Mott-VRH, Efros–Shklovskii-VRH and activation energy supported VRH. On the basis of transport behavior and confirmed by characterization analyses, the RGO film is modeled as GQD arrays where graphitic (sp2) domains behave as QDs and oxygenated (sp3) domains between interdots act as tunneling barriers. Temperature dependent resistance and current–voltage (I–V) characteristics indicate high sensitivity where sensor resistance changes by almost six orders of magnitude as the temperature is varied between 300 and 12 K. In convection mode, the developed temperature sensor shows a temperature coefficient of resistance (TCR) of ∼−1999% K−1 in the 300–77 K temperature range, which is much higher than the TCR values reported so far. Additionally, the sensor exhibits an extremely fast response (∼0.3 s) and recovery (0.8 s) time; and such high TCR leads to ultra high resolution of ∼ μK. The sensor shows excellent repeatability with negligible drift over several cycles. These studies are crucial for modern day thermal management and sensitive cryogenic applications.We report an ultra-sensitive temperature sensor derived from graphene quantum dots (GQDs) embedded in a self-standing reduced graphene oxide (RGO) film. The GQDs are obtained as a natural derivative during synthesis of GO to RGO. A fundamental study on low temperature transport mechanisms reveals the applicability of temperature zone specific ‘variable range hopping (VRH)’ conduction models,  We report an ultra-sensitive temperature sensor derived from graphene quantum dots (GQDs) embedded in a self-standing reduced graphene oxide (RGO) film. Irrespective of the dot size, graphene quantum dots (GQDs) possess distinctive graphene structures inside the dots which endows them with some of the remarkable properties of graphene;22–26 rendering them ideal systems for observing a novel quantum phenomena and building blocks for futuristic electronic devices in sensor applications.24,26,27 Nonetheless, the handling and device fabrication pose challenges, thereby necessitating the search for alternative graphene like materials which are easy to fabricate and possess properties close to graphene. An enormous quality research study is underway to search for such materials with mass production and ease of device fabrication.Since its inception, carbon nanotechnology has opened new prospects for the synthesis and analysis of one- and two-dimensional materials wherein fundamental physics converges with device applications.28–32 One such route is a chemical synthesis technique where exfoliated graphene oxide (GO) is produced in the first step and then reduced to obtain reduced graphene oxide (rGO).33–36 This technique is most suitable for economic and large scale synthesis of rGO. Despite having lowered electrical and thermal properties in comparison to pristine graphene, rGO is interesting owing to its unique properties which are an amalgamation of properties similar as well as dissimilar to those of pristine graphene.37,38 Various functional groups, such as epoxy (–O–) and hydroxyl (–OH) covalently attach on the basal plane, whereas carboxylic (–COOH) and carbonyl  scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg>O) groups primarily append at the sheet edges.18 Besides, sp3 → sp2 carbon transformation in rGO is not hundred percent complete and causes considerable defects or disorders which can trap charge carriers.39 Additionally, the conductivity of an rGO film can be controlled by varying the film thickness which in turn depends upon the synthesis parameters .3 Thus, rGO has gained immense scientific interest due to – (a) large scale production; (b) modifiable properties which can be controlled by tuning the sp2 to sp3 carbon ratio; and (c) a possibility of further modifying the rGO structure (and hence its properties) via functionalization with different nanoparticles as well as organic molecules.40 This behavior is attributed to the presence of significant disorders on the rGO films. Thus, a comprehensive study needs to be undertaken to analyze the effect of synthesis conditions on all these parameters.The most successful routes to synthesize such materials rely on using graphite as the precursor.41–43 Although a substantial amount of work has been done on the carrier transport mechanism in graphene,17,38,44–48 hardly any studies are available on the development of temperature sensors either from graphene or GQD arrays, exploiting their carrier transport properties. In the case of carrier transport in graphene or 2D materials, the current flow is controlled by scattering events;49–52 whereas in GQDs, the current will exist only if carriers overcome the Coulomb blockade potential.53,54 The temperature dependent differential change in resistance/current in the circuit is the key factor for the development of a highly sensitive temperature sensor. Two major sensing parameters, i.e. temperature coefficient of resistance (TCR) and thermal hysteresis (HTh) are very crucial since both of them control the quantification of heat sensing characteristic parameters including sensitivity, response- and recovery-time, resolution and repeatability. Interestingly, not many reports are present on GQD based temperature sensors and no concrete report is presented so far.Interestingly, much of the initial work on graphene based devices, in general, has been on field effect transistors (FETs). Development of a smart temperature sensor from graphene is a relatively new area of research.vis-à-vis its room temperature value. The rGO film has been converted into nanometre sized graphitic zones (termed as graphene quantum dots or GQDs) caged within insulating zones (barrier). The GQDs, distributed in disordered arrays, demonstrate elevated temperature (T ∼ 300 K) activated hopping, and direct quantum tunneling depending on the spatial interdot separation and operating temperature. The temperature sensor fabricated from this film shows TCR values of −7946.5% K−1 and −1999.8% K−1 in the temperature ranges of 300–12 K and 300–77 K, respectively. Such a high TCR leads to the achievement of ultra-resolution of ∼ μK, and a response-and recovery-time of 0.3 s and 0.8 s respectively in convection mode. Cycling tests conducted at 77 K show negligible drift over several cycles. Additionally, this work presents an example of reversible metal–insulator transition in a solid-state system implying the prospects of producing novel solid-state materials, based on GQDs, wherein the electronic band structure of the material can be ‘tailored’ by adjusting the electronic wave function overlap between neighboring dots.In this article, we present the rich transport phenomena observed in the rGO/alumina self-standing film where the resistance can be tuned over more than six orders of magnitude 2.2.1.via a chemical conversion technique has surfaced as a convenient method to produce graphene like monolayers with considerable yield.51,52,55 Modified Hummers’ method based oxidative treatment of natural graphite flakes was performed to prepare graphite oxide.56 Unlike pristine graphite, GO is highly oxidized having oxygenated epoxide bonds on the basal plane, whereas carbonyl and carboxyl groups are present at the sheet edges. Single- to few-layered GO sheets were obtained by exfoliation of graphite oxide in DI using a bath sonicator (90 minutes). The presence of oxygen moieties makes GO sheets readily soluble in DI, resulting in stable aqueous suspensions of nearly 1 nm thick sheets under mild sonication conditions.57 GO was then washed and dried under ambient conditions. Chemical reduction of the GO powder was carried out by dissolving 500 mg GO in 500 mL DI followed by 1 h of sonication. To this mixture, 50 μL hydrazine hydrate was mixed and the solution was refluxed at a constant temperature (98 °C) for varying time periods . At this point, the color changed from yellowish brown to black, indicating the conversion of GO to rGO. The RGO powder was obtained by suction filtering and washing the suspension with DI followed by subsequent drying at 60 °C in an inert (N2) ambient atmosphere for 10 h.51,52 The reduction process removes epoxy groups from the basal plane resulting in regeneration of C–C bonds.55Transformation of graphite to graphene oxide (GO) 58 where three out of four valence electrons from each C atom are covalently bound to three neighboring C atoms. These bound electrons are called σ-electrons and the corresponding bonds are termed as σ-bonds or C–C bonds. The fourth electron from each C atom (termed as π-electron) is free to move around the graphene layer, and thus acts as a charge carrier. In the hexagonal lattice of graphene, these π-electrons occupy six individual corners of the first Brillouin zone .58 During oxidation, each oxygen (O) atom uses two π-electrons from two C-atoms and meets its octet requirements, thereby forming epoxy bonds. However, neither σ-electrons nor σ-bonds are altered to fulfill octet requirements because the bond energy of C–O (358 kJ mol−1) is much lower vis-à-vis C–C (473 kJ mol−1) in graphene.58–60 Four types of oxygenated functional groups exist in GO, these are61To understand the mechanism of the above reactions, we can consider a graphene lattice,(a) Epoxide (–O–): attached to the basal plane.(b) Hydroxyl (–OH): along the basal plane.O): distributed along the edges.(c) Carbonyl (–C(d) Carboxyl (–COOH): attached to the edges.38 During reduction by a chemical route or thermal treatment, the C–O bonds break, thereby again freeing the π-electrons. In this way, graphene oxide recovers its pristine state by clearing the oxygen debris created during the oxidation process. Further, there was no apparent involvement of either σ-electrons or σ-bonds (or C–C bonds) unless aggressive oxidation/reduction is pursued. Thus, the graphene lattice is usually intact and no considerable point defects are introduced. Additionally, carbonyl and carboxyl groups cannot be removed without destroying the graphene basal plane.62Out of these, epoxide and hydroxyl groups dominate the total oxygen functionalities glued to GO sheets.3 to sp2 bonding. The graphene region (sp2 bonding) shows semi-metallic behavior whereas the remaining sp3 region is insulating. Physical properties of rGO are considerably affected by the sp2/sp3 hybridized carbon ratio, and the presence of different functional moieties. Therefore, the electrical conduction process through the rGO sheet becomes complex and it shows the Coulomb blockade phenomena for charge transport, specifically at low temperatures.23,55,63,64 Growth of QDs in the rGO film have been studied by several groups,23,63–66 where the degree of reduction during the synthesis process and atomic scale features are studied and characterized with various microscopic and spectroscopic techniques.GO to rGO transformation leads to the creation of graphene quantum dots (GQDs) within the rGO maze and their sizes vary from a few nm to 100 nm depending on the degree of conversion from sp2.2.2.2.1.3 defects alter the intrinsic π state of sp2 sites,35,55,62,67,68 residual sp2 carbon fraction becomes a vital clue for rGO sheets and is often described as the reduction efficiency. O bond ; and (4) the C(O)O or OC–OH bond . 2 carbon fraction, while the oxygenated functional moieties present at the basal plane of the sheets and sheet edges signify sp3-hybridized carbon fraction.38,67,69,70 The sp2 carbon fraction is obtained from the ratio of the integrated peak areas of the C–C peak to the total area under the C1's spectrum:40The changes in carbon and oxygen groups were analyzed by studying the gradual removal of oxygen containing functional groups by employing X-ray photoelectron spectroscopy (XPS). Since sp2 carbon fraction (or reduction efficiency) of the rGO sheet increases from ∼57% for GO (0 h) to ∼80% for rGO treated for 5 h.The results in 2.2.2.2 C–C bonds. Nevertheless, a continuous sp2 phase is not observed, rather these sp2 zones remain spatially separated by ‘islands’ of sp3 clusters. Thus, the reduction of GO creates numerous sp2 clusters of small size.71 This has been observed by Erickson et al. in 2 zones appear to be surrounded by disordered sp3 domains. The size of these sp2 zones is found to vary from 2 to 6 nm.As indicated in 2.2.3.