Subsequent chemical functionalization enabled us to develop an epoxidized graphene grid (EG-grid™), which effectively adsorbs protein particles for electron cryomicroscopy (cryoEM) image analysis. The EG-grid dramatically improved the particle density and orientation distribution. The density maps of GroEL and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were reconstructed at 1.99 and 2.16 Å resolution from only 504 and 241 micrographs, respectively. A sample solution of 0.1 mg ml−1 was sufficient to reconstruct a 3.10 Å resolution map of SARS-CoV-2 spike protein from 1163 micrographs. The map resolutions of β-galactosidase and apoferritin easily reached 1.81 Å and 1.29 Å resolution, respectively, indicating its atomic-resolution imaging capability. Thus, the EG-grid will be an extremely powerful tool for highly efficient high-resolution cryoEM structural analysis of biological macromolecules.Functionalization of graphene is one of the most important fundamental technologies in a wide variety of fields including industry and biochemistry. We have successfully achieved a novel oxidative modification of graphene using photoactivated ClO Automated vitrification devices have been developed5, and various efforts have been made to produce good grids with well-dispersed particles and without much sample loss by denaturation8. In general, proteins often localize to the air–water interface and denature, resulting in poor particle density and preferred orientation and thereby making high-resolution 3D reconstruction inefficient9. Due to these problems, optimizing the conditions for cryoEM grid preparation requires much time and effort11. To overcome these problems, thin support films, such as graphene, have been used to change the physical properties of grid surface14, and various types of functionalization have been achieved: Nα, Nα-dicarboxymethyllysine (NTA)16; SpyTag/SpyCatcher17; amino-polyethylene glycol (PEG) linkers18; as well as various organic compounds19. Although synthesis of graphene oxide is the first important step for graphene functionalization, a limited number of methods have been available: modified Hummers’ methods21 ; conventional glow discharge; and chemical modification by plasma treatment22 . However, these methods suffer from problems such as undesirable breakage of the graphene framework (C–C bonds) and low selectivity of reaction points between the basal plane and the edges of graphene due to the harsh reaction conditions. It would be more useful if we can oxidize graphene in a milder condition and introduce appropriate functional groups into the basal plane of graphene selectively.Despite the recent improvements in hardware and software for electron cryomicroscopy (cryoEM) single particle image analysis for high-throughput and high-resolution structure determination of biological macromolecules, specimen grid preparation remains a bottleneck due to various factors. In single particle analysis (SPA), protein particles must be well dispersed in aqueous solution to be flash-frozen and embedded in thin vitreous ice film formed within micron-sized holes on a few 10s nm thick carbon film laid on a metal grid substrate2· gas as a oxidant; it can convert a gaseous fuels of methane into liquid fuels of methanol and formic acid without producing CO223 and can also make surface oxidation modification of polymers, such as polypropylene24. We applied this oxidation reaction to introduce hydroxy groups into the basal plane of graphene surface on the EM grid and confirmed the oxidized graphene grid is storable with its functionality for at least three months under N2 atmosphere. This is the first effective method as a chemical dry process for oxidation of graphene on substrate. We then successfully introduced epoxy groups by epichlorohydrin (ECH) treatment, which has been used for functionalization of graphene oxide26. The resulting epoxidized graphene grid (EG-grid™) effectively adsorbed proteins so that they stayed on the surface even after washing with buffer. This was confirmed using electron cryotomography (cryo-ET), which showed that the protein particles were located close to the graphene surface and away from the air–water interface to avoid their denaturation.We have recently discovered a useful chemical reaction using photoactivated ClOThe EG-grid allows protein particles to be immobilized on graphene