However, the effective work function values of metal gate in graphene FET are significantly deviated from their vacuum work function, which is similar to the Fermi level pinning effect observed in silicon MOSFETs with high-k dielectric. The degree of deviation represented by a pinning factor was much worse with HfO2 (pinning factor (S) = 0.19) than with Al2O3 (S = 0.69). We propose that the significant pinning-like behaviors induced by HfO2 are correlated with the oxygen exchange reactions occurred at the interface of graphene and HfO2.High-k materials such as Al Especially, graphene field-effect transistors (GFETs) have been extensively studied as a promising alternative for post-silicon metal–oxide–semiconductor field-effect transistors (MOSFETs)6. Recently, graphene barristors and related variations have achieved very high on/off ratios of up to 1078, even without any bandgap. Such progress is motivating further research on gate stacks that are compatible with graphene. Since graphene barristors rely on the sliding of the graphene Fermi level (EF) at the interface with the semiconductor, it is desirable to minimize the Fermi level pinning in the metal gate/high-k dielectric stack on a graphene channel.Graphene has received considerable attention because of its extraordinary electrical and physical properties, such as its high carrier mobility, excellent thermal stability, and top-down processing compatibilityin situ ozone treatments, functionalization of the graphene, or addition of a metal seed layer on the graphene to aid nucleation11. While the physical scaling of the gate dielectric on graphene has substantially progressed owing to these efforts, the electrical performance—such as quality and reliability—of the gate dielectric has not been thoroughly investigated. Alternatively, hBN has been extensively studied as a thin gate dielectric material for graphene15. The possibility of direct growth on a graphene due to the small lattice mismatch is a strong merit, but high leakage current with a dielectric constant lower than SiO2 (~3–4) indicates that the process is less matured for gate dielectric applications.So far, the physical scaling of gate dielectric has been the primary focus of study in the gate stack research for graphene because it is difficult to form a thin gate dielectric on graphene using atomic layer deposition (ALD) process because graphene does not have enough dangling bonds to initiate the chemical reactions for ALD. Thus, various processes have been explored, including surface modification by via interface dipole formation and Fermi level pinning18. Furthermore, the redistribution of the oxygen into the substrate strongly influences the effective work function of the metal gate. Thus, gate stacks on GaAs substrates are less influenced by Fermi level pinning than those on silicon, because the oxygen diffusivity in GaAs is lower than that in silicon20. These findings were critically important for the development of nanoscale silicon MOSFETs, because controlling the effective work function of the metal gate is crucial in controlling the threshold voltage of MOSFETs. Likewise, a proper understanding of the effective work function of the metal electrodes in GFETs will be very important to properly control the Dirac voltage (VDirac) of the graphene layers used in various graphene-based devices21.On the other hand, the metal gate might be the least studied component in GFET structure. In silicon MOSFETs, the redistribution of oxygen vacancies at the metal gate/high-k dielectric interface causes a work function roll-off phenomenon; this occurs 2 and Al2O3, and various metal gates covering a wide range of vacuum work functions from 4.28 to 5.65 eV. We found that the effective work function values of metal gates on the high-k dielectric and graphene stack are significantly deviated from the vacuum work function, which is similar to the Fermi level pinning effect of a metal gate/high-k dielectric stack in silicon MOSFETs. Fermi level pinning factors representing the degree of deviation were extracted from the slope of Dirac voltage and vacuum work function of top gate metal. The pinning factors were ~0.1914 for HfO2 and ~0.6912 for Al2O3, indicating that the Al2O3 gate dielectric is more suitable for controlling the Dirac voltage of GFETs. Finally, the origin of the Fermi level pinning effect in GFETs was studied using a discharge current analysis (DCA) method.In this work, the interactions between the gate metal, gate dielectric, and graphene in the graphene FETs have been investigated using two different dielectrics, HfO22.To compare the influences of different metal gates and high-k dielectrics on multiple GFETs, the baseline GFETs used in this work should have relatively small scattering in the device characteristics. Especially, the device should show symmetric I-V characteristics to accurately assess the impact of Fermi level pinning using the distribution of Dirac points. We applied various addition annealing and passivation processes to achieve symmetric I-V characteristics with minimal hysteresis and residual charges as described below2/highly p-doped silicon substrate (resistance < 0.005 Ω) was sonicated sequentially in acetone, methanol, and deionized (DI) water. The substrate was then cleaned using the SC1 process . Monolayer graphene was grown on a Cu foil (≥99.8%) using thermal chemical vapor deposition (CVD), and then transferred to the SiO2 substrate. First, the graphene was coated with poly(methyl methacrylate) (PMMA) at 500 rpm for 5 s and 3000 rpm for 50 s, and then baked at 80 °C for 5 min. Then, the Cu foil was etched at 50 °C using a Cu etchant. The PMMA/graphene film was cleaned with diluted HCl (HCl and H2O in a ratio of 5:13) then rinsed with DI water. A final DI water rinse was performed for 5 min, and then the PMMA/graphene film was transferred to the substrate and dried overnight.The 90 nm SiO−6 Torr). The Raman spectrum of the transferred graphene fits well with a Lorentzian function. As shown in Fig. To remove the PMMA layer, the samples were baked at 150 °C for 10 min and then submerged in acetone heated to 85 °C for 3 hr. Finally, thermal annealing was performed at 300 °C for 1 hr in a vacuum  evaporator, followed by the patterning of the channel and contact regions by photolithography and an Au etchant. Then, the graphene was etched by oxygen plasma according to the Au hard mask, which was performed at 50 W in a 200 mTorr environment for 2 min. The final graphene channel width and length was 30 µm and 9 µm, respectively. Then, the source/drain electrodes (100 nm Au) were formed by photolithography and a metal wet etch process.2O3 or HfO2 was deposited on the graphene channel as a top-gate dielectric using ALD. For HfO2, a Hf seed layer (2 nm) was deposited using the e-beam evaporator because of the nonuniform nucleation that arises without the seed layer11. Before the ALD process, the Hf seed layer was oxidized using 10 s H2O/30 s N2 purge steps (10 cycles) at 100 °C. Then, HfO2 was deposited over 400 ALD cycles using tetrakis(ethylmethylamino)hafnium /N2 purge (10 s)/H2O (0.2 s)/N2 purge (10 s) steps at 250 °C. In the case of Al2O3, the trimethylaluminum /N2 purge (20 s)/H2O (0.2 s)/N2 purge (20 s) cycle was repeated 300 times at 130 °C. Both dielectrics were densified at 300 °C for 1 hr under vacuum (~10−6 Torr). The thicknesses of the Al2O3 and HfO2 layers measured with a spectroscopic ellipsometer were both ~30 nm. Finally, four different top-gate metals—Pt, Au, Ti, and Al—were deposited and pattered using a lift-off process. The top-gate metals were chosen so their work functions covered the range from 4.28 to 5.65 eV. Figure −6 Torr) at 300 °C for 1 hr before the electrical tests were performed.AlI–V measurement unit (Keithley 4225-RPM)26. A pulsed I–V measurement method was used to minimize the influence of slow charge trapping effects. The pulse width and rise/fall time was 1 ms and 100 µs, respectively, which were the optimized test conditions for the top-gated GFETs used in this work. The gate voltage was increased in one direction starting from near the Dirac voltage to minimize hysteresis. Since the dielectric constant of Al2O3 is 6 and that of HfO2 is 20.6, the gate voltages were biased from −10 V to 10 V for the Al2O3 device and from −2.9 V to 2.9 V for the HfO2 device to generate matching maximum electric fields of around ±5.1 MV/cm. The mobility, hysteresis-induced charge trap density, and Dirac voltage was extracted from the electron-doped branch of the pulsed Id–Vg curve. The charge trap density of the top-gated GFETs at the Fermi level of graphene was extracted using the DCA method25. For the DCA measurements, square pulses were applied to the top-gate, ranging from 10 kHz to 1 MHz for Al2O3 and from 10 kHz to 500 kHz for HfO2. The frequency ranges were adjusted considering the influence of the different time constants of the charge traps in each dielectric. The capacitance–voltage curves of the top-gated GFETs were also measured using an impedance analyzer (Agilent 4294 A) to extract the EF value of graphene using the quantum capacitance. A detailed description of the DCA setup and analysis is given in Ref.25.The device characteristics were measured using a Keithley 4200 Parameter Analyzer with a pulsed Id – Vg) curves of the top-gated GFETs having different top-gate metals are shown in Fig. 2, which is much smaller than that expected based on the range of vacuum work function values. In contrast, the range of Al2O3 Dirac voltages was 1.10 V. This behavior is somewhat similar to the Fermi level pinning effects observed in silicon MOSFETs which limits the swing range of the threshold voltage so that it is much smaller than the range of the vacuum work function of metal gates. However, since the Fermi level pinning in silicon MOSFETs is related to trap states at the dielectric/silicon interface, whether we can attribute this observation to Fermi level pinning remains uncertain.The representative pulsed drain current–gate voltage  reactions—can be measured separately according to the response time; redox components primarily appear at higher frequencies23. Moreover, the quantities of these two types of charge generation mechanism can be extracted using the DCA method.To investigate this interesting phenomenon, the differences in the graphene and high-k dielectric interfacial reactions were investigated using the DCA method2O3 gave a charge density that was ten times lower than that of HfO2. This result indicates that there are significantly more oxygen-related charge generation events at the HfO2/graphene interface. This is a reasonable result considering that oxygen ions diffuse much faster in HfO2 than in Al2O333. Oxygen vacancies in dielectrics can create an interfacial dipole layer through electron transfer that affects the Fermi level pinning35. Hence, the high density of charge traps in the top-gated GFETs containing HfO2 corresponds with the small pinning factor extracted from the relationship between the Dirac voltage and the work function of the top-gate metal.Figure 2 can affect the Fermi level pinning. Figure I–V curve shift via Fermi level pinning is more pronounced at negative biases, i.e., lower work functions. This result implies that the electron doping of the graphene is weaker than expected for the lower work function metal gates, i.e. there is a mechanism prohibiting the electron doping of the graphene, especially in the case of HfO2, as graphically shown in Fig. − charging at the graphene surface and thus prevent electron doping. Figure 2, in contrast to Al2O3, providing corroborating evidence for our model. Thus, we can conclude that the strong Fermi level pinning observed for the GFETs fabricated with the HfO2 gate dielectric is due to the inhibition of electron doping, which occurs via counter-charge generation at the dielectric/graphene interface due to the interfacial oxygen accumulation. This mechanism is quite different from the original Fermi level pinning mechanism because the chemically induced charges are limiting the movement of Fermi level of graphene rather than the defect states.Especially, oxygen accumulated at the bottom interface of HfODct,redox) in the top-gated GFETs with HfO2 were 0.1914 and ~1014 cm−2 eV−1, respectively. These values show that HfO2 devices experience a more significant pinning effect than Al2O3 devices, which have a pinning factor of 0.6912 and Dct,redox = ~1013 cm−2 eV−1. These results indicate that the pinning effect is caused by redox reactions at the interface between the high-k dielectric and graphene. These results indicate that the scaling of gate dielectrics using higher-k materials will be more difficult for GFETs and will require stringent control over the interface and its passivation.In this research, the dependence of the Fermi level pinning effect on the dielectric was investigated using pulsed current–voltage and DCA methods. From the analysis of our measurements, the pinning factor and charge trap density induced by the redox component (Figure S1


