During the treatment, NH3 plasma radicals not only flattened the Cu substrate such that the root-mean-square roughness value gradually decreased from 51.9 nm to 15.5 nm but also enhanced the nitrogen content in the Cu substrate. The smooth surface of copper enables good control of graphene growth and the decoupling of height fluctuations and ripple effects, which compensate for the Coulomb scattering by nitrogen incorporation. On the other hand, the nitrogen atoms on the pre-treated Cu surface enable nitrogen incorporation with low defect densities, causing less damage to the graphene structure during the process. Most incorporated nitrogen atoms are found in the pyrrolic configuration, with the nitrogen fraction ranging from 1.64% to 3.05%, while the samples exhibit low defect densities, as revealed by Raman spectroscopy. In the top-gated graphene transistor measurement, N-doped graphene exhibits n-type behavior, and the obtained carrier mobilities are greater than 1100 cm2·V−1·s−1. In this study, an efficient and minimally damaging n-doping approach was proposed for graphene nanoelectronic applications.N-doped graphene with low intrinsic defect densities was obtained by combining a solid source doping technique and chemical vapor deposition (CVD). The solid source for N-doping was embedded into the copper substrate by NH To obtain these promising properties and applications, researchers have continuously endeavored to synthesis N-doped graphene without damaging the graphene crystal structure and deteriorating its carrier mobility.Graphene is a promising next generation electronic material, and its electronic properties can be modified by the substitutional doping of heteroatoms  pos3 plasma ,15,16,17lasma [3  and (ii)4/H2/NH3  and pyri4/H2/NH3  are simu4/H2/NH3 ,21. Ther3, covalent C–N bonds form at intrinsic defects, vacancies or sites with weak C–C bonds in which the carbon atoms can be easily replaced by nitrogen atoms. However, considering the mismatch of the thermal expansion coefficients between graphene and the underlying substrate and the electrical disturbance induced by the closer contact between graphene and the underlying substrate after high-temperature post-annealing, extensive research must be devoted to its transport performance after high-temperature post-annealing [x group-based etching effect that induces increased defect densities [x groups was limited during the growth process [To obtain N-doped graphene with low defect densities, Cory et al. determined the ion energy window for nitrogen doping in the graphene lattice with the balance between the substitutional reaction efficiency and the vacancies or defect formation . Moreovennealing . For theensities . The sec process ,25. The 3 plasma at room temperature, and atomic force microscopy (AFM) was performed to examine the surface morphology variation. This process embeds nitrogen atoms into the Cu substrate, which are subsequently used as the solid-state doping source. Then, N-doped graphene was grown by CVD on the nitrogen-doped copper substrate. X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy were used to analyze the bonding and lattice properties of N-doped graphene. Top-gated graphene field-effect transistors (GFETs) were fabricated using N-doped graphene as the channel, and 1100 cm2·V−1·s−1 carrier mobilities were achieved without carrier mobility degradation, which is comparable to the corresponding pristine graphene.To overcome the bottleneck in the direct synthesis of N-doped graphene in the conventional chemical vapor deposition (CVD) process and to obtain high transport properties for N-doped graphene, a solid state doping technique was introduced in this study. The solid state doping technique is broadly used for efficient and uniform doping with low amounts of structural defects in low dimensional materials, such as nanowires and conducting polymers ,27,28; h3 pretreatment and N-doped graphene synthesis. As illustrated in 3 plasma treatment, nitrogen radicals are embedded into the Cu surface, and N–Cu bonds are formed after the surface oxidation and after the organic residues were reduced by hydrogen radicals in the NH3 plasma [3 plasma treatment [3 plasma pretreatment were loaded into a low-pressure chemical vapor deposition (LPCVD) chamber for graphene growth separately, as shown in 3 plasma ,31. Mean3 plasma . The crireatment ,31, and reatment . Then, t3 plasma pretreatment times, acquired in a 5 μm × 5 μm area prior to graphene CVD growth, are shown in 3 plasma pretreatment exhibits a periodically corrugated structure with a maximum height of 202.4 nm and a surface roughness with a root mean square (RMS) value of 51.9 nm. Samples with an NH3 plasma pretreatment of 1, 3, and 5 min were labelled as NG1, NG3, and NG5, respectively. The Cu surface morphology becomes less rough as the NH3 plasma pretreatment time increases. For the Cu with a 1, 3, and 5 min plasma treatment, the height of the Cu surface corrugation structure and surface roughness decrease to 46.2 nm and 15.5 nm, respectively. The surface-flattening effect of the plasma treatment is comparable to the effects of other Cu pretreatment processes, such as electrochemical polishing or pre-annealing [2 substrate for further measurement and analysis .The distinct apparent morphologies of these Cu foil samples with different NHnnealing . After t2 substrates. The deconvoluted C1s spectra of pristine graphene and NG1 are shown in 3 plasma pretreatment on the Cu surface. Note that the hydrogen annealing process may remove some surface nitrogen atoms. However, based on these results, the amount of surface nitrogen is sufficient for the graphene dopants. As shown in 3 plasma. According to the recent report on atomic-level scanning tunneling microscopy (STM) analysis, nitrogen atoms are preferentially located at the graphene grain boundaries, while there is no indication of nitrogen in the atomically flat graphene regions [To investigate the bonding configurations of N atoms in the graphene lattice, XPS was performed on pristine graphene and N-doped graphene samples after transfer onto the SiO regions . Thus, t−1 range as shown in −1 (defect mode), (b) a G peak at 1586 cm−1 , (c) a D’ peak at 1620 cm−1 (disorder mode), and (d) a 2D peak at 2707 cm−1 (two-phonon vibration mode). The locations of the G peak and the 2D peak of pristine graphene and N-doped graphene are shown in −1, respectively, and a downshift of the 2D peak from 2707 to 2701, 2701, and 2701 cm−1, respectively, indicating n-type doping from pristine graphene to NG1, NG3, and NG5 [I2D/IG ratios for both pristine graphene and N-doped graphene were greater than 2, as shown in ID/IG ratios of pristine graphene and N-doped graphene range from 0.168 to 0.171, 0.173, and 0.191. Based on the ID/IG ratio, the defect density for each sample can be calculated using the average defect distances based on the Tuinstra-Koenig (TK) relation of [−10)λ4(ID/IG)−1 as 71.4 nm (pristine graphene), 70.1 nm (NG1), 69.3 nm (NG3), and 62,8 nm (NG5). The slightly increasing trend of defect mode intensity is consistent with our XPS survey because more structural defects are induced by pyrrolic N than graphitic N. Moreover, the D’ peak  is weak compared to the G peak, indicating that a relatively low disorder accompanies the nitrogen doping [2-hybridized carbon neighbors with adjacent carbon vacancies. In addition, nitrogen incorporation may influence and even interact with the defect formation process during the kinetic growth of graphene because the nitrogen atoms are predominantly distributed at grain boundary regions where defects and vacancies are also easily formed to release the interfacial stress originating from the lattice mismatch between Cu and graphene [4 gas in the processes of gas phase deposition, molecular decomposition, and long-range Cu surface kinetic diffusion.N-doped graphene samples were also analyzed using their Raman spectra in the 1200–3000 cm and NG5 . The I2Dn doping . The incgraphene . The int2 substrates. The graphene samples without and with NH3 plasma in 1, 3, and 5 min treatments were also examined by Raman spectroscopy, and the Raman spectra are shown in 3 plasma treatments, strong lattice structural damage was found in which splitting was observed for the D and G peaks, and the 2D peak showed an obvious decrease. For the graphene samples under a 5 min NH3 plasma treatment, the typical vibration modes of graphene  almost disappeared. During the NH3 plasma treatment directly on graphene, high additional plasma energy after nitrogen displacement remained in the graphene lattice and transferred into in-plane collisions [For comparison, the same plasma treatment process was directly applied to the graphene samples after the transfer onto SiOllisions . Thus, tIdVd curves in IdVg measurement results and the capacitance of graphene transistors shown in Cg(Vg − VDirac)/e), where Cg is the gate dielectric capacitance [2·v−1·s−1 (at 3 × 1012/cm2), respectively. The electron mobilities of pristine graphene, NG1, NG3, and NG5 were 1060, 1061, 1121, and 1151 cm2·v−1·s−1 (at 3 × 1012/cm2), respectively. The opposite ambipolar mobility behaviors of pristine graphene and N-doped graphene can be clarified using Novikov’s calculation showing that attractive scattering is dominant relative to the repulsive scattering for Dirac fermions in graphene [3 plasma treatments was also fabricated, and no Id–Vg curves were obtained because the graphene was severely damaged during the direct high-energy plasma treatment.Finally, to measure the electrical properties, a top-gated GFET was fabricated, as shown in acitance . As showgraphene . Therefographene . For comID/IG ratios, Dirac points, and carrier mobilities. Starting with exfoliated graphene, followed by ion implantation and high-temperature NH3 annealing, N-doped graphene exhibits a high mobility, reaching 6000 cm2·V−1·s−1. However, this post-annealing doping method for pristine graphene results in a relatively low doping concentration, possibly due to the low probability of graphene interface hybridization between nitrogen atoms and the continuous atomic lattice structure [ID/IG ratio to values ranging from 0.3 to 2 . Both nitrogen incorporation  [2·v−1·s−1 (CVD with a mixed gas precursor) [2·v−1·s−1 (plasma enhanced CVD-grown) [2·v−1·s−1  [2·v−1·s−1 (substrate doping) [2 as the gate dielectric, which is more compatible with the integrated circuit (IC) architectures and operates in the low gate voltage mode, but the