in a variety of orientation, not only making the adjustment of sample protein concentration less needed but also solving the problem of preferred orientation caused by adsorption to the air–water interface. In its application tests using standard proteins, such as GroEL and β-galactosidase, as well as difficult proteins with preferred orientation, such as glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and SARS-CoV-2 spike protein, the numbers of cryoEM images needed for high-resolution reconstruction were much fewer than the cases where standard holey carbon grids were used. The resolution of apoferritin easily reached 1.29 Å resolution, also indicating its atomic-resolution imaging capability. We believe this EG-grid will become a powerful tool for accelerating high-resolution cryoEM structure determination.13 with some modification. Then, the surface of graphene is oxidized by photoactivated ClO2· treatment for 10 min to introduce hydroxy groups (see “Methods” for detail and Supplementary Movie 1). The following reaction with ECH in the third step introduces epoxy groups on the surface of graphene. The hydrophilicity of graphene grids was evaluated by the water contact angle (WCA) measurement. The WCA of graphene on the Au grid decreased from 76° ± 0.4° to 60° ± 0.3° by the ClO2· oxidation treatment  showed several characteristic peaks such as those at 3400 cm−1, v(C–OH); 1620 cm−1, v(C=C); and 1300 cm−1, 850 cm−1, v(C–O–C), suggesting that oxygen-containing functional groups were successfully introduced to the graphene surface. Contrary to this, the plasma-treated graphene showed a peak at 1740 cm−1 corresponds to the C=O bond and almost no C–O bond derived peaks. The Raman spectra of GP and GO are shown in Fig. −1) and 2D (2690 cm−1), and their intensity ratio (I2D/IG) was about 1.8, proving the high quality of single layer graphene. After oxidation, the D and D’ peaks appeared whereas the 2D peak nearly diminished. These features of the Raman spectra indicated the formation of sp3-carbon by the introduction of functional groups to graphene basal plane.To confirm the oxidation state of the graphene surface, we performed infrared (IR) and Raman spectral measurements and analyses. While pristine graphene (GP) was IR inactive as shown in Fig. 2· treatment. After plasma treatment, the composition ratios of the C–O groups (corresponding to the peak at 286.0 eV) and the C=O groups (corresponding to the peak at 287.8 eV) were 22.5% and 29.2%, respectively . On the other hand, in the ClO2·-treated graphene, they were 28.4% (C–O) and 8.5% (C=O), respectively . Generally, the increase of C=O ratio means the cleavage of the C=C bond of graphene. The increase of C–O ratio indicated that the hydroxy groups were introduced dominantly to the basal plane in the ClO2·-treated graphene rather than the cleavage of the C=C bond in the plasma-treated graphene, in agreement with the IR measurement.To investigate the effect of oxidation on the chemical state of the graphene surface more comprehensively, the chemical compositions of the functional groups were determined by X-ray photoelectron spectroscopy (XPS) measurement. Graphene was transferred onto a silicon wafer and then oxidized either by plasma or photoactivated ClOTo confirm the introduction of epoxy groups, we used 1H,1H-undecafluorohexylamine (UFHA) because the amino groups in this compound should react with the epoxy groups on the graphene surface and fluorine peaks can be clearly observed in XPS measurement . Only carbon and oxygen were detected with non-oxidized graphene (black line). No fluorine was detected with the UFHA-treated oxidized graphene and ECH-/UFHA-treated graphene (red and green lines), while a clear fluorine peak appeared with the UFHA-treated EG-grid (purple line). These results indicated that UFHA was successfully introduced on the graphene surface and that the epoxy groups enabled further chemical modification of the EG-grid.2· oxidized graphene grid (Ox-Gra); and EG-grid, and compared the peak intensities of the diffraction spots. We counted the maximum values of six equivalent diffraction peaks from the {2· oxidation, epoxidation, and vitrification.We observed the EG-grid using an electron cryomicroscope (cryoTEM) equipped with a cold field emission gun and an Ω-type in-column energy filter (JEM-Z300FSC (CRYO ARM™ 300), JEOL) operated at 300 