By transferring graphene layers to plasmonic nanoparticles immobilized on a glass substrate, the scattering profiles of the nanoprobes were adjusted in terms of the position, width, and intensity of the peaks to determine the optimal conditions for measuring the PRET signal. Using the optimized graphene-covered plasmonic nanoprobe, we obtained calibration curves over a wide concentration range from femtomoles to millimoles for hydrogen peroxide based on the change in the PRET signal. Before monitoring cellular ROS, we confirmed that a high density of cells adhered well to the graphene-covered plasmonic interface by observing immunofluorescence images of the cytoskeleton of the immobilized cells. Finally, we monitored the real-time ROS generated by the cells under oxidative stress conditions by directly measuring the spectral changes of the probes around the cells. We believe that the proposed graphene-covered tunable plasmonic interface has versatile applicability for investigating cellular stress and disease progression by monitoring ROS levels under various cellular conditions.Reactive oxygen species (ROS) regulate various physiological and pathological conditions in cells by interacting with signaling molecules and inducing oxidative stress. Therefore, sensitive monitoring of ROS levels in living cells is important to track cellular state and study the complex role of ROS in the development of various pathologies. Herein, we present an optically tunable plasmonic interface covered with graphene to monitor cellular ROS levels with superior sensitivity and cellular comfortability. As a sensing principle, we employed plasmon resonance energy transfer (PRET)-based spectral quenching dips modulated by redox-active cytochrome The online version contains supplementary material available at 10.1186/s40580-022-00315-9. Reactive oxygen species (ROS), like superoxide anions, hydrogen peroxide, and hydroxyl radicals, are molecules with strong oxidative power and high reactivity that regulate a variety of biological processes –4. In noc (Cyt c) induces unique spectral quenching dips in the scattering profile of the plasmonic probe, which can be changed by the presence of ROS. Before cellular ROS monitoring, it would be better to use optically adjustable and biocompatible interfaces for monitoring oxidative stress in cells. Because the PRET signals stem from the spectral overlap between the scattering of the plasmonic nanoprobe and the absorption of the redox-active Cyt c, fine-tuning of the scattering spectra of the probes is essential to achieve high sensitivity. Moreover, a comfortable interface should be provided to cells to monitor the oxidative stress experienced under certain extracellular or intracellular conditions. Recent reports have shown that diverse cell types, including fibroblasts [Various methods have been developed to detect ROS generated by cells using fluorescence –27, chemroblasts , neuronsroblasts , and ostroblasts , adhere c reacted with ROS. By measuring PRET signals in real time, we can quantitatively detect ROS levels and monitor cellular ROS.In this study, we demonstrate a plasmonic interface covered with graphene layers to optically monitor the ROS generated from living cells. As illustrated in Fig. 2SO4, 95%), hydrogen peroxide , ammonium persulfate (98%), ethanol (99.9%), dimethyl sulfoxide (99.8%), acetone (99.5%), 2-propanol , and nitric acid (69%) were purchased from Samchun Pure Chemical Co. Ltd. . Silver nanoparticle , cytochrome c (Cyt c) from the equine heart (≥ 95%), l-ascorbic acid (AA), 3-aminopropyltriethoxysilane (APTES), phosphate-buffered saline (PBS), poly-l-lysine (PLL) solution (0.01%), sodium (meta) arsenite (≥ 90%), Triton X-100, bovine serum albumin (BSA), and Hoechst 33258 were purchased from Sigma-Aldrich . Gold nanoparticles  were purchased from BBI solutions . The human dermal fibroblast cell line (HDF) was obtained from NeoRegen Biotech Co., Ltd. . Dulbecco’s modified Eagle medium (DMEM), penicillin/streptomycin (Penstrep), and 0.05% trypsin-EDTA were purchased from Gibco-Life Technologies . Fetal bovine serum (FBS) was purchased from MP Biomedicals . Formaldehyde (4%) and Alexa Fluor 488 phalloidin were purchased from Thermo Fisher Scientific . Rabbit monoclonal anti-α-tubulin and Alexa 647-conjugated anti-rabbit secondary antibodies were purchased from Abcam .A poly(polydimethylsiloxane) (PDMS) elastomer kit (Sylgard 184) was purchased from Dow Corning . Copper (Cu) foil  was purchased from Alfa Aesar . Sulfuric acid .4 and H2 as precursor gases. The substrate was first annealed at 1000 ℃ for 30 min under an H2 flow (100 sccm). Subsequently, the carbon source gas, CH4 (10 sccm), was introduced into the quartz tube. After 30 min of growth, the CH4 gas was turned off, and the copper substrate was removed from the heating area of the furnace to cool down at room temperature under H2 flow.Before graphene growth, the Cu foils were cut into 10 cm × 10 cm pieces. Nitric acid was diluted in deionized (DI) water under stirring. The Cu foils were immersed in dilute nitric acid for approximately 30 s. The cleaned Cu foils were successively washed with isopropanol, acetone, and DI water three times. Monolayer graphene was then grown on the cleaned copper foil via a CVD system using CHTEM analysis was performed to characterize plasmonic NPs. To prepare the specimens, 10 µL of each NP solution was dropped onto a carbon-coated 300-mesh TEM grid . TEM images were obtained using a transmission electron microscope  operating at an acceleration voltage of 120 kV.2SO4/H2O2 = 7:3 v/v) for 1 h, rinsed with ethanol and DI water, and then dried in a stream of N2. The cleaned glass slide was immersed in an ethanol solution of 1 mM APTES for 24 h. The surface modified with APTES was washed with pure ethanol and dried in a stream of N2. To immobilize plasmonic NPs on the glass slide, 200 µL of colloidal NP solution was dropped onto the APTES-modified glass slide for 30 s and washed with DI water to remove excess NPs. Prior to transferring the graphene layers onto the NP-immobilized glass substrate, the graphene was floated in a 1 M ammonium persulfate aqueous solution for 6 h to completely dissolve the Cu foil. Then, it was washed twice with DI water for 15 min and washed with HCl for 10 min at room temperature (RT) for complete etching of Cu. After the etched graphene was placed on a glass slide, the PMMA layer was removed with acetone and IPA.The glass slide was treated with piranha solution  at an accelerating voltage of 10 kV.The surface topologies and height profiles of the transferred graphene layers were also obtained using an AFM  in the non-contact mode.A micro-Raman system combined with a spectrometer SR-303i (Andor Technology) and a 50 mW 532-nm laser module PSU-III-FDA  was used for the characterization of graphene. The system was comprised of an integral Olympus BX51 microscope with a 20× objective lens. The Raman spectra were collected at an exposure time of 1 s (five accumulations).Dark-field scattering imaging of graphene-covered plasmonic NPs according to the number of graphene layers and the corresponding spectral analysis under each condition were performed using a dark-field microscope  equipped with a hyperspectral imaging spectrophotometer . A 20× objective lens was used for imaging, and the integration time for collecting the scattering spectra was 0.3 s. For all data, the reliabilities of the measured intensity, spectral shifts, and full width at half maximum (FWHM) were evaluated based on the standard deviation from 60 different nanoprobes.The photoluminescence (PL) properties of the plasmonic NP-graphene interface were characterized using a fluorescence spectrometer  with an excitation picosecond laser at 520 nm in the range 520–900 nm.2O2 concentration were measured using the above-mentioned dark-field-based hyperspectral system. Prior to the measurement of the quenching dips, the intrinsic spectra of the single NPs were measured under DI water in the PDMS well. To measure the initial spectra of the NPs before exposure to H2O2, DI water in the reaction well was replaced with a reduced Cyt c aqueous solution as a redox probe, which was prepared by preincubation with 100 µM Cyt c and 50 mM AA (5:1 v/v) for 1 h. Then, H2O2 was injected into the PDMS well, and the ratio between the reduced Cyt c and H2O2 was set to 5:1 v/v. After a 2 min reaction, the scattering spectra of the single NPs because of H2O2 exposure were collected. All collected spectra were normalized, and changes in the spectral quenching dips were measured before and after H2O2 exposure. For all the data, the reliability of the observed spectral shift and intensity was examined using the