This study presents a simple equation for the conductivity of graphene-filled nanocomposites by considering graphene size, amount of filler in the net, interphase deepness, tunneling size, and properties of the net. The amount of nanoparticles in the net is related to the percolation threshold and effective filler content. The novel model is analyzed using the measured conductivity of numerous examples and the factors’ impacts on the conductivity. Both experienced data and parametric examinations verify the correctness of the novel model. Among the studied factors, filler amount and interphase deepness implicitly manage the conductivity from 0 to 7 S/m. It is explained that the interphase amount affects the operative quantity of nanofiller, percolation threshold, and amount of nets. The high aspect ratio (ratio of diameter to thickness) of graphene causes an extremely low percolation threshold, which can cause significant conductivity by low filler concentration. Hence, graphene-based nanocomposites are perfect for fabricating low-cost and efficient sensors, primarily owing to their unique properties, such as high sensitivity, good selectivity, and low-limit detection15.Graphene nano-sheets in one or several flat layers of carbon particles tightly crowded in a two-dimensional (2D) space effectively increase the electrical conductivity of matrices21. Previous studies have tried to overcome these problems by producing nanocomposites exhibiting high conductivity by low filler concentration. The nanocomposite containing graphene suggests a lower percolation threshold and better conductivity compared to CNT samples22, primarily owing to the large aspect ratio and immense specific superficial area of graphene nano-sheets24, although aggregation/agglomeration, crimping, and hard nets of graphene may diminish its efficiency25.Obtaining high-quality graphene in substantial quantities and the uniform dispersion of graphene in the matrices are major challenges to realize its excellent properties32. There are many models that can predict the conductivity of CNT polymer products34. The models assume the effects of a tunneling mechanism, polymer-filler interfacial energy, accumulation, and curliness of CNT on the conductivity38. However, there are limited models for the conductivity of polymer graphene nanocomposite. The conductivity of polymer graphene nanocomposite was extensively evaluated using a simple power-law equation41; however, the power equation cannot consider the innovative and attractive facets of nanoparticles in conductivity.Many parameters control the conductivity of systems, such as the amount, conduction, size, and dispersion of nanofiller beside the interphase section43. The impact of interphase on the rigidity of nanocomposites has been reported46. Moreover, the interphase can provide the continuous nets in the system, which speeds up the percolation threshold, thereby growing the nets48. However, it warrants further study. In addition, electron tunneling primarily manages the electrical conductivity of polymer nanocomposite, since the electrons can be transported among adjoining nanoparticles through the tunneling effect52. Consequently, the nanoparticles can cause conductivity even when they are separated by a small distance. Nevertheless, the existing models for the conductivity of graphene systems have not considered the roles of these key parameters.The interphase nearby the nanofiller primarily affects the properties of systemsIn this study, a model is proposed for the conductivity of CNT systems by considering the terms for the nanocomposites containing graphene. In fact, an innovative model is developed by considering net parameters, filler conduction, the volume portion of nanoparticles belonging to the conductive nets, and tunneling distance. Furthermore, the percolation threshold and effective quantity of graphene are correlated to the filler size, interphase deepness, and tunneling size. Additionally, the net parameter is defined expressing the dimensionality and density of the nets in the product. Therefore, the developed model associates the conductivity of nanocomposite to net properties, net percentage, effective filler concentration, interphase depth, graphene conduction, tunneling distance, and graphene dimensions. The innovative model is analyzed using the tested data of several samples from previous studies and analysis of factors.53 as:0” and “σf” denote the conductivity of polymer medium and nanofiller, respectively, “f” denotes the percentage of nanosheets in the network after percolation threshold and “0” is approximately 10−15 S/m, which is marginalized. Moreover, it was observed in previous studies that the conductivity indicates a rapid increment at the percolation threshold56; so, it cannot linearly depend on the filler concentration. Besides, this equation improperly over-estimates the conductivity of graphene-filled samples, primarily because it neglects the effects of some critical issues, such as interphase, tunnels, and net.A linear equation for conductivity of CNT-based systems  was offered57 is calculated as follows:i” denote the thickness of graphene nano-sheets and interphase, respectively.The interphase regions around nanoparticles can add to the filler nets, and they raise the filler efficiency of the product. The volume percentage of interphase in graphene systemsThe operative amount of graphene in the samples is provided by the following:61. Consequently, the tunneling distance (d) affects the conductivity of samples.Moreover, the electrical conductivity in graphene nanocomposite is controlled by tunneling where electrons are moved via neighboring sheets62 as:The percolation threshold in graphite samples (randomly arranged particles) was suggestedAs mentioned previously, the interphase and tunnels desirably change the percolation threshold. Therefore, the latter equation can be developed assuming the roles of these parameters as:In addition to linking to the geometry of nano-sheets, the percolation threshold also correlates to the interphase deepness and tunneling size.Furthermore, only the graphene nano-sheets in the nets can affect the conductivity by moving electrons.63 can be written as:The ratio of CNT participating in the netsThe interphase and tunnels handle the effective filler amount and percolation threshold modifying the “f” as:The volume fraction of nano-sheets precipitating in the conductive nets is calculated by “f” as:i = 4 nm, and d = 5 nm by contour plot. As observed, the thin and large nano-sheets produce the highest “i” and “d” on “i”, and “d” has a negligibly effect. As shown, i > 9.5 nm, while i = d = 2 nm. Therefore, thin and large nano-sheets plus a deep interphase can effectively govern the amount of networked graphene.Figure mentclass2pt{minimFurthermore, the size, dimensionality, and compactness of nets affect the conductivity. However, existing models have mostly disregarded their roles. Although it is difficult to characterize the properties of nets, it is known that these parameters directly affect the conductivity. Accordingly, a definite and dimensionless factor (K) can be suggested as a net parameter to consider the impacts of net properties on the conductivity. The higher the “K”, the larger and denser the network causing higher conductivity in nanocomposites.Based on the above clarifications, Eq.  can be dThe innovative model can be expressed using the volume fraction of networked graphene Eq. , as:11\dFirst, the innovative equations and model are evaluated using the tested results for the samples from former articles, because considerable experimental data for various types of graphene-filled samples are required to confirm the developed model. The details on how the data were acquired, how graphene was mixed with the polymers, and the characterization of graphene dimensions were reported in the references.54, PVA (D = 2 μm and t = 2 nm)55, PET (D = 2 μm and t = 2 nm)64, and PS (D = 2 μm and t = 1 nm)56. It can be observed that the innovative model expresses satisfactory predictions compared to tested data. Consequently, the experimental results confirm the precision of the novel model for polymer graphene materials. The extents of interphase deepness, tunneling distance, and “K” net parameter are determined using advanced equations.Figure i” and “d” were calculated by fitting the measured percolation threshold of samples to Eq.  levels as , , , and  nm for the reported samples, respectively. Accordingly, PI and PS nanocomposites exhibit the densest interphase and the biggest tunnels, since they exhibit the smallest percolation threshold among the specimens.Here, “ts to Eq. . The pers to Eq.  results Furthermore, the values of “K” representing the properties of conductive nets can be calculated when the measurements of conductivity are used in the novel model. The “K” values are obtained as 16, 0.14, 2.6, and 3.5 for PI/graphene, PVA/graphene, PET/graphene, and PS/graphene samples, respectively. Therefore, it can be concluded that the most efficient nets are shown in the PI/graphene sample, while PVA/graphene nanocomposite displays the weakest ones. The results for “K” as the properties of nets conform to the percolation threshold and size of interphase and tunnels. The best nets are observed in PI/graphene sample based on “K” level. Moreover, thick interphase and large tunneling distance produce the most efficient nets calculated for the considered sample. Accordingly, the percolation threshold, interphase deepness, tunneling size, and “K” net parameter exhibit similar trends, which approve the correctness of the proposed equations in this study. Decisively, the proposed equations can forecast the mentioned parameters in the graphene system by the experimented conductivity.In the second step, the impacts of all variables on the conductivity are investigated to indorse the predictability of the novel model.f” on the conductivity at t = 2 nm, i = 4 nm, d = 5 nm, and K = 2. The conductivity of 0.25 S/m is acquired at f = 2.5 × 105 S/m, while f < 0.7 × 105 S/m yield the conductivity of 0.02 S/m. Accordingly, the least percolation threshold and the highest graphene conduction cause the best conductivity. In contrast, a low conductivity can be detected at a high percolation threshold and low filler conduction.Figure 56. Meanwhile, the high conduction of graphene obviously enhances the conductivity, primarily because the nanocomposite contains the insulated matrix and conductive graphene. In other words, only conductive nano-sheets of graphene improve the conductivity. Consequently, the innovative model correctly illustrates the inspirations of “f” on the conductivity of graphene systems.According to Eq. , the peri = 4 nm, d = 5 nm, σf = 105 S/m, and K = 2. Thick nano-sheets significantly decrease the conductivity, whereas thin and large nano-sheets create high conductivity. As illustrated, t > 2 nm produce a conductivity of zero; however, t = 1 nm and D > 2.5 μm lead to a conductivity of 0.6 S/m. Resultantly, it can be stated that thin and large nano-sheets can produce a desirable conductivity in the graphene nanocomposite.The influence of graphene size on the conductivity of the nanocomposite are depicted in Fig. 65 producing big nets, which desirably