kV and recorded images on a K3 direct electron detector . Most part (> 90–95%) of the EG-grid was covered with graphene  while we sometimes found graphene-detached area by recognizing rolled-up edges of graphene . The surface of graphene within the holes was quite clean , and the existence of suspended graphene was confirmed by electron diffraction patterns . To estimate the graphene damage caused by the oxidation and epoxidation, we recorded diffraction images of suspended graphene in 5 holes of three different types of grids: non-oxidized graphene grid (Gra); ClOEscherichia coli (Molecular weight (Mw) ~ 800 kDa) as a benchmark protein because the resolution of the cryoEM 3D reconstruction of this protein has been limited to 3.26 Å possibly by limited number of particles due to elimination of damaged particle images and by preferred orientation towards its end-on views27. A 3 μl solution of GroEL at a protein concentration of 0.3 mg ml−1 was applied on an EG-grid, and the protein solution was blotted after 5 min incubation. The grid was washed three times with 5 μl of buffer each, and then the grid was negatively stained with uranyl acetate. Many GroEL particles remained on the EG-grid and were packed densely . We also prepared a grid in the same way except skipping the epoxidation process by ECH and observed no or very small number of GroEL particles , which indicates that the EG-grid strongly adsorbed the protein particles.To investigate the protein binding activity of the EG-grid, we used GroEL from −1 GroEL solution was loaded onto the EG-grid and allowed to stand for 5 min at room temperature, followed by blotting and plunge freezing using Vitrobot™ (Thermo Fisher Scientific). A short blotting time around 1.0–1.5 s usually worked well for getting appropriate ice thickness on the EG-grid. We observed the grid and collected a dataset using CRYO ARM™ 300 operated at 300 kV (referred to as GroEL-EG). For comparison with a conventional grid preparation method, we also prepared a cryoEM grid using a Quantifoil grid and collected a dataset under the same EM imaging condition (referred to as GroEL-Q). We collected around 500 micrographs in both cases. The micrographs of GroEL-EG showed much more densely packed GroEL particles with more side view orientations than GroEL-Q  showed a much lower particle density than GroEL-EG , while those obtained from some areas of the other grid (dataset referred to as GroEL-Gh) showed densely packed and side-view oriented particles similarly to GroEL-EG . GroEL-Gh showed more side-view preference compared to GroEL-Gl , indicating that the orientation distribution of GroEL is somehow strongly affected by the particle density. To collect about 200,000 particle images of GroEL for these two datasets, we collected 5614 micrographs for GroEL-Gl and 649 for GroEL-Gh. The final reconstructions were obtained from 215,011 and 198,677 particle images, with resolutions of 2.08 and 2.09 Å for the GroEL-Gl and GroEL-Gh datasets, respectively  was smaller (better) than those of GroEL-Q (98.5 Å2), GroEL-Gl (83.3 Å2), and GroEL-Gh (85.4 Å2) , demonstrating a higher homogeneity of the particle structures on the EG-grid to reach such higher resolution. We also calculated the FSC curves and sphericities of these datasets using the 3DFSC server30 and found that the sphericity of GroEL-EG (0.991) was slightly higher than those of GroEL-Q (0.964), GroEL-Gl (0.985) and GroEL-Gh (0.985) . The efficiency , which represents the uniformity of orientation distribution used in the reconstruction (the perfect orientation distribution is presented as Eod = 1.0), of GroEL-EG (0.71) was slightly lower than those of GroEL-Gl (0.75) and GroEL-Gh (0.74) , which would reflect the efficiency of data collection. For the GroEL-EG and GroEL-Gh datasets, 315 and 306 particles/image were used for final reconstruction, respectively, while only 36 and 38 particles/image were used for the GroEL-Q and GroEL-Gl datasets, respectively (Supplementary Table 1). Although both the GroEL-EG and GroEL-Gh datasets showed high particle density and side-preferred orientation, the resolution of the final map of GroEL-EG (1.99 Å) was slightly higher than that of GroEL-Gh 2.09 Å) . It was clear from highly tilted images that these ice contaminants were located on the surface of vitreous ice . Reconstruction of tomographic images from this dataset