However, it is unclear whether tumor aggressiveness and patient survival are influenced more by the proliferative or the migratory properties of cancer cells. To address this question, we experimentally measured proliferation and migration phenotypes across more than 40 breast cancer cell-lines. Based on the latter, we built and validated individual predictors of breast cancer proliferation and migration levels from the cells’ transcriptomics. We then apply these predictors to estimate the proliferation and migration levels of more than 1000 TCGA breast cancer tumors. Reassuringly, both estimates increase with tumor’s aggressiveness, as qualified by its stage, grade, and subtype. However, predicted tumor migration levels are significantly more strongly associated with patient survival than the proliferation levels. We confirmed these findings by conducting siRNA knock-down experiments on the highly migratory MDA-MB-231 cell lines and deriving gene knock-down based proliferation and migration signatures. We show that cytoskeletal drugs might be more beneficial in patients with high predicted migration levels. Taken together, these results testify to the importance of migration levels in determining patient survival.The efficacy of prospective cancer treatments is routinely estimated by  Only 1 out of 5000 drugs from pre-clinical studies enter the market after successful clinical testing2. Cancer drugs show the highest proportion of failures on the road to clinics3. Currently, the prevailing experimental method to initially estimate the pre-clinical efficacy of cancer drug candidates is by measuring their effects on in vitro proliferation rates10. However, even after filtering these findings in animal models, only a fraction of emerging candidates has successfully translated into human trails13. Many factors contribute to the failure of drugs that are effective in pre-clinical systems. For starters, in vitro and in vivo systems are obviously only approximate models of patients that do not capture many aspects of human biology. However, another naturally arising possibility is that other cellular phenotypes, such as migration or invasion, may be better indices of tumor response in patients than cellular proliferation. Addressing this question, we aimed here to quantify the relative weight of proliferation versus migration in determining cancer aggressiveness and patient survival.Drug development risk is a major contributing factor for spiraling drug pricesin vivo to study their association with patient survival and treatment response. However, regrettably, such measurements are yet infeasible. We, therefore, set out to build and validate predictors of proliferation and migration levels in breast cancer cell-lines based on gene expression information. With such predictors, we applied them to predict the proliferation and migration levels of TCGA14 breast cancer patient samples from their gene expression. We tested and verified that the predicted levels of these phenotypes in the tumors are indeed strongly associated with patient survival in the direction expected and that they are associated with cancer aggressiveness as expected. We find that migration is more strongly associated with breast cancer aggressiveness and more importantly, patient survival, than proliferation.Ideally, one would have liked to directly measure proliferation and migration levels directly in tumors CellToPhenotype predictors, using least absolute shrinkage and selection operator (LASSO) based regression15. The predictors were tested on the cell-line data using a standard cross-validation procedure  with patient survival (in an independent dataset – METABRIC)16. Second, CellToPhenotype uses LASSO regressor to regularize the predictor that enables a data-driven feature selection using a cross-validation. A five-fold cross validation procedure to compute the minimum λ value for LASSO. Applying the predictor learned above to the gene expression of breast cancer tumor samples, we can predict migratory and proliferation level for each sample/individual. For each test sample, we iterate this procedure 50 times (to obtain a robust estimate) and take the median value of the migration and proliferation levels as the final estimates.CellToPhenotype predictors (Supplementary Note) consists of two expressions based supervised regression – one for predicting cell migration and other for predicting cell proliferation. Each predictor was trained on Doubling times for 46 breast cancer cell lines were estimated by plating a known number of cells and measuring the total number of cells once the culture reached an estimated 80% confluency. Proliferation rates were then calculated using the doubling time measurements. Cell line migration was estimated in 43 breast cancer cell lines using a live cell imaging-based random cell migration assay. The mean speed of cell migration was then quantified as the final migration estimate.CellToPhenotype predictors, which given the expression of a given cell-line, predict its migration and proliferation levels. These predictors were constructed using least absolute shrinkage and selection operator (LASSO) based regression15, considering as features the genes whose expression is significantly associated with survival in the METABRIC breast cancer collection  than that obtained by the CellToPhenotype predictor. Second, as a control, MIB-1 expression, a marker of tumor cell proliferation and determinant of patient survival in prostate cancer22, shows no correlation with experimental measured proliferation in breast cancer cell lines . Finally, TPX2 expression, a well-known marker of migration in breast cancer23, is significantly correlated with the experimental measurements of migration in breast cancer cell lines . Again, this correlation is smaller compared to correlation via the CellToPhenotype migration predictor. Overall, these results show that the latter provides a better estimate of in vitro proliferation and migration than known marker genes. We also check that the data used in training the CellToPhenotype predictors do not suffer from multicollinearity (Supplementary Note).Next, we compared CellToPhenotype predictions with the expression of three reported gene markers of migration and proliferation (expression data obtained from CCLE project)24, P < 5.5e-20 and P < 4.4e-20 respectively, Fig. We then applied the CellToPhenotype proliferation and migration predictors to analyze breast cancer TCGA tumor data. Given an input tumor sample, each predictor (migration or proliferation) receives as input the levels of expression of its feature genes in that sample and outputs the predicted migration or proliferation levels. We first tested if the CellToPhenotype predicted proliferation and migration levels are higher in the TCGA breast tumors than the matched adjacent normal tissues . Reassuringly, the predicted migration and proliferation levels are significantly higher in the tumors than in the matched non-cancerous tissues  exhibit the highest predicted migration and proliferation levels amongst subtypes. Luminal A patients (n = 224) exhibit the lowest predicted proliferation amongst subtypes and significantly lower migration levels than TNBC (Supplementary Note), consistent with the observation that they have low metastasis levels and respond relatively well to treatment. Luminal B (n = 127) patients have higher predicted proliferation levels than Luminal A patients, consistent with the observation that Luminal B has larger tumor size and higher cellular proliferation index25, and higher rates of lymph node involvement than Luminal A26 . For this analysis, we first built new predictors of migration and proliferation by analyzing the 40 breast cancer cell lines for which we have both migration and proliferation measurements to enable a head-to-head comparison of the effects of migration and proliferation on survival, when built from exactly the same cell-lines (the construction itself followed the same predictor generation procedure described above). Given these CellToPhenotype predictors, we estimated the migration and proliferation levels of the 1043 TCGA breast tumors from their expression data, as before. We then employed Cox regression to examine the association between the predicted values and the patients’ survival, controlling for various covariates including age, race, and genomic instability. We find a stronger association of predicted migration levels with patient survival  than the association between proliferation and survival  Fig. . In bothTo estimate the significance of the difference of predictability, we additionally performed a likelihood ratio test comparing the survival predictive power of combined migration and proliferation compared to only proliferation or only migration. We see that the migration + proliferation model is significantly better than a proliferation only model  but not significantly better from a migration only model . Thus, adding migration to a proliferation-only model improves the survival prediction, however adding proliferation does not add significant predictive power to a migration-only model., A migration only model is significantly better than a proliferation only model  in predicting patient survival Fig. . Finallymigration-suppressive genes. Similarly, we also carried out siRNA KD experiments on 227 proteins whose gene expression levels are significantly positively correlated with experimentally determined migration values in these 40 breast cancer cell lines, and determined the effect of each knockdown on cell migration. Among these, we identified all genes whose knockdown significantly decreased cellular migration in MDA-MB-231 cell line and termed them as migration-enhancer genes. Migration suppressive genes  experiments that we have conducted in a highly migratory breast cancer cell line, MDA-MB-231. Keeping translational goals in mind, we considered around 4600 druggable proteins. Most of these genes come from protein kinases and GPCRs (G-protein-coupled receptors). (Kinases which are often over expressed and activated in cancer are easily targetable through their ATP binding sites.) Among these, we knocked down 248 proteins whose gene expression levels are significantly negatively correlated with experimentally determined migration values in the 40 breast cancer cell lines that we studied above. We experimentally measured the effect of each knockdown on cell migration using a 2D migration assay. We termed the genes whose knockdown significantly enhances cellular migration The number of migration-suppressive genes that had low expression were downregulated in a given breast cancer cell-line (S-count) was highly correlated with its CellToPhenotype predicted migration levels  and also with its experimentally measured migration values . This suggests that the S-count could be considered as an approximation of cellular migration. Similarly, the number of migration-enhancer genes that had high expression in a given breast cancer cell-line denotes their migration-enhancer scores (E-count). The E-score is also highly correlated with the predicted migration levels  and the experimentally measured migration values , suggesting that E-count also provide approximate estimates of cellular migration. The sum of S-count and E-counts, termed the KD-migration-score, has a slightly higher correlation with the predicted migration levels  and proliferation levels of these tumors . We then examined the association of the KD-migration and KD-proliferation scores of the TCGA breast cancer tumors and patient survival, after controlling for age, race, and genomic instability via Cox regression. The results reinforce the trend observed previously with the CellToPhenotype analysis, as we find a significant association of KD-migration-scores with patient survival  but a lower association between KD-proliferation-scores and survival   Fig. . Analysi32. Accordingly, our working hypothesis has been that tumors with high migratory estimates may respond better to cytoskeletal drugs and conversely, tumors with high proliferation estimates will respond better to cytotoxic drugs.We next asked if predicted migration or proliferation levels determine patient response to cytoskeletal vs cytotoxic