The multiple-sandwich structure of UV-light sensors with graphene having high conductivity, flexibility, and impermeability is suggested to be beneficial for the facile fabrication of UV photodetectors with extremely efficient performances.Ultraviolet (UV) light photodetectors constructed from solely inorganic semiconductors still remain unsatisfactory because of their low electrical performances. To overcome this limitation, the hybridization is one of the key approaches that have been recently adopted to enhance the photocurrent. High-performance UV photodetectors showing stable on-off switching and excellent spectral selectivity have been fabricated based on the hybrid structure of solution-grown ZnS nanobelts and CVD-grown graphene. Sandwiched structures and multilayer stacking strategies have been applied to expand effective junction between graphene and photoactive ZnS nanobelts. A multiply sandwich-structured photodetector of graphene/ZnS has shown a photocurrent of 0.115 mA under illumination of 1.2 mWcm Photodetectors are a type of electronic devices for sensing light, and they have been found to have broad applications including flame monitoring, missile-plume detection, and space communication1233456789115131516171920212224262728313234356 times than the reported values of graphene-free UV photodetectors based on ZnS nanobeltsIn this work, we present that high-performance UV photodetectors showing stable on-off switching and spectral selectivity have been fabricated via a facile process based on the hybrid structure of solution-grown ZnS nanobelts and CVD-grown graphene. Sandwich structures and multi-layer stacking strategies have been applied to the photodetectors for the increment of the effective-junction region between graphene and ZnS. Three kinds of UV photodetectors, ZnS spin-coated on the surface of double-layer graphene (D-G/ZnS), ZnS sandwiched between two graphene layers (S-G/ZnS), and multiply sandwiched graphene and ZnS (MS-G/ZnS), have been demonstrated in ambient conditions at a low bias voltage. The photo-response behavior of a photodetector has been found to depend considerably on the stacking sequence of graphene and ZnS, as well as on the number of layers. An optimized photodetector based on S-G/ZnS shows a high photocurrent of 37 μA, which is higher 102 wafer  and the 2D-band (at 2692 cm−1)−1)The TEM image of −2 on and off periodically in ambient conditions. The net photocurrent can be obtained by subtracting the dark current from the light current. For a S-G/ZnS photodetector (Rλ), defined as the photocurrent generated per unit power of the incident light on the active area of the device, and the external quantum efficiency (EQE), defined as the number of electrons detected per incident photon, are crucial parameters for photodetectorsλ and EQE can be expressed as Iλ/(PλS) and hcRλ/(eλ), respectively, where Iλ is the difference between the photocurrent and the dark current, Pλ is the light power, S is the active area of the device, h is the Planck’s constant, c is the velocity of light, λ is the excitation wavelength, and e is the electronic charge. Our as-fabricated device has a channel length of 80 μm and a channel width of 20 μm. Therefore, the active area of the device is 1.6 × 10−5 cm2. According to our experimental results, Rλ and EQE for a S-G/ZnS photodetector have been calculated as 1.9 × 103 AW−1 and 8.0 × 105%, respectively, under an applied bias voltage of 1 V with 300 nm illumination of 1.2 mWcm−2. The response time is another important parameter of a photodetector. The rise and decay times  of a S-G/ZnS photodetector have been measured as 2.8 and 7.5 s, respectively. The multiple stacking of graphene and ZnS (MS-G/ZnS) has facilitated the photocurrent enhancement up to 115 μA  and H2 (3 SCCM) as the reaction source. After coating a poly(methylmethacrylate) (PMMA) polymer layer on the as-grown graphene of one side of the Cu foil, the graphene on the other side of the Cu foil was removed by oxygen plasma. After the Cu foil was etched with an ammonium persulfate (APS) solution and rinsed with distilled water several times, the graphene was transferred on a target substrate. The sample was soaked in acetone to remove the PMMA layer.Graphene was synthesized on a copper foil  via a chemical vapor deposition (CVD) method at 1,000 °C using a mixed gas of CH2+ was prepared by adding 1.0 mmol of ZnCl2(s) into 7.5 mL of water and 7.5 mL of C2H4(NH2)2(l) slowly with mild stirring; the stock solution was stirred further for 20 min to make a clear solution. A stock solution of S2− was prepared by adding 1.0 mmol of S(s) into 15 mL of N2H4·H2O(l) with vigorous stirring for 20 min. The stock solutions of Zn2+ and S2− were mixed together and stirred vigorously for 5 min. The mixture solution was then loaded into a Teflon-lined stainless-steel autoclave of 50 mL capacity, placed in a preheated oven at 180 °C for 9 h, and cooled to room temperature. A white precipitate produced in the reaction mixture was washed three times with water and two times with ethanol, vacuum-dried, and kept in a vial for further characterization.Single-crystalline wurtzite ZnS nanobelts were prepared via a hydrothermal process according to the reported method2 (300 nm) wafer by thermal evaporation, and graphene was transferred on the top of the electrodes. After spin coating of ZnS nanobelts dispersed ethanol on graphene for 20 s at 500 rpm, additional graphene was transferred on the top of the as-prepared sample.Titanium (3 nm) and gold (30 nm) electrodes were deposited on a Si/SiORaman spectra and their mapping profiles were obtained by a Raman spectrometer , and AFM images were measured at a noncontact mode using an atomic force microscope . TEM images and their SAED and FFT patterns were obtained with an electron microscope  operating at 300 kV, and FE-SEM images were measured with another electron microscope . While optical microscopy images were obtained with a microscope , HRXRD patterns were obtained with a diffractometer  using Cu Kα radiation (0.15418 nm). Extinction spectra were obtained with a UV-vis spectrophotometer . The current-voltage behaviors of photodetectors were measured using a probe nanovoltmeter  with a Xe lamp  as the light source.How to cite this article: Kim, Y. et al. High-performance ultraviolet photodetectors based on solution-grown ZnS nanobelts sandwiched between graphene layers. Sci. Rep.5, 12345; doi: 10.1038/srep12345 (2015).


Saccharomyces cerevisiae, appear to be beneficial during growth under stress conditions. In order to reveal the pattern of intron retention within intron-poor yeast species and better understand the mechanisms of intron evolution, we generated a comprehensive set of 250 orthologous introns in the 20 species that comprise the Saccharomycetaceae, by analyzing RNA deep-sequencing data and alignments of intron-containing genes. Analysis of these intron sets shows that intron loss is at least two orders of magnitude more frequent than intron gain. Fine mapping of intron positions shows that intron sliding is rare, and that introns are almost always removed without changing the primary sequence of the encoded protein. The latter finding is consistent with the prevailing view that homologous recombination between reverse-transcribed mature mRNAs and the corresponding genomic locus is the primary mechanism of intron loss. However, we also find evidence that loss of a small number of introns is mediated by micro-homology, and that the number of intron losses is diminished in yeast species that have lost the microhomology end joining and nonhomologous end joining machinery.Introns in protein-coding genes are very rare in hemiascomycetous yeast genomes. It has been suggested that these species have experienced extensive intron loss during their evolution from the postulated intron-rich fungal ancestor. However, no intron-devoid yeast species have been identified and some of the introns remaining within the genomes of intron-poor species, such as  The origin and evolution of introns in eukaryotic genomes are intensively debated topics . Two lonDaphnia pulex , followed by extensive loss of one copy of most paralogous genes (S. cerevisiae have been well annotated due to the extensive efforts of the yeast scientific community (Saccharomyces Genome Database [SGD]). However, introns in most other yeast species are poorly annotated, and intron analyses therefore rely either on automatic annotation or small manually curated sets of intron orthologs. This impedes the estimation of intron number and subsequent evolutionary studies. Nonetheless, even with limited intron information, interesting evolutionary observations have been made for the Saccharomycotina subphylum. For example, hemiascomycetous yeasts contain only the U2 spliceosome; all the components specific to U12 spliceosome have been lost in the Ascomycota  , togetheSaccharomyces cerevisiae strain BY4741, Saccharomyces kudriavzevii, and Naumovia castellii were cultured in standard YPD media in 30 °C and Saccharomyces uvarum NCYC 2669 at 28 °C with shaking at 200 rpm, to an absorbance of 0.5 at 600 nm. RNA of S. cerevisiae was extracted using Trizol , precipitated in lithium chloride , washed twice with 70% ethanol and the pellet resuspended in dH2O. RNA of the other three species was extracted using Qiagen RNA extraction kit according to manufacturer’s instructions. Ten micrograms of total RNA from each S. cerevisiae, S. kudriavzevii, S. uvarum, and N. castellii was processed with the RiboMinus Transcriptome Isolation Kit for Yeast and Bacteria (Invitrogen) to deplete the rRNA. cDNA libraries for each species were constructed and sequenced using the SOLiD 4.0 System from Life Technologies according to the standard manufacturer’s protocol. Saccharomyces cerevisiae cDNA was deposited on one-quadrant of a slide with one other barcoded library (not presented here). The other three libraries were barcoded and deposited on one-quadrant of a slide. Sequencing yielded a total of 77,286,181 reads for S. cerevisiae, 23,993,647 reads for S. uvarum, 6,287,432 reads for S. kudriavzevii, and 7,909,274 reads for N. castellii, all of 50 bps. Raw reads were filtered to obtain only reads with an average quality >20 using the approach described by S. cerevisiae genes according to SGD, release 64) (ease 64) . The RNAS. cerevisiae were extracted from SGD . The orthologs of the intron-containing genes in the S. cerevisiae clade were retrieved from the YGOB Pillar file   , which ph mLAGAN , and theh mLAGAN  to ensurh mLAGAN . BrieflyS. cerevisiae (SGD). We then used the resulting alignments as seeds for iterative HMMER searches as described above. We analysed the sequence surrounding the potential snoRNA matches to establish if they are encoded within introns of paralogous genes. Lastly, we used syntenic information provided by YGOB to establish if the snoRNA hits lie in regions paralogous to those included in the seed alignment.To detect orthologs of intronic snoRNAs, we extracted