Replicating prior work, L2 speaking rates were significantly slower than L1 speaking rates both across-groups , and across L1 and L2 within bilinguals. Critically, within the bilingual group, L1 speaking rate significantly predicted L2 speaking rate, suggesting that a significant portion of inter-talker variation in L2 speech is derived from inter-talker variation in L1 speech, and that individual variability in L2 spoken language production may be best understood within the context of individual variability in L1 spoken language production.Second-language (L2) speech is consistently slower than first-language (L1) speech, and L1 speaking rate varies within- and across-talkers depending on many individual, situational, linguistic, and sociolinguistic factors. It is asked whether speaking rate is also determined by a language-independent talker-specific trait such that, across a group of bilinguals, L1 speaking rate significantly predicts L2 speaking rate. Two measurements of speaking rate were automatically extracted from recordings of read and spontaneous speech by English monolinguals ( I.Cross-language and cross-talker variability are salient features of human speech production. Cross-language variability at the phonetic level is evident in the observation that similar speech sounds in different languages  often vary in their articulatory and acoustic details, giving rise to language-specific or dialect-specific articulatory settings . SimilarWithin the language/dialect-specific phonetic setting for a given language or dialect, acoustic-phonetic variation across individual talkers can exceed the extent expected based solely on physical variation across individuals. For example, within Standard American English, individual talkers differ in their characteristic voice onset times (VOTs) for voiceless stops. That is, controlling for speaking rate, some talkers produce longer VOTs than other talkers, and these talker differences are consistent across various places of articulation  or a consequence of more central, rather than peripheral, control processes in language and speech production. For example, working memory, IQ, and, lexical retrieval speed, amongst other cognitive and/or linguistic characteristics, may influence both first-language (L1) and second-language (L2) speech production. It is also possible that individual personality traits may bear some relation to patterns of speech production , as well as about who is saying it . For example, using sine-wave replicas of English speech in which the spectro-temporal dynamics of the signal  are preserved but the vocal source characteristics  are removed, The integration of linguistic (contrastive phonetic) and indexical  information suggests that some degree of idiolectal variability is likely constrained by language-specific structural features . In the case of bilingual talkers, for whom two linguistic sound systems must coexist, this may mean that the need to maintain a greater number of phonetic distinctions along any given acoustic dimension might further constrain the range of idiolectal variability. (Note that this account assumes integrated L1-L2 phonetic systems with constant co-activation of both languages.) Moreover, acoustic-phonetic variation along global acoustic-phonetic dimensions that are shared across a bilingual's L1 and L2 may be independently controlled in the two languages. For example, within a given language, speaking rate (in terms of syllables per second) may be substantially constrained by dialectal affiliation  of linguistically diverse bilinguals . We chose to focus primarily on timing in terms of speaking rate  because it can be automatically and consistently measured across languages. Moreover, as a global feature that sets a temporal frame (or tempo) for an utterance rather than conveys phonemic contrasts, speaking rate is exactly the type of acoustic feature that could be subject to both language/dialect- and talker-specific control. It is also more likely to be independent of the anatomical and physiological constraints of a particular vocal tract than other global features such as fundamental frequency or the long-term average speech spectrum. This set of conditions thus allows for the possibility of a dissociation between average L1 and L2 speaking rates in absolute terms (with L2 speaking rate invariably being slower than L1 speaking rate), but an association of relative L1 and L2 speaking rates across a group of bilingual talkers . The dissociation of average rates in L1 and L2 would establish that the parameter in question (speaking rate) is not an automatic consequence of the talker's vocal anatomy and physiology, but is instead a learned and/or centrally  controlled property. This then provides the necessary condition for investigating the relationship between relative L1 and L2 speaking rates as an indicator of the relationship between language- and talker-specificity in bilinguals.The present study seeks evidence for language-independent, talker-specific characteristics by examining a global, talker-controlled feature across both languages of a group of bilingual talkers. Specifically, we examined speech-timing patterns in a corpus that includes comparable recordings in each of the two languages of a group  than in a task that emphasizes speech production without language generation at the conceptual level (paragraph reading).II.A.Recordings for this study were all taken from a corpus of digital speech recordings that includes both read speech and spontaneous speech by bilingual and monolingual English speakers. A key feature of this corpus is that it includes recordings in both the L1 and the L2 (English) of each bilingual talker. All talkers in the corpus were recorded producing a common set of materials that was selected to cover both read and spontaneous speech, and to be comparable across languages. The full set of recordings in each language consisted of six distinct subsets, three of which were sentence lists, one of which was a paragraph-length reading passage, and two of which were designed to elicit spontaneous speech. The sentence recordings are intended primarily for use as stimuli in speech perception experiments and were not analyzed for the present study. The paragraph is the widely translated fable, “The North Wind and the Sun,” as available for all languages included in the To date, this corpus  includes recordings from 119 bilingual talkers from 21 different L1 backgrounds, plus 27 monolingual English talkers who provided English recordings only. Additional and current information about the continuously updated and expanded corpus can be found online.In the present study, we analyzed the recordings of the paragraph-length reading passage (NWS), the picture story narratives (ST), and the open response questions (Q&A) by 86 bilinguals in both of their languages and by 27 monolingual English talkers (English only).B.n = 27) in the corpus.The bilingual talkers included in this study came from ten different L1 backgrounds . While English proficiency of the bilingual talkers varied, all talkers had achieved a level of English language proficiency that was sufficient to gain entry into a graduate program at Northwestern. Nevertheless, most of these bilingual talkers were enrolled, either by choice or by requirement, in intensive English language instruction as offered by the Northwestern University English Language Programs. Standardized English test scores  and their speech was recorded direct to disk onto an Intel Core 2 Duo iMac . All talkers completed a language background questionnaire before beginning the recording of the sentences, paragraph, and spontaneous speech in English. The bilingual talkers returned the following day for a second recording session during which they recorded the sentences, paragraph, and spontaneous speech recordings in their native language (the L1 recordings). All scripted materials were presented in the standard orthography of the language. Each session took ∼1–1.5 h.C.The primary measure of interest for the current study was speaking rate, which we measured in both the L1 and L2 speech samples from the read speech (NWS paragraph) and spontaneous speech recordings . We obtained two measures of speaking rate, speech rate and articulation rate, from each of the three speech samples in each language, both of which were based directly on the number of (acoustic) syllables  per second. The two speaking rate measures differed only with respect to the inclusion (speech rate) or exclusion (articulation rate) of silent pauses.From each speech sample we obtained the number of syllables using an automatic syllable detection algorithm implemented as a Praat script . This scAdditionally, we obtained the average number of syllables per utterance where utterance was defined as a stretch of speech surrounded by pauses of at least 100 milliseconds. This measure of average utterance length  was included as a predictor of speech rate and articulation rate in the statistical analyses because prior work  and within the data for each task ]. The specific dependent variable entered into each statistical model was a set of such z-transformed acoustic measurements. It is important to note that, while an untransformed speech rate measure is necessarily always slower than the matched untransformed articulation rate measure (they have the same numerator but different denominators), a z-transformed speech rate measure can be either larger, equal to, or smaller in magnitude than its z-transformed articulation rate counterpart due to the fact that the duration of pauses varies substantially within stretches of speech. Therefore, a given talker's average articulation rate measure (pauses excluded) may be close to the group-wise average articulation rate measure, but this same talker's average speech rate measure (pauses included) may be quite far (in either direction) from the group-wise average speech rate measure. This would indicate that the talker in question exhibits quite typical articulation rate but rather atypical pausing behavior relative to the group.The speech rate and articulation rate data were analyzed with linear mixed effects regression models using the lme4 . Five hyFor all analyses, the fit of the base model  was compared to the fit of additional models that included additional predictors . Models were compared by means of the anova function from the lmerTest package .H1. L1 speaking rate varies across languagesH2a. L1 English speaking rate by monolinguals is faster than L2 English speaking rate by bilingualsH2b. Within bilingual talkers, L1 speaking rate is faster than L2 speaking rateH3a. Within bilingual talkers, L1 speaking rate predicts L2 speaking rateH3b. Within bilingual talkers, L1 speaking rate predicts L2 speaking rate, when controlled for proficiency levelsThe specific hypotheses we tested are as follows:z-transformed as described above) for