trade-off is the series resistance in the access region.A brief comparison of the N-doped graphene with different CVD synthesis approaches is presented in tructure . For CVDttering)  and its ttering)  degrade ttering) . Recentlttering) . Comparettering) . In thisttering) ,34, compecursor) , 100–400D-grown) , 365 cm2g on Cu) , and 105 doping) . Note th3 plasma pretreatment of the Cu foil catalyst was proposed. Approximately 80% of the nitrogen dopant atoms in the graphene lattice are present in the pyrrolic configuration, and the nitrogen concentration was negatively correlated with Cu surface roughness. For synthesis on the pre-treated Cu catalyst, N-doping does not induce a high density of defects or disorder in the graphene lattice, as revealed by the Raman spectra. The electrical transport measurement shows that N-doping effectively modulates the electrical properties of graphene to n-type behavior with high conductivity and carrier mobility. This study provides a low defect density approach for N-doped graphene synthesis and significantly advances the modulation and improvement of the performance of graphene in nanoelectronic applications.In summary, a CVD synthesis process for low defect density N-doped graphene based on the NH3 flowed into the chamber at 20 sccm while the pressure was maintained at 0.5 Torr, and radio frequency power of 100 W was applied to generate the NH3 plasma. The degree of plasma treatment of each sample was adjusted by controlling the exposure time (from 1 min to 5 min) after the plasma was ignited. The Cu surface morphology was scanned using an Innova AFM  equipped with an Olympus AC160TS probe . The AFM tip was operated in tapping mode and set to map a 5 × 5 μm2 area across the samples.Cu foil  was loaded into the plasma-enhanced CVD (PECVD) system, and the chamber was evacuated to 2 mTorr at room temperature. NH3 plasma pretreatment were loaded into a low-pressure tube furnace separately [2 flow rate of 50 standard cubic centimeters per minute  to remove the native Cu oxide. Then, 20 sccm CH4 was introduced to grow graphene on the Cu foils for 20 min. After the graphene synthesis was complete, the furnace was cooled to room temperature at a rate of ~5 °C/s. A layer of polymethyl methacrylate (PMMA) was spin-coated onto the graphene on the Cu surface. The films were then treated with a Cu etchant and rinsed with deionized (DI) water before they were transferred to the 300 nm SiO2/Si substrates. The PMMA support layer was removed from each film using acetone, and the remaining graphene films were rinsed with isopropyl alcohol.Copper foils with and without NHparately . The gro−1 was used as the calibration signal for each sample. For Raman probing, each sample was exposed for 3 s, which was repeated 3 times to obtain an average value and to eliminate unintentional cosmic ray effects. The chemical configurations were determined using an X-ray photoelectron spectrometer  with an Auger electron beam gun (LEG 300-F electron column). The energies were calibrated relative to the C1s peak to eliminate the charge distribution of the sample during analysis.Raman spectra were collected using a RAMaker confocal Raman microscope system . The Raman peak of Si at 520 cm2/Si substrate, 50 nm of Ni was deposited as the source and drain (100 μm × 100 μm). The graphene channel (20 µm in width and 50 µm in length) was patterned using lithography methods followed by an O2 plasma etching step; finally, 1500 nm of Al was deposited followed by a 1.5-nm Al deposition as a buffer layer. Both of these Al layers were exposed to air for more than 8 h to form a thin AlOx dielectric layer between the graphene channel and the Al gate [After transferring the graphene onto the 300 nm SiO Al gate . Graphen


The effects of graphene stacking are investigated by comparing the results of methane adsorption energy, electronic performance, and the doping feasibility of five dopants  via first-principles theory. Both zigzag and armchair graphenes are considered. It is found that the zigzag graphene with Bernal stacking has the largest adsorption energy on methane, while the armchair graphene with Order stacking is opposite. In addition, both the Order and Bernal stacked graphenes possess a positive linear relationship between adsorption energy and layer number. Furthermore, they always have larger adsorption energy in zigzag graphene. For electronic properties, the results show that the stacking effects on band gap are significant, but it does not cause big changes to band structure and density of states. In the comparison of distance, the average interlamellar spacing of the Order stacked graphene is the largest. Moreover, the adsorption effect is the result of the interactions between graphene and methane combined with the change of graphene’s structure. Lastly, the armchair graphene with Order stacking possesses the lowest formation energy in these five dopants. It could be the best choice for doping to improve the methane adsorption. Formaterial . Yang etAr, etc. . HoweverGenerally, graphene can be stacked in various ways to form different multilayered graphenes (MLG), which have different properties. Typical graphene stacking includes Order, Bernal (AB), and Rhombus stacking (ABC) ,16,17. IWhile the effects of graphene stacking on MLG’s properties had been well acknowledged, a comparative study of different ways of stacking is still lacking. Although the stacking effects on the adsorption, the band gap, and the doping of MLG are considered important, they are not yet understood. For instance, some research has shown that the stacking possessed a nonnegligible influence on electrical performance, but the changes are really diverse and messy. Furthermore, the studies of graphene stacking effects on methane adsorption are still lacking. In addition, doping is good for methane adsorption, such as the dopants of B, N, Al, Si, and P. The doping of Al and P would lead to a dramatic increase in methane adsorption energy, but some research revealed that the doping of Al and P in graphene is difficult. However, there are no reports on the graphene stacking effects on doping. Adsorption energy is an important and direct criterion to evaluate the adsorption effect. Band gap can partly reflect the electrical properties of graphene. In addition, doping is often used as a means to improve the performance of the target material. However, the studies of graphene stacking in these aspects are still lacking, which is exactly what we need to know. This is why we investigate the effects of graphene stacking on graphene’s properties, methane adsorption, and doping.In this paper, the effects of graphene stacking on methane adsorption and doping are first reported using the first-principles theory. The DFT-D has demonstrated that it is more appropriate for the multilayered graphene calculations than DFT. Both the zigzag and armchair graphenes are considered. First, the effect of graphene stacking on the adsorption is studied. It is found that the AB-stacked graphene has the highest adsorption energy on methane, while the adsorption effect of the ABC-stacked graphene is the worst. Second, the changes of band gap related to different number layers are compared. The effect of stacking on band gap is significant, but it does not cause big changes to band structure and density of states. Then, the distances of methane–graphene systems are also analyzed. In the comparison of distances, the average interlamellar spacing of the Order stacked graphene is the largest. Moreover, the adsorption effect is the result of the interactions between graphene and methane combined with the change of graphene’s structure. Third, the effect of graphene stacking on the doping is discussed. The formation energies of the five dopants of B, N, Al, Si, and P in different stacked MLG are reported. The armchair graphene with Order stacking possesses the lowest formation energy in these five dopants, which is the best choice for doping to improve the methane adsorption. 3 code base on density functional theory (DFT) [RI and r are the coordinates of the atomic nucleus and the electron, respectively. Equations (1) and (2) describe a time-independent and solvable Schrödinger equation, where the first two terms of Equation (2) represent the kinetic energy of atoms and electrons, respectively, and the latter three items denote the system Coulomb exclusion potential. The generalized gradient approximation (GGA) and local density approximation (LDA) are the exchange–correlation functionals commonly used in quantum mechanics calculation. GGA was chosen in this theoretical study and can be described in Equation (3):−6 eV, and the MAX force is 0.002 Ha/Å.All the calculations were performed in Dmolry (DFT) , which iry (DFT) ,27. DFT ry (DFT) . The Schrst–Pack  k-point rst–Pack  smearingBecause of the sheet structure of graphene, the multilayered graphene should be restricted by Van der Waals force. However, DFT calculations cannot estimate the weak interaction, which would lead to the poor calculation accuracy of methane adsorption. In order to solve this problem, the dispersion correction in DFT (DFT-D) was introduced in our work .C6,ijRij−6 term, where C6,ij is a material-specific, so-called dispersion coefficient between any atom pair i and j at distance Rij. At short range, the long-range expression is matched to the DFT potential by multiplication with a damping function f , which reduces the additional dispersion contribution to zero, subject to a cutoff defined by some suitably calculated combination Rij° of the vdW radii of the atom pair. The dispersion-corrected exchange-correlation functional is then formed by simply adding the correction potential to the ordinary DFT exchange-correlation functional. As C6,ij coefficients are additive, the dispersion-corrected total energy Etot can be written as:EDFT is the standard DFT total energy and the sums go over all N atoms in the system.In the semiempirical dispersion-correction approach, the missing dispersion contribution to the interatomic interaction is approximated by a simple isotropic potential. At long range, this potential is given by the Much research has demonstrated that DFT-D can improve the calculation accuracy. Dappe et al. found that the interlayer equilibrium distance of graphene calculated by DFT-D is more closely with the experimental evidence . Vanin eEa) is defined in Equation (5):Egraphene + gas molecule is the total energy of graphene adsorbed system, Egraphene