govern the conductivity. In contrast, thick nano-sheets decrease the effective volume fraction of nanoparticles  recovers the conductivity of the entire product, primarily because the polymer is insulated. Therefore, it is logical to observe a high conductivity at high filler concentrations. In contrast, a profuse interphase near the filler positively changes the percolation threshold Eq.  and enhaf = 105 S/m, ti = 4 nm and K = 2 is depicted in Fig. The conductivity of nanocomposites at various amounts of “d” and “f” and 68. The maximum tunneling distance in polymer CNT nanocomposites has been reported as 10 nm69, indicating that a separation distance between nanoparticles below 10 nm can cause the tunneling mechanism and distant graphene nano-sheets to be excluded from the conductivity. Thus, the inverse relation between conductivity and tunneling size is expected, because the distant nano-sheets cannot transport the electrons through the tunneling mechanism.The graphene nano-sheets can provide a tunneling effect when the distance between two adjacent nano-sheets does not exceed several nanometersA high “f” grows the performance and efficiency of nets in the samples, primarily because “f” determines the number of nano-sheets included in the net regions. A large “f” indicates the large and dense nets, while a low “f” exhibits a large number of dispersed nano-sheets in the nanocomposite. Consequently, “f” shows the net size, which controls the conductivity, as recommended by the innovative model.f = 105 S/m). The uppermost conductivity was obtained as 0.7 S/m at K = 3 and Figure 70. Undoubtedly, a higher level of “K” shows more scale and concentration of filler nets in the nanocomposite. Since the scale of conductive nets controls the charge transferring in the nanocomposite, it is reasonable to express a direct correlation between the conductivity and “K”. Meanwhile, the volume fraction of conductive nets determines the actual number of nano-sheets, which affects the conductivity of nanocomposite, because some nano-sheets are dispersed in the nanocomposite and do not participate in the nets. Therefore, ““K” dimensionless parameter represents a function of size and compactness of nets depending on the dispersion and distribution of nanoparticles in the nanocompositementclass2pt{minimThe developed model is applicable for graphene-filled samples with randomly arranged nano-sheets after percolation onset. Moreover, the samples should include the interphase and tunneling parts. The developed model also considers the long and thin nano-sheets dispersed in the nanocomposite. However, the developed model cannot predict the conductivity in samples containing oriented graphene below the percolation onset. Furthermore, the developed model cannot be applied in the absence of interphase and tunneling parts or when the samples contain short and thick nano-sheets.An innovative model was established for the conductivity of graphene systems by considering the effects of graphene dimensions, volume fraction of networked sheets, interphase size, tunneling size, and net properties. The recommended model was analyzed by the tested conductivity of samples and factor examination. Both experimented values and factors evaluations ratify the precision of the innovative model. A small percolation threshold and a high graphene conduction produces a desirable conductivity. Moreover, thick nano-sheets meaningfully decrease the conductivity. However, thin and large nano-sheets produce high conductivity. Furthermore, the high levels of filler quantity and interphase deepness significantly enhance the conductivity; however, small and medium levels of these factors result in poor conductivity. An extraordinary conductivity was obtained when the tunneling size was small with a high proportion of nano-sheets in the nets. Furthermore, “K” is representative of net properties and the volume fraction of nets positively affects the conductivity. It is worthwhile mentioning that some factors, such as filler amount, interphase size, tunneling size, and the number of nano-sheets in the nets expressively govern the conductivity. Consequently, these parameters should be managed accordingly to achieve a desirable conductivity in a graphene nanocomposite structure.


Ag/AgCl) determined by cyclic voltammetry. A single MFC with a voltage of 0.389–0.460 V and a current of 0.221–0.257 mA could drive electrochemical hydrodechlorination of PCE effectively with Ni-doped graphene as the cathode catalyst, and the removal rate of PCE was significantly higher than that with single Ni or graphene as the cathode catalyst. Moreover, neutral conditions were more suitable for Ni-doped graphene to electrochemically hydrodechlorinate PCE in groundwater and no byproduct was accumulated.Enhancing the activity of the cathode and reducing the voltage for electrochemical hydrodechlorination of chlorohydrocarbon were always the challenges in the area of electrochemical remediation. In this study, a novel cathode material of Ni-doped graphene generated by Ni nanoparticles dispersed evenly on graphene was prepared to electrochemically dechlorinate PCE in groundwater. The reduction potential of Ni-doped graphene for PCE electrochemical hydrodechlorination was −0.24 V ( Ni-doped graphene was prepared to electrochemically dechlorinate PCE driven by a microbial fuel cell. Dechlorination efficiency and reduction potential were significantly higher than for bare Ni or graphene. First, the difficulty of controlling anaerobic dechlorinating bacteria limits the reliability of microbial hydrodechlorination because the ability to completely dechlorinate PCE seems to be restricted to microorganisms belonging to the genus Dehalococcoides.15–17 Second, fierce competition for a carbon source and hydrogen between dechlorinating bacteria and other bacteria (such as sulfate-reducing bacteria and methanogenic bacteria) will decrease the dechlorination effects.16 Third, a chemical electron donor (e.g. acetate) is necessary for dechlorinating bacteria, and the accumulation of microbial fermentation products and microorganisms can cause groundwater clogging.15,18Microbial hydrodechlorination usually occurs with dechlorinating bacteria under strict anaerobic conditions.e.g. Zero Valent Iron, ZVI) as electron donor for PCE dechlorination.19 To enhance the hydrodechlorination efficiency, bimetals (e.g. Fe/Ni) are widely used for hydrodechlorination of chlorinated aliphatic hydrocarbons.20 In our previous study, we found that bimetals nano-Fe/Ni was more effective than single nano-Fe for PCE hydrodechlorination in groundwater. However, both nano-Fe and nano-Ni tend to aggregate due to their high interface energy and inherent magnetism, which can significantly decrease the hydrodechlorination efficiency.20 In addition, nano-Fe and nano-Ni will certainly cause groundwater contaminated by heavy metals according to the reactions: Fe − 2e− → Fe2+, Ni − 2e− → Ni2+.Chemical hydrodechlorination generally uses chemical reductant (e.g. PCE) can be dechlorinated on the catalytic cathode through obtaining electrons and protons.21,22 Compared to microbial hydrodechlorination and chemical hydrodechlorination, electrochemical hydrodechlorination needs not to cultivate dechlorinating bacteria and inject chemical electron donors into groundwater. Hence, electrochemical hydrodechlorination is recognized as an efficient technology with potential and development for chlorohydrocarbon-contaminated groundwater remediation. However, electrochemical hydrodechlorination usually is driven by external power with the voltages varied from 5–20 V, which may cause undesired reactions (e.g. water electrolysis) and consume plenty of electric energy.18,23 In addition, cathode material has great influences on the results of electrochemical hydrodechlorination.24 Catalytic cathode can combine electrons and protons (or water moleculars) to generate activated hydrogen atoms which subsequently act on chlorohydrocarbon (e.g. PCE) so as to accomplish hydrodechlorination process.25Electrochemical hydrodechlorination directly utilizes external power as the electron donor, chlorinated aliphatic hydrocarbons (e.g. particle graphite) and metal materials  are widely used as cathode materials.26–29 Especially noble metals such as Pt and Pd have excellent catalytic property for electrochemical hydrodechlorination due to their low electric potential of producing hydrogen and high capacity of adsorbing hydrogen.30–32 However, high cost actually limits the application of noble metals Pt and Pd.33 Fortunately, it had been verified that metal Ni also has the catalytic property for electrochemical hydrodechlorination, although the catalytic activity is lower than Pt and Pd.34–36 Therefore, some efforts such as decreasing dechlorinating voltage, saving electric energy and enhancing the catalytic activity of Ni-cathode, should be made to improve the electrochemical hydrodechlorination of chlorohydrocarbon. Recent years, graphene is widely used as catalyst carrier due to its excellent characteristics such as low resistivity, high thermal conductivity and mechanical strength.37–39 When metal nanoparticles load on the surface of graphene, the catalytic activity can be enhanced significantly, and agglomeration of magnetic nanoparticles (e.g. nano-Ni) can be decreased effectively.40,41 However, electrochemical hydrodechlorination of PCE in groundwater on Ni-doped graphene cathode has no report so far.Currently, carbon materials  can be electrochemically dechlorinated under high cathode potentials varied from −450 to −550 mV (vs. SHE).16,17 Hennebel also found that TCE can be effectively dechlorinated under the voltage of 0.8 V in microbial electrolysis cells with biogenic palladium nanoparticles.23 These demonstrated that it is feasible to utilize MFC to electrochemically dechlorinate PCE as long as the hydrodechlorinating catalyst on cathode is proper. However, electrochemical hydrodechlorination of PCE in groundwater with MFC has not been reported yet.Microbial fuel cell (MFC) has been widely studied in the area of anaerobic biodegradation of organic pollutants in recent years, which can not only eliminate organic pollution with anaerobic electrogenesis microorganisms, but also produce electric energy through electronic transportation in external circuit. However, it is a pity that the open circuit voltage of single MFC is so small (the maximum is about 0.7 V) that the electric energy is hard to collect or utilize directly.This study aimed to prepare Ni-doped graphene as the cathode material and use MFC (based on anaerobic sanitary sewage treatment) as the electric power to electrochemically dechlorinate PCE in groundwater. This study would develop an efficient cathode material for electrochemical hydrodechlorination and exhibit a novel remediation technology for PCE-contaminated groundwater.2.2.1D50 : 7–12 μm, monolayer content >80%) were dispersed sufficiently in absolute ethanol according to stoichiometric ratio 1 : 10. Then, the solid mixture of Ni and graphene heated in tube furnace under 400 °C for 2 hours with nitrogen flow after ethanol evaporated completely under room temperature.Ni  and graphene (2.2The microstructure of Ni-doped graphene was indicated by transmission electron