Supplemental Digital Content is available in the text An increasing number of studies have examined the ability of programmed death-ligand 1 (PD-L1) to function as a marker for tumor prognosis. However, whether PD-L1 expression is a prognostic factor for the poor outcomes in many human cancers remains controversial. This study aims to investigate the prognostic role of PD-L1 expression through a meta-analysis update of 60 studies.The studies were identified by searching PubMed, Embase, Google Scholar, and Cochrane Library, and were assessed by further quality evaluation. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for total and stratified analyses were calculated to investigate the association between the PD-L1 expression and the overall (OS) and disease-free (DFS) or progression-free survivals (PFS) of cancer patients. Heterogeneity and publication bias were also investigated.P <.000) and DFS/PFS . Subgroup analyses showed that PD-L1 overexpression was significantly related to the poor OS in patients with breast , urothelial , renal , and gastric cancers . Furthermore, PD-L1 overexpresion was significantly associated with poor DFS/PFS in patients with hepatocellular carcinoma (HCC) , melanoma , and renal carcinoma, . The adverse prognostic impact of PD-L1 was observed in patients of different ethnicities.The results indicated that PD-L1 overexpression can predict a poor OS (HR = 1.58, 95% CI = 1.38–1.81, The findings of this meta-analysis suggest the correlation of PD-L1 overexpression with worse OS in patients with solid tumors. However, the correlations differed according to tumor types. Although PD-1 cell surface protein expression can be discovered within 24 hours of stimulation, the functional effects of PD-1 ligation are observed within a few hours following T-cell activation. Tumors express antigens recognized by host T cells, although the immunologic clearance of tumors is rare. Part of this failure is attributed to the immune suppression by the tumor microenvironment. PD-1 interacts with 2 ligands, PD-L1  and PD-L2. The PD-L1 expression on many tumors is an integrant part of this suppressive milieu and may act in cooperation with other immunosuppressive signals. PD-L1 is expressed in several tumor types, such as melanoma, glioblastoma, and cancers in the lung, kidney, head and neck, stomach, colon, pancreas, breast, cervix, and ovary. PD-L1 inhibits T-cell proliferation and adhesion, as well as cytokine production. Furthermore, it appears to regulate T-cell function in peripheral tissues.Programmed cell death 1 (PD-1), an immunoinhibitory receptor was first described in 1992 by Ishida et al.–7 However, the prognostic role of PD-L1 remains unclear. Other studies have found that PD-L1 expression is correlated with both poor prognosis and no prognostic significance, making it difficult for researchers to present definitive conclusions. Thus, the PD-L1 expression in cancers and its prognostic significance are still uncertain.PD-L1-positive tumors may indicate immune active tumors that can be a response to anti-PD-1 and/or PD-L1 therapies because they are correlated with the poor prognosis of many of these malignancies, including lung adenocarcinoma, melanoma, and refractory Hodgkin's lymphoma.In this study, we conducted a systematic review and meta-analysis to summarize the global findings in using PD-L1 for the prediction of clinical results for cancer patients.22.1We performed a network search using PubMed, Embase, Google Scholar, and Cochrane Library for original articles that analyzed the prognostic value of PD-L1 in various cancers. We selected studies using different combinations of the following keywords: “Neoplasm,” “cancer,” “carcinoma,” “PD-L1,” “programmed cell death ligand 1,” “B4-H1,” and “CD-274.” The final search was performed on November 2015. We increased the integrity and accuracy of the search process by manually screening the reference lists for included articles to explore potential studies.As the present meta-analysis was performed based on previous published studies,thus no ethical approval and patient consent are required.2.2The studies were considered eligible if they met all of the following inclusion criteria: diagnosis of solid tumor was proven in human; the expression of PD-L1 through immunochemistry was evaluated; PD-L1 was correlated with overall survival (OS), disease-free survival (DFS), or progression-free survival (PFS); and hazard ratio (HR) and their corresponding 95% confidence intervals (CI) were estimated by sufficient data. Articles were excluded based on the following criteria: review or laboratory articles, as well as case studies or letters; articles that described the survival outcome of other indicators; articles from 1 author and studies with repeated samples of the same patients; and unpublished studies.2.3Two investigators (Qianqian Wang and Fang Liu) independently extracted data from each eligible study. The following information were extracted in each study: author, year of publication, country, number of patients, median age, median follow-up period, binded reading, tumor stage, PD-L1 overexpression cut-off value, prognostic outcomes of interest, analytical method, and HR with its 95% CI. Disagreements were resolved through discussion, and consensus between the 2 authors.2.4 Williamson et al, and Tierney et al. Statistical heterogeneity between studies was quantified using Cochran's Q test and Higgins I-squared statistic. Heterogeneity was defined as P <.05 or I2 >50%. If there was statistically significant heterogeneity, a random effects model was selected to combine the data. Otherwise, a fixed effects model was used. By convention, poor outcome for PD-L1 overexpression was considered when the described HR >1, and would be considered statistically significant if the 95% CI did not overlap 1. Subgroup analysis was further performed to explore the heterogeneity source. Publication bias was investigated by visually assessing the asymmetry of an inverted funnel plot. Moreover, Begg's and Egger's tests were conducted to quantitatively support the publication bias. All analyses were performed using STATA 12.0. A P-value <.05 was considered statistically significant.Positive or negative of PD-L1 was defined according to the cut-off values provided by the authors. The impact of PD-L1 overexpression on prognosis in solid tumors was estimated by HR and its 95% CI. We directly used crude values when HRs were reported in the original studies. Otherwise, the values were calculated using Kaplan–Meier curves according to the methods described by Parmar,33.1 Thus, 61 studies were included in this meta-analysis . Furthermore, significant heterogeneity was noted among the studies , as shown in Fig. P = .001), which suggested that PD-L1 overexpression was a poor prognosis indicator for multiple solid cancers, and significant heterogeneity was found across the studies , as shown in Fig. The combined HR of 60 included studies, which included 10,310 cancer patients, showed that the PD-L1 overexpression was associated with poor OS , urothelial , renal , and gastric cancers . No significant correlation between PD-L1 and OS was found in the other subgroup analyses, as shown in Fig. P = .003), melanoma , and renal carcinoma,  or Caucasian patients  and Caucasian patients   Fig. A. Sensit4,71 To evade from the immune system's monitoring, tumor cells in tumor microenvironment can upregulate the expression of PD-L1 through a variety of mechanisms, and bind themselves with negative immune checkpoint PD-1 on the surface of T cells, thereby inhibiting T cells function, losing its killing effect on tumor cells.PD-1 and its ligands, PD-L1 and PD-L2 expressed within the tumor microenviroment, can modulate the balance between T-cell activation, tolerance, and immunopathology during long-term antigen exposure.,73 Several clinical trials, which combine anti-PD-1 mAbs with cancer vaccines , antitumor mAbs (lymphoma), or chemotherapies (pancreatic cancer and NSCLC), have been planned or are in progress. Thus, PD-L1 may be used as a prognostic marker and therapeutic target for those cancers, which provides impetus for the further investigation of its functions.The correlations between PD-L1 expressions and different tumors have been studied by numerous researches. However, the results were inconsistent and conflicting. To arrive at a reasonable conclusion, we performed this meta-analysis and aimed to evaluate the relationship between PD-L1 overexpression and the prognosis of solid tumor patients. Our analysis included 60 studies with 10,310 cases. The results showed that PD-L1 overexpression is a poor prognostic factor in multiple solid tumors with statistical significances for OS , and DFS/PFS . We conducted subgroup analyses according to different tumor types and ethinicities. For tumor type, the analysis indicated a statistically significant detrimental effect of PD-L1 on the OS in breast, urothelial, renal, and gastric cancers. Furthermore, PD-L1 overexpression was also significantly associated with worse PFS in HCC, melanoma, and renal carcinoma. For ethnicities, the adverse prognostic impact of PD-L1 was retained in patients with different ethnicities. Other studies showed that antibodies blocking PD-1 or PD-L1 are likely to provide a new benchmark for antitumor activity in immunotherapy.The robustness of the results in our meta-analysis was confirmed by a sensitivity analysis, which did not significantly alter the pooled effect size for OS and DFS/PFS. Furthermore, HRs extracted from Kaplan–Meier curves seemed to be less reliable than those reported directly. However, we did not discover any obvious primary deviation in the publications when comparing our extracted OS HRs and 95% CIs with the outcomes of the published studies. Nevertheless, this meta-analysis still has many deficiencies. First, only published studies and studies in English were included. We may have missed unpublished and non-English studies which are unsuitable for meta-analysis, meanwhile there might be selection bias. Second, high heterogeneity was observed among studies, which will influence the analysis, interpretation, and conclusions of this study. To minimize the effect of the heterogeneity on OS and DFS/PFS, we used a random effect model. Nevertheless, the heterogeneity within and between studies only disappeared in urothelial cancer, melanoma, and renal carcinoma subgroups, we could not determine the origin of the heterogeneity. Third, no consistent standard for cut-off values were found in our included studies, which can cause clinical and statistical heterogeneity. Finally, the publication bias between the relationship between PD-L1 overexpression and OS has been shown on Egger tests and funnel plots, and we found evidence of the publication biases across the included studies. However, our finding that the corrected pooled effect size remained statistically significant after using the “Trim and Fill” method to adjust for the effect of potential publication bias on the results of the meta-analyses. Thus, the reliability of our results could be confirmed. In addition, studies with different follow-up times can also result in bias.5In conclusion, we demonstrated that PD-L1 overexpression is significantly associated with poor OS and DFS/ PFS, especially in renal carcinoma and urothelial cancer. PD-L1 overexpression may predict poor prognosis in different cancers. Furthermore, the PD-L1 target can become an effective anti-cancer therapy as well.Authors thank all the authors whose publications were included in our meta-analysis.


