects, a macroscopic decrease of dc conductivity occurs.It is assumed in this study that the mean value of conductivity at the last 100 s represents the dc conductivity, which is 3.18 × 1037. With further increasing the content to 0.5 wt%, the filler content is above a percolation threshold, leading to the thermally-activated field-enhanced carrier hopping at the polymer-filler interfaces under applied stress. In this case, a large number of charge carriers will pass through the thin polymer layer between neighboring fillers, thereby resulting in a dramatic rise of dc conductivity as shown in Fig. 36.Figure 2O3 showed a lower electrical conduction and the improvement of breakdown properties38. It is known that the trapping and recombination of charge carriers would produce hot electrons due to non-radiative transition of energy via an Auger-type process39. The hot electrons may have sufficient energy to collide with a molecule and dissociate into free radicals. According to the theory proposed by Kao, this process can be expressed as ref. Figure This process is continuous, resulting in a formation of low-density regions in the amorphous area, in which electrons are more easily to be accelerated by electric field and gain kinetic energies to cause chain scission. Consequently, the chain scissions or microvoids cause partial discharge and further develop into breakdown of polymer.Figure By comparing the space charge behaviors in Fig. However, further increasing the filler content would lead to an aggravation of space charge accumulation. As shown in Fig. I) of NCs as a function of depolarization time (t). It is found that the discharge current decreases gradually with the depolarization time and finally reach a quasi-steady state, since deeper and deeper traps are involved in the charge-release mechanism. Based on the theory of IDC proposed by Simmons, the density of traps (Nt(E)) and the energy of traps (Et) can be calculated by following equations41.d is the thickness of the film and l is the penetration depth of injected electrons. e is the electronic charge quantity. k is the Boltzmann constant. T is the absolute temperature (323 K). ν is the escape frequency of trapped electrons, which is approximately equal to 1012 s−1 in LDPE.The experimental results presented above consistently show that the transport of charge carriers in 0.005 wt% LDPE/graphene NCs is significantly suppressed, resulting in a lower dc conductivity, a higher breakdown strength and a smaller amount of space charge accumulation than the neat LDPE. It is believed that the transport of charge carriers is closely associated with the trap level distributions in polymer. In this study, isothermal discharge current (IDC) tests are employed to characterize the trap level distributions of LDPE/graphene NCs. Figure 2O3)15, silica-doped silicone rubber (SiR/SiO2)16 and titanium oxide-doped polyimide (PI/TiO2) NCs17. Tanaka proposed a multi-core model for typical spherical nanoparticles, in which the polymer-graphene interface consists of a bonded layer (the first layer), a bound layer (the second layer), a loose layer (the third layer) and an electric double layer overlapping the above three layers14. A large quantity of deep traps for electrons and holes are distributed in the bonded and bound layer, which capture charges within polymer and increase the average hopping distance for the charge carriers, thus suppressing the transport of charge carriers. As illustrated in Fig. The trap level distributions of LDPE/graphene NCs are obtained and shown in Fig. 2/g in this study, which is much larger than those of the spherical nanoparticles, such as Al2O3, SiO2 and TiO2. That is, graphene fillers provide much more deep traps than the other nanoparticles under the condition of same mass fraction. The large numbers of deep traps in the plate-like regions would capture holes and electrons and significantly suppress the transport of charge carriers in the microview, leading to the decrease of volume conductivity. As shown in Fig. In the case of low filler content as shown in Fig. With the further increase of the filler content to 0.1 wt%, the NDNF becomes at the level of sub-micrometer or nanometer, as shown in Fig. In this paper, the effects of graphene addition on the dc conductivity, breakdown strength and space charge behaviors of LDPE/graphene composites are investigated, and their trap level distributions are analyzed. It can be summarized that the composites with a filler content of 0.005 wt% have a lower dc conductivity, a higher breakdown strength and a much smaller amount of space charge accumulation