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.


The formation of layered nanosheets relies on exfoliation driven by rapid evaporation of water and/or other gas molecules generated under annealing. Our route allows simple, efficient and inexpensive production of 2D oxides. As a demonstration, we evaluate Cr2O3 nanosheets prepared by our method as anodes in lithium-ion batteries and find superior performance in comparison with their microcrystalline counterparts.Two-dimensional (2D) nanoscale oxides have attracted research interest owing to their electronic, magnetic optical and catalytic properties. If they could be manufactured on a large scale, 2D oxides would be attractive for applications ranging from electronics to energy conversion and storage. Herein, we report facile fabrication of oxide nanosheets by rapid thermal annealing of corresponding hydrous-chloride compounds. By heating CrCl Oxide nanosheets are of interest for a range of applications. Here the authors demonstrate a simple route combining gas evolution and exfoliation of hydrated chloride precursors for making large quantities of oxide nanosheets of various compositions. These oe sample . Typicale sample  indicatee sample . The rea3·6H2O crystals on nickel foam and heated the crystals in the flame of an alcohol lamp for 30 s. We again observed marked volume expansion . The thickness of the amorphous Cr2O3 obtained by alcohol lamp heating reached 1.9 nm . We placed CrClxpansion . Most ofxpansion  and TEM xpansion . The nanxpansion  and the xpansion . EDS anaxpansion  indicatexpansion , we posid 1.9 nm . Howeverd 1.9 nm . In addi nm , the tap density of the expanded Cr2O3 was very low, ∼0.0332, g ml−1. Using this method, we could mass-produce Cr2O3 nanosheets as shown in 2O3 nanosheets in ∼2 h in the lab using only a small muffle furnace , low average charging voltage (∼1.2 V) and low electromotive force value (1.085 V versus Li/Li+)362O3 electrodes is poor because of a loss of electronic contact of active materials and poor electron and ion transport properties within the active material2O3 nanosheets as active materials could address these limitations. We fabricated graphene/2D Cr2O3 composite electrodes. The graphene and Cr2O3 were assembled layer by layer because of their similar 2D layered structure. The stacked 2D structure can provide good electrical contact between the graphene and Cr2O3 nanosheets, improving electron transport. The ultrathin, 2D layered structure can help access to the active material by the electrolyte and aid ion transport.Among TMOs, Cr2O3 (2D) electrodes and graphene/Cr2O3 (particle)/polyvinylidene difluoride (PVDF) electrodes, as mentioned in the Methods section. The cells were tested over a voltage range from 0 to 3 V versus Li+/Li. During the test, the initial lithiation and delithiation capacity of the graphene/Cr2O3 (particle)/PVDF electrodes was only ∼738 and 316 mAh g−1, respectively, and the delithiation capacity decreased to only 168 mAh g−1 after ∼10 cycles at the current density of 0.2 A g−1 (2O3 (2D) electrodes exhibited a high capacity and stable cycling performance. The graphene/Cr2O3 (2D) electrodes achieved an initial delithiation capacity of 974 mAh g−1 at a rate of 0.2 A g−1. After 297 cycles, the capacity was ∼986 mAh g−1, and the cell continued to function well (2O3 (2D) electrodes is excellent compared with the reported performance of other Cr2O3 electrodes383940412O3 electrodes are included in 432O3 (particle)/PVDF electrode were 12.3 and 56.9 Ω, respectively. However, the graphene/Cr2O3 (2D) composites showed lower SEI and charge transfer resistances, which were 6.6 and 21.4 Ω. The electrochemical impedance spectroscopy results indicated the interface of graphene/Cr2O3 (2D) electrodes is more stable and confirmed the 2D structure is attributed to the charge transfer and charge diffusion, which could support the good electrochemical and rate performances of graphene/Cr2O3 (2D) electrodes. Importantly, the graphene/Cr2O3 (2D) composites exhibited strong adhesion to the current collector (copper foil) and between the layers. The graphene/Cr2O3 (2D) electrodes and graphene/Cr2O3 (particle)/PVDF electrodes were immersed in the electrolyte solution; we then sonicated the electrodes in the electrolyte solution with ultrasonic cleaning  for 5 min. For the graphene/Cr2O3 (particle)/PVDF electrodes, the PVDF binder did not prevent the spalling of the