microscope (JEM-2200FS JEOL Japan). The crystal lattice structures of Ni-doped graphene were showed by XRD (D/max-2550 RigaKu Japan) and HRTEM (JEM-2200FS JEOL Japan).2.3Electrochemical performance of Ni-doped graphene electrode was determined using a CHI660E electrochemical workstation . Three electrode systems consisting of Ni-doped graphene electrode, Ag/AgCl electrode, and Pt wire as working, reference, and counting electrodes, respectively, were used. The electrochemical analysis was performed with cyclic voltammetry in a 1 mM PCE solution containing 0.1 M KCl. All analytical measurements were performed at room temperature.2.4−1 of salinity, 0.76 mg L−1 of K+, 65.53 mg L−1 of Na+, 162.27 mg L−1 of Ca2+, 231.51 mg L−1 of HCO3−, 193.08 mg L−1 of Cl−, 122.25 mg L−1 of SO42−, 1.30 mg L−1 of NO3−, 0.01 mg L−1 of NO2−, 0.46 mg L−1 of F−, 7.05 of pH. Before adding PCE, the raw groundwater was put into anaerobic glove box  until dissolved oxygen (DO) was not detected by DO meter .PCE-contaminated groundwater was prepared in laboratory with the actual uncontaminated groundwater which was taken from a groundwater well of Changchun City. The hydrochemical components of groundwater contained 661.94 mg L2.5Two-chambered MFC was constructed with anaerobic microbial anode and air-aerated cathode. Anaerobic activated sludge was taken from a sewage treatment plant of Changchun City. Anolyte was the raw groundwater dissolved with beef extract, which was simulated as sanitary sewage. The substrate of anode was a piece of graphite felt (100 mm × 50 mm × 2 mm). Salt bridge composed of agar and saturated KCl was used to connect anolyte and catholyte. Anode and cathode were connected by copper wires with a battery  which was used to induce anaerobic electrogenesis microorganisms to preferentially inhabit on the graphite felt fastly. The battery would be dismantled as soon as the open circuit voltage and electric current of MFC maintained above 0.45 V and 0.25 mA, respectively.2.6The cathode of MFC was changed into Ni-doped graphene cathode instead of air-aerated cathode. The catholyte was PCE-contaminated groundwater which was sealed with rubber stopper to guard against oxygen intrusion and PCE volatilization. Ni-doped graphene cathode was prepared with Ni-doped graphene powder and graphite plate (50 mm × 50 mm × 3 mm). The Ni-doped graphene powder loaded on one side of graphite plate (50 mm × 50 mm) through heating the graphite plate on which the solution containing 11 mg Ni-doped graphene (10 mg graphene and 1 mg Ni) dispersed in ethanol was coated evenly in tube furnace under 400 °C for 2 hours with nitrogen flow. During the experimental process, concentration of PCE and corresponding degradation products such as TCE, cDCE, VC and ETH, were determined by Gas Chromatography-Flame Ionization Detector . The open circuit voltage and electric current of MFC were also monitored periodically by an accurate multimeter . The experimental schematic was described in 2.7ηd is the dechlorination efficiency, unit: %; C−Cl is the concentration of chloridion, unit: mmol L−1; CPCE is the initial concentration of PCE, unit: mmol L−1; the number 4 represents one PCE corresponding to four chloridion.The calculation formula of dechlorination efficiency for PCE was described as follow:ηc is the coulombic efficiency, unit: %; Qcurrent is the electric quantity of external circuit, unit: C; QCOD is the theoretical electric quantity produced by microbial electrogenesis of anolyte COD, unit: C; I is the current of external circuit, unit: A; δt is the time frame to calculate coulombic efficiency, unit: s; δCCOD is the concentration difference of anolyte COD, unit: mg L−1; Va is the volume of anolyte, unit: L; qe is electronic charge, 1.6 × 10−19 C; NA is Avogadro constant, 6.02 × 1023 mol−1.The calculation formula of coulombic efficiency for microbial anode was described as follow:ηe is the current efficiency, unit: %; Q−Cl is the total electric quantity used for electrochemical hydrodechlorination of PCE, unit: C; Qcurrent is same as formula C−Cl is the concentration of chloridion, unit: mmol L−1; Vc is the volume of catholyte, unit: L; qe is electronic charge, 1.6 × 10−19 C; NA is Avogadro constant, 6.02 × 1023 mol−1.The calculation formula of current efficiency for Ni-doped graphene cathode can be described as follow:3.3.1Transmission electron micrographies of Ni-doped graphene had been recorded using a copper grid dipped in a solution containing Ni-doped graphene particles dispersed in ethanol by ultrasonication and presented in A high-resolution transmission electron microscopic (HRTEM) image was given in 43,44θ angles from 20° to 90° , (200) and (220) crystal planes of cubic nickel lattice, respectively.43,44 These results confirmed that Ni nanoparticles had been dispersed on the graphene evently. However, the peaks corresponding to (111) and (220) crystal planes of nickel oxide lattice appeared at the 2θ angles of 38.4° and 65.0°, which indicated that some nickel oxide was produced due to the exposure of Ni-doped graphene to air.46,47The phase and crystallinity of Ni-doped graphene were characterized by using a Rigaku X-ray diffractometer with Cu K radiation over a range of 2° to 90° . A sharp3.2vs. Ag/AgCl) was observed in the cyclic voltammogram of Ni-doped graphene. The cyclic voltammograms of single Ni and graphene were also conducted as the experimental controls. It was found that the reductive peak of graphene was located at −0.33 V (vs. Ag/AgCl) which was significantly lower than the reduction potential of Ni-doped graphene. An interesting result was shown that single Ni had no reductive peak over a range of potential from −0.50 to 0 V (vs. Ag/AgCl). These results demonstrated that the Ni-doped graphene can be used as catalytic composited cathode material for electrochemical hydrodechlorination of PCE under low voltage (0.24 V) which was significantly lower than those reported up to now.16–18,23Cyclic voltammogram of Ni-doped graphene was shown in 3.3Electrochemical hydrodechlorination of PCE in groundwater driven by MFC was carried out on the graphite cathode coated with Ni-doped graphene. As the experimental controls, single Ni and graphene were also used as cathode materials to electrochemically dechlorinate PCE in groundwater, respectively . The res−1, respectively. These results suggested that the electrochemical hydrodechlorination efficiency of PCE had positive correlation with PCE concentration in groundwater. For low concentration of PCE, it would be spent more time to eliminate PCE contamination completely in groundwater. In addition, the total amount of nickel in Ni-doped graphene cathode was only 1 mg, which may lead to low removal rate of PCE. Therefore, the amount of Ni-doped graphene coated on cathode would be optimized in future study.The concentration of PCE in actual groundwater are varied along the contamination plume. Therefore, the effects of PCE concentration on electrochemical hydrodechlorination efficiency was investigated in this study and the results were shown in 25 Generally speaking, the pH of groundwater often varies from 5 to 9. Therefore, effects of pH on electrochemical hydrodechlorination efficiency of PCE in groundwater was investigated in this study and the results were shown in p < 0.05). At the remediation time of 96 h, the removal rates of PCE were 41.4%, 43.5%, 46.3%, 42.1% and 40.3% under different initial pH of 5, 6, 7, 8 and 9, respectively. These results demonstrated that neutral condition was more suitable for Ni-doped graphene to electrochemically hydrodechlorinate PCE. The cathode material of Ni-doped graphene prepared in this study can be better used for electrochemical hydrodechlorination remediation of actual PCE-contaminated groundwater.Proton is an important reactant for electrochemical hydrodechlorination of PCE. According to the hydrodechlorination mechanism of PCE, protons combined with electrons would replace the chlorines of PCE.3.4To electrochemically dechlorinate PCE in groundwater with aboveground MFC as the electric driver, salt bridge had to be utilized to connect the microbial anode chamber and PCE-contaminated groundwater cathode chamber. However, the resisitance of entire electrochemical remediation system was so high  is more difficult to be dechlorinated than PCE due to asymmetric p–π conjugation effects. Hence, the dechlorination process from cDCE and VC to ETH usually is the limited step for dechlorinating PCE into ETH.18 Therefore, Ni-doped graphene most probably can be better used for electrochemical hydrodechlorination of less chlorinated ethylenes. In addition, the detection of chloridion  didn't mean that more electric energy would be gained from MFC. On the contrary, low concentration (<196 mg L−1) of anolyte COD would enhance the coulombic efficiency of MFC although the total electric energy was relative less. The reason for increasing coulombic efficiency most probably was that electrons produced by microorganisms were partially absorbed by the electron acceptors  in anolyte before 54 h, rather than flowed into cathode via external circuit. After all, it is easy for O2 and NO3− to trap electron and react as follow:17Coulombic efficiency of MFC is used to assess the transfer efficiency of microbial fuel in anolyte from chemical energy to electric energy. It can be seen from 2 and NO3− rather than electrode were the microbial electron acceptors, which directly led to low coulombic efficiency before 54 h. Therefore, the anolyte of MFC should be optimized before application for electrochemical hydrodechlorination of PCE so as to save microbial fuel and raise the electric energy yield.In addition, microorganisms would consume more COD when O3− and SO42− in groundwater.48 In addition, production of H2 on the Ni-doped graphene could also decreased the current efficiency due to the lower hydrogen evolution overpotential of nickel.48 This is why electrochemical hydrodechlorination is hard to completely eliminate PCE in groundwater. Therefore, the effects of multi-electron acceptors on electrochemical hydrodechlorination of PCE would be investigated in future study.Current efficiency is used to assess the utilization efficiency of current produced by MFC on cathode. It was obvious that current efficiency of cathode was steady and the average current efficiency was only 9.28% . The rea4.vs. Ag/AgCl), which was significantly lower than those reported up to now. Electrochemical hydrodechlorination of PCE with Ni-doped graphene could be driven by low-voltage MFC, and the hydrodechlorination efficiency of PCE with Ni-doped graphene as the cathode material was obviously higher than that with single Ni or graphene. Most important was that Ni-doped graphene had the best PCE removal efficiency under neutral condition and no byproduct was accumulated.A novel cathode material of Ni-doped graphene for electrochemical hydrodechlorination of PCE was prepared and investigated successfully in this study. Ni nanoparticles with 5–10 nm size dispersed on the graphene evenly. The reduction potential of Ni-doped graphene for PCE electrochemical hydrodechlorination was −0.24 V (There are no conflicts to declare.RA-008-C8RA06951D-s001