standard deviation of 60 different NPs.To demonstrate PRET-based ROS quantification, changes in the quenching dips of the single plasmonic NPs depending on the HTo examine the biocompatibility of the plasmonic NP-graphene interface, cells incubated for 24 h were fixed with 4% formaldehyde in PBS for 15 min at RT and permeabilized with 0.4% Triton X-100 for 15 min. The cells were washed twice with PBS for 15 min. For imaging microtubules, after blocking the cells with 5% BSA in PBS for 1 h, cells were incubated with a rabbit monoclonal anti-α-tubulin antibody  overnight at 4 ℃ and washed five times with PBS for 2 min over 5 times. The cells were further incubated with an Alexa 647-conjugated anti-rabbit secondary antibody  for 1 h at RT. F-actin was stained with Alexa Fluor 488-phalloidin (Invitrogen) for 1 h at RT. Nucleic acids were stained with the DNA-binding dye Hoechst 33258 . Stained cells were visualized using a confocal laser scanning microscope .3 cells/200 µL) and A375P (5 × 103 cells/200 µL) were seeded in 0.01% PLL-treated plasmonic NP-graphene interface and incubated at 37 °C and 5% CO2 atmosphere for 24 h. Then, 0.2 M NaAsO2 and reduced Cyt c diluted in DMEM were injected onto the plasmonic NP-graphene interface in the PDMS well. The change in the scattering spectra induced by the ROS was monitored using a dark-field-based hyperspectral system.HDF cells . After incubation with the toxicants for 1 h, the cells were washed with PBS and exposed to 20 µM 2,7-dichlorofluoroscein diacetate (DCFDA) diluted in DMEM. After incubation for 50 min at 37 °C in the dark, the cells were washed with PBS and imaged using a confocal laser-scanning microscope.Cells were seeded at 0.01% PLL-treated plasmonic NP-graphene interface and incubated at 37 ℃ in a 5% COc in this study) are near the plasmonic nanoprobes. That is, the PRET signal is a result of light energy transfer from plasmonic nanoparticles (NP) to light-absorbing molecules [c. As summarized in Fig. c were clearly observed without omitting or truncating the dips. This revealed that there is an optimal condition for measuring the Cyt c-mediated PRET signal in terms of the position, width, and intensity of the scattering peaks of the plasmonic nanoprobes. When we layer the optically transparent and ultra-thin graphene—an excellent electrical conductor—onto plasmonic nanoprobes, we expected that electron transfer between them can modulate the scattering properties of the probes according to the type of plasmonic NPs, as shown in Fig. In principle, PRET signals are measured in the form of spectral quenching dips in the scattering spectrum of the plasmonic nanoprobe matching with the molecular absorption bands when the light-absorbing molecules (Cyt olecules –47. We nsc) of the nanoparticles was calculated by varying the sizes of the SNP and GNP and the number of graphene layers. For example, diameters of 100, 110, and 120 nm were considered for SNP, and diameters of 50, 55, and 60 nm were considered for GNP. In the case of graphene layers, 0–4 layers were considered . The scattering cross-section  and bi-layered graphene (~ 2 nm). Additionally, we measured the Raman scattering to further confirm the presence of the graphene layer in the plasmonic NP-graphene interface. As shown in Fig. − 1 (G peak) and 2670 cm− 1 (2D peak), which are well known as characteristic peaks of graphene [To prepare the plasmonic NP-graphene interface, plasmonic NPs were first immobilized on an amine-functionalized glass substrate, followed by transferring the graphene layer onto the immobilized NPs, as illustrated in Fig. graphene , 52. Thegraphene , 53. TheWe next investigated the optical tunability of plasmonic NPs by increasing the number of graphene layers. Dark-field scattering images showed that the scattering colors of the SNP probes gradually changed from green to red as the number of graphene layers increased Fig. e. Figurec was evaluated using the two types of plasmonic NPs (SNP and GNP) and their hybrid interfaces covered with different numbers of graphene layers. As expected, the quenching dips by PRET were the largest when the scattering peak of the nanoprobe exactly matched the absorption wavelength of Cyt c  was calculated as 2 fM, which is six orders of magnitude lower than that of the commercially available ROS assay kit (ca. 1 µM) [c, as described in Fig. For ROS detection, we intentionally prepared fully reduced Cyt ed Cyt c . ROS-ind8 M Fig. f. The SNa. 1 µM) . This isa. 1 µM)  and Au-Pa. 1 µM)  without Before monitoring the ROS in living cells, cellular adhesion on the plasmonic SNP-graphene interface was examined using immunofluorescence staining for F-actin (green), α-tubulin (red), and nuclei (blue). Human dermal fibroblasts (HDF) and human melanoma cell lines A375P) were seeded on the prepared SNP-graphene interface and on a glass slide (as a control) , oxidative stress-induced cells , and cancer cells , as displayed in Fig. Having demonstrated that the graphene-covered SNP serves as an excellent probe for ultrasensitive detection of ROS and provides a comfortable interface to cells, we next monitored the ROS generated from living cells in real time. The reduced Cyt i.e., A37P, as disi.e., A37P, as dis2 clearly shows that the change in the quenching dip at 550 nm is greater than that of the unexposed HDF. Within 10 min, ROS levels were measured to the ranges comparable to the 10 nM–10 µM H2O2 for the three cases, when estimated from the calibration curve. After 60 min, the extracellular ROS levels were more clearly distinguishable according to the cellular conditions, with ROS levels comparable to 1 mM, 100 mM, and over 100 mM H2O2 for HDF, NaAsO2-exposed HDF, and A375P, respectively. Through our real-time measurements, we observed that stressed cells released ROS more rapidly into the extracellular environment than normal cells. The observed difference in ROS release kinetics is consistent with the fluorescence intensity for intracellular ROS production measured after additional incubation with a ROS-indicating dye, 2,7-dichlorofluoroscein diacetate (DCFDA)  and number of graphene layers . We observed enhanced Cyt c-mediated PRET signals by modulation of electron movement through the interface between the NP and graphene. Using the optimized probes, we detected H2O2, a representative ROS, at the femtomolar level, which is six orders of magnitude lower than that of the commercially available ROS assay kit (ca. 1 µM). In addition, we demonstrated that a plasmonic-graphene hybrid interface provides an optically excellent and comfortable surface for real-time monitoring of cellular ROS from living cells. The proposed graphene-covered tunable plasmonic interface has versatile applications for studying cellular stress and disease progression by monitoring ROS levels under various cellular conditions.We demonstrated the in situ optical monitoring of ROS generated from living cells on the plasmonic NP-graphene interface based on the redox-active Cyt Additional file 1: Figure S1. Simulation of scattering spectra according to the number of graphene layers on SNP. (a) 110 nm SNP. (b) 120 nm SNP. (i) Full spectrum. (ii-iv) Plots showing shifts of λmax (ii), FWHM (iii), and scattering cross-section (σsc) (iv). Figure S2. Simulation of scattering spectra according to the number of graphene layers on GNP. (a) 55 nm GNP. (b) 60 nm GNP. (i) Full spectrum. (ii–iv) Plots showing shifts of λmax (ii), FWHM (iii), and scattering cross-section (σsc) (iv). Figure S3. Representative TEM images of the used SNPs. The average size (for n = 40) was observed to be 101.6 ± 5.0 nm . Scale bars represent 25 nm. Figure S4. TEM images of the used GNPs. Average size (for n = 40) was 49.5 ± 2.6 nm. Scale bars represent 25 nm. Figure S5. Scattering properties of the plasmonic GNP-graphene interface. (a) Dark-field scattering images of the graphene covered-plasmonic GNP with increasing number of graphene layers. The scale bars represent 10 µm. (b) Corresponding scattering spectra measured for the GNPs with increasing number of graphene layers. (c) Plots for the shifts in terms of λmax (i), FWHM (ii), and intensity (iii) with increasing the graphene layer on the GNP. Figure S6. Changes in photoluminescence (PL) intensities of graphene-covered NPs. (a) SNP. (b) GNP. (i) Schematic diagram, (ii) PL spectrum, and (iii) Plot for the change in PL intensity of NP at 550 nm in the presence of graphene layer. Figure S7. Fluorescence images of intracellular ROS in cells. (a) HDF, (b) NaAsO2-exposed HDF, and (c) A375P. The green fluorescence indicates intracellular ROS visualized by staining with a ROS indicating dye, 2,7-dichlorofluoroscein diacetate (DCFDA). The scale bars represent 50 µm.