drugs. Although migration and proliferation occur via partially overlapping cellular processes, as a first approximation cytotoxic drugs mainly target proliferation by inhibiting nucleotide synthesis or inducing DNA break while cytoskeletal drugs are known to affect cell migration by targeting microtubules To test this hypothesis, we analyzed data of TCGA breast cancer patients, out of which 389 patients were given at least one of the 7 cytoskeletal drugs and 331 patients were given at least one of 31 cytotoxic drugs Table . In the 33 in Cox regression  gives similar results as before, i.e. migration is a stronger predictor of breast cancer patient survival than proliferation . We also find that patients with high predicted migration levels respond better to cytoskeletal drugs than patients with low predicted levels. siRNA-based predictors of migration and proliferation that we additionally built further testify that migration is indeed a better predictor of survival and the association may even have a causal basis. The choice of MDA-MB-231 cell-line (for the siRNA-based predictor) is unlikely to bias this conclusion because MDA-MB-231 is both highly proliferative and migratory cell line , and since the expression levels of the selected genes (features) are significantly correlated with the experimentally measured migration values in the 40 breast cancer cell lines. Our conclusion about the relative importance of migration over proliferation is robust to a few different migration like wound-healing assays  that the predicted migration levels are higher in circulating tumor cells than in normal samples or TCGA cancer samples  identified many additional genes  in the early stages of cancer drug development, which currently focusses on proliferation screens.In summary, our analysis highlights the importance of tumor migration in determining its aggressiveness and patients’ survival. It puts forward the need to put more effort on 6, Number of cells at harvest = 20 × 106, days in culture = 4 days, then the doubling time would be 2 days or 48 hours (4 days/2 PDL). The proliferation rate for each cell lines was computed using the equation 70/(doubling time). We did this for 46 breast cancer cell lines.Breast cancer cell line population doublings were estimated by plating a known number of cells at day 0 and measuring the total number of cells once the culture reached an estimated 80% confluency . Cell numbers were calculated using a hemocytometer and population doublings (PDL) were determined using the following formula: PDL = 3.32 ). The doubling time was calculated by dividing the number of days or hours between harvest and seeding by the PDL. Example: Number of cells plated at day 0 = 5 × 1038. Before imaging, the cells were pre-exposed for 30–45 min to 0.1 µg/ml Hoechst 33342  to visualize the nuclei. The plates were placed on a Nikon Eclipse TE2000-E microscope fitted with a 37 °C incubation chamber and 5% CO2 supplier, a 20x objective , an automated stage and perfect focus system. Up to four positions per well were automatically defined and nuclei (stained with live Hoechst) were imaged overnight every 10 to 20 minutes using NIS controlling software (Nikon) and a CCD camera (Pixel size: 0.78 or 0.32 µm). The.nd2 files acquired from NIS were exported to.tiff files as mono image for each channel and then converted to.avi files and analyzed using custom made ImagePro Plus macros as previously described39. The mean speed of cell migration was quantified per time-lapse by tracking each nucleus separately over time. This was done for 43 breast cancer cell lines.Cells were seeded on 96-well glass bottom plates  coated with 10 µg/ml collagen type I (isolated from rat tails) in PBS for 1 hour at 37 °C40. ‘Transient siRNA knockdown was achieved by reverse transfection of 50 nM single or SMARTpool siRNA in 2,500-5,000 cells/well in a 96-well plate format (PKT assay) using the transfection reagent INTERFERin  according to the manufacturer’s guidelines. The medium was refreshed after 20 h and transfected cells were used for various assays between 65 to 72 h after transfection’40. .Human siRNA of 475 proteins  were coated with 10 µg/ml fibronectin  for 1 h at 37 °C. Plates were washed twice with PBS, using a HydroFlex platewasher . Subsequently, the plates were coated with white carboxylate modified latex beads  for 1 h at 37 °C, after which the plate was washed 7 times with PBS. 65 h after siRNA transfection, transfected cells were washed twice with PBS-EDTA and trypsinized. Cells were resuspended into single cell suspensions, then diluted, and finally seeded at low density (~100 cells/well) in the beads-coated plate. Cells were allowed to migrate for 7 h, after which the cells were fixed for 10 min with 4% formaldehyde and washed twice with PBS. For each transfection, duplicate bead plates were generated ; transfection of each siRNA library was also performed in duplicate . Procedures for transfection, medium refreshment and PKT assay were optimized for laboratory automation by a liquid-handling robot ’40. .‘PKT assays were performed as described before40. . Major Axis score (Z-score) as a measure of cell speed was further used and computed in the modeling.‘Migratory tracks were visualized by acquiring whole well montages (6 × 6 images) on a BD Pathway 855 BioImager  using transmitted light and a 10x objective (0.40 NA). A Twister II robotic microplate handler  was used for automated imaging of multiple plates. Montages were analyzed using WIS PhagoTracker. Migratory tracks without cells or with more than 1 cell were excluded during image analysis. The quantitative output of PhagoTracker was further analyzed using KNIME. Wells with <10 accepted tracks were excluded. Next, data was normalized to mock to obtain a robust Z-score for each treatment and each parameter. After normalization, an average Z-score of the 4 replicates was calculated. Knockdowns with <3 images were removed, as well as knockdowns with <150 accepted tracks’18. Each predictor was trained on in vitro cell migration or proliferation as the dependent variable and gene expression of cell lines as the independent variables in the regression. CellToPhenotype adopts two level feature selection to reduce testing error. First genes that are significantly associated with patient survival (in an independent dataset – METABRIC) were selected to be included in the subsequent regression model. Secondly, CellToPhenotype uses LASSO shrinkage to regularize the predictor that enables a data-driven feature selection using a cross-validation. Both above feature selection was conducted in dataset independent of the testing set on which performance of CellToPhenotye was evaluated. This ensures an unbiased evaluation of the predictive power of CellToPhenotype.CellToPhenotype predictors consists of two expressions based supervised predictors – one for predicting cell proliferation and other for cell migration. The gene expression data was obtained from the Cancer Cell Line Encyclopedia projectTo achieve the robust final estimate of migration and proliferation, CellToPhenotype uses bootstrapping. CellToPhenotype predictors were conducted on the training data and then phenotypes were predicted for test samples. The process is repeated for 50 bootstraps. Median of each bootstrap are taken as the final estimates of migration and proliferation levels (details are provided in the Supplementary Note).in vitro data leaving one sample. Migration and proliferation was estimated for the left-out sample. Spearman ρ between the predicted and actual phenotypes was computed.Leave-one-out cross validation was conducted to assess the CellToPhenotype predictive power as follows. The migration and proliferation models were trained on all Using CellToPhenotype predictors, migration and proliferation levels were predicted for 937 breast cancer patients in TCGA Table . Out of Using CellToPhenotype predictors, migration and proliferation levels were predicted for 1706 breast cancer patients whose cancer grade information was available in the METABRIC dataset . Accordingly, the above model systematically controls for these confounders. Strata (race) in the above model implies Cox regression was conducted in each patient stratification based on race separately and likelihood were combined. We repeated the procedure for 10 iterations, and median coefficients (risk factor) of migration were computed. The association of survival with proliferation was estimated similarly. Each Kaplan Myer (KM) analysis was done by comparing the migration/proliferation levels of on the top 25 percentile of patients with bottom 25 percentile patients.To estimate the relative contribution of migration and proliferation to predict patient survival we fit following Cox regression, which also controls for age, race, and genomic instability:42. Following the siRNA, we experimental measured change cell migration by measuring factors including Major Axis score (Z-score). Migration-suppressive genes (n = 26) were identified by selecting genes whose knockdown led to high migration (above 90 percentile) in MDA-MB-231 cell line and whose gene expression was significantly negatively correlated with experimentally determined migration values in 40 cell lines. Migration-enhancer genes (n = 24) are those whose knockdown significantly decreased cellular migration (below 10 percentile) and whose gene expression was significantly positively correlated with experimentally determined migration values in 40 cell lines. We count the number of lowly expressed  migration-suppressive genes and highly expressed (above 50 percentile) upregulated migration-enhancer genes; and assign the count as KD-migration-score of each sample (cell line or patient). For this analysis, we use the median expression of all genes as the threshold for upregulation or downregulation.Out of around ~4600 druggable genes that we considered, we selected 475 genes whose expression was significantly correlated with migration across 40 cell lines . Out of this, 248 genes are negatively correlated and 227 genes are positively correlated. We conducted siRNA knockdown of 475 genes described above in MDA-MB-231 breast cancer cell line. MDA-MB-231 was chosen because it is highly migratory breast cancer cell lineet al.30. Among these genes, we select 1248 genes whose expressions are significantly correlated with experimentally determined proliferation values in 40 BC cell lines (606 genes positively correlated and 642 genes negatively correlated). Proliferation-suppressive genes were identified by selecting genes whose knockdown led to high proliferation (above 90 percentile) in MDA-MB-231 cell line and whose gene expression was significantly negatively correlated with experimentally determined proliferation values in 40 cell lines. Proliferation-enhancer genes are those whose knockdown significantly decreased cellular proliferation (below 10 percentile) and whose gene expression was significantly positively correlated with experimentally determined proliferation values in 40 cell lines. The count of lowly expressed proliferation suppressive and highly expressed proliferation-enhancer genes in a sample was assigned as KD-proliferation-score of the sample.siRNA/shRNA-based KD-proliferation-score of a sample is determined in an analogous manner described for migration. Briefly, we used proliferation measurement post ~15400 genes shRNA knockout in MDA-MB-231 cell lines from Marcotte Drug response information is available for 720 TCGA breast cancer patients in TCGA: 389 patients administering at least one of the 7 cytoskeletal drugs, and 331 patients administering at least one of the 31 drugs targeting only proliferation . The siRNA-based analysis data based on MDA-MB-231 cell line is from Fokkelman et al.40. The R code for developing CellToPhenotype predictors is available as free software with the GNU General Public License in the GitHub repository (https://github.com/nishanth83/CellToPhenotype).The experimentally measured proliferation values are added in Table Supplementary note and figuresSupplementary Table S1Supplementary Table S2Supplementary Table S3