We have used a graphene field-effect transistor (G-FET) device whose geometrical capacitance is enhanced by incorporating an aluminum back-gate electrode with a naturally oxidized aluminum surface as an insulating layer. When the graphene, the top-side of the device, is exposed to NO2, the quantum capacitance of graphene and, thus, the measured capacitance of the device, changed in accordance with NO2 concentrations ranging from 1–100 parts per million (ppm). The operational principle of the proposed system is also explained with the changes in gate voltage-dependent capacitance of the G-FET exposed to various concentrations of NO2. Further analyses regarding carrier density changes and potential variances under various concentrations of NO2 are also presented to strengthen the argument. The results demonstrate the feasibility of capacitive NO2 sensing using graphene and the operational principle of capacitive NO2 sensing.A graphene-based capacitive NO However, most of related research has been focused on measuring a change in resistance (or conductance) when chemicals are adsorbed on a graphene surface [Since the discovery of graphene, a two-dimensional carbon atomic layer prepared by mechanical exfoliation of highly oriented pyrolytic graphite, it has garnered the attention of many researchers due to its unique electronic, optical, and mechanical properties ,4,5,6,7.2S, etc. ,9,10,11.2S, etc. ,14,15,162S, etc. , such astrations ,19. In t surface ,21,22,232 sensing has not yet been reported. The total capacitance  of a graphene-based capacitive sensing device consists of the series connection of geometrical capacitance (Cgeo) and quantum capacitance (CQ). The quantum capacitance effect is the main reason behind the changes in Ctotal upon the adsorption of chemical molecules on the graphene surface. However, due to the electrical characteristics of a system of capacitors, the system requires greater Cgeo compared to the minimum CQ , in order for Ctotal to be dominantly determined by CQ. This indicates that the sensitivity of the capacitive sensing device can be improved by enhancing the Cgeo [Although graphene-based capacitive sensing applications have been studied in recent years with various chemicals and gas/vapors ,25,26,27the Cgeo ,28,29. Fthe Cgeo ,31,32,332 sensing performance of an Al back-gated G-FET. Various gas molecules  can be adsorbed on graphene surfaces due to various reasons on graphene surfaces, resulting in changes in electronic response. The issue of functionalizing a graphene surface to increase the selectivity of a target molecule is a completely different issue yet to be resolved. However, we can still test the electrical responses of graphene-based devices by creating isolated environments to test the sensing procedure. Furthermore, the adsorbed gas molecules on graphene can be easily detached by thermal treatment or UV exposure, which allows us to recycle the prepared devices for the test under various conditions. To investigate the capacitive response of graphene for NO2, we restricted the test gases to a NO2/N2 mixture and adopted UV exposure for the recovery process. Water molecules in the air affect the quantum capacitance of graphene by adsorbing it onto graphene surfaces [2 in dry air was detected well using graphene-based devices by measuring its resistance [2 diluted in ambient air as a function of humidity should be further studied.In this work, we report capacitive NOsurfaces . Oxygen sistance . In orde2 sensing performance, and later, the electronic properties of graphene exposed to NO2 molecules will be discussed to strengthen our argument regarding the working principle of the device. Changes in the capacitance of the device were measured before and after the vacuum test chamber was filled with various concentrations of NO2 . Furthermore, the electronic properties, such as residual carrier density and potential variance at different NO2 concentrations, were extracted from the gate voltage-dependent Ctotal. Our results demonstrate the feasibility of capacitive NO2 sensing and, thus, provide preliminary research for capacitive gas sensing using graphene.We focus on capacitive NO2 sensing device was fabricated using CVD-grown graphene on Cu foil (LG electronics). Monolayer graphene was confirmed by Raman spectroscopy, as shown in x) on the surface of the Al gate electrode. Later, this extra thin layer of AlOx would serve as a high-k insulating layer for the graphene-based capacitor with a relatively large geometric capacitance. Next, CVD graphene was transferred onto the sapphire substrate. The Cu foil with top-side graphene was spin-coated with poly(methyl methacrylate) (PMMA) (4% in anisole) and then baked on a hot plate at 120 °C for 10 min. The reverse side of the graphene (lacking PMMA) was etched using O2 reactive-ion etching (RIE). Afterwards, PMMA/graphene on the Cu foil was placed in ammonium persulfate solution (5 wt% in distilled water) for more than 3 h to remove the Cu foil. After finishing the Cu foil etching, the graphene/PMMA film was transferred to distilled water three times to rinse the residual chemicals beneath the graphene. Subsequently, the PMMA-supported graphene film was transferred to the target substrate. Then, the sample was dried in an oven at 60 °C for more than 5 min. The PMMA layer on the substrate was removed by soaking in acetone for more than an hour. The graphene channel was patterned using photolithography and O2 RIE. Finally, 40 nm of Au source and drain electrodes were formed on the graphene channel using photolithography and electron beam evaporation. A cross-sectional view and an optical microscopy image of the fabricated device are shown in A capacitive NO2 99.999%) and 100 ppm NO2  gas, mass flow controllers (MFCs), a UV lamp , a measuring instrument , a mechanical pump, and a quartz window, as shown in 2 sensing performance, various concentrations (1–100 ppm) of NO2 gas are introduced into the test chamber by varying the ratio of the flow rate of 100 ppm NO2 and pure N2 using MFCs at a total flow rate of 10 standard liters per minute (SLM). The gas sensing performance of the device under test (DUT) is monitored by the change in capacitance caused by gas molecule adsorption on the graphene surface. The UV lamp outside the chamber is used during the recovery process; the distance between the device under test and the UV lamp is approximately 7 cm. All electrical measurements were carried out using a Keithley 4200-SCS at room temperature.Gas sensing measurements were carried out in a test chamber equipped with pure nitrogen . Then, all the valves are closed to isolate the chamber. The device is exposed to an NO2 environment for approximately 2 h to check capacitance saturation time (stage ➁). To characterize sensing performance, the response is calculated using the following equation:Cg and C0 are the capacitance before and after the exposure to NO2, respectively [Ctotal is measured by sweeping the back-gate voltage, VBG from +2 V to −2 V, to analyze the electronic properties of graphene exposed to different concentrations of NO2 (stage ➂).Before beginning the sensing measurements, the mechanical pump evacuates the chamber to its initialized state at a pressure of around 10ectively . Then, C2 adsorbates via hole recombination, e.g., hole + NO2− → NO2 (gas), on graphene [2 have been known to exhibit excellent durability and reliability for the UV-assisted recovery process [Thermal treatment and UV illumination can be used to detach adsorbates on graphene ,22,34,36graphene . Though graphene ,38. Furt2 molecules and possible introduction of O2 in the chamber (stage ➃). While pumping, the UV light is turned on to detach the adsorbed gas molecules from the graphene surface (stage ➄) and then tuned off (stage ➅) until the capacitance of the device returns to the initial state (the capacitance before NO2 exposure). Then, the response, gate sweeping, and recovery processes are repeated with different concentrations of NO2.Thus, during the recovery process, the chamber is first evacuated by a mechanical pump to reduce the residual NO2 sensing performance, the real-time capacitive response of the device at different NO2 concentrations was investigated, as shown in 2 concentrations. Note that actual measured capacitance for calculating the response in 2 with a desired concentration from 1 to 100 ppm while measuring the capacitance in real-time. The t = 0 value in 2 after the test chamber is filled with the test gas. The NO2 molecules in the test chamber adhere to the graphene surface and act as electron acceptors; they attracts electrons from graphene, leaving holes [CQ of the graphene exposed to NO2, because CQ of graphene is proportional to the square root of the carrier density of graphene [Cgeo of AlOx and CQ of graphene. This leads to changes in the measured capacitance of the device. The capacitive response immediately increases after the relatively high concentrations of 10–100 ppm NO2 are injected into the test chamber. On the other hand, the response to the relatively low concentrations of 1–5 ppm NO2 begins to change in a few seconds. Relatively high concentrations of NO2 are enough to dope graphene at the moment of the injection, so the response graph shows a dramatic change in value at the very early stages of exposure. The response at low concentrations of NO2 takes some time to dope graphene since, NO2 molecules in diluted gases are not enough to dope graphene at the moment of the injection. However, at the lower concentrations, our device can detect the differences in concentrations with higher precision within a moderately allowed time interval.To evaluate the NOng holes . The holgraphene . The meat = 75 s (roughly when the chamber pressure reaches atmospheric pressure) and 150 s are plotted as a function of NO2 concentration, as shown in 2 concentration in the two regions, namely 1–5 and 10–100 ppm. Although the response time under the higher NO2 concentration mixture is much faster simply due to the introduction of the large number of NO2 molecules in the system, the slope of response graph for lower concentrations (1–5 ppm) at the early stages of exposure is greater compared to that of larger concentration cases (10–100 ppm), which means the change in response provides a more precise distinction between various NO2 concentrations below 10 ppm. We believe that this distinction is drawn simply due to the total number of NO2 molecules our device can hold, and that the numbers can be modulated further by redesigning the size of the active channel. The change in the slope shown in Ctotal as a function of the VG curves. A detailed discussion will be given later. The response at 150 s (solid symbols) is increased with a similar trend to the response at 75 s. The changes between 75 and 150 s at high NO2 concentrations are lower than the low concentrations, which means that the response at high concentrations saturates faster than at low concentrations. To make sure of the effect of the pure N2 as a diluent gas, the response of 0 ppm NO2 was tested. When the test chamber was filled with pure N2, the response was changed by lower than 0.1% for 200 s. Impurities (the rest of 99.999% N2) in pure N2 do not significantly affect the capacitive sensing for 200 s, confirming that the N2 is only used to dilute 100 ppm NO2.The response values