one of the three tasks .The overall structure of the data set for these analyses consisted of multiple entries for each talker in each language  with each entry representing a speaking rate measure . For this analysis, the fixed effect factors in the best fitting, maximal model were L1 , and a second within bilingual talkers . In the first regression model , the fixed effect factors of the best fitting, maximal model were language status , task , measure , and utterance length. All possible two- and three-way interactions among language status, task, and measure were also included as fixed effect factors. Language status, task, and measure were contrast coded; utterance length was centered. The random intercept was talker, with task and measure as random slopes. The multicollinearity of this model was low, with a condition number of 3.77. In the second regression model , the fixed effect factors of the best fitting, maximal model were language status , task , measure , L1 . In this multivariate analysis, the dependent variables were L2 speech rate and L2 articulation rate. The dataset consisted of multiple entries for each talker in each language with each entry representing a speaking rate measure  to test for the influence of L2 (English) proficiency on L2 rate, and to see if the effect of L1 rate on L2 rate remains when variation in L2 proficiency is controlled (H3b). In this model, the fixed effect factors were L1 rate, task , measure , L1  and articulation rate (excludes silent pauses) could be performed. The analysis of speaking rate differences across the various L1s showed that all of the main effects  reached significance except for talker age. (See z-transformed) was significantly faster than the speech rate measure (z-transformed). Finally, as expected based on prior work  is beyond the scope of the present study as it would require a detailed analysis of the morpho-phonological and syntactic features of the various languages. There was also a significant L1 by task interaction, involving either a relatively small read-spontaneous difference (Russian), a relatively great read-spontaneous difference (Korean and Turkish), or a different-from-typical difference within the two types of spontaneous speech (Russian and Hebrew). The task by measure interaction was also significant, as was the three-way interaction between L1, task, and measure, due to the fact that two languages (Mandarin and Hindi) showed a smaller read-spontaneous difference in speech rate than in articulation rate, whereas two other languages (Korean and Russian) showed a greater read-spontaneous difference in speech rate than in articulation rate. Overall, while this analysis across L1s showed some notable patterns, there was no clear, interpretable, and systematic trend that could be taken as a reliable indicator of a strong language-specific effect on speaking rate , measure (articulation rate faster than speech rate), and utterance length (faster rates for longer utterances).  for whom proficiency (Versant) scores were available], the critical relation between L1 and L2 speaking rates remained.  than in a task that emphasizes speech production without language generation at the conceptual level (reading). However, in the present study, the relationship between L1 and L2 speaking rates within bilinguals was similarly evident in both read and spontaneous speech . Nevertheless, in the analysis of L1 and L2 speaking rate across the various L1s and the analysis that directly compared L1 and L2 speaking rates, the data showed that speaking rate was generally modulated by task such that read speech was produced with a faster speaking rate than spontaneous speech in both L1 and L2 speech production. Moreover, within spontaneous speech, the task with picture prompts elicited speech with a faster rate than the free-form, question-and-answer task. Thus, speaking rate in both L1 and L2 decreased with increasing task complexity. Furthermore, the data showed an interaction between language status (as L1 or L2) and task, such that the L1 vs L2 speaking rate difference was smaller in the simpler task (reading) than in the less constrained spontaneous speech tasks whether picture-guided or question-prompted. Thus, while task differences did not appear to amplify or diminish talker-specificity in speaking rate, the difficulties of L2 speaking seem to accumulate across levels of processing such that L2 speech is particularly slow in complex tasks that require language generation at the semantic and syntactic levels compared to read speech where the talker does not need to generate well-formed phrases and sentences in addition to having to produce intelligible speech  languages of that individual talker. How, then, might this aspect of talker-specificity serve as a language-independent idiolectal marker? One possibility is that listeners develop expectations about the range of speaking rates to expect for L1 and L2 speech. Then, when an individual bilingual talker is encountered, and once the L1 or L2 status has been determined on the basis of the wide range of acoustic-phonetic deviations that typically characterize L1 vs L2 speech in a given language , listeners could adjust to that individual's speaking rate within the appropriate range. It is then possible that this L1- or L2-specific adjustment could form the basis for generalization to the other of this particular bilingual's language. If a listener has experience with a relatively fast talker in L1, then this listener may expect that this talker will also be a relatively fast talker in L2. This experience-based expectation about speaking rate may then combine with the other talker-specific factors  that facilitate talker identification and recognition of that talker's speech in either the L1 or the L2.It is also possible that talker-specific, rate-based expectations that listeners develop on the basis of exposure to one language  may extend to other rate-dependent acoustic-phonetic features that can quite easily generalize across languages. For example, even for an L1-L2 pair with different duration-based phonological contrasts , experience with a given talker's realization of the L1 contrast in relation to the L1 norms could potentially provide the basis for expected durations in this talker's realization of an analogous L2 contrast. Moreover, since fast and slow speech are associated with general hypo- and hyper-articulation, respectively, it may be possible for listeners to develop quite extensive, language-independent, talker-specific, rate-based expectations for L1 (or L2) speech on the basis of prior experience with L2 (or L1) speech. Such expectations could then facilitate recognition of this talker's speech across both languages even if prior exposure has been to only one of the talker's languages.In conclusion, our analyses of speaking rate in L1 and L2 within bilingual talkers has suggested that a significant portion of variation in L2 speech is derived from talker-specific variation in L1 speech. Notwithstanding the strong influence of the relationship between first- and second-language structure, and the similarly strong influence of experience- and training-related second-language proficiency, individual variability in L2 spoken language production is probably best understood within the context of individual variability in L1 spoken language production.


In classical electrodynamics, nanostructured graphene is commonly modeled by the computationally demanding problem of a three-dimensional conducting film of atomic-scale thickness. Here, we propose an efficient alternative two-dimensional electrostatic approach where all calculation procedures are restricted to the graphene sheet. Furthermore, to explore possible quantum effects, we perform tight-binding calculations, adopting a random-phase approximation. We investigate multiple plasmon modes in 20 nm equilateral triangles of graphene, treating the optical response classically as well as quantum mechanically. Compared to the classical plasmonic spectrum which is “blind” to the edge termination, we find that the quantum plasmon frequencies exhibit blueshifts in the case of armchair edge termination of the underlying atomic lattice, while redshifts are found for zigzag edges. Furthermore, we find spectral features in the zigzag case which are associated with electronic edge states not present for armchair termination. Merging pairs of triangles into dimers, plasmon hybridization leads to energy splitting that appears strongest in classical calculations while splitting is lower for armchair edges and even more reduced for zigzag edges. Our various results illustrate a surprising phenomenon: Even 20 nm large graphene structures clearly exhibit quantum plasmonic features due to atomic-scale details in the edge termination. The collective excitations of conduction electrons in noble metals have been of great interest for a very long time. These excitations known as plasmons play an important role in the optical properties of metals. Through strong plasmon-photon interactions, metals can support important phenomena, such as focusing beyond the diffraction limit122/Vs at room temperature242628Graphene and other low-dimensional crystals are now emerging as interesting materials for exciting science and technology151617182122ε is a function of both frequency ω and momentum q. This is different from common three-dimensional (3D) photonic materials which are usually well-described by frequency-dependent functions, while spatial dispersion is negligible for good dielectrics and most metals . Two common approximations in the modelling of graphene structures are to adopt the local-response approximation  and to model graphene as a very thin conducting film, yet preserving its 3D representation33R 1516In this article, we study plasmon properties in individual graphene nanostructures and in dimers of such structures by means of both classical and quantum methods. In particular, we consider triangles of graphene and bow-tie structures formed by such triangles, while our methods can also be applied to other geometries (as we show in the Supplementary Information). Here, we focus on the plasmonic aspects due to doping with a significant number of electrons, while there are also appealing aspects in the single-electron doping regime25In our classical electrodynamical considerations, we treat the nanostructures as 2D materials characterized by a smooth surface conductivity (employing the sheet conductivity derived for bulk graphene), and formulate a closed-form eigenvalue problem on a 2D domain. Numerical solutions in arbitrarily shaped geometries are enabled by finite-element calculations. By its nature, this classical approach neglects the atomic details of the graphene flake. Some aspects e.g. of zigzag termination can be effectively accounted for by additional conductive channelsIn our quantum treatment, we employ a tight-binding description40tg. This assumed 3D film has an effective bulk permittivity, ε(ω) = ε0 + iσ(ω)/(ωtg), where ε0 is the vacuum permability and σ(ω) denotes the surface conductivity as obtained from e.g. the local-response limit of the RPA33tg should be chosen sufficiently small compared with all other characteristic and physical dimensions, yet sufficiently large that meshing hopefully stays computationally feasible and the numerical problem remains tractable. Optimizing this thickness tradeoff does not necessarily give an efficient method. An even more critical issue is that there are no formal proofs, at least to the best of our knowledge, that numerically computed fields  is then much larger than the geometrical extent of the hosting graphene nanostructures (~10–100 nm). For such problems the electrostatic approximation is excellent. As a computationally very attractive consequence, the electric and constitutive response are governed by two coupled scalar equations for the potential φ and the induced density ρ. In particular, we note that the