Studies on depositions of chemical vapour deposition (CVD) diamond films have shown that flame combustion has the highest deposition rates without involving microwave plasma and direct current arc. Thus, here we report on our study of few-layer graphene grown by flame deposition. A horizontal CVD reactor was modified for the synthesis of flame deposition of few-layer graphene on a Cu substrate. It was found that graphene obtained has comparable quality to that obtained with other flame deposition setups reported in the literature as determined from Raman spectroscopy, sheet resistance, and transmission electron microscopy. Calculation of the chemical kinetics reveals a gas phase species that has a close correlation to the growth rate of graphene. This was further correlated with van't Hoff analysis of the reaction, which shows that the growth reaction has a single dominating mechanism for temperatures in the range of 400 °C to 1000 °C. Arrhenius analysis also was found to be in good agreement with this result. This study shows few-layer graphene growth proceeds through different pathways from a CVD grown graphene and also highlights flame deposition as a viable method for graphene growth. Growth kinetics of few-layer graphene grown by flame deposition correlates to BIN20J species in a methane to soot mechanism model. One of them is that due to its batch production coupled with long reaction times at high temperatures, CVD becomes a very energy intensive method equating to increased costs. Through this method, graphene with resistivity as low as 143 Ω cm has been achieved.2 Bae et al.3 growing graphene films reports a sheet resistance of 30 Ω per square. In comparison, pristine graphene obtained from exfoliation of graphite has 10−6 Ω cm resistivity4 with a maximum current density5 larger than 108 A cm−2.Current production of high-quality large-area graphene can only be achieved through chemical vapour deposition. No other method comes close to producing the quality of graphene produced by this method.6 Most recently, gallium and cobalt–copper alloy substrates were shown to grow graphene at lower temperatures.7,8So far, much progress has been made to reduce its energy consumption through the development of better catalysts and growth methods. In general, copper has been the choice substrate for graphene growth due to its wide availability. When compared to other elements, it is cheaper compared to other metals but its high energy barrier creates a need for high reaction temperatures for graphene to grow.9,10 However, the need for a high-power plasma generator and a good vacuum system makes the setup very complex and has high energy consumption. Thus, graphene grown by this method is very expensive due to its batch production capabilities and very strict control of the growth conditions.While such catalytic substrates could potentially reduce the reaction temperature and improve reaction rate, there is still a need for better production methods as it will ultimately determine the scale of which graphene can be produced. For now, modifications to the conventional CVD methods have been shown to allow good quality graphene to be produced at even lower temperatures. Usage of plasma has been widely reported to have reduced the growth temperatures of graphene and commercially manufactured reactors are already available for plasma enhanced CVD (PECVD). Instead of thermally activating the reactants within the gas phase or on the substrate, reactants are first turned to active species within the plasma itself. As a result, growth temperature could be lowered as low as <420 °C while maintaining a high-quality growth.11 Previous to graphene synthesis by flame deposition, Wal et al. reports of the growth of carbon nanotubes by flame deposition only requiring reaction times in the order of milliseconds.12 The combustion itself raises the temperature allowing the remaining hydrocarbons to form the desired product. One of the earliest reports of graphene grown by flame deposition used a dual flame setup.13,14 Nickel substrate was kept within a flame sheath to prevent oxidation while another carburizing flame heats and provides carbon to the substrate. The authors reported that graphene synthesized through this method did not have full coverage and contains many defects. Another group built a micro combustor for the flame deposition of graphene on Cu and Ni wires where they found high Reynolds number yields better graphene qualities.15 A setup developed by Memon et al.16 for large area graphene growth used an inverse diffusion flame. The inverse diffusion flame getters excess O2 and provides a stable flame. They reported a graphene deposit which has a monolayer ratio (I2D/IG) of 0.77 to 0.59 and defect ratio (ID/IG) of 0.35 grown at 1000 °C for 10 minutes. Our group have also reported on achieving similar quality graphene using a horizontal CVD reactor modified for flame deposition.17 This highlights the potential that this method possesses for producing graphene at lowered temperatures.Here, we explore an alternative way which could emulate plasma enhanced CVD using flame deposition. Historically, deposition of diamond by flame combustion have been widely studied and it has been shown to have a very high deposition rate when compared to other methods except for direct current arc.18 there are no known studies relating to growth mechanisms for graphene grown by flame deposition. However, combustion reactions of hydrocarbon have been studied in relation to diamond film formation.19,20 In fact, there are various models that have been built which even allows predictions of the resulting equilibrium species. In this study, we aim to study the growth kinetics of graphene grown on Cu substrate using a flame deposition method. Complimenting the experimental results are simulations of equilibrium species based upon a soot production model.21While there are many discussions on the growth mechanism for CVD grown graphene,2) gas enters from the ceramic nozzle to mix with methane (CH4) and hydrogen (H2) gas entering from a separate ingress. 2 R-type thermocouples were used to regulate and observe the temperature. One was placed with the substrate and another outside of the quartz tube within the isothermal region.Graphene was grown using a homemade horizontal hot-wall furnace fitted with a ceramic nozzle to allow combustion. The furnace core is a ceramic tube wrapped with Kanthal wires and insulated with ceramic fibres . An ignition system was attached to the nozzle to initiate combustion as shown in Substrate preparation before the deposition is as follows. A copper foil  was polished and cleaned thoroughly before use. First, various grits of sandpaper were used until reaching #2000 grit. Next, alumina polish  was used as the final finishing before cleaning with acetone  followed by distilled water in an ultrasonic bath for 30 minutes. Once done, it was dried thoroughly and stored in a dry place. For the deposition, the foils were cut into 1 cm by 1.5 cm.2 atmosphere with a flow rate of 100 sccm. It was then removed from the furnace hot-zone and allowed to cool under Ar gas flow till room temperature. Once cooled, the substrate with Ar gas, CH4 and H2 were introduced followed by O2 gas. Upon introducing O2 gas, flame ignition was initiated, and the substrate was inserted back into the heated furnace at a temperature of 1000 °C for graphene deposition. After 10 minutes deposition, the substrate was taken out from the hot-zone and allowed to cool to room temperature in an Ar atmosphere. The whole reaction takes place at atmospheric pressure and its overall procedure is shown in Before deposition, an annealing step was performed at 1000 °C for 20 minutes in a H4, O2, and H2. Partial pressure for each reactant was changed while keeping the remaining 2 reactants at the same ratio to each other. Graphene deposits obtained were transferred and characterized.3 parameters were tested, which are partial pressures of CHRs was then converted to deposit thickness, t by the following ρ = 4 × 10−5 Ω cm) was used.Graphene deposits on Cu substrate were transferred using methyl methacrylate found commonly in super glue with tissue fibres as a reinforcing matrix. Sheet resistance measurements were done on these transferred deposits using a 4-point co-linear probe with a potentiostat . Sheet resistance, 22Additionally, graphene was also transferred to a silicon wafer and copper mesh grid  for analysis by Raman spectroscopy  and transmission electron microscopy (TEM) . Fast Fourier Transform analysis was also done on bright field images from TEM using FIJI software.23 set to conditions similar to those stated in the experiments using a mechanism file containing 296 species.21Apart from experimental data, numerical analysis of the concentrations of equilibrium species was also done using Cantera4 partial pressure, P(CH4) while H2/O2 ratio was set to unity results in thicker deposit as P(CH4) increases. However, deposit thickness reaches a maximum between 0.6 and 0.8 atm P(CH4). For the dependency of P(O2), while CH4/H2 is set to unity, deposit thickness reaches a maximum at P(O2) = 0.2 atm before decreasing again as seen in P(H2). However, it should be noted that the increase in deposit thickness due to P(H2) is very small when compared to changes due to P(CH4) and P(O2). For P(H2) deposit thickness increased from 0.5 Å to 2.1 Å whereas, graphene thickness increased by 100 fold for P(CH4) and 10 fold for P(O2). In general, conditions that yields thin deposits have a larger deviation which gets smaller as the deposits get thicker. Unsurprisingly, thinner deposits are harder to reproduce as any slight changes induces a significant change in thickness. This is clearly demonstrated in Deposit thickness calculated from the measured sheet resistance is shown in P(CH4), the low initial P(CH4) coupled with high P(O2) leaves only a small amount of unreacted hydrocarbon to serve as the precursor for graphene. As it increases, there are more excess hydrocarbon allowing for thicker deposits. In P(O2) was varied, even a small addition of O2 was found to promote thicker deposits until a certain threshold which is in line with reported observations.24 It was noted that O2 was found to assist in the pyrolysis of CH4 which improves growth rate but higher P(O2) instead oxidizes and depletes aromatic hydrocarbons vital for the production of solid carbons.For 4 partial pressure was done and TEM analysis was performed on the deposit grown at CH4 : O2 : H2 = 0.8 : 0.1 : 0.1 atm which is shown in ID/IG) and monolayer ratio (I2D/IG) in P(CH4) yield deposits that are higher in defects and thicker which is in good agreement with thickness calculated from sheet resistance P(CH4). Again, a transition point was observed at the 0.4 atm to 0.5 atm range similar to the one in sheet resistance where thickness growth rate undergoes a change. Growth rate slows down beyond this point as O2 and H2 get further diluted by CH4 which hints of an optimal O2 and H2 composition in aiding the growth of graphene.Additionally, Raman characterization of deposits for the dependency on CH25 Observing another region with spherical carbon nodules yields an FFT image with rings and devoid of any spots. Raman spectra of the same deposit in −1 was detected even on blank Cu  values also shows few-layer graphene as observed by Raman analysis in TEM analysis of the deposit reveals the presence of graphene structures and amorphous carbon. Fast Fourier Transformation (FFT) of a region with moiré pattern in u Fig. S1 without 27 The absence of d = 0.213 nm diffraction spots by graphene indicates that the deposit is few-layer graphene.25 Transferring the graphene on a Cu mesh introduces additional crumples and folds which will lead to regions of multilayered polycrystalline graphene which could be concluded from the twisted diffraction rings. Such defects are not originally present on the deposit itself. Overall, we could conclude that the deposit obtained contains amorphous carbon, multilayer polycrystalline graphene as well as diamond-like carbon.Analysis of diffraction patterns of the bright spots in 4, O2, and H2 were performed using Cantera. Focussing on the effects