Graphene deposition was conducted at low pressures (35 Torr) with a mixture of methane/hydrogen and a substrate temperature of 970 °C followed by spontaneous cooling to room temperature. A thin copper-strip was deposited in the middle of the SiO2/Si substrate as catalytic material. Raman spectroscopy mapping and atomic force microscopy measurements indicate the growth of few-layers of graphene over the entire SiO2/Si substrate, far beyond the thin copper-strip, while X-ray photoelectron spectroscopy and energy-dispersive X-ray spectroscopy showed negligible amounts of copper next to the initially deposited strip. The scale of the graphene nanocrystal was estimated by Raman spectroscopy and scanning electron microscopy.We report the first direct synthesis of graphene on SiO This table summarizes these methodologies under two classifications: Catalyst-free and metal catalyzed direct growth CVD, where both regular and plasma enhanced CVD (PECVD) are used [Since graphene was first obtained by microexfoliation of graphite in 2004  it has bd others . To dateare used .2 at low temperatures (400 °C) by using the electron cyclotron resonance CVD (ECR-CVD) method [In the first methodology , the majority of the graphene growth experiments on non-metallic substrates are conducted at high temperatures (1060–1100 °C) over a long deposition time . Liu et ) method .2/Si wafers as a catalyst, which was subsequently delaminated using adhesive tape, leaving behind the graphene layer on the substrate [2/Si substrate to synthesize graphene using a CVD tube furnace [3 solution [2/Si) to promote graphene growth [2/Si [In the metal-catalyzed direct growth method, many experiments have used a sacrificial metal layer to stimulate graphene growth. McNerny et al. deposited a nickel layer on SiOubstrate . They obubstrate . Dong et furnace . They cosolution . Similare growth . They foe growth . Kato etth [2/Si . They deth [2/Si .2/Si substrates by HFCVD. This technique allows the deposition of graphene over the entire substrate by using the metal-catalyzed method in a limited manner. A thin copper-strip was deposited on the middle of the SiO2/Si substrate allowing the methane dehydrogenation and the carbon absorption to occur and leaving the rest of the surface free of metal. Structural, morphological, and compositional analyses were made on the graphene grown on the SiO2/Si in areas on top of and next to the copper strip. This research targets SiO2/Si substrates due to their ubiquity in graphene applications, such as photodetectors, gas sensors, solar energy, and others [This paper reports a novel method suitable for industrial scale-up production to directly grow high-quality graphene on SiOd others . In addid others .2/Si wafers with a top oxide layer of 285 nm and a thickness of 500 ± 25 µm manufactured by Graphene Supermarket . These wafers were cut into 2 × 2 cm pieces and cleaned with: deionized water, trichlorethylene, acetone (histology grade), and isopropanol (histology grade); the last three reagents were obtained from Fisher Scientific . A mixture of sulfuric acid (H2SO4 purity range of 95–98%) and hydrogen peroxide (H2O2 solution at 30% w/w in H2O), both provided by Sigma Aldrich , was prepared for a further cleaning of the substrate. A thin copper-strip (3 mm width) was deposited in the middle of the SiO2/Si substrate by sputtering  with a deposition time of 1 minute  was used for the graphene deposition. The reactor consists of a heated substrate holder that is positioned below three heated filaments of rhenium. The gases enter the chamber from the top with a shower-like turbulent flow, (cf. 2/Si substrates (4 cm2) with the thin copper-strip (0.3 cm × 2.0 cm) were submitted to the graphene synthesis procedure at different growth parameters. The substrate was placed in the HFCVD as shown in A commercial HFCVD instrument  and hydrogen (10–50 sccm) for 30 to 120 min. Finally, the samples were cooled by spontaneous convection to room temperature. As a control study, SiO−1 with a spot size of 0.7 μm. In addition, Raman mappings were taken over an area of 150 × 100 μm2 and a step size of 2 μm; the collecting time for each point in the Raman mappings was 20 s. A morphological study of the synthesized graphene was done using a scanning electron microscope, SEM  at different magnifications  and an atomic force microscope, AFM  in tapping mode over an area of 3 × 3 µm. Compositional analyses of the graphene samples were done by energy-dispersive X-ray spectroscopy, EDS (JEOL JSM 6480LV) and X-ray photoelectron spectroscopy, XPS  over an energy range of 0 to 1200 eV.The structural characterization of graphene was conducted by Raman spectroscopy  equipped with an excitation laser operating at 532 nm. The spectra were collected over a frequency range of 1100 to 3100 cm2/Si substrate.A structural (Raman), morphological (SEM and AFM) and compositional (EDS and XPS) analysis was done on the synthesized graphene, both on top of and next to the copper- strip deposited in the SiO2 carbon atoms, the 2D peak appears in response to a two-phonon vibrational process and the D peak is activated by the edges or defects in graphene [2/Si substrate. For the control samples without a copper strip, these graphene peaks were not observed, indicating that the copper metal is necessary for the growth of graphene under our experimental conditions. 2/Si substrate, respectively. The red and green spectra show two different signals next to the copper strip  yields an estimate of the graphene grain size [D/G values, 0.45 ± 0.07 and 0.87 ± 0.03, were found on top of the metal strip. Although, we had a significant observed D peak, the average of the full width at half maximum (FWHM) of the D, G and 2D peaks indicates good quality crystallites [−1, 25 ± 1 cm−1, and 56 ± 3 cm−1, respectively and in areas next to the copper strip were: 38 ± 2 cm−1, 29 ± 1 cm−1 and 52 ± 2 cm−1.The average intensity ratio between the D and G peaks  measured by SEM (vide infra):To calculate the crystal size from the Raman data, we employ the Cancado equation (Equation (1)) , where LD represents the inter-defect distance, EL is the excitation energy and the defect concentration corresponds to 1/D in areas next to and on top of copper strip were 18 nm and 10 nm, respectively. We also estimate the defect concentration for both areas, next to and on top of copper strip with values of 3 × 10−3/nm 2 and 7 × 10−3/nm 2, respectively. These results confirm that some point defects are present in the nanocrystals and contribute to the ID/IG ratio intensity. In addition, we corroborate that higher concentration of defective crystals are present on top of the copper strip versus next to this metal film:The difference in the particle size estimates is likely due to the multiple phonon dispersion produced by defects inside of the graphene crystallites ,43. Thesto 1/LD2 . Our resThe G and 2D peaks characteristically correspond to the signal for graphitic materials , where t2/Si substrate and to estimate the number of graphene layers through the intensity ratio of the 2D/G peaks [2. In Raman mapping cf.  was done/G peaks ,39. In Fks 2. In ,32,35. Hks 2. In .From the SEM images, it was possible to estimate the size of the graphene crystals from the scale bar to ca.100 nm. By measuring many crystals, we obtained an average size of 120 nm and a range of 100 to 140 nm for particles next to the copper strip, and smaller particles  on top of the copper-strip. At lower magnification (5000×), no copper particles were observed next to the copper film.2/Si substrate for both next to  were observed on top of the copper-strip (3/2: 706.7–710.9 eV), Co (Co 2p3/2: 778.1–780.2 eV) and Ni (Ni 2p3/2: 852.5–854.4 eV) were not observed on areas next to and on top of the copper-strip. The absence of other metals demonstrates that the graphene growth was either catalyst free or catalyzed by copper [XPS measurements were taken both next to and on top of the copper-strip. er-strip f, as exper-strip c. Signaly copper . ; however, defects in the crystal due to phonon scattering may lead to an error in this estimate. These defects inside of the graphene nanocrystal were corroborated by the calculation of the inter-distance defect (Equation (2)).The structural (Raman), morphological (SEM and AFM) and compositional (EDS and XPS) characteristics of the graphene on SiOThe real size was confirmed through the images taken by the SEM technique where the particle size was in a range of 35 to 140 nm with an average of 120 nm (next to the