Stainless steel 316L is a material commonly used in cardiovascular medicine. Despite the various methods applied in stent production, the rates of in-stent restenosis and thrombosis remain high. In this study graphene was used to coat the surface of 316L substrate for enhanced bio- and hemocompatibility of the substrate. The presence of graphene layers applied to the substrate was investigated using cutting-edge imaging technology: energy-filtered low-voltage FE-SEM approach, scanning electron microscopy (SEM), Raman spectroscopy, and atomic force microscopy (AFM). The potential of G-316L surface to influence endothelial cells phenotype and endothelial-to-mesenchymal transition (EndoMT) has been determined. Our results show that the bio- and hemocompatible properties of graphene coatings along with known radial force of 316L make G-316L a promising candidate for intracoronary implants. EndoMT is a biological process in which endothelial cells lose their specific phenotype markers such as vascular endothelial cadherin and acquire a mesenchymal or myofibroblastic phenotype. They also express mesenchymal cell products such as α smooth muscle actin (α-SMA) and type I collagen   sulforhodamine B solution in 1% acetic acid for 30 min. The unincorporated dye was removed with 1% acetic acid while the incorporated dye was then liberated from the cells with 10 nM Tris base solution. After gentle stirring, 200 μl of solution was transferred into 96-well plate and the absorbance was measured spectrometrically at 490 nm. The absorbance was proportional to total biomass, corresponding to cell proliferation rate.To assess the proliferation of HCAEC after 24h and 72h, the cells were fixed with ice cold 10% (w/v) trichloroacetic acid for 1h in 4°C, washed (5 times) with ddHTo assess the cytotoxicity of graphene coated discs, the commercial In Vitro Toxicology Assay Kit from Sigma Aldrich was used. The test was performed according to the manufacturer's instruction.To discriminate viable and nonviable HPCAEC, the trypan blue staining of cells after 24h and 72h of cultivation was performed. Cell viability was calculated as the number of viable cells divided by the total number of cells within the grids on the hemocytometer.4 cells/cm2), washed 3x with PBS, and stained with 1 μM FDA for 15 min, 37°C in a dark and humid chamber. DAPI was used to stain the cell nuclei. Nikon Eclipse E600 fluorescence microscope was used to estimate the bright green fluorescent viable cells. The measured signal served as indicator of cell metabolic activity, as the conversion of FDA is esterase dependent.To determine the number of viable, metabolically active cells, the simultaneous double-staining procedure, using fluorescein diacetate  and DAPI, was used. Briefly, the HCAEC were cultured on 316Land G-316L discs for 72h (5x104 cells/cm2 on 316L and G-316L discs and cultured for 24h and 72h under conditions described above. The cells were fixed in 4% (w/v) paraformaldehyde in PBS pH 7.4, permeabilized with PBS containing 0.1% (v/v) Triton X-100, and incubated with mix of mouse monoclonal anti-endothelial cell antibody [BW-200]  BSA in PBS) and rabbit polyclonal anti-FAP-α  BSA in PBS) overnight at 4°C. The discs were washed and stained with secondary antibody: goat anti-mouse IgG H&L antibody  and donkey anti-rabbit IgG H&L antibody , respectively, at 1/500 dilution in PBS containing 0,2 % (w/v) BSA for 45 min at RT, in a dark and humid chamber. DAPI was used to stain the cell nuclei. After washing, the discs were transferred on a glass slide and mounted with Fluoromount Aqueous Mounting Media (Sigma Aldrich). The cells were counted with Nikon Eclipse E600 fluorescence microscope, captured with a video camera module , and analyzed with ImageJ 1.50b14. Briefly, color images were first converted to 8-bit image in grey scale . Then, the area of positive immunostaining was estimated by the number of RGB pixels within measuring mode, by separate measurements with red, green, and blue channels of RGB image. Thus, the area of positive reaction was determined as the percentage of red, green, and blue pixels area.Briefly, cells were seeded at density of 1×10μl of PRP containing 2.0 x 105 of platelets was transferred onto each plate. Platelets were allowed to adhere to graphene covered (n=6) and steel discs (n=3) for 15 min at 37°C. Nonadherent platelets were removed by rinsing the discs with PBS (3 times). Subsequently, adherent platelets were fixed with 10% PFA (10 min at RT), rerinsed three times with PBS, permeabilized with 0.1% Triton X-100 for 5 min at RT, washed again, and stained with conjugated phalloidin-FITC (5 μg/ml) for 20 min. After staining, platelets were washed three times with PBS and photographed by Nikon Eclipse E600. The extent of platelet activation was determined qualitatively from the aggregates formation and quantitatively by the measurement of mean area surface of platelets with ImageJ 1.46r.The hemocompatibility of G-316L in comparison to 316L was evaluated by the assessment of platelet adhesion and activation. Blood samples for platelets preparation were obtained from a volunteer, who declared lack of any diseases and anticoagulants administration. Blood was collected by venipuncture to tube containing 3.8% (wt/vol.) sodium citrate (9:1) and immediately centrifuged at 100 x g for 20 min (37°C) to obtain platelet-rich plasma (PRP). 2/3 of PRP fraction was transferred to a new 15 ml tube and centrifuged at 100 x g for 20 min (37°C) to pellet of contaminating red and white blood cells. The number of platelets in PRP was assessed by use of Cell-Dyn 1800 (Abbott); 500 GraphPad Prism v. 5.0 was used to perform the statistical analysis. Student's t-test or Mann-Whitney nonparametric test were used as appropriate. Results were expressed as mean±SD. P<0.05 was the criterion for statistical significance.Novel techniques of stent surface modifications to resolve the problem of undesired clinical outcomes of intravascular implants are still an ongoing research area. Surface modification technologies can be divided into two main categories: passive and bioactive material surface and coatings . PassiveGraphene, a single atom thick layer of sp2 carbon, has shown to be a suitable candidate for highly biocompatible, passive coating of the implant surface. Graphene is chemically inert and durable. Recently, the possible utilization of graphene for NiTi stents has been presented. NiTi stents, due to the various technical aspects of the deployment method, are routinely used in peripheral arterial disease but not for coronary interventions . In this−1) and 2D (2690 cm−1) bands and the ratio between their intensities. The shape of the bands (sharp peaks) and the high 2D/G ratio clearly indicate the presence of a single or few layers of graphene [To confirm the quality of obtained graphene layers after the transfer, Raman spectroscopy was carried out. The resulting spectrum  allows ngraphene .−1), D' (1620 cm−1), and D+G (2940 cm−1) [∗ peak at 2450 cm−1 is also visible although its interpretation in graphene and graphene-related structures is still a matter of debate. However, it is already known that this peak is absent in graphite spectra and was observed most frequently in single- and few-layered systems of graphene structures. It provides further indication that the obtained layers are of preferable properties.Taking into account the difficulties of polymer-based transfer onto a nontypical substrate of mechanically polished steel, the achieved quality of the graphene layers confirmed by the Raman scattering measurement is exceptionally good. Due to the nature of the material, transferred from its pristine substrate onto another, the 2D/G ratio is smaller than 2. A value above 2 would be typical for a perfect graphene monolayer. A lower ratio is expected in materials obtained using such transfer methods. The other important bands in the interpretation of Raman spectra of graphene-related materials are the D (located at 1350 cm40 cm−1) . In the SEM images of the obtained samples show that the layers are of good quality and their adhesion is exceptional. They remained on the substrate despite a more difficult target surface of the mechanically polished steel (in comparison to the perfectly flat surface of silicon wafers used in electronics) and intense chemical rinsing required for electron microscope imagery. The effects of the process are shown in In our AFM study the relative height difference between the substrate and graphene sheets surface was (23.32 ± 5.65 nm). It is known that the height of graphene monolayer is about 0.76 nm [EsB “in-Lens” detector allows blocking all electrons with kinetic energies lower than the strengths of the retarding field acting in front of the EsB detector . As a re4 of endothelial cells per cm2 and incubated in conditions described in Materials and Methods. After 24h of cultivation, the cell nuclei were stained with DAPI and cell bodies were stained with antiendothelium conjugate to facilitate visualization and counting. Results showed that G-316L surface enhanced the adhesion of endothelial cells 24 h after seeding in comparison to uncoated 316L steel  to evaluate whether graphene creates more favorable conditions for cell proliferation than steel does. To assess the rate of proliferation, an increase of biomass (by means of sulforhodamine staining) between 24h and 72h of cultivation was determined. The data showed more enhanced proliferation of endothelium on graphene-overlaid discs (3 times greater number of cells after 72h in comparison to number of cells adhering at 24h of cultivation) than on steel discs (2.4-fold increase in the same time period), p=0.001  has been used and cell vitality was assessed by trypan blue staining. The experiment showed no cytotoxic effect on either steel or graphene-overlaid discs 24h and 72h after cultivation  and Rat Aortic Endothelial Cells (RAECs) , we showTo evaluate whether G-316L surface has a potential to influence the endothelial cells phenotype, endothelial-to-mesenchymal transition (EndoMT) has been determined. The endothelium cultivated on uncoated steel and graphene coated discs was stained with conjugate containing anti-human FAPa  antibody (indicating fibrocytes phenotype) or anti-human endothelium antibody (indicating endothelium phenotype) and DyLight 488 or DyLight 550 secondary antibody, respectively. Interestingly, the experiment showed greater number of fibroblast phenotype cells on uncoated steel discs and greater number of endothelium phenotype cells on graphene-overlaid discs (p<0.05) .α-SMA and fibronectin expression) [Kato et al. showed that surgical and catheter-based interventions on pulmonary veins were associated with pulmonary vein stenosis due to endothelial-mesenchymal transition. The loss of endothelial and gain of mesenchymal marker expression or coexpression of endothelial and mesenchymal antigens in banded pulmonary vein intimal cells was observed . EndoMT ression) . Since gTo determine whether graphene covered discs adversely affect the platelets adhesion and/or activation, G-316L and 316L discs were covered with a large number of platelets according to protocol described earlier. The data showed that the number of platelets attached to G-316L surface was approximately two times lower than to uncoated steel  . MoreoveBlood platelets adhesion to the biomaterials surface is one of the primary indicators of their hemocompatibility . The plaThe high-quality CVD-grown graphene has been moved to the steel (316L) substrate using the PMMA-based method. The achieved quality of the transferred graphene layer was exceptionally good and confirmed by the Raman scattering measurement, low-voltage FE-SEM images, and the AFM analysis. It was confirmed that graphene coating on 316L (G-316L) supports the adhesion and proliferation of human primary coronary artery endothelial cells (HPCAEC) to a greater extent than the uncoated substrate. It was proved also that obtained surface has a unique potential to affect the endothelial cell phenotype by diminishing the endothelial-to-mesenchymal transition (EndoMT) and thus stronger reducing the risk of in-stent restenosis. This finding further confirms the biocompatibility of graphene coating. All of these characteristics of the coating along with excellent mechanical properties of 316L make a G-316L a suitable candidate for intravascular implants.