revealed three unique layers from the top to bottom. In the top layer, small ice contaminants were observed and no GroEL particles were found . The middle layer 240 Å below the top layer contained densely packed GroEL particles . Unexpectedly, at 225 Å below the middle layer, there were a few GroEL particles that seemed to be bound on the back side of the graphene layer . The estimated distance from the air–water interface to the bottom of the major GroEL layer was ~ 160 Å, which is reasonable to accommodate a single GroEL layer . These results indicate that GroEL particles were located close to the graphene surface and away from the air–water interface . We also collected a cryo-ET dataset from the GroEL-Gh grid and selected a hole containing both areas with and without graphene support for comparison. The graphene area showed a high density of GroEL particles in a single layer like the EG-grid, demonstrating the compatibility with the EG-grid, while fewer particles were observed in the non-graphene area .2· oxidized graphene grids are storable or not. After ClO2· treatment for 10 min, the grids were washed with water, air-dried, and stored under N2 atmosphere at room temperature for 3 months. Then, the grids were chemically modified by 1% (v/v) ECH solution, and 3 μl of 1.5 mg ml−1 GroEL solution was loaded in the same way as GroEL-EG. We found many GroEL particles adsorbed on the EG-grid , and the calculated sphericity (0.976) and efficiency factor (0.70) were comparable to those of GroEL-EG  and NPPI-final (104.1) compared to the GroEL-EG dataset (Supplementary Table 1) may reflect the way of the data collection, because, unlike the GroEL dataset described above, we collected three micrographs from one hole in the GroEL-EG3m, leading to carbon areas included in some micrographs to cause many particles on the carbon areas picked automatically.We also tested whether the ClOMw ~ 420 kDa), for which the present demand for high-resolution structures is quite high. We prepared three specimen grids: EG-grid (referred to as Spike-EG) and glow discharged graphene grid (referred to as Spike-G), both at a protein concentration of 0.1 mg ml−1, and conventional Quantifoil grid (referred to as Spike-Q) at a protein concentration of 0.5 mg ml−1. None of the grids were washed with the buffer after protein loading. The particle density was much higher in Spike-EG than that in Spike-Q, although the protein concentration was five times lower for Spike-EG  were all similar in Spike-EG and Spike-Q  . The value of NPPI-final for Spike-EG (129.2) was about twice as much as that for Spike-Q (56.5), while the FI ratio values were quite similar (24.9% and 23.8%) (Supplementary Table 1).Next, we examined the performance of the EG-grid on the SARS-CoV-2 spike protein trimer Mw ~ 20 kDa, f1-ATPase) composed of the A3B3 hexametric ring from Thermus thermophilus and the DF subunits from Homo sapiens (Mw ~ 400 kDa), the tetramer of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (Mw ~ 144 kDa), β-galactosidase  (Mw ~ 460 kDa), and apoferritin (Mw ~ 500 kDa). The GAPDH structure has not been solved by cryoEM except for the part of the large ternary complex32. We loaded each sample solution onto an EG-grid and made a cryoEM grid by plunge freezing in the same way as we did for GroEL. In all samples, we saw many protein particles in the micrographs . Datasets were collected and processed . The 2D class averages showed wide varieties in particle orientations . During 3D classification of V1-ATPase-EG, we found two classes, one with and the other without the human DF subunits corresponding to the central rotary shaft. We only selected the class without the shaft, because the particle number of this class was much larger than that of the other. The 3D map and model with the shaft will be presented elsewhere. The orientation distributions of the particles used for the final maps were quite uniform as expected from the 2D class averages . The overall map resolutions of V1-ATPase, GAPDH, β-galactosidase, and apoferritin reached 3.03 Å, 2.16 Å, 1.81 Å, and 1.29 Å. respectively  and apoferritin-EG (36.4 Å2) are also as small as those of the previous reconstructions35 . So the EG-grid allowed to reach the highest resolution of β-galactosidase with 40% less number of micrographs. The FI ratios and the NPPI-finals of V1-ATPase, GAPDH, β-galactosidase, and