We investigate ablation of CVD monolayer graphene by femtosecond pulses in the single shot regime. We show that the ablation probability of flat graphene drastically reduces for small illumination diameters even if the ablation threshold is exceeded. However, the presence of graphene wrinkles enhances the ablation probability. This is interpreted in terms of electron and energy diffusion within the graphene layer. This differentiated behavior is a drawback for single shot laser nanopatterning. The morphology of the holes with minimal diameter depends on the fluence distribution at ablation threshold. Strong fluence gradients due to strong focussing produce an explosive folding of graphene during ablation. Chemical vapour deposition (CVD) has emerged as a promising technology to produce graphene for large-scale electronic devices because it can be combined with standard wafer-scale lithographic methods; it is compatible with integrated circuit fabrication processes with low cost and high efficiency. Most graphene-based devices for photonic and optoelectronic applications will require nanopatterning arrays of holes with diameters ranging from 100 nm to 1 µm3.Since its isolation, graphene has attracted attention due to its properties as a material for next generation technologies. Graphene has potential applications in different fields such as optoelectronics, photonics or photovoltaics4, electron beam lithography with reactive ion etching6, UV nanoimprint lithography7, and direct etching with an electron beam in a transmission electron microscope8. Most of these procedures require vacuum and multiple steps, which are difficult to use for mass production of graphene-based devices.Different methods of patterning graphene at nanometer scale have been developed including focused ion beam11.Ultrafast laser processing is a promising technique because it requires no vacuum, it is single step, easily reconfigurable and large areas can be rapidly patterned. In addition, ultrafast pulses generate small heat affected zones in the surrounding materials. Importantly, the non-ablated regions of graphene maintain the pristine structure, which was confirmed by Raman microscopy13. In this context, direct laser ablation of graphene has been investigated in the single shot regime for holes on the order of 1 to 10 µm14, and in the multishot regime for smaller diameters down to below 500 nm16. Nanoribbons with width in the sub 100-nm range have been achieved in thermal accumulation regime with femtosecond pulses17. However, the processing speed was incompatible with industrial scale fabrication.The marked threshold effect of the femtosecond laser ablation process enables sub-spot and even sub-wavelength processing. For instance, hole diameters down to 100 nm have been processed at the surface of transparent materials or in thin films  using ultrashort laser pulses at 800 nm wavelength18.Here, our objective is to characterize ablation characteristics in the single shot regime close to ablation threshold with tight and loose focussing conditions. The single shot regime is a priori more interesting in terms of processing speed. However, in this regime, a recent study of the ablation probability with multiple spots determined a strong decrease of the ablation probability when the beam diameter was decreasing below ~1 µm20 and therefore on electrical and optical properties. Graphene wrinkles can be easily visualized under Scanning Electron Microscopy (SEM)  and 1.6 µm .A technical difficulty to investigate near-threshold ablation, when the targeted crater diameters are in the ~100 nm range is to precisely maintain the focus of the beam on the sampleover-threshold beam diameter as the lateral diameter of the central Bessel beam core over which the local fluence exceeds the ablation threshold, which was previously determined at 139 mJ/cm2 for 130 fs pulses18. This was measured in conditions where the hole diameter exceeds 1 µm and is consistent with literature16. For large diameters, the effect of fluence distribution within the over-threshold spot has a negligible effect, so that different focussing conditions lead to the same damage size if the over-threshold diameter is the same. We will investigate here conditions where the over-threshold diameter is on the order of 1 µm and below.We also need a quantitative element to compare between illumination conditions. Since the fluence distribution is different between the beams, we defined the 24. We used two Bessel beams with different cone angle, i.e. the angle made by the geometrical rays with the optical axis: 26° and 10°. The Bessel beam with 26° cone angle has a diameter of 0.65 µm FWHM and its Bessel zone extends over ~27 µm. The Bessel beam with 10° cone angle has a diameter of 1.6 µm FWHM (ie nearly 2.5 times larger than the previous one), extending over ~75 µm. The graphene sample is CVD-grown graphene monolayer on Corning Eagle-XG AMLCD glass substrate. We stress that the fluences used (<0.72 J/cm2) remained always below the damage threshold of the substrate.  The graphene monolayer was facing the incident beam, placed at the peak of the longitudinal intensity distribution within 2 µm precision.In our experiments, we used an amplified Ti:Sapphire laser system delivering 130 fs pulses at 800 nm. The laser operates at 5 kHz repetition rate and pulse-picking was implemented to operate in single-shot regime. Bessel beams were generated by applying a phase mask onto a Spatial Light Modulator  to generate a virtual axicon. The SLM is associated to an optical demagnification system (1/278 factor). The setup includes spatial Fourier filtering and was described in detail in ref.18 and no statistical difference has been observed between the first and last point of the series.To investigate the ablation statistics, we repeated the illumination 400 times in a matrix, where the sample was moved between each illumination. The illumination sites were determined on SEM images from the positions of holes performed at fluence well above threshold on two lines, on the left and right sides of the matrix as shown in Fig. 2 . But the comparison between the sites reveals that the ablation probability very quickly drops to zero for flat graphene islands, while wrinkles enhance the ablation probability so that it still exceeds 20% at over-threshold beam diameter of 0.6 µm.From 0.95 µm of over-threshold beam diameter and above, the ablation is 100%. The probability rapidly drops for diameters below this value. Importantly, this value is the same as was determined with lower focussing conditions , high gradients are involved, so that free-electron diffusion and/or heat diffusion are non-negligible. This explains the deviations observed in Fig. 13.In the ablation process, what determines the amplitude of the phase transformation is the deposited energy The difference of ablation statistics if the illumination occurs close to a wrinkle or on a flat graphene island can be understood in this framework. Indeed, energy diffusion outside the “over-threshold” area is reduced in the vicinity of a graphene wrinkle because it acts as a barrier of potential for conduction band electrons. Phonon density is also reduced at defects sites. Energy confinement is therefore enhanced by the wrinkle, which explains why ablation is possible with lower amounts of deposited energy than for the case of flat graphene islands. We also note that our study could not discriminate point defects or grain boundaries which are at scales well below what can be observed under SEM. These defects will also produce similar energy confinement and increase of the ablation probability.We finally note that for the smallest holes obtained at 26° cone angle, the surface of the folded structure exactly corresponds to the ablated one: negligible material amount was actually removed from the sample in these conditions, since the explosion made the graphene layer to fold. The extreme mechanical toughness of graphene is an important parameter which hampers material removal at nanometric scales. For over-threshold beam diameter below 1 µm, the intensity distribution, ie beam shape, is an important factor for the morphology of the crater.We have identified a decrease of the single-shot ablation probability of CVD graphene when the over-threshold beam diameters decreases below 1 µm, with two different behaviours depending on the presence or absence of graphene wrinkles in the vicinity of the ultrafast laser illuminated impact. Ablation probability in flat graphene islands has a step-like decrease to zero when the over-threshold beam diameter decreases below 1 µm. However, in the case of laser illumination in the vicinity of wrinkles, the ablation probability decreases gradually with decreasing input pulse energy. These results can be qualitatively understood because of the high electronic and thermal conductivities of graphene. The limitations in terms of diameter make preferable to use multiple shot illumination approaches to overcome the effect of diffusion and folding.We also report a high dependence on the ablation behaviour and hole morphology with the laser fluence distribution. For low fluence gradients we observed that minimal hole diameter is ~1 µm for single shot even if the over-threshold is down to ~200 nm. We note that accurate control of laser fluence gradients could lead to a selective formation of folded graphene petals for potential applications requiring a precise combination of mono- and bilayer graphene.The samples were CVD graphene monolayer on Corning Eagle-XG AMLCD glass substrates from Graphene supermarket. The graphene monolayers were transferred on the substrate after growth.The sample position was controlled with sub-micron precision with a 5 axis motorized translation stages and planarity was ensured to be better than 1 µm over the whole sample. The accuracy of the sample positioning is enabled by an independent imaging system including a high numerical aperture objective (NA 0.8), ensuring better than 1 µm longitudinal sectioning.