−1, 1580 cm−1, and ∼2700 cm−1 respectively.38,73 The D-band arises from disorders in atomic arrangement, edge effects, ripples, or charge puddles of the graphene sheet. A broad D-band having a higher intensity than that of the G-band signifies high disorderness of rGO. The G-band arises from in-plane vibrations of sp2-hybridized carbons, whereas the 2D-band is due to the second order Raman scattering and is particularly dominant in graphene vis-à-vis bulk graphite.74 The intensity ratio (ID/IG) of D- and G-bands can be used to quantify disorders, as represented by the sp2/sp3 carbon ratio.73,7538,40,76 relating the ID/IG ratio and crystallite size of the graphitic sp2 domain, it is found that the rGO sheets embrace ordered graphitic zones having a size of 3.3 nm (for 5 h reduction treatment) enclosed by domains of oxidized carbon atoms or point defects.38The most remarkable peaks in Raman spectra of graphene and alternative graphitic materials comprise D, G, and 2D peaks at ∼1350 cm2.2.4.51,52 This peak initially remains narrow with increasing reduction time up until 3 h of treatment and then starts broadening .Chemical reduction was performed to convert GO to rGO and the degree of transformation was monitored by XRD studies . As can 2.2.5.51 The gradual increase in areal intensity endorses the gradual restoration of C–C bonding with the increase in reduction time. Besides, there is a gradual red-shift of the peak at 239 nm (corresponding to GO) with the reduction time and the shift virtually saturates at the reduction time of around 5 h (267 nm).51,52UV-Vis spectra, obtained at different reduction steps of the chemically driven sample are shown in 2.3.via sol–gel processing where 0.6 wt% rGO powder was mechanically added to 99.4 wt% alumina sol as elaborated in The rGO (treated for 5 h) prepared in Section 2.1 was used for temperature sensor fabrication. A self-standing film of the rGO/alumina nanocomposite was prepared 3.3.1.I–V) characteristics of the fabricated sensor were studied in a four probe helium cryostat (Oxford Instruments) where cryo-pumping maintained the base pressure at ∼10−7 torr throughout the experiment. Proper care was taken to prevent stray radiation and condensation of residual moisture on the rGO film mounted inside the cryostat. The system is designed for low current measurements of the order of picoampere (pA). Current and voltage sweeps were programmed via LabView programme.Current–voltage  and disorders in rGO.38,80–89 Several distinct regions are clearly observed (as demonstrated in the TEM image), similar to disordered semiconductors.80,85The resistance i.e. Mott variable range hopping (Mott-VRH),38,86 Efros–Shklovskii variable range hopping (ES-VRH),55,87,88 and thermal activation52,89 supported VRH – also known as Arrhenius type; and these are highly temperature zone specific. While looking into the theoretical aspects, there is a serious disagreement on the temperature scaling factor even for an identical temperature range; and this is because the results were obtained with different sample geometries, reduction processes, reduction temperatures and the reduction routes.88 Therefore, the transport mechanism in GQD arrays in rGO is an important subject in the field of graphene based electronics.On the basis of XPS and Raman data, the rGO sheet may be considered as a two-dimensional (2D) array of GQDs where graphitic domains behave as QDs while oxygenated domains act as tunnel barriers. The temperature dependent charge-transport mechanism in rGO is generally supported by three independent theories, 38,88 theory relies on three specific criteria – (1) localized defect states are created near the Fermi level during GO to rGO conversion; and carrier transport occurs via hopping from one state to another by taking energy either from a phonon or directly from an externally applied electric field; (2) at low temperatures, the nearest neighbor hopping will not dominate; rather the hopping electron will always try to achieve the lowest activation energy (ΔE) and shortest hopping distance. Generally, these two conditions are not fulfilled simultaneously; and there exists an optimum hopping distance rhop, corresponding to maximum hopping probability; and (3) there is no Coulomb interaction between the localized states. The standard Mott-VRH relation is:40R0 is the resistance at zero temperature and T0 is the Mott characteristic temperature. The scaling factor p depends upon the dimensions of the system or the shape of density of states (DOS).85,90 This concept results in the mathematical expression where R is proportional to (T)−1/3.The temperature dependent resistance behavior shown in via hopping and there is Coulomb interaction among carriers. This idea results in R scaling as (T)−1/2.40,90Efros–Shklovskii (ES) proposed that defect states are localized around the Fermi level, transport occurs p turns out to be −1 in the case of Arrhenius theory of activation energy induced hopping and the relation is:80The scaling factor  in the temperature range of 90–12 K pointing toward ES-VRH as a plausible charge-transport phenomenon in rGO layers consisting of GQD arrays. The other fitted values are  in the 146–90 K temperature range where Mott-VRH is applicable. The temperature range of 273–146 K comes under Arrhenius type of conduction via activation energy where p = 1 is the best fit. The temperature range of 100–80 K may be a mixed zone where ES-VRH and Mott-VRH both coexist.Best linear fits were obtained for ξ by plotting R against T−1/2 in a semi-log scale  as a function of temperature. Here, ΔR = R(T) − R; R denotes resistance at 273 K and R(T) denotes the resistance of the sensor at a set temperature of T °C. The sensor exhibits an exceptionally high change in resistance leading to an ultra high TCR.The ES-VRH suggests strong localization of wave functions in GQDs. Its further analysis allows the calculation of localization length og scale .91–93 Fr3.2.The temperature sensing measurement was conducted in a two probe system fitted with a liquid nitrogen closed cycle system . The hea3.3.i.e. thermal (κ) and electrical (σ) conductivities; and both are responsible for heat and carrier transportation. Their interdependence determines the temperature coefficient of resistance (TCR) and thermal hysteresis loss (HTh), thereby playing an important role in efficient operation of the sensing device. A large TCR is critical for improved sensitivity, resolution, drift, and response- and recovery time.52,77,78In a temperature sensor, we encounter two interdependent physical properties of the material, i.e. convection mode  the de-Haas van Alphen effect under a magnetic field,96 and (3) the superposition of a group of waves having slightly different wavelengths from each other while propagating in a medium.97 Notably, the oscillations of the envelope as well as the wave pattern within the envelope were found periodic and their frequency of oscillation is 0.61 Hz and 10 Hz respectively. In the present case, the first two cases can be discarded as there is neither a high frequency (carrier wave) source nor any magnetic field applied to the sensing system. Therefore, the last one is most probable and needs further explanation.Interestingly, the envelope pattern resembles – (1) the superposition of the signal and carrier frequency as seen in the case of amplitude modulation in communication engineering,98–100 The ratio of interdot separation (D) and size (L) of the QD clearly measures the coupling strength between dots. While assuming L as a constant, the coupling strength may be classified in terms of D as followsWe propose these to be the Coulomb blockade effects in terms of tunneling strength between coupled dots. The type of coupling between QDs influences the character of the electronic states and the nature of transport through the QD array. Theoretical prediction supports phase transition for tunneling in coupled GQD arrays.D, there is no interaction among QDs and the CB of individual QDs is preserved, i.e. the number of peaks is equal to the number of energy levels within a single QD. Each peak signifies the addition of electrons to the array, one to each GQD at the same time.(1) For a large D is the intermediate, the CB of individual QDs is lifted and a combined CB effect across the QD array materializes.(2) When D, original energy levels split in subbands; these will cross each other and the CB will be completely diluted.(3) For low 99–104Due to the formation of high density QDs, there is a possibility to have charge particle interactions between the dots and it may be supported from the second point (2) mentioned above. As a result, the de-Broglie waves of the electrons may superimpose and the oscillatory envelope function may evolve from individual to collective electronic states in a dense QD assembly.D and L parameters are taken as average values because GQDs were self-created as a consequence of the chemical reduction process. Therefore, the Coulomb force due to interdot coupling is affected by the dilution effect arising from high temperature as well as high bias voltage. At a high bias voltage, the charging energy becomes negligible and the envelope disappears. Similarly, when the temperature is very high, the Coulomb force between GQDs gets diluted resulting in the disappearance of the envelope.In our case, 3.5.T) is micro-tuned to reach the targeted temperature and the corresponding change in resistance (ΔR) was measured accordingly. At a target temperature of 77 K, ΔR was found to be ∼MΩ for ΔT = 0.1 K (after extrapolation of the curve). For a gross simplification, if we assume the measurable signal strength to be within the 10–100 Ω range, then the signal obtained for ΔT = 0.1 K will correspond to a measurable temperature resolution of ∼μK. Measurements beyond 0.1 K change could not be carried out in our system due to the limitation of the temperature controller employed. As seen from 105–117 A comparison is presented in vis-à-vis some of the recently reported temperature sensors.The performance of the fabricated sensor is found to be much better than most of the existing sensors reported so far.4.i.e. an ultra-sensitive low temperature thermometer with very high resolution along with the fastest response and recovery. TCR and thermal hysteresis loss are considered as the main characteristic parameters that control the quality of most of the sensor parameters and the sensor ratings. Surprisingly, both of them appeared to be exceptional in making the sensing device ultra-sensitive both in cryogenic and high temperature measurement. The cycling test revealed that the sensor is stable for over 50 cycles with an ultra-fast response time. A highly sensitive temperature sensor is in great demand for cryogenic temperature measurements in industry, healthcare, and R&D laboratories; and the proposed sensor may fulfill the need of these sectors. An interesting fact is that the sensors are cheap and reproducible. Identical rGO sheets were developed and tested successfully to justify our claim. Further, no such comprehensive report is available in the literature on the development of graphene quantum dot based temperature sensor to date.GQD arrays, developed in the rGO film during the chemical reduction process from GO to rGO, act as a maze where poly-dispersed graphene QDs are semimetallic, and the interdot space is an insulating zone, called the tunnel barrier. Coulomb blockade and charge carrier tunneling through the tunnel barrier is largely associated with metal and semiconductor quantum dots. The carrier transport is explained using suitable VRH models where the ESH–VRH model fits well in the low temperature regime. The average QD size or the electron localization length was found to be ∼3.2 nm. This has allowed us to translate these phenomena into making a real device, There are no conflicts to declare.