at dC/dt) as a function of time. Once the graphene is exposed to 10–100 ppm NO2, dC/dt starts to immediately increase until it reaches the maximum, and then it approaches 0; on the other hand, dC/dt exposed to 1–5 ppm NO2 shows a relatively slowly increase to the maximum and then approaches 0. The dC/dt at 6.6 s  is shown in dC/dt is a nearly linear relation with the NO2 concentration, suggesting that the device can detect a high concentration of NO2 by measuring dC/dt at the early stage. dC/dt at the moment the pressure of the chamber reaches atmospheric pressure (75 s). The dC/dt has an inversely linear relation with NO2 concentrations, except for 0 ppm, which is a different result from 2 concentration saturates faster than at the low NO2 concentration, as expected.2 introductions into the system.Response change for an up to 2 h time span is shown in 2 (99.999% N2) is almost constant for approximately 15 min after the test chamber is filled. The response starts to slowly increase after a quarter hour. We speculate that 99.999% N2 as a diluent containing 0.001% unexpected impurities acting as acceptors slowly dopes graphene. However, when 100 ppm NO2 is diluted by 99.999% N2, responses of 1–50 ppm NO2  do not increase after a quarter hour, indicating that NO2 is binding with graphene far more strongly compared to any other possible impurities that might be present in the balancing gas (N2). This also suggests that N2 is a proper diluent for adjusting NO2 concentration in this test.The response at 0 ppm NOCtotal from 2 V to −2 V after finishing the NO2 sensing measurement (in 2 h), in order to extract carrier density and change in potential fluctuations induced by adsorbed NO2 molecules [2 molecules shift the Dirac point voltage (VDP) to the positive gate voltage side and causes a rounding of the curves near the VDP, which is the voltage with the minimum Ctotal. The shift in VDP can reasonably be explained by charge transfer between the graphene and NO2 molecules [2 molecules act as electron acceptors, which perform a similar function as the applied negative gate voltage [olecules ,33,39. Tolecules . Adsorbe voltage .Ctotal curves is attributed to the enhancement of local geometrical capacitance caused by NO2 molecules near graphene. The NO2 molecules which are polar molecules can be intercalated between graphene and the Al electrode, similar to intercalated H2O molecules between a substrate and graphene [2 molecules enhance the effective dielectric constant of the insulating layer in the graphene/AlOx/Al capacitor. The NO2 molecules on graphene are also one of possible contributions to the enhanced electric field originating from the gate electrode. Due to the low density of the state of graphene, the graphene does not screen all of the electric field, which affects NO2 molecules on graphene when Cgeo is comparable to the minimum CQ. The NO2 molecules on the graphene are aligned by the penetrated electric field, resulting in the enhancement of the effective geometrical capacitance.The upward shift of the graphene . The intCtotal curves in 2 concentration in the chamber increases, the capacitance curves shift up to the right, resulting in changes in Ctotal at VBG = 0 V. This is the mechanism of capacitive NO2 sensing. The dramatic change in the Ctotal curve at 0 ppm near VDP explains the slope in the two regions in R1ppm (the difference between the response with 1 ppm and the response with 0 ppm) after 2 h exposure is 23.8%, suggesting that the device can detect sub-ppm NO2 through the capacitance measurement. The expected response at concentrations between 0 ppm and 1 ppm would be between 11.8% and 35.6%.The 2 adsorbed graphene, the gate voltage-dependent Ctotal in Cgeo, parasitic capacitance, and δV  as fitting parameters [V of graphene exposed to NO2 for 2 h as a function of NO2 concertation is plotted in 2 molecules causes a local potential fluctuation near the adsorbed NO2 molecules. Since the local potential fluctuations are affected by charged impurities near graphene, such as the density of adsorbed NO2 molecules, δV becomes higher as NO2 concentration increases. In addition, the residual carrier densities were calculated from the quantum capacitance minimum extracted from fitting using the equation 2 molecules cause charge transfer from graphene and produce potential fluctuation. Holes produced from the charge transfer shift the VDP to the right. The potential fluctuations round the capacitance curve near VDP. These changes are determined by absorbed NO2 molecules and explain changes in the capacitance at zero gate voltage in To investigate the electronic properties of NOrameters . The ext2 sensing performance and the electronic properties of NO2 adsorbed graphene. The quantum capacitance effect caused by enhanced Cgeo of naturally oxidized Al allowed for capacitive sensing. The capacitance of the device exposed to 1–100 ppm NO2 was changed by 21–51% compared to the initial capacitance at t = 150 s. The ΔR1ppm at t = 150 s is 21%, indicating that the device would detect sub-ppm NO2 by the capacitance measurement of the device. Furthermore, the capacitive NO2 sensing mechanism is explained by the gate voltage-dependent Ctotal. Adsorbed NO2 molecules on the graphene surface shift VDP as a function of concentrations of NO2. Carrier density and potential variations in the device caused by absorbed NO2 molecules were extracted from quantum capacitance by fitting. These results demonstrate the fundamental understanding of the absorbed NO2 effect on graphene from capacitance measurement, as well as that capacitive NO2 sensing is possible by enhancing the Cgeo of graphene-based devices.In this study, Al back-gated G-FET was fabricated to measure capacitive NO


With cellular immunotherapy, the individuals’ medication could ablate (or enhance) any therapeutic benefit of the transferred cells. Murine and human macrophages activated with IL-4 ) improve wound healing and reduce the severity of disease in murine models of colitis. Advancing the position that autologous M(IL4) could be a novel approach to IBD, a critical question arises: will concurrent medication impact the M(IL4)s anti-colitic effect? To address this, we tested if prednisolone, a synthetic, anti-inflammatory glucocorticoid used to induce remission in IBD flares,impacts human M(IL4) phenotype and function.To determine if prednisolone suppresses or enhances a human M(IL4) phenotype as defined by canonical marker molecules and wound healing and anti-colitic activities.in vitro wound healing assay. Retrieved M(IL4) and M were characterized by mRNA expression of CD206 (mannose receptor), RAMP1 (CGRP receptor), and CD14 (LPS co-receptor). One million murine bone marrow-derived M(IL4) or M were injected into BALB/c mice 48h prior to intra-rectal DNBS (3mg), and colitis was assessed 72h-post DNBS.Macrophages were differentiated from the blood monocytes of healthy volunteers using M-CSF (7 days) and treated with GMP-grade IL-4  ± a 24h treatment with prednisolone (1μg/mL). Subsequently, conditioned medium was collected for TGFb measurement by ELISA and for use in a T84 epithelial cell Human M(IL4)s displayed increased mRNA expression of CD206 and RAMP1, and reduced CD14 compared to M(0), with the CD206 and RAMP1 being further increased by prednisolone treatment. M produced more TGF-β than M(IL4) upon LPS stimulation , which would predict an enhanced wound healing capacity. Stimulated M produced more IL-10 than M(IL4). Furthermore, murine M retained an anti-colitic capacity comparable to M(IL4) as determined by disease activity score in the DNBS model.Human M(IL4)s subsequently exposed to the potent immunomodulatory glucocorticoid, prednisolone show increased expression of phenotypic markers and increased output of TGFb and IL-10. Crucially M retained an anti-colic effect in the murine DNBS model of colitis. Interpreting these data, we suggest that the anti-colitic effect of M(IL4) immunotherapy would not be adversely offset by the individuals concomitant use of steroids. Our preliminary findings support pursuing M(IL4) transfers as a novel approach to the management of IBD.OtherHelmsley Charitable TrustNone Declared


Electrochemical exfoliation is an efficient and scalable method to obtain liquid-phase graphene. Graphene in solution, obtained through electrochemical exfoliation or other methods, is typically polydisperse, containing particles of various sizes, which is not optimal for applications. We employed cascade centrifugation to select specific particle sizes in solution and prepared thin films from those graphene particles using the Langmuir–Blodgett assembly. Employing centrifugation speeds of 3, 4, and 5 krpm, further diluting the solutions in different volumes of solvent, we reliably and consistently obtained films of tunable thickness. We show that there is a limit to how thin these films can be, which is imposed by the percolation threshold. The percolation threshold is quantitatively compared to results found in literature that are obtained using other, more complex graphene film fabrication methods, and is found to occur with a percolation exponent and percolative figure of merit that are of the same order as results in literature. A maximum optical transparency of 82.4% at a wavelength of 660 nm is obtained for these films, which is in agreement with earlier works on Langmuir–Blodgett assembled ultrasonic-assisted liquid-phase exfoliated graphene. Our work demonstrates that films that are in all respects on par with films of graphene obtained through other solution-based processes can be produced from inexpensive and widely available centrifugal post-processing of existing commercially available solutions of electrochemically exfoliated graphene. The demonstrated methodology will lower the entry barriers for new research and industrial uses, since it allows researchers with no exfoliation experience to make use of widely available graphene materials. N-methylpyrrolidone (NMP) was first proposed by Coleman et al. [The interest in graphene and other 2D materials keeps growing, especially since the initial delve into fundamental properties was augmented with an outlook towards potential applications . Over thn et al.  as a synn et al. –14.N,N-dimethylacetamide (DMA), γ-butyrolactone (GBL), 1,3-dimethyl-2-imidazolidinone (DMEU), and ortho-dichlorobenzene (o-DCB) [The mechanism of ultrasonic exfoliation involves ultrasonic waves in liquid media creating bubbles or voids in the liquid, which generate shear forces or cavitation bubbles upon collapsing, which then break up the bulk 2D materials into mono- and few-layer nanosheets –16. The  (o-DCB) ,17–18. E (o-DCB) .An alternative to LPE that has subsequently been developed is electrochemical exfoliation, whereby graphene is exfoliated in an electrolyte from an electrode made of graphite . In elecBoth ultrasound-assisted LPE and electrochemical exfoliation result in solutions that contain flakes of different sizes, that is, the solutions are polydisperse. Polydispersity is a significant problem regarding the use of solution-processed graphene, because many applications are size-dependent. On the one hand, for example, for use in composites, flakes with lateral sizes larger than 1 µm are preferred . On the The flake size can either be controlled during exfoliation or selected after exfoliation. Processing parameters that control flake size during exfoliation include the choice and concentration of solvent , a proceAlthough size selection through post-processing with cascade centrifugation has been demonstrated in the context of ultrasonic LPE materials –31, to tg). The obtained centrifugation sediment contained the largest nanosheets of the initial dispersion. The supernatant was subsequently centrifuged at higher rates: 2, 3, 4, and 5 krpm . At each step of centrifugation, the sediments with slightly smaller graphene sheets were gathered and the supernatants were used for the following cascade step. The sediments were collected and redispersed in a specific volume of NMP (Sigma-Aldrich M79603), which ranged between 250 and 1000 µL. This method is schematically described in In order to achieve homogeneous films with defined particle sizes, a dispersion of electrochemically exfoliated graphene from Sixonia Tech GmbH  was processed by cascade centrifugation . The commercially obtained solution contained a dispersion of graphene in NMP. Although many solvents are commercially available, NMP was the solvent of choice because of its favorable properties regarding LB deposition . 