of changing P(CH4), 2O, CO2, CO, and CH3O reach a peak at around P(CH4) = 0.5 atm and declines with higher P(CH4) since there is less O2 available. Formation of complex hydrocarbons (C6H6 and C8H10), H2 and H* radical undergoes a sharp increase as it transitions from a fuel lean mixture to a fuel rich mixture at P(CH4) = 0.2 atm. At higher concentrations, hydrocarbon species also increases but slow down after about P(CH4) = 0.5 atm. The growth rate of graphene observed in Calculations for the concentration of equilibrium species for each dependency on CHP(O2) and its equilibrium species concentration do not present any notable similarities. However, as expected, P(O2) > 0.5 atm as the gas composition turns into a fuel-lean mixture. Also, the point P(O2) = 0.2 atm where deposit thickness was highest was also the point where most hydrocarbons undergo a sharp decline in equilibrium concentration as O2 increases. Judging from the increased concentration of combustion products, oxidation reactions are the cause for this. Whereas according to P(H2) even as initial CH4 gets reduced due to Le Chatelier's principle to counter the increased P(H2). Deposit thickness in P(H2). Regardless, equilibrium species considered here does not closely follow the changes in deposition thickness from experiments. It is probable that there are other species that have a better correlation to our experimental data.Comparisons between dependency of graphene thickness on 12972032H1622016 (codename: BIN20J) is a possible candidate as it possesses the best correlation to our experimentally obtained data as shown in In order to find the chemical species vital for graphene growth, each species available in the model was evaluated. From all the species contained within the mechanism, it was found that a chemical species CP(CH4) and P(O2) in st order reaction which explains the close correlation of BIN20J concentration to deposit thickness. Calculations on the yield and selectivity of BIN20J was also done  increases as more hydrocarbon by-products are also produced at higher methane concentration. Selectivity for BIN20J increases above P(O2) = 0.2 atm but its yield reaches a plateau at that point. This is believed to be due to its consumption by excess oxygen despite its increased selectivity. Changes to deposit thickness in relation to P(H2) shows poor correlation to the predicted values of BIN20J. However as noted previously, changes in deposit thickness due to H2 dependency were an order of magnitude smaller than those due to CH4 and O2. Studies have noted that graphene could still be obtained by flame deposition even without H2 gas being used28 and chemisorbed hydrogen on Cu functions as a catalyst for CH4 heterogenous dissociation on Cu.29 From this, we believe that H2 has a small influence in gas phase kinetics and instead has a bigger role in affecting the substrate itself directly. Since the model involves only gas phase reactions, such heterogeneous reactions are left unaccounted for thus leading to the inconsistency between experimental and predicted model. Regardless, the predicted values of BIN20J has been shown to have a good correlation to experimental data.The equilibrium concentration for BIN20J displays a direct correlation to deposit thickness for e Fig. S3, it show30 An Arrhenius plot  that is less affected to the gas phase kinetics.In −1) which is markedly lower than graphene grown by conventional CVD methods as seen in 4 as determined by ab initio studies33 and mass transport is responsible for the rate-limiting step at APCVD34,35 which generally leads to an increased energy barrier  closest to our experimental values.45,46While a thorough understanding of the growth mechanism of graphene by flame deposition still eludes us, our results provides certain insights on the growth of graphene by flame deposition. First, graphene growth follows closely with the BIN20J formation using a soot formation mechanism reported by Ergut I2D/IG) of 0.65 and defect ratio (ID/IG) of 0.58 which was comparable to literature. We also show that numerical analysis by an open source software using an appropriate mechanism model provides a close prediction to the actual graphene deposition. Changes to deposit thickness when CH4 and O2 were varied was found to agree with simulated values for BIN20J. Deviations between experimental and simulation results for P(H2) suggests H2 has a minor role in gas phase kinetics and influences the heterogeneous reaction on the substrate which is not accounted for by the mechanism file used. At this current stage only the gas phase mechanism was considered but with proper improvements, a model that could predict the actual deposition is certainly possible. Considerations of the growth kinetics lead us to believe that the growth mechanism includes the gas phase formation of BIN20J followed by its adsorption on Cu substrate which then grows into graphene.An experimental flame deposition setup has been built for the growth of few-layer graphene. Raman analysis shows multilayer graphene with a monolayer ratio (There are no conflicts to declare.RA-009-C9RA01257E-s001


Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salute 4.0 Competence Center. The envisioned interventions are based on promoting behaviour change in various domains such as physical activity, mental health and nutrition. In particular, the nutrition component is a self-monitoring module that collects dietary habits to analyse them and recommend healthier eating behaviours. Dietary assessment is completed using a Food Frequency Questionnaire on the Mediterranean diet that is presented to the user as a grid of images. The questionnaire returns feedback on 11 aspects of nutrition. Although the questionnaire used in the application only consists of 24 questions, it still could be a bit overwhelming and a bit crowded when shown on the screen. In this paper, we tried to find a machine-learning-based solution to reduce the number of questions in the questionnaire. We proposed a method that uses the user’s previous answers as additional information to find the goals that need more attention. We compared this method with a case where the subset of questions is randomly selected and with a case where the subset is chosen using feature selection. We also explored how large the subset should be to obtain good predictions. All the experiments are conducted as a multi-target regression problem, which means several goals are predicted simultaneously. The proposed method adjusts well to the user in question and has the slightest error when predicting the goals. Food Frequency Questionnaires (FFQs) are inexpensive and probably one of the most commonly used dietary tools for self-assessment. Therefore, several applications incorporate FFQs to monitor dietary habits ,2. For eThe FFQ in the LBC application is based on a Mediterranean diet, which has been shown to have a wide range of benefits for health , for insAlthough performing well, most automated approaches for generating or adapting FFQs rely heavily on expert knowledge. For instance, Molag et al.  developeIn our previous work, we explored a few approaches that could help automate the development of FFQs and adjust the questionnaires to the users’ needs. So far, we have worked on single-target problems, trying to predict only one nutrition aspect at a time. First, we explored the ranking of questions in FFQs , which cIn this paper, we explore different approaches to reduce the number of questions shown on the screen in the LBC mobile application. We consider the problem of predicting the quality of several nutrition aspects from the questionnaire as a multi-target regression problem, which means we predict several values simultaneously ,17. We pThe FFQ used in this paper follows the principles of the Mediterranean diet and is currently implemented in the LBC application  developequestions for food items from this point on.The questionnaire asks the user about the daily consumption of 24 food items: bread, pasta, breakfast cereals, baked sweets, potatoes, fruit, vegetables, red meat, white meat, fish, conserved fish, eggs, lentils, milk/yoghurt, cheese, olive oil, other oils/butter, dried fruit, water, processed meat, sweets/snacks, sugar, honey/marmalade and sweet drinks intake. The outcomes of the questionnaire are quality scores of eating habits regarding 11 nutrition aspects: fruit and vegetables, carbohydrates, proteins, milk, oil, water, dried fruit, processed meats, sweets/snacks, sweet drinks, and sugar. The questionnaire is presented with a grid of images (buttons), and the system asks the user about his daily consumption of the 24 food items. The number of button presses on each image indicates the number of consumed portions of the food item presented with the corresponding image/question . For simWhile implementing the FFQ in the LBC application offers a good user experience, it also makes it easy to provide partial responses—the users may answer only the questions they deem important. This is not a problem for the users, as the application is intended for self-monitoring, and they can use it as they see fit, but the quality of the data collected with the application was low. Since the method proposed in this paper is general and can be used on any population without particularly unusual dietary habits, we thus used a higher-quality dataset to develop and test it. The questionnaire in the SiMenu had 104 questions, asking the users about the frequency of consumption of different food items. As the questions included in the LBC application questionnaire are a direct subset of the questions in the SiMenu questionnaire, the transformation of the answers to fit the LBC application questionnaire was very straightforward.FFQ representation using a grid of images to list questions is user-friendly and allows a more interactive way of self-reporting dietary habits. However, although the number of questions is only 24 and the questionnaire is not very extensive, the mobile application screen still seems crowded, slightly overwhelming and challenging to read for some users, especially elderly ones. Therefore, our task was to find a way to reduce the list of questions and use machine learning to predict the quality of achieving goals that the user has activated (decided to follow) from the 11 goals listed in The main idea was to find the subset of questions that would allow the algorithm to highlight more problematic goals from those the user chose to follow . Users usually have better dietary habits regarding some goals than others. For example, a user might have decided to use the application to self-monitor his vegetable, protein and sugar consumption habits. In his meals, he usually includes a portion of vegetables and a portion of fish or meat. He always finishes his lunch and dinner with a dessert. When comparing his quality scores to the optimal amounts of vegetables, protein and sweets, the algorithm will detect that the user eats enough protein and should maybe include more vegetables, but he overeats sugar. We wanted to teach our algorithm to pay more attention to the problematic goals. The most attention should go to sugar consumption, a little less (but still some) attention should go to the goal regarding vegetable intake, and the least attention should go to protein intake (as it is the best). This is included in the algorithm as weights assigned to goals and helps better assess the more problematic goals, which can then be used to give better recommendations or identify the important questions for the user.In this section, we first describe the pipeline used in the experiments. Next, we describe the machine-learning techniques used in the experimental setup. Finally, we describe a function that personalises the questions’ importance by transforming distance to the optimal quality scores for the activated goals into weights. With this, the application can determine the most informative questions based on the user’s previous answers.When reducing the number of questions, we strove to keep half the questions at most and explored options where we tried to predict targets by using 4, 6, 9 or 12 questions. These numbers are chosen based on the mobile