electrode material from the current collector during the ultrasonic cleaning process (2O3 (2D) composite materials remained on the current collector (2O3 (2D) electrodes maintained structural stability in all three solutions, whereas the graphene/Cr2O3 (particle)/PVDF electrodes exhibited different degrees of spalling (2O3 (particle)/PVDF electrode was easily peeled off, the graphene/Cr2O3 (2D) electrodes remained intact and no stripping happened  electrodes. We suggest the strong adhesion has three potential causes. First, the 2D structure of Cr2O3 provides a high contact area with graphene and copper foil, and the calendaring process ensures good contact between Cr2O3 (2D), graphene and copper foil. Second, according to zeta potential measurements under neutral conditions, graphene was negatively charged (−9.26 mV) and the 2D Cr2O3 was positively charged (32.5 mV), suggesting that electrostatic attraction contributes to the adhesion between 2D Cr2O3 and graphene. Third, and finally, we speculate that some chemical bonding may occur at the interface between the electrode material and current collector, between amorphous Cr2O3 nanosheets and the copper foil due to the high-valence Cr and low-coordinated surface atoms, leading to robust adhesion to the copper foil45We fabricated graphene/Cr.2 A g−1 . By contion well . This el process . Howeverollector . We alsospalling . We usedhappened . The obs3·6H2O, we applied the rapid heating method to other hydrous chlorides to obtain metal oxides such as ZrO2, Al2O3 and Y2O3. The ZrOCl2·8H2O, AlCl3·6H2O and YCl3·6H2O crystals were not layered nanosheets before the rapid heating treatment  in water and painted the solution onto copper foil. The copper foil was then dried in an oven at 60 °C for ∼30 min to obtain a hydrous AlCl3 thin film. We heated the hydrous AlCl3 on the copper foil using the heating gun (∼500 °C) until the light-yellow solid appeared. We observed that the solid formed on the copper foil was exfoliated Al2O3 , but the crystallinity was poor. We measured the thickness of the metal oxide nanosheets using AFM. The thinnest layers of the ZrO2, Al2O3 and Y2O3 obtained by rapid heating methods exhibited layer thicknesses of 3.5, 2.7 and 4.0 nm, respectively  or without layered crystal structure (such as AlNO3·9H2O) were also treated by the rapid heating process, however the 2D oxides were not obtained in these cases  by rapidly heating hydrous chlorides; the 2D Cr2O3 showed excellent electrochemical performance in Li-ion battery and surprisingly strong adhesion to the copper foil substrate. We also demonstrated that this method of producing ultrathin nanosheets could be generalized to allow rapid production of other oxides such as ZrO2, Al2O3 and Y2O3. We believe that this concept provides a practically promising avenue for simple, efficient, fast and inexpensive production of large quantities of large-area, ultrathin 2D nanosheets.We produced the 2D Cr3·6H2O crystals were placed in a glass bottle and heated using a microwave oven for ∼5 min. Then, the obtained CrOCl materials were transferred and stored in an Ar-filled glove box. The CrOCl nanosheets were dispersed in benzene to prepare TEM samples.The CrCl3·6H2O crystals were placed on nickel foam and heated using an alcohol lamp for ∼2 min; or placed in a quartz crucible and heated in a muffle furnace at 400 °C for ∼15 min. The ZrOCl2·8H2O crystals were placed in a glass bottle and heated using a microwave oven for ∼10 min. The AlCl3·6H2O crystals were dissolved in deionized water at a concentration of 1 g ml−1, and the solution was painted onto copper foil. The copper foil was dried in an oven at 60 °C for 30 min to obtain the hydrous AlCl3 thin film. We heated the hydrous AlCl3 on the copper foil using a heating gun until a white or light-yellow solid appeared. The white or light-yellow solid was Al2O3 nanosheets. The YCl3·6H2O crystals were placed in an alumina crucible and heated in the rapid heat treatment furnace at 600 °C for ∼2 min. The obtained metal oxide products contain incompletely reacted intermediate products. We dispersed the obtained products in deionized water to remove the soluble impurities. The dispersion of products was centrifuged for 5 min at 12,000 r.p.m. to obtain the sediment. We repeated the centrifuging process three times to clean the products . The obtained sediment was dried in an oven at 80 °C.The CrCl2O3 nanosheets (90 wt%) in water