total potential φ/2 the averaged dielectric constant of the medium above and below graphene. For simplicity, we only consider freely suspended graphene, so we will use εs = ε0 throughout the remaining part of the paper. The other scalar equation is obtained by inserting the constitutive equation J2D = −σ(ω)∇2Dφ(iωρ(2D·J2D(r restricted to the plane of the graphene structure gives ρ in (2) is restricted to the graphene plane. It may be obtained from a closed-form equation by eliminating the potential in (2) with the help of (1) ρ within the graphene plane is thus obtained, the potential φ in the entire space can be evaluated via (1).With the typical sub-eV doping levels, plasmonic resonances typically occur in the mid-infrared regime. The associated free-space wavelength (~10 φ = φext + (4sLπε)−1ρA and ρ = iσ(ω)ω−1Bφ, which we combine to getA and B are geometry-dependent square matrices, respectively representing the Coulomb integral in f(ω) = iσ(ω)/(4sLωπε) is a geometry-independent scalarεCLA(ω) ≡ 1 − f(ω)BA, connecting total and external potentials via εCLA(ω)φ = φext. In the absence of an external potential (φext = 0), BA. The resulting eigenvalues nλ are associated with plasmon frequencies nω through ρn in a finite-element representation. The corresponding eigenpotentials are denoted as φn, and within the graphene plane they can be computed directly as φn = ρAn. Following this classical approach, all plasmonic eigenmodes for a specific structure can be obtained as the solution of a single eigenvalue problem. This constitutes an attractive computational approach that can give direct insight in the plasmonic eigenstates that one would be able to probe with various experimental techniques.Within the framework of the finite-element method (FEM), π and π* bands  originating from the carbon σ bands arising from sp2 hybridization. The dynamics of low-energy excitations in graphene is well-described by inclusion of just the π bands, which can be determined by a simple tight-binding model in a nearest-neighbor approximation51N carbon atoms results in an N × N matrix representation of the tight-binding Hamiltonian with elements determined by the N eigenvalues and eigenvectors, corresponding to the electronic energy levels and the wave functions, respectively. The non-interacting density response function, or polarizability matrix l labels each of the carbon atoms), while kB and T = 300 K corresponding to a thermal energy of εs), and the characteristic structure dimensions (L). For details, we refer to the Supplementary Information.In a quantum mechanical formalism, there are two key computational components: (i) electronic band structure, and (ii) determination of response functions. The graphene l ≠ l′, and a self-interaction of 0.58 atomic units at l = l′εRPA(ω) is a matrix, we in principle seek the eigenvalues nε(ω) of the εRPA(ω) whose real part approaches zeronε(ω) are therefore complex-valued, with the imaginary parts denoting the plasmon peak broadening. With this in mind, we finally define the plasmon resonance frequencies from the local maxima of In the following, we use an efficient method to compute the non-interacting density response matrix nε(ω) are obtained by diagonalizing the RPA dielectric function εRPA(ω) for each frequency. An N-atom nanostructure entails n = 1,2,3, …, N distinct eigenvalues. Out of these we focus in the following on eigenvalues with largest and second-largest values of ρn, and similarly the eigenpotentials φn can be obtained by performing the Coulomb integral. For comparison with the quantum treatment, we also calculate the eigenvalue loss spectrum in the classical framework by carrying out diagonalization of the classical effective dielectric function εCLA(ω).Numerically, the eigenvalues m-fold rotational symmetry where the mC point group leads to either non-degenerate eigenstates or pairs of eigenstates with a double degeneracyBA of n = 1,2,3,…), corresponding to the eigenmode enumeration in The calculated eigenvalue loss spectrum for 20 nm graphene equilateral triangles is shown in The plasmon modes 1 through 8, being doubly degenerate, are either symmetric or antisymmetric with respect to the mirror symmetry plane. The dipole modes, 1 and 2, with the electric field being polarized orthogonal to each other, are of particular interest due to their strong coupling to optical fields. They can be excited directly by far-field techniques, and the plasmonic local field enhancement is concentrated at the vertices. The modes 3 through 8 penetrate significantly into the bulk, and can be considered as hybridized modes originating from interaction between dipole and bulk modes, because the patterns at the vertices are similar to dipole modes 1 and 2; in addition, the modes 3–6 have finite net dipole momenta, and can couple to far-field radiation. The modes 9–12 are not doubly degenerate, and exhibit threefold rotational symmetry around the center. Although optically dark, these modes are still detectable by suitable near-field techniques. As an example, in an EELS experiment the breathing mode 12 would exhibit the strongest coupling to a nanometer-sized electron beam if this beam passes through the center of the graphene triangleHaving described our classical results for graphene triangles, let us now turn to our corresponding tight-binding quantum results. In the quantum description, we calculate the eigenvalue loss spectrum, identify the plasmon mode eigenfrequencies, and then extract the corresponding eigenmodes. Due to the geometrical symmetry, the plasmon eigenmodes should exhibit the same energy degeneracy features as the equilateral triangles in classical calculations, for instance in n = 1,2,3,4) in the classical calculations, originating from the four (accounting degeneracy) low-energy dipole modes of the two uncoupled triangles. The hybridization process is illustrated in x-polarized dipole  exhibits large energy splitting, and the corresponding bonding  mode has lower energy. However, for the y-polarized dipole  the reduced mode-overlap causes a very small energy splitting. In both cases, the bonding modes are optically active with a net dipole polarization along x and y direction, respectively.Plasmon hybridization is of both fundamental and practical importance59x-polarized dipole exhibits no appreciable energy splitting when compared to the line width of the uncoupled resonances. As a result of this approximate degeneracy, the coupled system exhibits a single broad peak with all four modes merged together. In contrast to the dipole modes, the higher-order plasmon modes show a weak lifting of degeneracy for antisymmetrical and symmetrical states. We mention that the hybridization picture given in We find a very similar behavior in the armchair-terminated bow-tie triangles shown in x-polarized dipoles. We calculate the eigenfrequencies of the hybridized plasmon modes as a function of the width gap, and show the results in The energy splitting or coupling strength depends on the gap width of the bow-tie structures, which can be investigated in the hybridization of We note that the hybridization of other dimer plasmon modes (other than dipole modes) can be analyzed with a similar result. Generally speaking, the eigenfrequencies of the resulting hybridized modes are decided by two factors: symmetry and coupling strength. Specifically, the antisymmetrically coupled modes (no matter which polarization) have lower energy and modes with less field concentration at the gap region cause weaker coupling and consequently exhibit smaller energy splitting. As a further evidence for this qualitative characterization, we show in In this article, we have considered and compared classical and quantum aspects of plasmonic eigenmodes in graphene triangular nanostructures. The 2D FEM-approach for calculation of the classical electromagnetic response represents a numerically highly efficient method for electrodynamics in general 2D morphologies of graphene structures in the electrostatic limit . The simple eigenvalue approach offers a direct pathway to extraction of all plasmonic eigenmodes, not limited to just the optically active, but including also dark modes and highly symmetric breathing modes. The quantum method adopted here is useful for investigating the quantum effects in plasmon excitations of smaller graphene structures, and it offers additional insight into the importance of the particular edge-termination of the underlying atomic lattice. By a sweep of the excitation energy, our calculation of the eigenvalue loss spectra enables direct identification of all plasmonic modes also in the quantum treatment.We have applied both methods to equilateral triangles, of 20 nm sidelength, both in isolated and in bow-tie configurations. For the isolated nanotriangle we find that the plasmonic response of armchair-terminated triangles is qualitatively similar to the classical case, albeit with a significant and consistent blueshift of all resonances due to nonlocal response. Conversely, the response of zigzag-terminated triangles exhibits several significant differences from its classical counterpart. As a consequence of the existence of localized electronic edge states near zigzag edges, the eigenmodes extend further into the bulk, and are less intense at the vertices. Additionally, we observe a redshift and an pronounced readjustment of the loss-function intensity relative to the classical case.In the bow-tie configuration we observe plasmon hybridization and associated eigenmode energy splitting, of varying degree depending on treatment; the largest splitting is observed in the classical approach, and the smallest in zigzag structures. Nevertheless, the effects of hybridization are qualitatively similar across the considered cases, with the antisymmetric hybridized modes exhibiting a lowered energy, and with the coupling strength - and associated energy splitting - decreasing when the constituent eigenmodes exhibit lower field intensities in the gap region.jth element by Ωj. With a sufficiently dense mesh, a faithful approximation of the Coulomb and Laplacian operators in The classical calculations are performed on the two-dimensional domain defined by the geometry of the graphene structure. The domain is discretized using a triangular mesh (see Supplementary Information for details), consisting of a set of elements jth element are denoted as jα, jβ, and jδ, and together define the element centroid coordinates, To proceed, we introduce the following notation: the vertices of the kφ, and the densities, kρ, at all vertex positions kth vertex to findj as js. Note that no explicit handling of a semi-divergent self-interaction is necessary since j. The matrix A can then be assembled by noting its equivalent definition in element form, m-sum running over the vertex sites of Ωj, i.e. m ∈ {jα,jβ,jδ}, and where the twice-subscripted δ denotes the Kronecker delta.The integration of B (the discretized representation of the Laplacian) from j. The value of the test function within Ωj is most conveniently specified in a local barycentric coordinate system with parameters η ∈  and j. Within each region Ωj we interpolate function values φ.To evaluate the right-hand side of j, and defining BL ≡ ΣjBjL and BR ≡ ΣjBjR, while noting that the test functions BLρ = −iσ(ω)ω−1BRφ, from which we identify the desired matrix B with the property ρ = iσ(ω)ω−1φB as B = −(BL)−1BR.Finally, by summing over all σ(ω) of graphene through its well-known local-response form6364e denotes the electron charge, θ(x) the Heaviside function, and ln(x) the natural logarithm.The classical material response is modeled by the bulk conductivity π-electrons is constructed by considering only nearest-neighbor interactions with a hopping strength t = 2.8 eV. The associated Hamiltonian matrix-representation is real-valued and symmetric, giving rise to real eigenvalues and eigenvectors.The tight-binding Hamiltonian for the N4 operations, which must additionally be repeated for each distinct frequency. Significant reduction of computational complexity, to ~N3, can be achieved with the aid of Hilbert and fast Fourier transforms (FFT), following a procedure developed in density-functional theory (DFT)65The direct evaluation of the noninteracting density response matrix Furthermore, consideration of the symmetry of Supplementary Information