application layout . We wantWe conducted three different experiments to reduce the number of questions: (1) the subsets of questions were chosen randomly; (2) we chose the most important questions by calculating their importance as features for predicting nutrition quality scores assuming all activated goals are equally important; (3) we calculated the distance of predicted quality scores’ values from the experiment to optimal quality scores for activated goals only, transformed them to weights, and recalculated feature importance .For the first experiment that was used as a baseline, we decided to choose questions randomly. Another option would be to use one of the dimensionality reduction methods such as Pearson correlation analysis , as usedWe conducted the experiments as multi-target regression problems, which means that several quality scores are predicted simultaneously . In our In the LBC application, users are free to activate (follow) from 1 to 11 goals. Therefore, the case in which the user chooses just one goal can be treated as a single-target problem, and in this case, experiment (3) does not differ from the experiment (2), which was already addressed in our previous work ,15. TherAnswers to the questions are first transformed into a ‘portion per day’ measure. A similar technique is used with the goals. In machine learning, the transformed answers to the questions are called features, and the quality scores are called targets. Both features and targets are then scaled using the min–max normalisation:1.Random features. We choose n random features, n being a number from a set of 2.Statistically optimised features. We choose the best n features, n being a number from a set of sklearn library, feature importance is calculated on each fold and then averaged over the folds. The chosen features are then used to build a model on the train set, and the evaluation of the model is completed on the test set. This experiment considers all of the goals equally important for the user.3.Personalised features. The third experiment is split into two steps. The first step is the same as the second experiment. The second step takes the predicted values from the second experiment and calculates the distance to the optimal values of the goals  or should not be over-reached . We calculate the distance asWe conducted three experiments: random features, statically optimised features, and personalised features. Additionally, we compared cases when we showed 4, 6, 9 or 12 questions on the image grid.There are 2037 combinations of goals in which at least two goals are activated. We present the results averaged by the number of activated goals, which is completed for all cases of selected features. This section presents the main results, and the remaining can be found in In n random equations. The second one uses feature selection to determine n best features (which correspond to questions), n being a number from the set of The LBC application integrates a Food Frequency Questionnaire in a rather innovative way. However, although the questionnaire is reasonably short (24 questions), the representation with the image grid still becomes quite hard to read, especially for elderly users. Therefore, in this paper, we investigated a way to reduce the number of questions. We compared three approaches. Two of them are pretty straightforward. The first one serves as a baseline and simply picks Based on the results in Another conclusion from the experiments is that the prediction error increases with more activated goals. It also decreases with the number of selected features. Based on these conclusions, nine or 12 features or questions would be the most reasonable choice to be implemented in the application.Another observation regarding We conducted our experiments on a dataset derived from a dataset from the SImenu study . While iIn this paper, we proposed a method to reduce the number of questions and FFQ used in the LBC application. The number of questions is reduced based on users’ chosen nutrition goals and how well they achieve them. Additionally, the application should give more attention to the more problematic goals related to nutrition habits requiring the most improvement.random features, statically optimised features and personalised features. We considered cases where the application shows 4, 5, 9 or 12 questions instead of 24. We concluded that the proposed method—personalised features, which considers that goals are not equally essential and considers users’ previous answers, performs the best for the task. In our future work regarding this particular FFQ, we would like to explore further the possibilities of adopting the questionnaire to the users’ preferences based on their answers. We would also like to integrate the proposed method into the existing application and test it in the field on the actual longitudinal data.We compared three methods for choosing the questions in the FFQ—In addition to working with this particular questionnaire, we plan to test the proposed on more complex questionnaires in which questions and goals are more intertwined than in the FFQ used in this paper and on questionnaires where questions are asked more conventionally: question by question. Finally, we would like to explore more options to personalise the questionnaires based on users’ previous answers by using active learning —where th


People with a diagnosis of schizophrenia (PSz) have difficulty engaging in social interaction, but little research has focused on dialogues involving PSz interacting with partners who are unaware of their diagnosis. Using quantitative and qualitative methods on a unique corpus of triadic dialogues of PSz first social encounters, we show that turn-taking is disrupted in dialogues involving a PSz. Specifically, there are on average longer gaps between turns in groups which contain a PSz compared to those which do not, particularly when the speaker switch occurs from one control (C) participant to the other. Furthermore, the expected link between gesture and repair is not present in dialogues with a PSz, particularly for C participants interacting with a PSz. As well as offering some insights into how the presence of a PSz affects an interaction, our results also demonstrate the flexibility of our mechanisms for interaction.This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’. Difficulty engaging in social interaction is one of the most debilitating aspects of the disorder with significant consequences for the lives of those diagnosed. People with a diagnosis of schizophrenia (PSz) have low rates of employment . Social Our understanding of the social difficulties in PSz is derived primarily from studies exploring social cognition in PSz, referring to the mental operations that underpin the process of social interaction such as perception and interpretation of social cues . Such st (a)hesitation particles: ‘uh’ ‘erm’), pauses and self-repairs such as repeated words or phrases or reformulations of the utterance in progress [By contrast to written language, talk in interaction is characterized by incomplete utterances, non-word fillers  account of turn-taking , takes set al.  distinguet al.  emphasizet al. , althouget al. . Additioet al. , and canFor PSz, individual tasks suggest that they have difficulty monitoring their own behaviour  and mismper se. PSz and their interacting partners use fewer hesitation particles than Cs, which may be owing to reduced competition for the floor, an empirical question requiring more in depth analyses of different corpora. For unfilled pauses (defined as gaps of greater than 200 ms between utterances where the same speaker speaks before and after the gap), these are more common within the turns of people interacting with a PSz. We hypothesize that this may be because floor holding and turn yielding cues are less useful in dialogues with a PSz.Studies using a unique corpus of triadic interactions in which half of the dialogues include a person with a diagnosis of PSz, but their interacting partners are not aware of the diagnosis (see §2), show that PSz use fewer gestures while speaking , and havThe triadic nature of the interactions in this corpus offers a unique opportunity to investigate the dynamics of turn exchange when there is competition for the position of speaker/addressee. This is not possible in dyadic interactions, and is more complex in larger multiparty interactions.The findings derived from this corpus to date on disfluencies and gesture ,34,36 ar. 2 (a)The corpus, described elsewhere  consists (b)M = 15.8, s.d. = 6.76; PANSS negative symptoms M = 9.95, s.d. = 3.36; PANSS general M = 28.41, s.d. = 10.42). PSz participants were not displaying overt symptoms at the time of the interactions, e.g. verbally responding to auditory hallucinations.Exclusion criteria included those presenting with motor side effects from anti-psychotic medication (e.g. muscle stiffness and involuntary muscle spasms) and non-fluent English speakers. Seventeen of our PSz participants were taking anti-psychotic medication  and three were medication free. All PSz participants were diagnosed as having PSz and were regularly attending psychiatric outpatient appointments. The positive and negative syndrome scale for schizophrenia (PANSS)  assessed (c)Participants were instructed to discuss a moral dilemma called the Balloon Task. This task  require (d)All speech data were transcribed in ELAN . The durSelf-repairs were annotated using strongly incremental repair detection  which a−1), we imputed the missing data using a linear trajectory, otherwise left the data as missing. Following the methodology in [Hand movement was automatically extracted from the raw motion capture data. In order to control for individual variation, for each participant we extracted the movement from each of the three hand/wrist markers, and calculated the mean and standard deviation (s.d.) of movement in any direction by frame in millimetres (mm). For frames with missing markers, if this was fewer than 50 frames .It should be borne in mind that although we believe that our automatically derived hand movement measures are a good proxy for gesture, they do not distinguish between gestural hand movement and other hand movement . It is also the case that the automatic hand movement annotation captures only the movement phases of a gesture—including preparation and retraction , and wilAnalyses were performed in SPSS 28.. 