is the energy of pristine graphene, and Egas molecule is the energy of the gas molecule. Generally speaking, adsorption energy can be a criterion to evaluate the adsorption effect, namely, the value of adsorption energy is bigger, and the adsorption effect is better. The adsorption energy (The adsorption energy and the distances including the average interlamellar spacing of optimized graphene (d1) and optimized methane-graphene (d2) and the distance between graphene and methane of optimized methane-graphene (d3) are all calculated by DFT and DFT-D (PBE-grimme) and are listed in It is well known that the adsorption site has some important impacts on adsorption effects. Therefore, different adsorption models were firstly discussed in our calculations. In Taking an example of zigzag graphene, Graphene has a large surface area and porosity. As a result, it has excellent adsorption properties of gases and incomparable advantages in the production of gas sensors. Graphene has a better adsorption effect on methane compared with other gases. We know that the natural graphene often has many stacked ways, such as Order, AB, and ABC, so what are the effects of stacking on graphene-based methane sensor? A comprehensive research of stacking effects on methane adsorption of graphene is learned in this section. Model 2 is selected as the reference standard. Graphene adsorption is closely related to the change of electronic properties. In this section, the electronic properties of the graphene with different stacking are analyzed. As shown in To understand the mechanism of stacking effect on methane adsorption, some distances in six types of stacked graphene are recorded in Eformation is the formation energy, Egraphene is the energy of pristine graphene, and EC and Ed are the chemical potentials determining carbon and doping atoms. The three-layered graphene and the atoms of B, N, Al, Si, and P are chosen to calculate their formation energy, as shown in Previous studies have found that the doping in graphene is helpful to the adsorption of gases ,38, suchObviously, the graphene stacking methods have great influence on the properties of graphene, methane adsorption, and doping. As for methane adsorption and doping, AB stacked zigzag graphene with four layers has the best adsorption capacity, while Order stacked armchair graphene has the better doping performance. In addition, the armchair graphene possesses better electrical properties, but it has larger layer spacing than that of zigzag graphene. Therefore, the type of graphene should be selected according to the purposes and requirements of the sensing material.In this paper, the effects of Order, AB, and ABC stacking on the properties of graphene were investigated comprehensively via first-principles theory. Comparing the adsorption sites of methane, model 2 is the best choice for methane adsorption. The adsorption energy of the Z-AB graphene is the largest among the six types of stacked graphenes; the maximum value is 0.357 eV. Both Z-Order and Z-AB stackings are good for the methane adsorption. As for the band gap, it would be smaller in armchair graphene, which means that the electronic performance of armchair graphene is better than zigzag graphene. The stacking does not cause big changes to band structure and DOS; it only makes some changes in the band gap. In the comparison of distance, both d1 and d2 of the Order stacked graphene are the largest among these three types of stacked graphenes. The distances of d1 and d2 in the AB-stacked graphene are the smallest. The distances of d1 and d2 in the ABC-stacked graphene are close to the AB-stacked graphene. The adsorption effect is the result of the interactions between graphene and methane combined with the change of graphene’s structure. The decrease of d2 can strengthen the adsorption effect of methane. Lastly, A-Order stacking processes the lowest formation energy in all types of stacked graphene. It is the best graphene structure for doping. In addition, it is also the best choice for methane adsorption. In summary, model 2 and Z-AB stacking are the best choices for the methane adsorption. When doping is needed to improve the methane adsorption effect, the A-Order stacking would be a good choice. These discoveries will contribute to the design and production of the graphene-based methane sensor.


From Raman spectroscopy, it was concluded that the PLD process also induced defects in graphene. Compared to unmodified graphene, the obtained chemiresistive sensor showed considerable improvement of sensing ammonia at room temperature. In addition, the response time, sensitivity and reversibility were essentially enhanced due to graphene functionalisation by laser deposited V2O5. This can be explained by an increased surface density of gas adsorption sites introduced by high energy atoms in laser ablation plasma and formation of nanophase boundaries between deposited V2O5 and graphene.Graphene has been recognized as a promising gas sensing material. The response of graphene-based sensors can be radically improved by introducing defects in graphene using, for example, metal or metal oxide nanoparticles. We have functionalised CVD grown, single-layer graphene by applying pulsed laser deposition (PLD) of V Graphene, being a thin (semi)conducting material, is a promising gas sensing system. Highly sensitive response, down to single molecule resolution, has been demonstrated with graphene-based devices under laboratory conditions –3. Howev2O5) – a good oxygen transfer catalyst that is  stable in air and vacuum [Transition metal oxides constitute an important class of catalysts and photosensitizers. Apart from the very first and last 3d elements, scandium and zinc, the rest of the metals possess several oxidation states. The presence of several stable oxidation states serves as a basis of catalytic activity in redox reactions and is most noticeable for vanadium, chromium, and manganese. In particular, vanadium has the highest oxidation state in vanadium pentoxide  sensing properties of chemical vapour deposition (CVD) grown, single-layer graphene in our previous work, using ZrO2 and Ag for functionalisation [PLD is a highly versatile method for relatively well-controlled preparation of thin films, and many advanced composite materials have been produced for diverse applications . The poslisation .2O5. The amount and chemical state of vanadium oxide on graphene was characterized by X-ray photoelectron spectroscopy and X-ray fluorescence. The impact of the PLD process on graphene defect structures was investigated using Raman spectroscopy. Based on the electrical conductivity modulation, the room temperature gas sensing properties of the manufactured sensor structure towards ammonia (NH3) and (for comparison) nitrogen dioxide (NO2) gases were investigated.In the present work, we demonstrate functionalisation of single-layer CVD graphene with a few layers of laser deposited V−1 and ≈2690 cm−1, and have full-widths at half-maximum of 11 cm−1 and 29 cm−1, respectively. These characteristics, together with the ratio of G to 2D band peak intensities of approximately 1:3, correspond to single-layer graphene [−1 indicates extremely low defect density [2O5 onto graphene , the defect-related D and D’ bands emerge in the Raman spectrum, and at the same time, the G and 2D bands decrease in intensity  lattice plane layers is about 2.5.The amount of vanadium that was deposited on the sensor substrate was evaluated using X-ray fluorescence (XRF) analysis by measuring the amount of vanadium in the film deposited onto a fused quartz substrate under the identical PLD procedure. According to XRF, the mass thickness of vanadium on the substrate was 0.11 µg/cm38 g/cm3 , an esti1/2 region is not usable for the analysis, owing to the overlap with the Mg Kβ satellite with O 1s peak. The main component of V 2p3/2 at a binding energy of 517.4 eV was assigned to V5+, whereas a faint component at 513.3 eV belongs to V2+/1+. The O 1s peak component at 530.9 eV was identified as oxygen bound to vanadium, in agreement with Biesinger’s XPS measurements on vanadium oxide [2 in the silica substrate that can possibly overlap with the C–O component of (partly oxidized) graphene at the same binding energy (533.1 eV) [2O5, with an average thickness of about 0.6 nm. Among other vanadium oxides, V2O5 is a relatively stable compound that can be easily deposited by the PLD method [The oxidation state of vanadium was determined by means of X-ray photoelectron spectroscopy (XPS). um oxide . The mai33.1 eV) . Thus, aD method .2 and NH3. All the gas measurements in this work were recorded under continuous excitation with ultraviolet (UV) light (λ = 365 nm) at room temperature (RT). Illumination by UV light can enhance the sensing performance of graphene-based gas sensors. An increased gas response and fast recovery under UV irradiation has been demonstrated for pristine and functionalised graphene-based sensors, possibly as a result of cleaning the surface of interfering or passivating gases [ng gases –26. The 2O5, the conductivity of the sensor decreases by a factor of 30. According to the Raman spectra  into the test chamber. Graphene is typically a p-type conductor under ambient conditions due to chemical doping by adsorbed oxygen and water molecules [3 acts as a hole acceptor and NO2 as a hole donor [2O5 functionalised sensor to 100 ppm CO. Although the sensor is sensitive to CO and a ≈7% decrease in conductivity is observed, the sensitivity to CO is much lower than to NH3.As compared to the pristine sensor, the response to both gases is clearly improved after the functionalisation by PLD. The response to 1 ppm NOolecules –28. Bear2O5 under NH3 gas at different gas concentrations. The functionalisation with V2O5 clearly improves the performance of the sensor. The response and recovery times of pristine and functionalised sensors for 8 ppm NH3 gas were determined by fitting the time curves with the suitable functions. Either single or double exponential functions were used in the following form:S(t) is the relative change of conductance and t0 is the initial time when the respective stepwise change in gas composition was introduced. The characteristic times t1 and t2 are related to the rate constants of adsorption and desorption of gas molecules onto pristine or V2O5-functionalised graphene. In principle, a single exponential type of response  for gases at room temperature. The introduction of defects and dopant