PD-1 and PD-L1 mRNA-expression in a cohort of ovarian cancer (OC) patients in relation to tumor mutations. We analyzed mRNA expression of PD-1, PD-L1 and IFNG by quantitative real-time PCR in tissue of 170 patients with low grade-serous (LGSOC), high-grade serous (HGSOC), endometrioid and clear cell OC compared to 28 non-diseased tissues  in relation to tumor protein 53 (TP53) and breast cancer gene 1/2 (BRCA1/2) mutation status. TP53-mutated OC strongly expressed PD-L1 compared to TP53 wild-type OC (p = 0.028) and BRCA1/2-mutated OC increasingly expressed PD-1 (p = 0.024) and PD-L1 (p = 0.012) compared to BRCA1/2 wild-type OC. For the first time in human, we noted a strong correlation between tumoral IFNG and PD-1 or PD-L1 mRNA-expression, respectively (p < 0.001). OC tissue increasingly expressed PD-1 compared to healthy controls . PD-1 and PD-L1 mRNA-expression increased with higher tumor grade  and younger age . Finally, in the major subgroup of our cohort, FIGO stage III/IV HGSOC, high PD-1 and PD-L1 mRNA-expression was associated with reduced progression-free (p = 0.024) and overall survival (p = 0.049) but only in the univariate analysis. Our study suggests that in OC PD-1/PD-L1 mRNA-expression is controlled by IFNγ and affected by TP53 and BRCA1/2 mutations. We suggest that these mutations might serve as potential predictive factors that guide anti-PD1/PD-L1 immunotherapy.Checkpoint molecules such as programmed cell death protein-1 (PD-1) and its ligand PD-L1 are critically required for tumor immune escape. The objective of this study was to investigate tumoral  Ovarian cancer (OC) is the major cause of death among gynecological cancer entities . In recePD-L1 is expressed by tumor cells  to inactThe rationale to test the efficacy of checkpoint inhibitors in OC arises from the observation that intratumoral T-cells directly correlate with clinical outcome  and thatPD-1 and PD-L1 mRNA expression in 170 epithelial OC in relation to 28 non-neoplastic tissues and to clinicopathological features to identify a subgroup of patients which may profit by checkpoint inhibitors. Since IFNγ was found to play an essential role in the adaptive immune resistance of tumors as an inducer of PD-L1 on tumor cells  were determined with the assistance of the computer program Primer Express . BLASTN searches were conducted to confirm the total gene specificity of the nucleotide sequences chosen for the primers and probes. PD-L1 forward primer: 5′-AATGATGGATGTGAAAAAATGTGG-3′; PD-L1 reverse-primer: 5′-AATGCTGGATTACGTCTCCTCC-3′; PD-L1 TaqMan probe: 5′-FAM-TCCAAGATACAAACTCAAAGAAGCAAAGTGATACACATT-3′-TAMRA. To prevent amplification of contaminating genomic DNA, the probe was placed at the junction between exons 6 and 7. Primers and probe for IFNG and PD-1 were purchased from Applied Biosystems . PCR reactions were performed as previously described [Primers and probes for the TATA box-binding protein . For the particular primary antibodies the following procedures were used: PD-L1  diluted at 1:100 was incubated for 32 minutes and the OptiView™ DAB detection kit system CC1 was used for 36 minutes. PD-1  ready to use incubated for 32 minutes followed by the UltraView™ DAB detection kit CC2 for 44 minutes. For counterstaining hematoxylin was used. The evaluation of immunohistochemistry was performed semiquantitatively.® and the NextSeq system . After sequencing, mutations in BRCA1, BRCA2 and TP53 were identified with the help of NextGene and Geneticist Assistant softwares. Pathogenicity for new mutations that were not found in the database was determined and categorized using prediction tools like SIFT, alignGVGD, mutation taster.Genomic DNA from 158 pulverized, quick-frozen OC specimens was isolated using the DNeasy tissue-kit . Targeted NGS was performed using the TruSight Cancer sequencing panel . The analyses were performed on the Illumina MiSequU test or Kruskal–Wallis test were applied to test for statistical significance between two groups or more than two groups, respectively. For parametric sample sets student’s two-tailed t-test was applied to test for statistical significance between two groups. The correlations between PD-1, PD-L1 and IFNγ mRNA expression were assessed by Spearman rank correlation analyses . Progression free survival (PFS) was defined as the time from diagnosis of the primary to tumor to the histopathological confirmation of recurrence or metastases and overall survival (OS) as the time from diagnosis of the primary to tumor to death from any cause or to the last clinical inspection. Univariate Kaplan-Meier analyses and multivariable Cox survival analyses were used to explore the association of PD-1 and PD-L1 expression with PFS and OS . For survival analyses, patients were dichotomized into low and high mRNA expression level groups by the optimal cut-off expression value calculated by the Youden’s index [P-values less than 0.05 were considered as statistically significant. Statistical analysis was performed using SPSS statistical software .The non-parametric Mann-Whitney ’s index . P-value


Solanum lycopersicum that contain different, defined chromosomal segments derived from the wild tomato relative, S. pennellii. Among these lines, IL4-4, in which the bottom of S. lycopersicum (cv. M82) chromosome 4 is replaced by the corresponding S. pennellii segment, is altered in many primary and secondary metabolites, including many related to fruit flavor and nutritional quality. Here, we provide a comprehensive profile of IL4-4 ripe fruit metabolites, the transcriptome and fine mapping of sub-ILs. Remarkably, out of 327 quantified metabolites, 185 were significantly changed in IL4-4 fruit, compared to the control. These altered metabolites include volatile organic compounds, primary and secondary metabolites. Partial least squares enhanced discriminant analysis of the metabolite levels among sub-ILs indicated that a genome region encompassing 20 putative open reading frames is responsible for most of the metabolic changes in IL4-4 fruit. This work provides comprehensive insights into IL4-4 fruit biochemistry, identifying a small region of the genome that has major effects on a large and diverse set of metabolites.A major resource for tomato quality improvement and gene discovery is the collection of introgression lines (ILs) of cultivated  Solanum pennellii, a wild South American relative, has a complete genome sequence  is a barrier to utilizing the genetic variation of wild relatives at the bottom of chromosome 4 for improving tomato fruit quality  (Agilent 5975 GC/MS)1. The list of quantified volatile compounds is presented in Supplementary Table Volatile organic compounds (VOCs) were collected from ripe fruits of sub-ILs R401, R2174, R2075, R100, R434 as well as the parent lines, IL4-4 and M82, grown in randomized, replicated plots. Each biological replicate was a mixture of five to six individual fruit at the red ripe stage. Collection of VOCs was performed as described previously . In brieThe extraction method was performed as described by Sugars  and acids  were quantified in Florida as previously described .For secondary metabolites the rest of polar phase (see above extraction protocol) was dried and residue was suspended in 200 μl of 80% methanol water (80:20). The extracts were then subjected to LC-MS analysis using a high-performance liquid chromatography , coupled to a Finnigan LTQ-XP system , Metabolite identification and annotation were performed using a combination of standard compounds and tomato metabolomics databases .2 in positive and negative ionization mode. Processing of chromatograms, peak detection and integration were performed using REFINER MS® 6.0 (GeneData)3. Processing of mass spectrometry data included the removal of the fragmentation information, isotopic peaks, as well as chemical noise. Obtained features (m/z at a certain retention time) were queried against an in-house lipid database for further annotation.For lipid extraction, the lipophilic phase (see extraction protocol above) was collected and vacuum-dried. Samples were processed using ultra-performance liquid chromatography coupled with Fourier transform mass spectrometry .Five ripe tomato fruits were homogenized in a blender for 30 s and frozen at -80°C until analysis. Samples were thawed and 1.5 mL was centrifuged at 16 000 × p < 0.01) was used to determine the QTL controlling metabolite content. All lines were compared with M82 and each other. If metabolite level of the line was significantly different from the M82 control (indicated by Dunnett’s t-test p < 0.01), a line was considered as harboring a QTL. A list of QTLs within the sub-ILs can be found at Supplementary Table To map the metabolite variation in the sub-ILs, a one-way analysis of variance  was performed by using the Excel add-in Multibase package .Unpaired Student’s Total RNA was extracted from frozen pericarp tissue of ripe fruits as described in S. lycopersicum and S. pennellii were aligned using ClustalW4, and polymorphic sequences were submitted to SIFT5 to predict the impact of amino acid substitutions on protein function . A dot represents the log2 of the RPKM ratio of a transcript between IL4-4 and control M82.Gene ontology enrichment analyses of DEGs were conducted with PANTHERT-test.Twenty fruits harvested from R2174, R434, R2075, IL4-4, and M82 were subjected to sensory evaluation. Samples were cut into wedges and labeled with random numbers. Sample taste tests were performed on two occasions with two separate harvests. Each taste panel had 14 panelists. Each sample was rated on a 10-point hedonic scale . Taste preference scores are an averaged score of all panelists. Significant differences were calculated using Student’s p < 0.05) . Chemicals with altered contents included 21 VOCs, 44 hydrophilic primary metabolites, 31 hydrophilic secondary metabolites and 89 lipids (Table 1). Consistent with previous results (Table 1). Interestingly, out of 18 quantified sugars and sugar alcohols, 12 were elevated, including the most important contributors to sweetness, glucose, and fructose, while only one unknown sugar was significantly decreased in IL4-4. As key precursors for phenylpropanoids biosynthesis, levels of all three AAA were increased in IL4-4, indicating potential regulators of the shikimate pathway may exist within the S. pennellii genomic segment included in IL4-4. In addition, 31 out of 49 hydrophilic secondary metabolites and 89 out of 170 lipids that were quantified were significantly altered (p < 0.05) (Table 1). These results together indicate a locus or loci at the bottom of S. pennellii chromosome 4 regulating multiple independent metabolic pathways during fruit ripening.To obtain an overview of the IL4-4 fruit