apoferritin were 20.0%, 29.4%, 47.0%, and 66.5%, and 116.4, 368.2, 71.4, and 70.3, respectively (Supplementary Table 1).We applied the EG-grid to four samples: the chimeric complex of the soluble domain of V/A-type ATPase  and 2D class averages with an improved orientation distribution , although the resolution of the 3D map was limited to 6–7 Å.We also applied the EG-grid to hemagglutinin (HA), which suffered from preferred orientation and needed grid tilting data collection for 3D reconstruction19. It has also been reported that UV/O3 or KMnO4 treatment leads to the introduction of carboxy groups, implying the cleavage of C=C bonds in graphene, and that was confirmed by the formation of nanopores13. As can be seen in these reports, it is difficult to oxidize chemically stable graphene to introduce oxygen functional groups on the surface without disruption of its atomic layer sheet structure. The distinctly different results of GroEL-Gl and GroEL-Gh obtained by plasma-treated graphene grids  also indicate the difficulty in controlling the property of graphene surface by plasma treatment to reproduce good results in the cryoEM grid preparation.Oxidation of graphene films on EM grids without damaging is not an easy task. In fact, the plasma method (glow discharge) has been reported to cause graphene to collapse in a short time2·. This method can introduce reactive functional groups such as hydroxy (OH) group, while maintaining the graphene framework intact. In our method, C–O single bond groups (hydroxy groups) are more preferentially introduced compared to C=O type functional groups such as carboxy groups , and therefore the intrinsic sheet structure of graphene is better maintained. We also confirmed that these hydroxyl groups react with ECH to produce epoxy groups and that they further react with amine compounds . CryoEM observation confirmed that little damage was introduced to the graphene after oxidation, epoxidation, and vitrification . This suggests not only a variety of applications of the EG-grid but also a much wider potential application of the method we used for graphene oxidation by combination with further chemical modifications.We have successfully developed a novel oxidative modification method of graphene on substrates using photoactivated ClO2· oxidized graphene grids can be stored for at least 3 months under N2 atmosphere at room temperature  compared to those of Spike-Q and Spike-G  and also proved that near 2 Å resolution is reachable with ~ 144 kDa protein only from ~ 240 images , or PMMA-coated single-layer CVD graphene on a Cu foil from any suppliers  can be used. If necessary, the backside (the opposite side of PMMA) of graphene was removed by mechanical polishing using a waterproof abrasive paper . The Cu foil was etched by floating the PMMA-coated CVD graphene on a 0.5 M ammonium persulfate (APS) solution for more than 30 min. Then, the PMMA-coated graphene was scooped up on a filter paper, transferred to water and left for 10 min. This washing process was repeated twice. Or, a much easier and quicker method is to use Trivial Transfer Graphene™ , which can be transferred to water directly without polishing, etching, and washing. The graphene was transferred to water in a stainless container in which a number of Quantifoil grids (R1.2/1.3 Au 200 mesh) were aligned at the bottom with their carbon side facing up. The water was slowly drained, and the graphene layer was deposited on the grids. After air-dried for more than 1 h, each grid was separated and baked at 130 °C for 15 min. The grids were immersed in acetic acid twice for PMMA removal: for 2–3 h in the first step and then overnight. Then the grids were soaked in isopropanol for 10 min, air-dried on a filter paper, and baked at 100 °C for 10 min. The grids were stored in a grid box in a desiccator.We prepared graphene grids using Quantifoil grids based on the protocol described previously with some modifications2· gas generated from an aqueous solution (20 ml) of NaClO2 (200 mg) and 37% HCl (aq) (100 μl) by irradiation with an UV LED lamp  for 10 min. A square-shaped dish-like glassware with an inner and outer compartment that we developed before  was used to perform this oxidation process. The aqueous solution of NaClO2 was filled into the outer compartment, the graphene grids were laid in the inner compartment, and a glass plate was