Polyimide matrix nanocomposites have gained more attention in recent years due to their high thermal stability, good interfacial bonding, light weight, and good wear resistance and corrosion, factors that make them find great applications in the field of aerospace and advanced equipment. Many advancements have been made in improving the thermal, mechanical, and wear properties of polyimide nanocomposites. The use of nanofillers such as carbon nanotubes, graphene, graphene oxide, clay, and alumina has been studied. Some challenges with nanofillers are dispersion in the polymer matrix and interfacial adhesion; this has led to surface modification of the fillers. In this study, the interaction between clay and graphene to enhance the thermal and thermal-oxidative stability of a nanocomposite was studied. A polyimide/graphene nanocomposite containing ~12.48 vol.% graphene was used as the base nanocomposite, into which varying amounts of clay were added (0.45–9 vol.% clay). Thermogravimetric studies of the nitrogen and air atmospheres showed an improvement in thermal decomposition temperature by up to 50 °C. The presence of both fillers leads to increased restriction in the mobility of polymer chains, and thus assists in char formation. It was observed that the presence of clay led to higher decomposition temperatures of the char formed in air atmosphere (up to 80 °C higher). This led to the conclusion that clay interacts with graphene in a synergistic manner, hence improving the overall stability of the polyimide/graphene/clay nanocomposites. Polyimide is an engineering polymer that finds wide applications in the fabrication of aircraft structures and microelectronic devices. It has excellent chemical, thermal, and mechanical properties . In receThe incorporation of small loadings of nanoscale filler materials in polymers leads to substantial property enhancements compared with the conventional micro- and macroscale composites ,5. The s2 hybridized carbon atoms [Among the various nanofillers, graphene has been widely used due to its exceptional thermal, mechanical, and electrical properties ,7,8,9. Ion atoms ,11. Grapon atoms ,13,14. Gon atoms .Liu et al. investigated the gas barrier and thermal properties of polyimide/graphene and polyimide/clay nanocomposites. They reported decreased water vapor transmission rates and improvement in the temperature of decomposition at 5% weight loss, concluding that addition of these nanofillers improved the thermal stability of the polyimide ,17. WangPolymer/layered silicate nanocomposites have attracted attention because of the improvement in material properties when compared with the virgin polymer. These include increased strength and heat resistance ,18, highVarious property enhancements due to the addition of graphene and clay nanoplatelets have been studied and reported. The thermal stability studies have mostly focused on the temperature at onset of decomposition as a determinant factor for the improvement or lack thereof in thermal stability. However, few/no studies exist that delve further into the mechanisms of decomposition of these polyimide/graphene or polyimide/clay nanocomposites. Factors such as the effect of the nanofillers on the rate of decomposition of the polymer and the nature and mechanism of char decomposition have not been thoroughly investigated. These are important factors in elucidating the mechanism of flame retardancy.In previous research work by this group, polyimide/graphene nanocomposites containing 10–50 wt.% graphene were fabricated, and various properties were reported ,30,31. TNanographene sheets of 98.48% purity were purchased from Angstron Materials, Dayton, OH, USA. The graphene sheets were 70–100 nm thick, with a lateral dimension of 2–7 µm. Montmorillonite organomodified Cloisite 30B clay of 100–200 nm thickness and 2–13 µm length was purchased from Southern Clay Products, Inc., Gonzales, TX, USA. Ammonium ion (III) was used in the Cloisite 30B, as the exchange cation is methyl, octadecyl, and bis-2-hydroxyethyl ammonium ion. 4,4-oxydianiline (ODA-98% purity), pyromellitic dianhydride (PMDA-99% purity) and N-methyl-pyrrolidone (NMP-99% purity) were purchased from Sigma Aldrich, St. Louis, MO, USA.The fabrication of the composites occurred in three stages. The first step involved the synthesis of the poly(amic acid)–graphene solution. The second step was the synthesis of the poly(amic acid)–clay solution. In each case, 5.2 g of ODA was added to 100 mL of NMP contained in a round bottomed flask, followed by mechanical stirring until the solid completely dissolved in the solvent. For the poly(amic acid)–graphene solution, a graphene quantity corresponding to ~12.48 vol.% of total solids was added to the ODA solution, and stirring continued for 8 h. PMDA was then added to the mixture, followed by stirring for 12 h, the temperature of the reactants being maintained at 10 °C. For the poly(amic acid)–clay solution, varying concentrations (0.45–9 vol.%) of clay were added to each solution, and the rest of the process was similar to that of the poly(amic acid)–graphene synthesis procedure. These solutions were then mixed in varying concentrations of clay (0–9 vol.%), while maintaining the amount of graphene at ~12.78 vol.%. The poly(amic acid)–graphene–clay mixtures were cast onto a glass substrate, followed by curing in a vacuum at 120 °C for 2 h and 200 °C for 1 h to obtain PI/graphene/clay composite films (PI–NGS–Clay).Thermogravimetric analysis was used to investigate the mechanism of decomposition and thermal stability. The films were subjected to heating rates of 30 °C/min from 25 °C to 1000 °C in air and nitrogen in a TGA Q50 Thermal Analyzer purchased from TA Instruments, New Castle, DE, USA.The films were subjected to heating rates of 10 °C/min from 25 °C to 725 °C in nitrogen atmospheres in a DSC Q20 thermal analyzer purchased from TA instruments, New Castle, DE, USAEnvironmental Scanning Electron Microscopy (ESEM), model FEI XL30 FEG , was used to study the morphology of the composite films. The samples were coated with silver to improve their conductivity.Scanning electron microscopy with EDAX was used to obtain images of the decomposed char in nitrogen and its elemental composition. This is shown in The thermogravimetric degradation profiles of neat PI, graphene and Cloisite 30B clay are shown in In the The char performance of the 0.45 vol.% clay sample is similar to that of the 0 vol.% clay sample, but the former also shows a remarkable increase in the onset temperature of degradation. This goes to show that with just a small quantity of clay added to the PI-NGS nanocomposite, the thermal stability of the system can be enhanced, while saving on cost and preserving and/or improving on the properties of the polymer, because the dispersibility of the filler in the matrix is still excellent at low loadings of clay.max) is observed to have an overall shift towards higher temperatures. This is shown in The temperature at maximum rate of decomposition  is shown to decrease in height with increasing clay content, confirming the effect of clay on the degradation of graphene char. The decrease in the rate of degradation of graphene is therefore as a direct result of the increasing clay content.The rate of degradation of the three regions in air is shown in x group in clay.DSC thermograms in The behavior of the samples in air as measured by DSC is different from the nitrogen trends shown above. In The temperature at the onset of degradation, shown in The thermal degradation and stability of PI–NGS–Clay nanocomposites was investigated. Cloisite 30B clay was observed to maintain high char content (~70%) in both inert and oxidizing atmosphere. The addition of clay to the PI–NGS nanocomposite was shown to increase the onset degradation temperature in both atmospheres, as observed in TGA and DSC. The thermal stability of the system is therefore enhanced by the addition of clay. DSC results show that the char for the hybrid system degrades at much higher temperatures. A synergistic effect is observed between graphene and clay, as the presence of clay tends to shift the temperature of degradation of graphene char to higher temperatures. It was demonstrated that incorporating a small amount of clay into a polyimide–graphene system would enhance the thermal stability of the system. Higher loadings of graphene to achieve greater stability would decrease the ability of the nanofiller to homogenously disperse in the polymer matrix. This would lower certain characteristics of the system. This can therefore be counteracted by using a graphene quantity to the level of optimal dispersibility and then adding small quantities of clay to further enhance the thermal stability of the system without affecting the dispersibility of the nanofillers in the polymer.