the portion of each intron alignment that corresponded to a snoRNA annotation in Syntenic relationships between intron-containing genes were extracted from YGOB. Paralogous genes arising from WGD were grouped to represent one ancestral gene, and multiple introns in the same gene were treated separately. The phylogenetic tree used to map intron gains and losses was taken from YGOB  and BP (TACTAAC) sequences that are indicative of Saccharomycetaceae introns. We found 216, 163, 200, and 155 predicted introns in S. cerevisiae, S. kudriavzevii, S. uvarum, and N. castellii, respectively. Of these, three introns in S. cerevisiae, four in S. uvarum and ten in N. castellii are novel—not present in the latest annotation in SGD—although some have been previously experimentally validated . Comparing the numbers of reads mapped to each intron among the various species revealed an even stronger correlation between species (B). These results provide preliminary evidence that intron splicing and expression is generally highly conserved among the Saccharomyces sensu stricto species, but more specific investigation of levels of intron splicing is warranted.Next, we counted the reads spanning each identified junction in nscripts A. Compar species B. These S. cerevisiae (SGD) and introns predicted from the RNAseq data obtained for S. cerevisiae, S. uvarum, and N. castellii. We extracted the orthologous genes from YGOB and investigated the presence of introns for those species in which the host genes were present. Manual refinement of these alignments highlighted five additional unannotated introns in genes HOP2, CGI121, YPR170W-B, YPR153W, and YPL109C, of which only the HOP2 intron is present in S. cerevisiae  we investigated the differences in the distance between the BP and the 3′-splice site (referred to as the S2 distance) among 20 species. As reported previously, we found increased S2 distances in C. glabrata, K. lactis and to lesser extent in Saccharomyces sensu stricto, Kazachstania sp. and Naumovozyma sp. All those species have lost the U2AF1 splicing factor postulated to be correlated with short S2 distances . We examined the U2AF1 gene and protein structure in detail, and found that T. blattae and T. phaffii are the only species without the intron splitting the initial cysteine codon of the first zinc finger domain. The T. blattae and T. phaffii U2AF1 proteins also contain 105 and 60 amino acid insertions, respectively, but maintain all crucial domains (B). The V. polyspora U2AF1 gene contains the intron in the typical position and does not have extensive insertions. It was previously suggested that the loss of the U2AF1 protein might be responsible for increased S2 distance . The majority of missing introns  left the gene replaced with its cDNA version . There were 33 instances in 14 genes when intron removal was accompanied by a deletion or insertion of 3–40 codons. We found one clear case of intron sliding in the RPS22B gene in Eremothecium cymbalariae, E. gossypii, and K. lactis, where the 5′-UTR intron has been moved into the open reading frame (ORF) and is present after the initial A (fig. 5B). Finally, 310 additional introns, mainly in 5′-UTRs, appear to have been lost, but the mechanism of loss is unclear.Across the 20 species and 235 ancestral genes, there were total of 5,553 “intron sites,” defined as sites that could contain an intron based on the presence of an intron at that position in at least one other species. In total, 1,168 of these intron sites are without an intron (0.21 introns missing per site per species) and 4,385 contained an intron . We found only 13 cases where an intron encoding a snoRNA was lost from the host gene. Only in the case of snR191 in the NOG2 gene of Naumovozyma dairenensis did intron loss lead to snoRNA removal from the genome. Kazachstania africana has lost the same intron, but an additional copy of snR191 is present in a region non-paralogous to NOG2. In the five cases where the intron encoding the snoRNA was lost, the snoRNA was retained within the intron of the paralogous gene, and for another six cases a copy of snoRNA was present in the paralogous region but without the associated protein-coding gene (fig. 7B). This last mode of gene loss with retention of the intronic snoRNAs was previously described as “snoRNA deintronization” and has been shown to be common in the evolution of snoRNA genes in the Saccharomycetaceae . This result, together with the strong conservation of spliced transcript number and intron expression . In summary, distinctive patterns for RPG intron conservation in different species lead us to conclude that the unusual characteristics of RPG introns are not shared by the whole Saccharomycetaceae, and are probably restricted to the Saccharomyces sensu stricto species.In the  as well A. This rpression , suggestnogaster , thus it introns B. In sumY. lipolytica  and the Vanderwaltozyma clade (represented by V. polyspora)  . In contrevisiae . We therSaccharomyces sensu stricto, individual species have lost between 15% and 46% of their introns, except for the Lachancea clade, where 7% of the introns tested appeared to have been lost on average. The most extreme case of intron retention is L. kluyveri for which we observe a total of nine introns missing, seven of which are probably intron gains in other species. Interestingly, L. kluyveri has undergone an unprecedented loss of the proteins mediating NHEJ and MMEJ pathways of DNA repair .


Community detection is the process of assigning nodes and links in significant communities  and its development has led to a better understanding of complex networks. When applied to sizable networks, we argue that most detection algorithms correctly identify prominent communities, but fail to do so across multiple scales. As a result, a significant fraction of the network is left uncharted. We show that this problem stems from larger or denser communities overshadowing smaller or sparser ones, and that this effect accounts for most of the undetected communities and unassigned links. We propose a generic cascading approach to community detection that circumvents the problem. Using real and artificial network datasets with three widely used community detection algorithms, we show how a simple cascading procedure allows for the detection of the missing communities. This work highlights a new detection limit of community structure, and we hope that our approach can inspire better community detection algorithms. Over the course of the last decade, network science has attracted an ever growing interest since it provides important insights on a large class of interacting complex systems. One of the features that has drawn much attention is the structure of interactions highlighted by the network representation. Indeed, it has become increasingly clear that global structural patterns emerge in most real networks . One sucWhile the exact definition of communities is still not agreed upon , the genBy developing techniques to extract this organization, one assumes that communities are encoded in the way nodes are interconnected, and that their structure may be recovered from limited and/or incomplete topological information. Various algorithms and models have been proposed to tackle the problem, each featuring a different definition of the community structure while sharing the same general objective. Although these tools have been used with success in several different contexts , 5, 6, ashadowing phenomenon in 3 widely used algorithms. Then, we propose and develop a general cascading approach to community detection that addresses this specific problem. Next, we validate our first implementation of the cascading method by applying our algorithms to real complex networks, and then to synthetic benchmark networks. The former set of calculations acts as a proof of concept and shows that, despite its apparent simplicity, the algorithm detects several shadowed communities. The latter set helps us determine under which conditions the algorithm is expected to perform adequately. Finally, based on our results, we gather in the Conclusion ways in which our method can be improved.The paper is organized as follows. First, we argue that current algorithms tend to overlook small communities found in the neighborhood of larger, denser ones, under very general conditions. In the following sections, we investigate the exact mechanisms that cause this so-called non-overlapping partitions of the network. This resolution limit has been rigorously shown to affect modularity  for the high mixing case. A precise and general criterion cannot be formulated, because its specifics depends on the hidden parameters of the network and algorithm of choice, as illustrated by this example.In a normal situation, where built-in communities are not known, the results shown in structural communities in real networks based on the above qualitative criterion, because their detected community structure is similar to that of LF networks (See μ〉 = 0.13) with average memberships number (〈Ω〉 = 2.06), and network densities (〈ρ〉 = 0.002) that all roughly correspond to the parameters used in the left panel of cv suggests that shadowing of structural communities definitely occurs in the Email (cv = 5.51), and Internet networks (cv = 2.42). No definitive conclusion can be drawn for the other networks, since their calculated variability of cv > 0.7 lie slightly below the optimal threshold for low mixing cases, except for the Power network (cv = 0.48). These results must be considered with care since LF networks do not capture all the structural complexity of real networks  (left to right). All networks consists of N = 5 000 nodes of average degree 〈k〉 = 20, that belongs to Ω = 2 communities (if they overlap), with a mixing parameter μ = 0.1. Each subplot shows the number of communities of x nodes in the neighborhood of communities of y nodes, for all x, y ≤ 100. Communities that share at least one node are defined as neighbors. In all cases, large communities are found in the vicinity of smaller communities of all sizes . This pattern is however more pronounced in the highly heterogeneous region (right-hand side of the figure), where shadowing occurs (See μ = 0.6 (not shown).This array samples 25 points of the parameter space, i.e. fractions of overlapping nodes set to f = /5, 1 (bo(PDF)Click here for additional data file.S3 Figx nodes in the neighborhood of communities of y nodes, for all x, y ≤ 100. Communities that share at least one node are defined as neighbors. To identify the possibility of shadowed communities, one must look for large communities in the vicinity of small ones, i.e. for off diagonal elements in the rows / columns corresponding to community sizes that are present in the network. For CPA, one need not look far away from the diagonal, since small communities can shadow even smaller communities , GCE (center) and LCA (right). Each subplot shows the number of communities of (PDF)Click here for additional data file.