at a concentration of 10 mg ml−1 . We also dispersed graphene (10 wt%), Cr2O3 particles  and PVDF (10 wt%) in N-methyl-2-pyrrolidone (NMP) at a concentration of 10 mg ml−1 . The composite slurry was dropped onto a flat copper foil and dried in an oven at ∼80 °C. The dried electrodes were pressed using a calendaring process (∼20 MPa). The mass loading of the electrodes was ∼0.5 mg cm−2. The electrodes were assembled into half-cells with Li metal foil (MTI) as a counter electrode in an Ar-filled glove box. We used a 25 μm-thick microporous polypropylene membrane as the separator (Asahi Kasei) and 1 M LiPF6 in ethylene carbonate/diethyl carbonate/fluoroethylene carbonate  as the electrolyte.We dispersed graphene (10 wt%) and Crα radiation source. The chemical states of the products were determined by XPS (Thermo Fisher ESCALAB 250Xi). The morphology of the samples was observed with a SEM . The chemical composition of the samples was analysed using EDS . HRTEM observation was carried out using JEOL-2100 TEM operated at 200 kV. The thickness of the nanosheets was determined by AFM . The electrochemical performances of the batteries were measured by a BS-9300R/10V2A MTI 8 channels battery analyser.The X-ray diffraction patterns of the rare materials and products were evaluated using a D/max-2500 diffractometer  equipped with a CuKThe data that support the findings of this study are available from the corresponding author on request.How to cite this article: Zhao, C. et al. Mass production of two-dimensional oxides by rapid heating of hydrous chlorides. Nat. Commun. 7:12543 doi: 10.1038/ncomms12543 (2016).Supplementary Figures 1-183˙6H2O starting material undergoes large volume expansion upon microwave heating.CrCl3˙6H2O crystals directly placed into a furnace at 400 C and heated for 5 minutes to obtain porous 2D Cr2O3 nanomaterials.CrCl2O3 nanosheets and copper foil substrate.Tape test showing strong adhesion between Cr


We propose a nonvolatile terahertz (THz) switch which is able to perform the switching with transient stimulus. The device utilizes graphene as its floating-gate layer, which changes the transmissivity of THz signal by trapping the tunneling charges. The conventional top-down electrode configuration is replaced by a left-right electrode configuration, so THz signals could transmit through this device with the transmissivity being controlled by voltage pulses. The two electrodes are made of metals with different work functions. The resultant asymmetrical energy band structure ensures that both electrical programming and erasing are viable. With the aid of localized surface plasmon resonances in graphene ribbon arrays, the modulation depth is 89% provided that the Femi level of graphene is tuned between 0 and 0.2 eV by proper voltage pulses. Various approaches, including those based on active metamaterials5 and two-dimensional materials8, have been investigated over the past decade to manipulate the propagation of electromagnetic waves from RF to THz frequencies. However, most of them are volatile devices, as they return to original states once external stimuluses are removed. Nonvolatile photonic devices, which can sustain the transformed properties with short triggering stimuluses rather than persistent signals, hold an apparent advantage of saving the energy in the advent of photonic technologies. More importantly, the persistent photonic memory effect of these devices lend themselves to a variety of applications including protective optical/terahertz circuitry, adaptable transformation-optics devices, dynamically reconfigurable optical/THz networks or metamaterials, electrically controllable photonic memory15, and so on. Furthermore, most planar lightwave circuits, especially those built on the silicon-on-insulator substrate that have high refractive index contrasts and strong optical confinements, are sensitive to fabrication tolerance. Therefore, many properties of practical photonic circuits such as the resonance wavelength would inevitably drift from their design values. Nonvolatile devices then can be incorporated into these photonics circuits as a post-trimming technique to compensate permanent errors.The terahertz technology has gained increased research attention due to its industrial and academic applications16, phase-change materials13, organic materials17, ferromagnetic materials18, have been well developed for data storage, few of them turn out