3 (a)In order to assess differences in turn-taking in the C and PSz dialogues, we compared the duration between each turn by group, participant type and who speaks next.f1,7068 = 8.513, p = 0.004). However, drilling down in the PSz groups suggests that this is not the complete picture. While there is no significant difference in the gap following a turn by the PSz and Cs in the PSz groups, this masks the difference which emerges when we look not only at the identity of the person who speaks the turn prior to the turn change but also the turn following the turn change, as shown in f3,7066 = 3.880, p = 0.009). Post hoc tests with a Bonferonni correction show no significant differences between groups except for between C to C in the control and PSz groups  −137.31 to −13.29). This means that turn exchanges between the two Cs in the PSz group have a longer gap than turn exchanges between Cs in the C group.As seen in These results, particularly in conjunction with the less fine-grained results presented in , provide (b)Alongside previous results on disfluencies, the above quantitative analysis suggests that in PSz interactions there are bigger gaps between turns between the two Cs in interactions including a PSz. But what does this mean in practice? We now turn to some examples from our data. Note that our examples are for illustrative purposes only. We do not claim that our discussion of them is exhaustive and nor do we describe the examples in the level of detail of CA. (i)a, as C3 is asking the question, both the PSz and C1 are looking at C3 as he speaks. C3’s gaze and postural orientation is towards the PSz, which is taken as an indication—at least by C1 who does not immediately proffer an answer to the question—as C3 selecting PSz as the next speaker [figure 2b), following a short gap in which none of the participants alters their posture or shifts their gaze, and no answer is forthcoming from PSz, C3 further specifies his initial question, increasing the expectation of a response. After approximately a second of the 2.39 s gap in line 5, rather than providing an answer, which both C3 and C1 seem to expect, PSz leans back slightly and shifts his gaze towards C1, at which point she takes the floor and provides the second part of the original adjacency pair. The 2.39 s gap in line 5 is over twice as long as the 1 s ‘standard maximum’ silence in dialogue proposed by Jefferson [1The clearest case of the pattern of pauses, which we hypothesize to be more common in the PSz interactions, is that where the PSz is passing up an apparent opportunity to take the floor, which is subsequently taken by the other C participant, after a gap. An example of this can be seen in figure 2 speaker . In lineefferson , and sucefferson . In this (ii)a) the speaker, C2, is actively orienting her body, gaze, head and gestures towards the PSz, with C3, also orienting towards the PSz. The PSz is unresponsive and as C2 begins to articulate the final word of the utterance in line 1–2 (‘thing’) she turns her gaze towards C3, who simultaneously turns her gaze towards C2 and provides an acknowledgement in overlap (figure 3b), line 3, as well as a non-verbal nodding response. This creates an environment in which the agreement or disagreement of the PSz becomes relevant—even without C2’s bodily orientation towards her, which the PSz does not take up in the following 1.81 s long gap. Interestingly, even before C3’s agreement, C2’s utterance in line 1–2 (which is oriented towards the PSz) seems to be looking to elicit agreement from the PSz, as it builds on a recent previous utterance from her; less than 10 s previously the PSz stated ‘I don’t reckon it’s that hard to fly a balloon’.The example sequence in figure 3c), line 4. As in the previous example, this first pair part sets up the expectation for the second part of the adjacency pair. This means that the PSz no longer simply has the opportunity to take a turn, but there is a stronger—socially normative—obligation for her to do so. At the start of the question in line 4, C3 is still gazing at C2, but she turns her gaze towards the PSz over the (1 s) duration of the question, showing that she also orients to the expectation of a response from the PSz [Towards the end of the long gap, C2 turns her gaze back towards the PSz, followed by asking her a direct question c, line 4 the PSz .d,e), turning her gaze back to C2 just after C2 utters ‘older’ in line 6 before finally taking the floor in (f), line 8. As can be seen in this example, the Cs are doing a lot of interactive work to include the PSz in the interaction.Following a 540 ms gap where the expected response is not forthcoming, C2 provides, in line 5, a reason for the question, which demonstrates that she expected the PSz to take a turn. The PSz once again avoids the now more explicit turn-taking cue by actively looking away, precisely as C2 begins the utterance in line 5 ‘you haven’t …’ d,e, turnfloor in f, line 8Taken together, the quantitative results and these examples suggest that there are cases in the PSz interactions where there are differences in turn-taking patterns. This may be because the PSz lacks awareness of the normative turn-taking expectations, or because they are actively avoiding cues directed towards them.We now turn to the relationship between gesture and repair in dialogues with a PSz. (c)As discussed, previous work shows that PSz have decreased gesture use while speaking  and use more while not speaking, while the Cs in the PSz group gesture proportionally less while speaking, as shown in 2 showed a significant main effect of speaking , such that hand movement is much more likely when someone is speaking, a significant main effect of group/condition , such that PSz produce more gesture than their C interlocutors, and a significant interaction effect of group and condition by speaking , with significant differences in the non-speaking condition between the PSz in the PSz group and both Cs in the PSz group (p < 0.01) and Cs in the C group (p = 0.016) and in the speaking condition between the PSz and Cs in the PSz group (p < 0.001). This means that PSz produce more gestures than both C groups when not speaking, and their interacting partner Cs produce fewer gestures than the PSz when speaking.Firstly, we should note that, in contrast to previously reported results based on participant level values and a less sensitive measure of gesture ,35, we df1,7103 = 5.23, p = 0.022), a main effect of number of words  and an interaction between group type and repair .3 This means that there is less hand movement in turns with repair in the PSz group than in the C group, and demonstrates that the main effect of group is driven by the differences in turns which contain repair. There was no main effect of repair .Turning to the relationship between gesture and repair (looking only at the hand movements which co-occur with speech), we can see that the presence of repair in a turn is a good predictor of amount of hand movement, but only in the C groups, as shown in b), with amount of hand movement of a PSz unaffected by the presence or absence or repair. A GLMM with the same settings and factors except with group/condition instead of group showed the same pattern of main and interaction effects.4 Post hoc tests show that in the turns with repair, Cs in the C group use more gesture than Cs in the PSz group , and that Cs in the C group use more gesture in turns with repair  than in turns without repair, in line with previous findings. By contrast, Cs in the PSz group show the opposite pattern, producing less gesture in turns containing repair . We will return to the possible reasons for this in the discussion.Interestingly, when we drill down by participant type, we see that the effect is more pronounced in the Cs in the PSz group b, with a5 we see that the unexpected effect of Cs producing less gesture in turns containing a repair only holds when the PSz is the next speaker .Further, this difference is sensitive to the turn-taking dynamics discussed in §3(a). As shown in . 4This mixed methods analysis provides an in-depth investigation of turn exchange behaviour in triadic interactions involving a person with a diagnosis of PSz. The findings demonstrate that PSz’ turn-taking behaviour deviates from expectations, in often subtle ways, resulting in an adaptation by others. Interactions involving PSz showed reduced competition for the floor, and a lack of clarity about who should take the floor. PSz’ use of floor change cues deviates from C participants’ expectations; missing or avoiding subtle turn exchange cues and failing to provide such cues during their own turns. This difference in response time is also consistent with work which suggests that competition for the floor in multiparty interactions usually reduces turn exchange times —as is thWe hypothesize that the differences seen in the turn-taking behaviours may be because the PSz lacks awareness of the normative turn-taking expectations, or because they are actively avoiding cues directed towards them: our analysis here, based on an automatic detection of silences between turns, does not distinguish between gaps which occur at a TRP and those that do not, which may be a factor here (though note that our broad observations about differences between the groups hold despite this lack of nuance). Future work will address these questions using both human annotation and recently developed automatic methods to detect TRPs , and wilfewer gestures in utterances that included self-repair. Overall, the presence of a person with a diagnosis of PSz in a social interaction changes the behaviour of those they are interacting with, despite their diagnosis being undisclosed.Furthermore, the multimodal coordination between gesture use and self-repair that persists in C interactions, is not present in interactions involving a person with PSz. PSz’ use of gesture was not related to their use of self-repair, while Cs interacting with a PSz displayed Our findings align with those of previous studies of communication in PSz derived from role-play or task-based methodologies, specifically, PSz have difficulty meshing their turns , have de‘gesture’. As such, in the current analysis, we employed a more sensitive measure of hand gesture based on the raw motion capture data of three motion capture markers, rather than one, alongside manual inspection of the data. This analytic approach revealed that, compared to Cs in either group PSz displayed more hand movements categorized as ‘gesture’ when they were not speaking. Observational annotation of these movements would be required to identify their nature, however, one working hypothesis is that these movements are indicative of displacement behaviours, which are self-directed behaviours, found to be correlated with states of heightened arousal, or anxiety . This rfewer hand gestures when speaking. Taken alongside the lack of competition for the floor, this pattern may suggest a reduction in the need to employ floor-holding techniques such as hand gesture. Although this may be the case, drilling further into the turn exchange dynamics we also identified a disruption in the relationship between gesture and speech in both PSz and their C partners, compared to the C group, where we saw the expected relationship between repair and increased gesture use .The most prominent finding is seen in turn exchanges where Cs pass their turn to the PSz; here Cs employed significantly fewer gestures when they have verbal difficulty, as indexed by the use of self-repair. In interactions with a PSz, the relationship between speech repair and gesture is disrupted, not only for PSz but for the interaction as a whole. This suggests that when passing a turn to a PSz, Cs prioritize use of one modality rather than coordinating modalities.This is puzzling. We hypothesize that the use of gesture in turns which contain self-repair is a normally productive strategy, signalling either the presence of a potential problem or the attempt to resolve it. In interactions with a PSz, where turn exchange may have greater ambiguity, this strategy does not seem to be employed. This might suggest that its usefulness is overridden by other considerations in the interaction. Although the current analyses do not identify the reason for this, we present a number of possible explanations. For example, if the potential misunderstanding involves the PSz, which may be more likely in cases where they take the next turn, their diminished responsiveness, potentially even a lack of shared gaze (see the example in The current analysis did not investigate the reasons for the behaviours observed, in either turn-taking or gesture and repair, nor do we suggest that any behavioural pattern is superior to another. This study merely presents an account of how communication in interactions involving PSz differs from those involving Cs. We do not know if the nature of the task, or the fact that it took place in a laboratory environment contributed to a level of anxiety in PSz or how this may have contributed to PSz’ behaviour. Future comparative studies in different contexts, using different conversational topics could explore these questions.. 5These investigations demonstrate the complex interconnectedness of participants in an interaction and the necessity to analyse them as a dynamic unit ,57. As w