However, there is a trade-off between the electrical resistance and the optical transmittance, and the fixed absorption rate in graphene limits is use when fabricating devices. Therefore, a more efficient method which continuously controls the optical and electrical properties of graphene is needed. Here, we introduce a method which controls the optical transmittance and the electrical resistance of graphene through various thicknesses of the top Cu layers with a Cu/Ni metal catalyst structure used to fabricate a planar mesh pattern of single and multi-layer graphene. We exhibit a continuous transmittance change from 85% (MLG) to 97.6% (SLG) at an incident light wavelength of 550 nm on graphene samples simultaneously grown in a CVD quartz tube. We also investigate the relationships between the sheet resistances.Graphene has been received a considerable amount of attention as a transparent conducting electrode (TCE) which may be able to replace indium tin oxide (ITO) to overcome the significant weakness of the poor flexibility of ITO. Given that graphene is the thinnest 2-dimensional (2D) material known, it shows extremely high flexibility, and its lateral periodic honeycomb structure of sp Cu and Ni are well-known metal catalysts for the growth of large-area single- and multilayer CVD graphene, respectively114151415In this report, Cu and Ni are the only elements used as catalysts for synthesizing large-area graphene, and control of the optical and electrical properties of simultaneously grown multilayer and single-layer graphenes can be obtained under the same growth conditions for SLG growth with Cu foil. We demonstrate these controlled properties with two methods: graphene growth with Cu thickness control on a Cu/Ni metal catalyst structure, and the size-width control of a SLG-MLG mesh pattern using the optimized SLG growth condition derived from the first method.2 substrate. Our graphene samples were grown simultaneously on substrates structured with the Cu/Ni metal catalyst with various upper Cu thicknesses ranging from 0.3 μm to 0.7 μm in increments of 0.1 μm. The thickness of the Ni layer in Cu/Ni structure was fixed at 0.3 μm for all substrates. The same growth condition for single-layer graphene growth with the Cu foil was applied to our CVD growth condition  is proportional to the degree of light absorption and the thick.% of Ni. Moreover, because the areas of Cu and Ni are equal, the volume % of Ni is proportional to dG,avg. Thus, we assume that our method to control the Cu thickness of the Cu/Ni structure results in controlling the volume % of Ni, which then controls the amount of carbon supplied to the growing graphene and add-layers. Furthermore, the sheet resistance is inversely proportional to the film thickness as described by the following equation, ρ is the resistivity and d is the thickness. The other fittings ; and T is the transmittance of light. Using G value is 1.26 × 106 S/m. This value is comparable to the previously reported result of 1.1 × 106 S/m from CVD-grown SLGwhere 2 substrate (Ni/SiO2/Si). 2 substrate shows that the SLG-MLG patterns were continuously connected. During the thermal annealing process, Cu and Ni may diffuse vertically and laterally affect each other at the interface between the SLG and the MLG. However, the SLG area maintains an optimal condition without add-layers, despite the sensitive control over the Cu thickness, as shown in 2D/IG) exceeds 2.In order to determine the applicability of our Cu-thickness-controlled graphene, we deposited a 0.7-μm-thick Cu pattern using a shadow mask on a 0.3-μm-thick Ni-deposited SiOD/IG ratio of approximately 0.1 and I2D/IG ratio close to 2 with small distributions.Furthermore, we succeeded in the fabrication of SLG array patterns . Althoug2) is etched away, followed by the formation of electrode patterns. If we use a MLG sheet with minimally patterned SLG at the graphene channel position, handling during the transfer process could be much easier than it is with large-area SLG-only graphene. Moreover, MLG can be utilized as an electrodeThis SLG-MLG patterning growth enables the selective positioning of SLG elements for wafer-scale graphene device fabrication. One of the main topics in the research on graphene is large-area graphene growth and transfer processes124142/Si substrate using a shadow mask. After CVD growth, we transferred the SLG-MLG mesh-patterned sheet onto flexible polyethersulfone (PES) film and a glass substrate with a supporting layer of PMMA. As shown in Although the fabrication of graphene-layer-controlled patterns for graphene-based devices is fascinating, forming various thickness-controlled Cu patterns on graphene sheets with various properties remains complicated. In order to achieve more efficient control of the optical and electrical properties of graphene such that these processes become applicable to TCE, we developed a mesh-patterned graphene sheet using only two optimal growth conditions of ‘Cu 0.7 μm/Ni 0.3 μm’ for SLG and ‘Ni 0.3 μm’ for MLG. 3) and 63 wt% nitric acid (HNO3) for 5 minutes to reduce the sheet resistance. Chloroform has been used to reduce PMMA residue, but it is known to contribute to the doping effect of graphene233 was reported as one of the most effective chemical agents for the p-doping of graphene6 S/m, and the chloroform and HNO3 treatment resulted in values of 1.77 × 106 S/m and 2.67 × 106 S/m, respectively. Therefore, we were able to control the electrical properties of mesh-patterned graphene by means of chemical doping without changing the optical properties.We analyzed the Tr value as a function of the SLG area ratio, finding a Rs value of 840 Ω/□ at a Tr (at 550 nm) value of 97.6% for the Cu 0.7 μm sample (SLG). The Ni-only (Cu 0 μm) sample exhibited an Rs value of 489 Ω/□ at a Tr value of 88% . Interestingly, the Tr results of the mesh-pattered films show linear variations between the MLG-only and the SLG-only films . We also2, glass substrates, and transparent polymer substrates , respectively. The Cu/Ni/SiO2/Si substrate was heated up to 1,000 °C inside of a quartz tube under H2 atmosphere, and then the graphene was grown on the Cu/Ni/SiO2/Si substrate flowing gas mixtures of H2 : CH4 = 10 : 15 (sccm) for 20 min. After growing the graphene, PMMA (950 PMMA A6), which served as an adhesive supporting layer, was spin-coated on a graphene at 3000 RPM for 30 s. The PMMA/graphene/metal catalyst was separated from Si substrate during floating on buffered HF (BOE). It takes several minutes for separation. Then, the PMMA/graphene/metal catalyst was floated on 0.1 M ammonium persulfate solution (APS) to etch the metal catalyst. After rinsed the PMMA/graphene in DI-water over a few hours, the PMMA/graphene was transferred to the target substrate and baked at 75 °C for few minutes for increasing the adhesion between graphene and target substrate. The PMMA was removed with acetone and IPA.The graphene was synthesized on Cu/Ni metal catalyst layers by the thermal chemical vapor deposition (T-CVD). For Cu (0.3 ~ 0.7 μm) and Ni (0.3 μm) metal catalysts, DC sputter and E-beam evaporator were used to deposit on a thermally oxidized SiOThe optical transmittance was obtained using S-4100 PDA UV-Vis spectrophotometer from SCINCO. The sheet resistance was measured based on the van der Pauw method by using HL5500IU Hall system from ACCENT. In order to carry out the optical property measurement, the graphene sample was transferred onto transparent substrate. Indium ingot  was used as a contact pad for measuring sheet resistance by attaching on 4 edges of rectangular shape transferred graphene.How to cite this article: Choi, J. S. et al. Facile fabrication of properties-controllable graphene sheet. Sci. Rep.6, 24525; doi: 10.1038/srep24525 (2016).


Simply integrating an inexpensive bioinspired polymer coating onto the Mn-graphene nanocomposite increased ORR performance significantly, with a peak potential shift of over +730 mV. This indicates that the material can reduce oxygen at a higher rate but with lower energy usage, revealing its excellent potential as an ORR electrocatalyst in fuel cells.Platinum electrodes are commonly used electrocatalysts for oxygen reduction reactions (ORR) in fuel cells. However, this material is not economical due to its high cost and scarcity. We prepared an Mn(III) catalyst supported on graphene and further coated with polydopamine, resulting in superior ORR activity compared to the uncoated PDA structures. During ORR, a peak potential at 0.433 V was recorded, which is a significant shift compared to the uncoated material’s −0.303 V (both versus SHE). All the materials reduced oxygen in a wide pH range via a four-electron pathway. Rotating disk electrode and rotating ring disk electrode studies of the polydopamine-coated material revealed ORR occurring via 4.14 and 4.00 electrons, respectively. A rate constant of 6.33 × 10 The electrochemical reduction of oxygen to water is an essential cathodic half reaction in proton exchange membrane fuel cells. Platinum-based catalysts are the most efficient materials for oxygen reduction reactions (ORR) and are currently used in commercial fuel cells1368p-styrenesulfonate) modified reduced graphene oxide18Graphene and MWCNTs possess unique physical, morphological, electrical, and chemical properties, including a large surface area and electrical conductivity, which make them excellent candidates as active components in energy-related applications111213One widely-used method to develop superior ORR catalysts is the integration of heteroatom dopants such as nitrogen into the nanomaterial framework222324An example of a “green” nitrogen precursor material is polydopamine (PDA), which is non-toxic and biocompatible. PDA is an eco-friendly, bio-inspired mussel adhesive molecule that is generated by self-polymerization of dopamine, which forms a thin film on various substrates without any surface pretreatment28306 mol−1s−1, which is, to our knowledge, the highest rate constant recorded among similar bioinspired synthetic catalysts employed for ORR.Here, we present a nanocomposite that combines the usefulness and efficacy of metal complexes, carbon nanomaterials, and PDA to perform superior ORR. We have designed a Mn(III) catalyst of an amidomacrocyclic ligand supported on graphene structures and thoroughly characterized by a combination of analytical techniques. This nanocomposite was further coated with PDA at room temperature to enhance its reactivity and stability in ORR studies . Electro1  analyses were run to determine the chemical states of some of the elements in the Mn-graphene nanocomposite and PDA-Mn-graphene nanocomposite  was set ectively . A narroectively . The firphene352 38, with phene352 37.2O32 in the Mn-graphene nanocomposite2, the Mn center is reduced from +3 to +2 oxidation state.Mn2p and Mn3s narrow scans of the Mn-graphene nanocomposite were collected (Scanning electron microscopy (SEM) image of the Mn-graphene nanocomposite  indicate40Atomic force microscopy (AFM) imaging of the Mn-graphene  and PDA-−1 (Mn-graphene) and 1345 and 1570 cm−1 , which has been observed in graphene materials. The G band is indicative of the graphene carbons, and the D band indicates the defect present within the graphene structure. The slight shift in the two bands in PDA-Mn-graphene was due to the presence of PDA, an effect that has been reported earlierD/IG) changed slightly from 1.53 to 1.11 in the Mn-graphene and PDA-coated materials, respectively. In the lower range of the spectra, the presence of 2 is observed. −1. The Mn-N bond in 2 is observed at 410 cm−1 in the Mn-graphene nanocomposite and slightly shifted to 416 cm−1 in the PDA-coated materialRaman spectroscopy of the Mn-graphene and the PDA-Mn-graphene material were compared, and the spectra are given in 2)- and nitrogen (N2)-saturated pH 2.0 electrolyte solution. As is shown in 2 and a low current density under anaerobic conditions. However, when the study was performed in an O2-saturated solution, the current density displayed a significant increase toward ORR. Additionally, the peak potential shifted positively about +60 mV.The electrochemical activity of the nanocomposite to catalytically reduce oxygen was evaluated using cyclic voltammetry (CV). Initial evaluation of the Mn-graphene nanocomposite compared the efficacy of the material under an oxygen , with an approximate current density value of 1.37 mA/cm2.Electrochemical studies of Next, the effect of the type of nanomaterial was tested by comparing graphene- and MWCNT-supported electrocatalysts. As 2 and graphene was compared to determine the optimal pairing of the two. As seen in 2 was grafted onto graphene, the current density decreased slightly. However, the peak potential shifted negatively with a slightly higher overpotential than the 1:2 ratio sample. At this point, the graphene material was being saturated with 2. The decreased oxygen reduction and higher overpotential resulted from the smaller amount of graphene reducing the ability to transfer electrons between the nanomaterial and 2.The ratio of 2-saturated solution with a much lower current density. In comparison to the Mn-graphene nanocomposite, the peak potential has a significant +730 mV shift. When compared to the PDA-coated graphene nanocomposite , the current density of the PDA-Mn-graphene material was almost twice as large and, therefore, demonstrates the need for the Mn catalyst for higher ORR activity.We then studied the effect of PDA on the Mn-graphene nanocomposite to observe any positive influences on peak potential and/or current density. 