to be a feasible solution to implement the nonvolatile THz switching. The reason is that most of those devices contain functional materials or top-gate electrodes that are nontransparent at THz frequencies. For example, the floating-gate (FG) shown in Fig. 20 graphene is employed as the FG layer to store charges, which exhibits improved performance in terms of endurance, writing speed and operation voltage. However, THz signals cannot propagate through this configuration, since it demands a metallic control gate on the top, which is highly lossy at THz frequencies. Although metamaterial structures12, for example, a periodic hole array patterned on an Au film as shown in Fig. Although a lot of nonvolatile devices, e.g., those based on floating gate structures2), electrons from the drain electrode can tunnel through the HfO2 layer and be stored in the FG (graphene) layer under a negative drain voltage. Meanwhile, since the tunneling barrier under the source electrode between graphene and HfO2 is higher than that between the drain electrode and HfO2, electrons that tunnel out to the source electrode from the FG can be neglected. Therefore, electrons can accumulate in the FG. Similarly, under a positive drain voltage, the electrons stored in the FG can tunnel back to the drain, while electrons that tunnel into the FG from the source is negligible. Electrons therefore can be erased from the FG also due to the unequal tunneling barrier heights at the two electrode terminals. The nonvolatile switching is finally realized by taking advantage of different tunneling barriers at the two terminals.In this paper, we theoretically propose a nonvolatile THz switch by employing the graphene as the floating gate layer. The extraordinary optoelectronic property of graphene allows stored carriers to significantly tune the transmissivity of the THz signal. The conventional control-gate layer that covers the full device is replaced by source and drain electrodes made of different metals at the two sides as shown in Fig. 22 or plasmonic waveguides25. We note that active graphene layers in many of these modulators can be readily replaced by the graphene-based floating-gate in Fig. Although the proof-of-principle design of the THz nonvolatile switch here is based on free-space optics with the THz wave normally incident on the graphene layer, this scheme is also well adapted to various waveguide-based switches operating at THz and optical wavelengths. A lot of graphene-based photonic modulators have been reported on either dielectricψg = 4.6 eV19) vertically stacking on a 300-nm-thick SiO2 layer works as the floating-gate layer. Another 4.5-nm-thick HfO2 layer (ψHfO2 = 2.5 eV26) acts as the tunneling/blocking layer. The drain and source electrodes on top are made of aluminum (ψAl = 4.08 eV) and gold (ψAu = 5.1 eV27), respectively. To reveal the operation principle, the finite elements methods (Comsol Multiphysics) is utilized to model the electrostatic potential distribution in this device. The distance between the two electrodes is set to be 50 nm to better display the vertical configuration. It should be noted that the width of this transmission window for THz beams is on the scale of centimeters in practical devices. However, the electrostatic potential distribution in practical devices is similar to that presented in Fig. 20. Therefore, the potential difference from drain to graphene almost equals to that from graphene to source as shown in Fig. A schematic diagram of the nonvolatile switch is shown in Fig. 28, which is far less than the distance between the two terminals of our device. Therefore, the band bending is neglected to simplify the band diagrams. Resultant directions of tunneling currents and band diagrams at different operation states are shown in Fig. VD < 0) is applied on the drain. As already analyzed in Fig. 2 (ϕB1 = ψAl − ψHfO2 = 1.58 eV) is lower than that between graphene and HfO2 (ϕB2 = ψg − ψHfO2 = 2.1 eV). Since the tunneling probability strongly depends on the barrier height, the tunneling current cross the right barrier is greater than that cross the left barrier (J1 ≫ J2), and then electrons accumulate in the FG layer. In the retention mode  in the FG layer, potential and Femi level of graphene rise by ~0.08 V and ~0.2 eV, respectively. Tunneling currents from the FG layer to the source and drain is negligible as a result of the weak electrical field in the blocking