Liver regeneration is a tissue growth process after loss or injury of liver tissue, which is a compensatory hyperplasia rather than true regeneration, mainly depending on hepatocyte proliferation. Currently, a large number of studies on hepatocyte proliferation have been conducted. However, studies on the regulation of long noncoding RNA (lncRNA) on hepatocyte proliferation are still limited. To identify specially expressed lncRNA during rat liver regeneration, high-throughput sequencing technology was performed, and a total of 2446 lncRNAs and 4091 mRNAs were identified as significantly differentially expressed. Gene ontology (GO) enrichment analysis was performed to analyze the role of differentially expressed mRNAs, and 695 mRNAs were identified to be related to cell proliferation. Then, an lncRNA-mRNA coexpression network based on the differentially expressed lncRNAs and proliferation-related genes was constructed to analyze the potential function of lncRNAs on hepatocyte proliferation, and ten lncRNAs, NONRATT003557.2, NONRATT005357.2, NONRATT003292.2, NONRATT001466.2, NONRATT003289.2, NONRATT001047.2, NONRATT005180.2, NONRATT004419.2, NONRATT005336.2, and NONRATT005335.2, were selected as key regulatory factors, which may play crucial roles in hepatocyte proliferation during rat liver regeneration. Finally, a protein-protein interaction (PPI) network was established to illuminate the interaction between proliferation-related genes, and ten hub genes  were screened with the MCC method in the PPI network, which may be important biomarkers involved in the hepatocyte proliferation during rat liver regeneration. These results may provide clues for a more comprehensive understanding of the molecular mechanism of hepatocyte proliferation during rat liver regeneration. The vast majority of the eukaryotic genomes are transcribed into noncoding RNAs, which can be divided into small noncoding RNAs <200 bp) and long noncoding RNAs  based on transcript size . lncRNAs00 bp andIn rodents and humans, the liver can grow rapidly after partial hepatectomy (PH) or acute chemical injury. This growth process is known as LR, which is a compensatory hyperplasia rather than true regeneration . During In the present study, high-throughput sequencing technology was used to identify DE lncRNAs and mRNAs during rat LR. Then, functional enrichment analysis of DE mRNAs was performed to screen proliferation-related genes. Finally, the lncRNA-mRNA coexpression network and PPI network were constructed based on DE lncRNAs and proliferation-related genes to elucidate the molecular mechanism of hepatocyte proliferation during LR. These results lay a foundation for understanding the regulatory function of lncRNAs on hepatocyte proliferation and provide an important clue for the study of the LR process.The healthy adult male Sprague Dawley (SD) rats weighing 210∼250 g were provided by the Laboratory Animal Center of Zhengzhou University . These rats were raised in a controlled temperature room of 19∼23°C with a relative humidity of 50∼70% and an illumination time of 12 h/d (8 : 00 to 20 : 00) and permitted to freely have water and food. A total of 60 rats were taken for the experiment with six rats per group: nine PH groups and one normal group (CG). The rats in PH groups were conducted 2/3 PH according to the method of Xu et al. The rats were anesthetized and condemned to death at 0, 2, 6, 12, 24, 30, 36, 72, 120, and 168 h after operation. The right liver lobes of six rats were mixed at each time point and stored at −80°C. All operations conformed to the Animal Protection Law of China and Animal Ethics.RNA sequencing was performed by the Shanghai OE Biotech . In brief, the mirVana miRNA Isolation Kit (Ambion) was used to extract the total RNA from liver tissues. The TruSeq Stranded Total RNA with Ribo-Zero Gold (Illumina) was used to construct cDNA libraries. The purified cDNA libraries were sequenced on Illumina HiSeq 2500 following the manufacturer's instruction. After filtrating the adaptor and low-quality reads, clean reads were obtained for subsequent analysis. The reads were matched to the rat reference genome using the hisat2 (v2.2.1.0) software. The StringTie2 (v1.3.3b) software was used to splice the aligned reads. lncRNA identification included two categories: one is known lncRNA, which completely matches with the known lncRNAs, and the other is candidate lncRNA lacking protein-coding ability, whose length is greater than 200 bp and exon is greater than or equal to 2. The software CPC (v0.9-r2), CNCI (v1.0), Pfam (v30), and PLEK (v1.2) were used to predict the protein-coding ability of transcripts. The expression of transcription was calculated by the fragments per kilobase of exon per million reads mapped (FPKM) method using the bowtie2 (v2.2.9) and eXpress (v1.5.1) software.p < 0.05 as the threshold. All DE lncRNAs in nine PH groups underwent hierarchical clustering analysis using the cluster3.0 and treeview software.The counts of lncRNAs in each sample were standardized by the baseMean value using the DESeq (1.18.0) software. Differentially expressed (DE) lncRNAs were identified with fold change ≥2 or ≤0.5 and https://david.ncifcrf.gov/). The enrichment analysis consisted of three parts: biological process (BP), molecular function (MF), and cellular component (CC). p < 0.05 is considered statistically significant, which was calculated by the EASE score.Gene ontology (GO) enrichment analysis of the DE mRNAs was conducted using David Bioinformatics Resources 6.8  were calculated between the DE lncRNAs and the proliferation-related genes, and only lncRNA-mRNA pairs with PCC ≥0.8 and https://string-db.org/) was used to construct a protein-protein interaction (PPI) network. Only the interacting pairs with combined score ≥0.4 were selected and considered to be significant. The PPI network was visualized by using the Cytoscape v3.6.1 software. Then, a Cytoscape plugin cytoHubba was used to identify the key genes adopting the MCC method.To illustrate interactions between proliferation-related DE mRNAs, the string database  using DESeq. A total of 2446 lncRNAs were determined to be differentially expressed during rat LR, with 1120 upregulated, 731 downregulated, and 595 up/downregulated lncRNAs  and . ToThrough high-throughput RNA-seq, the expression profile of 28635 mRNAs was measured. Among them, 4091 mRNAs were found to be differentially expressed, of which 2,256 were upregulated, 1,686 were downregulated, and 149 were up/downregulated  and . HiGO enrichment analysis was employed to determine the function of deregulated genes during rat LR, and 689 significant GO terms  were enriched. The top 30 GO terms of the three groups are listed in The coexpression analysis of the screened proliferation-related DE mRNAs and DE lncRNAs found 18343 significant coexpression pairs, of which 17547 pairs were positively correlated (PCC ≥ 0.8) and 796 pairs were negatively correlated (PCC ≤ −0.8) . The expTo better elucidate the interaction network of proliferation-related genes, a PPI network containing 551 nodes with scores greater than or equal to 0.4 was constructed using the string database . MoreoveWe validated the high-throughput RNA-seq results by performing qRT-PCR analysis of differentially expressed lncRNAs; the expression patterns revealed similar conclusions . Our RNAIn rodents and humans, the liver can grow rapidly after partial hepatectomy or acute chemical injury. This growth process is known as LR, which is a compensatory hyperplasia rather than true regeneration, mainly depending on the proliferation of hepatocytes . To explThrough coexpression network analysis, a total of 18343 coexpression pairs were obtained based on DE lncRNAs and proliferation-related DE mRNAs. Among these coexpression pairs, 95.7% were positively correlated and 4.3% were negatively correlated. These results indicate that lncRNAs may regulate the expression of genes mainly in a positive way. It was also found that some lncRNAs could be coexpressed with multiple mRNAs, suggesting that lncRNAs could regulate multiple mRNAs. According to nodes and connections, the top 10 lncRNAs  were selected. These lncRNAs were connected to 158 common DE mRNAs, respectively, such as Slc25a16, Cdk1, Rfc3, Brca2, Mcm7, Cdca7, Hat1, Chaf1b, Rfc4, and Mcm4. These findings indicated that these lncRNAs and their interaction genes may play an important role in hepatocyte proliferation during rat LR.κB signaling pathway [β-catenin signaling pathway and p38 MAPK signaling pathway. Bub1b (BUB1 mitotic checkpoint serine/threonine kinase B) and Mad2l1 (mitotic arrest deficient 2 like 1) were involved in the spindle checkpoint during mitosis [β-actin mRNA and cell motility through physically interacting with ZBP1, which could govern the direction of migration by responding to directional cues in chemotaxis [cytoHubba, a Cytoscape plugin, could provide 11 topological analysis methods to explore important nodes in biological networks including maximal clique centrality (MCC). MCC, a new method, has a better precision in identifying hub proteins. Through the MCC method, 10 hub mRNAs were selected including Aurkb, Cdk1, Cdc20, Bub1b, Mad2l1, Kif11, Prc1, Ccna2, Top2a, and Ccnb1. Aurkb (aurora kinase B) was involved in regulation of chromatin, glycolysis metabolism, regulation of telomerase activity, regulation of zygote development, and turnover of kinetochore microtubules –22. It w pathway , 23–27.  pathway –35. It a pathway –40. Cdc2 pathway –44. It w mitosis –48. Kif1emotaxis , 50. Prcemotaxis , 52. Ccnemotaxis , 54. It emotaxis –57. Top2emotaxis . Top2a wemotaxis . Ccnb1 (emotaxis . High exemotaxis . TherefoIn this study, the comprehensive expression abundance of lncRNAs and mRNAs was identified by RNA-seq analysis during rat LR. The lncRNA-mRNA coexpression network and PPI network based on lncRNAs and proliferation-related genes were constructed, and 10 key lncRNAs and 10 key mRNAs were determined that may play crucial roles in hepatocyte proliferation. Our study provides a new idea to better understand the mechanism of LR.