copper-strip). The growth mechanism most likely begins with dehydrogenation of methane by the hot filament . In the 2/Si by HFCVD, avoiding a complex graphene transfer process. In this method, a thin copper-strip of 0.3 cm × 2.0 cm was deposited in the middle of a 4 cm2 substrate, leaving most of the substrate surface free of this metal. The structural analysis was done by Raman spectra to verify the graphene growth characteristics. SEM and AFM images allowed us to determine the graphene’s topography on the SiO2/Si substrate. Additionally, copper residues were observed on top of the copper-strip areas, but these were not present in areas next to the metal. A compositional study was made through EDS and XPS measurements, indicating the presence of carbon in all samples and the virtual absence of copper in areas next to the metal-strip. This work demonstrates that the thin copper-strip deposited on the middle of the SiO2/Si enables the graphene growth over all the substrate. By eliminating the need for a mechanical transfer step in the device fabrication process, this accomplishment opens up the possibility of integrating graphene with currently available silicon device technologies. Further research, needed to continuously improve the quality of the graphene deposition, is ongoing in our laboratories. One approach is the reduction of the nucleation density [This study demonstrates, for the first time, a method to deposit polycrystalline graphene directly onto SiO density ,54,55,562/Si by HFCVD, using the metal catalyzed method in a limited manner. The crystal size, structure, and inter-defect distance of the nanocrystalline graphene were estimated by SEM, AFM, and Raman measurements, respectively. EDS and XPS analyses confirmed the presence of graphene on SiO2/Si with negligible amount of copper in the area next to the copper strip. Our study allows the possibility of growing graphene directly on dielectrics without a transfer process and the opportunity to produce it on an industrial scale.This work presents an approach to directly grow graphene on SiO


Moreover, ID/IG increased from 0.87 to 0.92, indicating that the defect density decreased because of desorbing oxygen functional groups after ageing. When the temperature exceeded 60°C, CO2 produced by decomposing graphene oxide was detected. The thermal decomposition changed after ageing. The decomposition peak temperature decreased from 216°C to 195°C. The CO2 amount produced remained almost unchanged; however, the amount of CO, SO2 and H2O decreased. After ageing, the apparent activation energy of graphene oxide decreased from 150 to 134 kJ mol−1.After long-term ageing, the structure of graphene oxide prepared by the modified Hummers method changed. Because of the desorption of oxygen-containing functional groups, the C/O ratio of graphene oxide increased from 1.96 to 2.76. However, the average interlayer distance decreased from 0.660 to 0.567 nm. The content of -CH- and -CH Currently, researchers believe that oxygen-containing groups of graphene oxide are primarily the hydroxyl, carboxyl and epoxy groups . Further2 g−1) [Graphene oxide is a high-energy, thermally unstable material prone to the disproportionation reaction even under mild heating conditions; it emits a considerable amount of heat during the thermal decomposition process . Because2 g−1) , which a2 g−1) ,7. FurthIn recent years, there have been many studies using graphene oxide to prepare energetic materials and composite energetic materials –12. Usuaet al. examined the swelling phenomenon of graphene oxide films prepared using the Hummers method after ageing in air, water and different alcohols [et al. reported that ageing does not significantly affect the decomposition enthalpy of graphene oxide, but slightly reduces the initial decomposition temperature [et al. [et al. [Many studies have been conducted on the ageing properties of graphene oxide. Iakunkov alcohols . Furtherperature . Holt et [et al.  examined [et al.  examined [et al.  used firAlthough long-term ageing is a tedious method, it can better approximate the actual use of graphene oxide. However, studies on the effects of long-term ageing on the structure and thermal decomposition of graphene oxide have not been reported.. 2. 2.1Natural flake graphite, potassium permanganate, concentrated sulfuric acid, phosphoric acid, 5% hydrochloric acid and barium chloride were used.. 2.2The preparation of graphene oxide can be roughly divided into the following three processes: intercalation, oxidation and exfoliation. Common preparation methods of graphene oxide are the Brodie method, Staudenmaier method, Hoffman method, Tour method and Hummers method . Because2SO4 and 12 ml of H3PO4 were mixed and placed in a water bath at 25°C. Then, 5 g of flake graphite was added to the mixture and stirred for 30 min. Subsequently, 30 g of KMnO4 was added slowly to the mixture and stirring continued for 12 h until the solution turned dark green. Then, 18 ml of H2O2 was slowly added to the mixture to remove excess KMnO4, and the solution was stirred for 30 min until it cooled. After the mixture was suction filtered, a large amount of deionized water and HCl were added for washing. The suction filtration and washing step were repeated several times until the sulfate ions could not be detected after adding BaCl2. Then, the filter cake was added to 250 ml of deionized water under ultrasonic stirring for 4 h. During the ultrasonic dispersion process, biological ice packs are required to be continuously replaced to avoid excessive temperature. The beaker was taken out and freeze-dried for 24 h to obtain graphene oxide.First, 108 ml of concentrated H. 2.3The graphene oxide prepared above was stored in a constant temperature and humidity box for 2 years and then removed for characterization. The humidity box was purchased from MS SHIMEI Electric . The temperature of the constant temperature and humidity box was set to 25°C, and the humidity was maintained in the range of 20–70%.In this study, GO refers to newly prepared graphene oxide, while GO-a refers to graphene oxide aged for 2 years.. 2.4λ = 1.5438 Å). Samples were scanned from 5° to 60° (2θ) with a step size of 0.02° and scanning speed of 5° min−1. The Fourier transform infrared (FTIR) spectrum was obtained using a Bruker TENSOR II FTIR spectrometer . The samples were prepared by the KBr tablet method, and the mass ratio of graphene oxide to KBr was 1 : 100. The defect density of graphene oxide was characterized using a HORIBA Jobin Yvon LabRAM HR Evolution laser confocal Raman spectrometer with a laser wavelength of 532 nm and a 50× objective lens. The layer thickness of graphene oxide was characterized using a Bruker NanoManVS type atomic force microscope (AFM). The graphene oxide aqueous solution with a concentration of 0.01 mg ml−1 was ultrasonically dispersed for 20 min, and then dropped on a mica sheet and dried at room temperature. The probe was then operated in tapping mode. Elemental analysis was performed on an X-ray photoelectron spectrometer (XPS) with an Al Ka anode . The components of the gaseous products were detected by STA 449F3 thermal analysis combined with an infrared and mass spectroscopy system  at a heating rate of 20°C min−1 from 50°C to 900°C in Ar. The flow rate of argon was 50 ml min−1. The infrared spectrum scanning range was from 4000 to 650 cm–1, and the gas pool temperature was 200°C. The heat of decomposition of graphene oxide was measured by a Netzsch STA 449 TG–DSC synchronous thermal analyser where the carrier gas was N2 with a flow rate of 50 ml min−1. The heating range was from room temperature to 320°C, and the heating rate was 5, 10, 15 and 20°C min−1.X-ray diffraction (XRD) was performed on a Rigaku Ultima IV X-ray diffractometer operated at 44 kV and 44 mA using copper X-ray radiation  plane close to 9.68°, while GO-a had a diffraction peak of the (002) plane close to 11.02°. This result indicates that the adsorbed water and part of the oxygen-containing groups desorbed during the ageing process, which reduced the layer spacing. After ageing, the full width at half maximum (FWHM) of the diffraction peak of the (002) plane of graphene oxide increased from 0.53527° to 0.91882°. The Debye–Scherrer formula was used to calculate the lattice size of graphene oxide. After ageing, the lattice size of graphene oxide decreased from 14.48 to 7.11 nm. Furthermore, GO had weak diffraction peaks close to 19.70° and 42.20°, corresponding to the (002) crystal planes of oxygen partly desorbed graphene