Our previous work has shown that JIP3 (JNK interacted protein 3) mediates anterograde axonal transport of TrkB through the direct binding of its coiled-coil domain 1 (CC1) with TrkB. Here, we constructed a fluorescent CC1 and enhanced green fluorescent protein (EGFP) fused protein, CC1-EGFP, and found that CC1-EGFP could specifically interrupt TrkB anterograde axonal transport and its localization at the pre-synaptic site. Consistent with this, TrkB-mediated pre-synaptic vesicle release and retrograde axonal signaling transmission were disrupted by CC1-EGFP. Neuronal expression of CC1-EGFP in the basolateral amygdala (BLA) impaired fear memory extinction. And, it blocked BDNF in the BLA-induced enhancement of TrkB phosphorylation in the infralimbic prefrontal cortex (IL). Together, this study not only suggests that pre-synaptic TrkB in BLA neurons is necessary for memory extinction and contributes to the BDNF signaling transduction from the BLA to IL, but also provides CC1-EGFP as a novel tool to specifically regulate pre-synaptic TrkB expression  Recent studies have focused on the reciprocal and functional connections between the basolateral amygdala (BLA) and infralimbic prefrontal cortex (IL) during memory extinction and revealed that fear memory extinction depends on the elevated neuronal activity of the BLA and IL.4 Brain-derived neurotrophic factor (BDNF), a mediator of synaptic plasticity, plays an essential role in fear memory extinction.6 A BDNF gene polymorphism  reducing its secretion has been reported to impair the extinction of fear memory.7 However, the mechanism underlying BDNF-regulated memory extinction is still unclear.Extinction of conditioned fear memory is the process of reducing the response to a conditioned stimulus (CS) when an animal is repeatedly exposed to the CS without an unconditioned stimulus (US). The process of memory extinction requires complex networks of brain regions, such as the activity of amygdala, prefrontal cortex, and hippocampus.11 Therefore, the BDNF-induced function in neurons depends on the proper trafficking and surface distribution of TrkB. Overexpression of TrkB.T1 in the BLA impaired conditioned fear memory consolidation and extinction, indicating that TrkB in the BLA plays an essential role in mice memory.12 However, as a dominant-negative truncation of TrkB, TrkB.T1 blocks both the pre- and post-synaptic functions of TrkB and is unable to distinguish the functional differences of dendritic and axonal TrkB in BLA neurons. To understand the TrkB-mediated memory extinction, it is important to clearly distinguish the function of pre- and post-synaptic TrkB.As a high affinity receptor for BDNF, TrkB exerts its function by activating several intracellular signaling cascades. BDNF binds pre- or post-synaptic TrkB to regulate neuronal activity, neurotransmitter release and mediate BDNF retrograde endocytosis signaling.13 In this study, we fused the CC1 domain with enhanced green fluorescent protein (EGFP) (CC1-EGFP), which acted as a specific inhibitor of TrkB anterograde axonal transport. We demonstrated that CC1-EGFP blocked the synaptic localization of TrkB, which impaired pre-synaptic TrkB function in primary cultured neurons. Furthermore, we found that the expression of CC1-EGFP in the BLA impaired conditioned fear memory extinction by inhibiting the anterograde transmission of BDNF from the BLA to IL. Our results suggested that pre-synaptic TrkB in BLA neurons is responsible for fear memory extinction and provided a new technique to investigate pre-synaptic TrkB in vivo.Here, we aimed to study the function of pre-synaptic TrkB of BLA neurons in fear memory extinction. TrkB is known to be generated in the cell body and delivered to axonal terminals through anterograde trafficking in neurons. Previously, we have demonstrated that JIP3 mediates TrkB anterograde axonal transport by directly binding to TrkB with its coiled-coil 1 (CC1) domain.14 and its first coiled-coil (CC1) domain has been verified to directly bind with TrkB.13 Therefore, we aimed to use the CC1 domain as a dominant negative peptide to inhibit the binding of TrkB with JIP3 and the expression of TrkB in the pre-synapse. To enhance CC1 peptide stability, we fused it with EGFP to form a CC1-EGFP construct , which has been reported to mediate TrkB anterograde transport bridged by JIP3.16 The results showed that CC1-EGFP significantly decreased the interaction between TrkB and KLC1 . The results showed that CC1-EGFP did not alter the density of synaptic vesicles  and advance neuronal survival.21 Therefore, we measured the activation levels of Erk5 to represent the effect of CC1-EGFP on BDNF-induced retrograde signaling transmission. The results showed that CC1-EGFP inhibited BDNF-triggered phosphorylation of Erk5 in cultured hippocampal neurons  as well as between the BLA and IL.n the IL . This reThen, we verified whether the memory extinction impairment by CC1-EGFP was caused by the blockade of the transmission of the BDNF signal from the BLA to IL. We microinjected BDNF into the BLA and activated the local TrkB . SpecifiOur study demonstrated that CC1-EGFP inhibited TrkB anterograde axonal transport, caused less TrkB distribution in the pre-synapse and disrupted BDNF-induced pre-synaptic vesicle release and TrkB retrograde signaling transmission. Moreover, with the help of CC1-EGFP, we found that the blockage of pre-synaptic TrkB in BLA neurons impaired fear memory extinction by blocking anterograde BDNF signal transmission from the BLA to IL. Thus, we provided evidence that pre-synaptic TrkB in BLA neurons is involved in transmission of the BDNF signal from the BLA to IL.in vivo. Studies with TrkB knockout mice and BLA region TrkB conditional knockout mice have shown memory formation impairment, suggesting that TrkB plays an essential role in memory behavior.27 The TrkB receptor is widely distributed in axons, dendrites and soma. Researchers have primarily studied the function of pre-synaptic or post-synaptic TrkB in cultured neurons by overexpression of TrkB.T1 and electrophysiological recording in vitro.28 However, there isn’t direct evidence to support the function of pre- or post-synaptic TrkB in rat memory behavior. In this study, CC1-EGFP, as the anterograde-specific inhibitor of TrkB transport, impaired the distribution and function of pre-synaptic TrkB  were individually maintained at 20 °C under a 12 h light/dark cycle. Water and food were available 13 The CC1 domain (QLSGEQEVLKGELEAAKQAKVKLENRIKELEEELKRVKSEAVTARREPREEVEDVSSYLCTELDKIPMAQ) was obtained from Myc-mJIP3 by PCR and sub-cloned into the pEGFP-N1 vector. CC1-EGFP was separately cloned into the FUGW and pAAV-CKII-mCherry vectors for lentivirus and AAV5 virus packaging . Rats siJIP3 target sequence has been referred before as the following: 5′-CAG GCC GAG GAGAAA UUC A-3′.The Flag-rTrkB-FL, Myc-mJIP3, Myc-hKLC1 and TrkB-RFP constructs have been described previously.2, and invariant temperature at 37 °C was utilized for cell culture. For immunofluorescence staining, coverslips coated with 0.1 mg/ml poly-D-lysine  in six-well plates were used. Neurons were electroporated with plasmids in a Nucleofector device  according to the manufacturer’s instructions.Cultured hippocampal neurons were from embryonic day 18.5 (E18.5) Wistar rats. Hippocampi were removed from the embryos and digested with 0.05% trypsin/EDTA. Neurons were cultured in neurobasal medium  supplemented with 2% B27 and 0.5 mM glutamine. An incubator with saturated humidity, 5% CO35 Briefly, Wistar rats pups at E18.5 were killed by exposure of CO2. Then, the brain was removed and the brainstems were sliced away. After that, the brain was placed ventral side up and a coronal cut was made anterior to the diencephalon and posterior to the optic chiasm. Along the lateral fissure, a diagonal cut was made and the amygdalar region was separated by peeling away from the cortex. The other cell culture processions are all the same as the upper hippocampal neuron culture instruction.The methods for amygdalar neurons culture were based on a previous report.Hippocampal neurons cultured for 3 days were fixed with 4% paraformaldehyde in PBS and permeabilized with 0.4% Triton X-100 in PBS. The samples were incubated with PBS containing 10% donkey serum for 1 h at room temperature and then were incubated with the primary antibodies: rabbit anti-GFP , goat anti-TrkB , mouse anti-GFP , or rabbit anti-JNK3  at 4 °C overnight. Samples were washed three times with PBS and incubated with fluorescent secondary antibodies conjugated to Alexa Fluor 488 or Alexa Fluor 594 (Invitrogen) for 1 h at room temperature. All of the images were captured with a Zeiss LSM780 confocal microscope fitted with a × 63 oil-immersion objective lens .9 TU/ml 10 μl per 10 m dish) and were lysed with TNE buffer 48 h later. Samples of lysates were incubated with mouse anti-JIP3 antibodies  or mouse IgG-Sepharose (Sigma) as a negative control. The immuno mix was precipitated using protein G-Sepharose beads (Sigma). The bound proteins were eluted and analyzed by immunoblotting with rabbit anti-JIP3 , rabbit anti-TrkB  and rabbit anti-GFP  antibodies.HEK293 cells were electroporated with plasmids in a Nucleofector device . Cells were then extracted 48 h later with TNE buffer containing 10 mM Tris, 150 mM NaCl, 1 mM EDTA, 1% NP-40, 10% glycerol and protease inhibitors. The cell lysates were precipitated with M2-Sepharose (Sigma) overnight at 4 °C. The beads were rinsed five times with the TNE buffer and boiled in sample buffer (Invitrogen) for SDS-PAGE. For investigating the endogenous interaction, DIV6 hippocampal neurons were infected with the control EGFP or CC1-EGFP lentivirus  and were cultured in glass-bottomed 35-mm dishes (Willco Wells) for 3 days. The performance of neurons was obtained using an Eclipse TE 2000-U inverted fluorescence microscope  and observed with a motorized Z drive using a × 63 oil-immersion objective lens (1.0 NA). Images of the cells selected for imaging were acquired every 1 s continuously for 1 min. The vesicle was handled and processed by the Meta-Morph software. A kymograph was generated by using NIH ImageJ.The protocol for live cell imaging has been reported before.36 Lentivirus-infected hippocampal neurons were incubated with high K+-containing medium  including 10 μM FM1-43 (Invitrogen) for 45 s. The cultures were then washed with normal saline solution  and 1 mM ADVASEP-7  for 5 min. BDNF (50 ng/ml) was delivered for 10 min to quench the FM dye. Images were acquired both before and after stimulation I and II. To prevent network activity from promoting dye staining throughout the experiment, the AMPA receptor antagonist CNQX  and NMDA receptor antagonist D-2-aminophosphonovaleric acid D-APV  were added. The fluorescence intensity of individual boutons was measured with NIH ImageJ.The protocol was carried out as previously described.Lentivirus-infected neurons were fixed with 4% PFA on DIV9 and labeled with antibodies against the presynaptic protein SV2 . Images were acquired with a × 63 oil-immersion lens. Threshold was set to twice the fluorescence intensity of the neuritis shaft devoid of synapses (background). The number and size of the SV2 puncta were determined by using the integrated morphometric analysis feature in NIH ImageJ.37 Briefly, the microfluidic chambers were fixed to poly-D-lysine-coated 60-mm dishes. Suspensions of neurons were plated into the compartment of the cell soma; the other side was filled with neurobasal medium supplemented with B27. After 4 days of culturing, axons grew through the microgrooves and extended into the axonal compartment. Lentivirus was added into the cell body side, and cells were cultured for another 48 h. Then, BDNF (50 ng/ml) was administered in each compartment for 30 min. Cells were then fixed with 4% PFA and stained with mouse anti-GFP  and rabbit anti-pErk5  antibodies. Pictures were captured with a × 63 oil-immersion objective lens in the Eclipse TE 2000-U inverted fluorescence microscope (Nikon). The fluorescence intensity was measured by NIH ImageJ.The method for cell culturing in a microfluidic chamber has been previously described.