Silicon-based impurities are ubiquitous in natural graphite. However, their role as a contaminant in exfoliated graphene and their influence on devices have been overlooked. Herein atomic resolution microscopy is used to highlight the existence of silicon-based contamination on various solution-processed graphene. We found these impurities are extremely persistent and thus utilising high purity graphite as a precursor is the only route to produce silicon-free graphene. These impurities are found to hamper the effective utilisation of graphene in whereby surface area is of paramount importance. When non-contaminated graphene is used to fabricate supercapacitor microelectrodes, a capacitance value closest to the predicted theoretical capacitance for graphene is obtained. We also demonstrate a versatile humidity sensor made from pure graphene oxide which achieves the highest sensitivity and the lowest limit of detection ever reported. Our findings constitute a vital milestone to achieve commercially viable and high performance graphene-based devices. Silicon-based contaminants are ubiquitous in natural graphite, and they are thus expected to be present in exfoliated graphene. Here, the authors show that such impurities play a non-negligible role in graphene-based devices, and use high-purity parent graphite to boost the performance of graphene sensors and supercapacitor microelectrodes. However, the current top–down synthesis methods are prone to contamination, which can adversely affect properties, such as optical absorption and emission, electrochemical properties, carrier mobility, biological activity and toxicological properties13. These contaminants can cause inferior or unpredictable properties and are at the heart of many seemingly inconsistent reports on the properties of 2D materials15. For instance, several contradictory reports on electrochemical properties, electrocatalytic activity and biological properties of graphene and graphene oxide (GO) were ultimately understood to be the result of metallic impurities or acidic residues20. Such inconsistency prevents the development of a robust regulatory framework governing the implementation of such layered nanomaterials, especially if they are destined to become the backbone of next-generation devices. Perhaps, more importantly, this has stymied the emergence of a major application or the so-called “killer app” for graphene-based systems21, a long-promised but as-yet unrealised goal. Graphene may therefore follow the same trajectory of carbon nanotubes21, which were once billed as being transformative but have so far failed to make a significant commercial impact.Large-scale, cost-effective methods for producing high-quality and functional atomically thin two-dimensional (2D) materials with unmatched properties are critical to realising commercial applications of these promising materials21. However, to date, no rigorous quality-control mechanism has been put in place to understand the detrimental effects of impurities on the final performance of devices, mainly due to the lack of knowledge of the types of impurities and their potential influence. As graphene is a 2D material system, which is basically only a surface, any contaminant can alter its intrinsic properties dramatically by affecting surface active sites14 and/or by reducing the available surface area, as shown schematically in Fig. 22. This leads to capacitance values much lower than the theoretical capacitance of graphene (550 F/g)23, with values of 409 F/g being the highest reported to date24. As the production capacity of graphene and other 2D materials grows exponentially, the demand for high-performance 2D material-based devices will become significant21. This calls for a broad effort to identify the types and extent of impurities present and the best way to deal with them to achieve the maximum potential of the broad family of 2D materials.The need for methods that can produce higher quality and purity graphene and other 2D-based materials is widely recognised for device fabrication. Despite this, there seems to have been little effort devoted to understanding what effect impurities are having on the performance. The graphene glut in recent years has resulted in an increase in the use of cheaper alternative feedstocksHere direct visualisation of the surface of various solution-processed graphene reveals the existence of significant amount of silicon-based contaminations at the atomic level. High-angle annular dark field (HAADF) imaging combined with energy-dispersive X-ray spectroscopy (EDS) in an aberration-corrected scanning transmission electron microscope (STEM) was employed to detect and image these impurities. The present study addresses the challenge of obtaining high-purity graphene and GO from graphite (top–down approach) and offers a broad and comprehensive perspective on how to dramatically enhance the final performance of such materials in diverse applications. We identify the most prominent factor impacting the performance of graphene-based devices, namely inherent contaminants arising from impure graphite feedstock. Ultra-high performance humidity sensors and supercapacitors from graphene materials were then achieved through eliminating the adverse impact of endogenous silicon impurities.25. However, the situation changes dramatically when using HAADF imaging. The atomic resolution capabilities of aberration-corrected STEM permits single-atom imaging in HAADF mode by virtue of its high atomic number sensitivity 26. Therefore, the higher atomic number of silicon with respect to carbon and oxygen ensures that Si (and other high atomic number element) atoms are visible as bright spots in HAADF micrographs, while this was not possible with conventional BF images. Figure The presence of molecularly dispersed silicon-based contamination was not evident through high-resolution TEM bright-field (BF) analysis of GO samples Fig. . Monodis28 and requires several chemical treatment steps any, or all of which, could contribute to the observed silicon-based contamination. However, the impurity was also present in solvent-exfoliated graphene layers prepared by bath sonication of graphite powder in a very pure exfoliating solvent  for the exfoliation process to give graphene in the liquid phase (monolayer and few layers) without any additional oxidation step29. This showed that silicon-based compounds are ubiquitous contamination in graphene-based materials when using top–down production approaches and is not caused solely by reagents or particular chemical processes (i.e. modified Hummers’ method used here28). Therefore, the silicon contamination originated from the graphite precursor.Oxidative exfoliation of graphite, i.e. modified Hummers’ method, was used here30. However, the floatation process is not able to remove high abundance mineral impurities, such as silicon. These impurities are commonly removed by using chemical or thermal treatments31. Graphite particles in the purity range of 80–98% are typically refined using only floatation. For purities >98%, additional refinement steps are carried out following floatation32. This provides two options to eliminate the contamination: (a) purification of the exfoliated materials and (b) employing purer graphite precursors.HAADF imaging of the parent graphite (98% purity) demonstrated a significant amount of silicon-based contamination Fig. . Detaile4F to remove the impurities proved to be unsuccessful  revealed that purities of ≥99.9% result in almost contaminant-free GO Fig. . Interes2), which would be expected at 103.5 eV  was reduced much more rapidly than the lower purity GO (98%), probably due to a more pristine and uncontaminated surface.X-ray photoelectron spectroscopic (XPS) measurements were performed to further characterise the silicon-based impurities. GO samples derived from graphite with two different purities, 98% and 99.9%, were prepared by drop casting on a gold-coated wafer. Since XPS is extremely surface-sensitive with a sampling depth of only a few nm, we decided to use XPS depth profiling in order to probe the chemical composition at different depths. The use of an Ar cluster source instead of a conventional Ar ion gun enables sputtering (etching) of a wide range of materials, including organic compounds, while minimising damage to the chemical structure during the sputtering process. A comparison of the high-resolution C 1s spectra of the two materials provided strong evidence that the GO in both cases was essentially identical Fig. . Similaring Fig. , suggesting Fig. . Subsequing Fig. –13, whic39. It appears that silicon-rich impurities can effectively hinder this electronic transition as well as being a physical barrier on the GO functional groups. It should be noted that the size of graphite  spectroscopy was used. Results similar to the XPS depth profiling measurement, were obtained with 0.04 ± 0.007 and 0.25 ± 0.01% silicon found in the pure and non-pure samples, respectively. Furthermore, silicon-based impurities adversely affected the photoluminescence (PL) property of the GO materials as shown in Fig. 11, indirect detection through electrochemical measurements is not feasible due to the lack of an electrochemical redox response. Other analytical methods, such as inductively coupled plasma mass spectrometry, are also not effective as silicon-based compounds (i.e. silicon oxides) are unreactive in all acids except hydrofluoric acid (HF) and all three Si isotopes are subject to N- and O-based interferences40. These factors may explain why the existence of such ubiquitous contamination on solution-processed graphene, and other similar 2D materials, has not been previously reported in the literature.Identifying atomically dispersed and relatively low molecular weight impurities in graphene and other 2D materials is very challenging, as they are not easily visualised by routine imaging methods. In the case of silicon-based impurities, in contrast to metallic impuritiesIt should be noted, although there are many methods to purify graphite such as hydrometallurgical purification methods  and pyrometallurgical methods , these methods are not applicable after the exfoliation in liquid media or device fabrication. In the case of hydrometallurgical purification methods, usually a purification level of up to 98% can be achieved. This method, although very efficient in the removal of metallic impurities, cannot be applied to remove Si-based impurities. On the other hand, heating the graphene at high temperature (>2700 °C) is not a viable option in most cases, as this will limit the versatility of end-product device fabrication. These limitations are much more significant in the case of delicate dispersions of graphene and GO. As such, using high-purity graphite  in the first place is recommended.41. Therefore, GO-based relative humidity (RH) sensors have shown great promise in this respect44. However, the detection range and moisture uptake reported so far, although promising, represent just an incremental improvement over existing technologies. We