Sodium alginate, a biopolymer extracted from brown algae, has shown great potential for many applications, mainly due to its remarkable biocompatibility and biodegradability. To broaden its fields of applications and improve material characteristics, the use of nanoreinforcements to prepare nanocomposites with enhanced properties, such as carbonaceous structures which could improve thermal and mechanical behavior and confer new functionalities, is being studied. In this work, graphene oxide was obtained from graphite by using modified Hummers’ method and exfoliation was assisted by sonication and centrifugation, and it was later used to prepare sodium alginate/graphene oxide nanocomposites. The effect that different variables, during preparation of graphene oxide, have on the final properties has been studied. Longer oxidation times showed higher degrees of oxidation and thus larger amount of oxygen-containing groups in the structure, whereas longer sonication times and higher centrifugation rates showed more exfoliated graphene sheets with lower sizes. The addition of graphene oxide to a biopolymeric matrix was also studied, considering the effect of processing and content of reinforcement on the material. Materials with reinforcement size-dependent properties were observed, showing nanocomposites with large flake sizes, better thermal stability, and more enhanced mechanical properties, reaching an improvement of 65.3% and 83.3% for tensile strength and Young’s modulus, respectively, for a composite containing 8 wt % of graphene oxide. Macrocystis pyrifera) and can also be synthesized from microorganisms [2+, Ba2+, and Zn2+ [In recent years, there is a growing interest in renewably sourced materials in the polymeric materials field, due to the fluctuations in oil price and the increase in environmental concern. Biopolymers, extracted from renewable sources, can be classified into three main groups: extracted from biomass, such as polysaccharides and proteins; synthesized from bioderived monomers, such as polylactic acid; and those produced by organisms or bacteria, such as bacterial cellulose ,2. Sodiurganisms ,6. Alginrganisms . Alginatand Zn2+ . These dand Zn2+ . Alginatand Zn2+ .When compared to commercial synthetic polymers, the main drawbacks of alginate and biopolymers, in general, are their inferior thermal and mechanical properties , as well−1 and 5000 W m−1 K−1, respectively [Similarly, when carbonaceous structures, such as graphene and graphene derivatives, are incorporated to alginate, not only do the mechanical and thermal behaviors improve and the hydrophilicity decreases, but nanocomposites with electrical conductivity and antibacterial properties are also achieved. Graphene is a 2D allotropic variety of carbon, constituted by a one-atom-thick layer of sp2-hybridized carbon atoms. It has been reported that a single-layered graphene exhibits a Young’s modulus of ∼1100 GPa, a tensile strength of 130 GPa, and electrical and thermal conductivities up to 6000 S/cmectively ,18. Grapectively , Staudenectively , and Humectively . Among aectively . Si et aectively  biosynthectively . GO has ectively .It has been observed that the properties of GO-containing nanocomposites are strongly dependent on the physical-chemical properties and morphology of GO, which are mainly influenced by the oxidation treatment and exfoliation degree. Long oxidation times lead to high contents of oxygenated groups and to high exfoliation degrees, which result in GO flakes with small size  and thickness usually formed by a low number of layers (less than 2 nm) ,26,27. TThere are many papers in the literature on the effect of graphene-obtaining treatment on the final properties of graphene, and quite a number of studies have assessed the effect of graphene content on the final properties of nanocomposites; however, not many studies are known, in which the effect of different types of graphene, with different characteristics depending on the treatment used to obtain them, on the final properties of nanocomposites is studied, and even less in the case of nanocomposites in which the matrix is a biopolymer. May et al.  concludeTherefore, this work focuses on obtaining graphene oxide with different characteristics for its subsequent incorporation (with varying content) to an alginate polymer matrix. In this sense, an extensive study of how oxidative treatment and graphene oxide isolation, assisted by centrifugation and sonication, affect its physical-chemical characteristics and morphology has been carried out. The effect of different sizes as well as thicknesses of graphene oxide flakes, determined by morphological studies, on the mechanical properties of nanocomposites has been analyzed.2SO4, 96%), sodium nitrate , potassium permanganate , hydrogen peroxide  and hydrochloric acid  were supplied by Panreac . Medium viscosity alginic acid sodium salt from brown algae  was purchased from Sigma-Aldrich .Graphite flakes were purchased from Aldrich. Sulfuric acid  at 4500 rpm for 20 min. This step was repeated 5 times. Then, the same centrifugation procedure was repeated using deionized water until neutral pH was achieved. Finally, the mixture was filtered through polyamide filters  and was vacuum-dried at 50 °C for 48 h. Graphite oxide films, GO15 and GO30, were obtained.The oxidation process of graphite was carried out according to modified Hummers’ method . Graphit−1), assisted by ultrasonication . Ultrasonication times of 3 h and 4 h were applied for samples oxidized for 30 min, and these fractions were designated as GO30S and GO30L, respectively. The graphene oxide flakes, thus obtained, were size selected by centrifugation. Firstly, they were centrifuged at 4000 and/or 3000 rpm for 30 min and the supernatant fractions (ca. 80%) were collected. The fractions were named according to the applied ultrasonication and centrifugation treatments as GO30L-4000, GO30L-3000, and GO30S-4000. The remaining sediments of GO30L-4000 and GO30L-3000 fractions were collected and redispersed in water for 15 min using an ultrasonic bath. Centrifugation and redispersion steps were repeated for 2000 and 1000 rpm centrifugation rates to obtain size-selected graphene oxide flakes [The obtained graphite oxide was exfoliated in water  and dried for 48 h at room temperature. The designation of GO fractions and the applied oxidation and exfoliation procedures are summarized in −1) were prepared by ultrasonication for 1 h. Afterwards, sodium alginate/GO films were obtained. For this, sodium alginate in water at 2 wt % was used to incorporate different volumes of the GO dispersions previously prepared, so that the amount of GO was 1, 4, 6, and 8 wt % in the nanocomposites. The films were obtained by solvent casting and evaporating the water for 7 days at room temperature, and the samples were designated as SA-GO30L-4000-x% and SA-GO30S-4000-x%, where x is the percentage of GO in the nanocomposites. The samples were stored in a desiccator until their characterization.First, dispersions of GO30L-4000 and GO30S-4000 fractions in water (5 mg mL−1 KBr) were employed for the analysis. Single-beam spectra of the samples were obtained after averaging 32 scans in the range of 4000 to 400 cm−1, with a resolution of 2 cm−1. For composites, an MKII Golden Gate accessory (Specac) with a diamond crystal at a nominal incidence angle of 45° and ZnSe lens were used. Spectra were recorded in attenuated total reflection (ATR) mode between 4000 and 650 cm−1 with a resolution of 4 cm−1 and 32 scans.Fourier transform infrared (FTIR) spectroscopy was used to identify the characteristic functional groups of graphite, graphite oxide, and nanocomposites. Measurements were performed with a Nicolet Nexus FTIR spectrometer . For carbonaceous nanostructure characterization, KBr pellets (0.0025 mg sample g-1 solvent) by using ethanol and deionized water for graphite and graphene oxide, respectively. The spectra were obtained in a UV-3600 UV-VIS-NIR spectrophotometer  in the wavelength range of 200 to 600 nm. The absorbance of graphite and graphene oxide was measured by ultraviolet-visible spectrophotometry (UV-Vis), using open-top quartz cells. For sample preparation, low concentration dispersions were prepared  spectrometer, coupled to a Leica DMLM microscope (50x), with a laser of 514 nm wavelength (ModuLaser) operating at 5% of nominal potency. Data were collected in the range of 150 to 3200 cmX-Ray diffraction (XRD) analyses were performed in a Philips X’Per PRO  automatic diffractometer, operating at 40 kV and 40 mA in theta-theta configuration. A secondary monochromator with radiation Cu-Kα  and the solid state detector PIXCEL (active length in 2θ: 3.347°) were used. Data were collected in the 2θ range of 5° to 70° (step size: 0.026 and time between steps: 60 s) in continuous mode.n is a natural number between 1 and n = 1), λ is the wavelength of the X-rays used in the analysis , d is the interplanar distance in crystal structure, and θ is the angle between the incident rays and the dispersion planes.The interplanar distance in the different samples was analyzed according to Bragg’s law ,33:nλ =The morphology of graphene oxide flakes was analyzed by atomic force microscopy (AFM). Height images were obtained in a Dimension Icon . scanning probe microscope equipped with a Nanoscope V controller (Bruker). Tapping mode was employed in air, using an integrated tip/cantilever (125 µm length with ca. 300 kHz resonant frequency). −1) using an ultrasonic tip for 1 h. A droplet of graphene oxide dispersion was put on a prewashed silicon wafer substrate and water was eliminated by spin coating at 1200 rpm for 120 s. Prior to analysis, samples were kept at room temperature for 48 h.For sample preparation, GO fractions were dispersed in water  performed in a TGA/STDA 851  equipment. Samples of around 5 mg were heated from room temperature to 700 °C at a heating rate of 10 °C min−1 and with an initial grip separation of 10 mm. Rectangular samples of 70 mm × 5 mm × 0.05 mm (length × width × thickness) were used. Tensile modulus (E), stress at yield (σy), stress at break (σb), and elongation at break (εb) were determined from stress-strain curves and averaged from five specimens. Young modulus, tensile strength, and elongation at break of nanocomposites were analyzed in an Instron 5697 equipment  using a load cell of 500 N in tensile mode. Tests were carried out at a crosshead speed of 2 mm minIn order to study the different types of functional groups formed in the oxidation process of graphite as well as the effects of the different oxidation times, FTIR analysis was used. The FTIR spectra of graphite and GO are shown in −1 [−1 was assigned to the stretching vibration of OH groups, derived from hydroxide and