put on the glassware to seal the entire chamber. To avoid any damage on the graphene layer by direct photoirradiation, an aluminum foil was laid on the grids to shield them from the UV light . The total reaction volume can be reduced to 5 ml. For the storage experiment (GroEL-EG3m), the ClO2· oxidized graphene grids were washed with 5 μl of ultrapure water three times, air-dried, and stored under N2 gas at room temperature for 3 months. The graphene on a silicon wafer were also oxidized in the same way. Because the ClO2· gas is toxic and potentially explosive especially at high concentration, all the oxidation process should be performed under controlled concentration in a fume hood. Plasma treated hydrophilized graphene on a silicon wafer were also prepared as a control for XPS measurement by glow discharge  using a JEC-3000FC sputter coater (JEOL) with an aluminum foil mask to shield them from plasma irradiation.The graphene grids prepared as above were oxidized by photoactivated ClOThe preparation of EG-grid  was performed as described in manuscript for negative staining. A 3 μl aqueous solution of 1% 1H,1H-undecafluorohexylamine (UFHA) was loaded on the EG-grid. After 5 min, the solution was blotted with a filter paper and the grid was washed with 5 μl water for 3 times. After washing process, the fluorinated graphene grid was dried in vacuo overnight.−1 over 16 scans in a scan range of 600–4000 cm−1. The surface chemical composition of the graphene was determined using a JPS-9010MC XPS instrument (JEOL). The XPS insert parameters included the power of analysis  and monochromatic Mg Kα radiation. The survey and high-resolution XPS profiles were obtained at the constant analyzer pass energies of 160 and 10 eV, respectively. The CasaXPS Version 2.3.15 software was used for the peak-differentiation-imitating analysis of the C1s narrow XPS profile. Two independent experiments were conducted to obtain the standard deviation of the data. The static water contact angles of the graphene surfaces were determined using a Drop Master DM300 (Kyowa Interface Science) contact angle meter. A 1.0 μl of water droplet was placed on the surface of the graphene on the grid, and the contact angle was determined 5 s (scanning time) after the attachment of the droplet. Data are obtained as the mean of three independent experiments with standard deviation. All the cryoEM images and diffraction patterns of the grids were acquired using SerialEM39 and JEM-Z300FSC  operated at 300 kV with a K3 direct electron detector . The peak values of the diffraction intensities were identified using Fiji40.Raman spectra were obtained with an NRS-3100 T laser Raman spectrometer (JASCO) with a 532 nm laser at room temperature. Fourier-transform infrared (FTIR) spectroscopic measurements were performed using a Spectrum Two spectrometer (Perkin-Elmer) equipped with an UATR two attachment. All the spectra were acquired at a resolution of 4 cmEscherichia coli was purchased from TaKaRa Bio Inc. (Product code 7330). A 5 mg powder sample of GroEL was dissolved with 500 μl of GroEL buffer , and it was purified using a Superdex 200 Increase 10/300 GL gel filtration column with the GroEL buffer. The main peak was collected and concentrated to 3.2 mg ml−1, flash-frozen with liquid nitrogen and stored at − 80 °C.GroEL from 2+ affinity chromatography using Ni Sepharose 6 Fast Flow (Cytiva), followed by size exclusion chromatography using Superdex 200 Increase 10/300 GL (Cytiva) equilibrated with a buffer containing 50 mM HEPES (pH 7.0) and 200 mM NaCl.The codon-optimized gene of SARS-CoV-2 Spike (S) protein (GenBank: QHD43416) was designed for expression in mammalian cells and synthesized from GeneArt DNA Synthesis (Thermo). For expression of recombinant S protein, the sequence encoding the S ectodomain (residues 1–1208) with proline substitutions at residues 986 and 987, a “GSAS” substitution at furin cleavage site (residues 682–685), and C-terminal foldon trimerization motif followed by an octa-histidine tag was cloned into a pcDNA3.1 expression vector (Invitrogen). Further, S protein D614G mutation was introduced by inverse PCR method. Recombinant S proteins were transiently expressed in Expi293f. cells (Thermo) maintained in HE400AZ medium . The expression vector was transfected by using Gxpress 293 Transfection Kit  as following the manufacturer’s protocol. After 