anticipated that silicon-based contamination might act as a barrier, blocking the moisture absorber sites and thus adversely affect the sensor performance. Therefore, the first test of our hypothesis was to evaluate this effect by fabricating an RH sensor from a variety of GO materials.An important question is whether the impurities described here have any undesirable effect in the final performance of practical devices. Oxygen-containing functional groups on the hydrophilic surface of GO provide a high potential to adsorb water molecules45. Such high sensitivity results in an exceptionally high signal-to-noise ratio (~2000 Hz/Hz) thus allowing for trace levels of humidity to be detected. This high level of sensitivity corresponded to a very high humidity uptake, ranging from 7.5 wt% at 2.5% RH up to 32 wt% at 90% RH. The highest purity GO sheets displayed an extremely low limit of detection (LOD) of 0.006% RH at 27 °C, which is equivalent to 1.5 mg/m3 (2 ppm) absolute humidity; a level which has not been reported to date. This LOD is at least two orders of magnitude superior to the best-performing 2D MXenes (LOD of 0.8% RH) and around one order of magnitude better than the lowest reported LOD in the literature46. Similar trend was found when the effect of temperature is considered  were deposited on a quartz crystal microbalance (QCM) to fabricate the RH sensors. All QCM-based humidity sensors Fig.  revealedred Fig. . The GO red Fig. , which p47. However, the structure is mainly hydrophilic leading to easy adsorption and adherence of moisture (water) molecules onto the surface48. Our results show the existence of organo-silicon based contaminants on the surface as a hydrophobic and non-hygroscopic component detrimentally affects the final performance of the sensor leading to lower device performance49. Interestingly, the selectivity was not much influenced by the impurity implying that the impurity acts only as a passive barrier. This superb performance can be attributed to the much higher available hydrophilic surface area of the employed GO (99.9% purity) when the surface contamination is eliminated. This sensors’ detection range is shown to be from trace levels to up to >90% RH with unparalleled accuracy and selectivity. Therefore, the sensor developed from high-purity GO can eliminate the need for employing multiple sensors to detect the different humidity levels for any given application. Moreover, as the amount of the material on each sensor is typically around 50 μg, the use of more expensive high-purity precursor does not affect the overall production cost significantly.GO is an amphiphilic material consisting of both hydrophobic domains (graphenic domains) and hydrophilic parts51. Thus the masking effect of the surface contamination can adversely affect the double-layer charge storage capability. The performance of the three types of reduced GO (rGO) was evaluated. These were compared based on their cyclic voltammetric (CV) responses at 100 mV/s : natural graphite flake (98%), natural graphite powder (99%), natural graphite flake (99.8%), natural graphite flake (99.9%), natural graphite powder (99.9995%), synthetic graphite (99.9995%), and natural graphite powder (99.9999%) from Alfa Aesar. A commercially available GO was also tested as a control. Briefly, graphite powder (1 g) and sulphuric acid (200 mL) were mixed and stirred in a flask for 1 h. Then KMnO4 (10 g) was added to the mixture and stirred for 1 day. The mixture was transferred into an ice bath, and Milli-Q (200 mL) water was added slowly before H2O2 (50 mL) were poured into the mixture. Having stirred for another 30 min, the GO particles were then washed and centrifuged three times with HCl solution (9:1 water/HCl by volume), then centrifuged again and washed with Milli-Q water until the pH of the solution became about 4–5.GO was synthesised with a method described previouslySpecimens for electron microscopy were prepared by deposition of GO suspensions on a holey carbon support film on a copper grid (a droplet of 50 μg/mL). The specimens were stored in glass desiccator to avoid contamination. STEM examination was carried out using an aberration-corrected JEOL ARM200F microscope operating at 80 kV to minimise radiation damage to the specimens. The instrument was fitted with a cold field emission electron source and a JEOL large area (1sr) EDS. This was coupled to a Noran System Seven analytical system. All imaging and analysis was carried in scanning transmission mode (STEM) using a high-resolution imaging probe of approximately 30 pA current and 0.1 nm diameter with a convergence semi-angle of 24.9 mrad. Imaging was carried out in HAADF and BF modes, yielding mass thickness and diffraction contrast information, respectively. The inner and outer acceptance angles for HAADF imaging were 68 and 280 mrad, respectively, and for BF imaging, the acceptance semi-angle was 17 mrad. Scanning images were captured using the Gatan’s DigiScan hardware and DigitalMicrograph software.2 layer), as described previously52. Briefly, silane solution was prepared by mixing 3‐aminopropyltriethoxysilane (Aldrich) with water (1:9 v/v) and one drop of hydrochloric acid (Sigma–Aldrich). Precut silicon substrates were silanised by immersing in aqueous silane solution for 30 min and then washed thoroughly with Millipore water. GO sheets were deposited onto silanised silicon substrates by immersing a silicon substrate into the GO dispersion (50 μg/mL) for 5 s and then into a second container containing Millipore water for 30 s and then air‐drying. As‐deposited GO sheets were directly examined by SEM (JEOL JSM‐7500FA). The lateral size distributions of ~500 isolated GO sheets were determined from several SEM images and analysed using the image analysis software . The lateral size of GO sheets was defined as the diameter of an equal-area circle. Similarly, the size of the graphite particles was measured using optical microscopy.Scanning electron microscopic (SEM) analysis were carried out by first depositing GO sheets from their dispersions on pre‐cleaned and silanised silicon wafer . The total pressure in the main vacuum chamber during analysis was typically between 10−9 and 10−8 mbar. Survey spectra were acquired at a pass energy of 160 eV. C 1s high-resolution spectra were recorded at 40 eV pass energy, yielding a typical peak width for polymers of 0.8–1.0 eV. Each specimen was analysed at an emission angle of 0° as measured from the surface normal with an analysis depth of between 5 and 10 nm. Depth profiling experiments were conducted using an Ar Gas Cluster Ion Source  operated at a cluster size of Ar1000+ with impact energy of 10 keV, equating to partition energy of 10 eV per atom. For the ion beam, a raster size of 1.4 × 1.4 mm2 was employed. WD-XRF spectrometer Model S4 Pioneer, Bruker AXS Gmbh, Karlsruhe, Germany was used to evaluate the average amounts of silicon contamination in the powder form.XPS analysis was performed using an AXIS Nova spectrometer  with a monochromated Al Kα source at a power of 180 W (15 kV × 12 mA), a hemispherical analyser operating in the fixed analyser transmission mode and the standard aperture , which has a frequency resolution of ±0.03 Hz. The QCM response magnitudes were normalised using the mass deposition data in order to obtain a better understanding of the affinity (based on mass) of each material towards humidity.The 10 MHz QCM devices were fabricated using optically polished AT-cut quartz substrates (Ø = 7.5 mm) upon the surfaces of which metal electrodes (Ø = 4.5 mm) were e-beam evaporated. The two metal electrodes were each made up of 300 nm of Ti and their sensitivities were calculated to be 4.39 ng/cm2/Hz was employed. Three almost identical thin films of GO were deposited on the QCM devices for testing. Commercially available GO was deposited on the first QCM while the other two consisted of synthesised GO in-house from graphite with purities of 98% and 99.9%. GO dispersions (1 mg/mL) from these sources were drop cast (75 µL) onto the QCM device and were dried at room temperature. The change in the QCM frequency  confirmed that similar masses of each GO material were deposited on their respective QCM devices. These deposited mass values were used to normalise the humidity uptake of the materials.In order to make a RH sensor for testing GO from different sources, a 10 MHz QCM transducer with sensitivity of 4.39 ng/cm3 of water vapour in air. This level of humidity was diluted (by mass flow controllers) to obtain the different concentrations of water vapour required. The humidity sensing experiments throughout the study were such that the sensors were exposed to humidity for 15 min prior to being allowed to recover for a further 15 min under a dry nitrogen atmosphere. The humidity exposure and recovery events combined are referred to as a “pulse” from here on. The signal was determined from the highest humidity exposure (response magnitude) while the noise was determined from a blank profile used in the LOD calculations54.The humidity uptake measurements were performed in a custom-built environmental chamber, which housed the QCM devices while maintaining a constant operating temperature of 27 °C. The total gas flow rate was kept constant at 200 mL/min throughout the experiments using a multi-channel gas delivery system, employing mass flow controllers . The humidity levels were generated using an RH generator (V-Gen from InstruQuest). This humidity level constantly produced by the generator was equivalent to 100% RH at 27 °C or 25.6 g/mt90), detection limit, sensitivity and selectivity parameters were used in this study54.The selectivity and repeatability experiments were performed with a 50% RH level at 27 °C. Selectivity tests involved exposure to ammonia, acetaldehyde, ethylmercaptan, dimethyl disulphide and methylethylketone with/without the presence of humidity. The contaminant gases and their concentrations were chosen due to their relevance in industrial environments. Repeatability experiments involved the exposure to 50% RH at 27 °C over 10 pulses in a continuous manner. For sensor performance comparison, the response time (2SO4 (1 M) electrolyte. Please note, in order to highlight the effect of silica contamination (as a barrier) and in order to minimise the effect of restacking of GO sheets on the performance, thin films (<1 µm thickness) of GO were deposited on the electrode. However, for large-scale device fabrication a much higher mass of active material is recommended. This is discussed in the literature56.The active layer on the electrodes was fabricated through deposition of 5 µL of GO solution (1 mg/mL) on Pt electrodes. The chemical reduction was performed by immersing the electrodes in a solution containing 5 wt% ascorbic acid at 80 °C for 4 h. The double-layer charge storage was investigated using CV experiments using a two-electrode set-up and HSupplementary InformationPeer Review File