metabolome, we quantified a set of metabolites that included VOCs, hydrophilic primary, hydrophilic secondary and lipophilic metabolites by utilizing targeted gas chromatography (targeted GC), gas chromatography-mass spectrometry (GC-MS) and high/ultra-performance liquid chromatography mass spectrometry (HP/UPLC-MS). An overlay heat map shows the metabolite contents in IL4-4 with respect to M82. The fully annotated data set is provided in Supplementary Table  results , many VO results , includiP-value < 0.05, we identified 2307 more abundant transcripts and 1635 less abundant transcripts in IL4-4 fruit tissue compared to M82 . GO analysis showed that the largest groups in biological process and molecular function were metabolic process (47.4%) and catalytic activity (45%), respectively.To gain insight into the molecular mechanisms underlying the observed metabolite changes, gene expression in ripe pericarp tissues of IL4-4 and M82 was determined using RNA-Seq. A total of 26,147 expressed genes were detected in IL4-4 and M82 . The mapped DEGs are involved in main primary and secondary metabolic biosynthetic processes such as sugar, acid, amino acid, phenylpropanoid, and lipid metabolism. Notably, light reaction-associated genes were significantly more abundant. This pattern suggests a possible increase in photosynthesis in IL4-4 fruit, which in turn could provide precursors for multiple metabolic pathways.In order to further investigate the potential roles of DEGs in metabolism, we generated a snapshot of these genes over the main metabolic pathways facilitated by MapMan. An overview of the map suggested a global transcriptional regulation of metabolism-associated genes by IL4-4 . We observed significantly higher levels of TomLOXB (Solyc01g099190), TomLOXF (Solyc01g006560) and HPL (Solyc07g049690) transcripts in IL4-4 fruit tissue while other members of the 13-LOX family remained unchanged . Increased transcript abundance of these genes is consistent with the higher levels of C5 and C6 volatiles in IL4-4 fruit .To better understand the relationship between transcription and metabolite synthesis in IL4-4, we examined transcript content of genes encoding key enzymes related to FA metabolism and the shikimate-phenylpropanoid pathway. 13-lipoxygenases (13-LOXs) and hydroperoxide lyase (HPL) are the main enzymes catalyzing conversion of C18 polyunsaturated FAs to C5 and C6 volatiles in tomato fruit . The transcripts associated with most genes in the shikimate pathway were not significantly different while there was a significant decrease in shikimate dehydrogenase (Solyc10g038080) in IL4-4. Transcripts encoding three genes in the tryptophan synthesis pathway  were elevated in IL4-4, while ADT (arogenate dehydratase: Solyc11g072520) in the phenylalanine pathway was reduced. In addition, we observed significantly higher transcript contents of PHENYLALANINE AMMONIA-LYASE (PAL: Solyc09g007910), which catalyzes the first step in phenylpropanoid synthesis from L-phenylalanine . A previous study suggested that expression of this PAL is the primary determinant of commitment to phenylpropanoid synthesis . Taken together, these results suggest that genetic factors within IL4-4 regulate metabolite levels via their influence on transcription of genes involved in multiple biosynthetic pathways.We observed significant increases in AAAs as well as nutrition and flavor contributing phenylpropanoids and glycoalkaloids in IL4-4. Therefore, we examined the transcript levels of genes encoding key steps in the shikimate and phenylpropanoid pathways . VOCs were collected from fruits of five selected sub-ILs  and the parents, M82 and IL4-4, while additional metabolite data were collected from all sub-ILs shown in Figure 5A. Partial least squares enhanced discriminant analysis (PLS-EDA) models were generated based on the metabolome data collected from the sub-ILs . The distribution patterns of different lines with and without the M genomic region suggest that the overall metabolic traits of all lines can be divided to two main clades, IL4-4-like and M82-like. Sub-ILs that contain S. pennellii genomic region M (defined by marker C2147 and C200952) are closer to IL4-4, while lines without the M region resemble M82. Thus, the S. pennellii M genome fragment contains the major locus affecting a wide range of metabolic changes in tomato fruit.IL4-4 includes a genomic region of ∼3.4Mb . To fineTable 1, we identified 185 significantly altered metabolites (p < 0.05). To further understand how the M genomic region regulates fruit quality, we reexamined all of these metabolites in all of the lines for significant effects using ANOVA. Using a statistical threshold of p < 0.01, we identified 72 metabolite QTLs in IL4-4. Of these QTLs, 43 were located within the M region . QTLs within this region affect metabolites synthesized from a wide range of both primary and secondary metabolism pathways. Interestingly, QTLs correlated with fructose, glucose, soluble solids (brix) and C5 volatile contents are located within this small segment, all traits likely to affect consumer taste preferences . There are also major effects on potential nutrition-associated metabolites, including flavonoids . To address the effects of these chemical alterations on flavor quality, we conducted a consumer panel where individuals rated fruits from M82, IL4-4, and sub-ILs. Consumers significantly preferred IL4-4 and a sub-IL containing the M region (R2174) over M82 or sub-ILs absent the M region . In the red fruit pericarp tissue, transcript levels of only three of these genes were significantly altered between IL4-4 and M82: ORF2 (Serine/threonine protein kinase), ORF10  and ORF17 . There were no premature stop codons in any open reading frames of the genes in either ortholog. To investigate the possible effects of amino acid sequence substitutions on the metabolic phenotype in IL4-4, a comparison of protein sequences of the 20 candidate genes from S. pennellii and S. lycopersicum located in the mapped M genome region was performed  responsible for altered metabolite biosynthesis.There are 20 annotated genes within the pe fruit . That skS. pennellii . Localization of the reticulated peel phenotype caused by CWP1  gene in the M region. Trehalose, which is regulated by T6PP, has been proposed to be a central signaling sugar that impacts sucrose content (Table 2). It is possible that altered expression of one or more of these genes could cause at least some of the observed metabolic changes.High-resolution fine mapping studies with tomato populations to identify loci regulating multiple fruit quantitative traits have been performed e.g., . Yates e by CWP1  to the Me fruits , higher  content . AlteredLOX transcripts as well as HPL were more abundant. For phenylpropanoids, the transcript encoding PAL (Solyc09g007910) was significantly higher in IL4-4. This transcript was previously shown to be significantly up-regulated in transgenic tomato fruits over-expressing the MYB transcription factor PhODO1  and the mechanism of action. Although the skin reticulation phenotype is not itself desirable, it may be possible to eliminate or minimize this effect while maximizing the overall effects on fruit metabolite contents.ZL, HK, AF, JG, and ZF organized and coordinated aspects of the research in their respective laboratories. ZL, SA, YB, DT and YZ performed experiments and data analysis. ZL, SA, HK, and AF wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


By using a highly doped p-Si/SiO2 substrate as the back gate, we analyzed the transport properties of the device and the dependence on the pressure and on the electron bombardment. We demonstrate herein that low energy irradiation is detrimental to the transistor current capability, resulting in an increase in contact resistance and a reduction in carrier mobility, even at electron doses as low as 30 e−/nm2. We also show that irradiated devices recover their pristine state after few repeated electrical measurements.We studied the effects of low-energy electron beam irradiation up to 10 keV on graphene-based field effect transistors. We fabricated metallic bilayer electrodes to contact mono- and bi-layer graphene flakes on SiO Graphene is a promising candidate for future nanoelectronics and has been attracting an enormous amount of attention from the scientific community since 2004, when graphene flakes were exfoliated from graphite for the first time in Manchester . Due to From an experimental viewpoint, the use of scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron beam lithography (EBL), and focus ion beam (FIB) processing in an ultra-high vacuum represents a necessary step for the fabrication and characterization of graphene-based devices. Consequently, graphene devices during fabrication or under test are necessarily exposed to a high vacuum and electron irradiation, which may considerably affect their electronic properties.Several experiments have shown that the irradiation of energetic particles, such as electrons ,14,15,16Raman spectroscopy has been largely used to study electron-beam induced structural modifications ,20,21, o2 and a carrier mobility as high as 4000 cm2·V−1·s−1 on a Si/SiO2 substrate. Electron irradiation affects the transistor current drive capability by reducing the carrier mobility and increasing the channel and contact resistance. We also show that, for low energy electron irradiation, the conditions of pristine devices are almost restored by successive gate voltage sweeps.In this paper, we study the modification of electronic transport properties of GFETs upon exposure to electron beam irradiation for scanning electron microscopy imaging with an acceleration energy up to 10 keV. An optimized fabrication process has been developed to obtain devices characterized by specific contact resistivity 2 (300-nm-thick) substrates. After optical identification, the mono- or bi-layer nature of the flakes were confirmed by Raman spectroscopy. Metal contacts to selected graphene flakes were realized by means of electron beam lithography and magnetron sputtering techniques. Spin coating of approximately 400 nm PMMA-A7 (poly-methyl methacrylate) at 4000 rpm was performed on the sample, and it was successively exposed by an EBL system, Raith Elphy Plus . Methyl isobutyl ketone and then isopropanol was used as a developer. The metal electrodes were fabricated by a three cathode RF Sputtering Magnetron  for in-situ multilayer deposition working at 10−7 mbar base pressure. The graphene flakes were contacted by a Nb/Au metallic bilayer (15 nm Nb/25 nm Au) with niobium contacting the graphene and gold working as a cap layer to prevent Nb oxidation and favor electrical connection with the probe tips. Metallic