The process of handling and bonding copper (Cu) and graphene inevitably creates defects. To use graphene/Cu composites as electronic devices with new physical properties, it is essential to evaluate the effect of such defects. Since graphene is an ultrathin anisotropic material having a hexagonal structure, an evaluation of graphene/Cu composites containing defects was conducted taking into account the inherent structural characteristics. The purpose of this study is to evaluate defects that may occur in the manufacturing process and to present a usable basic method for the stable design research and development of copper/graphene composites essential for commercialization of copper/graphene composites. In the future, when performing analytical calculations on various copper/graphene composites and defect shapes in addition to the defect conditions presented in this paper, it is considered that it can be used as a useful method considering defects that occur during application to products of desired thickness and size. Herein, density functional theory was used to evaluate the behavior of graphene/Cu composites containing defects. The density of states (DOS) values were also calculated. The analysis was implemented using three kinds of models comprising defect-free graphene and two- and four-layered graphene/Cu composites containing defects. DOS and Fermi energy levels were used to gage the effect of defects on electrical properties. PB. PB26]. The analysis model was constructed for a total of eight cases as a The laminate was modeled according to In the case of four layers, the graphene layer into which the defects were inserted was changed. We subdivided the case to compare the following designs: four layers with the defects inserted into the external graphene layer (first layer) a, four lx-axis corresponds to the energy level function value and the y-axis to DOS value. DOS value of point A (the maximum)   compared with graphene. In addition, in model 2, DOS value was negligible before point C  are presented in −1 and that of point B  and point B is lower by about 50.63% (17.44 e/eV−1). This implies that defects in graphene reduce DOS value and decrease electrical performance.Defects were inserted into the graphene layers of the two- and four-layered graphene/Cu composites to establish the effect of defects on DOS analysis. −1 and point B  and point B is higher by 2.74 e/eV−1 (about 6.61%). Although defects in the external graphene layer (first layer) in the four-layered graphene/Cu composites led to a slight decrease in DOS value of point A, defects are nevertheless considered to play a doping-like role and increase electrical performance at point B.To examine the effect of these defects in detail, DOS analysis of model 7 was performed by fixing the number of layers and changing only the defect location. Accordingly, the defects were inserted into the middle graphene layer (third layer) instead of the external graphene layer (first layer). The results are presented in −1 and point B (−1. Compared with −1 (about 13.95%), and point B by 6.31 e/eV−1 (about 15.22%). In the four-layered graphene/Cu composites, defects in the middle graphene layer (third layer) led to a slight decrease in DOS value of point A, similar to the effect of defects in the external graphene layer (first layer). However, DOS of point B indicates that defects play a doping-like role, facilitating electron movement and thereby improving electrical performance.DOS analysis results of model 7 are presented in The behavior of points A and B in DOS analyses was examined in detail using model 8 c. In thi−1 and point B  is −0.00053 e/bohr3. In model 8, the range of ρ(max) is 12.009 e/bohr3 and ρ(min) is −0.00056 e/bohr3. Therefore, it can be seen that the charge density decreases by about 3.167% based on ρ(max) due to defects. It is judged that this is because the existence of defects prevented the bonding force of electrons.Molecular weight(MW) of model 5 is 4906.88 g/mol. MW of model 8 is 4762.75 g/mol. In model 5, the range of ρ(max) is 12.402 e/bohrDOS analyses of graphene/Cu composites containing defects were conducted to estimate the effect of defects on electrical performance. The following conclusions were drawn:DOS behavior of the two-layered graphene/Cu composite (model 3) resembled that of a single graphene layer, i.e., a stable DOS value was observed across the entire section of Compared with the two-layered case, DOS value of the four-layered graphene/Cu composite was higher by ≥14.69% at point A, having the highest DOS value. Notably, DOS values over the sections In the two-layered graphene/Cu composite with graphene defects, point A was lower by about 22.85%, and point B by 50.63%, compared with the defect-free two-layered graphene/Cu composite.In the four-layered graphene/Cu composite containing defects in the external graphene layer (first layer), DOS value at point A was about 3.47% lower than that of the defect-free four-layered graphene/Cu composite, while DOS value of point B was higher by about 6.61%. In the four-layered graphene/Cu composite containing defects in the middle graphene layer (third layer), point A was lower by about 13.95% and B was higher by about 15.22%. Therefore, it is likely that the presence of graphene defects in four-layered graphene/Cu composites decreases the number of electrons at point A, and plays a doping-like role to increase electrical performance at point B. In addition, the external graphene defects performed better than the middle graphene defects at point A, but not at point B.In the four-layered graphene/Cu composites containing defects both in the external graphene layer (first layer) and middle graphene layer (third layer), DOS value of point A was only about 0.05% lower than that for the defect-free four-layered graphene/Cu composite, while the value of point B was about 25.42% higher. This means that the graphene defects decreased the number of electrons at point A and played a doping-like role, facilitating movement of electrons at point B and thereby improving electrical performance.This study presents basic data concerning the electrical behavior of graphene/Cu composites containing defects. Understanding the influence of such defects is essential for the commercialization of graphene, and to control the physical properties of graphene/Cu composites.


The incidence of complications in Gram negative bloodstream infection (GN-BSI) is not clearly defined. This retrospective cohort study evaluates the incidence of complications within 90 days of GN-BSI and examines the predictors for these complications.Hospitalized adult patients with monomicrobial GN-BSI at Prisma Health-Midlands hospitals in South Carolina between 1/1/2012 and 6/30/2015 were evaluated. Complications of GN-BSI were defined as endocarditis, septic arthritis, osteomyelitis, spinal infections, deep seated abscesses, and recurrent GN-BSI within 90 days of the initial episode. Clinical and microbiological variables were assessed as potential risk factors for complications, including initial response to antimicrobial therapy within the first 72-96 hours of GN-BSI using the early clinical failure criteria. Kaplan-Meier analysis and multivariate Cox proportional hazards regression were used to examine the incidence and risk factors of complicated GN-BSI, respectively.Escherichia coli was the most common bacteria . Overall, 13.9% developed complications within 90 days of GN-BSI. The median time to identification of these complications was 5.2 days from the index GN-BSI (interquartile range 1-28 days). The incidence of complications was notably higher in BSI due to Serratia species (39.7%), Proteus mirabilis (35.7%), and Pseudomonas aeruginosa (21.5%) than other bacteria . Independent risk factors for complications included early clinical failure criteria, non-urinary source, presence of indwelling prosthetic devices, BSI due to Serratia species, P. mirabilis or P. aeruginosa, and persistent GN-BSI (Table).A total 752 patients with GN-BSI were included in the study. The median age was 66 years and 380 (50.6%) were women. The urinary tract was the most common source of GN-BSI  and At least 13.9% of patients developed complications within 90 days of GN-BSI. Risk of development of complications may be predicted by specific host and microbiological factors. Stratification of patients based on these risk factors may aid in identifying patients requiring further diagnostic work up for early detection of complications.Julie Ann Justo, PharmD, MS, FIDSA, BCPS-AQ ID, bioMerieux: Honoraria|bioMerieux: Honoraria|Entasis Therapeutics: Advisor/Consultant|Entasis Therapeutics: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Merck & Co: Advisor/Consultant|Merck & Co: Advisor/Consultant|Shionogi: Advisor/Consultant|Shionogi Inc.: Advisor/Consultant|Spero Therapeutics: Honoraria|Spero Therapeutics: Honoraria|Vaxart: Stocks/Bonds Julie Ann Justo, PharmD, MS, FIDSA, BCPS-AQ ID, bioMerieux: Honoraria|bioMerieux: Honoraria|Entasis Therapeutics: Advisor/Consultant|Entasis Therapeutics: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Merck & Co: Advisor/Consultant|Merck & Co: Advisor/Consultant|Shionogi: Advisor/Consultant|Shionogi Inc.: Advisor/Consultant|Spero Therapeutics: Honoraria|Spero Therapeutics: Honoraria|Vaxart: Stocks/Bonds P. Brandon Bookstaver, PharmD, Spero Therapeutics: Advisor/Consultant.