layer. Therefore, electrons could be stored in the FG layer for a long period before completely leaking out. In the erasing mode of Fig. J3 ≫ J4 as shown in Fig. To explain the necessity of using different metals for source and drain, we at first examine the case of both electrodes being made of the same metal, for example, Aluminum (Al). It is known that in a graphene MOS structure, charges will accumulate in graphene provided that work functions of graphene and metal are different. This localized metal-induced doping effect leads to internal built-in electrical fields inside the graphene, and hence bend its energy band. However, these fields only exist in narrow regions ~0.2 μm) adjacent to drain/graphene and source/graphene interfaces μm adjacode Fig. , the volϕB3 > ϕB4) as shown in Fig. J3 is accordingly insignificant as compared with J4. Therefore, the stored electrons could be erased by a positive voltage due to the asymmetric-electrode configuration.To solve this issue, the Al source is replaced by a Gold (Au) source. The different work functions of Al and Au lead to an asymmetrical energy band structure as shown in Fig. 30. However, a strict calculation based on this approach is much more cumbersome than that based on the Fowler-Nordheim (FN) theory31. The FN theory is initially built on the free-electron theory of metals and leads to a very simple relationship between the density of emission current and the applied electric field. Strictly, the FN model applies only to bulk systems, e.g., metal/insulator/crystalline solid. However, FN equations are also frequently used as an approximation to describe low dimensional systems, including nanocrystals33 and two-dimension materials35. For example, the FN model captures measured tunneling currents in a graphene/insulator/metal system quite well with proper modifying factors in ref.35. Based on these considerations, we employ the FN theory without considering the linear energy-momentum relation of graphene. This greatly simplifies the calculation with acceptable accuracy. Regarding the applicability of the FN model to the graphene/insulator/metal interfaces in our device, we discuss how the density of states of graphene affects the tunneling current density in the Supplementary Information. However, a rigorous treatment of the tunneling current is still suggested for our future work.A quantitative calculation is provided below. Considering the two-dimensional character and band structure peculiarities of graphene, a rigorous treatment should resort to the Bardeen transfer Hamiltonian approach, as it has been utilized to explain tunneling currents in relevant two-dimensional systemsED > qϕB1/d). It can be used to calculate tunneling currents in programming/erasing modes, but is not suitable for the retention mode (VD ≈ 0) of very weak electrical field. Generalized formulas are derived by Simmons which enable the calculation of tunneling currents for several different voltage ranges36. According to Simmons’ formulas, tunneling current densities J at programming/erasing and retention modes can be calculated asA and C are: A1 = q2(2 m*)1/2/(4d π2ℏ2) and C1 = 2d(2 m*)1/2/ A2 = q3/(16π2ħϕB) and C2 = 4(2 m*)1/2(ϕB)3/2/3ħq, here m* denotes the effective mass of the electron, ϕB is the barrier height and q is the electron charge. The electrical field between source and FG (ED) and that between drain and FG (ES) are determined by the potential of the floating gate layer VFG, which depends on the amount of stored electrons QFGVS, VD, and VG are the potentials of source, drain and ground, respectively. As shown in Fig. CS, CD, and CG represent the capacitances (per unit area) between graphene/source, graphene/drain, and graphene/ground, respectively; and CT is their sum (CT = CS + CD + CG)37.The conventional FN formula of tunneling current describe the field-assisted emission of electrons under a very high electrical field  of graphene. A part of the applied voltage drops in the graphene layer itself, and this effect can be captured in the quantum capacitance Cq of graphene, which can be calculated as38EF = 0, the quantum capacitance reaches its minimum value of CGox = 11.5 nF/cm2, which is far less than the quantum capacitance. Hence, the influence of the quantum capacitance on CG can be neglected. On the other hand, as the thickness of the HfO2 layer is very thin, its capacitance is comparable with the quantum capacitance. Therefore, both