atoms into graphene can drastically increase both the adsorption of pollutant molecules and the influence of gas adsorption on the electronic properties of graphene [Ea) of an NH3 molecule on regular graphene is relatively small , but 0.11 eV ,31) or i 0.11 eV ,30,32). not used ,31. Howen et al.  for carbn et al. . The bense of Ag .2 molecules than to any other gas, including NH3. The binding energy is large and intermolecular charge transfer can occur when NO2 adsorbs on either pristine, defective, or doped graphene [2 and NH3 responses of our devices, and also of several other sensors reported in the literature, all based on a single-layer graphene. Only qualitative discussion is possible, because in most cases, the gas concentrations differ. It can be seen that most sensors are more responsive to NO2, as compared to NH3. However, concerning the relative responses, the results of Yavari et al. [2O5-functionalised sensor. According to the measured Raman spectra, the CVD grown graphene used in these experiments contains a relatively large concentration of defects of unspecified nature [3 sensing is well illustrated by Lee et al. [3 gas increased by 600% after creation of defects in the pristine defect-free graphene by reactive ion etching. At the same time, the response to NO2 gas increased only by 33% [2 in our previous work [2 than to 20 ppm NH3  NOgraphene ,31. Tabli et al.  are compd nature . The rolous work  showed aThe introduction of dopants and clusters of atoms into graphene strongly increases the interaction with adsorbing molecules, due to an increased charge transfer and formation of chemical bonds between the dopant and adsorbate ,32. More2O5 cluster growth at the defect site as the PLD process continues. The V2O5 clusters are probably chemically bound to the defect sites of the graphene sheet, yielding a strong and stable contact between the two phases. Chemical bonding is accompanied by charge transfer between V2O5 clusters and graphene. A similar situation was modelled previously by Lim and Wilcox [2 molecule onto a platinum nanocluster which was chemically bound to graphene resulted in a large influence on the charge density distribution of the system [According to the Raman analysis shown in d Wilcox  where lae system .2O5 is known for its catalytic properties and as a good NH3 adsorber [3 gas sensing properties of V2O5 thin films and nanofibers have been demonstrated by Huotari et al. [3 with two V2O5 adsorption sites, one with a surface OH group, forming positively charged NH4+, and the other with oxygen vacancy, forming species denoted as “coordinated NH3”. At the same time, the intensity of the V5+=O related band in the infrared reflectance spectra decreases, which is an indication of the reduction of V2O5 [2O5 catalytic surface. A variety of redox processes are possible, for example:Vadsorber –8. The gi et al.  and Modai et al. . Two str of V2O5 ,9. The cAmbient oxygen can render the process reversible, as desired for sensor reset, for example:2O5 can modify the charge distribution in the graphene–V2O5 system, thus modulating its conductivity.Obviously, strongly bound adsorbate species and the accompanying reduction of V2 can adsorb to V2O5 to form nitrato groups (V5+–NO3), and this process is reversible:According to Gao et al. , NO2 can2 adsorption on the conductivity of the V2O5–graphene system is lower, as expected. Additionally, other energetically favourable adsorption sites can exist at phase boundaries or at the point defects in the parts of graphene exposed to the gas, since the V2O5 material is on average only 2.5 layers thick and can cover the graphene surface unevenly.As there are no redox reactions involved in this adsorption process, the influence of NO2O5. The emergence of defect-related D and D’ peaks and the suppression of the 2D peak in the Raman spectrum suggest that a high concentration of defects was introduced during the deposition. According to XPS, the deposited vanadium was in the state V5+, which is the highest stable oxidation state of this element. As a result, an enhancement in the sensing properties of graphene towards the reducing NH3 gas was achieved. Such an improvement can arise from the strong adsorption ability of NH3 on V2O5 and possible redox reactions on the surface of V2O5, together with the activation of surface processes by using UV light illumination. The changing oxidation state of vanadium can modulate the electrical conductivity of a strongly coupled graphene–V2O5 system. Additionally, gas adsorption may be enhanced at the phase boundaries between a very thin nanostructured V2O5 layer and graphene substrate.CVD graphene was functionalised by laser deposition of a sub-nanometre layer of catalytically active V2 flow  for 60 min, and then exposed to the mixture of 10% CH4  in Ar at the same temperature for 120 min. The sample was allowed to cool slowly, 15 °C/min in Ar flow. The as-grown graphene film was transferred onto a Si/SiO2 substrate by using poly(methyl methacrylate)  as a supporting material. The PMMA solution (1% in chlorobenzene) was spin-coated onto graphene/Cu, dried, and the Cu foil was dissolved in ammonium persulfate solution overnight. The floating PMMA/graphene film was rinsed with deionized water and transferred onto the Si/SiO2 substrate  using a home-built CVD reactor. First, the foil was annealed at 1000 °C in Ar/Hrate see  equipped2O5 pellet was used as an ablation target. The sensor substrate was held in place by a shadow mask through which V2O5 was deposited onto graphene. A KrF excimer laser  was used for deposition. For the PLD target, a fine microcrystalline powder of V2O5  was pelleted at 740 MPa and subsequently baked at 550 °C in air for 5 h. Before starting the PLD procedure, the PLD chamber was evacuated and the sensor substrates were heated in situ at 150 °C for 1.5 h and then cooled down to room temperature. The heating procedure was established in order to clean the graphene surface and minimise the effect of trace contaminants left from the manufacturing process of the graphene sensor. The V2O5 target was ablated by using a laser pulse energy density of 5.0 J/cm2 in the presence of 5 × 10−2 mbar of O2. During the deposition, the sensor substrates were kept at room temperature, with other typical process parameters as follows: laser pulse repetition rate 5 Hz, number of laser pulses 120, and the distance between the substrate and the target 75 mm. Under exactly the same experimental conditions, another V2O5 layer was deposited onto a fused quartz substrate for the evaluation of the mass thickness of the deposited layer in the XRF experiment.In the PLD process, a ceramic VThe structural characterization of graphene was performed by using a micro-Raman spectroscopic system  with an excitation wavelength of 514 nm. The vanadium concentration in the deposited layer was analysed with X-ray fluorescence . The oxidation state of the deposited vanadium was also determined by X-ray fluorescence. The XPS spectra were acquired using a Scienta SES-100 spectrometer. The excitation source was a polychromatic twin-anode X-ray tube  with Mg Kα (1253.6 eV) 300 W irradiation at an analyser–source angle of 45°. The semi-quantitative analysis was conducted using the methods described by Seah et al.  for quan3 equipped with a gas mixing system based on mass flow controllers . The voltage applied between the electrodes was 100 mV. The gases used in our measurements N2, O2, CO/N2, NO2/N2, and NH3/N2 were all 99.999% pure. A synthetic N2 and O2 air mixture  was used as a carrier gas and the relative concentration of N2 and O2 gases was held constant during measurements. The gas flow through the sample chamber was kept constant at 200 sccm while the concentration of the test gas was regulated by the ratio of the flow rates of individual gas components. The nominal relative humidity of the testing gas was 20% during the measurements. All the sensor measurements were done at room temperature. The sensor system was exposed to light from a Xe/Hg high pressure lamp (Hamamatsu). The UV wavelength of 356 nm was selected with a narrow-band interference filter (Andover). The light intensity on the sample was 10–20 mW/cm2.The measurements of the electrical characteristics and gas sensitivity were carried out with a source meter  in a sample chamber with a volume of 7 cm


The accumulation of protein aggregates in neurons is a typical pathological hallmark of the motor neuron disease amyotrophic lateral sclerosis (ALS) and of frontotemporal dementia (FTD). In many cases, these aggregates are composed of the 43 kDa TAR DNA-binding protein (TDP 43). Using a yeast model for TDP 43 proteinopathies, we observed that the vacuole  markedly contributed to the degradation of TDP 43. This clearance occurred via TDP 43-containing vesicles fusing with the vacuole through the concerted action of the endosomal-vacuolar  pathway and autophagy. In line with its dominant role in the clearance of TDP 43, endosomal-vacuolar pathway activity protected cells from the detrimental effects of TDP 43. In contrast, enhanced autophagy contributed to TDP 43 cytotoxicity, despite being involved in TDP 43 degradation. TDP 43’s interference with endosomal-vacuolar pathway activity may have two deleterious consequences. First, it interferes with its own degradation via this pathway, resulting in TDP 43 accumulation. Second, it affects vacuolar proteolytic activity, which requires endosomal-vacuolar trafficking. We speculate that the latter contributes to aberrant autophagy. In sum, we propose that ameliorating endolysosomal pathway activity enhances cell survival in TDP 43-associated diseases. Our study highlights the pivotal role of endolysosomal pathway activity in TDP 43 proteinopathies. Whereas previous studies focused on the role of autophagy in the clearance of cytotoxic TDP 43, we propose that the endolysosomal pathway is more important than autophagy for the survival of cells accumulating detrimental TDP 43. In both pathways, protein substrates (such as TDP 43) are sequestered into vesicles, either multivesicular bodies (MVBs) or autophagosomes . These vesicles fuse with the lysosomal membrane and subsequently release their content into the lysosomal lumen, where protein degradation occurs via proteases such as Pep4/Cathepsin D. Both vesicular pathways are interconnected. In mammalian cells (but not in yeast), some MVBs fuse with autophagosomes, leading to the formation of amphisomes which then fuse with