In recent decades, chemiresistive gas sensors (CGS) have been widely studied due to their unique advantages of expedient miniaturization, simple fabrication, easy operation, and low cost. As one ubiquitous interference factor, humidity dramatically affects the performance of CGS, which has been neglected for a long time. With the rapid development of technologies based on gas sensors, including the internet of things (IoT), healthcare, environment monitoring, and food quality assessing, the humidity interference on gas sensors has been attracting increasing attention. Inspiringly, various anti-humidity strategies have been proposed to alleviate the humidity interference in this field; however, comprehensive summaries of these strategies are rarely reported. Therefore, this review aims to summarize the latest research advances on humidity-independent CGS. First, we discussed the humidity interference mechanism on gas sensors. Then, the anti-humidity strategies mainly including surface engineering, physical isolation, working parameters modulation, humidity compensation, and developing novel gas-sensing materials were successively introduced in detail. Finally, challenges and perspectives of improving the humidity tolerance of gas sensors were proposed for future research. In the last decades, gas-detection technologies have been widely employed in various application scenarios, such as the internet of things (IoT), healthcare, industrial and agricultural safety, environment monitoring, and food quality assessment ,2,3,4,5.2, In2O3, etc.), and two-dimensional layered nanomaterials , black phosphorus, etc.) /[Sn] was 0.05) sensor surpassed the pure SnO2 in humidity tolerance with a lower response drift (20% vs. 52%) value toward 20 ppm acetone at 450 °C when humidity changed from a dry condition to 80% RH  of pure WO3 counterpart. In addition to lanthanide elements, other metal elements were employed to reduce the water-poisoning effect of gas sensors. Suematsu et al.  = 1, 2 ppm in [RH] = 0%, 30%, 60% RH. The input training set was exhibited in 2O3/rGO sensors simultaneously for each group. 2 clusters, with 95.2% of variances explained. Moreover, the classification system realized the quantification of six classes of  conditions with five classes above 95% accuracy and an overall classification accuracy of 97.3%, as shown in 2O3 gas sensor array with high-humidity endurance. The sensor array (five sensors) was evaluated using PCA and neural network-based classification in terms of the gas sensor data type/amount, neural network algorithms, sensor combinations, and environmental factors. Furthermore, the discrimination of five types of VOCs under different humidity conditions  was realized by deep learning algorithms. The above-mentioned algorism compensations were based on a large amount of data in regard to the features of sensors. Therefore, sensors with high stability are very important to ensure constant sensing features for a long time.In regard to CGS, the interference caused by water vapor not only includes the variation of baseline resistance and response to target gas, but also the response/recovery time. Humidity compensation based on mathematical models and neural network could utilize these features to realize the accurate calculation for target gas concentration. Yan et al.  proposedon model b. Howeveetection d. The str (MSE)) e. Wu et 2Te nanowires for trace NO2 detection. When the Ag2Te sensor was exposed to 1 ppm NO2 with humidity changing from 50% to 90% RH, the sensor exhibited a small fluctuation of baseline resistance drift of 1.84% and response drift of 14.14%, suggesting a remarkable humidity tolerance. AgTe2 did not possess any hydrophilic functional groups, such as oxygen-containing functional groups anchored on the surface, which provided the operation stability in humid conditions. Our group initiatively proposed one humidity tolerant NO2 sensor based on nanoplate Bi2Se3 film at room temperature as shown in 2Se3 sensor exhibited excellent humidity resistance at a humidity range of 0.3–60% RH with a small baseline drift of 26.1% (2Se3 sensor toward NO2 (5 ppm) was enhanced under moist conditions  through spark plasma sintering (SPS). After the reduction process, oxygenate functional groups in GO were effectively removed to a minimal content of 8.8%, leading to the sharp decrease in the surface energy of GO materials. Therefore, the rough surface with unique hierarchical structures and reduced surface energy of the prepared rGO sample led to the transformation from hydrophilic GO to superhydrophobic rGO (water contact angle: 53° vs. 154°). Moreover, the response of rGO sensors to 1 ppm NO2 exhibited a small decline of 5% when the humidity changed from 0% to 70% RH, indicating excellent resistance to humidity. Furthermore, Li et al. [p-type metal oxide-based gas sensors, such as CuO, NiO, and Co3O4 have received significantly less attention compared with n-type metal oxides due to their lower sensitivity [p-type metal oxides may be more humidity-tolerant than n-type ones [2 detection. The NiO sensors exhibited stable response to NO2 at 150 °C under humid conditions (40% RH), indicating the less humidity-dependent characteristics of p-type metal oxides. Similarly, Wilson et al. [2 with staircase concentration (0.8–7 ppm) for two cyclic measurements at 50% RH. Miao et al. [2S gas sensor based on a monolayer film of CuO nanosheets, as shown in 2 and S during the reaction between hydroxyl groups and H2S on the surface of CuO under humid conditions served as the resistive surface layer to screen the humidity effect on gas sensing, as depicted in 2S in the shrinking pores and thus producing the CuxS/S resistive layer. These results suggest that p-type metal oxides are promising materials to design humidity-tolerant sensors, which warrants more extensive investigation.As traditional sensing materials could not well meet the demanding scenario requirements of future gas sensors, developing novel nanomaterials or new nanostructures is one promising strategy to improve the humidity tolerance of gas sensors. Yuan et al.  synthesiof 26.1% b. In connditions c, which u et al.  synthesihobicity d. The hyi et al.  utilizedsitivity . Inspiriype ones . For insype ones  proposedn et al.  preparedo et al.  reportedIn this review, recent progress in a series of anti-humidity strategies of CGS is summarized, including surface engineering, physical isolation, working parameters modulation, humidity compensation, and developing novel gas-sensing materials. Based on these anti-humidity strategies, recently reported studies have achieved inspiring humidity tolerance in gas detection. Despite the significant achievements, there are still some challenges to be overcome in the design of anti-humidity gas sensors in the future:2 molecules, and heterojunctions could promote sensitivity. Therefore, surface engineering with dual functions should be considered preferentially.(I) As presented in this review, surface engineering is the most widely studied anti-humidity strategy. In regard to traditional gas-sensing materials that have been intensively investigated, direct surface engineering is one simple method, which mainly includes five forms of functionalization of noble metals, element doping, modification with hydrophobic materials, composites with hydrophilic materials, and post-treatment. The basic principle of these five surface engineering methods is introducing additives to functionalize gas-sensing materials. Therefore, the adsorption sites on gas-sensing materials are inevitably occupied, leading to response degradation. This phenomenon is prevalent for surface engineering-type gas sensors as demonstrated in (II) Utilizing semipermeable membranes or molecular sieves to isolate sensing film from water vapor could effectively promote the humidity tolerance of gas sensors. Whereas, the diffusion of target gases would be hindered by these membrane or molecular sieves to some extent, leading to a decline in sensitivity . Further(III) In regard to CGS, sensitivity is closely related to the operating temperature. Although the elevation of operating temperature could improve the humidity tolerance of sensors, if the operating temperature does not match the optimal one, then the elevation of operating temperature for humidity tolerance probably would lead to a sensitivity recession. Light illumination has been intensively used to improve the sensitivity of gas sensors. Meanwhile, introducing light illumination to enhance humidity tolerance can be seen as one good way to kill two birds with one stone. However, in regard to working parameters modulation for anti-humidity performance, both the elevation of operating temperature and addition of illumination would increase the complexity of systems and energy consumption.(IV) An overwhelming majority of studies in regard to anti-humidity gas sensor focused on minimizing the attenuation of sensor response to target gas within humid conditions. In fact, adsorbed water molecules may promote the reaction between adsorption sites on the sensing film and target gas molecules, and thus improve the sensor sensitivity. Humidity compensation based on mathematical models and neural network is one promising way to obtain the real concentration of target gas regardless of the negative or positive interferences from humidity. Of note, only one compensation model probably cannot fit in a wide humidity range, especially considering the physisorption of water molecules at high humidity. In regard to compensation based on the neural network, abundant data with gas-sensing features under different humidity conditions are essential to train the neural network. Therefore, sensors with high stability are very important to ensure constant sensing features for a long time.p-type metal oxides in the past deserve more attention due to their humidity tolerance. Whereas, the process to develop new materials may be lengthy and uncertain. With the emerging of new technologies, machine learning and big data technology can theoretically design new materials before preparation, which can effectively shorten the time of developing new materials and propose various application-oriented solutions [(V) Developing novel gas-sensing materials with humidity tolerance renders the possibility to thoroughly eliminate humidity interference for gas sensors. The underappreciated olutions ,132.(VI) Almost all the previous reports in this review only utilized a single strategy to improve the humidity resistance of gas sensors. Resembling co-doping hydrophobic APTES and Ag nanoparticles to achieve the stable acetone sensing of Si nanowires , a combiIn the process of summarizing the latest research advances of anti-humidity gas sensors, we realized that the studies in this field are still insufficient. Moreover, in regard to the emerging subject, many endeavors need to be devoted to overcoming these challenges in the development of anti-humidity gas sensors. Therefore, we hope this review will enlighten readers and open new avenues to further explore novel gas sensors with excellent anti-humidity features without other performance cost indexes.