2 carbon network in graphene, which creates defects in the adjacent sites. This, in turn, changes in the charge density, resistance in charge transfer, and hydrophilicity of the material to assist in ORR2 carbon framework in graphene and the lone pairs of electrons on the nitrogen in PDA to give better electrochemical transfer towards ORR with a lower overpotential.A possible explanation for the dramatic shift observed in the ORR potential lies within the interaction between PDA and graphene. The nitrogen atoms in PDA interacts with the spRatio studies were also performed with the PDA-Mn-graphene nanocomposite to observe any changes in the current density and/or peak potential . From th2 (as noted in The Mn-graphene nanocomposite was further tested in a wide range of pH conditions. lim), Levich (JLev) and kinetic currents (Jk). Theoretical slopes where the number of electrons, n, equals 2 and 4 were also plotted for comparison. From the slope, the Levich equation was used to calculate the number of electrons in the electrochemical reaction.RDE was conducted in both pH 2.0 and 10.0 at an increasing number of rotations. In pH 2.0, RDE  showed tk = 103 nFkCΓ, where k is the rate constant and Γ is the concentration of the catalyst deposited on the electrode surface. In acidic media, the rate constant was 1.3 × 106 mol−1s−1, which is larger than that of a similar Co electrocatalyst5 mol−1s−1)5 mol−1s−1)5 mol−1s−1. The decrease resulted from the higher overpotential encountered at higher pH, which decreased the rate of ORR. The PDA-Mn-graphene nanocomposite gave a rate constant of 6.33 × 106 mol−1s−1, which is over twenty-four times higher than the non-PDA-coated nanocomposite at pH 10.0 and higher than that of a Co-porphyrin-MWCNT electrocatalyst (1.3 × 106 mol−1s−1)2-porphyrin (3.0 × 105 mol−1s−1)5 mol−1s−1)c oxidaseThe rate constant was determined for each experimental condition by the kinetic current using the equation JWe also conducted RRDE, which possesses a platinum ring surrounded by a graphite disk. The nanocomposite was deposited onto the disk, carefully avoiding the platinum ring. As the electrode rotates at a constant speed, the initial oxygen reduction occurs at the disk. Any formation of peroxide intermediate is diffused outward towards the platinum ring, where it is further reduced to water and any accumulation of peroxide will be observed by a ring current.The RRDE in pH 2.0 is plotted for the Mn-graphene  and the −) in higher pH. The Mn(III) interacts with oxygen and water in the presence of two electrons to yield a hydroperoxo intermediate. Next, the hydroperoxide species is rapidly reduced via a two-electron process to hydroxide and regenerates the Mn(III) catalyst.The proposed general mechanism of the Mn-graphene nanocomposite in acidic and alkaline media is given below. From the XPS results, the Mn2p and Mn3s narrow scans showed the presence of a Mn(III) species in the Mn-graphene nanocomposite. It has been previously shown that PDA’s catechol groups can covalently graft with metal salts and even reduce the metal ions to their corresponding metallic form495048In order to show the effect of acidic media on the Mn catalyst’s stability, we conducted stability studies. The Mn-graphene modified electrode was left in pH 2.0 phosphate buffer solution for several days. Periodically, CV was performed to observe changes in the peak potential position and/or current density. 2. Using time dependence studies, UV/Vis was conducted for 2 and after the addition of the pH 10.0 buffer solution , a peak at 580 nm is associated with the dark yellow solution of 2. This peak has been found in other Mn(III) complexesStudies with the alkaline medium were also conducted using ultraviolet-visible spectroscopy (UV/Vis) to observe any changes in the absorbance of solution . Scans wIn this study, an Mn(III)-amidomacrocyclic complex was synthesized and used for the first time in ORR activity. The catalyst was supported on graphene and tested in a wide range of pH. CV of the nanocomposite showed increased current density in comparison to the Mn catalyst and graphene alone. This revealed that the synergy between the catalyst and graphene produces a nanocomposite capable of reducing oxygen more efficiently than the two individual components. When the nanocomposite was further coated with PDA, the multicomponent material revealed a significant increase in ORR peak potential from −0.303 V to 0.433 V (both versus SHE).6 mol−1s−1—for quick reduction of oxygen. During the formation of the PDA coating, the Mn(III) complex was reduced to a Mn(II) complex. This oxidation state change, along with the possible interaction of PDA with graphene, furnished a nanocomposite with enhanced ORR activity. Furthermore, the RRDE ring current did not increase, which suggests that no hydroperoxide intermediate is involved in the ORR mechanism. To our knowledge, this is the first use of a PDA-Mn-graphene nanocomposite for ORR applications.Varying the pH revealed increased current density in alkaline media accompanied with slightly higher overpotential. The PDA-coated nanocomposite also showed catalytic activity in a wide pH range, which is advantageous in fuel cell applications. Reaction kinetics from the hydrodynamic studies revealed a four-electron pathway with one of the highest rate constants—6.33 × 10−1 Raman shift.Chemicals used in this study were purchased from Sigma-Aldrich, USA, Acros Organics, USA, or VWR International and were used without further purification unless otherwise noted. Graphene was obtained from Angstron Materials  and used without any additional modification. MWCNTs were purchased from Bayer MaterialScience (Baytubes C150P RD SAM) and used as received. Nitrogen gas (ultrahigh purity) and oxygen gas (ultrahigh purity) were obtained from NLR Welding Supply Inc. Buffer solutions of different pH  were prepared using published procedure1 was synthesized as previously described  was added and dissolved with dry THF (20 mL) under N2. The temperature was lowered to 0 °C by placing the flask in an ice bath. Once the solution was cooled, n-butyllithium  was added, followed by anhydrous manganese(II) chloride . The reaction was slowly heated to room temperature and allowed to stir overnight. The reaction yielded a yellowish-brown precipitate. The solution was exposed to air to allow the metal to oxidize from Mn(II) to Mn(III). The solvent was removed via rotary evaporator to yield a solid yellowish brown product and dried under vacuum. Yield: 75% (247.5 mg). λmax = 408 nm, ε = 1.21 × 102 M−1cm−1. This catalyst was used in aqueous solutions for all the UV/Vis studies. The remaining studies used 2 with PPh4+ as the counter ion, which was synthesized following literature procedure2 was dissolved in distilled water, from which the PPh4+ salt of the catalyst was precipitated using PPh4Cl. The precipitate was filtered and dried under vacuum. Anal. Calcd for [C43H34MnN4O4P]·H2O: C, 66.66; H, 4.68; N, 7.23. Found: C, 66.92; H, 4.50; N, 7.37%.The amidomacrocyclic ligand escribed 5758. Syn dry THF  mL under2 was dissolved in 5 mL of methanol. From this solution, 2 mL of the Mn catalyst was transferred to a new vial. 4 mg of the carbon nanomaterial (MWCNTs or graphene) was added to the solution and methanol was further added to give a 1 mg/mL suspension. The mixture was sonicated for 30 min. 40 μL of Nafion® was added and the suspension was sonicated for an additional 30 min. Other ratios used in this study were prepared similarly by changing the amount of 2 and carbon nanomaterial.5 mg of 2 and 10 mg of graphene were added and suspended in 50 mL NaOH solution (pH 8). This was sonicated for 15–30 min. 125 mg (0.659 mmol) dopamine was added to the suspension and vigorously stirred for 3 h. 40 μL of Nafion® was added and the suspension was sonicated for an additional 30 min. Additional ratios of the PDA-coated nanocomposite were prepared similarly by changing the amount of 2 and graphene used.The PDA-coated nanocomposites were prepared by following literature procedure with some alterationsCV was performed with a potential range of 0.6 V to −1.0 V (versus Ag/AgCl) at a scan rate of 100 mV/s, unless otherwise noted. CV studies were performed in a three-electrode system using a glassy carbon working electrode, Ag/AgCl reference electrode, and platinum counter electrode. The glassy carbon working electrode was then modified with the previously prepared Mn-graphene or PDA-Mn-graphene nanocomposite. A 10 μL aliquot of the nanocomposite was drop casted onto the electrode and dried at room temperature. Within the electrochemical cell, a phosphate buffer solution was added and purged with either oxygen or nitrogen gas for at least 1 h prior to electrochemical testing. The peak potential values are given with respect to SHE.−9 mbar. Samples were then mounted to the sample holder using double-sided tape. All spectra were collected with the charge neutralization flood gun turned on. The typical pressure during the analysis with the flood gun on was 2 × 10−7 mbar. The collected data were processed using the Thermo Scientific Advantage XPS software package. Spectral charge correction was performed using the main C1s peak due to hydrocarbon (C-C/C-H bonds) set to 284.8 eV. Mixed Gaussian-Lorentzian peak shapes as well as a Shirley/Smart type background subtraction were used for the peak analysis and fitting.XPS studies were carried out by using a Thermo Scientific Model K-Alpha XPS instrument using monochromatic Al Kα radiation (1486.7 eV) with the X-ray spot size 200 μm for each sample. The base pressure in the analysis chamber was typically 1 × 10TEM observations of nanocomposite samples were performed by JEOL JEM 2100F equipped with a field emission gun at 80 kV acceleration voltage and an EDS (EDAX Corporation) used for elemental analysis. Each sample was dispersed into ethanol and a few drops were deposited and allowed to dry on a TEM holey carbon covered copper grid.Nanocomposite was suspended in methanol, and the resulting solutions were pipetted onto freshly cleaved mica . Tapping mode AFM images were acquired using an Asylum Research MFP-3D AFM. Silicon probes  were used with force constants of 7–8 N/m, nominal resonant frequencies of 150 kHz, and average tip radios less than 6 nm.How to cite this article: Parnell, C. M. et al. Polydopamine-Coated Manganese Complex/Graphene Nanocomposite for Enhanced Electrocatalytic Activity Towards Oxygen Reduction. Sci. Rep.6, 31415; doi: 10.1038/srep31415 (2016).