CS and CD should be considered as series connections of their respective oxide capacitances and quantum capacitances: 1/CS = 1/CSox + 1/CSq and 1/CD = 1/CDox + 1/CDq.According to the MOS theory, W1/W2 is the width ratio of the electrode and the FG layer as shown in Fig. VG can be neglected due to the fact that CG is far less than CS and CD.The stored-charge density in the FG layer is related to the tunneling currents across the two barriers. It can be calculated asentclass1pt{minima41. If these effects need to be reflected, VD should be replaced by corresponding effective voltages: VD_eff = VD − VD0. Here VD0 is drain voltage at the Dirac point without external voltages, whose role is very similar to that of the flat-band voltages in conventional MOS transistors. Hence electric fields ED and ES can be approximately written as, ε0, εr are the vacuum permittivity and relative permittivity of HfO2, respectively, d is the thickness of the tunneling layer.The discussion above has not considered the influences of different work functions between metal and graphene, and the resultant inevitable doping of grapheneJ1, J2) and erasing  modes. Parameters in the calculation are: εr = 1742, W1 = 1 mm, W2 = 1 cm, d = 4.5 nm, VD = −5.5 V and +6 V in programming and erasing modes. In Fig. J1 ≫ J2) in the programming mode. However, in the erasing mode the electron density in the FG layer keeps on growing as J4 ≪ J3, and therefore they cannot be erased. In Fig. With the aid of equations –5, we cad of the HfO2 layer. We calculate time responses of tunneling current densities in programming mode, electrical field strength between drain and graphene, density of stored charge, i.e., J1, ED, and QFG, in Fig. d as a variable. The drain voltage in the calculation is −5.5 V. It can be seen that reducing the thickness d could enhance the electrical ED and consequently the amplitude of tunneling current density J1, which implies a reduced programming time for storing the same amount of electrons in graphene. However, reducing the thickness d would deteriorate the retention time, and even the programming process forbidden. As shown in Fig. J2 becomes equal to J1 at t = 0.5 μs for d = 3.5 nm. Therefore, a very thin tunneling layer is not advisable. Based on the trade-off between the programming speed and the retention time, a 4.5-nm-thick HfO2 is chosen as the tunneling layer.A critical parameter which decides the device performance is the thickness 2 (17 MV/cm in42) and the stability of device. The drain voltages in the programming and erasing modes are finally set as VD = −5.5 V and +6 V, respectively. The absolute amplitude of VD in the erasing mode is higher and operating time is longer than those in programming mode. This is due to the fact that tunneling electrons experience a higher barrier height in the erasing mode as shown in Fig. −10 A/cm2 provided that the charge density in graphene layer is ~3 × 1012/cm2. This order of magnitude is in accordance with the calculations in refs44, and implies a very long retention time in theory. However, since insulating abilities of practical thin dielectric layers cannot be ideal, leakage currents in realistic nonvolatile devices are usually much larger than this theoretical estimation. Here RC time constant is not considered in calculation, since it is usually on the order of picoseconds (ps)7.In Fig. QFG) change the transmission of THz signal by varying its optical conductivity (σg(ω)). Since charges can be stored for a long period before an opposite erasing voltage comes, the device hence acts as a nonvolatile THz switch. Optical absorption in graphene includes interband and intraband transitions. However, in THz range the intraband transition dominates, so its optical conductivity can be described by a Drude-like dispersion formula45νF = 106 m/s is the Fermi velocity of Dirac fermions. The transmissivity of THz signal can be calculated as46ε1 and ε2 are the permittivities of covering layer and substrate, respectively.In this part, we study the switching characteristic and the dynamic response of this nonvolatile device. The charges stored in graphene (11/cm2. EF of graphene then rises by 0.1 eV. The modulation depth (MD), defined as |T(ns) − T(n0)|/T(n0), is calculated to be 9.5% at 5 THz according to equation (12/cm2) are stored, and thus the MD is enhanced to 20%. By further prolonging the programming and erasing time , QFG can reach