oxide and the (100) crystal planes of graphite, respectively [As shown in figure 1ectively . The difa shows the FWHM of peaks close to 20°. After ageing, the lattice size of partly reduced graphene oxide decreased from 12.16 to 1.47 nm. The increase in peak intensity close to 21.04° shows that additional oxygen-containing groups desorbed from GO-a. Graphene oxide heated at 200°C had a strong peak at approximately 20° [Figure 1tely 20° . After ately 20° .aL is the average grain size perpendicular to the crystal plane, λ is the X-ray wavelength, β is the FWHM of the diffraction peak and θ is the diffraction angle.The Debye–Scherer formula  is listed is the interplanar spacing, θ is the incident angle, n is the diffraction order and λ is the X-ray wavelength.The Bragg equation  is as beb shows the mid-infrared spectrum of graphene oxide. For the as-prepared graphene oxide, the infrared absorption peaks close to 3423 and 1400 cm–1 are the stretching and bending vibration peaks of OH, respectively [–1 are attributed to the stretching vibration peaks of the carbonyl group (C=O), the aromatic ring skeleton vibration peak, and the stretching vibration peaks of the epoxy and C–O bonds, respectively [–1 correspond to the C–H stretching vibration of the CH2 group [–1 corresponds to the antisymmetric stretching vibration of CH3 [3 and CH2 in the infrared spectrum of GO-a are not obvious. This result shows that CH3 and CH2 were degraded during the ageing process. In the infrared spectrum of GO-a, the absorption peaks at 3393, 1393, 1725, 1586, 1224 and 1053 cm–1 correspond to the stretching vibration mode of OH, the bending vibration mode of OH, the stretching vibration mode of the carbonyl group (C=O), the stretching vibration mode of aromatic C=C bonds, the stretching vibration mode of epoxy and the C–O bond, respectively. These results show that the type of oxygen-containing functional groups in graphene oxide did not change after ageing. Because of the desorption of surface water, the infrared vibration peak caused by OH bending vibration and aromatic C=C bonds close to 1622 cm–1 was red-shifted to 1585 cm–1.Figure 1ectively . The infectively . The infH2 group ,28,29, an of CH3 . Comparec shows the Raman spectrum of graphene oxide. The D band (1349 cm–1) of graphene oxide is the respiration mode of A1g symmetric k-point phonons, originating in the graphite region with a small domain size, which is believed to reflect the local defects and disorder of the crystal lattice, particularly the defects and disorder at the edges of the graphite sheet. The G band (1597 cm–1) is usually attributed to the first-order E2g phonon plane vibration of sp2 hybridized carbon atoms, which corresponds to the plane stretching of the graphite lattice [ID/IG ratio, the greater the defect density of graphene oxide; however, as per studies by Cancado et al. [et al. [ID/IG value of graphene oxide increased from 0.87 to 0.92. When the average defect distance LD is less than 4 nm, materials with additional defects have a smaller value of ID/IG. During the ageing progress, the formation of CO2 created vacancy defects on the basal plane of graphene oxide; however, the reduction in the number of oxygen-containing functional groups was considerably greater than the formation of vacancy defects, which makes the average defect distance (LD) of GO-a; i.e. the defect density decreased.Figure 1 lattice . Many reo et al.  and Lucc [et al. , this via,c shows the morphology of GO and GO-a, respectively. Graphene oxide had an irregular flake structure; after ageing, the size of graphene oxide decreased. The research results of Acik et al. demonstrated that the epoxy groups on graphene relax carbon bonds when they are arranged during the oxidation process. Ether groups are generated when the epoxy groups decompose, leading to the unzipping of graphene oxide [Figure 2b,d shows the thickness curves of GO and GO-a, respectively. Previous studies reported that the thickness of single-layer graphene oxide ranges from 0.5 to 1.2 nm. Therefore, the average thickness of GO (1.320 nm) and GO-a (1.134 nm) in figure 2b,d may belong to two-layer graphene oxide; i.e. the thickness of single-layer GO was 0.660 nm, which decreased to 0.567 nm of GO-a after ageing. The decrease in sheet thickness can be attributed to the desorption of oxygen functional groups.Figure 2α anode. The corrected binding energy of C 1s was 284.6 eV. Figure 3a shows the survey XPS spectra of graphene oxide. The intensity of the O 1s peak of GO-a was significantly lower than that of GO. After ageing, the C/O ratio of graphene oxide increased from 1.96 to 2.67; the results revealed that graphene oxide lost part of its oxygen-containing groups at 25°C and humidity of not greater than 70%. These results correspond to the XRD test.The surface elements of graphene oxide were characterized by a ULVAC PHI Quantera II XPS with an Al Kb shows a magnified view of the box in figure 3a. The peaks close to 231 and 168 eV are attributed to the 2s and 2p peaks of sulfur, respectively. Figure 3c,d present the C 1s and O 1s spectra of graphene oxide after deconvolution, respectively. After deconvolution of the C 1s spectrum of graphene oxide, there are five peaks close to 284.5 ± 0.1, 285.4 ± 0.2, 286.5 ± 0.3, 287.6 ± 0.2 and 288.9 ± 0.3 eV, corresponding to the sp2 hybrid carbon atom , sp3 hybrid carbon atom , hydroxyl/epoxy/ether , carbonyl/quinone  and carboxyl group/ester/anhydride  [Figure 3 purple) , respect2 carbon atoms significantly increased, while the relative content of C–O significantly decreased. This indicates that a large amount of oxygen-containing functional groups is desorbed to increase the relative content of aromatic domains. This result agrees with that observed in the XRD and Raman spectra. The relative content of sp3 carbon atoms decreased from 12.11 to 8.29%, indicating that the content of aliphatic carbocyclic structures decreased, which is consistent with the weakening of the corresponding CH2 spectral intensity in the FTIR spectra. After ageing, the relative content of C=O decreased from 6.48 to 3.73%, and the relative content of O=C–O increased from 3.80 to 5.48%. The relative area of the sub-peak of the O 1s peak of GO-a and the absolute content of all oxygen-containing functional groups decreased.After ageing, the C 1s spectrum of graphene oxide significantly changed and the peak area of the O 1s spectrum was significantly reduced. The content ratio of each group can be estimated as per the area of the secondary peak . Table 1et al. [2. Another study by Zhou & Bongiorno revealed that the reaction of C–H with epoxide and hydroxyl occurs easily. They argued that these reactions may be attributed to the metastable properties of multilayer extended graphene oxide. This conclusion agrees with the lack of a C–H absorption peak in the infrared spectrum of GO-a [Zhou & Bongiorno  demonstret al.  demonstr of GO-a .−1, as illustrated in figure 4a. The thermal decomposition of graphene oxide was a disproportionation reaction. During the thermal decomposition process, a part of C atoms was oxidized to escape in the form of CO2 or CO, while another part of C atoms was reduced. Calculating the second derivative of the TG curve to identify the corresponding inflection points of the curve could be used to roughly divide the decomposition stages of the sample. The thermal decomposition process of GO after preparation could be divided into four stages. The first stage was from 50°C to 112°C, the mass loss in this stage was approximately 5.92%, which was primarily attributed to the volatilization of adsorbed water. The second stage was from 112°C to 215°C. The weight loss in this stage was approximately 67.52%, which was mainly due to the desorption of oxygen-containing groups. The third stage was from 215°C to 522°C, and the weight in this stage first increased by approximately 1.93% (to approx. 323°C) and then decreased by approximately 3.72%. This may have been attributed to the adsorption of free radicals during the decomposition process and subsequent depth decomposition of intermediate products of graphene oxide. The fourth stage was from 522°C to 900°C. The weight in this stage first increased by approximately 2.76% (to 532°C) and then decreased by approximately 1.26%. This may reflect the process of adsorption and desorption of trace O2 in the protective gas.Thermogravimetric (TG) curve of graphene oxide was obtained at a heating rate of 20°C min−1. The third stage is from 195°C to 365°C, and the weight loss was approximately 1.50%. The fourth stage was from 365°C to 900°C, in which the weight first increased by 0.45% (to approx. 