carboxylic acid groups, as well as from some moisture traces [−1. The peak at 1735 cm−1 was assigned to the stretching vibration of C=O bonds in carboxylic acid [−1 in graphite oxide samples, which was related to the stretching vibration of C–O–C [The peak corresponding to the stretching vibration of C=C bonds was clearly observed at around 1590 cm−1  for grape traces . Moreovelic acid . Finallyof C–O–C . These rThe differences in oxidation grades of graphite oxide samples were analyzed by UV-Vis spectroscopy. The obtained spectra are shown in The graphite sample did not show remarkable peaks. The spectra of graphite oxide samples showed two absorption maximums at around 230 nm and 300 nm. The peak around 230 nm was attributed to the π–π* transitions of aromatic C–C bonds, while the peak at 300 nm corresponded to the n–π* transitions of carbonyl (C=O) groups ,37 and b2 domain, thereby reducing the concentration of π electrons. As a consequence, more energy is needed for π–π* transitions [The absorption peak of π–π* (C–C) transitions was studied in more detail , inset. nsitions ,39.2 carbon pairs and the 2D band is related to the second order of zone-boundary phonons [−1, respectively.Results of Raman spectroscopy analyses of graphite and graphite oxide are shown in  phonons ,40. The  phonons ,42. In t−1 and 1600 cm−1 wavenumber values for GO15 and GO30 samples, respectively. This shift to higher wavenumbers suggested a reduction of the in-plane sp2 domains as a result of the oxidation of graphite [−1 and 1351 cm−1 for GO15 and GO30, respectively). This may indicate the presence of more defects and disorders caused by hetero-atoms, grain boundaries, aliphatic chains, etc. as a consequence of stronger oxidation [−1, denoted as D + G band and related to defects, was also noted in the spectra of GO. For the 2D band, a slight decrease in intensity was observed in the GO30 sample, which can be explained by the breaking of the stacking order of graphene sheets along the z-axis due to oxidation [In graphite oxide samples b, a shifgraphite . In the xidation . These ra is the average size of sp2 domain crystals, λ1 is the input laser energy, ID is the intensity of D band, and IG is the intensity of G band. The relative intensity of D and G bands can be taken as indicative of crystallite size, according to the equation proposed by Cancado et al. :(2)La=[ and the values obtained from the diffractograms . Results are shown in According to the results, it was concluded that the oxidation process was carried out satisfactorily and that the GO30 sample presented a higher degree of oxidation. Graphite residues were observed in the XRD patterns of GO15 and GO30 samples, indicating that a fraction of graphite was not oxidized. In view of these results, the sample GO30 was subjected to an exfoliation and posterior centrifugation process to eliminate the residual graphite fraction and obtain small-thickness graphene oxide flakes, according to the previously described procedure. The GO30 sample was selected due to the higher content of oxygen-containing hydrophilic groups that make graphite oxide easier to exfoliate in a polar medium.To analyze the effect of sonication times (L: 4 h and S: 3 h) and centrifugation rates  on the characteristics of GO30 flakes, GO30L-4000, GO30L-2000, GO30L-1000, and GO30S-4000 samples were analyzed by XRD analysis and AFM. XRD spectra of GO30 samples are shown in N) can be related by Equation (3) [AFMt is the thickness measured by AFM, 0.4 is the factor that takes into account substrate–graphene and graphene–tip interactions, and dspacing corresponds to interplane spacing in each sample. When this equation was applied to graphene oxide, measured thickness values for GO30L-1000 and GO30L-2000 fractions corresponded to multilayer GO flakes. For fraction GO30L-4000, thickness values at around 2 nm were observed, which may be related to few-layer graphene oxide flakes. The morphology of the GO flakes was observed by AFM and the results are shown in tion (3) ,48:(3)N=Comparing the effect of ultrasonication time on flake morphology in GO30L-4000 and GO30S-4000 fractions  was chosen since the higher presence of oxygen-containing groups could favor the dispersibility of graphene oxide in water, as well as the interaction with the matrix. A centrifugation rate of 4000 rpm was chosen due to the presence of better exfoliated graphene flakes.FTIR spectra of the nanocomposites and pure SA matrix are shown in Thermogravimetric (TG) and derivative thermogravimetric (DTG) results for the SA matrix and GO30L-4000, as well as SA-GO30L-4000-8% and SA-GO30S-4000-8% nanocomposites, are shown in parts a and b of Concerning DTG curves, shown in The effect of GO content in the mechanical properties of nanocomposites was assessed by tensile tests. Young’s modulus values of nanocomposites showed a slight increase with respect to the SA matrix. In general, an increase in tensile strength was observed with the incorporation of GO. Pure SA matrix showed a tensile strength of 101.4 MPa, while the maximum value obtained was 119.1 MPa for the nanocomposite with 8% of GO content. This resulted in an increase of 17.4% with respect to the matrix. The rest of the nanocomposites also showed improved tensile strength with respect to pure SA matrix. The improvement of mechanical properties was attributed to the uniform dispersion of GO in the SA matrix, as well as to the effective interfacial interactions, resulting in a good transference of stress from the SA matrix to rigid GO nanoreinforcements ,50,51. TTo analyze the effect of GO flake size on mechanical properties, SA-GO30L-4000 and SA-GO30S-4000 nanocomposites series were compared, with a GO content of 6 wt % and 8 wt %. In AFM analysis, GO30L-4000 fraction showed thickness and flake size values around of 2 nm and 250 nm, respectively, while the thickness and flake size values for the GO30S-4000 fraction were around 3 nm and 450 nm, respectively. The stress-strain curves obtained for these samples are shown in A significant increase was observed in Young’s modulus and tensile strength when the GO30S-4000 fraction was used as a nanoreinforcement. Concerning tensile strength, an improvement of 65.3% was observed for sample SA-GO30S-4000-8% when compared with the matrix (167.7 MPa vs. 101.4 MPa). For the SA-GO30S-4000-6% sample, an improvement of 58.8% was observed with respect to the matrix (160.8 MPa vs. 101.4 MPa), while for the SA-GO30L-4000-6% sample, an improvement of 6.7% was observed with respect to the SA matrix. Young’s modulus also increased significantly when the GO30S-4000 fraction was used. The values obtained for SA-GO30S-4000-6% and SA-GO30S-4000-8% nanocomposites were 5.1 GPa and 5.5 GPa, respectively, while for SA-GO30L-4000-6% and SA-GO30L-4000-8% nanocomposites, values of 3.6 GPa and 3.7 GPa were obtained, respectively. The elongation at break values diminished in the SA-GO30S-4000 series with respect to the SA-GO30L-4000 series. These results suggested that the effect of flake size is an important factor influencing the reinforcement effect when flakes of similar thickness are employed. Similar results were reported with other matrices by other authors ,29.In order to assess the effect of the addition of GO and its size on the structure of alginate and, thus, in final composite properties, X-ray analyses were carried out. The resulting diffractograms are shown in C is the sum of areas under the crystalline peaks and AA is the area under the amorphous halo.For composites, a clear increase in the intensity of the crystalline zones took place. A new peak centered around 2θ = 26° was also present, attributed to the (002) plane of the remaining graphitic structure of the carbonaceous reinforcements. In order to further analyze the system structure and crystallinity, numerical analyses were carried out to determine the crystalline phase content of each system. For that, diffractograms were deconvoluted with originPro9 using Gauss function and Equation (4) was used to calculate each value.The crystallinity values for SA, SA-30GOL-4000-8%, and SA-30GOS-4000-8% were 74%, 78%, and 87%, respectively. The addition of GO resulted in more crystalline materials, which was greatly affected by the size of the flakes. Larger flakes, produced by shorter sonication times, resulted in systems with higher crystallinity degrees; this dependence on reinforcement shape has been previously reported by Szparaga et al. [The crystallinity values were in agreement with mechanical behaviors shown by the samples. The higher crystallinity degree shown by reinforced composites, specially SA-30GO3-4000-8%, could explain the lower elongation at break values shown by these systems. This higher crystallinity could also add to the reinforcement supplied by the GO flakes in increasing the strength and Young’s modulus.When graphite was subjected to the described oxidative process, it was observed that oxygen-containing groups were satisfactorily introduced into the graphitic structure. As the time of oxidative treatment increased (30 min vs. 15 min), a higher degree of oxidation, as well as a higher disruption of the graphitic structure, was observed. During the sonication and centrifugation processes to obtain graphene oxide, it was observed that higher centrifugation rates resulted in a graphene oxide fraction with lower amount of residual graphite. In the same fashion, graphene oxide flakes of lower size and thickness were isolated as centrifugation rate increased. According to AFM results, it was observed that, when using a centrifugation rate of 4000 rpm, few-layer graphene oxide flakes were obtained. When sonication time was decreased from 4 h to 3 h and final centrifugation rate was maintained at 4000 rpm, an increase in flake size was observed, while flake thickness values remained unchanged. In view of these results, it was concluded that ultrasonication time is related to the breaking of graphene oxide flakes, while flake thickness is more dependent on the centrifugation rate.The nanocomposites showed evidences of hydrogen-bonding interactions between the SA matrix and graphene oxide, because of the oxygen-containing groups introduced during the oxidative process. The incorporation of GO increased the resistance to thermal degradation of the nanocomposites, probably due to the restrictions in SA chains motion as a consequence of interactions with graphene oxide. Higher flake sizes resulted in an improvement in the resistance to thermal degradation. In general, the incorporation of graphene oxide improved the tensile strength and Young’s modulus of the SA matrix. It was observed that, at similar graphene oxide thickness values, the increase in flake size significantly improved the mechanical properties.