than the neat LDPE, which presents that the transport of charge carriers is suppressed by the well-distributed graphene. The density of deep traps increases with increasing filler content from 0 to 0.005 wt%, and then decreases with a further increase of filler content to 0.1 wt%, which is similar with the electrical performance. Accordingly, a schematic model is proposed for illustrating the effect of the polymer-filler interaction zones on charge carrier transport. It is concluded that large quantities of deep carrier traps distributed in the bonded layer and bound layer could capture the charge carriers in the vicinity of electrodes, thus enhancing the electric field required for charge injection and reducing the space charge accumulation in the dielectric bulk. Additionally, the formation of hot electrons with high energies are suppressed, resulting in the improvement of breakdown strength. However, further increasing of the filler content may lead to the charge transport through the overlapping sites of loose layers, and even an occurrence of thermally-activated field-enhanced carriers hopping between neighbor fillers, thus enhancing the dc conductivity macroscopically and reducing the breakdown strength of NCs. In conclusion, the deep trap modulated charge carrier transport is benefit to reduce the dc conductivity, modify the space charge behaviors and enhance the breakdown strength, which is of great significance in improving electrical properties of polymer dielectrics.3 is supplied by Dow Chemical Company (USA). The graphene nanoplatelets with a diameter of 0.5~5 μm and a specific surface area of 1217 m2/g are purchased from Hengqiu Graphene Technology Co., LTD (China). The graphene with different filler content of 0.005 wt%, 0.01 wt%, 0.1 wt% and 0.5 wt% are mechanically mixed with LDPE at the processing temperature of 453 K. To achieve proper dispersion of nanofillers in polymer matrix, the graphene is previously treated by surface modification agent. The mixed compounds are hot-pressed in a stainless steel mold at 433 K under a pressure of 15 MPa for 15 minutes. Then the prepared films are cooled down to room temperature.LDPE (DFDB-6005 NT) with density of 0.92 g/cm−1 to record the crystal melting.The cross-sections of specimens with different filler content are observed by scanning electron microscopy . The specimens for SEM observing are previously fractured in liquid nitrogen and sputtered with thin gold layer. Crystallisation and melting of the LDPE/graphene NCs were studied in a Mettler-Toledo differential scanning calorimeter (DSC). The specimens (4.0 ± 0.5 mg) were heated to 180 °C and kept at this temperature for 5 min in order to erase the previous thermal history, cooled to 20 °C, and finally heated to 180 °C at a scanning rate of 10 °C minσ) can be obtained by the following equation.I is the mean value of the charging current during the final 100 s. E is the strength of the electric field. d is the diameter of the main electrode and g is the gap between the main electrode and cylindrical electrode. It should be emphasized that for each value of conductivity, a new specimen is used for current measurement to ensure the accuracy.DC conductive characteristics of specimens with a thickness of 240 ± 5 μm are measured at room temperature. The experiments follow a standard procedure using a three-electrode system, where the high-voltage electrode is a stainless steel cylindrical electrode with a diameter of 30 mm, and the main electrode is 25 mm in diameter, where the guard ring eliminated surface currents. The charging current in an electrical field of 10 kV/mm is measured by an electrometer (Keithley 6517B) for 2100 s. Then the conductivity  tests under an electrical field of 50 kV/mm. The specimens with a thickness of 240 ± 5 μm and a diameter of 8 cm are sandwiched between an aluminum electrode with a diameter of 12 cm and a semiconductive polymer electrode with a diameter of 2 cm. The voltage-on test is performed for 30 minutes, and the voltage-off test continues for 15 minutes. The space charge behavior of each group is confirmed by repeating the tests for five times.IDC tests are employed to obtain the trap level distribution of composites with different filler content. The specimen with a thickness of 240 ± 5 μm is placed between two gold-coated stainless steel electrodes with a diameter of 2 cm. The tested specimen is first polarized under 30 kV/mm at 323 K for 30 min, and then short-circuited for 50 min to release the polarization charges. The isothermal discharge current curve is recorded by an electrometer (Keithley 6517B).