μl Hamilton microsyringe motored by a microinjection pump . Infusions were administered with a volume of 0.5 μl over the course of 2 min, and an additional 2 min was allowed for diffusion before the infusion cannulas were removed. BDNF  was administered into the BLA, and AAV5-CamkIIα-EGFP , AAV5-CamkIIα-CC1-EGFP , and AAV5-CamkIIα-TrkB-mCherry  were injected into the BLA 24 h after acquisition of the auditory fear memory.Rats anesthetized with 5% chloral hydrate were implanted bilaterally with guide cannulas to the BLA or IL. The coordinates were as follows: BLA: anteroposterior (AP), −2.97 mm; lateral (L), ±5.1 mm; dorsoventral (V), −7.0 mm; IL: anteroposterior (AP), +3.14 mm; lateral (L), ±0.3 mm; dorsoventral (V), −4.0 mm. To prevent clogging, a stylus was placed into the cannula. Bilateral infusion cannulas (28 gauges) were inserted 7 days later, extending 1.5 mm beyond the tip of the guide cannulas. The injection cannula was linked via PE20 tubing to a 10-38 the rats were placed into a standard fear conditioning chamber . After 10 min of habituation without any stimulation, rats received three trials with 20 s, 5 kHz, 80 dB tones (CS), simultaneously ending with a 1 s, 0.75 mA foot shock (US). Each trial was separated by a 180-s inter-trial interval. After the last shock, the rats remained in the chamber for 60 s and were then placed back in their home cages.According to the previously reported protocol,On the next day, rats received an extinction training session that consisted of 19 CS exposures without a shock presentation. Each CS exposure was separated by a 180-s interval. After each session, the experimental chamber was cleaned with 70% alcohol. The rats received a test session consisting of four CS exposures 24 h later to assess the extinction of the cued fear memory.μm on a Leica VT1200S vibratome. Immunohistological staining was performed on free-floating sections. Slices were blocked in a solution containing 0.4% Triton X-100 and 10% donkey serum and then stained with rabbit anti-GFP , mouse anti-CamkIIα  and mouse anti-GAD67  antibodies before incubation with the appropriate fluorescence-conjugated secondary antibodies.Rats were anesthetized with 5% chloral hydrate anesthesia  and transcardially perfused with saline and 4% PFA. Brains were post-fixed in 4% PFA overnight and coronal sections were serially cut at 40 β-actin  antibodies. Ratios of p-TrkB/total TrkB derived from control groups were normalized to 1.0.BLA and IL brain tissue samples were removed and lysed with TNE buffer. The protein concentration of each sample was detected by the BCA reagent (Thermo). For analysis of TrkB phosphorylation (p-TrkB), 5 mg protein was utilized for immunoprecipitation with the rabbit anti-TrkB antibody , followed by immunoblotting with anti-phospho-tyrosine pY99 , goat anti-TrkB  and rabbit anti-t-test, one-way ANOVA or two-way ANOVA, followed by LSD post hoc comparisons. All values in the text and figures represent the mean±S.E.M., and P<0.05 was considered significant.Data were analyzed by Student’s


Working at this unique regime has allowed the study of carbon ad-atom migration as well as the influence of various substrate materials on e-beam sculpting of graphene. We also show that this information was pivotal to fabricating functional graphene nanopores for studying DNA with increased spatial resolution which is attributed to atomically thin membranes.The use of atomically thin graphene for molecular sensing has attracted tremendous attention over the years and, in some instances, could displace the use of classical thin films. For nanopore sensing, graphene must be suspended over an aperture so that a single pore can be formed in the free-standing region. Nanopores are typically drilled using an electron beam (e-beam) which is tightly focused until a desired pore size is obtained. E-beam sculpting of graphene however is not just dependent on the ability to displace atoms but also the ability to hinder the migration of ad-atoms on the surface of graphene. Using relatively lower e-beam fluxes from a thermionic electron source, the C-atom knockout rate seems to be comparable to the rate of carbon ad-atom attraction and accumulation at the e-beam/graphene interface (i.e., R The guided migration of biological molecules through a pore and their real-time electrical detection is the fundamental premise of most nanopore-based sensors ,2,3,4,5.The geometry of the nanopore is the most fundamental aspect of a nanopore experiment and should be tailored to the molecule being sensed. In most cases, changes in the material, or more commonly, the fabrication technique, will result in a change in pore geometry ,27,28,296) have much lower electron beam fluxes that are typically not intense enough to drill nanopores in a reasonable amount of time  = 1 M). Upon adding DNA, transient reductions in the current are produced when a single DNA molecule traverses the pore A. Since ct λ-DNA . knockout ≈ REBID). Working at this unique regime has allowed the study of C-atom ad-atom migration as well as the influence of various substrate materials on graphene pore formation. Since the e-beam is able to elevate the temperature of a material, in combination with the fact that graphene has a high thermal conductivity, there seems to be long-range effects on accumulation of surface ad-atoms. The temperature rise in the vicinity of the e-beam is likely to play a role in the diffusion of surface ad-toms. In the model that was presented in this work, diffusion must be overcome in order to be attracted towards the e-beam. Therefore, higher temperatures promotes diffusion and limits the ability accumulate ad-atoms near the e-beam. Since the drawing and storing of heat depends both on conductivity and the amount of supporting material, thin carbon grids led to successful drilling at virtually any aperture size whereas SiN membranes requires aperture sizes >600 nm. The fabrication of graphene nanopores, and more generally graphene nanostructures, has opened the door to a range of sensing applications and the development of novel devices over the years. This particular report aims at making graphene fabrication more robust by understanding the e-beam sculpting process. The understanding of these processes has led to several publications in the field of nanopore sensing including the ability to sense proteins using graphene [In conclusion, e-beam sculpting of graphene is not just dependent on the ability to displace atoms but also the ability to hinder the self-healing properties of graphene. Using relatively lower e-beam fluxes from a thermionic electron source, the C-atom knockout rate seems to be comparable to the rate of C-atom ad-atom attraction and accumulation at the e-beam/graphene interface (i.e., Rgraphene  and the graphene .


The key to this approach is the catalyst metal, which is not popular for graphene growth by chemical vapor deposition. A catalyst metal film of 500 nm thick was deposited onto an amorphous C (50 nm thick) coated SiO2/Si substrate. The sample was then annealed at 250 °C under vacuum condition. Raman spectra measured after the removal of the catalyst by chemical etching showed intense G and 2D peaks together with a small D and intense SiO2 related peaks, confirming the transfer free growth of multilayer graphene on SiO2/Si. The domain size of the graphene confirmed by optical microscope and atomic force microscope was about 5 μm in an average. Thus, this approach will open up a new route for transfer free graphene growth at low temperatures.Low-temperature growth, as well as the transfer free growth on substrates, is the major concern of graphene research for its practical applications. Here we propose a simple method to achieve the transfer free graphene growth on SiO Rummeli et al. demonstrated the CVD graphene growth at 325 °C on dielectric insulators using acetylene as a carbon source, although the grown graphene was of sub-micrometer dimension. Jang et al. reported graphene formation at 300 °C using benzene using a CVD process161718192021et al. demonstrated the low-temperature growth of graphene film using Ni catalyst2 ambient to yield a strong 111-texture of Ni is necessary. If the low-temperature graphene growth without any high-temperature heat treatment is achieved, it will be very fascinating.Extraordinary electrical, mechanical and chemical properties of graphene make it suitable for various applications12357810111213Very recently, we noticed that a variety of metals can be used for graphene growth based on the solid-liquid-solid reaction. In the solid-liquid-solid reaction method, although it is a high-temperature process at the moment, graphene can be grown directly on insulating substrates2 layer (SiO2/Si) was used as a substrate for the experiments. Annealing of the Sn/C stack layer on SiO2/Si substrate was performed at 250 °C in a PLD system at a pressure of 5 × 10−5 Pa. After the annealing, the Sn layer was removed by a chemical etching process using diluted nitric acid (10%) solution. The characterization of graphene sample thus prepared involved optical microscopy , Raman spectroscopy (NRS 3300 laser Raman spectrometer with a laser excitation energy of 532.08 nm), atomic force microscopy (AFM) (JSPM-5200) and Auger electron spectroscopy (AES) (JAMP-7800).In the present work, we employed a simple solid-liquid-solid phase reaction method by which graphene grows just by heating of layered films of catalyst metal and carbon on a substrate in a vacuum condition. In this method, the amorphous carbon film can be available as a carbon source, which is advantageous to skip the process of decomposition of carbon-containing molecules by a catalyst, unlike CVD. Pulsed laser deposition (PLD) technique was used to carry out the controlled deposition of a thin C and a catalytic Sn layers. Deposition of 50-nm thickness C layer and 500-nm Sn layer was carried out with a laser pulse energy and frequency of 30 mJ and 10 Hz, respectively, at room temperature. A Si wafer coated with a 100 nm thick SiO−1 and 2711 cm−1, respectively, with a less intense D peak at 1354 cm−1, while region Z shows an amorphous carbon (a-C) peak. Raman analysis confirms the formation of graphene patches only at regions X and Y surrounded by a-C (Region Z). Thus, it is concluded that graphene grows on Sn even at a temperature as low as 250 °C.In order to study the carbon state on the sample surface shown in 2 surface or only on the catalyst metal surface, the sample was kept in 10% nitric acid (HNO3) solution for 2 hours (short time) to remove metal from the sample surface. To confirm whether the formation of graphene occurred directly also on SiO2 related Raman peak. This result must suggest the evidence of graphene growth directly on the SiO2 substrate. Further chemical etching was performed to establish a firm conclusion. 