Graphene is one of the most important nanomaterials. The twisted bilayer graphene shows superior electronic properties compared to graphene. Here, we demonstrate via molecular dynamics simulations that twisted bilayer graphene possesses outstanding mechanical properties. We find that the mechanical strain rate and the presence of cracks have negligible effects on the linear elastic properties, but not the nonlinear mechanical properties, including fracture toughness. The “two-peak” pattern in the stress-strain curves of the bilayer composites of defective and pristine graphene indicates a sequential failure of the two layers. Our study provides a safe-guide for the design and applications of multilayer grapheme-based nanoelectronic devices. Graphene, a single layer of carbon atoms arranged in a hexagonal lattice, is known for its extraordinary physical, chemical, and mechanical properties ,6,7,8,9.Although graphene has such superior properties and great applications, it has a zero band gap, which prevents it from being a suitable alternative to silicon in the electronics industry . It has There is a decent amount of studies on twisted graphene bilayers ,30,31,32x-axis, and zigzag edges along the y-axis. The second layer of graphene was shifted parallel relative to the first layer, with a disorientation angle γ = 25.2°. The primitive unit cell of such a twisted-bilayer contained 532 carbon atoms. The model was periodic along the two in-plane directions (x and y), and the third direction was fixed (z). We used a 4 × 4 × 1 super cell (8512 atoms) in this study to reduce the self-image interactions of the defects.The MD simulations were carried out using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)  software3, and contained 8512 carbon atoms as shown in x axis , which is denoted as an armchair direction, in line with the literature. All the strain, stress, and the Young’s modulus, were specified to this direction in this study.All the simulations were carried out at a temperature of T = 300 K. All systems were fully equilibrated at a pressure of atmosphere (P = 0.0001 GPa) using the isothermal–isobaric ensemble  before any mechanical loading. The simulation time step was 0.0005 picoseconds (ps). The size of the equilibrated simulation box was 11.24 × 9.7 × 5 nmFrom the simulated stress–strain curves, the Young’s modulus−2 nm2, which corresponded to the C–C bond length with a component of 0.1405 nm parallel to the projected plane (x-y plane). It was slightly smaller than the experiment value of 0.142 nm and the original AIREBO value of 0.141 nm. Such a 0.46% contraction, referring to the ideal theoretical potential, indicates a tilt angle of 0.26°, due to out-of-plane buckling.We firstly relaxed the twisted bilayer graphene using the isothermal–isobaric ensemble (NPT) for 550 ps with T = 300 K and P = 0.0001 GPa (representing ambient condition), to remove the residual stress of the system. The equilibrated system is depicted in 3, with a standard deviation of 3, while the thickness of 5 nm was fixed. The bulk modulus The temperature and pressure of the system fluctuated around the target value after equilibrium. The thermal fluctuations of the last 50 ps of the equilibration are shown in  1.2 TPa .It is worth mentioning that the graphene monolayer was non-isotropic. The twisted-bilayer graphene was in principle also non-isotropic and influenced by the twist angle. However, this angle-dependence should be less than that of monolayer graphene. The angle-dependent properties in bilayer graphene deserve further studies. L(t) = L0 exp(η × t), where L(t) is the length of the simulation box at time t, L0 is the initial length, and η is the true strain rate, in unit of s−1. Thickness is one of the most challenge questions for dealing with the mechanical properties of atomic-thick 2D materials, because their thickness (third-dimension) is not well defined, which is totally different from conventional bulk materials. For the convenience of comparison, we set all the thicknesses to be 0.668 nm, which corresponded to the thickness of two atomic layers of carbons in bulk graphite. The tensile stresses were computed from the normal Cauchy stresses, multiplied by a factor of 7.485 which was the ratio of the thickness of the simulation box (5 nm) to the thickness of double layer graphene (0.668 nm). Our results of the tensile test modeling of the pure twisted bilayer graphene are illustrated in The stress–strain relationship plays an essential role in the characterization of mechanical properties of a material or structure. The stress-strain relationship is, in general, obtained from tensile tests. To model the tensile tests, we elongated the simulation box in the direction. The strain was measured as “true” strain, which means that the box dimension changes non-linearly with time from its initial to final value. The variation of the simulation box length as a function of time was described as In a small regime of strain of about a few percent, when the tensile strain increases, the stress that the system experienced enlarges linearly. Such linearity is measured by Young’s moduli, which is calculated as the initial slope of the stress–strain curve. Our stress–strain relationship revealed a Young’s modulus of 0.96 TPa. When a large strain was applied, the stress of the system responded non-linearly to the strain until the system’s failure. The ultimate tensile strength was the maxima in the stress-strain curve, indicating the upper strength limit of the system. The corresponding strain was defined as the ultimate tensile strain, which reflected the flexibility of the system. The ultimate tensile strength and strain were 94.9 GPa and 0.1545 respectively. Our results of the Young’s modulus, the ultimate tensile stress, and the strain indicated that the modulus of the twisted bilayer graphene was the same as the single layer graphene. To verify this point, we examined the stress–strain relationships of single layer graphene. The upper layer graphene behaved the same as the lower layer in the tensile tests, as illustrated in Fracture toughness describes the ability of a material containing a crack to resist fracture. As an intrinsic property, fracture toughness is one of the most important mechanical properties of any material. Through tensile tests of the pure twisted bilayer graphene, we obtained the ultimate tensile stress which intrinsically governed the uniform breaking of atomic bonds (C–C bonds) in a perfect system. Considering the wide applications, the useful strength with engineering relevance is usually determined by its fracture toughness ,45.3. A total of 48 carbon atoms were removed within this regime, 24 atoms on each layer. The length of the pre-crack measured by the remaining atoms was 2.12 nm, as shown in We generated a pre-crack by removing atoms within a regime defined by a rectangular box of 0.305 × 2.0 × 5.0 nmWe observed that the Young’s modulus of the pre-cracked system was the same as the pure system, which indicates that the pre-crack had little effect on the linear elastic properties. However, the non-linear mechanical properties were very different. With the crack, the ultimate tensile stress reduced to 41.03 GPa, a drop of 57% from the perfect system, which was 94.9 GPa. The ultimate tensile strain also had a large (68.4%) drop, from 0.1545 to 0.0488. Such a large drop in ultimate stress is very much expected when a crack is present due to concentration of stresses (increase in compliance). In addition, the drop in peak stress seemed to be more or less similar in both the monolayer and twisted layer, which indicates that the physics was not all that different. This could be understood as the coupling between the two layers not being strong because of the bond nature of weak Van der Waals interactions between layers. 2, agreeing with MD studies in the literature, as reviewed in [Both the perfect and pre-cracked twisted bilayer graphene undergoes brittle failure at large mechanical loads. Once the crack is initiated, it will propagate across the whole system immediately and cause global failure. Our observation agrees well with the experimental investigation ,45. Acco4.0 MPam . The difiewed in .We have also examined the mechanical behaviors of the individual components of the two layers. The critical stress and strain are 18.98 GPa, 0.0458 and 21.58 GPa, 0.0528 for the upper and lower layer graphene, respectively. The differences might stem from the different orientations to the mechanical loading due to the anisotropic mechanical behaviors of graphene. However, such differences are “averaged” in the system, which indicates that there are still some strong couplings between layers. This reasoning is verified by the observation of inter-layer bonding around the critical loading.When periodic boundary conditions are applied to a defective system, it is inevitable that the defects interact with its own periodic images or self-images, due to the long range of the elastic field. As a result, the system size in general has an effect on the defective system. To reduce this artificial effect, the system size should be large enough. The system size effect is examined in this study through three super cells which contain 2 × 2, 4 × 4, and 8 × 8 unitcells respectively. The number of carbon atoms are 2128, 8512, and 34,048, respectively before the generation of the pre-crack. Since the pre-cracks generated in all the three systems have the same siz e, the system size effect on the fracture toughness is equivalent to the effect on the critical stress As seen from 9 s−1, much larger than those used in experimental studies, which are around 10−3–103 s−1. The medium system of the pre-cracked twisted bilayer graphene was examined with tensile tests under four different strain rates: 4.0 × 108, 1.0 × 109, 4.0× 109, 1.0 × 1010. The results of the stress-strain relationships are illustrated in It is well known that the strain rate influences the mechanical properties, especially the fracture toughness, because it takes time for the system to respond to the applied mechanical loading. Due to computational resource limitations, the strain rates in the molecular dynamics simulations are in the order of 10Our results show that the strain rate had little effect on the linear elastic properties, as