1 mL ofIn order to study the optical properties of the produced graphene films, graphene from solution was deposited onto glass substrates with dimensions of ca. 2 cm × 1 cm . AlthougTo study optical properties of the fabricated samples, UV–vis spectroscopy was performed . A xenon lamp was used as a light source. The glass samples were mounted on a holder and inserted into the light path. Optical transmittance was measured by subtracting the signal from a baseline reference signal obtained when a clean glass substrate without graphene was inserted into the light path.Graphene film resistance was measured by inserting the substrates with electrodes into an electrode connector  and the acquiring resistance with a handheld digital multimeter.−4 Pa with electron beam voltages set between 1 and 30 kV, depending on the film. Films that are shown in optical dark-field microscopy and SEM have been made from solutions that have been diluted with 500 µL of NMP.Optical dark-field microscopy of the films was performed with a magnification of 10× (Olympus BX53M). Scanning electron microscopy (SEM) was performed with a FESEM  at a chamber pressure of 1 × 10Films deposited from solutions obtained from different centrifugation protocols are expected to have different thicknesses, due to the different size of the flakes in the solutions. A visual inspection of the images in We quantify the number of scattering centers by analyzing the dark-field images using the software imageJ. First, we set the brightness threshold for what counts as a scattering particle. As these are dark-field images, anything that is not completely dark is counted as a scattering particle. Thus, we set the threshold at a brightness value of 3 . Such an analysis yields results that are consistent with intuitive observation .g) and at 4 krpm (1465g). In these three cases, SEM reveals continuous graphene films. The films contain some wrinkles or flake edges, showing up as bright lines in SEM. Upon closer inspection, the contrast varies slightly across the films, which is likely due to local thickness variations. Overall, the films resemble those made earlier with ultrasonic liquid-phase exfoliation followed by LB deposition [g) is strikingly different from the ones made with lower centrifugation speeds. The 5 krpm (2289g) film has an irregular structure, resembling a fractal coastline. It is also noted that the conductivity of this film is much lower than those of the other three films, as SEM operation quickly leads to surface charging effects.The film structure was further investigated with SEM, shown in t 4 krpm 65g. In tUV–vis spectra of the deposited graphene films at different centrifugation rates, redispersed in specified volumes of NMP, are given in Although atomic force microscopy (AFM) is often employed to characterize graphene films ,12,14,44To analyze the electrical properties of the synthesized graphene films, resistance values of each film were measured. Resistance values, optical transparency values, and the average number of layers in each graphene film are shown in The images in RS obey the following equation:Regardless of how the film thickness is controlled, an analysis of Z0 is the impedance of free space, σop is the optical conductivity, and σdc,B is the bulk dc conductivity of the film. For film thicknesses below the percolation threshold, the transmittance and sheet resistance obey the following equation:where n is the percolation exponent.where Π is the percolative figure of merit (FOM) as described by De and Coleman  and n isn = 3.6, values which are of the same order as values previously observed for graphene [We fit the above two equations to our data in the two observed regimes independently. The solid red line in graphene . The gooThe two regimes exhibit a crossover that likely indicates the percolation threshold. The value of optical transparency at which we observe the threshold is 82.4%, matching the value found in an earlier work that showed particle size selection by control of liquid-phase exfoliation time , possiblSolution-processed graphene holds potential for applications across a diverse range of industries. There exist several production methods for solution-processed graphene, some of which are highly scalable. However, all graphene solutions resulting from those processes are polydisperse, containing a wide distribution of particle sizes, which is unfavorable for applications. It was previously shown that cascade centrifugation can be used as a common method for the separation of graphene particles by size, in the case of ultrasound-assisted liquid-phase exfoliated graphene. In this paper, we demonstrated that cascade centrifugation of electrochemically exfoliated graphene can be used in conjunction with Langmuir–Blodgett assembly to produce thin graphene films. By tuning centrifugation speed and solvent dilution volume, we produced films of different controlled thicknesses. We have shown that the optical transparency and electrical resistance of these films behave similarly to those of other films made of graphene from solution. Namely, the films exhibit a percolation threshold, at which optoelectronic properties experience a critical change. The percolation exponent and percolative FOM are found to be of the same order as in the case of other graphene films found in literature, which indicates that our films are of a similar quality. However, the method that we have used, which relies on cascade centrifugation of a commercially available solution of graphene, is easily accessible to researchers who have no exfoliation experience or equipment. We have shown that ownership of a centrifuge is the only prerequisite for making practical use of widely available graphene in solution.


Sarcoidosis is a systemic granulomatous disorder affecting multiple organs including the lungs, skin, kidneys, and eyes. We used microRNA (miRNA) microarrays to investigate serum miRNA profiles of patients with ocular sarcoidosis as diagnosed by specific criteria (diagnosed ocular sarcoidosis), and patients with idiopathic uveitis characterized by ocular manifestations of sarcoidosis (suspected ocular sarcoidosis). Principal component analysis (PCA) and hierarchical clustering showed that serum miRNA profiles of diagnosed ocular sarcoidosis and suspected ocular sarcoidosis were both clearly distinguishable from healthy controls. Furthermore, comparative analysis of the miRNA profiles showed highly similar patterns between diagnosed ocular sarcoidosis and suspected ocular sarcoidosis. Pathway analysis revealed common pathways were involved in the two groups, including those of WNT signaling and TGF-beta signaling. Our study demonstrated a high overlap of differentially expressed serum miRNAs in patients with diagnosed ocular sarcoidosis and suspected ocular sarcoidosis, suggesting that these groups share a similar underlying pathology and may represent possible variants of the disease. Characterization of serum miRNA profiles may provide an opportunity for earlier diagnosis and treatment, and may inform more accurate clinical prognosis in patients with an ocular sarcoidosis phenotype. Uveitis can cause significant visual loss , and itsSarcoidosis is a systemic granulomatous disorder affecting multiple organs including the lungs, skin, kidneys, and eyes ,6. OculaThere are circulating cell-free nucleic acids released into the serum/plasma by various tissues and cells, including DNA, RNA, microRNA (miRNA), and long-noncoding RNA (lncRNA) ,11. ReceWe conducted a multicenter study to investigate serum miRNA profiles in patients with diagnosed ocular sarcoidosis and patients with idiopathic uveitis characterized by the presence of ocular manifestations of sarcoidosis, as well as in healthy controls. We were able to identify a shared miRNA profile in the two patient groups, and also examined the feasibility of machine learning using leave-one-out cross-validation (LOOCV) to classify patients based on miRNA expression profile.n = 21) and healthy control subjects (n = 16) who participated in this study are summarized in The demographic characteristics of uveitis patients , either up-regulated (11 miRNAs with >2.0-fold change) or down-regulated (7 miRNAs with <0.5-fold change), in diagnosed ocular sarcoidosis compared to healthy controls (We employed GeneChipcontrols . In ordep < 0.05), either up-regulated (7 miRNAs with >2.0-fold change) or down-regulated (11 miRNAs with <0.5-fold change), in suspected ocular sarcoidosis compared to healthy controls  method. As shown in As uveitis specialists, we sometimes encounter pediatric patients with chronic iridocyclitis that appears identical to the type of uveitis observed in patients diagnosed with juvenile idiopathic arthritis, but without any arthritis signs or symptoms ,18. SimiRosenbaum and colleagues developed a machine learning algorithm to aid in reclassifying idiopathic uveitis based on peripheral whole blood gene expression profiles. Using the algorithm, the diagnostic likelihood of four specific uveitic diseases associated with systemic manifestations (one was sarcoidosis) was assessed in 38 patients with idiopathic uveitis, and the results suggested reclassification of 11 of the patients . AlthougThe present study compared the serum miRNA signatures of patients with either diagnosed or suspected ocular sarcoidosis versus healthy controls. PCA and unsupervised hierarchical clustering displayed a distinct profile between diagnosed ocular sarcoidosis and healthy controls. By KEGG pathway analysis of associated miRNAs, 8 pathways appeared to be potentially up-regulated and 11 pathways appeared to be potentially down-regulated in diagnosed ocular sarcoidosis. Our results are consistent with a recent report , demonstThis study has several limitations. First, the sample size of each group was relatively small. Second, confirmation by polymerase chain reaction of the differentially expressed miRNAs was not performed. Third, although all patients underwent chest X-ray evaluation to assess for possible pulmonary lesions consistent with sarcoidosis, only some patients had chest computed tomography performed. Therefore, some of our patients with suspected ocular sarcoidosis may in fact have fulfilled the IWOS criteria for full diagnosis if more radiographic testing had been pursued ,24, FourThis study was conducted in accordance with the tenets of the Declaration of Helsinki, and was approved by the Institutional Review Boards of Kyorin University Hospital, Toyama University Hospital, and Jikei University Hospital . All patients and healthy control subjects provided written informed consent at the time of enrollment in the study.Peripheral blood samples (4 mL) were obtained between January 2020 and October 2021 from 11 patients with diagnosed ocular sarcoidosis, 10 patients with suspected ocular sarcoidosis, and 16 healthy control subjects. All patients had active intraocular inflammation (panuveitis or posterior uveitis) and were not receiving systemic immunosuppressive treatment at the time of serum collection. The healthy control subjects were confirmed by careful questioning to have no history of uveitis, systemic inflammatory disorders, malignancy, or diabetes mellitus. Serum was isolated from blood samples and stored at −80 °C prior to processing.