leads were sputtered at low power density (<0.7 W·cm−2) and small deposition rates (0.3 nm/s for Nb and 1.2 nm/s for Au) to prevent graphene damages.Graphene flakes were obtained from highly oriented pyrolytic graphite (from NGS Naturgraphit GmbH) by a scotch tape method and were placed on standard p-Si/SiOElectrical characterization was performed by means of a Janis Research ST-500 cryogenic probe station  connected to a Keithley 4200  Semiconductor Characterization System (SCS) working in wide ranges of current (100 fA to 0.1 A) and voltage (10 μV to 200 V). To study the effect of e-beam irradiation on transistors, the SCS was connected to a scanning electron microscope Zeiss LEO 1430  equipped with Kleindeik nanomanipulators MM3A , which allowed in-situ electrical measurements with the sample inside the high-vacuum SEM chamber to prevent adsorbate contamination.In order to characterize the contact resistance, we designed a device with standard geometry to apply the transfer length method (TLM), a structure consisting of a series of spaced electrodes up to 10 µm apart a.IDS vs. VDS) has been measured for each possible combination of contacts: the drain current (IDS) linearly increases with source-drain voltage (VDS), which is a typical behavior at low bias  indicate width and length of each contact, respectively. From the linear fitting of Reff vs. L . The negative value indicates that the graphene is n-doped. The device was measured as produced, without any electrical annealing (stress), which is suitable for inducing the desorption of surface contaminants and improving the metal-graphene coupling, thus reducing the contact resistance [IDS vs. VDS for several VGate values in the range −60 V to +60 V) and the transfer characteristic (at fixed VDS) measured before and after an electrical stress event that stabilize the device improving its performances. The black arrow in the figure identifies the voltage at which the device was suddenly modified, switching from a total resistance of about 250 kΩ to 150 kΩ, for the effect of current annealing. After such modification, the device was routinely measured, showing extreme stability without further modification of the total resistance RDS, which we report as a function of VGate in the insets of RDS is the series of the contact resistance and the channel resistance, RDS = RC + Rchannel, where the channel resistance can be expressed as L and W the length and width of the channel, respectively, μ is the carrier mobility, and q is the unit charge [n0 is the intrinsic carrier concentration, and nind is the carrier concentration induced by the back gate. nind can be expressed in terms of gate oxide capacitance as R vs. VGate allows for the extraction of the contact resistance and carrier mobility as fitting parameters.We also tested the current modulation of this device when used as a field effect transistor with the Si substrate as the back-gate electrode. In t charge . The tot2·cm−1·s−1 to 3900 V2·cm−1·s−1. The electrical stress increased the graphene–metal coupling and worked to clean the channel. The mobility values are comparable to values already reported for Nb-contacted GFETs [p-branch clearly away from the expected theoretical behavior. This can be explained in terms of the reduced coupling between the Nb electrode and the graphene channel (corresponding to large contact resistance), a situation that can cause asymmetry, a double dip, or both in such curves as reported in [RDS − Rcontact we also confirmed the improvement of the channel resistance.Using the transfer characteristics measured before and after the electrical stress, we found that the contact resistance improved (reduced from 200 kΩ to 90 kΩ), while the carrier mobility increased from 3600 Ved GFETs . We alsoorted in ,34. The −7 mbar) for different gate voltage values. The ohmic nature of the contacts is confirmed by the linearity of such characteristics.In VDS = 1 mV. Remarkably, the current annealing and the long high vacuum storage produced very stable devices with low contact resistance (5.0 kΩ < Rcontact < 5.5 kΩ). The high fabrication quality is confirmed by the small contact resistance, the low noise, and the high carrier mobility, which is 4000 V2·cm−1·s−1 < µ < 4400 V2·cm−1·s−1. The Dirac point at a bias between −40 V and −60 V indicates a strong n-doping that is favored by the vacuum and the electron irradiation .In p-dopants, shift the Dirac point towards positive biases, increase the contact resistance and reduce the carrier mobility [Rcontact ≈ 6.7 kΩ a value 35% larger than the value in the high vacuum, while the carrier mobility was reduced to µ ≈ 4100 V2·cm−1·s−1. The inset shows the evolution of the Dirac point from −55 V in the high vacuum to −30 V in air. This observation confirms the importance of performing the electrical measurements in-situ when studying irradiation effects to distinguish the electron beam from other environment-induced phenomena.As soon as the devices are exposed to air, the graphene collects adsorbates that, generally acting as mobility ,37,38,39Ibeam = 0.2 nA. We used an exposure time of 10 s, which resulted in an electron irradiation dose of about 30 e−/nm2. Differently from other works [2·cm−1·s−1 to about 3600 V2·cm−1·s−1 (as obtained from the first sweep measurement). The initial value is restored by the successive sweeps. A consistent behavior is shown by the total resistance, which is increased by the irradiation and recovers with an increasing number of sweeps. The increase in total resistance, as a consequence of the e-beam irradiation, has also been observed on chemical vapor deposition (CVD) grown graphene [0. Mobility and resistance degradation can be explained as increased long-range coulomb scattering [In the following, we consider the effect of electron beam irradiation on the GFETs. In particular, we consider electron beam energy up to 10 keV, i.e., the energy range typically used for SEM imaging. Larger energy (about 30 keV) is normally used for e-beam lithography or imaging in STEM mode. The irradiation was performed on an area of 20 µm × 20 µm, covering most of the graphene channel, with constant beam current er works , we perfer works ,41. By cer works  to estimgraphene . Figure attering  by elect2 substrate with Nb/Au metallic bilayers as contacting electrodes. Electrical characterization evidenced high-quality devices with carrier mobility as high as 4000 cm2·V−1·s−1 and specific contact resistivity of about 19 kΩ·µm2. The effect of 10 keV electron irradiation, with a dose of 30 e−/nm2, on the transport properties has been reported, evidencing a significant reduction in carrier mobility and an increase in contact resistance. Finally, we show here that, for low energy irradiation, the pristine conditions are almost restored after several electrical sweeps, which we explain as a gradual removal of electrons piled up in the gate oxide during e-beam exposure.We realized graphene-based field effect transistors on a Si/SiO


Polyurethane/graphene nanocomposites were synthesized using commercial thermoplastic polyurethane , and two types of graphene derivatives: graphene nanoplatelets (GNP) and reduced graphene oxide (RGO). Fourier Transformation Infrared Spectroscopy Fourier Transformation Infrared Spectroscopy (FTIR) spectroscopy, TEM, and SEM microscopy and XRD techniques were used to chemically and structurally characterize GNP and RGO nanofillers. The properties of the new TPU nanocomposite materials were studied using thermal analysis techniques , Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TG)) to describe the influence of graphene nanofillers on polyurethane matrix. Our investigation describes the comparison of two types of graphene derivatives, commercial one (GNP) and synthesized (RGO) on thermoplastic polyurethanes. These nanofillers provides opportunities to achieve compatibility with the TPU matrix. The property enhancements are attributed commonly to high aspect ratio of graphene nanoplatelets and filler–polymer interactions at the interface. The obtained nanocomposites exhibit higher thermal and mechanical properties due to the good dispersion of both nanofillers into TPU matrix. It was found that the addition of 2 wt % of the nanofiller could lead to a significant reinforcement effect on the TPU matrix. Also, with high content of nanofiller (GNP and RGO), the Payne effect was observed. Since the first scientific on the mechanical exfoliation of graphite into graphene monolayers by scotch-tape, several interesting studies have been published on graphene ,2,3. GraBottom-up methods allow for the production of wafer graphene, which covers the demand of the electronic industry. The high quality of graphene monolayer shows the high electrical conductivity and transparency of this material. Quality of graphene produced by bottom-up approach shows well-builded one-layer structure without atomic defects . The metTop-down approach of obtaining graphene is based on graphite as a raw material. In this way, graphite is exfoliated as the result of breaking inter-plane π-π interactions of stacked graphene in graphite crystal. Exfoliation can be carried in a mechanical, chemical, electrochemical, and plasma assisted way. According to top-down production methods, the obtained graphenes can differ in chemical and physical properties. The main differences are in crystal defects, various multi-layer stacked structure, polydispersity of basal plane, and thickness. The monolayer structure of top-down graphene can be folded and sharp-edged. We can also add function groups by various chemical modifications. These modifications have a strong effect on the electrical, mechanical, and optical properties of graphene obtained by top-down ways.Top-down graphene is promising nanofiller for segmented polyurethane matrix. It can be introduced to matrix via in-situ polymerization, solvent-methods and during extrusion ,11,12, oElastomeric materials can be successfully modified with many nanofillers , especiaSegmented polyurethanes contain flexible and rigid chains alternately in the polymer back bone. The precursor of soft flexible segments is polymeric macromolecules with hydroxyl-end groups, while rigid segments are obtained because of reaction diisocyanate with low-molecular diol or diamine. Due to the chemical incompatibility between the two types of segments (soft and hard), segmented polyurethanes are phase separated on a nanoscale. This phenomenon leads to the creation hard and soft domains, in which hard domains (HS) are rich in rigid segments and soft domains (SS) are rich in flexible chains. Hard domains are responsible for mechanical strength and hardness, and on the other hand, soft domains provide elasticity . FurtherThe aim of