Substitutional chemical doping is one way of introducing an electronic bandgap in otherwise semimetallic graphene. A small change in dopant arrangement can convert graphene from a semiconducting to a semimetallic state. Based on ab initio Density Functional Theory calculations, we discuss the electron structure of BN-doped graphene with Bravais and non-Bravais lattice-type defect patterns, identifying semiconducting/semimetallic configurations. Semimetallic behavior of graphene with non-Bravais lattice-type defect patterns can be explained by a phase cancellation in the scattering amplitude. Our investigation reveals for the first time that the symmetry of defect islands and the periodicity of defect modulation limit the phase cancellation which controls the semimetal-semiconductor transition in doped graphene. For instance, it has been shown that electronic bandgap of graphene antidot lattices depends on the shape and size of the holesh-BN) islands in zigzag-edged hexagonal supercells. The periodicity of supercells can in general be defined by two lattice vectors, et al., a supercell is denoted by four integers n, such that supercell geometry is described by the combination n × n. Both Bravais lattice-type patterns, where supercell lattice points are replaced by single defect sites and non-Bravais lattice-type patterns, where supercell lattice points are replaced by more than one defect site, are considered in this work.We considered two types of B/N-based defects: atomically separated B and N (isolated scatterers) and hexagonal BN  was used for the exchange and correlation functional with a 45 Ry energy cutoff for plane wave expansion. A 6 × 6 × 1 Monkhorst-Pack grid was used to sample the Brillouin zone. All geometries were optimized to forces less than 0.025 eV/Å.All calculations were done using ab initio Density Functional Theory (DFT) as it is implemented in the h-BN islands in n × n zigzag-edged hexagonal periodic supercells.In this section, we discuss how the electronic properties of graphene depend on periodicity of dopant modulation by considering Bravais lattice-type defect patterns. We considered two defect types: isolated B scatterers and n × n zigzag-edged hexagonal periodic supercells  as depicted in n × 3n where n is an integer, the bandgap closes , and the bandgap decreases when the dopant concentration is reduced otherwise25n × 3n or into K (or K′) point if the defect periodicity is n × 3n, two Dirac points merge into the Γ point destroying intervalley scattering. Thus no bandgap opening is observed. Therefore, semimetal-semiconductor transition is possible as the periodicity of defect modulation changes2425We considered isolated B scatterers in h-BN islands in zigzag-edged hexagonal supercell of graphene as shown in h-BN islands. Electronic bandgap is observed at the Γ point or K point depending on the supercell periodicity. The bandgap decreases with the decreasing defect concentration. However, this pattern is slightly deviated for 3n × 3n defect periodicity as shown in B and N co-doping can break the A-B sublattice symmetry of host lattice of graphene; thus are attractive in graphene defects engineeringWithin these two observations, it is clear that both translational and sublattice symmetry breaking are important in tuning the bandgap of graphene. To explore more about the effect of symmetry on bandgap tuning, we considered defects with non-Bravais lattice-type patternsk is the wave vector and τj is the position of the jth defect site. A phase cancellation can occur for particular defect patterns, which destroys the scattering between two sublattices at the Dirac point. For a given chemical composition, a small change in the defect pattern can change graphene from semimetallic to semiconducting behavior. In order to understannd the effect of sublattice symmetry breaking and the effect of symmetry of defect sites on the semimetal-semiconductor transition, we investigated electronic structures of several non-Bravais lattice-type defect patterns in graphene.In non-Bravais lattice-type defect patterns, more than one defect site replace the lattice points described by two vectors We considered four cases, where there are two isolated B and/or N scatterers in 8 × 8 zigzag-edged hexagonal supercell, as shown in g = 0.10 eV –g = 0.15 eV –When there is only a single B scatterer in the 8 × 8 zigzag-edged hexagonal supercell of graphene (Bravais lattice-type defect pattern), 0.06 eV electronic bandgap is observed . Bandgaph-BN islands. h-BN island in the 8 × 8 zigzag-edged hexagonal supercell. B and N atoms of the h-BN island take the positions of sublattice A and sublattice B respectively. In addition to the h-BN island, g ~ 0.12 eV is observed for graphene 8 × 8 zigzag-edged supercell with a single h-BN island , it is not possible for the structure factor to be zero, irrespective of the atomic arrangement. Therefore, no semimetal-semiconductor transition is observed for inverted and non-inverted systems in the 9 × 9 systems. This suggests that a semimetal-semiconductor transition in graphene with non-Bravais lattice-type defect patterns is limited by the supercell periodicity of the system.The same investigation was carried out for systems with two h-BN islands both of which have C3 symmetry shows an electronic bandgap 0.66 eV. B and N atoms take positions of same sublattice in these two h-BN islands. Semimetallic behaviour is observed for the configuration shown in h-BN island is restored by the second h-BN island. In contrast, the configuration in h-BN islands where similar defect atoms take the same sublattice positions shows a 0.68 eV bandgap, whereas the bandgap does not close when similar defect atoms take positions of opposite sublattices .


Staphylococcus aureus), by varying the ratios between AuNPs and graphene-PDDA. AuNPs/graphene-PDDA in the ratio of AuNPs/graphene-PDDA = 4:1 exhibited the strongest SERS signal in SERS detection of adenine and S. aureus. Thus, it is promising in the application of rapid and label-free bio-detection of bacteria or tumor cells.In this research, graphene nanosheets were functionalized with cationic poly  (PDDA) and citrate-capped gold nanoparticles (AuNPs) for surface-enhanced Raman scattering (SERS) bio-detection application. AuNPs were synthesized by the traditional citrate thermal reduction method and then adsorbed onto graphene-PDDA nanohybrid sheets with electrostatic interaction. The nanohybrids were subject to characterization including X-ray diffraction (XRD), transmission electron microscopy (TEM), zeta potential, and X-ray photoelectron spectroscopy (XPS). The results showed that the diameter of AuNPs is about 15–20 nm immobilized on the graphene-PDDA sheets, and the zeta potential of various AuNPs/graphene-PDDA ratio is 7.7–38.4 mV. Furthermore, the resulting nanohybrids of AuNPs/graphene-PDDA were used for SERS detection of small molecules (adenine) and microorganisms ( Escherichia coli (E. coli) and various molecules. This is possible because of the enhancement of the Raman signal. Gold and silver nanoparticles are widely used for SERS enhancement [9, thereby easily detecting the presence of various bacteria or molecules. Once metal (gold and silver) become nanoparticles with a unique size and morphology, their optical, electrical, and magnetic properties also change. Recent research on SERS technology emphasize on controlling the size and morphology of the nanoparticles. When the gap of the metal nanoparticles is within 10 nm, it will produce “hot spot” effect, which will further enhance the intensity of the SERS signal. Therefore, it is important to develop the SERS bio-detection by controlling the gap and the particle size of the metals nanoparticles.Raman scattering was discovered by CV Raman in 1928, and further, the use of surface-enhanced Raman scattering (SERS) technology was developed by Fleischman and others in 1974. SERS is used widely in various applications such as label-free sensing of bacteria ancement  via thei2/Vs with a resistance value (10–6 Ω·cm) even lower than silver or copper [Graphene  is an alr copper , which ir copper –12.Poly (PDDA) is a homopolymer and synthesized by George Butler in 1957 –14. PDDAStaphylococcus aureus for SERS rapid detection. Various ratios of AuNPs/graphene-PDDA were evaluated in order to create an optimum surface-enhanced Raman scattering (SERS) signal for SERS bio-detection [(S. aureus).In this experiment, Au/graphene-PDDA nanocomposite was fabricated by adsorbed gold nanoparticles (AuNPs) onto the graphene-PDDA nanosheets as shown in Fig. etection –20 of sm2SO4, 96.5 %, Baker; fuming nitric acid, HNO3, ≧99.5 %, Sigma-Aldrich; potassium permanganate, KMnO4, Baker; PDDA, 35 % , Aldrich; hydrogen peroxide, H2O2(aq), 35 %, Acros; hydrochloric acid, HCl(aq), 37 %, Scharlau; sodium citrate dehydrate, Na3Ct·2H2O, ≧99.5, Sigma-Aldrich; hydrogen tetrachloroaurate(III) trihydrate, HAuCl4·3H2O, 99 %, Sigma-Aldrich; nitric acid, HNO3(aq), 69 %, Panreac; silicon Oil, Choneye Pure Chemical; Luria-Bertani (LB broth), Difco™ , Oxoid; and adenine, C5H5N5, ≧99 %, Sigma.The materials used in this study were as follows: graphite powder, <20 μm, synthetic, Aldrich; sulfuric acid, H3Ct·2H2O) as a reducing agent to reduce Au3+ of HAuCl4·3H2O. The experimental procedure is as follows. (1) Ninety-six milliliters of 0.307 mM tetrachloroauric acid solution was prepared. (2) The solution was heated, and after boiling for 10 min, 4 mL of 1 % sodium citrate solution was added and the color changed from yellow to dark red.Citrate thermal reduction method was used to prepare gold nanoparticles. It is an oxidation-reduction reaction, which uses sodium citrate (NaGraphene oxide (GO) was prepared by using the modified Hummers method. Potassium permanganate was added to the graphite, and the surface of the graphite will have many oxidized functional groups. The experimental procedure is as follows. (1) Thirty-six milliliters of concentrated sulfuric acid was added to 1.0 g of graphite powder and stirred for 1 h. (2) The solution was stirred continuously in ice bath, 12 mL of fuming nitric acid was added dropwise, and then, 5 g of potassium permanganate was slowly added. (3) The solution was stirred for 120 h under room temperature. (4) One hundred twenty milliliters of deionized water was added slowly and stirred for 2 h under room temperature. (5) Six milliliters of hydrogen peroxide solution was added and stirred for 2 h and left at room temperature for 24 h. (6) The top layer of the clear solution was removed, and 200 mL of deionized water, 1 mL of hydrogen peroxide solution, and 1 mL of hydrochloric acid were added. The solution was mixed for 2 h and centrifuged. (7) Step 6 was repeated three times. (8) The top layer was washed with deionized water until the solid pH is close to 7. (9) After the solid was collected, it was placed in a vacuum oven and dried at 40 °C for 48 h. The final product was graphene oxide powder.The experimental procedure is as follows. (1) Sixty milligrams of graphene oxide powder was mixed with 20 mL of deionized water. (2) The solution was sonicated for 10 min. (3) Eight hundred microliters of PDDA was added and stirred for 10 min. (4) The solution was heated to 90 °C under reflux for 12 h. (5) The solution was centrifuged, and the upper layer solution was removed. The step was repeated for several times. (6) Deionized water was added to the final product.4 solutions were prepared as shown in Table 4 to graphene-PDDA were as follows: 1:2, 2:1, 4:1, 8:1, 16:1, referred as Au1/G2, Au2/G1, Au4/G1, Au8/G1, and Au16/G1.Different proportions of graphene-PDDA and HAuClS. aureus (ATCC 6538P). The experimental procedure is as follows. (1) Fifty microliters of the varied AuNPs/graphene-PDDA and 50 μL of S. aureus solutions (1 × 105 CFU/mL grown for 18 h at 37 °C) or adenine (concentration of adenine