2 substrate. As indicated by the arrow labeled with number 2 in the 2 interface. Raman spectrum measured on position 1, 2 and 3 labeled in −1 while that for position 3 is at 2692 cm−1. This shift in 2D peak confirms that some layers are removed along with Sn.In order to identify the carbon state more microscopically, a detailed Raman analysis was performed for the respective regions A–D in 2 substrate was recognizable all over the sample surface and dark blue patches corresponding to region D in 2 were detected and no metal was observable. As confirmed by the Raman spectrum shown in After the long-time removal of the metal, SiO2 carbon bonding on its surface27282 carbon bonding formation, namely graphene formation, would be enhanced on the molten Sn surface. This graphitization would occur not only at the top surface but also at the interface between Sn and SiO2 substrate. After the chemical removal of Sn, the only graphene formed at the interface between Sn and SiO2 would have survived on the SiO2 surface and the graphene formed on the top of Sn surface would have dispersed in the chemical solution.From the above-described experimental data, the annealing and etching processes may be summarized as follows together with the schematic representation in In order to probe the thickness of the formed graphene, AFM was carried out at around a typical dark blue patch after long-time (24 hours) removal of the Sn metal. In order to confirm the size of the graphene flakes, surface Raman mapping was also performed. sp2 hybridized honeycomb arrangement. The reason that sp2 hybridized honeycomb arrangement is preferred compared to other bonding states during catalyst motion through a-C might be due to the fact that sp2 hybridized honeycomb arrangement does not require any extra change in enthalpy to form while sp3 and sp1 doUsually, it is easier to graphitize a-C at higher temperature312/Si interfaces. This graphene becomes visible only after the chemical etching process. In order to check the role of substrate in graphene formation, similar experiments replacing SiO2 by glass substrates were also performed. It was also the case for glass substrates, thus indicating that graphene would be growable directly on any insulating substrate using this technique. The next step required for this technique is the optimization of the layer thickness of C and Sn and the results will be dealt with in a forthcoming paper.As described above, although graphene has been easily formed on top of the Sn surface, it has also been formed between the Sn and SiO2 substrate at 250 °C has been attempted. Raman microscopy, AES and AFM measurements confirmed the formation of multilayer graphene on SiO2 as well as on Sn. Although the formed graphene is still of micrometer order at the moment, this transfer free low-temperature synthesis approach is believed to explore new dimensions of graphene properties and applications.In this work, a step towards low-temperature graphene growth using a new catalyst metal, Sn, has been executed and transfer free graphene growth on SiOHow to cite this article: Vishwakarma, R. et al. Transfer free graphene growth on SiO2 substrate at 250 °C. Sci. Rep.7, 43756; doi: 10.1038/srep43756 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Abrupt shifts in natural resources and their markets are a ubiquitous challenge to human communities. Building resilient social-ecological systems requires approaches that are robust to uncertainty and to regime shifts. Harvesting diverse portfolios of natural resources and adapting portfolios in response to change could stabilize economies reliant on natural resources and their markets, both of which are prone to unpredictable shifts. Here we use fisheries catch and revenue data from Alaskan fishing communities over 34 years to test whether diversification and turnover in the composition of fishing opportunities increased economic stability during major ocean and market regime shifts in 1989. More than 85% of communities show reduced fishing revenues following these regime shifts. However, communities with the highest portfolio diversity and those that could opportunistically shift the composition of resources they harvest, experienced negligible or even positive changes in revenue. Maintaining diversity in economic opportunities and enabling turnover facilitates sustainability of communities reliant on renewable resources facing uncertain futures. et al. show that communities relying on diverse fisheries were more resilient to major ocean and market regime shifts in 1989.Economies dependent on natural resources could gain resilience to abrupt ecosystem and market shifts through proactive risk-buffering approaches. Using data from Alaskan fisheries, Cline  Ecosystem dynamics are notoriously unpredictable, especially considering the effects of ongoing climate change, ocean acidification and other poorly understood and interacting perturbations. It is often argued that more mechanistic science will lead to greater understanding of ecosystem functioning and improved forecasting for variability and changeA large proportion of the world's population lives in coastal communities and is critically dependent on highly volatile marine resources71213Forecasting and insurance are two strategies that have been used extensively to help buffer people against unexpected perturbations to natural resource economies; the former is used to avoid risks while the latter is used to transfer damages from negative perturbations6112021Abrupt shifts in ecosystems and fish stocks are common in the marine environment77102513Here we use a well-documented oceanographic regime shift with far reaching biological consequences in the North Pacific Ocean24Our results show that diversification and turnover of fishing opportunities significantly reduce the negative impacts of regime shifts on the revenues generated by fishing communities. We find that >85% of fishing communities saw large decreases in their revenues following major ocean regime and market shifts. However, communities that participated in a more diverse collection of fisheries saw smaller changes in revenue and had more opportunities to adapt to the changes. In some cases, communities actually increased their revenue from fishing as they were able to capitalize on emerging opportunities. Enabling communities to create and maintain flexibility in fishing opportunities appears to be a tangible mechanism for providing resilience against unpredictable changes in ecosystems and markets. Building sustainable communities will require employing strategies that are robust to irreducible uncertainties about future changes in coupled human-natural systems.The ecosystems of the North Pacific Ocean are notoriously variable, expressing change at interannual and interdecadal time scales large enough to seriously challenge the sustainability of fishery-dependent communities. These unpredictable changes in productivity and species composition are due mostly to oceanographic changes that favour certain stocks or species. Importantly, these changes do not result in the unilateral collapse or benefit of all species; rather they cause a reshuffling of the relative abundances of important commercial species2424To understand how the 1989 regime shift affected commercial fisheries across Alaska, we evaluated changes over time in commercial fisheries harvest by stock. Using dynamic factor analysis (DFA)r=0.86, Pearson correlation) with the common trend in commercial catches  with the price-per-pound paid to fishers for Pacific salmon species, the dominant commercial fisheries by value in Alaska . In Alaska, we see patterns of diversification that follow geographic access to resources as well as economic development . CommuniDiversification also carries risks such as loss of efficiency due to lack of specific knowledge as well as high capital costs. Diversification has been mostly thought of as an individual level strategyOver the last decade, there has been considerable interest and effort towards implementing ecosystem-based fisheries management under which multiple objectives and stakeholders, rather than single species, are considered in the management of fishing sectors and ecosystemsHere we show that diversification and the ability to opportunistically shift the composition of species exploited can buffer communities against unexpected and large-scale ocean regime shifts and changing markets. Ecosystems can undergo significant shifts in composition, but often maintain their overall productivity. While delivery of ecosystem services is provided primarily by abundant speciesThe state of Alaska manages all fisheries operating within three nautical miles of shore and a small set of fisheries operating in the USA exclusive economic zones . Alaskan commercial fisheries are delineated by taxa (either species or species group), the area harvested, and the gear used to harvest them. State managed fisheries are mostly managed using some form of limited entry where access is controlled, but can be bought or sold among parties. Any individual harvesting in a state managed fishery requires a permit specific to the taxa, area fished and gear used. All landed catches and revenue in Alaskan commercial fisheries are required to be reported. The Commercial Fisheries Entry Commission (CFEC) tracks landings and revenue by permit and permit-holder residency. Therefore, reported catch and revenue are assigned to communities where the permit holder resides. For confidentiality reasons, the CFEC cannot report very small fisheries that have less than four permit holders per community. Therefore, these fisheries are not included in our analysis. Management of sablefish and halibut fisheries switched to individual transferable quota systems in 1995, which could alter the revenue and catches in these fisheries. However, excluding catches and revenues for these species does not change the results in this study.http://www.bls.gov/cpi/). To evaluate spatial patterns in diversification, we used revenue data aggregated to USA census areas (www.census.gov). Salmon price data was provided by CFEC summary data. Prices were adjusted for inflation to 2013 USD using the USA annual average consumer price index.We used community level gross fishing revenues data collated by the CFEC. These revenues represent the sum of annual gross fishing revenues attributable to CFEC commercial fishing permits registered to a specific Alaskan community. Some communities have seen changes and permit migration. We ran these analyses using revenue per permit fished to account for these changes, and there is no change in the results or conclusions. All revenue data were adjusted for inflation to 2013 USD using the US annual average consumer price index  to observations (y).Observation equation relates trend (t (yt) are modelled as linear combinations of hidden trend (xt) and factor loadings on the hidden trend for each stock (community) (Z). vt and wt represent the observation and process error structures, respectively. To make the model estimableQ) was set to a diagonal matrix of value 1. Observation errors (R) are from a multivariate normal distribution. The number of trends and the error structure are determined through model selection. Candidate models were compared using AIC based on the maximum likelihood of the model fit. Model selection results for DFA analyses are in Here the vector of observations for catches in each stock (or revenue in each community) at time n=60). This increased the overall diversity of taxa and locations represented in the data. We compared models with one or two hidden trends as well as two error structures; where all stocks share a single observation error  and where all stocks have individual observation error . All data were z-scored to account for differences in means and intrinsic variance dynamics.To evaluate common trends in commercial fisheries harvest among stocks around Alaska, we applied the DFA to time series of fisheries delineated by taxa and location harvest . This yielded an increased number of complete time series of catch. We limited stocks to only those that had no more than three zero-catch values (n=105). We compared models with 1 or 2 hidden trends as well as two error structures; where all stocks share a single observation error  and where all stocks have individual observation error . All data were z-scored to account for differences in means and intrinsic variance dynamics. DFA models were fit using the ‘MARSS' package in RIn a separate analysis, we applied the DFA to time series of fishing community revenues allocated to Alaskan cities. For fishing communities we included only those that had non-zero revenue in all years between 1980 and 1999 . For an even comparison we used only the 10 years post-1989 (1990–1999).Diversity was computed for each community over the 10 years before the 1989 (1980–1989). We used the reciprocal Simpson's index (1/D) where diversity (D)pi is the proportion of total revenue by fisheries (taxa and location). To compute an overall diversity metric for each community we weighted each year by the relative proportion of that year to the total revenue for all years.where J) index between these two time periods, A (1980–1989) and B (1990–1999).To measure turnover in the relative composition of a community's commercial fishing catches, we compared the commercial fisheries catches from before and after 1989. For each community a relative composition by fishery was computed based catches on for each of two periods, 1980–1989 and 1990–1999. Specifically, each fishery's contribution to the total catch for each period in total was computed. Then, the relative change in composition was computed using Jaccard's dissimilarity How to cite this article: Cline, T. J. et al. Fisheries portfolio diversification and turnover buffer Alaskan fishing communities from abrupt resource and market changes. Nat. Commun.8, 14042 doi: 10.1038/ncomms14042 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures and Supplementary Tables