this study is to explore the influence of graphene-like nano structured carbon fillers, RGO (reduced graphene oxide), and GNP (graphene nanoplatelets) on the morphology and proprieties of segmented polyurethane thermoplastic elastomers. The filler dispersion, filler orientation and filler-filler networking (Payne effect) have been carefully studied and compared. Morphology-property correlation has been established. Techniques such as high-resolution transmission electron microscopy (HRTEM), DSC, TGA, XRD, Fourier Transformation Infrared Spectroscopy (FTIR), and UTM have been employed to characterize the morphology and proprieties of the system. Theoretical models have been used to fit the experimental data.4, Stanlab, >99%), hydrogen peroxide , hydrochloric acid , phosphoric acid , sulfuric acid , ethanol , and diethyl ether ((C2H5)2O), all from POCh S.A., Poland, were used in GO synthesis. Compressed argon  was used in thermal reduction of GO to reduced graphene oxide (RGO). Graphene platelets (GNP) were supplied by ACS Materials . Thermoplastic polyester urethane (TPU) 52DE55  was used as a nanocomposite polymer matrix. N,N-Dimethylmethanamide (DMF) ((HCON(CH3)2, 99%, POCh S.A., Gliwice, Poland) was used as a solvent in composite preparation.In the following research graphite flakes, Micro 850  was used as a precursor to obtain Graphite Oxide (GO). Also, potassium permanganate (KMnOReduced graphene oxide (RGO) was obtained in two-step synthesis. On the first step, graphite oxide (GO) was obtained using modified Hummer’s method described in the following article . On the Nanocomposites were prepared by the solvent blending method. TPU was dissolved by adding it to DMF at temperature of 80 °C with continuously stirring, the final concentration of TPU/DMF solutions was 25% by weight. Next, the proper amounts of nanofillers was added to prepared solution and was homogenized by high-shear mixer  with 2500 rpm for 30 min. Obtained polymer nanocomposite solutions, with good quality of dispersion, were poured onto Petri pans and the DMF was fully evaporated at 70 °C for 72 h. The obtained nanocomposites were compression molded (under a pressure of 1 ton) to thin film with 0.7 mm thickness at the temperature of 195 °C. Press molded films  was used−1. The measurement was carried in standard conditions .Fourier Transformation Infrared Spectroscopy of polyurethane materials has been carried using ThermoElectron Corporation  Nicolet 8700 Spectrometer in the Attenuated Total Reflectance  mode in the range of 500–4000 cmWide-angle X-ray scattering measurements of nanofillers and polyurethane composites were performed using a Bragg–Brentano X’PERT PHILIPS diffractometer , equipped with a Cu Anode X-ray tube and diffracted beam monochromator . TPU samples were scanned in 2θ range from 5° to 35°.Microscopy analysis (for nanofiller GNP and RGO) was performed by means of STEM-EDX technique using Transmission Electron Microscope FEI Europe , Tecnai F20 X-Twin coupled with EDX Spectrometer  (samples were cut in cryo-mode using ultramicrotome). HRTEM (for nanocomposite systems) is from JEOL , model JEM-2100. The samples were sliced with LEICA EM UC7 Ultra Microtome with cryo-mode  using glass knife. The thickness of the samples was 150 nm and the slices were deposited on a 200 mesh Cu Grid.Thermogravimetry  ISO 11358) of obtained materials was conducted by Netzsch TG209F3 TG analyzer . Degradation process has been performed in the temperature range from 35 °C to 700 °C, nitrogen atmosphere with gas flow rate 40 mL/min, and heating rate 20 °C/min.Differential Scanning Calorimetry  measurements have been carried by Netzsch DSC209F1 instrument  in the temperature range from −85 °C to 250 °C. The samples weight was about 10 mg, and analysis was conducted in nitrogen atmosphere with gas flow 40 mL/min. The samples were pre-heated (1st-heating) to eliminate thermal history, then cooling and 2nd-heating at rate 10 °C/min.3. Tensile (in static mode) tests were carried out using universal testing machine (UTM) Zwick/Roell Z020 . All of the tests were performed according to ISO 527-1 standard.Dynamical Mechanical Analysis  ASTM D4065, D7028-07) has been carried by TA Instruments DMA Q800 analyzer . Temperature investigation mode has been performed in uniaxial tension mode, in the temperature range −100–100 °C, 1Hz frequency amplitude of deformation 20 µm and heating rate 4 °C/min using liquid nitrogen as cooling medium. Strain sweep analysis for Payne effect investigation has been conducted in three different temperatures , in the same tension mode and frequency of deformation, but in the amplitude range from 0.25 to 170 µm. The dimensions of thin samples were 5.0 × 5.0 × 0.7 mmPrepared reduced graphene oxide (RGO) nanofiller possess good exfoliated structures a. Our pr−1, were assigned to stretching vibrations of N–H groups that were present in both types of TPU spectra. The wide base of the identified peak of NH stretching may be related to the presence of hydrogen bonds in the TPU and GNP or RGO modified TPU structure. The very strong carbonyl stretching, in the case of obtained TPUs and nanocomposite systems, appeared at 1726 cm−1. The FTIR analysis confirmed that the performed synthesis lead to the generation of obtain TPUs . For PURE TPU polyurethane matrix, DSC curve indicates that there are no crystalline regions within the soft domain, while at 216 °C there is a maximum corresponding to the melting of crystalline hard domains (Tm). Thus, the polyurethane matrix is composed of two amorphous regions, which are arranged in rigid segments consisting of hard domains. Polyurethane matrix possesses soft and hard segments what was also confirmed using XRD investigation and correlate with DSC results. The visibility of diffraction maxima’s in XRD studies (c), which is related to the GNP activity as nucleation process in the heterocrystallization by increasing its temperature. In the TPU/RGO nanocomposites, the crystallization temperatures have practically remained unchanged, indicating its less influential factor.For many group of polymers, which possess semicrystalline structure, the incorporation of nanofiller causes changes in the crystallization degree, size of the crystalline aggregates, and changes in crystallization rate and in the maximum crystallization temperature (Trmograms . What isrmograms . Additiormograms . It was 4–40 °C)  were regMA study  [Tg ≈ −2 studies  indicate studies , where m studies . The DSCDynamic mechanical properties were analyzed for all of the obtained materials. Based on curve behavior, it was visible high hard segments (HS) content for non-modified material (PURE TPU) . For modg region. There is only a slight change of Tg value of TPU nanocomposite in comparison to the PURE TPU  of nanocomposite systems increased with nanofiller (RGO or GNP) loadings , Table 5Based on this study it was successfully obtained polyurethane nanocomposites containing reduced graphene oxide (RGO) or graphene nanoplatelets (GNP). The addition of RGO or GNP undoubtedly influenced investigated properties of the polyurethane matrix.g), respectively, than those of the PURE TPU material. It is seen (maxδ) that the addition of the nanofillers improves the mechanical strength of TPU matrix. Enhanced mechanical properties were observed when add maximum 2 wt % of nanofiller and higher tensile strength properties obtain for RGO nanofiller in comparison to the GNP modificator.The authors showed the interaction behavior of the nanofillers with the polyurethane matrix by using spectroscopic and diffraction investigation. Morphology investigation confirmed good exfoliation nanoparticles into the polyurethane matrix and shows a better compatibly of RGO nanofiller to the matrix, in comparison to the GNP  and synthesized reduced graphene oxide (RGO) nanofiller. We could observe agglomeration (strong filler/filler interaction) at higher filler loading, which was evidenced by the high Payne effect. When comparing with results in literature  based on


Ultrathin metal layers can be highly active carbon dioxide electroreduction catalysts, but may also be prone to oxidation. Here we construct a model of graphene confined ultrathin layers of highly reactive metals, taking the synthetic highly reactive tin quantum sheets confined in graphene as an example. The higher electrochemical active area ensures 9 times larger carbon dioxide adsorption capacity relative to bulk tin, while the highly-conductive graphene favours rate-determining electron transfer from carbon dioxide to its radical anion. The lowered tin–tin coordination numbers, revealed by X-ray absorption fine structure spectroscopy, enable tin quantum sheets confined in graphene to efficiently stabilize the carbon dioxide radical anion, verified by 0.13 volts lowered potential of hydroxyl ion adsorption compared with bulk tin. Hence, the tin quantum sheets confined in graphene show enhanced electrocatalytic activity and stability. This work may provide a promising lead for designing efficient and robust catalysts for electrolytic fuel synthesis. Ultrathin metal layers can be highly active carbon dioxide electroreduction catalysts but may also be prone to oxidation. Here, the authors report the fabrication of reactive tin quantum nanosheets confined in graphene and demonstrate their enhanced electrocatalytic activity and stability. Furthermore, the intimate contact between ultrathin metal layers and graphene in a sandwich-like fashion could ensure excellent long-term stability.Motivated by the above consideration, we construct a material model of ultrathin metal layers confined in graphene, in which the protection of graphene could avoid the oxidation of the highly reactive metals at ambient atmosphere . The met2 to formate582 reduction performances. Therefore, it is highly warranted for controllably synthesizing Sn ultrathin layers in the confined space of graphene. Herein, metallic Sn quantum sheets confined in graphene were first successfully synthesized through a spatially confined reduction strategy  oscillation curves in 713To further identify the phase and unravel the local atomic arrangements of the synthetic Sn quantum sheets, synchrotron radiation XAFS measurements were performed. As shown in 2 electroreduction performances in comparison with the Sn nanoparticles as well as the bulk counterpart. To verify the higher activity of the unique sandwich-like 2D structure, CO2 reduction activities for the Sn quantum sheets confined in graphene, 15 nm Sn nanoparticles mixed with graphene, 15 nm Sn nanoparticles and bulk Sn were determined in CO2-saturated 0.1 M NaHCO3 solution. As revealed by linear sweep voltammetry (LSV) in −2 at −1.8 V versus SCE, which was roughly 2, 2.5 