Graphene, which is one of the most promising materials for its state-of-the-art applications, has received extensive attention because of its superior mechanical properties. However, there is little experimental evidence related to the mechanical properties of graphene at the atomic level because of the challenges associated with transferring atomically-thin two-dimensional (2D) materials onto microelectromechanical systems (MEMS) devices. In this study, we show successful dry transfer with a gel material of a stable, clean, and free-standing exfoliated graphene film onto a push-to-pull (PTP) device, which is a MEMS device used for uniaxial tensile testing in in situ transmission electron microscopy (TEM). Through the results of optical microscopy, Raman spectroscopy, and TEM, we demonstrate high quality exfoliated graphene on the PTP device. Finally, the stress–strain results corresponding to propagating cracks in folded graphene were simultaneously obtained during the tensile tests in TEM. The zigzag and armchair edges of graphene confirmed that the fracture occurred in association with the hexagonal lattice structure of graphene while the tensile testing. In the wake of the results, we envision the dedicated preparation and in situ TEM tensile experiments advance the understanding of the relationship between the mechanical properties and structural characteristics of 2D materials.The online version of this article (10.1007/s42649-019-0005-5) contains supplementary material, which is available to authorized users. Since graphene was first prepared from graphite via mechanical exfoliation, so called Scotch tape method in 2004, graphene with a distinctive hexagonal lattice structure has attracted intensive attention because of its extraordinary properties  have been speculated to represent the mechanical properties of graphene  or by wet or dry transfer in the case of a graphene prepared by exfoliation or chemical vapor deposition  device, which is one of the MEMS devices used in uniaxial tensile testing, by using dry transfer with a gel material; we then conducted tensile testing on the exfoliated graphene. Optical microscopy and cross-sectional high-resolution transmission electron microscopy (HRTEM) images confirmed that our technique led to the successful transfer to the PTP device. HRTEM images and Raman spectra confirmed that the gel material did not influence the mechanical properties of the graphene. Furthermore, we studied the mechanical properties of exfoliated graphene via stress–strain (S–S) curves obtained by in situ TEM tensile testing. Crack propagation in graphene was also observed. Our developed technique enables the in situ TEM tensile testing of graphene at an atomic level.We used natural graphite crystals . The graphite was sonicated with isopropyl alcohol (IPA) to thin the graphite and weaken the interactions between the thin flakes. The graphite was then heated in a box furnace  for 1 h at 500 °C to evaporate the IPA  at an acceleration voltage of 80 kV to reduce knock-on damage in the exfoliated graphene. The holder system used in the in situ experiments was a Hysitron PI 95 TEM PicoIndenter, and the flat punch probe pushed the semi-circular part of the PTP device to perform tensile testing. Given that the PTP device itself has an identical spring with high stiffness in the lateral direction, we calibrated the S–S curve against broken exfoliated graphene. ImageJ software was used to confirm the distribution of the gel material on the exfoliated graphene. We removed reflections related to the periodic graphene lattice in the fast Fourier transform (FFT) images of the HRTEM images, and the background was inversed in the FFT image to enhance the contrast of the gel material. The density of the gel material was measured manually.The PTP device capable of performing the uniaxial tensile testing of exfoliated graphene is shown in Fig. 2 on a Si substrate for the measurement of the thickness of the exfoliated graphene via optical contrast difference between the substrate and exfoliated graphene with different layer numbers  Optical images of 1 L to 8 L exfoliated graphene with a PF film on 300-nm SiO2/Si. The scale bars shown in (a–h) are 10 μm. (i) Graph of optical contrast difference in the number of layers in exfoliated graphene. (j) Optical image for three-layer exfoliated graphene. The scale bar is 5 μm. (k) Height profile obtained from the solid line shown in (j). (TIF 793 kb)Additional file 2:Figure S2. The homemade position aligner used to transfer exfoliated graphene onto the region of interest in the PTP device. (TIF 1103 kb)Additional file 3:Figure S3. Optical images for exfoliated graphene on a PTP device (a) before and (b) after the in situ heating Raman experiment. The scale bars are 20 μm. The red crosses indicate the area analyzed by Raman spectroscopy. (c) The Raman spectra before and after the sample was heated at 300 °C. The intensity ratios between the D and G peaks are 0.58 and 0.39, respectively. The remaining peaks shown in the “before” heating result correspond to the peaks from the gel material, which is a proprietary product, so details are omitted. (TIF 487 kb)Additional file 4:Movie S1. In situ heating movie under the optical microscope that the gel material was evaporated when the PTP device was heated to 300 °C. (WMV 3161 kb)Additional file 5:Figure S4. (a) High-angle annular dark field scanning transmission electron microscopy image of the penetrated gel materials under the exfoliated graphene. (b–d) Energy-dispersive X-ray spectroscopy elemental maps of (b) Si, (c) O, and (d) C. The result of element carbon corresponds to penetrated gel materials. The scale bar is 4 nm. (TIF 467 kb)Additional file 6:Figure S5. (a–b) Optical images of the gel materials on or under the exfoliated graphene on a Si substrate. (c–d) Optical images after the gel materials were annealed at 500 °C for 5 min. The scale bar is 50 μm. (TIF 1592 kb)Additional file 7:Figure S6. (a) The result of image processing after the lattice of graphene and the background were removed using software to enhance the gel material. The scale bar is 2 nm. (b) FFT image with the graphene lattice removed by mask filtering. (TIF 655 kb)Additional file 8:Movie S2. In situ TEM tensile test of the exfoliated graphene in the PTP device. (WMV 4208 kb)Additional file 9:Movie S3. Movie for brittle mechanical properties of graphene layers. (WMV 583 kb)Additional file 10:Movie S4. In situ TEM tensile test of other exfoliated graphene sample. (WMV 2388 kb)Additional file 11:Figure S7. TEM image of exfoliated graphene after in situ TEM tensile testing. We matched the orientation of the crack propagation and graphene armchair or zigzag edges through the inset figure SADP. The scale bar is 200 nm. (TIF 1083 kb)


Recently, graphene has gained a lot of attention in the electronic industry due to its unique properties and has paved the way for realizing novel devices in the field of electronics. For the development of new device applications, it is necessary to grow large wafer-sized monolayer graphene samples. Among the methods to synthesize large graphene films, chemical vapor deposition (CVD) is one of the promising and common techniques. However, during the growth and transfer of the CVD graphene monolayer, defects such as wrinkles, cracks, and holes appear on the graphene surface. These defects can influence the electrical properties and it is of interest to know the quality of graphene samples non-destructively. Electrical impedance tomography (EIT) can be applied as an alternate method to determine conductivity distribution non-destructively. The EIT inverse problem of reconstructing conductivity is highly non-linear and is heavily dependent on measurement accuracy and modeling errors related to an accurate knowledge of electrode location, contact resistances, the exact outer boundary of the graphene wafer, etc. In practical situations, it is difficult to eliminate these modeling errors as complete knowledge of the electrode contact impedance and outer domain boundary is not fully available, and this leads to an undesirable solution. In this paper, a difference imaging approach is proposed to estimate the conductivity change of graphene with respect to the reference distribution from the data sets collected before and after the change. The estimated conductivity change can be used to locate the defects on the graphene surface caused due to the CVD transfer process or environment interaction. Numerical and experimental results with graphene sample of size 2.5 × 2.5 cm are performed to determine the change in conductivity distribution and the results show that the proposed difference imaging approach handles the modeling errors and estimates the conductivity distribution with good accuracy.  GraphenD materiakness ~3. Å, and hy nature ,7,8 to nGraphene properties are dependent on the number and thickness of the graphene layers . MoreoveAmong the developed methods, micromechanical cleavage produces high-quality graphene, but it can yield very small flakes of order few microns. For the device applications, large grown wafer-sized graphene samples are necessary. Among the different methods to obtain large graphene films, chemical vapor deposition (CVD) is a particularly promising technique ,15. HoweIn terms of electrical characterization for graphene, there is not much advancement in manufacturing methods to develop good throughput and quality. The popular way to evaluate the suitability of graphene films for a specific application is the four-point probe method or Hall effect measurements ,18,20. TNon-contact methods for characterizing the electrical properties of graphene sheets covering a large area such as Terahertz domain spectroscopy (TDS) mapping ,26, InfrRecently, electrical impedance tomography (EIT) is applied as an alternate nondestructive method to map the conductivity distribution of graphene assuming point electrodes . Point eL electrodes lth electrode, lth electrode, In EIT to obtain the conductivity distribution of the graphene sheet, an array of termined . Reconstgiven as (2)u+zlσal level (5)∑l=1LA numerical solution based on the finite element method is used to solve the forward problem. In the 2D finite element formulation, the computation domain i.e., graphene layer surface is discretized into triangular elements where within each element the resistivity distribution is assumed to constant. For the complete details about 2D finite element formulation for the EIT forward solution see . Here itN are basis functions and the number of finite element nodes considered, respectively. In Equation (7), And the corresponding boundary voltages on electrodes are described asA is the system matrix, e) is considered to be white Gaussian, the EIT observation equation can be formulated asUsing Equations (6) and (7) and the boundary conditions Equations (2)–(5), the FEM solution results in a set of algebraic equations and is represented in a matrix formTo obtain the solution for the forward problem, Equation (9) is solved as For the conductivity estimation, let us consider the domain is discretized into small pixels as N). Also, the Hessian matrix is ill-conditioned as the change in voltage due to conductivity in a given pixel is non-uniform. Hence, to have a stable solution, quite often regularization methods are used to reduce the ill-conditioned and ill-posedness of the EIT inverse problem. The regularized cost functional is therefore expressed as [In EIT the inverse problem is ill-posed i.e., the number of voltage data ΦIn absolute imaging, the solution is highly dependent on the accuracy of forward solution and measured voltages. Apart from the measurement noise, there are uncertainties due to