377°C) and then decreased by 8.74%, which may be related to the free decomposition process. The adsorption of radicals and the decomposition of carbon atoms caused by the reaction with trace oxygen in the carrier gas under high-temperature conditions were related. Zhang et al. [The thermal decomposition process of GO-a was similar to that of GO and could be roughly divided into four stages. The first stage was from 50°C to 147°C, and the weight loss was approximately 11.39%, primarily because of the volatilization of adsorbed water and the desorption of oxygen-containing functional groups. The second stage was from 147°C to 195°C, and the weight loss was approximately 50.82%. In this stage, the GO-a was violently decomposed to produce a large amount of gas, which blew away certain samples from the crucible. This occurred in the TG test of GO at a heating rate of 20°C ming et al.  reportedb shows the relationship between gas generated during the thermal decomposition of graphene oxide and temperature. After ageing, the infrared signal of gas released by the thermal decomposition of graphene oxide was significantly weaker, indicating that the amount of gas produced was smaller. The peak temperature of gas generated by the decomposition of GO-a was close to 195°C; however, that of GO was close to 216°C. This demonstrates that ageing can reduce the thermal decomposition temperature of graphene oxide, which is consistent with the phenomenon reflected by the TG curve. Interestingly, before the violent decomposition of graphene oxide, the infrared absorption intensity of the gas produced by the decomposition of graphene oxide briefly decreased. As illustrated in the inset of figure 4b, for GO, this temperature point appears close to 210°C, while the corresponding temperature point for GO-a appears close to 188°C.Figure 4a,b). Figure 5a shows that the gaseous decomposition products of graphene oxide exhibit a strong infrared absorption signal that corresponds to the severe weight loss during decomposition. As the temperature increases, the gaseous products produced by the decomposition of graphene oxide lead to a weak, continuous gaseous infrared absorption signal. This indicates that the thermal decomposition process of graphene oxide is a continuous and uninterrupted process when the temperature increases. The thermal decomposition process of GO-a illustrated in figure 5b is similar to that of GO. Both GO and GO-a are violently decomposed close to 200°C and maintain weaker decomposition under high-temperature conditions. The difference is that the temperature corresponding to the violent decomposition of GO-a is 195°C, whereas the temperature corresponding to that of GO is 216°C.To examine the thermal decomposition process of GO and GO-a more comprehensively, contour plots were drawn of the infrared absorption signals of the decomposition products a,b. Figua, the decomposition products of GO at 216°C exhibit several obvious infrared absorption signals, which correspond to the infrared absorption of water vapour between 1400–1750 and 3483–3759 cm–1; the infrared absorption peaks of CO2 close to 671, 1377 and 2358 cm–1; and the infrared absorption peaks of CO close to 2115 and 2183 cm–1 [figure 6b exhibits infrared absorption bands of CO2, CO and water vapour at the corresponding positions during the thermal decomposition process at 195°C. Furthermore, it is difficult to completely remove sulfur during the preparation of graphene oxide. It is possible that the weak infrared absorption band close to 1168 cm–1 in figure 6a,b corresponds to SO2 [figure 6a,b correspond to the infrared absorption signals of water vapour. The infrared absorption intensity of water vapour in figure 6a is obvious, while that in figure 6b is only obvious at the peak temperature (197°C). This indicates that the decomposition of GO produces more water vapour than the decomposition of GO-a.The three-dimensional infrared absorption spectrum of the gaseous decomposition products can be analysed by the temperature profile to examine the gas types at different temperatures, as illustrated in figure 6183 cm–1 –43. Simis to SO2 . The gre2 (2358 cm–1), CO (2115 cm–1), water vapour (1509 cm–1) and SO2 (1168 cm–1). As per the Lambert–Beer law, infrared absorption intensity is positively correlated with substance content. Figure 7a–d presents the changes in infrared absorbance of CO2, CO, water vapour and SO2 with temperature, respectively. As illustrated in figure 7a–d, the CO2, CO, water vapour and SO2 generated by the decomposition of graphene oxide all have peaks during the violent decomposition, regardless of whether the sample has been aged. The difference is that the content of CO and water vapour generated by the decomposition of GO-a is slightly lower than that of GO, and the amount of SO2 generated is considerably lower than that of GO; however, the amount of CO2 generated does not appear to change.To determine the relationship between the gaseous product content and temperature more accurately, infrared signals with greater intensity and less interference were selected to represent the corresponding gaseous products, such as COe, GO can be decomposed to produce CO2 at 60°C, and the amount of production increases with an increase in temperature. In figure 7f, there is little difference between the infrared absorption intensity at 50°C and 60°C. The infrared intensity of CO2 increases at 70°C, which indicates that GO-a may generate CO2 close to 70°C. The corresponding temperature of GO is approximately 60°C.In the middle-temperature section (greater than 300°C), almost no water vapour generated by the GO-a decomposition is observed. Compared with GO-a, GO contains more C-H and generates additional water vapour during the decomposition process. Furthermore, it decomposes to generate water vapour when the temperature is greater than 300°C. The decrease in CO content generated by the decomposition of GO-a may be related to the decrease in oxygen content. As illustrated in figure 7−1) differential scanning calorimetry tests were performed on GO and GO-a, as illustrated in figures Ea) was calculated by the Kissinger formula. The results demonstrate that after ageing, the apparent activation energy (Ea) of graphene oxide decreased from 150 to 134 kJ mol−1.To clarify the influence of ageing on kinetic parameters of the thermal decomposition of graphene oxide, non-isothermal (5, 10, 15 and 20°C min. 42 produced at different temperatures, it could be inferred that graphene oxide may have begun to decompose at approximately 70°C. When the temperature exceeded 100°C, the weight loss rate of GO-a was significantly accelerated.The results presented in this study suggest that the aliphatic hydrocarbon groups and oxygen-containing functional groups in the graphene oxide structure are degraded during long-term ageing. The average interlayer spacing of graphene oxide decreased from 0.660 to 0.567 nm. The process of desorption of physically adsorbed water in the graphene oxide interlayer during the ageing process caused the average layer spacing of graphene oxide to decrease. The desorption of water and oxygen-containing functional groups resulted in the appearance of graphene sheets with a lower degree of oxidation, which appeared as a broad peak at 21.04° in the XRD pattern. During ageing, the types of graphene oxide functional groups did not significantly change, but the proportion changed. The content of oxygen-containing functional groups significantly decreased. By comparing the amount of CO−1. Thus, after reaching a certain time limit, graphene oxide may not be able to meet the requirements of users. Because light affects the decomposition of graphene oxide [The results of thermal analysis–mass spectrometry indicated that the ion current intensity peak of OH radicals appeared at approximately 190°C during the decomposition of graphene oxide, and it then began to violently decompose at approximately 195°C. The lower decomposition temperature of graphene oxide limits its application under higher temperature conditions. The graphene oxide underwent slow structural changes when stored at room temperature, and the structural changes led to changes in the physical and chemical properties. Prolonged contact with moisture in the air caused the activation energy of graphene oxide to decrease from 150 to 134 kJ molClick here for additional data file.