and 13 times larger than that of the 15 nm Sn nanoparticles mixed with graphene, 15 nm Sn nanoparticles and bulk Sn, respectively. In addition, the Sn quantum sheets confined in graphene exhibited an onset potential of −0.85 V versus SCE, which was much positive relative to other three samples  may be one of the main contributors since larger ECSA could afford more catalytically active sites−1. The remarkably increased ECSA for the Sn quantum sheets confined in graphene could afford larger amount of active sites to efficiently adsorb CO2, further confirmed by their 1.75, 2 and 9 times higher CO2 adsorption capacity compared with the 15 nm Sn nanoparticles mixed with graphene, 15 nm Sn nanoparticles and bulk Sn. The lowest interfacial charge-transfer resistance, benefiting from the highly-conductive graphene, facilitates the rate-limiting electron transfer from CO2 to CO2− intermediate. In addition, compared to the 15 nm Sn nanoparticles mixed with graphene, 15 nm Sn nanoparticles and bulk Sn, the 0.05, 0.06 and 0.13 V lowered potential of OH− adsorption indicate that the Sn quantum sheets confined in graphene possess the strongest adsorption affinity of CO2− intermediate, and hence they efficiently stabilize the CO2− intermediate, thus definitely lowering the overall reaction barrier. As an outcome, the Sn quantum sheets confined in graphene display a current density of 21.1 mA cm−2 at −1.8 V versus SCE, which is roughly 2, 2.5 and 13 times larger than that of the 15 nm Sn nanoparticles mixed with graphene, 15 nm Sn nanoparticles and bulk Sn. Also, the formate Faradaic efficiency for Sn quantum sheets confined in graphene is always larger than 85% during the long test period of 50 h. Thus, the present work demonstrates that the unique structure of highly reactive metal quantum sheets confined in graphene could fully optimize CO2 electroreduction performances.In conclusion, we first put forward an ideal material model of ultrathin metal layers confined in graphene, in which the protection of graphene could avoid the oxidation of the highly reactive metals in air. As an example, highly reactive metallic Sn quantum sheets, confined in few-layered graphene, are first successfully synthesized through a spatially confined reduction strategy. Synchrotron radiation XAFS results disclose that Sn–Sn coordination numbers for the Sn quantum sheets confined in graphene reduce from 2 and 4 to 1.4 and 2.7 compared with that of bulk counterpart, which implies the former's higher intrinsic catalytic activity, further demonstrated by their lowered Tafel slope from 176 to 83 mV dec2 layers, synthesized according to our previous work , were dispersed in 40 ml 0.03 M aqueous glucose solution under mild stirring for 30 min. Then the above mixture was transferred into a 50 ml Teflon-lined autoclave, sealed and heated at 180 °C for 10 h. After the initial hydrothermal method, the ultrathin SnO2 layers were homogenerously encapsulated in the carbon layer. Then, the carbon-coated ultrathin SnO2 layers were initially annealed at 500 °C for 2 h, and then immediately annealed at 1,000 °C for 5 min, and hence cooled down rapidly (within 1 min) to room temperature under the protection of argon atmosphere. During this temperature-programed process, the amorphous carbon turned into graphene, while the ultrathin SnO2 layers simultaneously reduced into monodispersed Sn quantum sheets in the confined space of graphene, thus forming a sandwich-like structure. By contrast, for the synthesis of 15 nm Sn nanoparticles mixed with graphene, the 15 nm Sn nanoparticles, synthesized according to the ref. of Nanotechnology, 2011, 22, 22,5701, were physically mixed with the commercial graphene under mild stirring for 1 h. Then, the 15 nm Sn nanoparticles could be dispersed on the surface of graphene.In a typical synthesis, 10 mg ultrathin SnO2 layers, synthesized according to ref. Angew. Chem. Int. Ed. 2013, 52, 10,569, were annealed at 500 °C for 5 h in H2 atmosphere and then cooled down to room temperature. The obtained powders were collected for further characterization.In a typical synthesis, 50 mg ultrathin SnOK-edge XAFS was measured at the Shanghai Synchrotron Radiation Factory and the National Synchrotron Radiation Laboratory, China. Thermal gravimetric analysis (TGA) of the as-synthesized samples were carried out on a Shimadzu TA-50 thermal analyser at a heating rate of 10 °C min−1 from room temperature to 700 °C in air. The liquid products were quantified by nuclear magnetic resonance (Bruker AVANCE AV III 400) spectroscopy. CO2 adsorption isotherms measurements for all the synthetic samples were carried out by using an automatic microporous physical and chemical gas adsorption analyser (ASAP 2020M+C).TEM images and high-resolution TEM image were performed by using a JEOL-2010 TEM with an acceleration voltage of 200 kV. The Sn 2 reduction experiments, LSV with a scan rate of 20 mV s−1 was carried out in CO2-saturated 0.1 M NaHCO3 solution (60 ml) (The NaHCO3 electrolyte was purged with CO2 for 30 min prior to the measurement.). ECSA was determined by measuring the capacitive current associated with double-layer charging from the scan-rate dependence of CVs. The Cdl was estimated by plotting the Δj (ja−jc) at −0.75 V versus SCE against the scan rate, where the Δj could be acquired by Cyclic voltammetry measurement under the potential windows of −0.8∼−0.7 V versus SCE (0.1 M NaHCO3 solution).Electrochemical measurements were implemented in a three-electrode system at an electrochemical station (CHI660E). The working electrode was glassy carbon electrode. The platinum gauze and the saturated calomel electrode (SCE) reference electrode served as counter and the reference electrodes, respectively. In a typical prepared procedure of the working electrode, 3 μl of the homogeneous ink, which was prepared by dispersing 5 mg sample and 40 μl Nafion solution (5 wt%) in 1 ml water-ethanol solution with volume ratio of 3:1, was loaded onto a glassy carbon electrode with 3 mm diameter. For COSample of 20 mg was homogeneously mixed with 100 mg graphite and hence pressed into circular pellets with a diameter of 10 mm for further extended X-ray absorption fine structure (EXAFS) measurement under ambient conditions. Then, the XAFS measurements were performed at BL14W1 station in Shanghai Synchrotron Radiation Facility, China. The storage rings of SSRF were operated at 3.5 GeV with the maximum current of 210 mA. Si(311) double-crystal monochromator crystals were used to monochromatize the X-ray beam. The energy resolution at Sn K-edge was about 2 eV. XAFS data were collected in transmission in the energy range from −200 below to 1,000 eV above the Sn K-edge. The detuning was done by 30% to remove harmonics.The authors declare that the data supporting the findings of this study are available within the article and its How to cite this article: Lei, F. et al. Metallic tin quantum sheets confined in graphene toward high-efficiency carbon dioxide electroreduction. Nat. Commun. 7:12697 doi: 10.1038/ncomms12697 (2016).Supplementary Figures 1-4


Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities.We propose a divide-and-conquer strategy to discover hierarchical community structure, nonoverlapping within each level. Our algorithm is based on the highly efficient rank-2 symmetric nonnegative matrix factorization. We solve several implementation challenges to boost its efficiency on modern computer architectures, specifically for very sparse adjacency matrices that represent a wide range of social networks.Empirical results have shown that our algorithm has competitive overall efficiency and leading performance in minimizing the average normalized cut, and that the nonoverlapping communities found by our algorithm recover the ground-truth communities better than state-of-the-art algorithms for overlapping community detection. In addition, we present a new dataset of the DBLP computer science bibliography network with richer meta-data and verifiable ground-truth knowledge, which can foster future research in community finding and interpretation of communities in large networks. However, it has been shown that linkage-based communities usually have strong correlation with functional communities [Community detection is a key task in the study of large networks, which has recently become a very important area of research –3. Althomunities . The purEarlier work in this area mainly focused on finding linkage-based communities on small networks. Various measures have been defined and optimized for the difference between intraconnections and interconnections among network nodes, such as normalized cut, conductance, and others, using a variety of methods . Manual This paper introduces a scalable algorithm based on rank-2 symmetric nonnegative matrix factorization (rank-2 SymNMF) for large-scale hierarchical community detection. After summarizing some related works, we discuss the problem domain for our new results and our solutions for particular problems within that domain. Then, some highlights and speedup in our implementations are discussed, after which we present comprehensive experimental results to evaluate our new algorithm.The study of network community detection dates back to the well-known Kernighan–Lin algorithm from the early 1970s . At thatk-means objective [A spectral clustering method that minimizes normalized cut was proposed as an image-segmentation algorithm , and it bjective  and applbjective , 14. ThiGirvan and Newman  producedorder statistics local optimization method (OSLOM), which was based on a measurement similar to modularity but was able to handle overlapping communities. Yang and Leskovec studied the properties of large-scale overlapping communities [The early overlapping community detection algorithms , 22 weremunities  and propmunities . They prmunities , 24. Whascalable community detection (SCD). Network communities of good quality should have stronger intraconnections than interconnections. Previous algorithms measure such strength of connectivity only through the number of edges. The uniqueness of SCD is that it is based on a triangular structure of edges. The goal is to identify communities where each node forms more triangles with nodes inside the community than those formed with nodes outside the community.Recently,  proposedOn the other hand, nonnegative matrix factorization (NMF)-based methods exhibit superior interpretability in many application areas such as text mining , 26 and As mentioned above, there is no universally accepted definition for network communities. Rather than defining community detection as optimizing some specific measurement criteria, we believe it would be more effective and flexible to understand