modeling errors associated with imprecise knowledge of electrodes position, the unknown shape of outer boundary, and contact impedances of electrodes . The mosfunction ,45(16)ΦThis section presents the numerical and experimental studies to reconstruct the conductivity change across the graphene sample. The graphene sample used for the experimental and numerical case has the same geometry and the preparation of CVD graphene is described below4 mS/cm and data 2 contains a defect or crack that has conductivity 5 × 10−9 mS/cm on the surface. In the second test case, it is assumed the graphene sample has a defect that is like a wrinkle located at the bottom left side that has conductivity 2 × 103 mS/cm, and after some time due to handling or transfer process, another defect is formed that has conductivity 5 × 10−9 mS/cm. The generated voltage data corresponding to data for the two cases is contaminated with 0.5% relative noise to account for instrument and numerical modeling errors.Numerical simulations are carried out using a graphene sample that is square-shaped and has the same geometry as the graphene sample used for the experiment. Fine mesh with 20,440 triangular elements is used to generate true data and coarse mesh with 5110 elements is used to estimate the conductivity distribution across the graphene . Here, tThe results for conductivity estimation for numerical test case 1 are shown in σ1) a and datσ1) b. The esNumerical results for conductivity estimation of case 2 with noise are shown in 2/Si wafer, target substrate. Finally, acetone was used to dissolve the PMMA. The graphene sample is now ready to use for experimental purposes. In this study, we have used a graphene sample of size 2.5 × 2.5 cm for examining the conductivity distribution using EIT. The graphene sample is then coated with copper electrodes using sputtering with a mask designed with the electrode location and shape. In For experiment purposes, the single-layer graphene synthesized on copper foil using the CVD process was purchased from Graphene Square Inc. As a mechanical supporter during the transfer process, poly(methyl-methacrylate) PMMA  is used as a data acquisition system to measure the resultant voltages. A total of 16 electrodes, four on each side of the graphene sample with electrodes placed equidistant on the boundary with a gap of 0.5 cm between them. As a current injection method, the adjacent method is used where neighboring electrodes are used for current injection and the resultant voltages across the other electrodes are measured. For a 16-electrode setup, with 0.1 mA injected current across the neighboring electrodes, there are 256 measurements for each data frame. EIT experiments are performed with a graphene sample of size 2.5 cm × 2.5 cm. Initial or reference data is obtained from a graphene plate without any external defects. After that on the graphene wafer, defects are made manually using a sharp pointed knife on the surface to investigate the conductivity distribution when the defects are present. In scenario 1 , we havee defect e. In thee defect f. In LDIThe experiment results for scenario 2 that has initial data and after change data with two defects are shown in The results for scenario 3 with initial and after change data containing three defects are shown in In this paper, we present electrical impedance tomography (EIT) based non-destructive electrical characterization method to determine the conductivity distribution of large-area monolayer graphene. Electrodes are placed on the boundary of the graphene outer boundary and by using the current-voltage relationship. The conductivity distribution can be reconstructed by solving the EIT forward and inverse solution. In practical situations, the exact knowledge about the electrode locations, contact impedances, and the outer boundary of the graphene is not known accurately. The associated modeling errors and measurement noise can affect the estimates of absolute imaging. Therefore, in this paper, a difference imaging-based approach is proposed to estimate the conductivity change based on the data corresponding initial and after the change. Difference imaging uses the data corresponding same geometry therefore the modeling errors are compensated to some extent and are useful to determine the conductivity profile of graphene. From the numerical and experimental studies with graphene wafer of size 2.5 × 2.5 cm it is found that LDI based conductivity estimates could identify the conductivity change and locate the defects with good accuracy as compared to ABI based solution that is affected due to modeling errors. LDI provides improved results and is computationally efficient as it is one-step and does not need any iterations however the solution is dependent on nominal or reference value and for situations where conductivity change is not very high. Absolute imaging of the conductivity profile is more practically useful for assessing the electrical properties and quality. However, the modeling errors have to be compensated. The simultaneous estimation of model parameters along with conductivity and use of the approximation error method can reduce the modeling errors in absolute imaging of the graphene conductivity profile, which will be studied in the future.


The heterostructures of two-dimensional (2D) and three-dimensional (3D) materials represent one of the focal points of current nanotechnology research and development. From an application perspective, the possibility of a direct integration of active 2D layers with exceptional optoelectronic and mechanical properties into the existing semiconductor manufacturing processes is extremely appealing. However, for this purpose, 2D materials should ideally be grown directly on 3D substrates to avoid the transferring step, which induces damage and contamination of the 2D layer. Alternatively, when such an approach is difficult—as is the case of graphene on noncatalytic substrates such as Si—inverted structures can be created, where the 3D material is deposited onto the 2D substrate. In the present work, we investigated the possibility of using plasma-enhanced chemical vapor deposition (PECVD) to deposit amorphous hydrogenated Si (a-Si:H) onto graphene resting on a catalytic copper foil. The resulting stacks created at different Si deposition temperatures were investigated by the combination of Raman spectroscopy (to quantify the damage and to estimate the change in resistivity of graphene), temperature-dependent dark conductivity, and constant photocurrent measurements (to monitor the changes in the electronic properties of a-Si:H). The results indicate that the optimum is 100 °C deposition temperature, where the graphene still retains most of its properties and the a-Si:H layer presents high-quality, device-ready characteristics. The research on 2D materials, first of all on graphene (Gr), belongs to the most exciting areas in condensed matter physics. The possibility of realizing various heterostructures based on the combination of atomically thin layers with 3D counterparts offers a particularly encouraging playground to investigate and modulate electronic or optical properties. The combination of graphene (2D) with silicon (3D) has been intensively studied recently ,14,15,16To realize a graphene/silicon heterostructure, chemical vapor-deposited (CVD) graphene is usually grown on a metal catalyst  and then transferred to a target silicon substrate by a sacrificial polymer-assisted method. Unfortunately, corrugations and cracks are formed, and the graphene layer can also be contaminated (by etchant and polymer residues) during the transfer process . TherefoIn this study, we propose an inverted course of action to produce graphene/silicon heterostructures, where a 2D material serves as a substrate for a-Si:H deposition performed by a well-known plasma-enhanced chemical vapor deposition (PECVD) process. The idea of silicon film deposition on CVD graphene has already been verified by Arezki et al.  and LupiThe graphene monolayer was synthesized on copper catalyst substrate using a low-pressure (47 Pa) CVD setup . The Cu py e.g., . The groe.g., μm . The Cu The intrinsic a-Si:H layers (approximately 20-nm-thick) were directly deposited on the graphene-Cu substrates (1 × 1 cmRaman spectroscopy measurements were conducted on a LabRAM HR spectrometer (Horiba Jobin-Yvon) equipped with an Olympus BX-41 microscope  and with the laser spot not exceeding 1 Constant photocurrent measurement (CPM) and the temperature dependence of the dark conductivity were investigated on Corning glass (C7059) substrates with coplanar electrodes in a homemade setup equipped with a Keithley 237 source-measure unit.The synthesis of graphene–silicon heterostructures requires the deposition of a device-quality silicon film on a graphene layer, which can be done by PECVD. However, the PECVD deposition of the silicon thin film leads the graphene to be exposed to quite violent conditions, with elevated temperature and various plasma species.The effects of the a-Si:H PECVD process on the graphene layer were examined by Raman spectroscopy . The Ramonolayer ,23. Addi lattice ,23.The level of structural disorder in graphene—expressed as the distance between the defects ( (ID/IG) ,25:(1)LDgraphite ,27. Howe≈2–3 nm) ,27. For ct bands . BesidesEquation . As can Equation , at thisdths see b,c. In t defects . A certa defects .A deeper insight into the nature of the defects can be gained by looking at the ratio between the Raman intensities of the D and D’ bands  ,33. In sThe state of graphene in terms of charge-transfer doping and strain can also be evaluated from the correlation of G and 2D frequencies. The method was introduced by Lee et al.  and has d on top . In brieubstrate , no chanubstrate ).The electronic quality of silicon films strongly depends on the fabrication conditions. To find the optimum temperature interval at which graphene and a-Si:H exhibit properties suitable for device implementation, a series of silicon films deposited at various temperatures was characterized by the temperature-dependent dark conductivity and the CPM. The silicon films are identical to those deposited on graphene; however, the deposition time had to be increased to obtain the roughly 500-nm-thick film needed for reliable electrical and optical measurements.k is the Boltzmann constant and Si bonds . The speeriments , we can 016 cm−3 . These d016 cm−3 , namely,016 cm−3 . The dev016 cm−3 . Hence, h energy . This pah energy .As shown in ectively , evincinThe inverted heterostructure of silicon grown directly on graphene resting on the catalytic Cu foil holds great promise towards circumventing the disorder and impurities that are imposed on the graphene when using the common transfer procedures to place the graphene on top of the silicon. After a-Si:H deposition by the PECVD method, the thus induced changes in graphene were monitored by Raman spectroscopy, allowing a direct quantification of the defect density and relating the possible changes in resistivity. The electronic properties of a-Si:H were assessed by temperature-dependent dark conductivity and constant photocurrent measurements, from which the activation energy of electronic conduction and dangling-bond density can be derived, respectively. An optimum a-Si:H growth temperature of 100 °C permitted us to fabricate a device-quality inverted graphene/silicon stack with minor graphene disorder and good electronic properties of the a-Si:H film. To conclude, we validated the a-Si:H growth by PECVD as a suitable method for the production of inverted graphene/silicon heterostructures, which can be relevant not only for possible photovoltaic applications but also for the “silicon” industry in general.