The pullout process of graphene from an epoxy/graphene composite filled with a carbon nanotube (CNT) was simulated by molecular dynamics simulations. The interaction energy and the interfacial adhesion energy were calculated to analyze the effect of CNT addition on the interfacial adhesion between the graphene and the epoxy matrix, with varying CNT radii, distances between the CNT and the graphene sheet, CNT axial directions, and the number of CNT walls. Generally, the addition of a CNT strengthens the interfacial adhesion between the graphene and the polymer matrix. Firstly, a larger CNT radius induces a stronger interfacial adhesion of graphene with the matrix. Secondly, when the CNT is farther away from the graphene sheet, the interfacial adhesion of graphene with the matrix becomes weaker. Thirdly, the CNT axial direction has little effect on the interfacial adhesion of graphene in the equilibrium structure. However, it plays an important role in the graphene pullout process. Finally, compared with a single-walled CNT, the interfacial adhesion between graphene and the matrix is stronger when a double-walled CNT is added to the matrix. In recent years, carbon nanotubes (CNT) and graphene have attracted significant attention due to their excellent mechanical, electrical, and thermal properties. They both show great potential for applications as nanofillers in polymer composites due to their superior properties. Composites filled with a CNT or graphene present various excellent properties . In addiSumfleth et al.  and YangIn this work, we focused on the interfacial adhesion between the carbon nanoparticles and the matrix. Many experiments have indicated that interfacial adhesion plays an important role in the synergetic effect between the CNT and graphene. Sa et al.  preparedIn recent years, molecular dynamics simulations are used to investigate interfacial adhesion for graphene/CNT/polymer systems. Liu et al. , investiAlthough it has been found that the interfacial adhesion between carbon nanoparticles plays an important role in the synergistic effect of a CNT and graphene, the mechanisms have not been well understood. In the present work, we focused on the interfacial adhesion of graphene with the matrix in epoxy/graphene/CNT composites. The pullout process of graphene from the composite model was conducted to investigate the effects of CNTs on interfacial adhesion using molecular dynamics simulations. The effects of the CNT radius, the distance between a CNT and graphene, the CNT axial direction, and the number of CNT walls were analyzed.3. Thus, the finial copolymerization degree of the polymer matrix was 75% . A single graphene sheet with the size of 43.46 × 43.79 Å2 and a CNT with a length of 42.54 Å were built into the simulation cell—the number of CNT walls and the CNT radii varied. Hydrogen atoms were added to the edges of the CNT and the graphene to saturate the dangling bonds. The whole simulation cell ).Diglycidyl ether of bisphenol A (DGEBA) and cyclohexylamine (CHA) were chosen as the epoxy monomer and linker, respectively a,b. The ion cell c size waIn this work, the Condensed-Phase Optimized Molecular Potentials for Atomistic Simulation Studies (COMPASS) force field was used . It is aTwo dynamic simulations were performed to obtain the equilibrium structures of the epoxy/graphene/CNT composites. First, to relax the epoxy matrix, a molecular dynamics simulation was adopted for 2 ns at 400 K with all the carbon atoms of the CNT and graphene being fixed. This meant that all the atoms, including the CNT and graphene, were able to move freely. Another molecular dynamics simulation was adopted for 4 ns at 300 K to relax the whole model. Each molecular dynamics simulation was performed with constant volume, temperature, and number of particles, which is also known as the NVT ensemble. The same procedure was used for all composite models. Once the equilibrium stage was completed, the model was subjected to the pullout simulation, as in Reference , of the Etotal refers to the total energy of the entire system . Egraphene refers to the energy of the graphene sheet. ECNT/epoxy refers to the energy of the CNT and all the epoxy molecules. Based on this definition, a negative interaction energy means there is an exothermic bonding between the graphene and the polymer matrix. In this work, two kinds of energies, the interaction energy and the interfacial adhesion energy, were calculated to analyze the interfacial adhesion of graphene and the polymer matrix (including CNT and epoxy). The interaction energy between the graphene and the matrix was calculated by Equation (1) below,E is the interaction energy between the graphene and the polymer matrix, calculated by Equation (1). A is the contact area between the graphene and the polymer matrix. The interfacial adhesion energy indicates the interaction energy per unit area.Equation (2) was used to calculate the interfacial adhesion energy (γ) of graphene,In this work, three composite models with different CNT radii  were built to study the effect of CNT radius on the interfacial adhesion between graphene and polymer matrix. The equilibrium structures of the three models after the equilibrium molecular dynamics simulations are shown in To analyze the pullout process of graphene from the composite model, the interaction energies at every pullout step were calculated . The intFor all three models, the absolute values of interaction energies decreased with the increase of the graphene displacement. During the pullout process, the contact area between the graphene surface and the matrix became smaller, leading to the decrease in interaction energy. The interaction energies tended to be zero when the graphene displacement was 50 Å, in each model, since the graphene sheet was totally pulled out from the polymer matrix.The interfacial adhesion energies of graphene during the pullout process are shown in d, indicating migration and adsorption of the epoxy molecules between them.Three initial distances between the CNT  and graphene  were chosen to investigate the effect of the CNT location on interfacial adhesion. The radius of the CNT was constant at 5.43 Å in all three models. Their equilibrium structures are shown in The interaction energies and the interfacial adhesion energies of the equilibrium structures at every step of the pullout process were calculated  to analyThe angle between the CNT  axial direction and pullout direction were set to 0°, 30°, 60°, and 90° in models A, B, C, and D, respectively. The equilibrium structures of models A to D are shown in For all four models, the interaction energy became lower during the pullout process due to a decrease in the contact area between the graphene and the matrix. Before pulling the graphene sheet out (at 0 Å displacement), the interaction energies in the four models were similar, around 975 kcal/mol. This indicates that the angle between the CNT axial direction and the pullout direction has little effect on the interfacial adhesion of graphene and the matrix in the equilibrium structures. In the early stages of the graphene pullout process (0 Å < displacement < 20 Å), the interaction energy increased the angle between the CNT axial direction and the pullout direction. The interaction energy of model A is the lowest, and model D’s interaction energy is the highest. In model A, the proportion of interaction between the graphene and the CNT was unchanged during the pullout process, because the angle between the CNT and the graphene was 0°. However, the angle was 90° in model D. At the beginning of the pullout process, the contact area between the graphene and the CNT stayed the same, while the contact area between the graphene and the epoxy decreased with an increase in the graphene displacement. This led to a larger proportion of the interaction between the graphene and the CNT in the total interaction between the graphene and the whole matrix. The interaction between the graphene and the CNT was larger than that of the graphene and epoxy, so model D shows the largest interaction energy at the beginning of the pullout process. In the later pullout stages (25 Å < displacement < 50 Å), the angle between the CNT axial direction and the pullout direction had a completely opposite effect on the interaction energy, i.e., the larger angle gave a smaller interaction energy. The interaction in model D was weakest, and model A’s interaction energy was the largest. In the later stages of the pullout process, the graphene passed through the CNT, leading to a decrease in the contact area of the graphene with the CNT. However, in model A, the proportion of the interaction between the graphene and the CNT stayed the same. Thus, model A provided the largest interaction energy. Based on the above analysis, the angle between the CNT axial direction and the pullout direction has little effect on the interfacial adhesion of graphene in the equilibrium structure, but it plays an important role in the graphene pullout process.To analyze the effect of the number of CNT walls on the interfacial adhesion of graphene with the matrix, a composite model with a double-walled CNT (radius of the outer layer was 5.43 Å) was simulated . Its intThe interaction energy and interfacial adhesion energy both suggest that the interfacial adhesion of graphene with the matrix including a double-walled CNT is larger than that of a single-walled CNT. For the single-walled CNT, only a single layer of carbon atoms interacted with the graphene sheet, while two layers of carbon atoms took effect with the double-walled CNT. Thus, the interfacial adhesion between the graphene and the matrix with a double-walled CNT was larger. In addition, the profile of the interfacial adhesion energy for the model with a double-walled CNT was more stable during the whole pullout process than that of a single-walled CNT b. It indMolecular dynamics simulations were conducted to investigate the interfacial adhesion of graphene with a CNT/epoxy matrix based on the graphene pullout process. The effects of the CNT radius, the distance between the CNT and the graphene, the CNT axial direction, and the number of CNT walls on interfacial adhesion were analyzed. We conclude that:1. The addition of a CNT strengthens interfacial adhesion between graphene and the polymer matrix. A larger CNT radius gives a larger area in contact with graphene, leading to a stronger interfacial adhesion of graphene with the matrix.2. When the CNT is farther away from the graphene sheet, the interfacial adhesion of graphene with the matrix becomes weaker, due to the interaction decreasing between the CNT and graphene.3. The CNT axial direction has little effect on the interfacial adhesion of graphene in the equilibrium structure, but it plays an important role in the graphene pullout process. At the beginning of the graphene pullout process, a larger angle between the CNT axial direction and the pullout direction gives a stronger interfacial adhesion, however, it offers a weaker interfacial adhesion at the later pullout stages.4. The interfacial adhesion between graphene and the matrix becomes stronger when a double-walled CNT is added to the matrix compared with a single-walled CNT.