to (~7.4 × 1012/cm2), meanwhile, its MD is increased to 27%.According to the analysis above, the amplitudes of the programming/erasing (negative/positive) pulses are chosen to be −5.5 V and +6 V. Waveforms of the programming/erasing pulses applied on the drain and the corresponding responses of the device are shown in Fig. QFG) relies on the duration time of the programming/erasing pulses as long as amplitudes of driving pulses are fixed. Enhancing the MD by using long pulses is in contradiction with improving the operation speed. In order to overcome this tradeoff, we can take advantage of graphene-based plasmons. As collective oscillations of charge carriers, plasmons can occur in patterned graphene47. For example, the graphene-ribbon array shown in Fig. 52. The simulation result shows that this structure helps to enhance the MD remarkably. The modeled resonance curves are presented in Fig. EF from 0 eV to 0.1 eV and to 0.2 eV, respectively. The power consumption is proportional to the total stored charges and hence the area of the floating-gate layer, the corresponding power consumptions per unit area are 0.0068 J/cm2 and 0.0085 J/cm2 to tune the Femi level from 0 to 0.1 eV (programming mode) and from 0.1 eV back to 0 eV (erasing mode), respectively. These values become 0.026 J/cm2 and 0.033 J/cm2 for tuning Femi level from 0 to 0.2 eV and from 0.2 to 0 eV, respectively. The simulation in Fig. 2 substrate according to data in52. It is observed that both impurities and phonons affect scattering rates of practical graphene layers55, so the value of scattering time is not fixed but depends on the particular processing condition. To investigate the impact of the scattering time, we also carry out the simulation in Fig. 51. It can be seen that a high-quality graphene layer of long scattering time is favorable to realize a narrow resonance curve with high resonance depth, since the conductivity of graphene is proportional to iτ−2.In accordance with Figs EF of graphene between 0 and 0.1/0.2 eV with voltage pulses of appropriate amplitude and duration. Moreover, the MD can be further improved to 89% by patterning the graphene layer into a ribbon array. The performance of this configuration can be further improved by different manners. For example, since charged impurities in oxides introduce disorders of graphene, h-BN can be used to replace HfO2 so as to increase the quality of graphene56; using multi-layer graphene instead of graphene monolayer is able to enhance the effective refraction index variation19, and so on.In conclusion, we propose a graphene-based nonvolatile THz intensity switch with asymmetric electrodes. If a uniform graphene layer is used as the floating gate layer, the modulation depths are 9.5%/20% by nonvolatile tuning 9. The width of electrodes (drain and source) is W1 = 1 mm; and that of the floating-gate layer is W2 = 1 cm. Since W1 and W2 are several orders of magnitude larger than the thickness of graphene, they are scaled down to 5 nm and 50 nm in the simulation for a better eye-capture.The numerical simulation of electrostatic potential distribution and the corresponding electrical distribution as shown in Fig. 41; the results are shown in Figs VD_eff is calculated via the equations in refs41. The voltage at the Dirac point is normally related to work function difference between metal and graphene as well as interface trap states. In this study we assume the graphene is ideal, so only the effect of different work functions is considered. The transmissivities through the monolayer graphene in Fig. The tunneling processes of electrons in programing/erasing modes are calculated by Matlab based on the model in refsy direction. A plane-wave source is normally incident (z-direction) on graphene. The periodic boundary condition is used along the y direction, while the anti-symmetric boundary condition is used along the x direction. The perfectly matched layer (PML) is set at the z-direction. The graphene material type (temperature of 300 K) together with the 2D rectangle geometry described in modeling methodology are employed to model the graphene layer following the surface conductivity approach. The grid size for graphene layer is Δx = Δy = 2.5 nm and Δz = 0.25 nm.The numerical simulation of the THz transmissivity through graphene ribbons is performed with the 3-D finite-different time domain method . The result is shown in Fig. Supplementary Information