depicted by the Young’s modulus. The critical stresses were 39.8, 41.3, 41.0, and 41.9 GPa, for the four strain rates, respectively. Although the strain rate differed by 25 times, there was no clear trend for the strain rate effect. In fact, at these high strain rates, the material had little time to respond to mechanical stimuli (adiabatic conditions). This is the reason for why almost all the results looked identical. Nevertheless, this study implied that the strain rate has negligible effect on the fracture toughness of the twisted bilayer graphene in molecular dynamics simulations. Further studies with low strain rate are interesting, but they are outside the scope of molecular dynamics simulations.Different from the monolayer graphene, the twisted bilayer graphene has structures with interlayer interactions. To examine this layered structure effects on the fracture toughness, we studied two additional hybrid structures or composites: the pre-crack that exists in only the upper or lower layers, denoted as “crack + graphene (GE)” and “GE + crack” respectively. The results of the stress-strain relationships from the tensile test simulations are illustrated in Our results demonstrated that all these cracks had little effect on the linear elastic properties, but a large effect on the fracture toughness and the flexibility. It is very interesting that there were two peaks in the stress–strain curves of the hybrid structures. For the “crack + GE” system, the two peaks were 43.7 and 43.4 GPa at the strains of 0.0498 and 0.1058. For the “GE + crack” system, the two peaks were 46.1 and 36.0 GPa at the strain of 0.0528 and 0.0848. Such distinctive characteristics suggest that there are different and complex fracture mechanisms in the hybrid structures. This difference might be attributed to the different orientations between the upper layer and the lower layer, referring to the tensile strain direction. The non-isotropic mechanical properties of graphene also affected the mechanical response of the composite with the heterogeneous structures. Nevertheless, the two hybrid structures shared the same patterns of double peaks in the stress–strain relationship, which clearly reflects the double-layered structure. The layer with a pre-crack failed first at a strain of about 0.05. The other graphene layer without a pre-crack still maintained system integrity, and the loading could be further sustained, until the failure of the second layer. The two-peak pattern revealed the sequential failure of the two layers. Our results of the two-peak pattern in the stress-strain curves suggest a new method to detect the number of layers in the multilayered structures via tensile tests.It is worth noting that the twisted bilayer graphene in this study was in a “free-standing” state without interaction with other objects. However, in most of the applications involving graphene, a substrate is in inevitable, which will affect these twisted bilayer graphene mechanics. The extent to which this occurs depends on the coupling between the substrate and the graphene. In general, such an interaction is through Van der Waals interaction, and it is small. A thorough study of the substrate influence is desirable for real applications of this twisted bilayer graphene.We have investigated the mechanical properties of twisted bilayer graphene via molecular dynamics simulations. The bulk modulus of twisted bilayer graphene is similar to the single layered graphene. The bilayer graphene has double the stiffness of single layered graphene, as implied in the stress–strain relationship. The system size effect on the fracture toughness is examined. The strain rate has negligible effect on the fracture toughness. The linear elastic properties are insensitive to the defects and strain rates, which are opposite to the non-linear mechanical properties, including fracture toughness. The hybrid structures combined with the defective (crack) and pristine graphene layers demonstrated a “two-peak” pattern in the stress-strain curves, indicating a sequential failure of the two layers, suggesting a method for characterizing the layered structure via tensile tests. Compared to the single layered graphene, the twisted graphene bilayer retains its extraordinary mechanical properties with the extra bonus in advanced electronic and optical properties, showing a great promise in a wide range of applications.


The good thermal transport properties of NPGs are attributed to the strong hydrogen-bonding interaction between nanocrystalline cellulose and functionalized graphene and the well alignment structure of NPGs.As a two-dimensional material, graphene has attracted increasing attention as heat dissipation material owing to its excellent thermal transport property. In this work, we fabricated sisal nanocrystalline cellulose/functionalized graphene papers (NPGs) with high thermal conductivity by vacuum-assisted self-assembly method. The papers exhibit in-plane thermal conductivity as high as 21.05 W m Studying high thermal conductivity (TC) of polymeric composites has been considered an effective way to solve this problem2. Graphene, a sp2-bond two-dimensional material, has a super thermal conductivity (~5300 W m−1 K−1), whose conjugated molecular structure provides an ideal two-dimensional channel for phonon propagation4. Besides, micron-grade graphene has a large contact area with the polymer matrix compared to the zero- or one-dimensional fillers, such as fullerene5, ceramics7, metal nanoparticles9, carbon nanofiber10 and so on. So graphene is regarded as a kind of favor fillers for high TC for polymer-based composites. For example, Shen et al. employed multilayer graphene as fillers associated with epoxy to fabricate thermal conductive composites. It was found that the obtained composites at 10 vol% graphene loading showed the TC of 5.1 W m−1 K−111. Li et al. fabricated nanofibrillated cellulose/graphite nanoplatelets (GNPs) composite papers reached a TC of 12.4 and 27.95 W m−1 K−1 at 10 wt% and 25 wt% GNPs loading, respectively12. Though the higher TC values will be enhanced with the increase of the graphene content, the enhancement of the TC grows slowly due to the poor dispersion of graphene in the polymer matrix. So how to improve the dispersion of graphene in polymer is still a challenging problem.Nowadays, solving the heat accumulation problem is a top priority for obtaining good-performance and long-lifetime flexible electronic devices, which have become more miniaturized, integrated and functional16. Generally, covalent functionalization introduces defects or distortions into the graphene basal plane during the synthesis of graphene oxide, compromising the sp2 structure of graphene lattices and leading to a loss of the electronic properties. In contrast, noncovalent functionalization, such as π–π stacking interaction is a better option because it does not alter the structure or electronic properties of the graphene while it simultaneously introduces new functional groups on the surface17. Teng et al. adopted functional segmented poly(glycidyl methadrylate) containing localized pyrene groups to functionalize graphene nanosheets (GNSs) through π–π stacking interaction and obtained a homogeneous dispersion liquid (Py-PGMA-GNS). The Py-PGMA-GNS was then dispersed in an epoxy matrix to fabricate Py-PGMA-GNS/epoxy composites. The TC of Py-PGMA-GNS/epoxy with only 4 phr loading achieved 1.19 W m−1 K−1 and was 20% higher than that of the composites with pristine graphene18. What’s more, different functional molecules including 1-pyrenebutyl, 1-pyrenebutyric and 1-pyrenebutylamine can reduce the interfacial thermal resistance by noncovalent functionalization reported by Wang et al.19. It provided a theoretical basis for improving thermal conductivity of polymer composites because the low thermal interface resistance can lead to an enhancement of thermal conductivity. Thus surface noncovalent functionalization for fillers is an efficient way to enhance the interaction between the fillers and the polymer matrix21.It was found that graphene functionalization  is a common and efficient method to enhance a good dispersion of graphene in polymers22. PBIs have a large electron-rich planar aromatic structures that is benefit for their strongly anchor onto the surface of graphene sheets via π–π stacking interaction23. Up to now, there were many reports related to the perylene dimides functionalized graphene and improved the dispersion of graphene27. In a recent report, a series of ionic formed polyamine-functionalized PBIs (HAPBI) were synthesized to disperse graphene via π–π stacking interaction, a high concentration of 1.0 mg ml−1 graphene dispersion was obtained at the 1:3 weight ratio of HAPBI to graphene27.Perylene-3, 4, 9, 10-tetracarboxylic acid dimide derivatives (PBI) have been extensively used in organic semiconductors, photovaltaic devices and fluorescence spectroscopy31. Nanocellulose includes cellulose nanofibers (CNF) and nanocrystalline cellulose (NCC). Particularly, NCC, rod-like nanoparticles (5–20 nm wide and 50–500 nm long), was conventionally prepared by acid hydrolysis, which introduces many hydrophilic groups on the surface of NCC34.Cellulose is a renewable and sustainable eco-friendly natural material. The nanocellulose-based thermal conductive nanocomposites have attracted much attention, which exhibited remarkable mechanical strength, anisotropy and flexibility due to its highly ordered hierarchical structures−1 using the PED as a stabilizer. Meanwhile, the PEG and NCC matrix can form stable dispersions due to the hydrogen bonding interaction, enhancing the miscibility and affinity between PEG and NCC matrix. Herein, it is favorable to form effective heat conduction paths, which assist to improve the TC along the plane. As a result, the TC value achieved 21.05 W m−1 K−1 with 90 wt% PEG loading.In this work, NCC was extracted from sisal fibers by three main steps, de-waxing process, bleaching process, grafting process and a series of mechanical process. Then high thermal conductive sisal nanocrystalline cellulose/noncovalent functionalized graphene composite papers (NPGs) were prepared by vacuum-assisted self-assembly method. A polyamine-functionalized PBI (PED) was synthesized and further used as the stabilizer of graphene. It was found that the high concentration of the stable graphene dispersion can reach up to 0.5 mg ml2, CH2ClCOOH, anhydrous ethanol, Na2SO4•10H2O, CH3COOH, NaOH, H2SO4 and H2O2 were purchased from Sinopharm Chemical Reagent Co., Ltd China. All other reagents and solvents were used without further purication.Graphene was purchased by Ningbo Moxi Tech. Co., Ltd. (China). 