Self-consistent density functional tight binding (SCC-DFTB) approaches were used to study optimized structures, energy, differential charge density, and Mülliken populations for the  carbon nanotubes (CNTs) connected to the graphene having different topology defects. Based on the calculations, nine seamless CNT-graphenes were selected. For these connected systems, geometric configurations of the graphene and nanotubes were characterized, and the nearest neighbor length of C-C atoms and average length were obtained. The intrinsic energy, energy gap, and chemical potential were analyzed, and they presented apparent differences for different connection modes. Differential charge densities of these connection modes were analyzed to present covalent bonds between the atoms. We have also thoroughly analyzed the Mülliken charge transfer among the C atoms at the junctions. Low dimensional carbon nanostructured materials have shown outstanding performance in various fields. Carbon nanotube (CNT)-graphene composites are composed of two carbon allotropes, concerning environmentally friendly carbon elements, which are used as a remarkable enhancer to improve plasma frequency , microwaIn laboratory, Li. et al. detected a unique seamlessly bonded CNT-graphene hybrid nanostructure introduced in an interlayer for efficient and stable perovskite solar cells, and the power conversion efficiency improved significantly . MaaroufIn the two tubes, the zigzag  tube has a small chemical hardness, and the carbon atoms in the cross section of the  tube are axisymmetric when it is adjacent to the graphene atoms. Therefore, one interesting issue arises naturally to understand connection patterns in the CNT-graphene. The study of the low dimensional nanomaterials for CNT-graphene is looking forward to a matter worthy of expectation to build devices having good binding and stability at the nanoscale. Accounting for the fact that electrical information determined by experiment is hardly possible for these connectors, computer simulations based on semi-empirical quantum mechanics, such as density functional tight binding (DFTB), are particularly well-suited to characterize microscopic details at atomic scale ,35,36,37In the present work, the structures and electrical properties are investigated by DFTB simulations for these connecting modes of the graphenes, respectively, having 16 defects linked to the  CNTs. These values are examined including geometric configurations, energy analysis, electron difference density, and Mülliken charge. The atomic simulations contribute to identify the defect’s role in the connection between the carbon nanotube and graphene as well as the bonding between the dangling atoms. The structural and electronic related information of carbon nanotube-graphene heterojunctions will be helpful in experimental characterization of the interface at atomic scale for novel 3D nanostructures based on carbon .The present simulations used DFTB+ software, which is developed by Bremen University in Germany . Within  qα0−qα) . Erep isHere, atomN atoms  and N electrons, molecular orbital is given below:A, and c orbit coefficient. Then,Mülliken populations were calculated from Linear Combination of Atomic Orbitals (LCAO) coefficients ,43,44. Fin to get the following,Integrating both sides of this equation, and multiplied by This equation can be used to analyze the charge distribution. The charge distributed on the atomic orbital isi, and the charge on the atomic orbital We sum the A, the charge of atom A is obtained:By summing all the atomic orbitals of atom A atom and the B atom isThe total overlapping charge of the The constructed  SWNTs having 96 carbon atoms were performed geometry optimization, where the C-C bond length was 1.420 Å being parallel to the tube axis and 1.434 Å being non-parallel to the tube axis, and the diameter was 6.337 Å. Meanwhile, the initial graphene was 18 Å in length and width. We started by removing the carbon atoms from the graphene to form the 16 possible defect patterns as given in the literature . SixteenX carbon atoms in the i  interval . Therefore, we define Fx as follows: CNT-graphene positions are described as the following. The maximum x-coordinate of carbon atoms in the graphene is Xmax and the minimum x-coordinate is Xmin. Xmax–Xmin is divided equally into intervals with an increment of ∆x. There are ∆Ny carbon atoms in the i  interval . Therefore, we define Fy = ∆Ny/N. The maximum z-coordinate of carbon atoms in the graphene is Zmax and the minimum z-coordinate is Zmin. Zmax–Zmin is divided equally into intervals with an increment of ∆z. There are ∆Nz carbon atoms in the i  interval . Therefore, we define Fz = ∆Nz/N.Here, it should be noted that N is the number of carbon atoms in the graphene for the CNT-graphene. Similarly, the maximum y-coordinate of carbon atoms in the graphene is Ymax and the minimum y-coordinate is Ymin. Ymax–Ymin is divided equally into intervals with an increment of ∆y. There are ∆NTo characterize the interaction of these atoms, the formula for the intrinsic energy per atom of these modes was calculated as follows:insE(CNT-graphene) is intrinsic energy per atom, cn is the number of carbon atom, and Hn is the number of hydrogen atom, and N is the total number of atoms. E(C) and E(H) are the monatomic energies of carbon and hydrogen, respectively. totE (CNT-graphene) is the total energy of CNT-graphene.Here, HOMO and the lowest unoccupied molecular orbital (LUMO) energy ELUMO. The energy gap is given by LUMOEg = E − HOMOE.In general, there is an energy difference between the highest occupied molecular orbital (HOMO) energy EAs shown x-axis or y-axis is 90 degrees, while the angle between the carbon nanotube axis and z-axis is 0 degree. For the nine seamless connection modes, the angle between the CNT tube and coordinate axis minus that corresponding to the initial position is the change values as illustrated in x-axis, the maximum positive value of the mode #13 corresponds to the positive maximum deflection of the graphene carbon atom along the x-axis. The #13 also has a large positive value between the tube axis and z-axis. The large positive angle between the tube axis and y-axis or z-axis for the #8 suggests that the tube of this connection also shows obvious inclination. The small angles for the #9 indicate that the tube is vertical on the graphene. The change of the angle between the tube axis and the coordinate axis for these carbon nanotubes is related to the deflection of carbon atoms in the graphene. In the meantime, the asymmetry of bond-length in the graphene is amplified near the tube, thus increasing the deflection density in these regions.In order to quantitatively analyze the geometries of the CNT-graphenes, we calculated fraction of the carbon atoms for the CNT-graphenes along three directions as given by the Equation (11). As illustrated in z-axis and close to the positive direction of the y-axis compared with that in the #1, and the angles with the y-axis and z-axis increases. For the #6, the length of four atomic pairs is 1.432 Å, the other four 1.414 Å. For the other connection modes, the scatter distribution of bond lengths suggests that there are also inclination angles between the tube and graphene. Especially for the #13 having the largest inclination angle, there is a larger length of four pairs and smaller length of the other four pairs.In the following For inorganic crystals composed of carbon or silicon, our previous works ,35,36,37x-axis. If there are not the two defect positions marked as “7” and “8” in the graphene, the #8 would be symmetrical just like the #9. The amount of charge obtained and lost among the carbon atoms at the junction of 7-7 and 8-8 for the #8 are higher than those for the #9. The carbon atoms near the “2-2” and “3-3” connections in the #10 have more gain and loss of charge than those near the other connections. With an eye to the #13, the defect arrangement of the graphene is symmetrical, resulting in the symmetrical arrangements of the atoms getting and losing charge at the junctions. Three of the sixteen atoms in the carbon nanotube gain charge and thirteen lose. The atoms in the graphene are dominated by charge loss.As shown in z-axis, and carbon tubes take forms mainly including upright and inclined. The phenomena can be also found in 3D graphene-CNT-Ni heteronanostructures. Here, the heteronanostructures embedded in electrodes and the ionic membrane of the artificial muscles present significantly bending deformation. Some CNT-graphene systems with vertical tubes have relatively high intrinsic energy and chemical stability. Owing to the pulling and pressure from the connected carbon tubes, the morphologies of the graphene present apparent differences, which greatly affect the distribution of electrical densities of the graphene. The locally morphological differences are similar to the localized part of experimentally growing CNTs on graphene, where optimized interfacial bonding state play a great significance in the strength/toughness and EMI shielding effectiveness. From the Mülliken population differences, it can be found that the lost amount of the charge on the tube’ atoms are higher than that the graphene’s ones. The transfer of Mülliken charge obviously occurs among the atoms at the junction between the tube and graphene, implying that there are weak ionic bonds between these atoms in addition to strong covalent bonds. The present calculations provide us the possible structures and charge distributions as well as the energy information for coupling CNTs with grapheme, and improve our understanding for such systems on electrical level. These will be helpful in constructing novel carbon nanotube arrays on 2D materials with excellent thermal properties.The geometric structural optimization and electronic information of the CNT-graphene are investigated by using SCC-DFTB simulations at atomic scale. CNT-graphene is a suitable choice for the carbon nanotubes linked with grapheme having topological defects within the present DFTB formalism. Carbon nanotubes having a larger diameter need to be connected with graphene having larger size topological defects, and this cannot be performed through even the semi-empirical DFTB calculations. Geometrically, thinner nanotubes can hardly form a seamless connection with graphene having topological defects. In the present work, the hydrogenation for the top C atoms of the nanotubes is used to represent their infinite length. For these 16 topology defects constructed in the graphene, there are only nine seamless connection modes with the  CNTs. After connection, the graphene shows obvious fluctuation along the


Drosophila. The sensilla housing Or42b neurons show cuticular deformations upon moist air stimuli, indicating a conversion of humidity into mechanical force. Accordingly, we found Or42b neurons directly respond to humidity changes and rely on the mechanosensitive ion channel TMEM63 to mediate humidity sensing (hygrosensation). Expressing human TMEM63B in Tmem63 mutant flies rescued their defective phenotype in moisture attraction, demonstrating functional conservation. Thus, our results reveal a role of Tmem63 in hygrosensation and support the strategy to detect humidity by transforming it into a mechanical stimulus, which is unique in sensory transduction.Birds, reptiles and insects have the ability to discriminate humidity levels that influence their survival and geographic distribution. Insects are particularly susceptible to humidity changes due to high surface area to volume ratios, but it remains unclear how humidity sensors transduce humidity signals. Here we identified Or42b-expressing olfactory sensory neurons, which are required for moisture attraction in  Drosophila, Or42b neurons, located in sensilla with humidity-dependent shape act as humidity sensors via the mechanically gated channel TMEM63.Humidity detection is a feature of many species. Here the authors show that in  Notably, insects have the ability to extract information about their surroundings via humidity sensation. For instance, the hawkmoth Hyles lineata uses humidity levels to assess the nectar availability of blooming flowers that produce a higher level of humidity compared with their ambiance7. In mosquitoes, the disease vectors, moisture serves as a key attractant for host seeking9. Drosophila are equipped with sophisticated hygrosensory organs to detect moisture levels and possess various humidity-induced behaviors19. Water-sated flies are averse to moisture, while dehydrated flies show attraction to moisture as well as moisture searching behavior18. Besides, multiple Drosophila species show diverse humidity preferences and have different distributions, for example, rainforest flies and desert-dwelling flies prefer 85% and 20% relative humidity (RH), respectively12. Although recent studies have revealed the neural and molecular basis underlying humidity preference in