is 10−4 M) were placed in 1.5 mL micro-centrifuge tubes and mixed well. (2) Five microliters of each sample was dropped on the aluminum sheet. Raman spectra in the range of 400 to 1800 cm−1 were evaluated for the samples. The intensity of the Raman signal at 733 cm−1  was investigated also for the samples.A Raman microscope  with He-Ne laser (632.8 nm) was used to detect the presence of The interaction between AuNPs and graphene-PDDA were analyzed by X-ray photoelectron spectroscope , and surface electric properties of AuNPs/graphene-PDDA samples were analyzed by zeta potential analyzer  as described below.Various ratios of AuNPs to graphene-PDDA nanohybrids were prepared, including Au1/G2 (AuNPs to graphene-PDDA ratio = 1:2), Au2/G1, Au4/G1, Au8/G1, and Au16/G1. The morphology and distribution of the Au/graphene-PDDA nanohybrids were analyzed by transmission electron microscopy (TEM), as shown in Fig. X-ray diffraction of Au/graphene-PDDA Fig.  shows th2 functional group in the PDDA, which enables it to attach to the negative charge of AuNPs. The higher addition of AuNPs would decrease the average zeta potential of the nanocomposites. In Fig. Zeta potential also confirmed successful fabrication of Au/graphene-PDDA, as shown in Fig. 7/2 (from 84.8 to 84.5 eV) and 4f5/2 (from 88.6 to 88.1 eV), respectively. The binding energy of pristine AuNPs in XPS peaks would decrease after some molecules were grated due to the increase of electrons [XPS analysis Fig.  shows thlectrons . It provS. aureus, SA) was used as a model for SERS detection, and integrated intensity of the Raman signal at 733 cm−1  was examined, as shown in Fig. S. aureus by Au/graphene-PDDA nanohybrids detection. The different ratios of Au/graphene-PDDA nanohybrids were used to detect SA for optimum SERS signal. The results showed that AuNPs/graphene-PDDA in the ratio of AuNPs/graphene-PDDA = 4:1 exhibited the strongest SERS signal in the detection of S. aureus.Bacterium (S. aureus or adenine. An increase in the ratio of 4:1 would cause the aggregation of AuNPs to induce the laser scattering or decrease the surface plasmon effects (hot junctions effects) due to the contact of AuNPs with each other. Therefore, SERS signal intensity would decrease.In addition, the small molecules  also were tested by SERS detection. Figure S. aureus and adenine. Graphite was chemically exfoliated, and PDDA was π-π stacked onto the surface of graphene nanosheets, and later, gold nanoparticles were synthesized and attached onto the surface of graphene-PDDA by surface charge interaction. The resulting Au/graphene-PDDA nanocomposites greatly enhanced the Raman signal of S. aureus and adenine. Various ratios of AuNPs to graphene-PDDA were tested to make optimum SERS enhancement effects. AuNPs/graphene-PDDA in the ratio of AuNPs/graphene-PDDA = 4:1 exhibited the strongest SERS signal in the bio-detection of small biomolecules (adenine) and microorganisms (S. aureus). AuNPs/graphene-PDDA was shown to have enhanced Raman signal capability and unique ability to adsorb onto the microorganisms. Thus, it can be further applied in the rapid and label-free bio-sensing of biomolecules and microorganisms.This paper demonstrates in detail the synthesis of Au/graphene-PDDA nanocomposites and its application in SERS detection of


We extensively investigated the conductivity of single-layer graphene decorated with iridium deposited in ultra-high vacuum at low temperature (7 K) as a function of Ir concentration, carrier density, temperature, and annealing conditions. Our results are consistent with the formation of Ir clusters of ~100 atoms at low temperature, with each cluster donating a single electronic charge to graphene. Annealing graphene increases the cluster size, reducing the doping and increasing the mobility. We do not observe any sign of an energy gap induced by spin-orbit coupling, possibly due to the clustering of Ir.Graphene decorated with 5 We measure the conductivity as a function of Ir concentration, carrier density, temperature, and annealing conditions. The results are consistent with the formation of clusters of Ir on graphene, even for deposition at low temperature, with each cluster containing ~100 Ir atoms and donating ~1 electron to graphene and acting as a charged impurity scattering center. Annealing Ir-decorated graphene to room temperature greatly reduces the doping and increases the mobility, consistent with greatly increased cluster size. No signature of any significant bandgap in graphene decorated with Ir adatoms was observed and is attributed to the formation of Ir clusters.In this work, we investigate the σ(Vg) was measured. σ(Vg) for the pristine device and the device with four different Ir doping concentrations. With increasing Ir deposition, several features become apparent: 1) the gate voltage of minimum conductivity Vg,min shifts to more negative values, 2) the mobility μ decreases, 3) the minimum conductivity σmin decreases. All of these features are similar to the effect of charged impurities on graphene, observed previously by deposition of potassiumIr was deposited via electron-beam evaporation in UHV. To vary the coverage, the device was exposed to a controlled flux with sequential exposures at a fixed sample temperature of 7 K. Following each deposition, the conductivity as a function of gate voltage σ(Vg) at high |Vg| toWe fit Vg − Vg,min > 0) and hole conduction  in order to determine the electron and hole mobilities μe and μh, the threshold shift ΔVg,min, and the residual conductivity σres, where n is the carrier density, e is the electronic charge and cg is the gate capacitance per area. μe and hole mobility 1/μh versus Ir coverage, both of which are linear, demonstrating the mobility depends inversely on the density of impurities 1/μ ∝ nimp (Matthiessen’s rule)separately for electron conduction  for electrons (holes), about two orders of magnitude larger than found for K adatoms, indicating Ir is about 2 order of magnitude less effective at scattering electrons in graphene. g,min and σmin as a function of 1/μe, respectively. The results of both ΔVg,min and σmin agree well with that of potassium adatoms and can be well described by the previous theoretical predictions generated for impurity charge Ze with Z = 1, and impurity–graphene distance d = 0.3 nm–1.0 nm3Vg,min = Znimp, the scattering cross-section of an impurity scales as Z2 however the density of impurities nimp scales as 1/Z, hence the mobility scales as Z. Thus the results strongly suggest scattering by charged impurities with Z ≈ 1. Together with the observation of C about two orders of magnitude larger for Ir adatoms than for K adatoms, we infer that scattering is due to clusterse. This is entirely consistent with the observation of ΔVg,min about two orders of magnitude lower at a given Ir concentration than for a similar concentration of K adataoms. Note also that the charge transferred by Ir in clusters is much smaller than the value for isolated Ir adatoms calculated by density functional theory (Z = 0.22)T = 7 K. However the calculated barrier for an Ir adatom to diffuse through the bridge site on graphene is very small, ~50 meVwhere ry Z = 0.T = 7 K. Vg,min as a function of Ir coverage; σ(Vg) was measured during the continuous deposition of Ir. At the beginning of deposition, Ir tends to form uniform clusters randomly distributed on the graphene surface, and Vg,min drops fast and is roughly linear with increasing Ir coverage. At higher coverages Vg, min drops at a slower rate, consistent with the formation of larger clusters, and finally reaches a saturated value at the Ir coverage of 1.2 ML. This presumably marks the transition from clusters to a continuous film. With increased coverage beyond this point, Vg,min gradually recovers. The results are similar to those obtained for Ptn-type doping, with a reduction or even reversal of doping as a continuous film is formed. Monolayer graphene on single crystal Ir is known to be slightly p-doped. We conclude that the larger the cluster size, the smaller the charge transfer efficiency. Although Vg, min begins to recover for coverages above 1.2 ML, the resistivity at the Dirac point (ρxx) continues to rise with increasing Ir coverage, as shown in the inset of We also explored very high Ir coverages (>1 ML). Vg, min of graphene with 0.085 ML Ir deposited at T = 7 K [σ(Vg) data shown in Vg,min first shifts slightly towards negative gate voltage and then for T > 90 K shifts more rapidly to positive, eventually reaching its initial value before Ir deposition. The Vg, min shift reflects the rearrangement of Ir clusters, while the diffusion, growth and nucleation of atoms on the surface is a complex process, we speculate that below 90 K, Ir clusters do not grow appreciably, while some movement of individual adatoms leads to the negative gate voltage shift of Vg,min. Above 90 K, Ir clusters grow by Ostwald ripening, reducing the charge transfer efficiency and positively shifting Vg,min. Unsurprisingly, formation of large clusters at higher temperature (350 K) is found to be irreversible when re-cooling to low temperature, i.e. the ripening process is irreversible, as shown in g) for pristine graphene and Ir-decorated graphene annealed to form large clusters. Vg,min remains almost the same, indicating there is no change in charge transfer between large Ir clusters and graphene, consistent with charged-impurity-dominated scattering, σmin is nearly unchanged, and μ barely decreases, from 7000 to 6000 cm2/Vs. In contrast to small clusters, large clusters have low impact on the conductivity of graphene. The results are similar to those seen with Au clusters4We also explored temperature as a means to tune the cluster size after depositionxx of graphene decorated with Ir to search for an energy gap induced by spin-orbit couplingxx increases with decreasing temperature, and is well described by ρxx(T) ~ ln(T). Since the Ir cluster ripening process is irreversible when increasing and decreasing temperature (as shown in xx(T) curves of warming and cooling do not overlap. Comparing to the cooling curve, the Ir clusters rearrangement also provides additional contribution to the T dependence when warming. We also plot ρxx in logarithmic scale as a function of inverse temperature (inset of xx(T) is poorly described by the simple thermal activation model Eg < 1 meV, which is nonphysical, in that it is smaller than the measurement temperature kBT and much smaller than the disorder energy scale of order 50 meV. The roughly logarithmic ρxx(T) may originate from increased weak localization in graphene, has been observed in other noble metal-decorated grapheneWe further studied the temperature dependence of ρinset of  and findd transitional metal Ir. Ir tends to form clusters on graphene, acting as charged impurity scattering centers with a single electronic charge per cluster. No topological gap induced by spin-orbit coupling is observed, either due to the lack of such a gap in graphene with clustered Ir, or the lack of a global gap in transport due to inhomogeneity in graphene with adatom clusters. These findings provide guidance for future experiments aimed at achieving strong spin-orbit coupling in metal-decorated graphene.In summary, we have investigated the electronic transport properties of graphene decorated with 52/Si substrate and are identified by color contrast in optical microscope imaging and confirmed by Raman spectroscopy. The electrical contacts are defined with standard electron beam lithography and thermally evaporated Cr/Au (5 nm/100 nm). After annealing in H2/Ar gas at 350 °C to remove resist residueGraphene flakes are obtained by mechanical exfoliation of graphite on a 300-nm-SiOHow to cite this article: Wang, Y. et al. Electronic transport properties of Ir-decorated graphene. Sci. Rep.5, 15764; doi: 10.1038/srep15764 (2015).