3, 4, 9, 10-Perylene tetracarboxylic anhydride (PTCDA) was purchased from the Xiya Chemical Reagent Company . 6-Aminocaproic acid, imidazole, triphenylphosphine (TPP) and Calcium chloride anhydrous were all purchased from Aladdin Chemistry Co., Ltd, China. Ethanediamine (EDA), Pyridine (Py), methanol, 30% hydrochloric acid, the solvent N-methyl-2-pyrrolidone (NMP) and N, N-dimethylformamide (DMF) were analytical grade, provided by Guangdong Guanghua Sci-Tech Co., Ltd, China. Sisal fibers were bought from Guangxi Sisal Company, China. NaClO35. 3,4,9,10-Perylene tetracarboxylic anhydride (PTCDA) , 6-aminocaproic acid  and imidazole (5 g) were stirred at 120 °C for 12 h under nitrogen atmosphere. Then added deionized water (50 ml) with stirring continues for another 2 h at 90 °C. Next, the dark-red solution was filtered to remove the unreacted PTCDA. The mixture was acidified with 2 M HCl aqueous solution until the pH reached a value of ~4. The resultant precipitate was filtered out several times until the filtrate was neutral and dried in a vacuum oven at 60 °C overnight. Secondly, the synthesis process of PED was as follows, the as-prepared PBI powder , Calcium chloride anhydrous (0.3 g) and NMP (30 ml) were added into a round-bottom flask and purged with nitrogen gas for 15 min. EDA , TPP (0.5 ml) and Py (0.4 ml) were added into the flask under nitrogen atmosphere and stirred for 12 h at 120 °C. The mixture was poured into the 1:1 methanol/deionized water and washed at least five times. The final product is a reddish brown solid after drying in a 60 °C vacuum oven for 8 h.The synthetic routes of PBI and PED are shown in Fig. π–π stacking interaction. The final product is a black solid powder after drying in 80 °C vacuum oven overnight, as shown in Fig. Noncovalent functionalization of GNS by PED was carried out as follows. GNS 10 mg) was dispersed in 30 ml DMF by mixing with PED (40 mg) by sonication in water bath for 3 h to give a black homogeneous suspension. The suspension was then centrifuged at 9500 rpm for 5 min to remove the unabsorbed GNS, and the process was repeated for three times to afford the pure PEG nanocomposites. After centrifugal, the supernatant turned to golden yellow due to 0 mg was 2SO4•10H2O (4 wt%) in 500 ml autoclaves at 170 °C for 2 h. Finally, the product were filtered and washed several times and dried in oven overnight. (2) Bleaching process: the obtained de-waxing sisal fibers, 12 g NaClO2, 5 ml CH3COOH and 350 ml deionized water were added into a 500 ml three-necked flask with stirring at 80 °C for 2 h. The product were washed and filtered as above and microcrystalline sisal fibers (MSF) were obtained. (3) Grafting process: firstly, the 7 g MSF, 35 g 10 wt% NaOH and 350 ml anhydrous ethanol were added into a 500 ml three-necked round bottomed flask with stirring at 30 °C for 30 min. Then added 3.5 g monochloroacetic acid (CH2ClCOOH) with stirring continues for another 3 h at 70 °C. The product was collected after washing many times by centrifugal precipitation until neutral. The crude product was dispersed and soaked in 500 ml deionized water for 1 h, shearing the product into small fragments by a high-shear dispersion homogenizer at a speed of 28000 rpm for 30 min. And the transparent gel of nanocrystalline cellulose (NCC) was obtained with a solid content of 0.25 wt% for later use.NCC was extracted from sisal fibers by three steps, de-waxing processing, bleaching processing, grafting processing and a series of mechanical processing. The details of the three processing were as follows. (1) De-waxing process: firstly, the sisal fibers were chopped to an approximate length of 5 mm and washed with deionized water to remove the grain shape lignin on the surface of sisal fibers. Then the sisal fibers were dried in an oven at 60 °C for later use. Secondly, the 50 g drying fibers were boiled in a mixture aqueous solution of NaOH (4 wt%) and NaAt first, a NCC aqueous dispersion with a solid concentration of 0.25 wt% was mixed with PEG via fast stirring for 6 h and then sonication for 30 min. The ultrasonic power was set at 120 W to avoid damaging the PEG. Next, the dispersion was vacuum-filtered on a mixed cellulose ester membranes with a diameter of 50 mm and a pore size of 0.22 µm. The films were dried in a vacuum oven at 30 °C overnight. Finally the drying hybrid films were compressed at a tablet press (10 MPa) for 3 min. The experimental details of the process of NCC/PEG composite papers are shown in Fig. α) of the nanocomposites was measured by the laser flash apparatus  at 25 °C. The thermal conductivity was calculated as follow;α and ρ are the thermal diffusivity and the density of the nanocomposites, respectively. Cp is specific heat capacity, and measured on differential scanning calorimetry , ρ was calculated according to the samples’ dimension and weight. The infrared (IR) photos were captured by IR camera .Fourier transform infrared spectra (FT-IR) were recorded on a Thermo Nexus 470 FTIR spectrometer (KBr disk). A thermal gravimetric analysis (TGA) instrument (Netzsch STA-449) was used to investigate the thermal stability up to 800 °C at a heating rate of 10 °C/min under nitrogen atmosphere. XPS measurements were recorded with an ESCALAB 250Xi instrument . UV-vis spectra in the wavelength range of 300–700 nm was obtained from a UV 3600 spectrophotometer . Fluorescence spectra were measured on a Cary Eclipse fluorescence at the excitation wavelength of 420 nm. The excitation/emission slits was set at 2.5 × 2.5 nm. Atomic force microscopy (AFM) images were taken on a multimode scanning probe microscope from Digital Instruments with Nanoscope IA controller. Field emission scanning electron microscopy  was used to observe the morphology of the specimens. Transmission electron microscopy (TEM) images were recorded on a JEM-2100F high-resolution transmission electron microscope at 200 kV. Samples were prepared by placing a drop of deionized water dispersion on the surface of ultrathin carbon film. The thermal diffusivity  that is ascribed to the loss of the amide chain, indicating the successful amide reaction between PBI and EDA. As shown in the Fig. The GNS, PBI, PED and PEG were analyzed using TGA under nitrogen atmosphere to 800 °C at a heating rate of 10 °C/min. As shown in Fig. 2 C, sp3 C, C-N and O=C-O groups25,26, respectively, as shown in Fig. 2 C (284.8 eV), sp3 C (285.4 eV), C-N/C-NH2 (2856.3 eV), C=O/O=C-N (288 eV) and O=C-O− (289.2 eV) groups, as shown in Fig. 3 C is decreases and the intensity peak of C=O/O=C-N is increases may due to the successful amide reaction. From the surface elements composition in the inset in Fig. 2 C (284.8 eV), sp3 C (285 eV), C-N/C-NH2 (285.8 eV), C=O/O=C-N (287 eV) and O=C-O− (289 eV) groups. The C-N/C-NH2 and C=O/O=C-N groups come from the PED and the high intensity of N 1s peak is consistent with the PED. Except for sp2 C, all groups have a downshift at a range of 0.5 to 1 eV compared to PED as shown in Fig. 26. These changes confirmed that PED has successfully attached into the GNS.XPS characterizations, as shown in Fig. π–π stacking interaction to disperse graphene40. The fluorescence spectroscope and the UV-vis absorbance spectroscope were commonly employed to characterize the π–π stacking interaction between graphene and polycyclic aromatic precursors, such as pyrene derivatives and porphyrin derivatives41. Perylene imide derivatives are a class of fluorescent substances and their fluorescence could be quenched when they are attached onto graphene nanosheets because of photoinduced electron transfer43. Figure π–π stacking interaction between PED and graphene. As shown in Fig. π–π stacking interaction between PED and graphene.There were many reports of polycyclic aromatic precursors with graphene through noncovalent functionalization of 44. On the other hand, the noncovalent functionalization of graphene with PED which not only improves the dispersion of GNS, but also introduces many –NH2 groups that can form the hydrogen bonding interaction, improving the compatibility of PEG and NCC. The cross-section morphology of the NPGs was carried out by SEM, as shown in Fig. 30. When PEG loading was over 70 wt%, the layered structure was not obvious which is due to the excessive PEG is not evenly dispersed in the NCC matrix.The digital photograph of high-transparent of NCC aqueous solution is displayed in Fig. α) of the NPGs, respectively. The neat NCC film has a high in-plane TC of 4.18 W m−1 K−1 at 25 °C, which is higher than other polymer matrix that have low TC values of 0.1–0.5 W m−1 K−145. According to a previous report, the in-plane TC of single nanocrystalline cellulose varies from 0.72 to 5.7 W m−1 K−146. This can be ascribed to the degree of NCCs alignment and the types of cellulose47. For the in-plane direction, the TC and α value increased with the increase of PEG loading and reached 21.05 W m−1 K− 1 and 12.07 mm2/s with a 90 wt% PEG, respectively. Furthermore, the NPGs-90 has a 403% thermal conductivity enhancement (TCE) relative to the neat NCC films (4.18 W m−1 K− 1) in the plane as shown in Fig. 48. For the through-plane direction, the TC reaches only 1.57 W m−1 K−1 at the loading of 70 wt% of PEG, which is attributed to highly aligned PEG network throughout the NCC matrix, demonstrating a high anisotropy values (presented as the in-plane TC divided through-plane TC), as shown in Table Figure 49. Figure The temperature dependence of the in-plane thermal conductivity was studied. As shown in Fig. −1 was obtained at the weight ratio of PED to graphene to 4:1. Meanwhile, it was found that PEG and NCC matrix can form stable dispersions due to the good compatibility of graphene-NCC through the hydrogen bonding interaction, which contributed to the PEG well-aligned in a NCC matrix. It is favorable to form effective heat conduction paths, which assist to improve the in-plane TC. As a result, the in-plane TC of NPGs-90 reached 21.05 W m−1 K−1 with 90 wt% PEG loading, which was 403% enhancement relative to the pure cellulose paper.In summary, we fabricated NPGs of high thermal conductivity by vacuum-assisted self-assembly method. With PED, high concentration of aqueous PEG dispersion up to 0.5 mg mlTailoring Thermal Transport Properties of Graphene Paper by Structural Engineering