Drosophila15, the mechanisms by which humidity stimuli are transduced into electrical signals and guide moisture attraction behavior remain largely obscure.Humidity provides a cue for terrestrial animals to migrate toward favorable environments, which ensure their survival and reproduction22. In the mechanical hygrometer model, change of humidity levels causes shape change of sensilla structures so that mechanosensitive molecules are activated to mediate hygrosensation. In the osmometer model, humidity-dependent osmolality change occurs in the sensilla lymph and evokes hygrosensation. Additionally, the psychrometer model posits that thermosensitive molecules function as humidity transducers, which detect the evaporation cooling from the sensilla lymph. These concepts are not mutually exclusive as sensory inputs from different pathways may act in concert to mediate hygrosensation by a multisensory integration mechanism. Previous studies have established several ionotropic receptors (IRs) including IR40a, IR68a, IR93a and IR25a as attractive candidates for humidity receptors15, but it remains unclear whether these molecules respond directly to mechanical or thermal stimulus. Thus, molecular evidence for those concepts regarding humidity transduction mechanisms has been lacking.Humidity signals are transduced and encoded by hygroreceptors; however, the identification of molecular receptors in hygrosensory neurons has been hampered by the elusive nature of humidity, which is the amount of water vapor in the atmosphere. Three concepts that explain how humidity changes activate a hygroreceptor have been proposedTmem63 mutant flies, demonstrating functional conservation in hygrosensation.In this study, we identified a mechanosensitive ion channel TMEM63, which functions in a group of olfactory receptor (OR)-expressing neurons to mediate hygrosensation. Using a newly developed assay, we first identified a group of olfactory sensory neurons (OSNs), the Or42b neurons, which work in synergy with IR68a-expressing moist cells to guide moisture attraction behavior. The sensilla that house the dendritic branches of Or42b neurons show shape change upon increasing humidity, which suggests humidity stimulus can be converted into mechanical force on the dendritic membrane. We further show that the humidity response of Or42b neurons depends on TMEM63, a member of the recently identified family of mechanosensitive channels, thus providing molecular evidence for the model in which a mechanosensory pathway contributes to humidity sensing. The physiological function of TMEM63 proteins in the animal kingdom is largely unknown, we found human TMEM63B rescues moisture attraction defects in 19. Briefly, flies were placed in a Petri dish that was covered with nylon net. After desiccation for 6 h, the dish was placed above a 24-well plate with 2 holes filled with super-saturated salt solutions23 immediately beneath the nylon net, creating a humidity gradient between 20% RH and 70% RH. The attraction index was then calculated as the percentage of flies in the region of higher humidity every 10 seconds. The chance level of attraction to the 70% RH region is estimated to be ~12% based on the proportion of the moist area in a random distribution. The humidity gradient was stable during the 120 s experimental period 31. This group of sensory neurons have been reported to be essential for innate attraction to vinegar and odor-induced food searching33. These studies together with our results indicate Or42b neurons play pivotal roles in detecting food and water, two essential factors for animals’ survival.Four antennal cell groups have been associated with hygrosensation, including dry cells and moist cells located near the sacculusoup Fig. . IR68a-G RH Fig. . Similarion Fig. . Or42b-G34. Although no temperature gradient was presented in our behavior assay , which suggests the curved sensilla are more responsive to humidity while straight sensilla may react poorly.We then investigated the mechanism by which Or42b neurons regulate animal behavior in the 20-70% RH gradient. Among existing models for the mechanism of hygrosensationels Fig. . When swels Fig. , which iels Fig. . Moreoveels Fig. . Consideels Fig. . A detainanchung has been reported to play essential roles in the thirst state sensor in central brain36, which can also regulate the moisture attraction behavior, we chose to perform RNAi-mediated knockdown of this gene in Or42b neurons. All of these tested lines behaved similarly to the control. Although inactive and nanchung were both required for the humidity choice between 0% and 100% RH in water sated flies11, our study excluded their roles in the 70% RH-induced attraction in desiccated flies. The differences in humidity settings and internal state of animals might lead to distinct results. In addition, neither a P-element insertion mutant for Or42b (Or42bEY) nor Orco2 mutants exhibited behavioral defects in humidity-guided attraction to 70% RH  in S2 cells transfected with DmTMEM63-GFP   located near the sacculus chamber II  to similar levels when tested in a 20% to 70% RH gradient  in these neurons 7094, LexAop-GCaMP6m, LexAop-Gal80, UAS-Kir2.1, Orco-RFP, UAS-RedStinger, UAS-CD4-tdGFP, UAS-GCaMP6m and UAS-tdTomato were obtained from the Bloomington Stock Center. UAS-Tmem63-RNAi and UAS-Dicer2 flies were obtained from the Vienna Drosophila Resource Center. UAS-ReaperHid was a gift from Hermann Steller lab. Ir68a-Gal4 was a gift from Paul Garrity lab. iav1, tmcGAL4, nompCf00914 and UAS-pzl-RNAi were gifts from Wei Zhang lab. UAS-ppk-RNAi and UAS-nan-RNAi were from Tsinghua Fly Center. ppk28Δ was from the Core Facility of Drosophila Resource and Technology in Shanghai Institutes of Biochemistry and Cell Biology. The flies were raised on standard medium at 25 °C and 60% humidity under a 12 h/ 12 h light-dark cycle.Drosophila S2 cells were cultured in Schneider’s medium supplemented with 10% FBS at 25 °C. Cells were plated into 35 mm petri dishes before transfection. TransIT-Insect (Mirus) was used to transfect cells according to the product instructions. All constructs with the pUAST backbone were cotransfected with pActin-Gal4.41 to obtain the Tmem63KO mutant allele with an attp site introduced to the first intron, then reporter constructs were integrated into the attP site through phiC31 mediated gene integration to generate the corresponding reporter lines.We used a previously described targeting strategyTmem63KO mutant lines were generated through homologous recombination in Drosophila embryos via the CRISPR/Cas9 system. The 5ʹ and 3ʹ homologous arms of Tmem63 were cloned from nos-Cas9 flies by PCR amplification.The Primers for 5ʹ homology arm amplification:5ʹ-GATGCCAGAACAAATACATCGAGA-3ʹ and 5ʹ-GATTGTTATCCGCTTTCATAGCAC-3ʹPrimers for 3ʹ homology arm amplification:5ʹ-ACTTTACACCGGGCACCTACC-3ʹ and 5ʹ-TTGCCTTGCTGTTATCCTCCTG-3ʹ.To generate the knock out targeting vector, the 5ʹ arm and 3ʹ arm flanking an attP-3P3-RFP-loxP cassette were cloned into pBSK+ vectors using a MultiS One Step Cloning Kit (Vazyme). The sgRNA sequences were as follows:sgRNA1: 5ʹ-AAATAAGTGAATGCGACGAA-3ʹ;sgRNA2: 5ʹ-GTTCTGGGCCTCCTGCACAG-3ʹ.Nos-Cas9 embryos. F1 flies with RFP-positive eyes were selected as mutant candidates and verified by genotyping. The mutant was confirmed by RT-PCR, with the Actin5C gene as a positive control. The primer pairs were as follows:The guide RNAs were expressed under U6b promoter control. A mixture of the targeting vector and two sgRNAs was injected into Tmem63:forward-primer, AGGGATACTCTCATGGTTAAACCAG;reverse-primer, TCCGAGGGAAGGCAGAATCATG.Actin5C:forward-primer, CTGGGACGATATGGAGAAGATC;reverse-primer, CAGCTCGTAGGACTTCTCCAAC.Tmem63 before the stop codon was amplified from w1118 flies and cloned into the pBSK-attB-EGFP-T2A-Gal4 and pBSK-attB-V5-T2A-LexA vectors41 using the ClonExpress II One Step Cloning Kit (Vazyme). These vectors were then separately injected into embryos from nos-phiC31 females crossed with Tmem63KO males. Knock-in Tmem63LexA and Tmem63EGFP-Gal4 lines were obtained from F1 flies with red eyes and verified by PCR. Finally, the knock-in lines were crossed to hs-Cre flies to remove screening markers in the genome.To generate the knock-in marker line, the replaced sequence and the rest genomic region of Drosophila Tmem63 (RE44586) was purchased from the Drosophila Genomics Resource Center, and the full length cDNA of human Tmem63a (NM_014698.3), Tmem63b (NM_001318792.1) and Tmem63c (NM_020431.4) were synthesized (Genewiz). The coding regions of these genes were obtained by PCR and subcloned into the pUAST vector using the ClonExpress II One Step Cloning Kit (Vazyme). UAS-DmTMEM63, UAS-HsTMEM63A, UAS-HsTMEM63B and UAS-HsTMEM63C transgenic flies were generated by conventional P-element-mediated germ-line transformation.The full length cDNA of Or42b gene promoter was PCR amplified from the genomic DNA of Or42b-Gal4 flies. The CD8-RFP coding region following the Or42b promoter were assembled to pBSK-attb vector via MultiS One Step Cloning Kit (Vazyme). Or42b-CD8-RFP flies were generated by phiC31 mediated gene integration to the attP2 site on the third chromosome.The 5 kb 19. In total, 20–40 male flies aged between 7 and 10 days that had been sorted into groups 2 days before testing were placed in a 60 mm Petri dish that was covered with 250 mesh nylon net. Petri dishes with flies were placed in a sealed chamber with desiccant and desiccated for 6 h prior to the test; no food was provided during desiccation. The dish with flies under desiccation stress was placed above a 24-well plate with 5 holes immediately beneath the dish. Different humidity levels were created with distilled water or saturated salt solutions23. The moist hole on one side provided high humidity, while the hole on the opposite side provided low humidity, which results in a humidity gradient between the two specific humidity levels. The room humidity was kept at 50% to 60% RH with household humidifier or dehumidifier during testing. The room temperature was set at 25 °C. Assays were performed during 17:00 and 22:00. The water-deprived flies were allowed to walk for 2 min, and the petri dishes were videotaped at 30 frames per second. The attraction index was then calculated for every 10 s time point as follows :The humidity-guided attraction behavior assay was modified from the hygrotaxis assayAttraction index = Number of flies above moist hole / Total number of flies56. Two male flies were introduced to a 60 mm petri dish for 6 h of desiccation and then videotaped at 30 frames per second for 2 min under the humidity conditions in which the flies were raised.The locomotor activity of the thirsty flies was assayed by using a previously established video-assisted tracking method57  to analyze the positions and movement trajectories of the flies during the assay. Then, the data were processed and plotted using custom programs and scripts in MATLAB based on generic codes from Plotly (https://plotly.com/matlab/2D-Histogram). The whole arena was divided into 20 ×20 pixels, and the total time that a fly spent on each pixel during 80–90 s after the onset of the assay was calculated as the cumulative time. Pixels with a cumulative time above 10 s suggest more than one fly staying in the same pixel.For quantification of the attraction index for distinct groups, an average index of the attraction indexes from the plateau stage was used. For spatial distribution analysis and locomotion analysis, flies were first tracked using Flytracker42. The room humidity was also kept at 50% to 60% RH with household humidifier or dehumidifier during experiments. Briefly, 3- to 5-day-old flies were mounted in a custom made stage using two component silica gel. The antennae were pushed toward with a thin sheet of plastic on top of the head. Then a small window was made in both the plastic sheet and the head capsule to allow access to the antennal lobes. A drop of Adult Hemolymph-Like Saline  was then added to cover the imaging window. For the humidity stimulation, airstreams from a 50 mL syringe