Growth differentiation factor 15 (GDF-15) is a highly divergent member of the TGF-β superfamily and has been implicated in various biological functions. However, the expression of GDF-15 in patients with acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is unclear.The study included 47 AE-IPF patients, 61 stable IPF (S-IPF) subjects, and 31 healthy controls (HCs). Serum GDF-15 levels and their expression in the lung were measured. The correlation between serum GDF-15 and other clinical parameters and the risk factors for AE occurrence and the survival of IPF patients were analyzed.Serum GDF-15 levels were significantly elevated in AE-IPF patients (1279.22 ± 540.02 pg/ml) as compared with HCs (891.30 ± 479.90 pg/ml) or S-IPF subjects (107.82 ± 14.21 pg/ml) (both p < 0.001). The protein and mRNA expressions of GDF-15 in the lung of AE-IPF patients were significantly increased as compared with S-IPF cases . The serum GDF-15 level was correlated with the clinical variables of inflammation, metabolism, and disease severity in IPF subjects . The GDF-15 serum concentration was significantly higher in decedents than in survivors (p = 0.005). A serum GDF-15 level above 989.3 pg/ml was a risk factor for AE occurrence (p = 0.04), and the level above 1,075.76 pg/ml was an independent predictor for survival in IPF cases (p = 0.007).The GDF-15 level was significantly elevated in subjects with AE-IPF. GDF-15 could be a promising biomarker for AE occurrence and survival in IPF patients. Idiopathic pulmonary fibrosis (IPF) is the most common and devastating idiopathic interstitial lung disease (ILD), thus resulting in a progressive decline of respiratory function and early mortality . During Growth differentiation factor 15 (GDF-15) is a divergent member of the transforming growth factor-β (TGF-β) superfamily, also named macrophage inhibitory cytokine-1 (MIC-1) . It is uFor patients with AE-IPF, diffuse ground glass opacity (GGO) and consolidation on chest imaging and DAD on histopathology, similar to what is observed in ARDS subjects. However, the expression of GDF-15 in the acute exacerbation of IPF remains unknown. In present study, we aimed to explore the expression and potential role of GDF-15 in a Chinese cohort of AE-IPF subjects.The current study recruited 108 IPF subjects  and 31 healthy controls (HCs) in Nanjing Drum Tower Hospital, Nanjing University Medical School from January 2015 to December 2018. The diagnoses criteria of IPF were based on the updated international practical guideline in 2018 . Lung spThe serum samples were collected, aliquoted, and stored at -80 °C. The date of sample collection was the day following admission . The serum concentrations of GDF-15  and leptin  were measured using enzyme-linked immunosorbent assay (ELISA) kits on the basis of the manufacturer’s instructions. Each sample was assayed in duplicate.Quantitative real-time PCR (RT-PCR) and western blot (WB) were used to detect the mRNA and protein expression of GDF-15 in the lung from IPF patients and HCs as described previously . ImmunohClinical data were collected from electronic medical records. The vital status was determined from medical records or a telephone follow-up. The deadline of the follow-up was November 17, 2019. The duration of survival was from the date of sample collection to the date of death or vital status confirmed. Concurrent infection was defined as patients with clinical symptoms of fever or/and purulent sputum, elevated white blood cell (WBC) counts, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and/or lactate dehydrogenase (LDH), or positive pathogen tests.The scores of gender–age–physiology (GAP) and chest high-resolution computed tomography (HRCT) were used to assess the disease severity of IPF patients. The total GAP score was calculated by the method proposed by Ley et al. . Chest H2) or Fisher exact test, and continuous variables were evaluated by independent sample t-test, Kruskal–Wallis, Mann–Whitney U test, or one-way ANOVA test. The correlations between serum GDF-15 and other clinical variables were assessed by Spearman’s or Pearson’s correlation analysis depending on the distribution of the variables. The survival curves were generated by Kaplan–Meier methods and compared by the log-rank test. The optimal cut-off value for predicting AE occurrence and the mortality was determined by receiver operating characteristic (ROC) curve. Logistic models were used to analyze the risk factors for AE and Cox regression models were built to examine the predictors of time-to-death in IPF patients. p < 0.05 was considered to be statistically significant. Statistical analyses were performed by IBM SPSS version 21  and Prism version 8 .The categorical variables were presented as numbers and percentages, and continuous variables were expressed as the mean ± standard deviation (SD). The comparisons of the differences of categorical parameters were assessed by the chi-square  , forced vital capacity (FVC), carbon monoxide–diffusing capacity (DLCO), GAP scores, and stages were significantly worse in the AE-IPF group compared with the counters   was significantly elevated compared with S-IPF subjects (891.30 ± 479.90 pg/ml) or HCs (107.82 ± 14.21 pg/ml)   (5 fields each sample).The protein and mRNA expressions of GDF-15 in the lung of AE-IPF subjects were increased significantly compared with HCs and S-IPF cases by WB and RT-PCR  .As shown in The areas under the ROC curves of serum GDF-15 levels were statistically significant in the classification of AE-IPF (n = 47) or S-IPF patients (n = 61)    of AE-IPF patients were significantly worse than S-IPF cases. The duration in the hospital of AE-IPF cases was significantly longer  and in-hospital mortality was significantly greater than S-IPF subjects   were significantly different between patients with S-IPF and AE-IPF. The published data showed that poor pulmonary physiological function, mechanical procedures, and pulmonary hypertension were associated with AE-IPF –exposed mice, and increased GDF-15 expression may contribute to CS-induced pulmonary inflammation . GDF-15 The circulating GDF-15 levels have a close correlation with known markers of fibrosis such as type I collagen and osteopontin . GDF-15 The present study has some limitations. First, the sample was small and all patients were from the same center. We have no data of serum GDF-15 concentrations from the same patient in different clinical conditions , which would be helpful to further support the utility of GDF-15 as a biomarker of IPF. A prospective and multicenter study of a larger sample cohort would be beneficial to provide additional evidence on GDF-15.In summary, the expressions of GDF-15 were increased significantly in patients with AE-IPF. An elevated serum GDF-15 level was associated with infection and inflammation, metabolism indicators, and the disease severity of IPF and could predict AE occurrence and the survival in IPF subjects.The authors would like to thank all patients for sending their consents. This work has been presented in the form of one late-breaking abstract at the European Respiratory Society Congress 2019 in Madrid.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethics Committee of Nanjing Drum Tower Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.MC and LS conceived and prepared the manuscript. MC, LS, LinG, ML, YuZ, and LilG contributed to the collection of clinical data, statistics, and experiments. FM and MC evaluated the histopathology. FM and JZ provided the lung tissue sample. MC, XQ, XG, MM, YT, XL, and YX contributed to the administration to the patients. LS, MC, FM, and YX reviewed the manuscript and took responsibility for the integrity and accuracy of data analysis. All authors reviewed and approved the final draft.This study was partly supported by National Natural Science Foundation of China .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Single/multilayer graphene holds great promise in withstanding impact/penetration as ideal protective material. In this work, dynamic penetration behaviors of graphene has been explored using molecular dynamics simulations. The crashworthiness performance of graphene is contingent upon the number of layers and impact velocity. The variables including residual velocity and kinetic energy loss under different layers or different impact velocities have been monitored during the hypervelocity impact. Results show that there exists deviation from the continuum Recht–Ipson and Rosenberg–Dekel models, but these models tend to hold to reasonably predict the ballistic limit velocity of graphene with increasing layers. Besides, fractal theory has been introduced here and proven valid to quantitatively describe the fracture morphology. Furthermore, Forrestal–Warren rigid body model II still can well estimate the depth of penetration of multilayer graphene under a certain range of velocity impact. Finally, one modified model has been proposed to correlate the specific penetration energy with the number of layer and impact velocity. Especially, its inherent strength exceeding 100 GPa, Young’s modulus as high as 1 TPa16 and low density of 2.2 g/cm3 make multi-layer graphene (MLG) an extraordinary armor material exhibiting excellent impact energy delocalization under a supersonic penetration event and hold great promise in the field of safety protection for lightweight body armor design17 and spacecraft protection18. So far, most of experimental tests are restricted to the quasi-static mechanical properties of graphene except the recent supersonic projectile penetration carried out by Lee et al.19 in 2014, in which the high-strain-rate behavior of multilayer graphene by using miniaturized ballistic tests has been investigated. It has been discovered that the specific penetration energy for multilayer graphene is about 10 times more than literature values for macroscopic steel sheets19. Such extraordinary findings have aroused great interests in the application of graphene into the field of impact protection.Because of the excellent properties, such as exceptionally high intrinsic strength and stiffness arising from the two-dimensional (2D) hexagonal lattice of covalently bonded carbon atoms20 has its unique advantages, which can be used to simulate hundreds of thousands or even millions of atoms by virtue of universally applicable force field relations. Therefore, MD simulation have provided much convenience and feasibility to understand the dynamic behaviors of 2D materials26. As a major two-dimensional material, there are lots of MD studies on graphene’s properties, especially its impact resistance. In terms of axial wave and cone wave propagation. Briefly, the failure mechanism of single-layer graphene under high-speed impact of diamond projectiles was explored by Xia et al.24 and found that the circular shape graphene possesses the best impact resistance for similar sample size. Furthermore, Bizao et al.27 simulated the process of 1–3 layers of graphene being subjected to the impact by 3.5–7.5 km/s of fullerene, evidencing the angle law of graphene crack observed from Lee’s experiment. It has been concluded that the high axial-wave and cone-wave velocity favor the momentum transfer and improvement of ballistic protection performance of graphene23. Meng et al.22 further explored the regularity of cone wave propagation relating to the boundary conditions and propose one approach to estimate the critical graphene membrane size, which offers theoretical guidance for the size engineering of graphene as a protective material. In addition, Bizao et al.21 put forward the law of scale effect to explain the discrepancy by one order of magnitude between experiments and MD simulations and correlate the specific penetration energy with the number of layers. In addition to graphene, other 2D material such as BC3and C3N whose structures is similar to graphene’s have also been explored by researchers using MD simulation30. Sadeghzadeh et al. explored the ballistic properties of C3N and BC3 nanosheets against hypersonic bullets with numbers greater than 6. The critical perforation conditions, and thus, the intrinsic impact strength of these 2D materials were determined by simulating ballistic curves of C3N and BC3 monolayers30. The mechanical properties of defective hybrid C3N–BC and defect-free hybrid C3N–BC3 also have been investigated. Results show that the effect of these flaws on Young’s modulus is less than their effect on failure stress and strain29. Further work has been performed to explore the mechanical and ballistic properties of Aluminum nanocomposites reinforced with monolayer polyaniline (C3N) and it is found that the nanocomposite can absorb 35% energy of nanoparticle when C3N was placed on the top of the aluminum surface28.Confronted with experimental challenges, it is experimentally harsh to