Vacancy defects are unavoidable in graphene sheets, and the random distribution of vacancy defects has a significant influence on the mechanical properties of graphene. This leads to a crucial issue in the research on nanomaterials. Previous methods, including the molecular dynamics theory and the continuous medium mechanics, have limitations in solving this problem. In this study, the Monte Carlo-based finite element method, one of the stochastic finite element methods, is proposed and simulated to analyze the buckling behavior of vacancy-defected graphene. The critical buckling stress of vacancy-defected graphene sheets deviated within a certain range. The histogram and regression graphs of the probability density distribution are also presented. Strengthening effects on the mechanical properties by vacancy defects were detected. For high-order buckling modes, the regularity and geometrical symmetry in the displacement of graphene were damaged because of a large amount of randomly dispersed vacancy defects. Graphene sheets is a promising nanomaterial with extraordinary properties for a wide range of applications. A great amount of academic research and experiments have been conducted to explore the properties of single-layer graphene sheets ,2,3,4. HThe main difficulty in the analysis of graphene’s mechanical behavior is the small size of this material ,8. It isIn this challenging field, effective attempts and efforts have been made to analyze the influence of different defects. Banhart et al.  pointed The Monte Carlo simulation (MCS), a sophisticated sampling method, can be used in program design and has been widely adopted in various fields of research including engineering ,21,22,23In this paper, the elastic buckling characteristics of single-layer graphene sheets are studied, and the effects of random vacancy defects are considered. In Since the carbon atoms in graphene sheets are combined with covalent bonds to form a honeycomb 2D lattice, the displacement of a single atom is the response of graphene sheet to external forces. The deformation and displacement of the carbon atoms are constrained by the C–C bonds. Therefore, modelling the C–C bonds in graphene sheets is an appropriate method to analyze the mechanical reaction and response in both static and dynamic states. Considering its technological development and sound mathematical foundation, the efficacy of FEM in the nanomaterial computation field was explored. The beam element was chosen as the finite element in the elastic buckling analysis of graphene sheets, as presented in For the elastic buckling analysis, the eigenvalue equation for the structure subjected to static load is expressed asIn the finite element model of graphene lattice, the global stiffness matrix Then, the critical buckling stress The validation of the modified Morse potential has been proved in previous studies to predict the mechanical properties of carbon nanotubes. The potential was successfully employed in the nonlinear response simulation of nanomaterials under tensile and torsional external forces . The efftial are (9)r0=1.On the basis of the above expression, the diameter Per, can be expressed asAs mentioned above, each beam in the FEM of graphene sheets has a specific number. If a number is chosen in the MCS, a corresponding beam is removed from the entire hexagon structure of graphene sheets. The defect density, Per. By the MCS, the vacancy defects are randomly distributed in graphene sheets. For the parameters related to the material and geometrical properties, the Young’s modulus and Poisson’s ratio in graphene sheets are settled as 1.2 TPa and 0.2, respectively; the length of the bond in the honeycomb lattice is 0.27 nm, and the diameter in the cross section of the beam finite element is 0.032 nm, according to the literature . For theWhen the number of samples is increased, the relative errors of MCS are obviously reduced. In other words, when the sampling space is large enough, the MCS can reach an acceptable accuracy. However, the required large sampling sets and repetitions of FEM significantly increase the costs of experimentation. Evidently, a trade-off exists between the result accuracy of MCS and the computational costs in numerical simulations. On the basis of a previous work , the numAfter validation of the FEM, the MC–FEM was applied until sufficient sampling was completed. By the MCS generator, the beams representing vacancy defects were chosen and removed from the pristine graphene sheets. The elastic buckling analysis was performed by the MC–FEM in the vacancy-defected graphene sheets.Per, is determined, one application of the MC–FEM is insufficient to describe the stochastic placement of the vacancy defects. It was, therefore, necessary to repeat the MCS and implement the FEM for a sufficient number of times for as thorough an analysis as possible.Given that vacancy defects are randomly distributed in graphene sheets, it is hard to evaluate or predict with certainty their locations throughout the graphene sheets. When the number of vacancy defects, Per exceeded 3%, the critical buckling stress decreased suddenly. When the amount of vacancy defects was minor, the reduction was slow and insignificant.Per was equal to 2%, the reduction rate of the critical buckling stress was 1.64% compared with graphene without defects. The influence of the vacancy defects on the elastic buckling behavior was not obvious and could be ignored. However, when Per was 3%, 4%, and 5%, the reduction of the critical buckling stress was 9.91%, 21.7%, and 44.07%, respectively, compared with graphene without defects. Therefore, when the vacancy defects exceeded a certain density, the vacancy defects had a great influence on the elastic buckling behavior of graphene. The existence of vacancy defects damages the structural symmetry and integrity of the graphene and has a deep effect on the buckling behavior.When As shown in Because of the randomly distributed placement of the vacancy defects in graphene sheets, the critical buckling stress was variable and fluctuated within certain ranges. In order to discuss the probability results more clearly, The probability analysis of the buckling behavior of vacancy-defected graphene sheets was successfully performed based on the results of the MC–FEM. Besides the probability results, the range of values of the critical buckling stress was determined in this study. From these results, the extreme situations and the corresponding critical buckling stress were analyzed and are shown in Per was small. The curve of the maximum critical buckling stress was not as smooth as those of the minimum and the mean critical buckling stress. It is clear that the curve of the maximum critical buckling stress was characterized by two different stages. In the first stage, the critical buckling stress became larger with the increase of vacancy defects. However, in the second stage, the critical buckling stress sharply reduced with the increase of vacancy defects. This phenomenon was also measured and tested in physical experiments [In addition, the stiffness strengthening effect was observed in the curve of the maximum critical buckling stress when the amount of vacancy defects was smaller than 2%, as reported in eriments , and theFurthermore, with the augmentation of the number of vacancy defects, not only the critical buckling stress itself was affected, but also the interval between the maximum and minimum values of the MC–FEM was amplified. In Similar to this study, kinetic lattice Monte Carlo simulation  was usedThe buckling of graphene is feasible and appropriate in the application of hydrogen storage and memcapacitor. The possibility of recruiting the buckled membrane as a plate of capacitor with memory was validated by MD simulations and elastic mechanical calculations . BesidesIn order to demonstrate the buckling behavior of vacancy-defected graphene sheets, With the increase of the number of vacancy defects, the vector sum of the displacement of the low-order buckling mode maintained geometrical symmetry. A deviation was not very evident, which as shown in For the deformation of the elastic buckling, the results of this study are in good agreement with quasi-static MD predictions. The buckling surfaces of highly cross-linked epoxy polymers under stress obeyed the paraboloid yield criterion at different temperatures . BesidesIn this paper, a detailed study of the elastic buckling of rectangular graphene sheets with different amounts of vacancy defects was carried out using the MC–FEM. The random dispersion of vacancy defects in graphene was taken into consideration, and the effects of the amount of vacancy defects and stochastically distributed placements were discussed. From the proposed MC–FEM, the following conclusion can be drawn:With the increase of vacancy defects in graphene, the critical buckling stress sharply decreased, and the standard variance for the buckling stress was evidently amplified when the number of vacancy defects exceeded 3%. When the vacancy defect percentage was equal to 5%, the reduction of the critical buckling was as large as 44%. The vacancy defects profoundly influenced the buckling behavior of the graphene lattice. With the increase of vacancy defects, the geometrical symmetry in the vector sum of displacement was obviously affected.The randomly distributed placement of vacancies caused a fluctuation and deviation in the buckling behavior of the graphene vibration. The intervals between the maximum and minimum values were amplified with the increase of vacancy defects. For different amounts of vacancy defects, the probability density distributions of the critical buckling stress were close to the Gaussian or t Location-Scale distributions.Furthermore, the stiffness strengthening effect of the vacancy defects on graphene was discussed in this study. The possibility of improving the mechanical properties of graphene by vacancy defects was confirmed.