Latency in video-mediated interaction can frustrate smooth turn-taking: it may cause participants to perceive silence at points where talk should occur, it may cause them to talk in overlap, and it impedes their ability to return to one-speaker-at-a-time. Whilst potentially frustrating for participants, this makes video-mediated interaction a perspicuous setting for the study of social interaction: it is an environment that nurtures the occurrence of turn-taking problems. For this paper, we conducted secondary analysis of 25 video consultations recorded for heart failure,  diabetes, and cancer services in the UK. By comparing video recordings of the patient's and clinician's side of the call, we provide a detailed analysis of how latency interferes with the turn-taking system, how participants understand problems, and how they address them. We conclude that in our data latency unnoticed until it becomes problematic: participants act as if they share the same reality. •Latency causes participants to perceive silence at points where talk should occur.•Gaze behavior is similarly misperceived and thereby misunderstood.•Participants treat these silences as gaps or lapses.•Silence resolution strategies frequently lead to overlapping talk.•Participants have a non-mutual reality, but act as if their reality is shared. The effects latency has on smooth turn-taking have featured centrally in a range of studies of social interaction  pose for conversational participants , 2015. Ieraction .natural breaching experiment  , but als1.1Turn-taking is a basic organizational principle of human social interaction . While tnoticeable silence as, for example, a refusal by the recipient to produce a next action  using a recording from the nurse's end of the call. If we consider the patient's end, we get a very different picture.on time: there is only a micropause between question and answer (line 6–8). Latency affects the nurse's perception of the patient's behavior. First, she receives his vocal answer significantly later than he produces it: he perceives her turn about 750 ms after she produces it and consequently she perceives his response 1.5s after finishing her turn. Second, she sees him turn his gaze to the screen, directing his attention to her, upon completion of her question (see 1a). When recipients gaze towards the speaker, this “displays recipiency” , while revealing that this sense-making is discrepant with the actual source of the problems (etic). It is this combined perspective that drives our conclusion that these participants routinely treat the interaction as instantaneous, until latency noticeably interferes with turn-taking. Finally, we discuss the implications of these findings both for participants engaged in video-mediated interaction, as well as scholars interested in researching video-mediated interaction.2We conducted secondary analysis of data that were collected between 2015 and 2018 in two separate studies on video-mediated consultations We transcribed the data according to conventions by We examined silences at each point where a speaker's turn came to possible completion (the transition-relevance place). However, silence can occur at these points even without latency. We needed to isolate cases in which the speaker attempted to resolve a post-completion silence, where that silence was caused by latency. In other words, our focus was on cases where latency caused speakers to perceive a noticeable silence, when there was no such silence for the recipient. We therefore excluded cases where the recipient did not take a turn . In these cases, latency would merely prolong the noticeable silence. We did not take the length of a silence as a criterion for inclusion. A silence becomes a noticeable silence when participants treat it as such, that is, when participants orient to that silence as the noticeable absence of talk .As with silence, overlapping talk can occur in any social interaction . To makeOur final collection consisted of 130 cases of noticeable silence and 172 cases of overlap. Since silence resolution strategies almost invariably caused overlapping talk, most cases of noticeable silence are also in the collection of overlap.We analyzed these data using Conversation Analysis , focusinBoth studies from which data were drawn received ethical approval for a detailed analysis of video recordings of video consultations. VOCAL was approved by the National Research Ethics Service Committee London–City Road and Hampstead in December 2014 (14/LO/1883) and OQTS by the South Central–Berkshire Research Ethics Committee in September 2015 (15/SC/0553). All participating staff and patients in both studies gave their informed consent to be audio and video recorded during consultations and for the data to be used for research purposes.3The turn-taking system provides for minimal silence between turns and minimal overlapping talk, and it has built-in repair mechanisms to address errors . However3.1normatively relevant, when the speaker has selected the recipient to produce a specific “type” of next action , or it can be possible, when the speaker has completed their turn without sequentially implicating a specific type of next action . When turn-transition could happen, a lapse emerges   .Silences thus become problematic and are constituted as a specific type of silence through the behavior of participants. In our data, both the practices used by participants to address silence and the timing with which they implement these practices indicates they do not understand these silences to be the result of latency, at least not initially. They overwhelmingly treat the silences as failures with turn transition,3.1.1That latency affects turn-taking can best be seen when speakers treat a post-completion silence as the noticeable absence of a projected next action: the recipient is not just heard to be not talking cf. , but to yeah to pursue (dis)confirmation after a yes/no-type question) , response prompts   (such asuestion)   their turn, that is, they perform self-initiated self-repair . The spethe pen type of insulin  . However, the doctor perceives a silence of 0.6 s . Following syntactic completion of her turn, the doctor adds another constituent that continues that turn, in this case the noun phrase uestion) . With thuestion) . She theIn all but three cases, speakers use practices that reinstantiate the transition relevance place. In these three exceptions, participants explicitly orient to the technology as the cause of the problem. However, in none of the three do they treat latency as the cause. Consider extracts 3a) and (3b), taken from the start of the consultation in (1a-b).a and  during which he maintains his gaze towards the nurse, demonstrably waiting for her to take a turn. He then remarks softly—possibly to the GP-researcher—that the nurse cannot hear him. He treats the silence as the noticeable absence of a projected response. He does not, however, pursue a response. Instead he indicates there is a problem with the technology that causes the nurse to not hear him. Note that the account the patient provides for the silence is not latency, but some form of audio trouble. He orients to the silence not as indicating a delay, but as indicating no response at all.Recurrently we find that participants in our data treat silence at a point where turn transition is relevant, as a failure in the organization of turn-taking. They use practices to pursue a response, meaning they treat the response not as delayed due to latency, but as missing entirely. By treating these silences as gaps, they show that at least initially they are not aware of latency as the true source of the problem.3.1.2According to the rules of turn-taking, when a speaker reaches possible completion and has not selected a next speaker, turn transition is optional. Recipients will have primary rights to self-select, but they may forego this opportunity. The ensuing silence will still be oriented to as silence where talk should occur , but theWe found that latency interfered with this system. At a point of possible completion where no next speaker has been selected, the current speaker may perceive a lapse even though the recipient has already self-selected. The following example from a diabetes consultation illustrates how speakers perceive and deal with a lapse where one does not occur. The patient has been talking for 1.5 min about a drug trial in which he took part. That story comes to potential completion in line 3, where he returns to describing the study and discusses the outlook .4Image 54oh right (okay to indicate her understanding that his story is done (The nurse receives the patient's story with a claim of understanding oh right  and subs is done . With th is done  before c is done ), and thThe consequences of this perceived but not actual lapse may seem small initially. There is some overlapping talk before they resume one-speaker-at-a-time with the patient answering the nurse's question . However, the patient does not return to the turn he had abandoned when the overlap happened. Instead, the talk moves away from the specifics of the study he was talking about. The opportunity to mention what he was going to mention does not arise afterwards and he does not create the opportunity for himself.We identified 33 lapses that were caused by latency (extract (5) below provides a further example). Lapses provide a different problem for participants than gaps. Latency causes speakers to perceive silence at a point where recipients may self-select. This silence is understood as the recipient foregoing the opportunity to take a turn, even when the recipient has in fact started talking. Because the solution provided by the turn-taking system is for the speaker to continue, the result consistently is overlapping talk, which then has to be resolved by one of the participants dropping out. But that itself is not always straightforward. We now turn to how latency frustrates participants’ ability to accomplish overlap resolution.3.2The turn-taking system provides for minimal overlapping talk . When ovrecognitional onset)transition space onset); (iii) when a next speaker begins a turn just after current speaker has continued (post-transition onset); and (iv) when a next speaker begins a turn at a point where current speaker is recognizably not approaching completion (interjacent onset).Overlap can occur at different structural positions in the construction of the turn : (i) wheThe implications of overlap happening at these