the problem by clearly defining what makes a community detection result good.F1 score, which assumes the existence of ground truth. Finally, we would like to know whether the result reveals some new and useful information about the network (Q3). This is mostly relevant for the study of small networks [In this paper, we focus on link-based community detection, and use functional communities (if known) as ground truth. In general, to evaluate link-based community detection results, we may ask several questions. The first is whether the result is coherent from the point of view of network links (Q1). There are many measurement scores defined based on the presumption that a community should have more intraconnections than interconnections. These measures include normalized cut, ratio cut, conductance, etc. The second question is whether the result agrees with prior knowledge, especially the ground truth if it is known (Q2). There have been largely two approaches. One is to manually analyze the results with some known meta-information (such as the entity each node represents), which is not scalable to large networks. Another approach is to compare the community detection result using some measures such as networks . For larGround-truth communities of large networks were not available to researchers until Yang and Leskovec  defined Based on the above observations, we conclude that link-based community detection algorithms have the ability to partially recover functional communities, but such ability is inherently limited by the essential differences between functional communities and link-based communities. This should be kept in mind when comparing link-based community detection results against ground-truth communities.Real-world network communities are usually overlapping. For example, it is common that one user joins a variety of groups in a social network. However, our current focus is on nonoverlapping community detection, since nonoverlapping community detection is also very useful for revealing the network structure, and our algorithm is designed to detect nonoverlapping communities efficiently. The results of a good-quality nonoverlapping community detection algorithm can be used as an effective starting point for overlapping community detection , 3.We present an algorithm called HierSymNMF2 for hierarchical community detection. HierSymNMF2 uses a fast SymNMF algorithm  with ranX, and each column in the matrix H can be seen as a low rank representation of the data item. Nonnegativity constraints allow such a low rank representation to be more interpretable than other low rank approximations such as SVD. This formulation can be applied to areas such as document clustering [m and n [k reaches a value on the order of thousands, NMF algorithms become slow. To solve this issue, [k to linear [Given the vast amounts of nonnegative data available for extracting critical information, the NMF has found a wealth of applications in such domains as image, text, and chemical data processing. It can be shown that applying algorithms to such data without constraining the solution can result in uninterpretable results such as negative chemical concentrations and possibly false negative and/or false positive detections, which could lead to meaningless results . For texustering  and can [m and n . Howevers issue,  developeo linear .S for SymNMF are adjacency matrix i. When S is the adjacency matrix, .The algorithm we introduce in this paper uses the framework shown in Algorithm 1, where a cluster is a community, and the task of splitting a community is performed by our rank-2 version of SymNMF. The decision to choose the next node to split is based on a criterion discussed in the next section. In the following sections, we denote H naturally induces a binary split of the community: suppose ith graph node.Splitting a community is achieved by rank-2 SymNMF of alternating nonnegative least squares (ANLS) algorithm for SymNMF from [A formal formulation of rank-2 SymNMF is the following optimization problem:lgorithm , 14. HowNMF from , we can W and W and H. Formulation  normalized cut determines whether a split is structurally effective since it measures the difference between intraconnections and interconnections among network nodes; and (2) for SymNMF, when S is the normalized adjacency matrix, the SymNMF objective function is equivalent to (a relaxation of) minimizing the normalized cut, which is the preferred choice in graph clustering [The “best” community to split further is chosen by computing and comparing ustering .u, v) is w. Note that for an unweighted graph, k pairwise disjoint subsets of V, where Suppose we have a graph For example, the graph shown in Fig. sparse–dense matrix multiplication (SpMM) was the main computational bottleneck for computing the solution. However, this is not the case with rank-2 SymNMF or HierSymNMF2 for community detection problems on typical large-scale networks. Suppose we have an z nonzeros as an input to rank-2 SymNMF. In Algorithm 2, i.e., Nonnegative Least Squares (NLS) with two unknowns, SpMM costs 2z floating-point operations (flops), while searching for the optimal active set (abbreviated as opt-act) costs 12n flops. Of the 12n flops for opt-act, 8n flops are required for solving n linear systems each of size n flops are incurred by lines 4–5. Note that comparison operations and the memory I/O required by opt-act are ignored.In the previous work on rank-2 NMF  that takThe above rough estimation of computational complexity reveals that if mentclass2pt{minimFigure SpMM is a required routine in lines 1, 4, and 5 of Algorithm 2. The problem can be written assparse matrix–vector multiplication). For the Intel Math Kernel LibraryA depends on the sparsity pattern of A.Most open-source and commercial software packages for sparse matrix manipulation have a single-threaded implementation for SpMM, for example, MatlabA that represents an undirected network. We exploit two important facts in order to reach high performance:A is not assumed to have any special sparsity pattern. Thus, we can store the matrix A in the commonly used generic storage, the compressed sparse column (CSC) format, as is practiced in the built-in sparse matrix type in Matlab. As a result, nonzeros of A are stored column-by-column.Since the nodes of the network are arranged in an arbitrary order, the matrix A is symmetric. This property enables us to build an SpMM routine for AX.The matrix The second fact above is particularly important: When A is stored in the CSC format, computing AX with multiple threads would incur atomic operations or mutex locks to avoid race conditions between different threads. Implementing multithreaded X on one thread. Our customized SpMM implementation is described in Algorithm 3.In this paper, we present a simple yet effective implementation to compute SpMM for a matrix In addition, the original adjacency matrix often has the value “1” as every nonzero entry, that is, all the edges in the network carry the same weight. Thus, multiplication operations are no longer needed in SpMM with such a sparse matrix. Therefore, we have developed a specialized routine for the case where the original adjacency matrix is provided as input to HierSymNMF2.advanced vector extensions) intrinsics.The search for the optimal active set, opt-act, is the most time-consuming step in the algorithm for NLS with two columns . For example, a 256-bit AVX register can process four double-precision floating point numbers (64-bit each) in one CPU cycle, which amounts to four times speed-up over a sequential program. AVX intrinsics are external libraries for exploiting AVX vector registers in native C code. These libraries are not part of the ANSI C standard but retain mostly the same interface on various operating systems . However, to obtain the best performance from vector registers, functions in the AVX libraries often require the operands having aligned memory addresses . The function calls for aligned memory allocation, which is completely platform dependent for native C code, means that our software would not be easily portable across various platforms if aligned memory allocation were managed in the C code. Therefore, in order to strike the right balance between computational efficiency and software portability, our strategy is to allocate memory within Matlab for the vectors involved in opt-act, since Matlab arrays are memory aligned in a cross-platform fashion.It turns out that a C/Matlab hybrid implementation is the preferred choice for achieving high performance with native C code. Intel CPUs are equipped with AVX vector registers, since the Sandy Bridge architecture and these vector registers are essential for applying the same instructions to multiple data entries . This large discrepancy in time cost would be substantial for vector–scalar operations. Therefore, we replace vector–scalar division, in the form of k is the number of communities to find. Theoretically, this will generate k communities. However, we remove fully disconnected communities, as outliers since they are often far from being significant because of their unusually small size and they correspond to all-zero submatrices in the graph adjacency matrix, which does not have a meaningful rank-2 representation. Therefore, the final number of communities are usually slightly smaller than k, as will be shown in “h2-n(g)(d)-a(x): These labels represent several versions of our algorithm. Here h2 stands for HierSymNMF2, n for the ncut_local criterion, ng for the ncut_global criterion, and ngd for the ncut_global_diff criterion (see previous sections for the definitions of these criteria); ‘a’ means that we compute the real normalized cut using the original adjacency matrix; and ‘x’ indicates that an approximated normalized cut is computed using the normalized adjacency matrix, which usually results in faster computations. We stop our algorithm after SCD: SCD algorithm .BigClam: BigClam algorithm .Graclus: Graclus algorithm .NISE: An improved version of NISE that is published in 2016 .We compare our algorithm with some recent algorithms mentioned in the “k communities Normalized cut  is a meaductance , which ik communities Alternatively, we can measure the qualities of detected communities by comparing them with ground truth. Suppose mentclass2pt{minimThe data used for the experimental results of this paper are mostly from SNAP datasets , 34. In DBLP06 is the only dataset with a complete raw dataset openly available to the public. The other five datasets  were obtained by crawling the web, and they are far from being complete. Crawling large complex graphs is challenging by itself that may need extensive and specialized research efforts. We do