“Diagnosed ocular sarcoidosis” was defined as uveitis with features meeting the IWOS criteria for ocular sarcoidosis, and “suspected ocular sarcoidosis” was defined as idiopathic uveitis with at least 3 of 7 IWOS ocular clinical signs suggestive of ocular sarcoidosis but not meeting the criteria for ocular sarcoidosis . The IWOFor the present study, the following data were collected from medical records: age at uveitis onset, sex, best-corrected visual acuity, laterality, anatomic type of uveitis, presence of IWOS intraocular findings, ocular complications, and ancillary test results. Anatomic type of uveitis was based on the Standardization of Uveitis Nomenclature (SUN) guidelines . In gene® Mini Kit  following the manufacturer’s protocol. For the microRNA assay, a 200 μL aliquot of each serum sample was used. Total RNA was eluted into 14 μL of RNAse-free water and the concentration of RNA was analyzed by measuring a 2 µL aliquot on a NanoDrop ND-3300 Fluorospectrometer . The miRNA was then prepared using the FlashTag™ Biotin-HSA RNA Labeling Kit for microarray analysis . Biotin-labeled RNA targets were hybridized to GeneChip® miRNA 4.0 arrays . Probe signal intensities were log2-transformed and normalized based on the total intensity of the arrays using Transcriptome Analysis Console (TAC) v4.0  and quality check of each sample was performed using QC metrics view on TAC v4.0. PCA was performed using probe set intensity of each sample.Since a recent study by Rosenbaum et al. demonstrated that transcriptional signatures from peripheral blood have the potential to aid in identifying specific subsets among patients with idiopathic uveitis, we hypothesized that a similar approach of “molecular characterization” using miRNAs could allow further classification of our patients with idiopathic uveitis . Total Rp-value < 0.05 between patients and healthy controls were used for filtering. Hierarchical clustering and Venn diagrams were generated for normalized samples.The miRNA probes with a >2.0-fold change or with a https://dianalab.e-ce.uth.gr/html/mirpathv3/index.php?r=mirpath, accessed on 22 July 2022) to identify target pathways using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and a p-value < 0.01 was considered to be statistically significant for this analysis.Differentially expressed miRNAs identified in the present study were mapped by DNA intelligent analysis (DIANA)-mirPath tool  was used to produce a machine learning model based on random forest classification with Boruta algorithm (0.30) in order to compare patients with diagnosed ocular sarcoidosis, patients with suspected ocular sarcoidosis and healthy controls. The random forest algorithm used the hyperparameters of number of trees = 5000, criterion = “gini”, max depth = none, mini sample lear = 1, and max features = “auto”. The Boruta algorithm used the hyperparameters of n estimators = “auto”, max iter = 100, and alpha = 0.05. The sensitivity and specificity of resulting classification scores were calculated. Leave-one-out cross-validation was employed to validate the model’s performance.t-test and the ANOVA was used for comparison of 3 or more groups. Categorical variables were analyzed with the Fisher exact probability test or chi-square test. Statistical analyses were performed using SPSS version 28. A p-value of < 0.05 was considered to be statistically significant unless otherwise indicated.The Shapiro–Wilk test was performed to evaluate data distribution. Continuous variables were compared between two groups using the Mann–Whitney U test or Student’s Our study demonstrated a high overlap of the differential expression of serum miRNAs in patients with diagnosed ocular sarcoidosis and patients with suspected ocular sarcoidosis, suggesting that both groups share similar underlying pathogenic mechanisms. Further characterization of serum miRNA profiles may provide new insights into diagnosis, therapeutic decisions, and visual prognosis in patients with idiopathic uveitis that have the features of ocular sarcoidosis but do not meet current diagnostic criteria.


The objective of this study was to compare the external load of training sessions using as a reference an official competition match in women’s football in order to find if the training sessions replicate the competition demands. Twenty-two semi-professional football players were analyzed during 17 weeks in the first phase of the competitive period of the 2020–2021 season of Spanish women’s football. In addition to the competition , four types of sessions were distinguished: strength or intensity (INT), endurance or extensity (EXT), velocity (VEL), and activation or pre-competitive (PREOM). The external load variables recorded were total distance (TD), high-speed running (HSR), sprint (Sprint), accelerations (ACC2), decelerations (DEC2), player load (PL), distance covered per minute (TDmin), high metabolic load distance (HMLD), and total impacts. The main results were that the external load demanded was different according to the type of session, being, in all cases, much lower than OM. The variables referring to the neuromuscular demands (ACC2 and DEC2) were higher in the INT sessions, the TD variable in the EXT sessions and the velocity variables (HSR and Sprint) in the VEL sessions. We can conclude that there was an alternating horizontal distribution of training loads within the competitive micro-cycle in women’s football, although the order was not the usual one for tactical periodization. In recent decades, there has been exponential global development of women’s football, both in its practice and in the entities and institutions that promote and manage it ,2. This It is essential to know the demands of women’s competition  in orderIn men’s football, the two usual planning strategies are the structured micro-cycle together with tactical periodization ,14. ElitIn order to improve the knowledge of types of load management strategies in women’s football in a novel way, the objective of the present study is to describe the external load in the different types of training and competitive matches in women’s football during a competitive micro-cycle. The starting hypothesis is that there is a horizontal alternation in the conditional demand between the training sessions in the competitive micro-cycle, but none of the sessions replicates the competition demand. In this sense, the results of this study will allow increasing the information on the type of periodization that is proposed in an elite women’s football team, which could help to facilitate load management in women’s football, which is still little known and under research.A total of 22 semi-professional female football players : 65.4 ± 17.9 mm) took part in the study. The data recording was carried out during 17 weeks of the competitive period of the Second Women’s Football Division (Reto Iberdrola) during the 2020–2021 season. Usually, the team completed four days of training and one day of competition per week.In order to obtain position data, the players were monitored with WIMU PRO devices  using the global positioning system (GPS). The GPS device used in this study can operate at 10 Hz, and it is compatible with the Galileo and American satellite constellation, which seems to provide more precision . For the2 ; decelerations of less than −2 m/s2 ; player load , distance traveled per minute ; high metabolic load distance , defined as the distance covered by a player when their metabolic power is above 25.5 W/kg1, ratios per kilogram [The duration of the session was recorded considering only the effective time, that is, the time in which the players were active, excluding times of inactivity . The external load variables were: total distance ; high speed running , established as 60% of the maximum individual velocity of the participants ; sprint kilogram ; and, ton = 12), the types of sessions were differentiated based on the priority conditional objective that was developed: strength or intensity session ; endurance or extensity session ; velocity session ; and activation session . In total, 805 recordings were collected from official matches (n = 49) and 756 training sessions, distributed as follows: VEL = 114, INT = 173, EXT = 239, and PREOM = 230. The number of records per player was 36.8 ± 10.6.Apart from the official matches  and before (OM−) of the OM, following the proposal of previous studies ,14.2). The VEL session was usually the first acquisition session, located 2 days after the OM (OM + 2) and 5 or 6 days before the next official match (OM − 6 or OM − 5), characterized by velocity work, locomotive-oriented and based on tasks of intermittent nature or in waves (with breaks of 1 or 2 min between drills). The tasks were mostly carried out with goalposts and goalkeepers and were made up of a large relative space per player  and 3 or 4 days before the next competition (OM − 3 or OM − 4), characterized by neuromuscular and mechanically-oriented work, based on reduced and positional games, with a relative space per player of less than 100 m2 per player, and with goalposts and goalkeepers.The EXT session was usually the third acquisition session of the micro-cycle, located 4 or 5 days after the OM (OM + 4 or OM + 5) and 2 or 3 days before the next competition (OM − 2 or OM − 3), characterized by cardiovascular work, based on large-format tasks, that is, with a moderate to high number of participants per team (>6), in a relative space equal to or greater than 250 m2 per player) or small  with a polarized orientation and a tactical and strategic approach. Pre-match day (PREOM) was the fine-tuning session. It was always conducted the day before match day (OM − 1). The contents that were developed in it were activation tasks based on activities with a large number of participants (>6) in a medium relative space .The last day of the week was match day (OM). To calculate the load on the day of the competition, the previous warm-up carried out by the players was