Therefore, epoxy/graphene nanocomposites have been prepared for the first time with DCB as a dispersant; DMF and ethanol have been chosen as the reference. The colloidal stability, mechanical properties, thermogravimetric analysis, dynamic mechanical analysis and scanning electron microscopic images of nanocomposites have been obtained. The results show that with the use of DCB, the tensile strength of graphene has been improved from 64.46 to 69.32 MPa, and its flexural strength has been increased from 97.17 to 104.77 MPa. DCB is found to be more effective than DMF and ethanol for making stable and homogeneous graphene dispersion and composites.It is generally recognized that dimethylformamide (DMF) and ethanol are good media to uniformly disperse graphene, and therefore have been used widely in the preparation of epoxy/graphene nanocomposites. However, as a solvent to disperse graphene, dichlorobenzene (DCB) has not been fully realized by the polymer community. Owing to high values of the dispersion component ( EpoxyFor epoxy/graphene nanocomposites, graphene can significantly improve the physical and chemical properties of matrix at extremely low loadings, and this improvement could only be achieved when the filler is homogeneously dispersed in the matrix ,5. The uTherefore, solvents are widely used as dispersants to overcome the van der Waals force between graphene nanosheets, and hence lead to uniform graphene dispersion. For example, ethanol –14 has bδd) of the Hildebrand solubility parameter are the best for making homogeneous and aggregate-free dispersions of graphene [δd of 19.2 MPa1/2. In the light of this, DCB tends to be suitable to produce stable graphene dispersion. Song et al. [et al. [Besides those commonly used solvents, dichlorobenzene (DCB) has also been widely reported as a good dispersant for preparing graphene dispersions because of the following reasons. Firstly, DCB is a commonly used reaction solvent for fullerenes and is known to form stable single-walled nanotube dispersions . Secondlgraphene ,43. DCB g et al.  disperse [et al.  surface- [et al. –53 also o-DCB has been chosen and used for the first time to prepare epoxy/graphene nanocomposites. Another two commonly used solvents, DMF (δd = 17.4 MPa1/2) and ethanol (δd = 15.8 MPa1/2), have been used for comparison in this work.However, though DCB has been used a lot in the dispersion of graphene, the use of DCB to prepare epoxy/graphene nanocomposites is not yet fully realized by the polymer community. To the best of our knowledge, there is no work reported using DCB as a dispersant for epoxy/graphene nanocomposites. In this work, 2.2.1.2 g−1. o-DCB, DMF and ethanol were purchased from Sigma-Aldrich Company with a purity of 99.9%.The epoxy system used in this study was purchased from Polyfibre UK Ltd. This system offers good all-round properties and consists of a EPOPHEN EL5 bisphenol A-based liquid epoxy and EPOPHENEHA 57 diamine hardener. Graphene nanoplatelets were offered by Graphene Laboratories Inc., USA with an average lateral size of 4.5 µm and a specific surface area of 80 m2.2.According to our previous research , 0.3 wt%One set of samples was prepared without any solvent, marked as G-0.3. DCB, DMF and ethanol were used to prepare another three sets of samples; 0.45 g of graphene was first dispersed in 100 ml of solvent  and bath-sonicated for 0.5 h; then 99.7 g of epoxy monomer was added to the dispersion and sonicated for another 0.5 h. To remove the solvent, the mixtures were heated with stirring; all the mixtures were then weighed to ensure evaporation of the solvents fully. Then the mixtures were cooled down to room temperature and 49.85 g of hardener was added with hand stirring for 5 min followed by 5 min of bath sonication; then the entrapped air bubbles were removed by vacuum degassing. Finally, the liquid mixtures were mould cast and cured for 6 h at room temperature and then for 6 h at 80°C.2.3.−1. Tensile tests were conducted according to ASTMD638 (Type V) with a specimen thickness of 4 mm. Flexural properties were measured according to ASTM D790 with a specimen dimension of 48 × 12.7 × 3 mm. A single-edge-notch three-point bend specimen was used to measure the fracture toughness (K1C) according to ASTM D5045; the specimen dimension was 36 × 6 × 3 mm with a crack of length of 3 mm. The K1C was calculated usingW is the sample width (millimetres), a is the crack length and kept between 0.45 and 0.55 W, B is the sample thickness (millimetres), f(a/w) was calculated using equation (2.2), which is constant and related to the sample geometry and Pmax is the maximum load of the load–displacement curve. The critical strain energy release rate (G1C) was calculated using equation (2.3), where v is Poisson's ratio of the polymer and taken as 0.35 and E is Young's modulus obtained from the tensile tests (MPa).Tensile properties, flexural properties and fracture toughness were measured by a universal mechanical testing machine (Instron 3382); for all tests the crosshead speed used was 2 mm minVickers microhardness was measured by Buehler Micromet II. A dwell time of 10 s with a load of 200 g was used for all the samples. Six samples were measured for each set of conditions and mean values were then reported.E′) and loss factor (tan δ). Sample dimensions of 30 × 8 × 2.5 mm were tested by the single cantilever method. All tests were carried out under the temperature sweeping mode (temperature ramp from 30°C to 150°C at 5°C min−1) at a constant frequency of 1 Hz. The glass transition temperature (Tg) was taken as the temperature value at the peak of tan δ curves. Thermogravimetric analysis (TGA) of the nanocomposites was carried out with a TA Instruments Q500 thermal analyser. The temperature range was from room temperature to 600°C at a ramp rate of 5°C min−1 under N2 atmosphere. To evaluate the fracture modes of the nanocomposites, a FEI Quanta 200 electron microscope was used to obtain scanning electron microscopy (SEM) images. A layer of gold with 10 nm thickness was applied to the samples' fractured surfaces using an Emscope sputter coater model SC500A.A Perkin Elmer-8000 dynamic mechanical analysis (DMA) instrument was used to conduct the analysis to determine the storage modulus  of the nanocomposites. As can be seen from the figure, the storage moduli of samples prepared with solvents increased obviously over the G-0.3 samples throughout the temperature range. Especially, DCB-prepared samples show a 2.61 GPa storage modulus, which is higher than the 2.45 GPa of DMF samples and 2.38 GPa of ethanol samples.Figure 5Tg), which was taken as the temperature value at the peak of tan δ curves and are shown in figure 5b. In this figure, it can be seen that the tan δ peak is observed at 69.28°C for G-0.3 samples. For nanocomposites processed by DCB, DMF and ethanol, the Tg were observed at higher temperatures. This can be ascribed to the phenomenon that the uniformly dispersed graphene has restricted the chain mobility of the epoxy matrix, and thus increased the Tg values [Tg of 76.57°C, which is a more than 7°C increment compared with the G-0.3 samples, while only slight increases (approx. 4°C) in Tg are obtained for samples prepared with DMF and ethanol. As mentioned above, the uniformly dispersed graphene increased the cross-linking density of the nanocomposites, and then plays a positive role in improving the thermal stability.The segmental motion of polymers is characterized by glass transition temperature (g values . Among a3.5.a, graphene aggregates are sparsely located on the surface; the inset of figure 6a shows the typical morphology of a graphene aggregate. This poorly dispersed surface indicates a poor interfacial interaction between the epoxy matrix and graphene, and features the brittle nature of the material and its poor resistance to crack initiation and propagation. Compared to the G-0.3 samples, the fracture surfaces of DCB samples are smoother, as shown in figure 6b. The clear fracture patterns show the fracture mechanism of sheet/sheet delamination of this nanocomposite, and reveals that the usage of DCB leads to uniform graphene dispersion. The homogeneously dispersed graphene is able to bridge growing cracks, impede crack propagation and thus improve the properties of nanomaterials. However, for DMF- and ethanol-prepared samples, as shown by the insets in figure 6c,d, some poorly dispersed graphene can still be seen on the surface. The non-uniformly dispersed graphene forms defects in the nanocomposites, which act to concentrate the stresses locally, effectively causing a localized weakness, thus decreasing the properties of the nanocomposites.The fracture surfaces of nanocomposites were examined by SEM and are shown in figure 64.A prerequisite for property enhancement of epoxy/graphene nanocomposites is the uniform dispersion of graphene in the matrix; however, the ultra-high specific surface area of graphene results in high van der Waals forces and thus induces a strong tendency to reaggregate. Solvents are able to provide a low-viscous medium, which is an ideal environment for graphene to disperse. While dispersing, lower viscosities are responsible for promoting the de-bundling of graphene sheets. This results in the triggering of the van der Waals force on graphene surfaces in lower proximity with each other, thus leading to dispersion. Therefore, the selection of dispersion media is very important for the final properties of nanocomposites.In this work, DCB was selected to test its effectiveness for epoxy/graphene nanocomposites preparation. The colloidal stability and mechanical properties were determined, and TGA, DMA and SEM imaging of nanocomposites were conducted. The results show that DCB is able to produce stable graphene dispersion; however, DMF and ethanol are not sufficient to disperse graphene homogeneously in the nanocomposites. Nanocomposites prepared with DCB also show higher mechanical properties and better thermal stability compared to the samples prepared with DMF and ethanol.In general, it is concluded that DCB is more efficient than DMF and ethanol for making uniform and stable graphene dispersions. The usage of DCB can endow the nanocomposites with outstanding mechanical properties and improved thermal stability. This finding can be significant in practical applications and gives an impetus for DCB usage not only in epoxy/graphene nanocomposites preparation, but also in other polymer nanocomposites where the usage of solvents is required in the processing.