lysosomes. Moreover, the endolysosomal pathway is required for directing important lysosomal proteases, thus decisively contributing to proteolytic capacity of this organelle.Our study raises a number of questions regarding TDP 43 degradation and cytotoxicity. For instance, in cells accumulating cytoplasmic TDP 43, different types of potentially cytotoxic TDP 43 species can be expected, e.g., monomers, oligomers, and different classes of protein aggregates.Which kinds of TDP 43 species are recruited into MVBs for degradation via the endosomal-vacuolar pathway, and which kinds of TDP 43 species are sequestered into Atg8-labelled cellular compartments for elimination via autophagy?For steric reasons, one would expect that MVBs contain smaller TDP 43 species than autophagosomes, which could easily enclose large protein aggregates. It is possible that the endosomal-vacuolar pathway is involved in the clearance of more cytotoxic species than autophagy. This could explain why loss of this pathway is more detrimental for TDP 43-expressing cells than loss of autophagy.What is the molecular machinery that determines whether TDP 43 is degraded via the endosomal-vacuolar pathway or via autophagy (or via the ubiquitin-proteasome system)?It is tempting to speculate that the ubiquitylation pattern of TDP 43 plays an important role. Lysine 63 (K63) polyubiquitin chains could favor the endosomal-vacuolar pathway, while K48 polyubiquitin chains could facilitate degradation via the proteasome, and both chains could be substrates for ubiquitin-dependent autophagy.What is the molecular machinery that recruits TDP 43 into MVBs for degradation via the endosomal-vacuolar pathway?Whereas there is a significant amount of information regarding the molecular machinery involved in recruiting protein aggregates into autophagosomes, the mechanisms that enable the recruitment of cytosolic proteins into MVBs remain largely unknown.Autophagy has been shown to be a cytoprotective mechanism to eliminate deleterious protein aggregates. Therefore, pharmacological induction of autophagy has been proposed to be an efficient mechanism to prevent neuronal loss in neurodegenerative disorders. However, this approach was not successful in our yeast TDP 43 model. In contrast, it increased cytotoxicity.Does the observed inhibitory effect of TDP 43 on endosomal-vacuolar pathway activity and vacuolar proteolytic activity reprogram  autophagy towards cytotoxicity?If this holds true, a therapeutic approach that addresses both endosomal-vacuolar pathway activity and autophagic turnover should be much more effective than targeting autophagy alone.Resolving these questions in yeast TDP 43 proteinopathy models and their validations in higher model systems  will improve our understanding of the molecular mechanisms of devastating TDP 43-associated human disorders.


L/IH = 6000 as relative humidity (RH) consecutively changes between 11% and 95%; small hysteresis of 8% RH; reliable repeatability and stability. In addition, a detailed mechanism analysis is performed to investigate the difference in water adsorption and ions transfer under various RH levels. Notably, the one-step supramolecular assembly method to prepare Pyr-rGO provides a new insight into developing novel functional humidity sensing materials with enhanced device performance.A facile one-step supramolecular assembly method is adopted to modify reduced graphene oxide (rGO) with functional organic molecule pyranine for achieving comprehensive humidity sensing performance. The fabricated humidity sensor based on pyranine modified-reduced graphene oxide (Pyr-rGO) exhibits excellent sensing performance with ultrafast (<2 s) and ultrahigh response of I So far, several effective methods are proposed to fabricate humidity sensors while it still remains a challenge to make the fabrication more facile. For the whole sensor fabricating process, the selection of sensing materials is the most critical stage. Great efforts have been made to investigate the different sensing characteristics of various materials, including ceramics5, metal oxide6, composites and organic polymers9. Nevertheless, it is hard to simultaneously realize the comprehensive humidity sensing properties such as rapid & high response, small hysteresis and reliable stability10. For instance, the ceramics materials possess excellent physical and chemical stability but the practical application is restricted since the need of auxiliary heat cleaning process. Thus, it is of great importance to explore a superior humidity sensing material which can endeavor more comprehensive properties.Humidity sensors have increasingly aroused interests due to the wide application in various realms, such as the indoor humidity measurement, industrial and agricultural environmental monitoring11. Several methods have been explored to prepare graphene such as mechanical exfoliation12, chemical vapor deposition14, and epitaxial growth15, but the most effective approach should be the chemical reduction mainly because the potential for the relatively low-cost and large-scale fabrication from the view of practical application16. Unfortunately, the pristine reduced graphene oxide (rGO) without proper modification is not satisfying when applied as sensors, exhibiting slow response and low sensitivity towards gas or humidity changes18. Moreover, the rGO nanosheets tend to irreversibly aggregate during the chemical reduction process, limiting the further devices fabrication. Hence, it is necessary to modify rGO with appropriate method.Graphene, a fascinating material with perfect 2D atom-thin structure, large specific surface areas and excellent electronic properties, has been considered as a promising candidate for extensive applications including humidity sensing19, electrostatic forces20, and π-π stacking21, is an alternative approach which will maximize the remain of inherent properties of rGO22. In addition, supramolecular assembly modification allows more possibilities for functional “guests” such as inorganic ions, organic molecules or nanoparticles18, which can be specific to enhance the dispersibility or humidity sensing performance of the as-prepared material.Covalent chemical modification of rGO is widely used and indeed the sensing properties can be enhanced prominently, but usually a complex reaction process is required and the instinct structure and electrical properties of rGO will be damaged to some degree. To realize the mild and effective modification of rGO, a facile supramolecular assembly modification based on non-covalent interactions such as hydrogen bondingL/IH = 6000) response. In addition, the humidity sensing mechanism under different RH levels has also been investigated in detail. It is worthy to mention that the one-step supramolecular assembly method has the potential for facilely preparing the novel functional materials with enhanced humidity sensing performance.In this work, we present a facile one-step supramolecular assembly method to modify rGO with functional organic molecule pyranine to form pyranine modified- reduced graphene oxide (Pyr-rGO) for achieving high performance humidity sensing materials. The as-prepared sensors exhibit excellent comprehensive humidity sensing properties including ultrafast (<2 s) and ultrahigh  compared to GO (ID/IG = 1.15), suggesting that the reduction process enhanced the defect content or edge area28. Interestingly, Pyr-rGO showed the same ID/IG value as rGO, proving that the supramolecular assembly modification is a mild approach without causing further disruption of rGO structure.Raman spectroscopy was used to analyze the structural changes during the supramolecular assembly and reduction process. As indicated in Fig. 29. Hence, the impedance of Pyr-rGO sensors at different RH levels was tested with frequency ranged from 50 Hz to 100000 Hz. As can be seen from Fig. It is generally known that the response of humidity sensors is dependent on the testing frequency which should be determined first before investigating the humidity sensing propertiesL) and 95% RH (IH), respectively. By calculation  and an ultrafast response time (<2 s). In addition, the recovery time could be controlled within 6 s for such a high-expansion response of Pyr-rGO sensors. It was noteworthy that the Pyr-rGO sensors also exhibited remarkable repeatability, and the response remained highly stable (IL/IH = 6000) with no attenuation in the impedance (IL and IH) even after 100 cycle continuous response-recovery test  with a long response time for more than 50 s. Moreover, the repeatability of rGO sensors was unqualified that the response and impedance (IL and IH) attenuated significantly after the five-cycle test as shown in Fig. For humidity sensors, the response, response-recovery time and repeatability are the key parameters to directly determine the accuracy and efficiency in practical test. Figure ion Fig. , the Pyrest Fig. . The PyrIn practical humidity sensing applications, the humidity hysteresis phenomenon is also an important parameter to evaluate the practicability of sensors, which is unavoidable but expected to be minimized. The reason for the existence of humidity hysteresis is that different energy is required during the humidification and dehumidification process. Larger desorption energy is always needed than adsorption energy, signifying the corresponding impedance cannot return to the adsorption impedance at the same time, and thus forms the humidity hysteresis. Figure 31. Figure ct represents the resistance of charge transfer, the constant phase element (CPE) stands for the sensing film capacitance (CPE1) and electrode/sensing film interface capacitance (CPE2).Complex impedance spectra (CIS) can be adopted to investigate the sensing mechanism of humidity sensors1). As well-known, the impedance of a CPE is generally defined as Equation (32At low RH range (11% and 33%), the CIS ran in almost straight lines and thus the EC model in this case could be described as a CPE , the curve of CIS was part of semicircle, which was mainly generated from the intrinsic impedance of the sensing film. It could be described by a simplified EC model comprising a resister and a capacitor connected in parallel37. Therefore, the EC model shown in Fig. 4b3 was suitable for the sensing mechanism of Pyr-rGO at this stage. At high RH range, apart from the chemical adsorption, the physical adsorption has upgraded as the main adsorption method. Thus, more serial water layers are formed to further