various points can be markedly different. For example, when recognitional onset occurs, turn completion provides a natural point for current speaker to drop out and thereby to resolve overlap. However, when one participant launches a turn at an interjacent position, they are making an active claim to the turn when another already has the right, and this can require more competitive practices . In videIn the majority of our data (147 cases), overlap is resolved quickly (in the other 25 one participant concedes the turn after both compete for it). One of the participants drops out within a few beats of overlapping talk. Extract (5) from a diabetes consultation illustrates this clearly. The nurse has just explained that while the patient had a slightly low potassium during her last blood test, there is no cause for concern. She then asks the patient for an update on her diabetes.yeah, with an audible glottal stop. Note that, although the patient concedes the turn to the nurse, she maintains her gaze away from the screen, likely indicating that she will resume or re-attempt to answer when the nurse completes her turn  comes to completion, she perceives a 0.6 s silence during which she sees the patient gazing to her. She pursues a response by re-asking her question, using a full clause this time, treating the silence as a gap. The patient's response comes off in post-transition overlap, but the nurse continues undisturbed. After her response pursuit, the patient provides an answer and the sequence unfolds. From the patient's perspective, however, she responds with no delay , her gaze aversion coordinated with the start of a TCU , and she perceives the onset of the overlap in the transition space.her turn . She seeThe overlapping talk here is a result of the nurse pursuing a response when, unbeknownst to her, one was already forthcoming. The trouble is quickly resolved. The patient quickly drops out, in line with what One problem that latency can cause for overlap resolution is that resolution practices are similarly delayed. This potentially leads to extensive sequences in which participants try to figure out who gets to talk. The following example from a heart failure consultation reveals how latency affects overlap onset and the problems it poses for overlap resolution. The patient has been explaining how his back pain affects his ability to perform household tasks and in line 1 he turns to talk about how he struggles to play croquet.yeah  completes the sequence, and since neither subsequently takes a turn, a lapse ensues. Both participants attempt to resolve this lapse by self-selecting: the nurse begins to offer a potential solution to some of the patient's problems, and the patient starts to say that he will keep trying. But because of latency, neither participant can immediately notice that the other has self-selected. As a result, each has already produced a partial turn when they hear the other's turn, leading to interjacent overlap at both ends ).The patient's turn reaches possible completion for the first time at line 7. At the patient's end (left column), the nurse does not take a turn and silence ensues. The patient then recompletes his turn by providing an increment . In overrepeat request, and finally when the patient attempts to self-select.In this example, latency has a significant effect on participants’ perception of where overlap onset happens. It also frustrates their attempts to figure out who gets to talk next. First when the participants attempt to give the right to talk to the other party by abandoning their turn, subsequently when they explicitly grant the other the right to talk through what Both participants initially attempt to resolve the overlap by abandoning their turn after a few overlapping beats, but due to latency these strategies create a new problem. From the patient's perspective (left column), he drops out quickly after the nurse starts her turn, and so she continues talking (line 16) without him talking in overlap. However, from the nurse's perspective (right column), she stops talking after about two beats of overlapping talk, and it is in fact the patient who can continue talking (line 16) without her talking in overlap. Whilst they solve the overlapping talk by both dropping out, they are faced with another problematic silence (line 17).sorry (line 18) he orients to his prior overlapping talk as an interruption and acknowledges that the nurse has the right to talk. The nurse responds with go on (line 19), thereby asking the patient to continue his turn. Here the problem could have been resolved. After he has heard the nurse's turn, the patient recognizably restarts his turn by repeating I've got (line 15 and 22). However, because latency delays the nurse's perception of his restart, she perceives 0.5 s of silence and then more actively prompts him to continue by repeating part of his turn: y'r gonna keep on. This prompt comes off in overlap the patient's restart. He abandons his turn and another silence ensues.To move out of this silence, one participant needs to self-select or grant the turn to the other, but again latency frustrates their moves for a solution. The patient first attempts to give the turn to the nurse: by saying yeah  how long a delay needs to be before it starts to disrupt turn-taking, and (b) to what extent the length of the delay matters for different types of actions and turn-transitions. Furthermore, the COVID-19 pandemic has led to an enormous increase in the use of video-mediated communication. Early indications are that people are now far more aware of the problems that latency can cause. Indeed, it seems to be one of the main frustrations. Whether and if so how this emerging experience re-shapes the turn-taking rules for video-mediated interaction is unclear. Participants may become better at addressing the problems, and develop new turn-taking conventions that are optimized for the medium.4.1Video-mediated interaction has been a subject of research for decades, and much of our social interaction has moved online as a result of the COVID-19 pandemic and social distancing protocols. Nonetheless, the literature around turn-taking in video is scarce and primarily about institutional talk. Since latency is omni-present, inter-turn silences will always be longer in video-mediated interaction than they are for in-person or telephone interaction. However, what kind of silences are tolerable and when video-mediated interaction can be understood as “smooth” has so far not been investigated. Our data suggests that with latency around 100 ms, turn-taking is barely affected, whereas with 700 ms, participants struggle routinely. Studies need to be set up to systematically address the nature of video-mediated turn-taking, focusing on its vocal, embodied and technological components in both dyadic and multi-party interaction. Given the challenge posed by social distancing protocols for data collection, such studies may prove difficult during the pandemic, but with screen capture software it should be possible to start exploring these questions in a more systematic way.emic perspective, we should focus on the individual's life world  [grant number PB-PG-1216-20012]; the 10.13039/100014461NIHR Biomedical Research Center, Oxford, UK [grant number BRC-1215-20008]; and the 10.13039/100004440Wellcome Trust [grant number WT104830MA]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.This work was supported by the Lucas Seuren: Writing – Original Draft, Data Curation, Investigation Joseph Wherton: Investigation, Writing – Review & Editing Trisha Greenhalgh: Funding acquisition, Writing – Review & Editing Sara Shaw: Supervision, Writing – Review & Editing, Project Administration.None.


The key to the preparation of polymer nanocomposites with new or improved properties resides in the homogeneous dispersion of the filler and in the efficient load transfer between components through strong filler/polymer interfacial interactions. This paper reports on the preparation of a series of nanocomposites of graphene and a polyolefin using different experimental approaches, with the final goal of obtaining multifunctional materials. A high-density polyethylene (HDPE) is employed as the matrix, while unmodified and chemically modified graphene fillers are used. By selecting the correct combination as well as the adequate preparation process, the nanocomposites display optimized thermal and mechanical properties, while also conferring good gas barrier properties and significant levels of electrical conductivity. At the frontiers of materials technology, the concept of synergistically combining various materials of very different nature is one of the most successful approaches to achieve multifunctionality. In this respect, the appropriate combination of graphene with polyolefins, two families of materials with very different but complementary properties, provides a route to the creation of versatile materials with countless applications, because it not only refers to the combination of two compounds but in the assembly of two families of materials.On the one hand, polyolefins are the most widely exploited classes of polymers on the planet due to their good chemical and physical properties, together with their low production costs, easy processing, extraordinary versatility, and their recyclability potential and application as sustainable materials. The annual global production of polyolefins in 2015 was estimated at over 180 million tons . The apparent solubility coefficient can be calculated byTaking into account that the permeability coefficient is defined as Values of the permeability, diffusion and apparent solubility coefficients for the gases in the nanocomposite membranes prepared by solution and melt mixing at 30 °C and 1 bar of pressure are shown in 2) > Φ(H2) > Φ(O2) > Φ(CH4) > Φ(N2). Regarding the diffusion coefficients: D(H2) > D(O2) > D(N2) > D(CO2) > D(CH4), closely related to the Lennard–Jones diameter of the diffusive species in such a way that the lower the diameter, the higher the diffusion coefficient. Finally, the solubility: S(CO2) > S(CH4) > S(O2) > S(N2) > S(H2). Evidently, carbon dioxide is the most condensable gas and exhibits the largest apparent solubility coefficient. The high value of the solubility coefficient of CO2 in comparison with that of H2 is responsible for the relatively high permeability coefficient of that gas in all nanocomposite membranes.An inspection of these tables shows that all membranes exhibit the