taken into account in addition to the two parts of the match, which had the following approximate load: duration was 20.1 ± 2.1 min, and the physical demands were as follows: TD: 1280 ± 266 m, HSR: 64.3 ± 37.7 m, Sprint: 5 ± 10.7 m, ACC2: 27.9 ± 9.6 n, DEC2: 28.4 ± 9.6 n, PL 20.6 ± 4.3 au and TDmin 64 ± 12.3  and a customized Microsoft Excel spreadsheet  for Windows.Descriptive statistics data from variables were presented using mean and standard deviation. Tests for normality (Shapiro–Wilk) and equality of variances (Levene) were applied. The null hypothesis was accepted because the distribution of the data met the normality criterion. Furthermore, the variances were homogeneous. Therefore, a one-way ANOVA analysis of variance for independent samples was used to test for differences in the variables between the different sessions . Significant results were then analyzed using post hoc Tukey’s test. Effect size (ES) was also calculated to determine meaningful differences with magnitudes classified as  trivial p < 0.05) between training sessions for all variables. The PREOM session had the lowest (p < 0.05) volume  but with higher density than the VEL and INT sessions. The INT session had the highest volume  but with a lower density than the EXT and PREOM sessions and higher than VEL.As shown in The aim of this study is to describe the distribution of external load in different training sessions and competitions in competitive micro-cycles in women’s football. According to the current state of the art, this is the first study carried out on the description of tactical periodization in women’s football. The results confirm the starting hypotheses because despite describing a distribution of the conditional demands  based on the horizontal alternation during the competitive micro-cycle sessions, none of them replicated the demands of the competition.In line with the results obtained in previous studies carried out in men’s football ,28, the On the other hand, unlike previous studies ,18, the 2 and −1.5–3 m/s2) and high (>3 m/s2 and <−3 m/s2) accelerations and decelerations during training were similar to the competition values in this type of session, in this research work, the competition values were significantly higher than the rest of the types of training sessions. Regarding the INT session, which was usually carried out on the central days of the week (OM − 5 or OM − 4) and prior to the EXT session, a predominance of the force variables (accelerations and decelerations) was described compared to the other sessions, as described in men’s football in previous works ,29. HoweWith regard to the EXT session, which was usually carried out in the middle of the week (OM − 4 or OM − 3), high values were described in TD and PL compared to the other sessions, showing a similar trend as in previous studies ,30. In eDespite the global coincidence of this approach, not all the works ,31,32,33This study describes a variant of tactical periodization, which, while respecting horizontal alternation, proposes a novel distribution in the orientation of content throughout the competitive micro-cycle. The possible practical application of this study is that the content of the training sessions with different conditional orientations could present a different ordering than the one proposed by the tactical periodization. In this way, instead of respecting the usual order of the sessions proposed by the tactical periodization , it could be altered by proposing VEL+INT+EXT when the contextual needs and/or the characteristics of the type of population to which it is directed requires adapting content to optimize their preparation process.It has been verified that none of the training sessions obtained higher values than those of the competition in all the variables . Therefore, coaches should be careful in the return-to-play process and should prepare players adequately because there could be an excessive gap between the demands of training sessions and those of competition, putting players at risk ,12,13.This work has some context limitations. In the first place, since just one team was analyzed, the periodization results should not be interpreted as something generalized in women’s football; more case studies would be required to approach an extrapolation. In addition, the inclusion of internal load variables  could haIn conclusion, this research work provides a description of the profile of training loads throughout a competitive week in women’s football. A horizontal alternation in the stimulation of physical capacities in female football was described, although the order of the training contents varied with respect to the original proposal of tactical periodization. This study could open the possibility of proposing a variant that can better adapt to a particular reality conditioned by the schedules and pitches established by the sports club of the players. In this sense, in ongoing works, a larger sample and other contexts will be included.


Epitaxial lateral overgrowth (ELO) of GaN epilayers on a sapphire substrate was studied by using a laser-patterned graphene interlayer. Monolayer graphene was transferred onto the sapphire substrate using a wet transfer technique, and its quality was confirmed by Raman spectroscopy. The graphene layer was ablated using a femtosecond laser, which produced well-defined patterns without damaging the underlying sapphire substrate. Different types of patterns were produced for ELO of GaN epilayers: stripe patterns were ablated along the  Lack of native substrate has always plagued the development of III-nitride materials and devices. GaN-based structures are usually deposited on various foreign substrates, such as sapphire or SiC; however, the large lattice and thermal mismatch causes high dislocation densities ,2. To imIn this work, we present graphene ablation on sapphire substrates with a femtosecond laser and evaluate if the resulting patterns are suitable for ELO of GaN epilayers. Monolayer graphene is transferred onto the sapphire substrate using a wet method; its quality is assessed by Raman spectroscopy. The laser-ablated patterned structures are analyzed by scanning electron microscopy (SEM). Afterward, the patterned structures are used for the growth of GaN layers, and the structural properties of the epilayers are studied.3) were used as Ga and N precursors, respectively. The epitaxy process was monitored by an in situ laser reflectometry system operating at 650 nm. Epi-ready 2-inch monocrystal (0001) orientation sapphire substrates were used for graphene layer transfer and further deposition of GaN epilayers. Commercially available transfer-ready poly(methyl methacrylate)-coated (PMMA-coated) graphene monolayers from Graphenea Inc.  were used to cover the sapphire substrates. Rapid thermal annealing (RTA) was used for the thermal treatment of the samples. The GaN epilayers were deposited by low-pressure metalorganic vapor phase epitaxy (MOVPE) using a flip-top close-coupled showerhead 3×2” reactor . Trimethylgallium (TMGa) and ammonia . The 532 nm laser excitation source with a power of 1.8 mW was focused on a 0.9 µm diameter spot on the sample surface, and 1800 lines/mm grating was used to record the Raman spectra. The wavenumber axis was calibrated using a polystyrene standard. Femtosecond laser microfabrication setup  based on Carbide laser  and Aerotech xy stages were used to perform laser ablation. The surface of the samples after ablation and epitaxy experiments was studied by SEM . Spatially-resolved cathodoluminescence (CL) measurements were performed using a hybrid CL-SEM system . The electron beam was accelerated to 4 keV; the collected light was dispersed using a spectrometer  and recorded using a CCD camera . All the measurements were performed at room temperature.The key processes involved in the sample preparation are schematically illustrated in 2 were transferred onto the sapphire substrates using the wet transfer procedure. First, the PMMA/graphene cuts were dipped into deionized water and left floating freely on the water surface for 5 min. Afterward, the sapphire substrate was put beneath the floating polymer/graphene piece, which was attached as close as possible to the center of the substrate. The sample was then dried in the air until there were no water droplets visible. Next, the sample was heated inside the RTA oven for 20 min at a temperature of 100 °C under an N2 atmosphere. The PMMA was dissolved by dipping the sample into acetone for 45 min at 40 °C and rinsing in isopropyl alcohol for another 45 min at 40 °C. Finally, the samples were annealed for 8 h using RTA at 300 °C in a vacuum to remove any organic surface residues left [Monolayer graphene pieces of size 1.3 × 1.3 cm−1. However, the intensity of D mode was low compared to G (IG/ID approx. 12), indicating the high quality of the graphene [2D/IG) was 3.8, while the position and full width at half maximum (FWHM) of the 2D peak were 2683 cm−1 and 44 cm−1, respectively, indicating monolayer graphene [The quality of transferred graphene on the sapphire substrate was verified by Raman spectroscopy. The obtained Raman spectrum, shown in graphene ,22. The graphene .v = 6 mm/s. To check the sensitivity of the experimental setup to surface roughness, the initial ablation experiments were performed without using the surface tracking system. 2, which was significantly above the ablation threshold of both graphene  and sapJ/cm2 [2 ). ChangiThe subsequent ablation experiments were performed employing the surface tracking system. To remove 2-µm-wide stripes of graphene without damaging the sapphire substrate, the focal plane was set at the 5 µm distance from the sample surface, and the pulse energy was reduced. The 2 µm width of the graphene window was selected to ensure fast island coalescence during the ELO process: the diameter of the GaN islands reaches a couple of microns before fully merging into a 2D layer . Several patterns with different distances between graphene windows were fabricated, as shown in SEM images in even see a. The evFor the following ELO of GaN epilayers, the graphene layer was divided into four quadrants  were evaluated in each zone using CL. The typical images of the spatial distribution of integrated CL intensity are shown in 9 cm−2 was obtained for GaN grown on square graphene islands (zone B). Somewhat lower values of 8.1 × 108 and 6.6 × 108 cm−2 were estimated for GaN grown on striped graphene pattern . Meanwhile, the lowest DD values of 5 × 108 cm−2 were measured both in GaN deposited on unmodified graphene (zone D) and in reference GaN grown on bare sapphire. The link between graphene pattern and dislocation density can be interpreted by considering the organization of dislocations along the lines parallel to the direction of stripes [The highest average DD value of 1.1 × 10 stripes , as indiTo summarize: femtosecond laser patterning of a graphene monolayer on a sapphire substrate is developed to fabricate a mask for ELO of GaN epilayers. Applicability of patterned graphene for lateral overgrowth is demonstrated: GaN seeds are formed mostly in the graphene windows during the standard MOVPE growth procedure. Growth utilizing masks comprised of graphene stripes confirmed higher GaN growth rate along the