not aim to solve this issue in this paper. The Orkut and Youtube datasets can be acquired from [Orkut and Youtube datasets are not complete. Such incompleteness in crawled datasets is expected due to intrinsic restrictions of web crawling such as rate limit and privacy protection. The Friendster data were crawled by the ArchiveTeam, and the LiveJournal data come from [Amazon data were crawled by the SNAP group [Although all of these datasets can be conveniently accessed on the SNAP website as a graph with ground-truth communities, red from . Detaileome from . The AmaAP group . HoweverYoutube, LiveJournal, Friendster, and Orkut), it is common that a user does not join any user groups; (4) SNAP used the dataset from [DBLP06 dataset, which was published in 2006. At that time, the DBLP database was not as mature and complete as it is today. Another issue of the above datasets is that all nodes are anonymized, which ensures protection of user privacy, but limits our ability to interpret community detection results.Possible reasons that many nodes in these datasets do not belong to any communities are (1) SNAP removed communities with less than three nodes, which caused some nodes to “lose” their memberships; (2) the well-known incompleteness of crawled datasets; (3) for social networks . Although the other datasets which we currently cannot improve are also valuable, our goal is to obtain new information from comparison of community detection results and ground-truth communities, rather than simply recovering the ground-truth communities.http://dblp.dagstuhl.de/xml/release/dblp-2015-06-02.xml.gz. The structure of this XML file is illustrated in Fig. Level 1 elements and the children of Level 1 elements Level 2 elements, and so on. Level 1 elements represent the individual data records [DBLP is an online reference for bibliographic information on major computer science publications . As of J records , such asOur goal is to obtain a coauthorship network and ground-truth information (venue–author relation) from the XML file. Although the XML file is highly structured, such a task is still not straightforward due to the ambiguity of entities, such as conflicts or changes of author names, various abbreviations, or even journal name change. DBLP resolves the author ambiguity issue by means of a unique number for each author. However, the venue ambiguity is still an issue in DBLP: there are no unique identifiers for venues. Fortunately, each record in DBLP has a unique key, and most paper keys contain the venue information as follows:Now unique identifiers for both authors and venues make extracting the network and community information very reasonable. The next step is to create a node for each author, and create a link between two authors if they have ever coauthored in the same publication. For community information, each venue is a community, and an author belongs to a community if he/she has published in the corresponding venue.A few authors do not have any coauthor in the DBLP database, and become isolated nodes in the generated network. Thus, we remove these authors. However, after removing those authors, some venues/communities become empty because all of their authors are removed. Hence, we remove those empty communities. After this cleaning, we obtained 1,509,944 authors in 5147 communities (venues).This cleaned network has 51,328 (weakly) connected components, where the largest connected component contains 1,357,781 nodes, which makes 89.9% of all nodes. The remaining 51,327 connected components are all small, the largest of which has only 37 nodes. We take the largest connected component as the network to study. By extracting the largest connected component, we obtain a network with 1,357,781 nodes, 6,369,212 edges and 5146 ground-truth communities. The ground-truth communities were divided into connected components, obtaining 93,824 communities. The divided ground-truth communities were used for comparison with detected communities.DBLP15 dataset is available at https://github.com/smallk/smallk_data/tree/master/dblp_ground_truth.The new We run our experiments on a server with two Intel E5-2620 processors, each having six cores, and 377-GB memory. The results are listed in Tables In the “internal measures” table, “coverage” measures the percentage of nodes which are assigned to at least one community; “algorithm time” and “total time” provide the runtime information. We list two measures of runtime since our algorithm  implemented in MATLAB directly uses a processed matrix in memory as its input. Other algorithms must first read the graph stored as an edge list or an adjacency list and convert the graph to the appropriate internal representation. Therefore, we use “algorithm time” to measure the algorithm runtime without the time needed for reading and converting the graph, which is reported by the algorithms themselves. The “total time” is the wall clock time for running the algorithm, including the time for reading and converting the graph, which is measured with an external timer. BigClam reports its algorithm time as the sum of time used in each core, and therefore, the results are not comparable. For completeness, we added the data-loading and data-preprocessing times, which are measured separately, to obtain a “total time” for the MATLAB algorithms .The “number of clusters” in the “internal measures” table are different across different methods due to the following reasons. The SCD algorithm does not provide an interface for specifying the number of communities to detect, and instead detects the number of communities automatically. For other algorithms, we specify the number of communities to detect as 5000. The actual number of communities generated by HierSymNMF2 is usually less than 5000, as discussed in “In the “external measures” table, the “reverse precision” and “reverse recall” refer to the scores computed as if the ground-truth communities are treated as detected communities and the detected communities are treated as the ground truth, respectively. Note that the number of clusters in “external measures” is less than the one in “internal measures” due to the removal of nodes that do not appear in the ground truth.Amazon dataset. On the Amazon dataset, HierSymNMF2 achieves much lower average normalized cut than SCD and BigClam, and the variant h2-ngd-a obtained comparable average normalized cut (0.1491) versus Graclus (0.1450), which is not as good as NISE (0.1118). (2) HierSymNMF2 runs slower than most other algorithms on DBLP06 and DBLP15. On the Youtube dataset, HierSymNMF2 runs faster than BigClam, Graclus and NISE. On the Amazon dataset, HierSymNMF2 runs faster than NISE, but slower than other methods. (3) HierSymNMF2 achieves better F1 score than BigClam and NISE on all the datasets we used. Graclus has better F1 score than HierSymNMF2 on DBLP06, Amazon, and Youtube datasets but obtained an unusually low F1 score on the DBLP15 dataset. SCD achieves higher F1 scores than HierSymNMF2. However, SCD often discovers a significantly larger number of (nonoverlapping) communities than expected and has very unbalanced precision and recall scores compared with other algorithms. The SCD algorithm finds the number of communities as it finds the communities and the number of communities cannot be given to SCD as an input. The SCD algorithm starts by assigning an initial partitioning of the graph heuristically. In short, in the initial partitioning, each node and all its neighbors form a community, and special care is taken to ensure that no node belongs to more than one community. As a result, this initial step often creates many more communities than the optimal number, although later refining procedures may reduce the number of communities. As can be seen from the experimental results, when compared with BigClam, Graclus, NISE, and our proposed algorithms that take the number of communities as an input, a much larger number of communities that the SCD generates does not necessarily translate to a better overall community detection result in terms of either normalized cut or F1 scores.We have the following observations from the experimental results: (1) Our HierSymNMF2 algorithm has significant advantages over other methods in average normalized cut on most datasets except the Overall, HierSymNMF2 is an effective community detection method that optimizes the average normalized cut very well, although it is not the fastest method.To address the quality issue of the ground-truth networks, we constructed a more complete and recent version of the DBLP dataset, where most nodes have at least one community membership and also the size of the data is significantly larger.F1 score because of the incompleteness of ground-truth communities and the difference between linkage-based communities (as detected) and functional communities (as in the ground truth). Therefore, it is important to use an internal measure such as the average normalized cut to evaluate an algorithm. In addition, we believe the current evaluation methods for large-scale community detection have certain limitations. These methods are mainly based on some quality scores, e.g., the F1 score. Such quality scores can be used to compare various algorithms. However, they do not provide much more information regarding the quality of results than the average performance. We assert that to better understand a community detection result, it is necessary to develop more comprehensive evaluation methods.We partially answered the three questions we raised in “For Q3, we think that the large scale of the network and the large number of communities make the community detection results hard to interpret. One way to understand the community structure better would be to develop better methods for community visualization.As a result of the growing popularity and utility of social media communications and other channels of communications between people and groups of people, there are vast amounts of data that contain latent community information. The amount of information is overwhelming and very demanding of our current technological capabilities, which may adversley impact the ability of stakeholders to make critical and timely decisions that are important in many domains such as natural disasters, local conflicts, healthcare, and law enforcement, to name a few. These domains typically involve groups of individuals with often hidden links. Thus, it is incumbent on the research community to develop fast and effective methods to first discover the communities formed by these links and then formulate useful summaries of the information provided by the algorithms and measures in order for decision makers to initiate appropriate actions as required.