The graphene nano-electro-mechanical switches are promising components due to their outstanding switching performance. However, most of the reported devices suffered from a large actuation voltages, hindering them from the integration in the conventional complementary metal-oxide-semiconductor (CMOS) circuit. In this work, we demonstrated the graphene nano-electro-mechanical switches with the local actuation electrode via conventional nanofabrication techniques. Both cantilever-type and double-clamped beam switches were fabricated. These devices exhibited the sharp switching, reversible operation cycles, high on/off ratio, and a low actuation voltage of below 5 V, which were compatible with the CMOS circuit requirements. Nano-electro-mechanical (NEM) switches, utilizing electrostatic forces to mechanically deflect the active element into physical contact with an electrode, are of great interest for future logic devices, relays and sensors . GraphenIn this work, we demonstrated the simple procedure to fabricate graphene NEM switches with both double-clamped beam and cantilever-type moving elements. By this method, the local top electrodes can be introduced for actuation. The fabricated devices were characterized with a two-terminal configuration, showing low pull-in voltages of less than 5 V. The results demonstrated the possibility of integrating such NEM switches with a conventional complementary metal-oxide-semiconductor (CMOS) circuit for future applications.2. The possible graphene flakes were firstly identified from their contrast to the substrate under an optical microscope. −1. For the bilayer graphene, four Lorentzian sub-peaks are fitted to the 2D band. In principle, the exfoliated graphene should be intact from significant lattice defects. The mild D peak observed in the measured spectra are ascribed to the defective edges of the flake near the probed areas. The fabrication process started with the mechanically-exfoliated monolayer graphene flakes on the p-doped silicon substrate covered with 300 nm thermal SiO2 layer was evaporated and capped onto graphene/HSQ as a sacrificial layer; (e) the top actuation electrode was defined with Cr/Au (5 nm/200 nm); and (f) etch all of the oxide layers  in the buffered hydrofluoric acid (1:5) to release graphene from the supporting substrate. Finally, the device was dried in a critical point drier to prevent surface tension-induced collapse of the suspended graphene. Vtb was applied between top and bottom electrodes and swept from 0 V to higher voltages, which could electrostatically deflect the graphene upwards to the top electrode, while the current between two electrodes Itb was monitored.All of the fabricated graphene switches were characterized in the vacuum condition (~0.1 Pa) to reduce the impacts from ambient, such as the molecular absorption and capillary force due to moisture. The two-terminal configuration is used to investigate switch performance  the pull-in voltage of the switch with a clean graphene, which has the thickness t of 0.34 nm, is estimated as 0.70 V. However, the contaminations and adsorbates on graphene are difficult to avoid after fabrication. By assuming a monolayer of water molecularly adsorbed on the graphene surface, it increases the beam thickness to ~0.7 nm; Vtb of 2.07 V is then calculated with Equation (1), which is close to the measurement. The current annealing process could be employed to remove the adsorbates [The pull-in voltage of a double-clamped beam can be theoretically calculated as :(1)Vpi=sorbates .Vtb of 2.3 V was measured in the third operation cycle. It is ascribed to the enhanced leakage current through the degraded substrate (read as the enhanced leakage current background), which effectively reduces the strength of the electric field flux at the graphene surface and, therefore, weakening the electrostatic force.We retracted the actuation voltage application immediately after the observation of pull-in to avoid the joule heating accumulation at the contact interface. This is known as the main reason of device failure, since it triggers the formation of chemical bonds between graphene and the contact metal and causes graphene sticking on contact . ReversiVpi of 3.8 V (A sharp “pull-in” behavior was observed at the of 3.8 V . Howeverof 3.8 V . Later, of 3.8 V b. k of a cantilever is:As is known, the spring constant ractions . We demonstrated the graphene NEM switch with local actuation electrode fabricated via simple fabrication methods. Sharp switching is realized at a low voltage of less than 5 V. These outstanding features benefit future applications of graphene NEM devices for high-performance switching components. In the future, the further lowered pull-in voltage can be achieved through the geometric design. More effort will also be devoted to the improvement of device reliability. For instance, contact interface modification will be utilized to prevent graphene from sticking on contact and increase the number of operation cycles.


Approximately 35% of patients with amyotrophic lateral sclerosis (ALS) exhibit mild cognitive deficits in executive functions, language and fluency, without dementia. The precise pathology of these extramotor symptoms has remained unknown. This study aimed to determine the pathological correlate of cognitive impairment in patients with non-demented ALS.In-depth neuropathological analysis of 27 patients with non-demented ALS who had undergone cognitive testing (Edinburgh Cognitive and Behaviour ALS Screen (ECAS)) during life. Analysis involved assessing 43 kDa Tar-DNA binding protein (TDP-43) accumulation in brain regions specifically involved in executive functions, language functions and verbal fluency to ascertain whether functional deficits would relate to a specific regional distribution of pathology.All patients with cognitive impairment had TDP-43 pathology in extramotor brain regions . The ECAS also predicted TDP-43 pathology with 100% specificity in brain regions associated with executive, language and fluency domains. We also detected a subgroup with no cognitive dysfunction, despite having substantial TDP-43 pathology, so called mismatch cases.Cognitive impairment as detected by the ECAS is a valid predictor of TDP-43 pathology in non-demented ALS. The profile of mild cognitive deficits specifically predicts regional cerebral involvement. These findings highlight the utility of the ECAS in accurately assessing the pathological burden of disease. Amyotrophic lateral sclerosis (ALS) has a heterogeneous clinical manifestation and is accompanied by cognitive and/or behaviour dysfunction in 35% of patients, with an additional 15% exhibiting a frontotemporal dementia (FTD) typically a behavioural variant.(SOD1) mutation, suggests a common underlying process.The presence of 43 kDa Tar-DNA binding protein (TDP-43), whose pathological misfolding and accumulation is observed in the brains and spinal cord of patients with ALS, except for cases of superoxide dismutase 1 In ALS brains, TDP-43 pathology can be widespread in cases of ALS-related/FTD and some have suggested a sequential spread.The aim was to determine the pathological correlate of cognitive impairment in patients with non-demented ALS. Our hypotheses are that (1) the specific profile of cognitive impairment in patients with non-demented ALS  is associated with TDP-43 pathology in corresponding brain regions identified a priori and (2) the ECAS assessed in vivo is a good predictor of TDP-43 pathology postmortem.We identified, from the Medical Research Council (MRC) Edinburgh Brain Bank, 27 ALS cases who had undergone neuropsychological testing with the ECAS during life. Additionally, all cases had separately undergone whole genome sequencing. All clinical data including the ECAS were collected as part of Scottish Motor Neuron Disease Register (SMNDR) and Care Audit Research and Evaluation for Motor Neuron Disease (CARE-MND) platform and all patients consented to the use of their data during life. All postmortem tissue was collected via the Edinburgh Brain Bank in line with the Human Tissue (Scotland) Act.https://ecas.psy.ed.ac.uk/). The ECAS is specifically designed to assess the profile of cognitive impairment in ALS and is adapted for patients with physical disability. It consists of 16 subtests across five cognitive domains: executive functions, language functions and letter fluency which combine to produce and ALS-specific score, while memory and visuospatial functions combine to produce an ALS-non-specific score. The ECAS also includes a carer behavioural interview based on criteria for diagnosing behavioural variant FTD.Cognitive impairment was measured using the ECAS, and all assessments were performed according to the standard procedure  is made up of the ALS-specific (maximum score of 100) and ALS-non-specific (maximum score of 36) scores. A person can be judged to be cognitively impaired (ALSci) if they have failed the ECAS (see 10.1136/jnnp-2019-320807.supp1Supplementary dataBrain tissue was taken at postmortem from standardised Brodmann areas (BA) and fixed in 10% formalin for a minimum of 24 hours. Tissue was dehydrated in an ascending alcohol series (70%–100%) followed by three successive 4-hour washes in xylene. Three successive 5-hour paraffin wax embedding stages were performed followed by cooling and sectioning of the formalin-fixed paraffin embedded (FFPE) tissue on a Leica microtome in 4 µm sections on to a superfrost microscope slide. Sections were dried overnight at 40°C and immunostaining was performed using the Novolink Polymer detection system with the Proteintech anti-phospho(409-410)-TDP-43 antibody at a 1 in 1000 dilution and 3,3′-diaminobenzidine chromogen and counterstained with haematoxylin, according to standard operating procedures.For the purpose of later analyses, regions of the brain were grouped according to their association with (1) executive, (2) language functions or (3) letter fluency based on functional imaging and pathological studies.Sensitivity, specificity, positive