directly perform hypervelocity impact. As an alternative approach to physical experiments, molecular dynamics (MD) simulation31, however, the inherent correlation between ballistic limit velocity and the number of layers of graphene are largely unknown. Is there a suitable way to predict the ballistic limit velocity of graphene at different layers? Are the continuum models related to penetration mechanics such as Recht–Ipson model32 still applicable at the ultra-small nanoscale? These need to be explored further. The description of the morphology of damaged graphene is usually qualitative and herein we make an effort to quantitatively to explain the morphology of the membrane after failure. What’s more, the residual velocity, kinetic energy consumption, impact displacement, total impact contact time and penetration depth in our work will be also discussed.As we see, much progress has been made on the impact resistance of graphene and other 2D materials, nonetheless unresolved issues but of scientific importance remain unclear. The ballistic limit velocity is one of the important indicators signifying the material’s capability of impact resistance. Sadeghzadeh and Meng both found the result that the ballistic limit velocity increase with the increased number of grapheneV0 ranging from 2500 to 7000 m/s. The residual velocity Vr, the kinetic energy loss ΔEK, i.e., V0, as discussed from Fig. Vbl. The kinetic energy loss ΔEk first increases with increasing the initial impact velocity to a peak value at around the ballistic limit velocity, followed by a drop and subsequent hike again as shown in Fig. In this part, the single or multi-layer graphene is subjected to the impact of the diamond nanosphere of 6.0 nm in diameter, acting as projectile, with an initial velocity mentclass2pt{minimmentclass2pt{minimThe simulation snapshots of projectile collided with the single layer graphene membrane at different initial impact velocities of 2000, 2750 and 3500 m/s are shown in Fig. Vr and ΔEk present complicated and non-linear trends. The whole region can be roughly divided into three parts: Region A, B and C. To begin with Region A, the bounce-back, i.e., ricochet region, in which the initial velocity is comparably small, the projectile fails to perforate graphene and is bounced back without exerting damage to the graphene membrane. Under this circumstance, the membrane maintains a relatively good elastic deformation as shown in Fig. As the initial impact velocity rises from 2 to 7 km/s, the corresponding results of both 32, the initial impact velocity V0, residual velocity and ballistic limit follow are correlated as follows,p, mp are the mass of projectile and the mass of plug ejected by projectile, respectively. When satisfying Mp ≫ mp, the above equation can be reduced toBallistic limit velocity is one of the important parameters in describing the anti-penetration capability of structures or materials. According to Recht–Ipson modelIn order to test the validity of the continuum model, a series of MD simulation have been performed by varying the initial impact velocity. Especially, a velocity increment of 10 m/s in the transitional region B is employed to accurately estimate the ballistic limit velocity. The theoretical predictions of ballistic limit velocity from the Recht–Ipson model for single layer to four layers are obtained by fitting the residual velocity to the Eq. , highligVbl for single, double, triple and quadruple layer graphene are separately measured using the Recht–Ipson model and MD simulations and listed in Table Vbl for nano-impact thin target, especially for larger thickness target.As observed from Fig. 33, which employs the conception of effective resisting stress that simplifies complex penetration process, taking the form ofp, H, σr are the density of projectile, the target thickness and effective resisting stress, respectively. leff is effective length of projectile obtained based on the principle of equivalent to a cylinder with equal cross-sectional area and mass.Although the governing equation from Recht–Ipson model is simple and easy to use, nonetheless it is not associated with materials properties of the projectile and the target and lacks significant physical meaning. In order to further explore the ballistic limit velocity, we refer to the Rosenberg–Dekel model34 MD, the ratio of σr and Yt is related to the thickness of the target plate (H) and the diameter of the projectile (here D = 6 nm), Yt is the strength of material. For the thin target (H/D < 1/3), there is:According to the empirical formula provided by Rosenberg and Dekel35. Normally, the maximum resisting force can be readily obtained by calculating the interaction forces between the projectile and target from the MD simulations. As for the corresponding contact area, it can be achieved with the aid of Materials Studio software, the corresponding surface area corresponding to the maximum resisting force can be calculated following the previous approach36, then the maximum impact stress is obtained. As per Eq. . The orange high stress area presents a hexagonal shape at 4.649 ps while the red central part represents higher stress caused by the compression between the membrane and the projectile. Interestingly, the border of the hexagon is the deformation bulge part of the membrane, which is just like a barrier converging stress waves to interact with each other within it. Subsequently, the hexagon area where shearing stress converges gradually shrinks to the size similar to the impact area until the strongest stress concentration appears at times of 6.249 and 6.749 ps. This can be vividly confirmed from the greatest resistance force exerting on the projectile by the graphene membrane as shown in Fig. 22.In order to demonstrate internal wave propagation and reveal the underlying mechanism of the failure of single layer graphene at 2750 m/s, shear stress distribution has been monitored at different simulation times. As observed from Fig. 27.It is of scientific importance to investigate the morphology of hole left in the graphene membrane after penetration and also the corresponding mass loss. In view of the irregularity of the holes after destruction, the shape factor in the fractal theory  model40 are widely used in describing the penetration depth.Depth of penetration (DOP) is an important indicator of impact resistance, therefore it becomes necessary to accurately estimate this parameter using MD simulations and test the validity of the continuum penetration depth models of rigid projectile at the atomic/nanoscale. For rigid projectile, Forrestal–Warren (F–W) modelP, L, ρP and ρt are the penetration depth, effective length of projectile, densities of projectile and target, respectively; σyt is the quasi-static yield strength of target, ψ = 0.5 for the hemispherical nosed projectile and thus the projectile nose coefficient N* = 0.5. L, ρP, ρt and σyt are 4 nm, 3.5 g/cm3, 2.2 g/cm3, 130 GPa, respectively.ρP and σyt have the same meanings and values as them in F–W model I.F–W Model II is the first-order Taylor expansion of F–W Model I, which is equivalent to the upper limit of the F–W model. Parameter t in Eq.  is smallt in Eq.  can be rEt, σft are the elastic modulus and flow stress of the target, which are 1 TPa and 140 GPa, respectively, for the graphene.41. Besides, the total impact displacement, which is the distance of the projectile travelling from the initial contact point in the approach process to the final contact point at the instant of initiated restitution where the kinetic energy becomes zero or perforation along the Z-axis. Correspondingly, the total time of impact is the time interval from the instant of the initial contact to that of the final contact between the projectile and graphene. These two parameters have also been monitored as shown in Fig. Since these theoretical models require that the shape of the projectile should be hemispherical nosed projectile. As per the principle of equal cross-sectional area and mass, the 6 nm rigid spherical projectile is equivalent to one hemispherical nosed projectile with a length of 2.0 nm and radius of 3.0 nm, as shown in Fig. 19 proposed the following relationship as per their experimental results,19. This has been discovered by Rafael’s research21 and the value of 21 and basically consistent with the results of Haque27. For instance, at impact velocity of 4500 and 5000 m/s, Haque accordingly obtained the 43.Lee et al.N, V0 to the ballistic limit velocity Vbl; NC, N’C, A, B are the coefficients as obtained from the best fitting.Although scale law has been put forward in terms of number of layer in Rafael’s research, nonetheless the influencing factor of impact velocity has not been properly considered. In order to further accurately predict 19 have been re-drawn and incorporated along with the simulated data for the fitting as displayed in Fig. NC, N’C, A, B are 6385, 25.89, 322, − 2, respectively. Then Eq. 46. The projectile is made of diamond nanosphere with a diameter of 6 nm, which is built by cutting from the diamond structured diamond with the lattice constant of 0.357 nm bulk using LAMMPS. The mass of the projectile containing 19,889 carbon atoms is 2.01 × 10–22 kg. Single layer, double-layer, triple-layer and quadruple-layer graphene are built using Visual Molecular Dynamics (VMD)47 with a dimension of 40 nm × 40 nm in x, y axes, respectively. The adaptive intermolecular reactive empirical bonder order (AIREBO) potential was adopted herein since it has been proved valid to describe the interaction between C–C and C–H atoms49 and reproduce the mechanical properties of carbon systems such as graphene27. But it is suitable for describing the system containing graphene and diamond. Therefore, for the interaction between graphene and diamond, we refer to previous studies and set the LJ parameter separately to describe the interaction between graphene and diamond37. Because, although both graphene and diamond are composed of carbon atoms, their atomic arrangement and structure are completely different. Besides, because of the high Young’s modulus of diamond and for the sake of comparison with the “rigid” assumption inherent in continuum model, the treatment of diamond projectiles as rigid bodies in the nano-scale impact process has also been adopted herein and elsewhere53. The effect of rigid assumption has been evaluated as shown by Figs. 54. The projectile is regarded as a rigid body24 The interaction between graphene and projectile is described by the 12-6 Lennard Jones (LJ) potential and the values of εLJ, σLJ are 0.035 eV and 3.46 Å55, respectively. Note that the boundary of graphene is fixed while the central circular area of 15.0 nm in radius is unconstrained. Since graphene is a two-dimensional and thin material, the entire graphene sheet will move at high speed when subjected to the collision with the projectile. In this case, it is difficult to further study the impact resistance of graphene. Thus, one feasible but carefully corroborated approach of fixed boundary has been adopted herein and the detailed validation process can refer to the Supplementary Information. The projectile is perpendicularly located about 10 nm apart from the center of the graphene membrane. An initial impact velocity ranging from 2000 to 7000 m/s was applied to projectile and the whole impact process is simulated using NVE ensemble with a time step of 0.5 fs43.In this paper, the MD simulation processes are performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)40, and the corresponding continuum theories are also different. Our paper mainly is associated with the rigid penetration, with a focus on the impact of “rigid” projectile with the graphene. The effect of “non-rigid” projectile and influence of “free” or “fixed” boundary conditions have also been provided in the Supplementary Information. The setting of “free” or “fixed” boundary conditions will affect the shockwave reflecting, but has little influence on the main conclusion regarding the penetration behaviors of multilayer graphene in this work.Note that penetration can be classified into rigid penetration, deformation penetration and eroding penetrationSupplementary Information.