Neuroimaging has revealed robust interactions between the prefrontal cortex and the hippocampus when people stop memory retrieval. Efforts to stop retrieval can arise when people encounter reminders to unpleasant thoughts they prefer not to think about. Retrieval stopping suppresses hippocampal and amygdala activity, especially when cues elicit aversive memory intrusions, via a broad inhibitory control capacity enabling prepotent response suppression. Repeated retrieval stopping reduces intrusions of unpleasant memories and diminishes their affective tone, outcomes resembling those achieved by the extinction of conditioned emotional responses. Despite this resemblance, the role of inhibitory fronto-hippocampal interactions and retrieval stopping broadly in extinction has received little attention. Here we integrate human and animal research on extinction and retrieval stopping. We argue that reconceptualising extinction to integrate mnemonic inhibitory control with learning would yield a greater understanding of extinction’s relevance to mental health. We hypothesize that fear extinction spontaneously engages retrieval stopping across species, and that controlled suppression of hippocampal and amygdala activity by the prefrontal cortex reduces fearful thoughts. Moreover, we argue that retrieval stopping recruits extinction circuitry to achieve affect regulation, linking extinction to how humans cope with intrusive thoughts. We discuss novel hypotheses derived from this theoretical synthesis. Thoughts and emotions, like actions, often need to be controlled. For example, when reminded of an unpleasant event, people often try to reduce distress by limiting awareness of the unwanted memory. Attempts to limit awareness are often achieved by retrieval stopping, an intentional effort to terminate the retrieval of content elicited by the reminder. Over the last two decades, evidence has mounted that the prefrontal cortex plays a key role in retrieval stopping. Retrieval stopping recruits a prefrontally-mediated inhibitory control mechanism that, over repeated exposures to reminders, suppresses unwanted traces, rendering them less intrusive, less unpleasant, and less likely to be retrieved –3. RetriIn the last two decades, knowledge about the neural mechanisms of fear extinction also has rapidly expanded –13. ExtiOur central thesis asserts that extinction recruits prefrontally-mediated inhibitory control mechanisms to suppress retrieval, contributing to the affective adaptations normally observed. Consider an example of retrieval suppression, reported by an acquaintance of the first author. One afternoon, the acquaintance was driving down a pleasant residential street when a boy ran out from behind a bush to retrieve a ball before it rolled into the road. Panicking, the friend nearly wrecked his car, even though there was no danger of hitting the boy. He overreacted because the boy’s abrupt appearance reminded him of roadside bomb attacks from his time in the Iraq war. In the ensuing seconds, he suppressed the unpleasant reminding, enabling him to calm down and resume driving. After being home from Iraq for several months, situations like this rarely reminded him of roadside attacks. This illustrates how cues can elicit unwelcome memories automatically, and how important it is to have the ability to stop such intrusions in the immediate and longer-term.This incident illustrates the kind of example researchers have in mind when arguing for extinction’s relevance to post-traumatic stress disorder (PTSD). Yet, a key aspect of the person’s coping behavior that goes unacknowledged in the extinction analogy is their effort to control retrieval of the intrusive thought through retrieval stopping. The person not only needed to adjust their emotional state, but also how their memories responded to environmental cues, a situation referred to as a memory adaptation problem . If peopTo illustrate the analogy, consider the structural similarities between action and retrieval stopping on the one hand, and extinction on the other Fig. . During The extinction of conditioned fear resembles action and retrieval stopping. On each trial, the person attends to a stimulus that has become associated to a threat (the conditioned stimulus), leading them to experience fear, and make defensive responses . On initial trials, these responses are appropriate, given the prior pairing of the conditioned and unconditioned (aversive) stimuli. But repeated exposures without the unconditioned stimulus make it clear that the conditioned stimulus no longer predicts threat. Correspondingly, the fear reaction ceases to be useful and so it declines. The formal similarity to stopping is unmistakable: in extinction learning, as with action and retrieval stopping, repeated exposures to a cue reduce its tendency to elicit the associated response, under conditions designed to communicate that the response is not needed. Extinction differs from stopping, however, in the presumed intentionality of the process reducing the response. Whereas retrieval and action stopping are accompanied by an explicit goal to terminate retrieval or action production, in extinction, no stopping instruction occurs; rather, any decision about the wisdom of stopping the emotional response, if it occurs, is left to the individual, based on their detection, over exposures to the stimulus, of a decrease in the likelihood of threat.Some differences among these three situations are more apparent than real. For example, because extinction training provides no overt stopping cue, declines in emotional responding with repetition are easily construed as reflecting an automatic learning process that incrementally adjusts the cue-response association. This interpretation may seem desirable because extinction arises in animals, for which many might be reticent to attribute intentionality; indeed, extinction in invertebrates indicates that some cases of extinction may reflect automatic processes. But all manifestations of extinction need not be painted with the same brush, simply because stopping is not signaled by the task. Absence of stopping cues is a poor reason to assume intentional stopping does not occur. Overt stopping cues rarely exist when action or retrieval stopping occurs “in the real world”; rather, the impetus to stop comes from changes detected by the organism. Stopping oneself from catching a hot pot is initiated by perceptual feedback indicating a threatening outcome; and stopping retrieval of an unwelcome thought begins when we detect a memory or thought that threatens our emotional state. Gradual declines in either conditioned responding or intrusive thoughts may reflect increasing certainty that these responses are no longer relevant and greater willingness to stop them. Under natural conditions, then, extinction and stopping do not intrinsically differ in overt cuing or their apparent intentionality.Extinction also appears to differ in the content that is controlled, with a strong focus on fear and threat responses. Here again, these differences are more apparent than real. For example, as the example of the Iraq war veteran suggests, episodic memories for conditioning experiences may accompany affective learning and be elicited by conditioned stimuli, at least for mammals with intact hippocampi. Indeed, in human participants, fear conditioning can lead people to develop intrusive images as conditioned responses –19, consGiven the foregoing parallels, it becomes important to consider whether retrieval suppression contributes to extinguishing conditioned fear. In our retrieval stopping model, we introduce several assumptions that bridge these phenomena. First, we assume that organisms find fear aversive for several reasons. Fear responses  can interfere with rewarding or survival-relevant pursuits such as seeking food, shelter, or mates. Fear states also undermine or bias high-level processes, such as planning, decision making, and behavioral control that are important for pursuing goals , 22. FinBehavioral and imaging studies usually measure retrieval stopping with the Think/No-Think paradigm . This pResearch using the TNT procedure shows that people can stop retrieval. Several notable effects support this conclusion. First, retrieval stopping abolishes the benefits of reminders on