Although stochastic and deterministic processes have been found to jointly shape structure of natural communities, the relative importance of both forces may vary across different environmental conditions and across levels of biological organization. We tested the effects of abiotic environmental conditions, altered trophic interactions and dispersal limitation on the structure of aquatic microfauna communities in Costa Rican tank bromeliads. Our approach combined natural gradients in environmental conditions with experimental manipulations of bottom-up interactions (resources), top-down interactions (predators) and dispersal at two spatial scales in the field. We found that resource addition strongly increased the abundance and reduced the richness of microfauna communities. Community composition shifted in a predictable way towards assemblages dominated by flagellates and ciliates but with lower abundance and richness of algae and amoebae. While all functional groups responded strongly and predictably to resource addition, similarity among communities at the species level decreased, suggesting a role of stochasticity in species-level assembly processes. Dispersal limitation did not affect the communities. Since our design excluded potential priority effects we can attribute the differences in community similarity to increased demographic stochasticity of resource-enriched communities related to erratic changes in population sizes of some species. In contrast to resources, predators and environmental conditions had negligible effects on community structure. Our results demonstrate that bromeliad microfauna communities are strongly controlled by bottom-up forces. They further suggest that the relative importance of stochasticity may change with productivity and with the organizational level at which communities are examined. Ecologists have long sought to understand which factors generate the variation within and among communities that is so ubiquitous in nature. Deterministic, niche-related processes such as environmental filtering , competiEcologists have yet to determine under which conditions, in which environments or for which organism groups particular structuring forces or their combinations prevail –13. Partin situ manipulations of community assembly. We focus on the microfauna communities in bromeliads, including all non-bacterial, non-viral microscopic organisms such as flagellates, ciliates, amoebae, algae and predatory microscopic metazoans such as rotifers and copepods. In nature, these communities are subject to varying environmental conditions within and among bromeliads such as water temperature and light regimes. Furthermore, resource input may vary strongly and the abundance of organisms at higher trophic levels may differ among bromeliads, resulting in differential strengths of bottom-up and top-down control on the microfauna. Dispersal is assumed to act as a neutral driver of community assembly since microfauna are mainly wind-, water- and vector-dispersed [In this study we use a natural system that can be relatively easily manipulated to conduct field tests of three deterministic structuring forces  and one stochastic force  identified as potentially important by more controlled but simplified systems . Tank brispersed  and diffHere, we examine the influence of environmental conditions, resources, predation and dispersal on bromeliad microfauna community structure. After homogenizing natural communities to exclude potential priority effects, we measured environmental variables and then explicitly manipulated resource input and predator presence in a field experiment. Because a spatial signal in community structure can result from effects of spatially correlated environmental variables as well as from dispersal limitation, we experimentally manipulated dispersal at local (among leaf axils) and regional spatial scales (among bromeliads). We then assessed effects of our experimental treatments by examining changes in the structure of microfauna communities consisting of five major functional groups .We hypothesized that:If deterministic processes dominate, then community structure will only respond to differences in environmental conditions and to the manipulation of species interactions such as resource competition and predation.If dispersal as a stochastic process dominates, then community structure will change as a response to the experimental manipulation of dispersal at both, local and regional scales.If deterministic processes and dispersal both contribute to community structure, then the experimental increase in regional dispersal rates (among bromeliads) should result in a much greater change in community composition than an experimental increase in local dispersal rates (within bromeliads). This is because distant sites will develop more different communities, through differences in environmental factors and species interactions, than communities within the same region, exposed to more similar conditions.http://www.acguanacaste.ac.cr), north-western Costa Rica under research permit N° ACG-PI-028-2010 . The station is situated at an altitude of 700m and is surrounded by primary and secondary tropical rainforest and horse pastures. Bromeliads occur in all of these habitats. We used three dominant tank bromeliad species: Werauhia sanguinolenta  J.R. Grant, W. gladioliflora (H. Wendland) J.R. Grant and W. kupperiana (Suessenguth) J.R. Grant and initiated the experiment after the first strong rainfall of the wet season.This study was carried out in April and May 2010 at the Estación Biológica Pitilla  in the Área de Conservación Guanacaste : 1.37±0.05m), in three habitats varying in canopy openness in order to include a large range in environmental conditions: secondary forest (relatively closed canopy with low light conditions), mixed habitat (mostly edge of the forest with mixed light conditions) and pasture (mostly open canopy with high light availability). We mixed water collected from 44 bromeliads growing in the same three habitats near our experimental bromeliads to include the same species of inhabiting organisms but homogenize the initial composition of experimental communities. We used a suction device with a tube to extract the water from the bromeliads after careful mixing to make sure all organisms were suspended in the water. With a filter with a mesh size of 850μm we removed large detritus particles and macroscopic organisms such as insect larvae (we checked for insect larvae emerging from eggs throughout the experiment and removed them instantly). Then, we added 35ml of this water, containing a mix of algae, protozoans and microscopic metazoans (hereafter collectively referred to as microfauna), bacteria, fungi and small detritus particles, to 50-ml falcon tubes. We placed 243 tubes containing the experimental communities into 27 bromeliads: nine bromeliads in each of the three habitats, nine tubes per bromeliad . The experimental communities in the predator-addition treatment each received one large  and two small  Culex jenningsi Dyar&Knab mosquito larvae. Those numbers represent average natural densities in bromeliads at the field site. Control communities did not receive any additions. All tubes were then covered with fine mosquito mesh to prevent insects from ovipositing and to monitor emerging adult mosquitoes. To keep predation pressure relatively constant over the course of the study, mosquito larvae were replaced whenever they pupated and emerged as adults. We could not observe the number of larvae during the study without disturbing the community, so we could not replace larvae that had died. However, this number was relatively low and all tubes in the predator treatment still contained mosquito larvae at the end of our study. The tubes were placed into separate leaf axils in the bromeliad with no water exchange between the tube and the bromeliad.The 27 experimental bromeliads were divided into three “trophic” treatments: control, increase of bottom-up effects by resource addition, addition of top-down effects by predator addition. For the resource-addition treatment, we collected leaf litter from the forest floor, chopped the material into 1-cmWe nested a hierarchical dispersal manipulation within each trophic treatment in each bromeliad. In each bromeliad, three of the nine experimental communities served as manipulations of “regional dispersal” between bromeliads, i.e., the removal of any dispersal limitation at the regional scale. After gently mixing the contents of the tube, we extracted 1 ml of water from each of the three experimental communities in each of the three bromeliads of the same trophic treatment in the same habitat. We then mixed the resultant 9 ml in a common vial and redistributed 1 ml of this mixture into each regional dispersal community. Manipulations of “local dispersal” within bromeliads  were treated similarly but only the 3 ml from the three “local” communities within a bromeliad were mixed. In three no-dispersal “control” communities we pipetted 1ml out of each community and placed the same liquid back into the tube to control for disturbance from pipetting. In these control communities, natural dispersal was the only means of exchange between communities, so patterns of dispersal limitation were allowed to develop over the course of the experiment. We performed the dispersal manipulations every four days over the four-week experimental duration.At the start of the experiment, we measured the key structural variables that we expected to influence microfauna communities. These were bromeliad diameter, number of live leaves (as a measure of habitat complexity and of light availability in each leaf compartment), height above the ground, natural water content of the bromeliad  and natural water content of each experimental leaf axil (in ml). Canopy openness was estimated by taking a photograph of the canopy above each bromeliad plant using a 35-mm lens and calculating the proportion of sky by pixel count. At the end of our study after four weeks, we measured pH, water temperature, volume, dissolved oxygen and turbidity in each tube to further describe the environment that the microfauna communities were experiencing. However, since these parameters can change across time and even within a day, their measurement at the end of the study only represents a snapshot of the environmental conditions and only large effects will be picked up by the analysis. We then collected microfauna samples from all 243 experimental communities and fixed them with acid 5%-iodine Lugol’s solution. The fixed samples were transferred to the University of British Columbia  where all organisms were counted under an inverted microscope (in 50-μl subsamples and 200x magnification). We used and expanded a photographic identification key developed for the microfauna living in bromeliads at our field site  to identWe analyzed the effects of resource addition, predation and dispersal on the overall abundance and species richness  and on the abundance and richness of the main functional groups. All abundance data were natural-log-transformed to achieve normality and homogeneity of variance. This conservative approach removes samples with zero abundance from the analysis. We repeated all analyses after adding a value smaller than the smallest recorded density to the data before log transformation. These additional analyses showed the same results and are therefore not presented.nlme [vegan [To account for the hierarchical error structure we analyzed abundance and richness with mixed effects models using the function lme in package nlme  in R, venlme . These ae [vegan . We addie [vegan . SpatialWe used multivariate analyses to test for the effects of canopy openness, temperature, trophic treatment and dispersal on community composition . Our resTo explore effects of varying alpha diversity (richness) on differences in community composition ,25,26 weWe counted a total of 62 species of protozoa and micrometazoa in our experimental communities and classified them into five major functional groups: algae (11 species), flagellates (17 species), ciliates (11 species), amoebae (11 species), predatory microfauna .Overall, our trophic treatments had strong effects on microfauna abundance , but lar2,23 = 0.267, P = 0.768 and F2,22 = 1.520, P = 0.241, respectively), suggesting that differences in abundance were the main cause for the richness differences in these groups. Differences between trophic treatments in the other groups remained significant after rarefaction, indicating that changes in abundance did not completely explain effects of trophic treatments on the richness of algae, flagellates and predatory microfauna.Diverging patterns emerged for individual functional groups  and 3. AOnly two environmental variables had significant effects on the experimental communities: canopy openness and water temperature. The other environmental variables were removed from further analyses. Canopy openness and water temperature both showed a spatial signal . Canopy openness influenced overall microfauna abundance  with higThe dispersal manipulation had no effect on abundance or richness of the combined microfaunal community, or of any of the functional groups Tables  and 2. I2,234 = 1.8099, P = 0.005 for linear effects, F4,232 = 2.8058, P = 0.005 for the four selected PCNM vectors) but this did not qualitatively affect any results. In the following section we present only results from analyses in which we removed the spatial trends.The spatial arrangement of the communities had an influence on their composition . This means that communities receiving added resources were more dissimilar from each other (higher beta diversity) than control communities and communities with predators. Communities with different dispersal manipulations did not differ in their community composition . We detected a small effect  may define the relative importance of deterministic and stochastic processes.Bottom-up control was the strongest and most prevalent effect driving several community characteristics, irrespective of differences in environmental conditions or experimental dispersal levels. Adding resources in the form of leaf litter increased overall microfauna abundance, decreased rarefied richness and shifted species composition. Abundances of certain fast-growing species were boosted strongly by the additional resources while overall species incidences remained unchanged, resulting in communities with less species per number of individuals. The decrease in alpha diversity with increased resources agrees with studies from grasslands  where stIn our study, resource addition also shifted functional group composition of microfauna communities. Flagellates (the most abundant group) increased most strongly as a response to resource addition, driving overall abundance patterns. Flagellates have short generation times  and haveUnexpectedly, only flagellate abundance increased with predator presence while overall microfauna abundance and the abundance of other groups did not change. Flagellates might have benefited from predators through a reduction in competition or predation by other groups possibly arising from size-selective feeding by mosquitoes , but morMecistogaster modesta Selys [It is not clear why the other groups did not change in response to the predators. Although we mimicked average natural densities of mosquitoes in bromeliads in the area , ourta Selys ,37. Moreta Selys ,39. For Deterministic processes: environmental conditionsOnly canopy openness and temperature had effects on community structure, and those were entirely due to high-temperature or high-canopy openness outlier communities. In a parallel observational study a number of habitat variables also influenced the structure of natural microfauna communities , however, explained variance was relatively low .a, water volume and water colour [The microfauna communities in Brazilian coastal bromeliads also show significant, but weak (17% of the variance) correlations with environmental factors, notably temperature, chlorophyll-r colour . One potWhile we detected a spatial signal in community composition, its inclusion in the models did not change the strong influence of the trophic treatment or the lack of dispersal effects. Furthermore, we did not find an influence of spatial arrangement on abundance and richness, and dispersal treatments had no effect on community structure. Instead, communities appeared to maintain their initial composition over the duration of the experiment, with most species being present at low abundance and extinctions being rare. We considered dispersal limitation a stochastic factor. However, had it affected only certain species, so operating in a deterministic fashion, we would similarly have seen changes in community composition following our dispersal manipulations.Environmental differences between bromeliads in the field did not lead to strong species sorting in our experiment, although natural bromeliad communities differ from each other . One interpretation of our results is that by homogenizing microfauna communities we overcame any priority effects of dispersal limitation that underlay these natural community differences. Whereas dispersal limitation has been detected in pitcher plant microfauna , and lowOur study of species-rich natural communities strengthens the results from more controlled experimental mesocosms  by showiCertain species benefitted from resource addition more than other species but the identity of those dominant species varied among replicate communities. This increase in stochasticity with increased resource availability has been shown previously from grasslands, where beta diversity of plant communities was positively related to nitrogen enrichment  or site In contrast to the increased stochasticity at the species level with higher resource availability, our study shows that at the functional-group level, shifts in composition towards communities dominated by flagellates and ciliates were strong and predictable. The general pattern emerging from this and other studies is that not only are stochastic and deterministic forces jointly structuring communities  but we aS1 Supporting Information(DOCX)Click here for additional data file.