same behavior when comparing results for different gases. Thus, the permeability coefficients follow the trends Φ to the experimental results, as can be seen in −7 cm2/s, respectively. These values fit very well with those of 0.81 Barrer and 1.76 × 10−7 cm2/s obtained by fitting Equation (5) to the experimental results. The values of Φ and D obtained from the As can be seen in The morphology of the interface plays an important role in the overall transport properties of composite materials, and according Moore and Koros , five ‘cThe experimental results shown in NC) can be described by the equationp and Φf are the gas permeability of the pure polymer and the filler, respectively and φf is the volume fraction of the filler, which takes values of 0.023 and 0.067 for samples with 1 wt % and 3 wt % of graphene, respectively.In order to analyze the differences in performance between GPE and G nanocomposites with the same filler loading experimental data were compared with those calculated from several theoretical models. The simplest model to predict the permeability of nanocomposite membranes is the Maxwell model that assumes an ideal filler/matrix contact where the polymer fully embedded the filler nanoparticles, but it does not feel the presence on the filler, maintaining its bulk properties ,45,46. Af = 0, and apply the Maxwell model, we see that the decrease of permeability is much lower than almost all the experimental data (int) would be related to that in the bulk by means of an immobilization factor, β according to the equationIf we assume that the graphene sheets are not impermeable, Φtal data , whateveφs is the filler volume fraction in the pseudo-dispersed phase, which in the case of 2D laminar particles (as in this study) can be estimated by the equationφint is the volume fraction of the filler in the interlayer region, which is a parameter that cannot be experimentally determined, and w and intl are the thicknesses of the filler and the interface, respectively. Note that the interface layer is formed on both sides of the filler sheets.Therefore, the effective permeability of the pseudo-dispersed phase (rigidified region plus dispersed particles) can be expressed by the equationWith this information, the effective permeability of the nanocomposites can be estimated by using a corrected Maxwell equation. For this simulation we focused on the materials with 1 wt % of filler. As the fillers differ (G and GPE), the graphene volume fraction in the nanocomposites are also different due to the contribution of the pendant short PE brushes in GPE. With the composition of the nanocomposites studied and using a filler thickness of 10 nm and interface thicknesses from 0 to 100 nm, we can simulate the variation of the permeability of the nanocomposites for different values of β, and the results are presented in Figure 10intl to adjust to the curves that provides an estimation of the extent of the rigidized region around the filler. In the evaluated range of β (2–50) the intl adopts values in the ranges of 3.4–8 nm, 17.5–39 nm, and 36.5–80 nm for G1S, GPE1S, and GPE1M, respectively. The variation of intl with β for these samples is represented in With the linear fits from the points in intl) around the filler is greater for the nanocomposites with GPE, which is a clear indication of better compatibility and stronger polymer/filler interface when graphene is appropriately functionalized. In addition, a greater effect is observed for the samples prepared by melt-compounding which may be explained in terms of some preferential orientation of the filler in the direction of extrusion. In the case of samples prepared in solution, the filler is more homogeneously dispersed in a random manner throughout the polymer matrix.From Although the results for oxygen permeability are shown, the same trend has also been obtained for nitrogen and carbon dioxide (not shown).Mathematical models have been proposed to describe the influence of the geometry and orientation of impermeable fillers on the barrier properties of different nanocomposites materials ,48,49,50NCD), as shown in PD is the diffusion coefficient of the pristine polymer and τ is the tortuosity factor. If we assume that the presence of the filler does not affect the permeation properties of the polymer matrix, the gas solubility can be expressed asThe dispersed impermeable nanofillers provoke a reduction in the diffusion coefficient of the composites (By combining Equations (9), (15), and (16), the relative permeability of the nanocomposite in relation to the neat polymer is obtained byα) and the volume fraction of filler, according toNielsen suggested a model based on the dispersion of impermeable ribbons of infinite length in a permeable matrix . This apThus, Equation (17) can be expressed asfφ < 0.01 whereas for higher volume fraction of graphene layers, the filler tends to aggregate and this model is not valid. In this last case, Cussler et al. [This theoretical model assumes a complete exfoliation of the nanoplatelets dispersed along the perpendicular direction of gas diffusion, and can predict the experimental results accurately when r et al. ,53 propoα) on the permeability decrease as a function of the filler volume fraction for Nielsen and Cussler models. The experimental values for nanocomposites prepared with GPE filler are also represented in α increases. Furthermore, our experimental data showed good agreements with the Nielsen model with an aspect ratio α = 10–20 and with the Cussler model with α = 10.As an example, S, which allows the calculation of zero and maximum tortuosity states [However, none of these models take into account the orientation of the platelets into the film thickness. Bharadwaj modified the Nielsen model including an orientation factor, y states (21)ΦNCΦS = −0.5 corresponds to the zero tortuosity case with the platelets oriented in a parallel direction respect to the gas diffusion. A value of S = 1 represents the maximum tortuosity with a perfect alignment of the platelets in the perpendicular orientation to the permeation direction. A value of S = 0 corresponds to a random distribution of graphene layers.This orientation factor takes values from −0.5 to 1. A value of In the same way, we can extend the Cussler model by including this orientation parameterα = 10 and different values for S. The results are shown in S = 0, whereas GPE1M and GPE3M fit very well with this model with 0.5 < S < 1. These results would mean that for membranes prepared from solution, the functionalized graphene platelets have a random orientation with respect to the permeation direction, while when melt-blending was employed, the graphene sheets seems to be partially aligned with the film surface. These results are in good agreement with the TEM images of Taking into account that the volume fractions of GPE used to prepare our nanocomposite membranes were higher than 0.01, which is the upper limit for achieving better applicability of the Nielsen model, we have compared our experimental data with the modified Cussler model of Equation (22), using Summarizing, the much lower permeability of the nanocomposites with GPE can be unequivocally attributed to the better compatibility of GPE with the matrix caused by strong interactions between the pendant short PE brushes in graphene with the HDPE, and the differences between samples prepared by different experimental approaches can be attributed to different dispersion, interphase morphology, and some orientation of the fillers during the sample preparation process.−14 S cm−1. Therefore, it is expected that the incorporation of small amounts of graphene with high aspect ratio can produce a significant increase in the electrical conductivity of the host polymer matrices. In fact, for graphene-based polymer nanocomposites with different polymer matrices, it has been shown that the electrical conductivity of the insulating polymer can be increased by several orders of magnitude in a percolative manner with the addition of critical amounts of graphene. The critical loading content, at which the conductivity suddenly increases, known as the electrical percolation threshold, depends on the type of matrix, the characteristics of graphene , the polymer/filler affinity and the mixing procedure. For certain matrices, the use of reduced graphene oxide has demonstrated to be very useful to achieve electrical conductivity [One of the main challenges for systems composed of polyolefins and highly conductive particles like graphene is to achieve polymer nanocomposites with reasonable electrical conductivity. Polyolefins are totally insulating, with conductivity values in the order of 10uctivity ,55. For uctivity ,25.−2 S cm−1 with 0.6% GNP) has been reported for ultra-high molecular weight polyethylene-GNP nanocomposites produced via solvent assisted dispersion and melt compression [In pression .At this point, it is worth noting that while the strength of the filler/polymer interface is fundamental to achieve nanocomposites with improved or new properties, the mixing approaches can produce opposite effects in electrical conductivity and gas barrier. While in the former the orientation of the filler can be detrimental for the electron transport through the nanocomposite, in the latter this is helpful to increase the tortuous path limiting the diffusion of gas molecules.The preparation of multifunctional nanocomposites by combination of HDPE and graphene has been described. The adequate combination of both components produces nanocomposites with better thermal and mechanical properties incorporating gas barrier and electrical conductivity. The importance of an appropriate selection of the type of filler and the mixing method for the preparation of HDPE/graphene nanocomposites has been highlighted. Consistent results demonstrate a clear influence of both parameters on the final properties of the nanocomposites. While thermal and mechanical properties are slightly affected, gas barrier and electrical conductivity are strongly dependent on the type of filler as well as on the mixing approach. While melt-compounding produces better membranes for gas barrier due to some orientation along the extrusion direction, this is disadvantageous for electrical conductivity at the graphene contents tested. Furthermore, the mechanical and barrier properties of the materials prepared here make them good candidates for food packaging, although in this particular case, issues related to reduced transparency must be addressed.