accelerate the ions transfer, and the water molecules can permeate into the Pyr-rGO film to induce the electrolytic conduction. Therefore, the impedance continuously decreases sharply at high RH range.At high RH range , the CIS exhibited a semicircle shape with a short straight line at low frequency region. As the RH increased, the straight line grew longer while part of the semicircle disappeared. Actually, the short straight line represented Warburg impedance, caused by the ions diffusion at sensing material/electrode interfacei.e. pyranine, realized the synergetic functionalization to enhance the dispersibility and humidity sensing properties. The fabricated Pyr-rGO humidity sensors exhibited comprehensive sensing performance such as ultrafast response (<2 s) and ultrahigh response (IL/IH = 6000) as RH alternately changed between 11% and 95%; small hysteresis of 8% RH; reliable repeatability and stability. Further, the complex impedance spectra under different RH was discussed and the corresponding equivalent circuits were built to investigate the humidity sensing mechanism in detail, verifying that different water molecules adsorption and ion transfer were adopted in different RH levels. Notably, we believe that the Pyr-rGO represents a new platform for developing novel functional materials in ultra-sensitive humidity sensors and shows the potential for facile one-step humidity sensing materials preparation.In summary, the Pyr-rGO based humidity sensing materials were prepared through a facile one-step supramolecular assembly method. The modification of rGO by organic molecule, i.e. Trisodium 8-hydroxypyrene-1,3,6-trisulphonate, >85%) and hydrazine hydrate (>98%) were purchased from Alfa Aesar. Ammonium hydroxide (28% NH3 in H2O), lithium chloride (≥99%), magnesium chloride (99.99%), magnesium nitrate (≥99%), sodium chloride (≥99%), potassium nitrate (≥99%), and potassium chloride (≥99%) were all purchased from Sigma-Aldrich. All the chemicals were analytical grade and used as received without further purifications.GO flakes were obtained from XianFeng NANO Co., Ltd. Pyranine (GO flakes were dispersed into deionized (DI) water under vigorous ultra-sonication for an hour to prepare a brown GO dispersion of 1 mg/mL. 5 mL GO dispersion (1 mg/mL) was transferred into a 50 mL round-bottom flask and diluted by 15 ml DI water. Subsequently, 80 μL ammonium hydroxide (30%) and 10 mL hydrazine hydrate (0.1%) were added respectively and heated at 95 °C for 1 h in oil bath. After cooling to ambient temperature, the mixture was filtrated to dry and then re-dispersed into 20 mL DI water under mild sonication for 5 min to prepare 0.25 mg/mL rGO dispersion.5 mL GO dispersion (1 mg/mL) was transferred into a 50 mL round-bottom flask and diluted by 15 ml DI water. Pyranine (100 mg), ammonium hydroxide and hydrazine hydrate  were added into the dispersion respectively under mild stir for 30 mins and then the mixture was further stirred at 95 °C for 1 h in oil bath. After cooling to ambient temperature, the dispersion was transferred to a 50 mL centrifuge tube and washed by DI water under 10 mins centrifugation at 12000 r/min for three times. The residuum was then re-dispersed into 20 mL DI water to prepare 0.25 mg/mL Pyr-rGO dispersion.In the fabrication of humidity sensors, a facile Drop & Dry method was applied by dropping approximately 5 μL Pyr-rGO dispersion on Ag-Pd Interdigital electrodes (IEs) and then dried on heating plate in air at 50 °C for 10 min. Finally, a thin sensing film was formed on the surface of IEs and thus the humidity sensors were prepared for testing as shown in Fig. 2, Mg(NO3)2, NaCl, KCl, and KNO3 with corresponding relative humidity (RH) of 11%, 33%, 54%, 75%, 85%, and 95% respectively. To ensure the accuracy of RH, 10 h stabilization of the flasks was necessary to get fully liquid-gas equilibrium before humidity sensing test and the ambient RH was maintained at 25% by an automatic drier. The ideal flow chart of the humidity sensing test was indicated in Supplementary Figure All the humidity sensing tests were performed by a humidity sensing system  at an operating temperature of 25 °C. The voltage in humidity test was 1 V AC, and the testing frequency varied from 50 Hz to 100000 Hz. The humidity gradient was controlled by several erlenmeyer flasks filled with different saturated salt solutions which were LiCl, MgClThe humidity sensing materials were characterized by several techniques. The morphology was characterized by transmission electron microscope (TEM) . The surface properties were examined by FT-IR , Raman-scattering spectroscopy (HORIBA Jobin Yvon Raman microscope (LabRAM HR800) and X-ray photoelectron spectra .Supplementary information


We developed new technology for precise seawater temperature control to examine warming effects on communities of bacteria, microbial eukaryotes (protists) and metazoans. Despite highly contrasting phylogenies, size spectra and diversity levels, the three community types responded similarly to seawater warming treatments of +3°C and +5°C, highlighting the critical and overarching importance of temperature in structuring communities. Temperature effects were detectable at coarse taxonomic resolutions and many taxa responded positively to warming, leading to increased abundances at the community-level. Novel field-based experimental approaches are essential to improve mechanistic understanding of how ocean warming will alter the structure and functioning of diverse marine communities.Temperature variability is a major driver of ecological pattern, with recent changes in average and extreme temperatures having significant impacts on populations, communities and ecosystems. In the marine realm, very few experiments have manipulated temperature  Recent alterations in environmental conditions, principally caused by human activities, have rendered this goal more pressing since a better understanding of ecological processes is necessary to improve our ability to detect and predict the impact of future changes ,2. The mClimate change is driving the redistribution of species, reorganization of communities and restructuring of entire ecosystems at global scales ,12. WarmMarine communities are particularly useful models for global change ecology studies because (i) they are often extremely diverse and comprise highly divergent taxa, from microbes to metazoans; (ii) they often comprise taxa that are short-lived and fast growing and thereby responsive to experimentation; and (iii) they are dominated by ectotherms that are strongly influenced by their surrounding environment and thermal variability. In contrast to both terrestrial ,21 and fin situ temperature manipulations within natural marine habitats that support complex communities and diverse species pools. We tested the following hypotheses: (i) that increased temperature will alter patterns of diversity and community structure across multiple diverse biological groupings (i.e. from unicellular microorganisms through to metazoans), and (ii) that the pervasive community-level effects of increased temperature will be detectable at coarse taxonomic resolutions.We developed a novel experimental tool to conduct 2.(a)in situ with a heated settlement panel system (HSPS). Briefly, the HSPS comprised three sets of 10 replicate panels  mounted onto a fibreglass lattice frame . The first set of 10 panels were each heated with an electrical heat pad fixed to the underside of a stainless steel plate, and controlled by a series of microprocessors linked to a temperature sensor embedded onto each panel surface. The second set of 10 panels were heated in a similar way and a third set of 10 panels served as experimental controls held at ambient seawater temperature . The desired temperature increase for each treatment  was programmed into a shore-based control unit , which maintained constant communications to the microprocessor units to precisely control temperatures over the panels.Seawater temperature was elevated in situ seawater temperature within a highly-dynamic coastal marine habitat, with the desired warming treatments of +3°C and +5°C above ambient sea temperature maintained for 40 days (a–e). Temperatures over the experimental panels precisely tracked natural variability in ambient sea temperature, which was of the order of 3°C throughout the experiment and was related to tidal cycles and storm events  and the target treatments were maintained within ±0.2°C throughout the experiment .The field experiment was conducted within a marina in Plymouth, UK, between 10 October and 18 November 2013. An experimental settling surface (polyester fabric with a 700 µm pore size) was mounted onto each panel to provide a surface for colonization by marine organisms. A glass microscope slide was also secured to each panel, ensuring that it was mounted against the settling surface and within the heated boundary layer. The HSPS was deployed horizontally, suspended from a pontoon at approximately 2 m depth, with the plate surfaces facing downwards. Microbial and metazoan communities were sampled after 18 and 40 days, respectively, which allowed adequate time for biofilms and sessile invertebrates to cover more than 50% of all microscope slides/panel surfaces. Previous work has shown that assemblages of bacteria and protists associated with submerged substrata in marine habitats reach maturity within this timeframe –26, whil 40 days a–e. Tempm events . Experim(b)in situ manipulation, microbial diversity was assessed by biofilm DNA extraction and bacterial 16S and eukaryote 18S rRNA gene high-throughput sequencing. Biofilms were removed from five replicate microscope slides (selected at random from each treatment) using a sterile razor blade before DNA was extracted and stored at −20°C. Standard PCR and sequencing techniques were employed  and QIIME [2 subunits. The metazoan taxa within each 1 × 1 mm grid-square were identified to the lowest taxonomic level possible  and the total number of subunits within which each taxon occurred was summed for each sample to quantify areal cover. The total abundance  of each taxon was also recorded.After 18 days of nd QIIME  was usednd QIIME ,31. In snd QIIME  and remo(c)p < 0.05) pairwise tests were conducted to determine which treatments differed from one another. Univariate community-level metrics were examined with permutational ANOVA. Similarity matrices based on Euclidean distances between untransformed abundance and richness values were constructed