were passed through either a gas washing bottle with desiccant or a gas washing bottle containing a water soaked sponge, generating a 24.66 ± 0.62% RH dry airstream or a 66.67 ± 0.19% RH humid airstream, respectively13. Humidity changes were achieved by alternating the two syringes producing the dry airstream or humid airstream. Odors were obtained from filter paper with the corresponding odor source in a gas bottle as previously described42. The required dilution of ACV in water or ethyl propionate in mineral oil was used as the odor stimulus. The airflow rate was ~500 mL/min. We used flies carrying Nan-Gal4 (ref. 36), an alternative driver for the Or42b neurons, to express GCaMP6 and RFP for calcium imaging. Images were acquired with FV10-ASW 4.2 software from an Olympus FV1200 confocal microscope equipped with a 10x water-immersion objective. GCaMP and red fluorescent proteins (as references) were excited by a 473-nm and a 559-nm laser, respectively. The average GCaMP signals from the first 3 s before the introduction of the odorant stimulus or a humidity change were taken as F0, and ΔF/F0 was calculated for each data point.Antennal lobe calcium imaging was performed at room temperature (25 °C) as described previouslyOr42b-Gal4 and UAS-CD4-tdGFP were used to perform confocal microscopy imaging. Acutely isolated antennae were mounted on a slide using double-sided tape (Scotch 3 M). A coverslip was placed onto the slide using spacers so that a thin tube (~0.2 mm diameter) can be inserted for the humidity stimulation. Humidity stimulation was applied as described in calcium imaging experiments. The room temperature was set at 25 °C and room humidity was set to ~50% RH. Images were obtained at the region of the GFP-positive sensillum with FV10-ASW 4.2 software on an Olympus FV1200 confocal microscope equipped with a 60x water-immersion lens at ~2 Hz.One day old adult flies carrying 58 according to the documentation on Github (https://github.com/brouhardlab/Kappa). We first aligned the image stack using the StackReg plugin of Fiji, only the image stacks in which the sensilla base displayed no movement were opened in Kappa. The time point in which the dry air was converted to moist air was defined as 0 s. We chose the images at time points −5 s, 2.5 s and 10 s for the next analysis. In Kappa plugin, five points that track the midline of the sensilla were first manually defined to make an initial curve. We then adjusted parameters such as the color channel, the brightness threshold, and the distance from the curve to make all GFP signals or bright pixels within the sensilla contour selected. Next, we fitted the initial curve to the chosen pixels by the least square algorithm in Kappa. After setting the scale factor (μm/pixel) of the images, we exported the average radius of curvature of the fitted curve for statistical analysis.The curvature analysis is conducted using the Kappa plugin of Fiji12. Male 2- to 5-day-old flies were dissected in Adult Hemolymph-Like Saline, and the 3rd antennal segments were fixed in 4% PFA at room temperature for 5 min. Brains of 5- to 7-day-old flies were dissected in 1x PBS and fixed in 4% PFA at room temperature for 20 min. After three washes, the samples were blocked in block buffer  for 30 min at room temperature. The samples were then incubated with primary antibody at 4 °C overnight. On the second day, tissues were washed three times and incubated in secondary antibodies at room temperature for 2 h. All samples were mounted in Rapiclear 1.47 (SunJin Lab) for confocal microscopy (Olympus FV1200). The following antibodies were used: rabbit anti-GFP , mouse anti-nc82 , donkey anti-rabbit Alexa488 , donkey anti-mouse Alexa647 .Whole-mount antennal staining was performed as previously describedDrosophila S2 cell staining, we used a PCR-based approach to introduce the in-frame fused myc-tag (EQKLISEEDL) behind the F21 or G724 site of DmTMEM63. The pUAST-21myc-Tmem63-mCherry or pUAST-724myc-Tmem63-mCherry construct was cotransfected with pActin-Gal4. 24-36 h after transfection, cells were plated onto ConA-coated coverslips for staining.For Drosophila Medium and incubated with transfected cells for 30 min at 25 °C. After fixation with 4% PFA for 30 min at 4 °C, the cells were blocked for 30 min at room temperature and then incubated with the secondary antibody  for 30 min.For nonpermeabilized staining, the primary antibody  was diluted 1:200 in Schneider’s For permeabilized staining, cells were fixed and incubated with PBST (PBS + 0.1% Triton) for 10 min. Then the cells were blocked and stained with the primary and secondary antibodies.DmTMEM63-GFP or GFP empty vectors and incubated for 24-36 h before recording. Outside-out patch recordings of S2 cells were carried out at 25 °C under an Olympus BX51WI microscope equipped with a 40x water-immersion lens as described before59. Recordings were performed using borosilicate glass pipettes with resistances of around 10 MΩ. The pipette solution contained 140 mM potassium gluconic acid and 10 mM HEPES. The bath solution contained 140 mM NaMES (sodium methanesulfonate) and 10 mM HEPES. All solutions were adjusted to 320 mOsm and pH 7.2. Negative pressure was applied to the excised membrane patches using a HSPC device (ALA-scientific). The sample rate was 20 kHz and filtered at 1 kHz (low-pass). A multiclamp 200B amplifier, DIGIDATA 1550 A and Clampex 10.5 software (Molecular Devices) were used to acquire and process the data.S2 cells were transfected with Humidity was measured using an custom 2.5 × 2.5 mm probe equipped with SHT31 sensor and recorded with the SHT31 Smart Gadget (Sensirion). To monitor the humidity gradients formed in the humidity-guided attraction behavior assay, the arena was divided into 10 × 10 measurement points to allow the insertion of humidity sensor for monitoring the humidity distribution over a 120 s time period.t test, one-way ANOVA followed by Holm-Sidak or Dunnett’s post hoc test, or the two-way ANOVA with Sidak’s multiple comparison test. Post hoc power analyses were performed in PASS 15 (https://www.ncss.com/software/pass/) to ensure that statistical power >0.8 for all the significant differences. All data in bar and line graphs are presented as means ± SEM and the exact P values are displayed in the Figures.Experimental animals and genetic controls were tested at the same condition, and data were collected from at least three independent experiments. Statistical analysis were carried out in Prism 7 (GraphPad). Statistical methods used include two-tailed Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Reporting Summary


The use of graphene in surface plasmon resonance sensors, covering a metallic (plasmonic) film, has a number of demonstrated advantages, such as protecting the film against corrosion/oxidation and facilitating the introduction of functional groups for selective sensing. Recently, a number of works have claimed that few-layer graphene can also increase the sensitivity of the sensor. However, graphene was treated as an isotropic thin film, with an out-of-plane refractive index that is identical to the in-plane index. Here, we critically examine the role of single and few layers of graphene in the sensitivity enhancement of surface plasmon resonance sensors. Graphene is introduced over the metallic film via three different descriptions: as an atomic-thick two-dimensional sheet, as a thin effective isotropic material (same conductivity in the three coordinate directions), and as an non-isotropic layer . We find that only the isotropic layer model, which is known to be incorrect for the optical modeling of graphene, provides sizable sensitivity increases, while the other, more accurate, models lead to a negligible contribution to the sensitivity. Lately, much attention has been paid to the development of new chemical and biological sensors based on low cost and fast diagnosis ,2,3,4. SThe primary design feature of a SPR sensor is the sensitivity, which, depending on the interrogation method, is defined as the ratio between the shift of the SPR resonance angle or wavelength and the change in the refractive index of the analyte. An increased sensitivity translates into the possibility of the detection of analytes with low molecular weight and low concentrations . In thisp to 50% . These pp to 50% ,14,15, gp to 50% ,17, as wp to 50% ,19,20,21In all of the previous reports, the sensitivity of the N layer SPR sensor was calculated through the transfer matrix method, where each layer of graphene is modeled by an effective thickness. Note that this means that after the multiplication of the matrices of the individual layers, we end up with a thicker homogeneous sample, in opposition to the experimental evidence that showed that, in few-layer graphene, each monolayer is minimally perturbed by the presence of the other layers ,23. Withp-polarized light impinging on the prism at a certain angle larger than the critical angle of the prism–analyte interface. Thus, the SPR condition is easily calculated through the reflectance of the multilayer structure r23=ke being the electron charge and ℏ the reduced Planck constant.In this expression, ≈σ0=e24ℏ ,27, withn). The relation among all of them is well established: The refractive index of graphene in the 3D isotropic model is obtained from the surface conductivity ( and n=ε . The surg=σ3Ddg) . Thus, bGraphene’s effective thickness can be assumed to be the interlayer distance in graphite: =0.34 nm ,30,31,32From the experimental perspective, the optical constants of graphene were obtained after fitting processes of reflectance, transmittance and ellipsometry measurements ,34,35,36s- and p-polarized waves is zero. This ensures that the reflectance of the four-layer system with the anisotropic graphene continues to be calculated by Equation , the contribution of graphene is included in the Fresnel reflection coefficient of the gold/analyte interface (Equation ) throughThe effect of the different approaches used to model the graphene monolayer is presented in At first sight, the four models produce similar reflectance curves, with graphene only slightly modifying the response of the sensor. However, the zoom in the reflectance curves provided in However, the most important result of the present work is to quantify the sensitivity of the sensor , which is shown in iscussed , yields Having established that the way a single sheet of graphene is modeled affects the obtained  sensor sensitivity, we now investigate the sensitivity as a function of the number of graphene layers. To model more than one layer, previous works treating graphene as a thin film ,13,30,39le range ). Figurentegrals  and consIn essence, the problem of the isotropic model is that it assumes the electronic response of graphene to light to be independent of light’s electric field orientation. However, graphene’s out-of-plane conductivity significantly differs from the in-plane conductivity. As a matter of fact, since the early days of graphene isolation, it has been well known that the optical response of few-layer graphene in the visible range can be taken to be that of a stack of non-interacting 2D electron gas sheets . For exaIn this paper, we theoretically studied the influence of graphene modeling to the sensitivity obtained in a refractometer based on gold’s SPR. When graphene is modeled as a surface (2D model) or an anisotropic thin film (anisotropic 3D model) the system’s sensitivity is practically unaffected by the presence and number of layers of graphene. However, when graphene is modeled as an isotropic thin film (isotropic 3D model), a significant non-physical sensitivity increase is observed. Therefore, we suggest that graphene modeling through the 2D or 3D anisotropic models are more accurate alternatives, which must be adopted for the study of graphene-assisted SPR sensors. It is important to highlight that by no means are we suggesting that graphene cannot effectively improve the sensitivity or the performance of SPR sensors by other means, in the visible range, as recently reported ,46,47. O