memory, as reflected by often large differences between Think and No-Think items on delayed tests Fig. . Indeed,Beyond its effects on explicit memory, repeatedly presenting a cue and stopping retrieval gradually reduces the memory’s tendency to intrude and people’s affective responses to the suppressed content. Because intrusions are, by nature, involuntary retrievals yielding no observable behavior, they must be probed with self-reports. For example, many studies identify intrusions by asking, after each No-Think trial, whether the cue elicited the memory, despite participants’ efforts to stop retrieval. Studies often collect these reports during an fMRI or EEG protocol to determine whether they identify distinct neural mechanisms of the intrusion response . Remarkably, whereas participants commonly experience involuntary intrusions on early trials , their incidence declines robustly after repeated stopping, showing proportional reductions of nearly 50% , 33–38. fMRI studies have revealed contributions of the prefrontal cortex to retrieval stopping, and how this region modulates brain activity relating to memory and emotion. Here we summarize which prefrontal regions suppression engages, and under what conditions, and evidence for how it modulates the hippocampus and the amygdala.To isolate prefrontal regions involved in retrieval stopping, neuroimaging studies have scanned participants as they performed the Think/No-Think task. All trials in this task present a cue from a trained pair. Some trials ask participants to retrieve the paired item , whereas others cue them to stop retrieval , typically signaled by green or red colored task cues, respectively. Regions more activated during No-Think than Think trials reflect recruitment of stopping-related processes above and beyond processes involved in cue processing and retrieval, as reflected in increased blood-oxygenation-level-dependent signal . Retrieval stopping studies have examined word pairs, face-scene pairs, word-scene pairs, word-face, and word-object pairings of both neutral and negative valence , 3, 29. Retrieval stopping engages right-lateralized prefrontal regions, including dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), posterior middle frontal gyrus (pMFG), and bilateral insula. Amongst these, the most extensive activations arise in right DLPFC, which often extend the anterior–posterior length of the MFG, in a region spanning Brodmann’s areas (BA) 9 and 46. Anteriorly, the right DLPFC activation usually extends into the posterior aspect of BA10, bordering 9/46. Indeed, DLPFC activations sometimes are restricted to this anterior BA9/46/10 area, suggesting it is a key DLPFC region of retrieval stopping ).Another benefit of the retrieval stopping model lies in its potential to forge stronger connections between translational neuroscience on extinction and the human psychiatric conditions it is intended to address. An uncomfortable and not always acknowledged disconnect exists between research on extinction and disorders such as PTSD and anxiety: Whereas extinction is thought to model how experience changes fear responses to conditioned stimuli, the real symptoms faced by patients also include intrusive memories, thoughts, and images, the content of which distress the patient , 147. InOne final implication of the retrieval stopping model concerns its consequences for clinical treatment. There is enthusiasm for the value of extinction learning as an experimental model of processes that might be deficient in clinical populations suffering from anxiety, PTSD and OCD , 148–150Although support for the retrieval stopping model is promising, further evidence is needed to evaluate its predictions, and to elaborate the mechanisms enabling the prefrontal cortex to regulate hippocampal activity. To encourage work on this hypothesis, we discuss areas of development in animal and human studies that could evaluate this model’s potential in translating foundational neuroscience to mental health.One area for development in animal research is to determine whether IL PFC exerts an inhibitory influence over hippocampal activity during extinction learning, and if so, whether it suppresses neurons representing the original conditioning event. Work by Lacagnina et al.  shows thFuture research also should compare retrieval stopping and extinction in humans more precisely, using imaging and behavioral experimentation. Is the rDLPFC region engaged during extinction also involved during retrieval stopping? Moreover, although the hippocampus shows reduced activity during extinction learning, as does retrieval stopping, we do not know whether these reductions are causally related to rDLPFC activity, an issue that could be examined with effective connectivity analyses. If the hippocampus is downregulated, does this disrupt memory for the conditioning events, as retrieval stopping predicts? Translational implications can also be examined. For example, if extinction spontaneously engages retrieval stopping, can the impact of extinction be improved by instructing people to stop retrieval during extinction trials, ensuring that it always contributes (cf. )? To whaWhen organisms stop actions, the result is visible to the naked eye; but when they stop thoughts or feelings, the outcome is not observable. This simple difference contributes to vastly different theoretical traditions in neuroscience relevant to stopping actions and thoughts. On one hand, the objective reality of response stopping has triggered a vast and detailed neurobiological account of how organisms stop actions, centered around constructs such as inhibitory control, which posit a dynamic, goal-related interruption of neural processes underlying action production. On the other hand, neuroscience paradigms such as extinction, are not conceptualized as stopping, even though incentives for organisms to stop memories and affect are ubiquitous. Rather, accounts of extinction focus on simple learning mechanisms applied incrementally over extinction trials. That conditioned emotional responses decrease gradually over unreinforced repetitions of a CS without special instructions or demands, in organisms simple and complex invites a basic learning account, especially given the understandable reticence to assign intentionality and control to animals. But if species such as mice and rats can engage the prefrontal cortex to stop their actions, why would they not be able to do so for affective and cognitive responses, especially when the persistence of those responses may be detrimental?Here we built a case for the value in reconceptualizing extinction as inhibitory control over memory. We argued that extinction naturally recruits retrieval stopping to downregulate activity in the hippocampus and amygdala during extinction trials and gradually adapts mnemonic responses to conditioned stimuli. In essence, extinction reflects the organism solving a memory adaptation problem . Accordi


A repeat expansion in C9orf72 is the major cause of both frontotemporal dementia and amyotrophic lateral sclerosis, accounting for approximately 1 in 12 cases of either disease. The expansion is translated to produce five dipeptide repeat proteins (DPRs) which aggregate in patient brain and are toxic in numerous models, though the mechanisms underlying this toxicity are poorly understood. Recent studies highlight nucleocytoplasmic transport impairments as a potential mechanism underlying neurodegeneration in C9orf72-linked disease, although the contribution of DPRs to this remains unclear. We expressed DPRs in HeLa cells, in the absence of repeat RNA. Crucially, we expressed DPRs at repeat-lengths found in patients (> 1000 units), ensuring our findings were relevant to disease. Immunofluorescence imaging was used to investigate the impact of each DPR on the nucleus, nucleocytoplasmic transport machinery and TDP-43 localisation. DPRs impaired the structural integrity of the nucleus, causing nuclear membrane disruption and misshapen nuclei. Ran and RanGAP, two proteins required for nucleocytoplasmic transport, were also mislocalised in DPR-expressing cells. Furthermore, DPRs triggered mislocalisation of TDP-43 to the cytoplasm, and this occurred in the same cells as Ran and RanGAP mislocalisation, suggesting a potential link between DPRs, nucleocytoplasmic transport impairments and TDP-43 pathology. The expanded region of C9orf72 is transcribed to produce long, repetitive G4C2 RNA transcripts which form intraneuronal nuclear foci in patient brain and sequester RNA-binding proteins1. The repeat region is also translated via unconventional repeat-associated non-ATG translation to generate five distinct dipeptide repeat proteins (DPRs): glycine–alanine (GA), glycine–arginine (GR), proline–arginine (PR), alanine–proline (AP) and glycine–proline (GP). These peptides form inclusions in patient brain4 and are highly toxic in various cell culture models8, as well as in Drosophila10, zebrafish11 and mice16. However, the mechanisms underlying DPR toxicity are unknown.Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are two devastating neurodegenerative diseases with considerable clinical, genetic and pathological overlap. A large G1. TDP-43 is mislocalised from the nucleus to cytoplasm where it becomes abnormally hyperphosphorylated and forms insoluble intraneuronal inclusions. TDP-43 pathology correlates well with neuronal loss in C9FTD/ALS, leading to the proposal of an “amyloid-like cascade” hypothesis of neurodegeneration, whereby the C9orf72 expansion produces RNA foci and DPRs which somehow trigger TDP-43 pathology and this causes neuronal death17. However, the link between RNA foci/DPRs and TDP-43 mislocalisation is not well established, and there are likely multiple mechanisms through which products of the C9orf72 expansion impair cellular function which converge to cause neurodegeneration in FTD/ALS.In addition to DPR pathology, the FTD/ALS-related protein TDP-43 (transactive response DNA-binding protein 43) also aggregates in C9orf72-positive patient brain22. These studies led to the novel hypothesis that the C9orf72 expansion causes neurodegeneration by disrupting nucleocytoplasmic transport, with a wide range of downstream consequences for cellular processes necessary for normal function and survival. However, the exact mechanisms underlying nucleocytoplasmic transport impairments in C9FTD/ALS remain unclear. Much of the literature published to date utilises models which express pure G4C2 repeat sequences, and therefore all five DPRs as well as G4C2 repeat RNA will be produced in those models, rendering it impossible to determine the specific cause of any observed phenotypes. Furthermore, those models which use alternative-codon sequences to express DPRs in the absence of G4C2 repeat RNA do so at extremely short repeat-lengths which are not found in patients. We have previously shown that repeat-length determines the impact of DPRs on cellular function and toxicity, with longer repeats causing more severe phenotypes11. As such, it is essential to model DPR pathology at repeat-lengths found in patients wherever technically possible, to ensure relevance to human disease.Recent literature has highlighted defects in nucleocytoplasmic transport as an important feature of C9FTD/ALS, with large-scale genetic screening studies identifying a number of key nucleocytoplasmic transport genes as modifiers of toxicity in fly and yeast models4C2 repeat-RNA, to investigate the specific impact of each dipeptide on the nuclear membrane and nucleocytoplasmic transport machinery. We also investigated the link between C9orf72 dipeptides and TDP-43, which is mislocalised to the cytoplasm in the brain of expansion-carrying patients. Crucially, we used previously characterised constructs8 to express each DPR at long repeat-lengths which are known to be found in patients (> 1000 units), ensuring our findings are relevant to human disease.Here we used alternative-codon sequences for expression of DPRs in the absence of G1020, GR1136, PR1100, AP1024 or empty pEGFP-N1 vector as a GFP-only control. Cells were stained with DAPI to visualise the nucleus and an antibody against lamin-B1, a key component of the lamin matrix which forms the nuclear membrane. A large proportion of cells containing GA, GR or PR inclusions had misshapen nuclei, indicating that DPRs cause structural abnormalities in the nucleus. Rather than forming smooth, rounded structures, nuclei were excessively folded to form “horseshoe” shapes or cause severe creasing in the nuclear membrane .Immunofluorescence imaging was performed on HeLa cells transfected with GAP = 0.0052), and 40.6% for PR (P = 0.0096) compared to 15.7% for GFP-only control (n = 3). Lamin B1 staining of cells containing GA, GR or PR inclusions demonstrated that the lamin matrix had a frayed appearance, suggesting that DPRs caused fragmentation of the nuclear membrane , 62.6% for GR (P = 0.0006), and 50.7% for PR (P = 0. 0071) compared to 22.2% for GFP-only control (n = 3). AP1024 did not cause structural damage to the nucleus, with only 22.5% of AP-transfected cells exhibiting misshapen nuclei (P = 0.6797) and 33.0% exhibiting nuclear membrane fragmentation .43.1% for GR (P = 0.0025), indicating that GR caused mislocalisation of Ran to the cytoplasm. Accumulation of Ran in the cytoplasm was also observed in cells containing GA inclusions, however this occurred less frequently (36.0% of cells) and was not statistically significant , although the severity of this phenotype varied between peptides with arginine-rich dipeptides causing RanGAP accumulation most frequently. The severity of RanGAP mislocalisation was also significantly different between most of the DPR treatments , 37.6% (P = 0.0011) and 29.5% (P = 0.0349) respectively. AP did not cause TDP-43 mislocalisation, with only 18.7% of staining contained within the cytoplasm . Statistical comparisons between each DPR treatment are shown in Table Since DPR expression caused both structural damage to the nucleus and nuclear membrane and disruption to key components of the system that powers nucleocytoplasmic transport, we next investigated whether TDP-43, a nuclear protein known to be mislocalised to the cytoplasm in C9FTD/ALS patient brain, was affected by DPR expression. HeLa cells were co-transfected with an expression construct for mCherry-tagged human TDP-43 and GFP-tagged DPRs or empty pEGFP-N1 control. Immunofluorescence imaging was used to determine whether DPR expression affected the nuclear localisation of TDP-43. Figure P = 0.0044; GR 81.9%, P = 0.0003; PR 71.5%, P = 0.0009), indicating that these two phenotypes occur alongside each other. The vast majority of cells exhibiting TDP-43 mislocalisation also exhibited granular RanGAP staining: 85.6% of cells for GA (P < 0.0001), GR 85.5% (P < 0.0001) and PR 82.1% (P < 0.0001). Therefore, both components of the Ran cycle were mislocalised in cells with DPR-induced TDP-43 mislocalisation to the cytoplasm, highlighting the possibility that defects in the nucleocytoplasmic machinery could be the connection between DPRs and TDP-43 pathology.In order to determine whether the mislocalisation of TDP-43 observed could be linked to DPR-induced disruption to the nucleocytoplasmic transport machinery, we next investigated whether these two things occurred together. Since not all cells containing DPR inclusions exhibited mislocalisation of either Ran, RanGAP or TDP-43, immunofluorescence imaging was performed to determine whether these phenotypes occurred in the same cells. HeLa cells were co-transfected with the mCherry-TDP-43 and DPR-GFP constructs, and a Cy5-conjugated antibody was used to visualise either Ran or RanGAP in these cells , and therefore it was not possible to determine whether this phenotype was caused by DPRs or repeat RNA23. Here, we demonstrate that breakdown of the nuclear membrane is caused by DPRs, specifically GA, GR and PR but not AP. Our finding is supported by a recent report that staining for both lamin-B1 and lamin-A/C was fragmented in a transgenic mouse expressing PR5013. Destabilisation of the lamin matrix which forms the nuclear membrane is likely to have detrimental consequences for cellular function and survival in a number of ways. Firstly, impaired structural integrity of the membrane is likely to impact the transport of proteins and other cargoes across it, leading to nucleocytoplasmic transport defects and perhaps breakdown of the barrier which maintains separation of the nuclear and cytoplasmic compartments. Secondly, lamins have several crucial functions besides supporting the structural integrity of the membrane, including