By analyzing the GH variation, the index sensitivity of such configuration can reach as high as 8.02 × 105 λ/RIU, which is 293.24 times of the Au-ITO structure and 177.43 times of the Au-ITO-graphene structure. The proposed SPR biosensor can be widely used in the precision metrology and optical sensing.In order to improve the performance of surface plasmon resonance (SPR) biosensor, the structure based on two-dimensional (2D) of graphene and transition metal dichalcogenides (TMDCs) are proposed to greatly enhance the Goos-Hänchen (GH) shift. It is theoretically proved that GH shift can be significantly enhanced in SPR structure coated with gold (Au)-indium tin oxide (ITO)-TMDCs-graphene heterostructure. In order to realize high GH shifts, the number of TMDCs and graphene layer are optimized. The highest GH shift (−801.7 λ) is obtained by Au-ITO-MoSe Surface plasmon resonance (SPR) is a kind of highly sensitive real-time spectral phenomenon, which can be used to measure the refractive index change on the surface of the metal film . The optAs we know, when total reflection occurs at the interface of two kinds of media, a small lateral displacement occurs between the incident light and the reflected light, which is called the Goos-Hänchen (GH) shift ,13. Artm2O3 (commonly used doping ratio is: In2O3:SnO2 = 9:1), and substituting Sn4+ atom for in3+ atom, it has the characteristics of high band gap and degeneracy [2, TMDCs, and direct band gap semiconductor in detail [2 are considered [2 heterojunction, the GH shift is significantly enhanced to 235.8 λ [Indium tin oxide (ITO) is a new kind of semiconductor material . Pass thgeneracy . It is cgeneracy ,23,24,25generacy ,27, trangeneracy ,29, blacgeneracy ,31, and generacy ; secondlgeneracy ; finallygeneracy ,35. Grapgeneracy . Howevergeneracy . Therefogeneracy . The hargeneracy ,40. Wu egeneracy . Comparen detail . The spinsidered . In the  235.8 λ .2/MoS2/MoSe2/WSe2) and graphene on GH shift in current literatures. Moreover, the combination effects of metal and ITO on GH shift has yet been reported, while in this paper, the GH shifts of TMDCs and graphene are compared, and the maximum GH shift is 801.7 λ for Au-ITO-MoSe2-graphene heterostructure. The optimum sensitivity based on GH shift is 8.02 × 105 λ/RIU, which is 293.24 times of Au-ITO structure and 177.43 times of Au-ITO-graphene structure. These results will provide a helpful guidance for the scholars who study 2D-materials-based GH shift.Although TMDCs and graphene are recognized as promising approaches to enhance the GH shift, there is no comparative analysis of various TMDCs n1=1.ollowing :(2)n2=1.According to the Drude–Lorentz mode, the third layer is the Au film with refractive index as following :(3)n3==1The fourth layer is the ITO film with refractive index as :(4)n4=3.The fifth layer of TMDCs with the refractive index and thickness of monolayer at λ = 632.8 nm is shown in 1 ≈ 5.446 μm−1. The thickness of monolayer of graphene is 0.34 nm. For the last layer, the sensing medium is water. In the λ = 632.8 nm, the refractive indices are n1 = 1.7786, n2 = 1.5151, n3 = 0.181 + 3.068i, n4 = 1.858 + 0.058i, n6 = 3.000 + 1.149i, n7 = 1.330 [bion. The dielectric constant of each layer is set to kε . The thickness (kd) of SF11 glass, BK7 glass, and sensing medium are 1d = 200 nm, 2d = 100 nm, and d7 =100 nm, respectively. For this SPR biosensor, we use the thickness of Au (3d) 50 nm and ITO (4d) 10 nm to excite the SPR. It is reasonable to take the individual graphene sheet as a non-interacting monolayer if the number of layers N ≤ 5 [In the sixth layer, the refractive index of graphene at visible range is obtained by the relation :(5)n6=3. = 1.330 , respectM is the characteristic TM of n-layer composite structure, which is obtained from the following relation of P-polarized light [kM is expresses as:kp and kα are written as:kd is the thickness of the kth layer. The matrix of the total reflection polarized light (pγ) can be expressed as [p1 and Np are the corresponding terms for the first layer and the Nth layer. The reflectivity (Rp) and phase (pψ) is shown as:In order to study the changes of the GH shift and reflectivity in SPR biosensors, we use the transfer matrix method (TMM) and the Fresnel equation based on N-layer model to perform a detailed analysis. The ed light :(6)M=∏k=essed as :(10)γp=S=−1The curve of reflectivity changing with incident angle is called SPR curve, once SPPs are excited, there will be a reflection angle and a corresponding sharp change of reflection phase. According to Equations (11)–(13), we can know that the SPR reflectivity (Rp), phase (ψp), and GH shift of Au-ITO film coated BK7 and SF11 glass, as shown in S = 63.89 λ. For bilayer and 3 layers, the GH shifts of this Au-ITO-graphene structure are 89.06 λ and 168.5 λ, respectively. For 4 layers, the minimum reflectivity is 1.9829 × 10−6 a.u. at resonance angle of 61.01°, the phase change to Lorentzian-like at resonance angle, and the highest GH shift of SPR biosensor structure is −241.2 λ. Therefore, when the phase change is Lorentzian-like, the GH shift is negative, and the greater the change of phase, the larger the value of GH shift. The GH shift of −134.7 λ is obtained by 5 layers. Hence, when the thickness of graphene is 4 layers, the GH shift reaches the maximum value S = −241.2 λ. Subsequently, different layers of MoSe2 are added to the Au-ITO structure, as demonstrated in −5 a.u. at resonance angle of 63.02°. The phase change to Lorentzian-like at resonance angle, and the highest GH shift of SPR biosensor is S = −492.6 λ. From 3 layers to 5 layers, the GH shift is −53.41 λ, −28.01 λ, and 20.14 λ, respectively. Therefore, the maximum GH shift of −492.6 λ is obtained by the MoSe2 bilayer. From 2 layer, the SPR resonance angle will show a larger GH shift, and the GH shift of MoSe2 is larger than that of graphene. Secondly, the bandwidth of the reflection curve will be broadened rapidly with the increase of the number of MoSe2/graphene layers, because the electronic energy loss of MoSe2 layer is related to its imaginary part of dielectric function. The increment of MoSe2 layer leads to a large electron energy loss [Then, the different layers of graphene and TMDCs are used to increase GH shift. First, the different layers of graphene are added to the Au-ITO structure. In rgy loss .2. In 2 (monolayer) hybrid structure change with angle of incidence. For monolayer, the GH shift of this structure is S = 186.4 λ at resonance angle of 61.53°. For the bilayer of graphene, the minimum reflectivity is 3.29 × 10−5 a.u. at resonance angle of 61.97°, the phase change to Lorentzian-like at resonance angle, and the highest GH shift of SPR biosensor is S = −801.7 λ. For the 3, 4, 5 layers, the GH shift is −114.1 λ, −58.14 λ, and −37.68 λ, respectively. Therefore, when the graphene and MoSe2 are bilayer and monolayer, respectively, the maximum GH shift of −801.7 λ is obtained by Au-ITO-MoSe2-graphene hybrid structure. Secondly, the different number of MoSe2 layers added to the Au-ITO-graphene (monolayer) structure. In 2 from layer 2 to layer 5, the lowest point of the reflection curve is more and more far away from zero, and the phase change is smaller, which shows that the light absorption is gradually weakened, and the SPR excitation are also weakened.In order to enhance the GH shift, the different layers of graphene and TMDCs are used to increase GH shift. Firstly, we investigate the angle of incidence for different number of graphene layers with monolayer of MoSe2/WS2/WSe2) are added to the monolayer of graphene. As shown in 2 monolayer, the highest GH shift is 409.9 λ. With the increment of MoS2 layers, the GH shift changes from positive to negative. However, the GH shift that become negative are smaller, which are −56.57 λ, −24.23 λ, −16.12 λ, and −13.03 λ, respectively. Therefore, the maximum GH shift of 409.9 λ is obtained with both monolayer of MoS2 and graphene. In 2 is from monolayer to 5 layer, the GH shift is 43.09 λ, 29.92 λ, 21.82 λ, 17.0 λ, and 14.28 λ, respectively. In 2.Subsequently, different number of other TMDCs  can be obtained. With WS2, the highest GH shift 382.4 λ is gained by WS2 monolayer and graphene 5 layers. Finally, the largest GH shift of −454.3 λ is obtained when the WSe2 is monolayer and graphene is 5 layers.With the increase of layers of TMDCs/graphene, the GH shift increases gradually. However, further increasing the layers, the absorbed energy will not be completely transferred to the enhanced evanescent field, which leads to the decrease of GH shift. This can be analyzed from the depth and width of the SPR curve. The closer the reflectance to zero the higher the modulation depth, and the greater the loss the broader the resonance . Therefon7), the GH shift will appear with a large variation. Hence, the structure can be used as a high sensitivity biosensor by monitoring the change of GH and the sensitivity (PS) is defined as [n7 is the change of refractive index of sensing medium. In n7 is plotted. When the n7 increases from 1.330 to 1.332, the maximum of GH shift reaches ΔGH = 5.47 λ . Therefore, we can calculate the sensitivity to be PS = 2735 λ/RIU. Similarly, in PS = 4520 λ/RIU. In 2 (monolayer)-graphene (monolayer) structure, so the sensitivity is PS = 2.142 × 104 λ/RIU. In 2 (monolayer) -graphene (bilayer) offers the maximum of GH shift ΔGH = 160.4 λ, when the n7 increases from 1.3300 to 1.3302, resulting in the highest sensitivity of 8.02 × 105 λ/RIU, which is 293.24 times larger than the Au-ITO structure and 177.43 times larger than the Au-ITO-graphene (monolayer) structure.We found that when we change the refractive index of sensing medium (fined as :(14)SP=ΔFor comparison, the performances of previously reported 2D-material-assisted GH shift sensors based on SPR sensors are summarized in 2 and bilayer of graphene. The maximum displacement is 801.7 times of the incident wavelength. Compared with the traditional SPR structure, the shift of the structure is increased by more than 2 orders of magnitude. Moreover, the GH shift can be positive or negative depending on the layer number of TMDCs and graphene. The sensitivity corresponding to the maximum GH shift can reach as high as 8.02 × 105 λ/RIU, which is 293.24 times of the Au-ITO structure and 177.43 times of the Au-ITO-graphene structure. Such configuration could pave the way to high precision optical sensing.In this paper, the high GH shift in SPR biosensor based on Au-ITO-TMDCs-graphene hybrid structure is analyzed. We theoretically prove the influence of the number of graphene and TMDCs layers on the GH shift, and a large GH shift is obtained by using the mixed structure of monolayer MoSe