Transactivating DNA-binding protein-43 (TDP-43) deposits represent a typical finding in almost all ALS patients, more than half of FTLD patients and patients with several other neurodegenerative disorders. It appears that perturbation of nucleo-cytoplasmic transport is an important event in these conditions but the mechanistic role and the fate of TDP-43 during neuronal degeneration remain elusive. We have developed an experimental system for visualising the perturbed nucleocytoplasmic transport of neuronal TDP-43 at the single-cell level in vivo using zebrafish spinal cord. This approach enabled us to image TDP-43-expressing motor neurons before and after experimental initiation of cell death. We report the formation of mobile TDP-43 deposits within degenerating motor neurons, which are normally phagocytosed by microglia. However, when microglial cells were depleted, injury-induced motor neuron degeneration follows a characteristic process that includes TDP-43 redistribution into the cytoplasm, axon and extracellular space. This is the first demonstration of perturbed TDP-43 nucleocytoplasmic transport in vivo, and suggests that impairment in microglial phagocytosis of dying neurons may contribute towards the formation of pathological TDP-43 presentations in ALS and FTLD.The online version of this article (10.1007/s00401-018-1875-2) contains supplementary material, which is available to authorized users. Pathological deposition of protein aggregates characterises a large group of neurodegenerative diseases in which the key event is considered to be either the malformation of a key protein, or the failing of a crucial metabolic cascade, leading to the generation of a pathological proteinaceous agent that can self-propagate and result in gain- or loss-of-function progressing toward cell death .An aggregating proteinopathy involving the TAR DNA-binding protein TDP-43) provides a molecular link between two progressive neurodegenerative diseases: amyotrophic lateral sclerosis (ALS) of the motor cortex, brainstem and spinal cord, and frontotemporal lobar degeneration (FTLD) of the frontal, insular, and temporal cortex TARDBP, or of an interacting partner, upsets the balance between aggregation and dissolution of TDP-43 .Transgenic lines were generated by co-injection of the Tol2-flanked constructs and transposase .PU.1 (spi1b)-targeted active MO: 5ʹ-GATATACTGATACTCCATTGGTGGT-3ʹPU.1 (spi1b) mismatch MO: 5ʹ-GATAAACTGTTACTCGATTGCTGGT-3ʹThe PU.1 morpholino and a mismatch control used in this study were the same as those of Rhodes et al. , obtaineD. rerio, GRCz10). Given the efficacy of the morpholino in this and previous studies . Similarly, for the motor neurons targeted by UV to induce degeneration, such as the neuron demonstrated in Fig. WT was detectable along the motor axon from the hillock to its conjunction with the axon bundle leading down into the myotome, whereas mTagBFP was present throughout the cytoplasm and along the axon. However, in two of the ten motor neurons that underwent UV-induced degeneration, we observed that the process of cellular expansion was accompanied by a rapid movement of eGFP-TDP43WT into the axon. The process is illustrated in Fig. WT intensity along the axon increased during the process of degeneration before spiking rapidly ~ 5 h after the initial UV stress (but prior to dissolution as described above). The observation of inducible spreading of TDP-43 into axons in a living organism points to the existence of a potential molecular mechanism for progressive transmission and redistribution of pathological TDP-43 to neighbouring and distal neurons within the motor system.In addition to morphological alterations of the cell body, we also observed beading and disintegration of the axon as a sequela of UV-induced degeneration of spinal motor neurons (Online Resource 8). To follow up on this, we investigated whether the mislocalisation of TDP-43 into the cytoplasm was accompanied by an increase in axonal TDP-43. To establish a baseline, we examined eGFP-TDP43Our understanding of the molecular pathogenesis of ALS and FTLD is limited by our incomplete understanding of the sequence of events underpinning the pathology. Many of the insights we currently have on the hallmarks of ALS/FTLD were derived from postmortem histological studies. In almost all ALS patients and more than half of FTLD patients, the key molecular histopathological hallmark is the presence of abnormal amounts of intracellularly deposited TDP-43. It typically forms cytoplasmic-insoluble aggregates within neurons  and thatThe subcellular redistribution of TDP-43 from the nucleus to the cytoplasm in injured motor neurons  was especially striking, and this study represents the first real-time in vivo observation of the phenomenon. This redistribution of TDP-43 to the cytoplasm has been suggested to represent a landmark feature of the onset and progression of ALS/FTLD , 43, andSignificant recent interest in the field has focussed on the potential cell-to-cell transfer of TDP-43 and/or associated proteins, and the possibility that this might facilitate propagation of the disease process through the motor system. In vitro, the intercellular transfer of TDP-43 has been demonstrated , 57, 67.UV injury-induced degeneration resulted in a phagocytic response by spinal microglia, which took up predominantly nuclear TDP-43. This represents the first in vivo demonstration of TDP-43 uptake by microglia as well as the prevention of TDP-43 spreading by normal microglia. Further study into the fate of TDP-43 and whether microglial function is altered following TDP-43 uptake in the zebrafish model may help expand upon the early evidence for microglial involvement in the progression of ALS/FTLD , 35, 46.WT inside axons of healthy spinal motor neurons. Neuronal cell cultures and drosophila imaging have identified axonal TDP-43 as a component of transport vesicles in keeping with a role as a chaperone of RNA transport [WT. This difference indicates the intriguing possibility that the incorporation of TDP-43 into transport vesicles may be differentially regulated between neuronal cell types, in an in vivo setting  or in distinct environmental or developmental contexts.Depletion of microglia through PU.1 knockdown allowed us to visualise intracellular spreading of TDP-43. In fact, we observed at least three pathways for TDP-43 dispersal following motor neuron degeneration after injury: this included diffusion of TDP-43 from the nucleus to the cytoplasm after neuronal swelling, compartmentalised TDP-43 release in discrete quanta, and the breakdown of homeostatic TDP-43 exclusion from the axon. Concerning the interesting axonal spreading of TDP-43 it is important to note that we consistently did not detect any eGFP-TDP43ransport , 19. We This study provides the first live-imaging data to support axonal-mediated spreading of TDP-43 , 45. In Taken together, nucleo-cytoplasmic transport of TDP-43 was studied in real time in the living spinal cord, and revealed that depletion of functional microglia leads to axonal and extracellular spreading of the pathological protein. This provides new insight into the potential direct role of microglia as well as pathways for protein distribution in the cascade of cellular and molecular events that cause ALS and FTLD.Online Resource 1 Video of motile TDP-43 accumulations in the motor neuron nucleus. 8 kHz resonance scan of a motor neuron cell body with nuclear eGFP-TDP43WT (green) and cytoplasmic mTagBFP (magenta). Scale=1 µmSupplementary material 1 (MP4 27379 kb) Online Resource 2 Video of spinal microglia  directed migration toward and engulfment of eGFP-TDP43WT expressing motor neuron during UV-induced degeneration. Scale=10 µmSupplementary material 2 (MP4 5934 kb) Online Resource 3 Video of spinal microglia  migration toward an eGFP-TDP43wt expressing motor neuron after UV stress. The neuron does not undergo cell death or demonstrate morphological signs of degeneration over the follow-up imaging period. The microglial cell approaches and makes contacts with the targeted cell and surrounding region, as well as appearing to phagocytose a nearby cell. The microglial cell finally migrates away from the target cell. Scale=10 µmSupplementary material 3 (MP4 3963 kb) Online Resource 4 Video of the expansion and dissolution of a motor neuron and its TDP-43 expression over the course of UV-induced degeneration. mTagBFP is expressed in the cytoplasm and eGFP-TDP43WT is initially localised predominantly to the nucleus. Scale=2 µmSupplementary material 4 (MP4 16244 kb) Online Resource 5 Video showing the blebbing of a motor neuron cell body over the course of UV-induced degeneration. Nuclear fragmentation and cytoplasmic extrusions occur during the degeneration process. Scale=2 µmSupplementary material 5 (MP4 2567 kb) Online Resource 6 High time resolution video of nuclear eGFP-TDP43WT redistribution to the cytoplasm during cell dysmorphia. An 8 kHz resonance scan shows the rapid redistribution of TDP-43 into the cytoplasm. Segments of the axon become visible as eGFP-TDP43WT localises to these regions. Scale=2 µmSupplementary material 6 (MP4 13465 kb) Online Resource 7 Video of TDP-43 fragments released from a neuron undergoing UV-induced degeneration. Scale=2 µmSupplementary material 7 (MP4 2416 kb) Online Resource 8 Video of progressive fragmentation of the axon in a motor neuron in the spinal cord occurring during UV-induced degeneration. The axon is visualised by a membrane bound mKO2-CAAX (yellow). Scale=10 µmSupplementary material 8 (MP4 1597 kb) Online Resource 9 Video of axonal TDP-43 redistribution during UV-induced degeneration. Top panel: Max projection of stacks for eGFP and mTagBFP channels. Fluorescence intensities have been optimised to highlight the axonal distribution of TDP-43. Scale=5 µm. Bottom panel: Fluorescence intensity of eGFP in segments along axon trajectory in 3D over the stackSupplementary material 9 (MP4 3045 kb) Online Resource 10 Supplementary figures 1-4Supplementary material 10 (PDF 1304 kb) Below is the link to the electronic supplementary material.