roles in DNA repair, chromatin remodelling, transcription and cell migration25. Furthermore, loss of function mutations in lamins are known to trigger ER stress and activate the unfolded protein response, leading to premature apoptosis27. It is therefore possible that DPR-induced mislocalisation of lamins directly contributes to neurodegeneration in C9FTD/ALS.We have shown that DPRs cause structural damage to the nucleus and fragmentation of the nuclear membrane when expressed at disease-relevant repeat-lengths of over 1000 units. Nuclear membrane fragmentation was previously reported in two 28. Ran and RanGAP mislocalisation have been previously reported to occur in (G4C2)30 flies and human patient iPSC-derived neurons20, however, as with the nuclear membrane fragmentation study, it was not possible to determine the specific cause of this phenotype since these models express G4C2 repeat RNA as well as all five DPRs. We have shown that both phenotypes were caused by DPRs, with all DPRs causing RanGAP to accumulate in the nucleus and primarily GR causing Ran mislocalisation to the cytoplasm. RanGAP puncta have been previously observed in brain tissue from mice expressing GA50, supporting our findings14. Of note, punctate accumulations of RanGAP are also present in post-mortem brain tissue from C9FTD/ALS patients20, demonstrating the relevance of our findings to human disease. Mislocalisation of these key proteins is likely to cause a loss of function phenotype, resulting in nucleocytoplasmic transport impairments. This is supported by genetic screening studies in Drosophila expressing (G4C2)30 which found that overexpression of RanGAP rescued toxicity in this model, while RanGAP knockdown worsened the phenotype20. Deletion of Ran or RanGAP also increased toxicity in separate fly models expressing (G4C2)5823 and PR5021, highlighting the importance of the Ran cycle in cell survival. Combined with our finding that DPRs impair the structural integrity of the nuclear membrane, our data suggests that DPRs are the driving factor causing nucleocytoplasmic transport defects in C9FTD/ALS. Of particular note, the different DPRs had distinct effects on the nucleocytoplasmic machinery, despite their previously demonstrated toxicity in cell culture models 8. This demonstrates that the observed phenotypes were not a by-product of DPR toxicity causing cell death through some other mechanism. Functional assays to investigate of the impact of DPR-induced Ran cycle defects on nucleocytoplasmic transport would be beneficial to fully understand their contribution to disease.We have also shown that DPRs differently impact the localisation of two proteins required for the Ran cycle: the small GTPase, Ran, and its activator protein, RanGAP. The Ran cycle is an essential process which provides the energy required for active transport of proteins across the nuclear membrane via hydrolysis of RanGTP to RanGDP, creating a concentration gradient across the nuclear membraneDrosophila expressing either pure G4C2 repeats or alternative-codon DPR sequences encoding GA64, GR64 or PR6429. GR200 has also recently been found to cause mislocalisation of TDP-43 in mice expressing GR20030, further supporting our findings. GA100 also caused cytoplasmic aggregation of phosphorylated TDP-43 in human neuroblastoma cells31. Interestingly, this study found that neither GR100 nor PR100 caused mislocalisation of TDP-43, indicating that longer repeats may be required to cause a phenotype in this in vitro model. Indeed, the authors note that GA triggered the formation of TDP-43 inclusions in a length-dependent manner, and we have previously shown that DPR repeat-length is strongly correlated with toxicity in human cell culture and zebrafish11.Since the nucleocytoplasmic transport machinery was impacted by DPR expression in our model, we next investigated potential downstream consequences of this by assessing the localisation of TDP-43. TDP-43 is a predominantly nuclear RNA-binding protein which is mislocalised to the cytoplasm in C9FTD/ALS patient brain, where it aggregates to form insoluble inclusions. We found that expression of GA, GR and PR, but not AP, all caused TDP-43 to be mislocalised to the cytoplasm, demonstrating a functional link between these disease proteins. This is supported by a recent report of TDP-43 mislocalisation to the cytoplasm in 4C2)30 fly model and in iPSC-derived neurons from expansion-carrying patients20, and our findings suggest that this was most likely triggered by DPRs. Nuclear depletion of Ran has also been reported to occur alongside TDP-43 mislocalisation to the cytoplasm in a GRN−/− mouse model of non-C9FTD, preceding neuronal loss in the retina32. Impaired nucleocytoplasmic transport may therefore be a common mechanism leading to TDP-43 mislocalisation and neurodegeneration in different subtypes of FTD.We found that DPR-induced TDP-43 mislocalisation occurred alongside DPR-induced Ran and RanGAP mislocalisation, implying that dysfunction of the nucleocytoplasmic transport machinery and TDP-43 pathology could be linked in DPR-expressing cells, although further work would be required to definitively confirm this. Ran and TDP-43 mislocalisation also occurred together in the  was used as a template for PCR using the forward primer (5′ GCTCAAGCTTATATGTCTGAATATATTCGGGTA 3′) and reverse (5′ GAGCGGATCCCTACATTCCCCAGCCAGAAGACT 3′) containing HindIII and BamHI sites respectively. After digestion the product was ligated in pmCherry-C1 (Clontech) in the same restriction sites to create the pmCherry-C1 TDP-43 WT construct. All constructs were verified by DNA sequencing.We previously generated and characterised expression of DPR constructs for expression of GA2 in Dulbecco’s Modified Medium (Sigma) supplemented with 10% foetal calf serum (Gibco), 2 mM l-glutamine (Sigma), 100 U/ml penicillin and 100 µg/ml streptomycin (Sigma). Cells were seeded on HCl-treated glass coverslips in 6-well plates overnight and transfected with DPR constructs or empty pEGFP-N1 control vector (800 ng/well) using FuGene HD . Cells were incubated at 37 °C for 48 h with a media change performed at 4 h to reduce DPR toxicity. Co-transfection of DPR and TDP-43 vectors was performed by addition of 500 ng of DPR vector or empty pEGFP-N1 control and 500 ng of TDP-43 vector or empty mCherry control and 9 µl FuGene HD. Cells were incubated at 37 °C for 48 h with a media change 4 h post-transfection.HeLa cells (ECACC #93021013) were maintained at 37 °C with 5% COCells were fixed in 4% paraformaldehyde 48 h post-transfection and permeabilised with 0.05% Triton-X. Primary antibodies were diluted in phosphate buffered saline (PBS) as follows: anti-lamin B1 (Abcam) 1:500, anti-Ran (Abcam) 1:200, anti-RanGAP (Abcam) 1:100. Donkey anti-rabbit AlexaFluor-594, goat anti-mouse AlexaFluor-594 and goat anti-rabbit Cy5 secondary antibodies were used . All antibody incubations were performed at room temperature for 30 min with 3 PBS washes in between. Coverslips were rinsed in MilliQ water and dried overnight before mounting on glass slides using ProLong Diamond Antifade Mounting Media with DAPI (ThermoFisher). All immunofluorescence was performed in triplicate from separate passages of cells.-Apochromat objective and captured using a Coolsnap HQ2 camera (Photometrics) through Micromanager software v1.4.23. Specific band pass filter sets for DAPI, FITC, Texas red and Cy5 were used to prevent bleed through from one channel to the next. Images were processed and analysed using Fiji ImageJ (http://imagej.net/Fiji/Downloads). The relative intensity of antibody staining in the nucleus and cytoplasm was determined by calculating corrected total cell fluorescence (CTCF) values on ImageJ. DAPI staining was used to determine the boundaries of the nucleus.Images were collected on a Zeiss Axioimager.D2 upright microscope using a 63x/1.4 EC PlanP values below 0.05 were considered statistically significant.All experiments were performed in triplicate, with a minimum of 30 cells (chosen from random fields of view based on the presence of GFP-tagged DPR expression) analysed per independent replicate. One-way ANOVAs with Dunnett’s test for multiple comparisons were performed for all experiments using GraphPad Prism. All data generated or analysed during this study are included in this published article.