and negative predictive values are expressed as percentages. CIs for sensitivity and specificity are exact Clopper-Pearson CIs. CIs for the likelihood ratios are calculated using the Log method. CIs for the predictive values are the standard logit CIs. GraphPad Prism V.5.0 was used to present graphical data. Non-parametric analysis of (1) median survival was performed using Mann-Whitney U test and (2) time between ECAS testing and death was performed using two-tailed Fisher’s exact text. Shapiro-Wilk test was performed to ensure data were normally distributed. One-way analysis of variance (ANOVA) was used to compare cognitively impaired and unimpaired cases with different cell-type specific distributions of TDP-43 .C9orf72 cases, two cases carrying the SOD1 (I114T) Scottish founder mutation and two cases with mutations of uncertain significance in the NEK1 gene.SOD1 mutation. No individual showed ALScibi, likely reflecting the small number of individuals with available behavioural data . Eleven individuals demonstrated cognitive impairment in specific subdomains; executive function only , language  and fluency  with two individuals having a mixed deficit affecting all three domains  from the Edinburgh Brain Bank who had undergone neuropsychological testing during life using the ECAS were identified . No pati domains . Only onTo determine whether cognitive impairment as detected by the ECAS was a good predictor of extramotor pathology in ALS we first assessed whether patients with impaired ECAS scores exhibited extramotor pathology assessed by TDP-43 aggregation in postmortem tissue.Three out of 27 patients met criteria for ALSbi; one of these had a NEK1 mutation, one had a SOD1 mutation and one was a sporadic case. The brain areas thought to predominantly be associated with behavioural dysfunction are (1) the orbitofrontal cortex (BA11/12), (2) ventral anterior cingulate BA24) and (3) medial prefrontal cortex (BA6). There was no TDP-43 present in the case with SOD1 mutation, in keeping with previous literature; however, the remaining two cases did have TDP-43 inclusions in two or more of these brain regions . 7 out o and (3) Three of the 27 patients demonstrated mild executive dysfunction when tested  and all three had corresponding TDP-43 pathology at postmortem in the brain areas associated with executive functions . Furthermore eight of the 27 patients demonstrated mild language impairment  when tested with ECAS , all of whom had TDP-43 pathology in corresponding brain areas at postmortem. Four of the 27 patients demonstrated mild fluency dysfunction when tested with ECAS  and all four had corresponding TDP-43 pathology at postmortem in the brain areas associated with fluency . There were no cases where cognitive impairment was present in the absence of TDP-43 pathology; however, there was a small subgroup (n=6) of patients who had TDP-43 pathology with no cognitive impairment . These results taken together gave rise to the ECAS accurately predicting TDP-43 pathology with a positive predictive value of 100% and 100% specificity in executive function, language and fluency domains .C9orf72 repeat expansion; all cases had extramotor TDP-43 pathology at postmortem, irrespective of cognitive impairment. This is in stark contrast to two patients in the cohort with a SOD1 (I114T) mutation (Scottish founder mutation), who had no detectable TDP-43 pathology and no cognitive dysfunction , consistent with previous neuropathological and neuropsychological assessments.C9orf72 repeat expansion cases, one had executive dysfunction and three had language impairment. We also identified two patients carrying a NEK1 mutation, one of these patients had ALSbi with TDP-43 pathology and the other had no cognitive impairment and no evidence of extramotor TDP-43 pathology at postmortem.Six out of the 27 cases identified had a genetic diagnosis of a Analysis of the cell-type specific distribution of TDP-43 pathology was performed demonstrating three predominating patterns: (1) predominantly glial (22.2% of cases), (2) mixed glial and neuronal (59.3% of cases) and (3) predominantly neuronal TDP-43 pathology  had TDP-43 pathology in extramotor areas. Furthermore, a more detailed analysis of the types of cognitive impairment (executive/language/fluency) revealed a direct association between TDP-43 pathology and specific corresponding brain regions of the frontal and temporal lobes. Specifically we showed that (1) executive dysfunction was associated with TDP-43 pathology in the following regions: the orbitofrontal cortex (BA11/12), ventral anterior cingulate (BA24), dorsolateral prefrontal cortex (BA46 and BA9) and the medial prefrontal cortex (BA6); (2) language dysfunction was associated with TDP-43 pathology in the inferior frontal gyrus , transverse temporal area , middle and inferior temporal gyri (BA20/21) and the angular gyrus (BA39); (3) fluency dysfunction was associated with TDP-43 pathology in the prefrontal cortex (BA9), inferior frontal gyrus , ventral anterior cingulate (BA24) and the transverse temporal area  and (4) behavioural impairment was associated with TDP-43 pathology in the orbitofrontal cortex (BA11/12), ventral anterior cingulate (BA24) and medial prefrontal cortex (BA6). We recognise that a potential limitation of these data is the difference in time between ECAS testing during life and time of death. However, we demonstrate that there was no effect of time between ECAS and death on our data. As such we are confident that our conclusions from these data are robust and meaningful.https://ecas.psy.ed.ac.uk), highlighting the potential impact and clinical utility of these findings. Cognitive changes are found in about half of cases of ALS and given that these clinical manifestations of the disease are likely related to TDP-43 accumulation, there is potential for targeted therapies to improve cognition aimed at reducing the burden of misfolded TDP-43. These findings raise the possibility of using ECAS as a stratification tool in clinical trials, aimed at reducing cognitive symptoms in at risk individuals. Given that no false positives would be identified, the use of the ECAS as a stratification tool would not result in anyone being incorrectly included in such a clinical trial.Staging of TDP-43 pathology at postmortem has been described previously and together with the current findings indicates that the pathological accumulation of TDP-43 is clearly related to the clinical manifestations of ALS and could be a promising biomarker.Although there were some cases with mild involvement of BA19 (five cases) and hippocampal pathology (four cases), there was no associated functional  impairment. We identified one case with language impairment that also had some mild BA19 pathology. In this case, there was no additional hippocampal pathology and it is therefore likely that they performed poorly on the story recall memory test due to other cognitive problems  and not because a primary impairment in memory.There were three predominating cell-type specific patterns of TDP-43 pathology: (1) predominantly glial (22.2% of cases); (2) mixed neuronal and glial (59.3% of cases) and (3) predominantly neuronal (7.4% of cases). This finding is clearly present in other published pathological data setsC9orf72 repeat expansion carrying patients and none of the patients with a SOD1 mutation had TDP-43 pathology at postmortem and neither TDP-43 severity nor cell-type distribution was associated with disease progression. Our data therefore show that there are two predictors of TDP-43 pathology at postmortem: (1) cognitive dysfunction assessed by the ECAS and (2) C9orf72/SOD1 status. Given the impact that cognition has on the quality of life of both patients and carers, future studies looking at the effects of therapies, targeting protein misfolding, on cognitive function in patients with ALS, independent of their effects on survival or motor function, are clearly warranted. Our data demonstrate the utility of the ECAS in predicting TDP-43 pathology in extra-motor brain regions in postmortem tissue. The primary advantage of a specificity of 100% is that no individual would be incorrectly included in a clinical trial aimed at reducing TDP-43 burden. However, the major limitation to this is the lower sensitivity of the ECAS in predicting this burden of pathology, meaning that some patients, who could plausibly benefit from such a trial, would not be identified. However, recently we identified a striking difference in spatial expression of a pathological marker called clusterin in mismatch cases that would not be identified by the ECAS.Furthermore, in line with previous neuropathological studies of patients with ALS, all