The high ratio of pyridinic and pyridone N-doped graphene sheets have been synthesized by functionalizing graphene oxide (GO) with different oxygen groups on its surface. The typical N-doped graphene was determined to be ~3–5 layers by transmission electron microscopy (TEM) and atomic force microscopy (AFM), and the nitrogen content was measured as 6.8–8 at. % by X-ray photoelectron spectroscopy (XPS). The structure of the N-doped graphene with different surface functional groups was characterized by Raman spectroscopy. The research result indicates that the carboxylation of GO is the key factor to obtain pyridinic and pyridone N types during the N atom doping process. Compared to general N-doped graphene, the electrochemical test shows that specific capacitance of the GO-OOH-N sample reaches up to 217 F/g at a discharge current density 1 A/g and stable cycling performance (keep 88.8 % specific capacitance after 500 cycles at the same discharge current density) when applied to the supercapacitor electrode materials.The online version of this article (doi:10.1186/s11671-015-1031-z) contains supplementary material, which is available to authorized users. Graphene is an allotrope of a new-type carbon with two-dimensional carbon atoms tightly packed into honeycomb lattice . The speBy doping nitrogen into graphene, the bandgap can be opened, which makes it possible to adjust the conductivity type and the electronic structure of graphene; thus, the physical and surface performance of graphene can be modulated , 11, 12.sp2-hybridized carbons will hinder the N atoms doping into graphene lattice, the quaternary-type N is difficult to form, and often with lower N content or even the failure of doping [In general, the graphene and nitrogen atoms could be combined together in four ways –21. Consf doping . Oxidizef doping , fuel cef doping , 25, andf doping .Supercapacitors are new energy storage device, and they have many advantages attracting tremendous attentions, such as high power density, excellent cycling stability, and fast charge/discharge capability . GraphenThe effect of N doping types of graphene on supercapacitor performances has been previously investigated , 29, 30.In this paper, we obtained the N-doped graphene with different N types and content by adjusting the oxygenic functional groups on the graphene surface. As a result, the graphene with a high percentage of both pyridinic and pyridone N was obtained from carboxylic graphene oxide. The graphene with designed N types exhibits superior capacitance and cycling performance in the electrochemical tests, suggesting that special oxygen functionalization of graphene surface is a promising method to synthesize N-doped graphene with optimized N-doped types for high-performance supercapacitors.4 , N2H4·H2O , H2SO4 , H3PO4 , HCl , H2O2 , C2H5OH , ClCH2CO2H , NH3·H2O (30 %), and home-made secondary deionized water. All medicines were not treated with further purification.Materials used were as follows: natural flake graphite , KMnOλ = 690 nm), and Nanoscope IIIa controller atomic force microscope (AFM).All the graphene and N-doped graphene sheets were characterized with the JOL2010 transmission electron microscope , PHI-5400 X-ray photoelectron spectroscopy (XPS), Rxn1-785 Raman spectrometer (VH2SO4:VH3PO4 = 9:1). Then 18 g of KMnO4 was slowly added. The temperature was reached to 50 °C and kept at the temperature for 60 h accompanied by magnetic stirring. Finally, the solution was stewed and cooled down to room temperature. After that, deionized water of 300 ml was slowly added for dilution, and 10 ml of H2O2 and 100 ml of HCl were added to remove impurities. Then a large amount of distilled water was used to make washing and centrifugation, which were repeated for several times until the pH of the solution was neutral. The brown precipitate was frozen and dried for preservation.Three grams of natural flake graphite was added into 400 ml of ice-mixed acid (2CO2H (7.5 g) were added, and the solution was stirred for 3 h. After the reaction was complete, the solution was washed repeatedly to remove impurity ions until the neutral state was reached. Vacuum drying was conducted at 60 °C to harvest GO-OOH.The as-prepared GO (0.5 g) was dispersed in 250 ml of distilled water using ultrasonic equipment. Yellow transparent colloidal solution was regained. Then NaOH (10 g) and ClCH3·H2O. Then 20 ml of N2H4·H2O solution was added followed by stirring for 30 min. The solution was then transferred into the reaction vessel and kept for 5 h at 130 °C, and black product was obtained. Centrifugal washing was performed for several times with distilled water and ethanol. N-RGO was finally obtained by vacuum drying at 60 °C.Twenty-five milliliters of GO solution (6 mg/ml) was taken, and its pH was adjusted to 10 by NHThe process for preparation is the same as GO-N, except that the react temperature is 180 °C.The process for preparation of GO-OOH-N is the same as GO-N, except that the precursor solution GO is replaced by GO-OOH.N,N-dimethylformamide(DMF) until they formed a slurry with the proper viscosity, and then the slurry was uniformly laid on a piece of Ni foam about 1 cm2 that was used as a current collector and then dried at 80 °C for 2 h. The Ni foam coated with the composite was pressed for 1 min under 1.0 MPa and dried at 120 °C for another 12 h. A Pt electrode and Ag/AgCl electrode filled with saturated KCl aqueous solution were used as the counter electrode and reference electrode, respectively. Cyclic voltammetry (CV), galvanostatic charge/discharge, and electrochemical impedance spectroscopy (EIS) were measured on a CHI760E electrochemical workstation in a three-electrode system. The specific capacitance is calculated according to equation C = It/V, where I is the charge/discharge current density (A/g), t is the discharge time (t), and V is the voltage (1 V). The reported specific capacitance and energy density are all normalized to the weight of sample.A mixture containing 80 wt % active materials (3 mg), 10 wt % acetylene black, and 10 wt % poly-tetrafluoroethylene (PTFE) was well mixed in Figure −1 and D band near 1350 cm−1, representing the degree of crystallization and the degree of disorder or defect in the samples, respectively. The former is caused by in-plane stretching vibration under the Eg2 mode, and the latter is caused by the scattering of phonons at the boundary of the disordered hexagonal Brillouin zone [ID/IG = 0.14). After oxidization treatment, the crystallization degree of GO decreased obviously due to the oxygen-containing groups induced. For the carboxylic graphene, broadening of D band indicates more disorder of the structure, which is related to the increase of oxygen content and the size reduction caused by bond breaking in a transition from C-O-C to -COOH. By nitrogen doping and reduction treatment, N-doped graphene samples were obtained, with a small reduction in ID/IG value (from 1.08 to 1.01). This is the result of the combined action of several factors: (1) reduction of oxygen-containing groups led to decrease of ID/IG value; and (2) more structural defects of graphene caused by N doping would increase the value of ID/IG; the ID/IG values of GO-N, GO-N-180, and GO-OOH-N were similar, indicating close defect density of the N-doped samples but different defect type and distribution, as confirmed by XPS results.Figure uin zone . It can sp2 C-C peak was enhanced. The possible reason is that the carboxyl groups with high binding energy were introduced to the edge of graphene, while the epoxyl groups were distributed inside in-plane [As an effective tool to characterize elemental contents and bonding states in the samples, XPS technique can be used to understand the changes in oxygen and nitrogen content and their existing forms in the samples before and after reaction. Figure in-plane . The redAfter nitrogen doping, C1s peak of all NG samples shows only one main peak at 284.6 eV and can be divided into three sub-peaks centered at approximately 284.6, 286, and 289 eV , constant current (A), discharge time (s), potential window (V), and mass of the active material (g), respectively. As shown in Fig. + and N atoms, giving rise to abundant electrolyte ions occupied on the electrode surface [2/g, respectively. The pore size and distribution are close for both samples , indicating that this markedly enhanced capacitance ability of GO-OOH-N ought to be attributed to the high N content and favored N structures benefiting from an artificial carboxylation on graphene oxide precursor instead of pore structure difference. Unexpectedly, the Csp of GO-OOH-N undergoes a visible drop from 1 to 2 A/g and reaches to a lower capacitance level compared to that of GO-N; this observation may be elucidated by the instability of the pseudocapacitive redox pairs at high current density [The specific capacitance of the electrode is obtained from the equation: Csp =  surface . Clearly density , which c density , 28.The impedance analyses of all products are measured in the frequency range of 100 kHz–0.01 Hz at open circuit potential to evaluate the kinetic and fundamental behavior of electrode materials and are shown in Fig. Figure In this article, the changes of N types in graphene were investigated by using different functionalized graphene oxide precursors under varied experimental conditions. The research result shows that low temperature was favorable for the generation of pyridone nitrogen, while high temperature was beneficial to the generation of pyridinic nitrogen. When GO-OOH was used as the precursor, the presence of carboxyl groups was favorable for the both generations of pyridinic nitrogen and pyridone nitrogen. The GO-OOH-N shows the highest capacitance, and an inferior of GO-OOH-N to GO-N in rate capability at high current density convinced the instability of pseudocapacitance in the fast charge/discharge process. Moreover, GO-OOH-N also showed an impressive specific capacitance and cyclic stability after 500 cycles at a discharge current density 1 A/g. These observations demonstrate that the regulation of nitrogen structures in graphene could be achieved by the well-controlled oxygen groups on graphene surface. We believe that the present way is beneficial for the supercapacitor applications or other fields.