prior to conducting 999 unique unrestricted permutations to test for differences between treatments. Where significant differences in multivariate community structure between warming treatments were detected (at p < 0.05), SIMPER analysis was conducted to determine which OTUs/taxa contributed most to the observed differences. All statistical procedures were conducted using PRIMER v7 software [Community-level response patterns were analysed using multivariate statistics. The relative abundances of the OTUs determined by molecular methods and the abundance/cover of taxa identified with traditional taxonomic approaches were analysed separately but in a directly comparable way. Abundance data were initially square-root transformed to down-weight the influence of highly abundant taxa before constructing Bray–Curtis similarity matrices. PERMANOVA  was usedsoftware  with thesoftware .3.a–c). PERMANOVA [d,e), as communities developed under warmed conditions were significantly different from those held at ambient temperature (f) and PERMANOVA tests did not detect significant between-treatment variability , suggesting that multivariate community structure, rather than multivariate dispersion (i.e. variability between plates), was impacted by increased seawater temperature.In total, 2465 and 388 distinct OTUs were determined for bacteria and protists, respectively, and 12 distinct metazoan taxa (mostly bryozoa and ascidia) were identified. Multivariate analyses showed that communities comprising bacteria, protists and metazoans responded analogously to warming, in that communities held under ambient conditions were distinct from those that had developed under the warming treatments a–c. PERMERMANOVA  tests shperature . For metperature f and PERiability . For alla–c) with, on average, communities comprising three to six times as many organisms/marker genes in the +5°C treatments compared with the controls. Variability between temperature treatments was significant for the bacteria and metazoan communities, but not for the microbial eukaryotes owing to high within-treatment variability . While this trend was apparent for all the groups, statistically significant differences between treatments were observed only for protists . For the protist communities, OTUs closely related to the diatom Melosira varians and the dinoflagellate Paradinium poucheti were less abundant at higher temperatures, whereas several other taxa  showed the opposite pattern and were important discriminators between treatments . The metazoan communities were dominated by colonial and solitary ascidians and cheilostome bryozoans, which were all more abundant on warmed panels compared with controls . The UK-native ascidians Diplosoma listerianum and Ciona intestinalis, and the non-native ascidian Corella eumyota, were major contributors to the observed dissimilarity between the communities, and were all more abundant on panels held at +3°C and +5°C compared with those held at ambient temperature .With regards to the responses of individual OTUs/taxa to warming, a SIMPER analysis was conducted to determine the variables that were the principal contributors to the observed dissimilarity in community structure between the treatments. For the bacterial communities, Community-level responses to increased temperature were examined across a range of taxonomic resolutions by consolidating species/OTU-level data according to their taxonomic position . For bacteria, partitioning in multivariate community structure by treatment was evident through to aggregation to order level . Order-level communities held at ambient temperature and at +5°C were significantly different, with overall dissimilarity at 21.8% , and, andin s between the temperature treatments, suggesting a high rate of turnover for these taxon-rich communities. Bacterial communities were particularly diverse and the number of unique taxa associated with each treatment was high (i.e. 250–400 OTUs); replacements of species in response to warming suggests some degree of selectivity or adaptation to temperature at the community-level. For the metazoans, however, there was no evidence of species turnover as multivariate communities based on presence/absence data did not differ between temperature treatments. Rather, shifts in metazoan community structure were related to changes in abundance rather than turnover of species or higher-order taxa. The higher richness of bacteria and protist communities and the greater diversity of the available species pool compared with metazoans may suggest that species turnover, and perhaps functional redundancy, in response to warming is comparatively limited for metazoans.The rate and trajectory of microbial biofilm development is likely to have mediated, to some degree, the development of macroscopic metazoan communities, as settlement of sessile invertebrates is known to be influenced by biofilm community structure ,54. For The most effective and reliable way of determining causation is through controlled experimentation , but manWhile other anthropogenic stressors, such as ocean acidification  and eutr


We propose and analyze nonlinear coupler based on a pair of single mode graphene-coated nanowires. Nonlinear wave interactions in such structure are analyzed by the coupled mode equations derived from the unconjugated Lorentz reciprocity theorem. We show that the routing of plasmons in the proposed structure can be controlled by the input power due to the third order nonlinear response of graphene layer. Our findings show that graphene nonlinearity can be exploited in tunable nanoplasmonic circuits based on low-loss, edgeless cylindrical graphene waveguides. Light control at the nanoscale is one of the important aims of the rapidly developing research field of nanophotonics. Conventional silicon-based photonic circuits cannot localize and manipulate light at the nanoscale due to the diffraction nature of light3689Graphene has nontrivial nonlinear properties. As an example, strong four wave mixing was experimentally observed in graphene, indicating that it exhibits third order nonlinear response17In this paper, we study the nonlinear coupling in a pair of single mode graphene coated nanowires. We first review the properties of the linear modes in a pair of graphene coated nanowires. Then, we derive the coupled mode equations for nonlinear coupling in the structure based on the unconjugated Lorentz reciprocity theorem and solve them numerically. Finally, we demonstrate the power-dependent behaviour of the GNW coupler that allows to route the output by input intensity, and we discuss the influence of structural parameters on coupling.R are covered by graphene, which is held in place by Van der Waals forceε = 2.5, and the spacing between the wires is d. For simplicity, it is assumed that the structure is placed in free space.Schematics of the structure is shown in σ = σ(1) + σ(3)|Eτ|2, where Eτ is the electric field tangential to the graphene surface. The linear surface conductivity σ(1) is a superposition of an interband and an intraband contributions, which are described by the Kubo formulaGraphene is characterized by its surface conductivity as e is electron charge, μc is chemical potential, kB is Boltzmann constant, τ is scattering time, and T is temperature. We assume that μc = 0.5, τ = 10 ps, corresponding to low loss high quality graphene1620T = 300 K.where σ(3) isThe third order nonlinear conductivityvf ≈ c/300 is the Fermi velocity.where A single graphene coated nanowire can support different order modes, which can be classified by azimuthal mode profileLB = π/Δβ with Δβ = βa − βs, βa and βs are propagation constants of odd and even modes, respectively.When two single-mode GNWs are placed parallel and are close enough, the hybridisation of the modes creates antisymmetric (odd) mode and symmetric (even) mode, as shown in the inset in em, hm} exp(−αmz) exp(iβmz), where βm is the wavenumber, αm is the attenuation of the m-th mode, {em, hm} are transverse profiles of electric and magnetic fields, and m is either “a” or “s”, denoting the antisymmetric or symmetric mode.The linear modes in GNW pair can be described as {z-invariant graphene plasmonic waveguide, the reciprocity theoremTo describe the nonlinear interactions, we aim to derive coupled mode equations23x0 denotes the position of graphene layer on the cross section of waveguide, JNL is the nonlinear surface current in graphene, {E, H} are the electromagnetic field in the presence of nonlinear current JNL, {eq, hq} is the q-th eigen mode of the waveguide, and S is the entire cross-section of the waveguide where the fields are present.where JNL does not modify the mode profile but only its amplitude, we can write electromagnetic field {E, H} in such waveguide as a superposition of linear modes. Considering the fact that GNW pair in this paper only supports two modes, we haveAssuming that the presence of As and Aa are the amplitude of even and odd modes with a dependence on z. Expressions for the magnetic field H can be written in a similar way.in which z component of electric field is much larger than other tangential field components. Then, the induced nonlinear surface current has only z-component and it is expressed asSince the even and odd modes originate from the hybridization of TM modes in a single GNWSubstituting (4) into (5), we haveAs(z). Substituting (4) and (6) into (3) and letting z-direction, we have amplitude equation for As asWe first consider the amplitude evolution of even mode where the nonlinear coupling coefficients areγ2, γ4 and γ5 vanish after integration. The amplitude equation for the odd mode Aa could be derived in a similar way. As a result, the coupled mode equations for the nonlinear coupling can be derived asDue to the field symmetry of even and odd modes, ηi can be calculated from (8) by replacing es,z− by ea,z−.where R = 50 nm, d = 150 nm and the working frequency is 30 THz. We calculate eigen mode profiles and their mode indices numerically using commercial simulation package COMSOL. We found that the mode indices of odd and even modes are 21.50 and 20.08, respectively, and this leads to the beat length LB of 3.52 μm.To show a particular example, we consider a pair of graphene coated nanowires with P0 is launched into the left nanowire. The input power in P0 = 0.01 mW, which is low enough so that all nonlinear terms in equations are negligible, i.e. at least 10 times smaller than linear terms. As the wave propagates, the energy