Over the last decade graphene based electronic devices have attracted the interest of researchers due to their exceptional chemical, electrical and optical properties. Graphene is very sensitive to any physical changes in its surrounding environment and, inherently, has very low electronic noise. This property of graphene makes it a suitable candidate for sensor applications. The purpose of the work presented in this article is to demonstrate the ability of graphene derivatives to detect toxic organic compounds like phenol and methanol. A novel method for the detection of organic compounds (phenol and methanol) has been introduced in this article. In this method, a change in the photocurrent, as well as electric current, have been used as detection signals to improve the sensor accuracy and selectivity for specific target molecules. A nanoscale electronic device simulator, Quantumwise Atomistix Toolkit (ATK), has been used to simulate graphene nanosheet and armchair graphene nanoribbon based sensors. Devices density of states (DOS), current–voltage curves and photocurrent curves have been calculated with the ATK simulator. In the proximity of target molecules, a significant change in DOS, electric current and photocurrent have been observed. The simulated graphene based structures can be converted into physical sensors to obtain a low cost, small sized, integrated sensing device. Graphene can be categorized from a single layer to several stacks of single layers called multilayers. It is mostly known as a zero-gap semiconductor, whose band gap can be tailored according to the application requirement [In modern society, the development of a cheap, portable and reliable gas sensor for domestic and industrial environments is of utmost importance. Industrial environment monitoring, for the health and safety of the workforce, demands highly reliable sensors for the detection of toxic compounds . The demuirement ,14. Singuirement . All theA wide range of graphene-based devices are being investigated for the detection of organic compounds and gases. Several advancements in the technology have revolutionized the field of graphene-based sensors and devices. Recently, laser induced graphene paper (LIGP) has emerged as a promising candidate to detect liquids and antibacterial media . GraphitMoreover, graphene-based gas and organic compound detectors have different working principles. They may include resistive method, micro-electro-mechanical systems (mems) method and field effect transistors (FET) methods. Most of the commercial gas sensors use the resistive method to detect foreign gas particles. The conductivity of the graphene changes with the change in the gas concentration in the environment . The preFurthermore, graphene has a unique electronic band structure that is different from semiconductors, insulators and conductors. It has a conical band structure above and below the Fermi level. These conical band structures are called Dirac cones . GraphenPhenol and methanol are toxic and flammable organic compounds that are harmful to human health so it is essential to detect their presence in domestic and industrial environments to avoid serious accidents ,33. In tIn that paper , phenol Phenol and methanol molecules have been detected with two types of devices. First, graphene nanosheet based structures, with gold electrodes, have been used to detect target molecules, as shown in In our previously published work , graphenDuring the characterization of the simulated device, DC bias voltage is applied to the gold electrodes of the simulated device. Current–voltage (IV) curves have been calculated for four different scenarios: In the absence of target molecules, in the presence of only four phenol molecules, in the presence of only four methanol molecules and in the presence of both molecules (two-phenol and two-methanol molecules). For the same scenarios, four different DOS have also been calculated under zero-bias conditions.This simulated structure is an extension to our previously published work . TerminaIn For the simulations of the structures described in A = 5, KB = 5 and KC = 50.This HPC has 232 computing machines with 1024 GB of total memory. With eight computing nodes, each IV-curve took about one week to calculate in HPC for these experiments. ATK-VNL follows a workflow to simulate electronic devices. In this workflow, first of all the structure of the device is formed using the ATK builder tool, then this simulated structure is sent to the script generator tool that generates the python file. During the script generation, an inbuilt calculator is added into the simulated structure with the desired parameters to be analyzed. The script contains a simulated device, new calculator and density of states . Finally, the script has been generated as a script.py file. The python code, generated by the scripter can be customized by using the custom scripter tool of ATK-VNL. In the last step, this generated script is run in the job manager. The script can be run on the local machine or on a remote high-power computing machine. After the execution of the job, the output file is obtained . Finally, the results are viewed in the viewer tab. The ATK-DFT in-built calculator was used for the simulations with K-point sampling of KThis section has been divided into two subsections, as two types of devices have been simulated. The density of states (DOS) and IV-curves have been calculated for graphene nanosheet and AGNR based devices.In In the third scenario c, both pMoreover, the difference in the electric current values is not very significant for different target molecules in the simulations. This could be due to a lesser concentration of target molecules that are exposed to the graphene nanosheet. The concentration of target molecules has been kept low (four molecules in each case) to minimize the computational time of the simulations. Even with this lesser concentration of target molecules, each IV-curve took more than one week to calculate in HPC. This difference in current values would be more obvious with an increased concentration of target molecules.‒2 to 10‒1 eV, as compared to that of the device in the absence of target molecules.In another set of experiments, armchair graphene nanoribbon (AGNR) of approximately the same dimensions as the graphene nanosheet  to generate a photocurrent. Then the change in photocurrent, in the presence of different target molecules, could be used as a signature signal to detect specific molecules. The AGNR based device was illuminated with photons, having energies between 0 to 3 eV, and the change in photocurrent was calculated for different target molecules, as shown in Moreover, the results show that with an increase in photon energies, the generation of photocurrent also increases, but the change in photocurrent, for different target molecules, was dissimilar, as shown in However, a very low photocurrent has been observed during the simulations in AGNR as shown in However, with heavy chemical doping, the absorption in a single layer of graphene can be increased physically up to 40%. The generation of a photocurrent also depends on the interaction of graphene plasmons with surface polar phonons of insulating substrates, like SiO2. This hybrid plasmon–phonon mode, between graphene plasmons and its substrate’s polar phonons, can be excited in the mid-infrared region under s-polarization. In this interaction, the phonon’s and electron’s temperature are raised and this increased temperature changes the electrical conductivity and increases the photocurrent in graphene. Due to the limitations of the simulator in controlling the polarization of photons and the activation of the plasmon-phonon coupling mode (in the case of using an insulating substrate for AGNR), high values of photocurrent could not be achieved in the simulations. However, in physically fabricated graphene-based devices, high photocurrents can be achieved by tuning intrinsic plasmons and activating the hybrid plasmon–phonon mode .Moreover, we are planning to conduct experiments based on differential measurements to study the effect of light on the generation of photocurrents in graphene-based devices. In these experiments two graphene strips will be placed in parallel within a controlled environment. In the experimental setup, one strip will be illuminated with a suitable light source and the other one will be kept in darkness. After that, the differential signal will be acquired from these two graphene strips with the help of a locally developed amplifier  to get rPristine graphene nanosheet and AGNRs were used to detect phenol and methanol molecules in our simulations. However, in practice, it is not possible to avoid defects in the large-scale fabrication process of graphene-based devices. Defects in graphene change its electronic properties. Sometimes, defects can contribute to the introduction of beneficial effects in graphene-based devices that can be utilized for different device applications. More significant effects of defects are self-heating and a degraded performance of the device .The purpose of the work that has been presented in this article is to access the feasibility of the graphene nanosheet and armchair graphene nanoribbon to detect phenol and methanol molecules prior to the physical fabrication of a sensor. The simulation results showed that both phenol and methanol could be detected with a graphene nanosheet and armchair nanoribbons, but the sensitivity of a graphene nanosheet is very low for the detection of the desired target molecules. The change in the electric current of the graphene nanosheet is very small in the presence of phenol and methanol as target molecules. In a low concentration of these target molecules, it is very difficult to differentiate between the presence of phenol, methanol and both phenol and methanol molecules. Whereas, the selectivity and sensitivity of armchair graphene nanoribbon, to detect phenol and methanol molecules, is better than the graphene nanosheet. A significant difference between the IV-curves of AGNR based device has been observed in the presence of phenol and methanol as target molecules. Furthermore, a change in the photocurrent through AGNR, in the presence of the above-mentioned target molecules, was also calculated, although, the values of the photo generated current were very small in the simulated devices. By controlling the polarization of photons and activating the plasmon-photon coupling mode (after selecting a suitable insulating substrate for AGNR), higher values of photocurrent could be physically achieved. In order to improve the sensor selectivity and accuracy, changes in the electric current, as well as the photocurrent, are suggested as signature signals for the detection of phenol and methanol molecules. These simulations revealed that AGNR is a better choice for the detection of phenol and methanol molecules as the change in IV-curves, in the presence of target molecules, is more obvious as compared to that of graphene nanosheet. The future intention is to develop a physical sensor based on AGNR to detect phenol and methanol molecules. Changes in the electric current and photocurrent, in the presence of specific target molecules, will be used as a detection signal for the AGNR based physical sensor.