couples from the left nanowire to the right one, with complete energy transfer after propagating a distance of LB. We note that the total energy decreases with propagation due to intrinsic loss in graphene. When the input power is large enough, the coupling changes due to graphene nonlinear response. P0 = 2.9 mW. After propagating a distance of LB, most of the energy still flows in the left nanowire with little coupling to the right waveguide. At the distance about LB/2, a small portion of energy is coupled into the right wire, and then coupled back to left wire while propagating toward LB.Pc0, = 2.29 mW, equal output power from both wires can be achieved. When P0 further increased to 2.9 mW, the power is predominantly localized in the left nanowire. It is worth noting that in our simulations, when the input power is 3 mW, the maximum electric field in graphene is 3 × 107 V/m, which is well below the breakdown threshold of grapheneTo further demonstrate the power dependence of the coupler performance, LB and vice versa, which can be inferred from LB, a longer coupler length means longer nonlinear interaction region, leading to larger nonlinear phase shift in the coupler and as a result smaller input power is required to achieve the nonlinear switching. At the same time, the length of the coupler cannot be too large, because of graphene absorption and finite plasmon propagation length. Thus we choose an intermediate spacing d = 150 nm in this work, which gives LB that is shorter than the graphene plasmon decay length, and long enough so that the nonlinear effects allow to observe the switching in the coupler.The spacing d has an important impact on the switching power. In conclusion, we have studied the nonlinear plasmon coupling in a pair of single mode graphene coated nanowires. Based on unconjugated Lorentz reciprocity theorem, we derived the coupled mode equations describing nonlinear wave propagation in such a structure. We demonstrated that the nonlinear switching of plasmons between the waveguides can be achieved by changing the input power, and due to the large third order nonlinearity and tight field confinement, relatively low power is sufficient for achieving efficient switching. Our results shows nonlinearity of graphene could be exploited as a new degree of freedom for designing active plasmonic devices based on cylindrical graphene structure.J and guided by a GNW pair satisfies Maxwell’s equationElectromagnetic wave excited by surface current x0 is the position vector of graphene layer on the waveguide cross section. The electric and magnetic fields {eq, hq} of the q-th eigen mode of GNW pair satisfies the source free Maxwell’s equations:where hq, eq}, respectively. Then, we multiply H, E}, and combining them we arrive at the reciprocity theorem in the formWe multiply By integrating both parts of this equation over a suitable volume and applying the divergence theorem, taking into account the bounded nature of guided wave, we derive J = σ(1)Eτ on the surface of nanowire.Finite element method (FEM) calculations for the linear modes in GNW pair are performed with the help of COMSOL Multiphysics modal analysis with perfectly matched layer (PML) enclosing the structure. Graphene is modelled as an electric field-induced surface current How to cite this article: Gao, Y. and Shadrivov, I. V. Nonlinear coupling in graphene-coated nanowires. Sci. Rep.6, 38924; doi: 10.1038/srep38924 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


We propose a novel plasmonic Bragg reflector (PBR) based on graphene with multiple-step silicon structure. The monolayer graphene bears locally variable optical properties by modulation of electric fields, and the periodical change of effective refractive index on graphene can be obtained by external bias voltage in the mid-infrared region. Through patterning the PBR units into multiple-step structures, we can decrease the insertion loss and suppress the rippling in transmission spectra. By introducing the defect into the multiple-step PBRs, the multiple resonance modes are formed inside the stopband by increasing the step number. This work may pave the ways for the further development of ultra-compact low-cost hyperspectral sensors in the mid-infrared region. Recently, graphene has become a popular research field and has attracted an increasing amount of attention . GraphenIn this paper, the plasmonic Bragg reflector (PBR) structure based on graphene with multiple-step units are numerically presented in the mid-infrared region. External bias voltages are applied to control the optical properties of the graphene, and the periodical modulation of effective refractive index on graphene is obtained to form a forbidden band on transmitted spectrum. The insertion loss is lower and the rippling in transmission is suppressed by increasing the step number of multiple-step structures. Furthermore, multiple peaks appear in the stopband while the defect introduces into the multiple-step PBRs. The finite element method (FEM) has been utilized to perform simulation work.biasV is applied between graphene and silicon grating, the periodic induced electric field can be obtained and modulated in graphene due to the periodic grating structure. This theoretic structure is not hard to build up experimentally. The grating silica/silicon substrate can be fabricated via electron beam lithography. The monolayer graphene can be prepared by the chemical vapor deposition method and then placed on the grating substrate. When a broadband mid-infrared irradiation is incident on the structure from the left interface, SPPs will be excited and propagate along the graphene layer. According the Bragg condition, only a narrowband of the spectrum can pass through the graphene waveguide and the rest will be reflected away., We set d(y), i.e., d1 and d2 labeled in 2 thin layer, which is a periodic step function of y, and it is far less than the width of the trench in the grating. We assume the field is given by the equation E(y) = εSiO2V/d(y); here, V = biasV, and εSiO2 is the relative permittivity of SiO2. Then, the surface charge density of graphene can be approximated by n(y) = [ε0E(y)]/e = ε0εSiO2V/ed(y); here, ε0 is the vacuum permittivity, and e is the elementary charge. Further, we can modulate the Fermi level according to fE(y) = fħν(sπn)1/2; here, fν = 106 m/s is the Fermi velocity of the electron in the graphene, and ħ is the reduced Planck constant. The Fermi level affects the scattering of electrons, characterized by the relaxation time τ = fμE(y)/feν2; here, μ is the carrier mobility. According to Kubo formula, the optical properties of a monolayer graphene can be characterized by a complex surface conductivity Gσ [The schematic of the PBR structure we proposed is shown in ivity σG :σG(y)=2Bk is the Boltzmann constant, T is the temperature, and w is the angular frequency of optical excitation. The first term and the second term correspond to intraband transitions and interband transitions, respectively, where bε and Gt are typically chosen as 2.5 and 1 nm, respectively. The optical parameters of other involved materials are εAir = 1, εSiO2 = 3.9, and εSi = 11.7 in the spectral region of interest. In our study, the values of voltages and the parameters of the structure are dependent on each other and should be reasonably chosen.In our work, the permittivity of graphene layer ,22,23 ca2-Si multi-layer structure. According to Maxwell’s equations and the boundary conditions, the general dispersion relation for the graphene waveguide in the multilayer structures can be solved by the following equation:η0 is the intrinsic impedance of free space. If ε1 = ε2= ε, the effective refractive can be reduced from Equation (3) as follows:We regard the structure as an Air-Graphene-SiOeffN  with bias voltage V and wavelength λ is well depicted in μ = 2 m2·V−1·s−1, and the real parts are invariant while the imaginary parts decrease dramatically as μ increases.The relation of the effective refractive eff-HN) and a low reflective index (eff-LN), as shown in biasV increases. Here, the parameters are set as d1 = 5 nm, d2 = 10 nm, w = 40 nm, and Ʌ = 80 nm.The loss of the graphene-based structure is mainly caused by the imaginary part of complex permittivity of graphene. Here, we firstly consider the real part of complex permittivity of graphene and its lossless style. When SPPs propagate in periodically modulating media, we can regard the graphene-based silica grating structure as a Bragg reflector made of alternating layers with a high reflective index (effN in the groove depth [N changing with deferent SiO2 thickness d and wavelength λ. After comparing the transmission spectra in Furthermore, we design the PBR units into the multi-step pattern to deal with the high insertion loss and severe rippling in the transmission spectra resulting from the abrupt change of ve depth ,25. The t = 100 nm, and the gate voltage gV = 2.2 V. From In order to make the practical application of this ideal platform more extensive, we designed one defect structure in the multiple-step PBR reflectors to realize the filtering, as shown in effN of graphene. This corresponds to the preceding statement. Another conspicuous feature is that the effect decreases with the increase in carrier mobility when it exceeds 20 m2·V−1·s−1. Thus, we can consider the losses negligible in this case, and it can be realized by using proper doping or compensating the gain media. In tλ and rλ in the case of biasV = 3.2 V in order to visualize the propagating principle of the excited SPPs.Finally, we need to consider the material losses of graphene. As shown in We have here presented some plasmonic Bragg reflectors based on graphene to realize the tunable low-loss filtering effect in the broadband multiple-step structures, and PBRs feasibilities have been adequately verified by FEM. By modulating the bias voltages of graphene, we can turn the transmission spectrum according to our need. Moreover, the transmission can reach above 80%, which means the losses are quite low as long as the carrier mobility is large enough. In addition, by designing the PRB units into multi-step structures, we can mitigate the insertion loss and suppress the rippling in transmission spectra. We introduced the defect structure in the multiple-step PBR reflectors to realize multiple filtering phenomena. These proposed designs were easily carried out in experiment. We hope they can help pave new ways in actively tunable sensoring applications.