5 days post-transfection, culture supernatants were harvested, and His-tagged S proteins were purified by NiEscherichia coli strain BL21-CodonPlus-RP (Stratagene) was used for expression of the human DF-V1-ATPase chimeric complex. The recombinant complex was isolated as described previously42. The expression plasmids for V1-ATPase containing DF from Homo sapiens were constructed by the same method as described previously41. The genes encoding the D and F subunits were amplified from human cDNA. The amplified fragment was then digested with appropriate restriction enzymes and inserted into the corresponding region of the Thermus thermophilus V1-ATPase expression plasmid42. The mutant V1-ATPase  was used for preparation of the complex.Escherichia coli strain, BL21 Star™ (DE3) (Thermo Fisher Scientific). The transformed cells were cultured in Luria–Bertani (LB) medium with 50 μg ml−1 Kanamycin at 37 °C until the OD600 reached ~ 0.6. After adding 1 mM isopropyl-β-D-thiogalactopyranoside (IPTG) and culturing for 16 h at 37 °C, cells were harvested by centrifugation, flash-frozen with liquid nitrogen, and stored at − 80 °C. Frozen bacteria pellets were resuspended in a buffer containing 20 mM Tris–HCl pH 8.0, 30 mM NaCl, 1 mM 1,4-dithiothreitol (DTT) supplemented with 10 μl Benzonase, 1 mg ml−1 lysozyme, and EDTA-free cOmplete™ protease inhibitor cocktail (Roche) and lysed by sonication. The suspension was centrifuged for 30 min at 200,000×g and 4 °C. The supernatant was collected and purified using a HisTrap HP 5 ml column  equilibrated with a buffer containing 20 mM Na4P2O7 pH 7.4, 500 mM NaCl, 30 mM imidazole, and subsequently eluted with the same buffer containing 500 mM imidazole. The desired fractions were collected and dialyzed overnight at 4 °C against a buffer composed of 20 mM Na4P2O7 pH 7.4, 200 mM NaCl supplemented with TEV protease. The protein was further purified using a HiLoad 16/600 Superdex 200 column  equilibrated with 20 mM Tris–HCl pH 8.0, 30 mM NaCl, 1 mM Tris(2-carboxyethyl)phosphine (TCEP). The desired fractions were collected, concentrated to ~ 10 mg ml−1, flash-frozen with liquid nitrogen, and stored at − 80 °C.The gene of human GAPDH was cloned into pET-28a(+) expression vector (Novagen) with an N-terminal His-tag followed by the Tobacco Etch Virus (TEV) protease cleavage site. The recombinant protein was expressed using an 2, 1 mM TCEP) to a final concentration of 4.0 mg ml−1. IPTG was supplemented to a final concentration of 5 mM, and the solution was incubated on ice for 30 min. β-gal was purified using Superdex 200 Increase 10/300 GL column  with the same buffer. The main peak was collected and concentrated to 2.0 mg ml−1, flash-frozen with liquid nitrogen and stored at − 80 °C.β-galactosidase  was purchased from SIGMA-Aldrich (Product code G5635) and dissolved with β-gal buffer  (Thermo Fisher Scientific). After the plasmid was transformed, the cells were cultured in 4 ml of LB medium with 50 μg ml−1 Kanamycin and 0.5% glucose overnight. The culture was transferred into 500 ml of fresh culture medium and cultured at 37 °C until the optical density at 600 nm reached 0.5. Then, 1 mM IPTG was added, and the cultivation was continued for 4 h at 37 °C. Cells were harvested and resuspended in buffer A  supplemented with 1 mg ml−1 lysozyme and lysed by sonication. The suspension was centrifuged, and the supernatant was incubated at 70 °C for 10 min. After centrifugation and collecting supernatant again, the protein was precipitated by adding ammonium sulfate to a final concentration of ~ 53% and stirring on ice for 30 min. After centrifugation and collecting pellet, the pellet was washed with buffer A supplemented with ~ 53% ammonium sulfate. This washing step was repeated twice. The washed pellet was resuspended in 2 ml of phosphate buffered saline (PBS) and dialyzed against 500 ml of PBS at 4 °C overnight. Precipitation was removed by ultracentrifugation, and the solution was concentrated to ~ 0.9 ml, which was further purified using a Superose 6 10/300 column  equilibrated with 20 mM HEPES pH 7.5, 150 mM NaCl. The desired fractions were collected separately, flash-frozen with liquid nitrogen and stored at − 80 °C.Mouse apoferritin was expressed using mFth1-pET24a plasmid and an −1.Influenza hemagglutinin trimer (HA)  was purchased from MyBioSource  and dissolved in 1× PBS buffer to a concentration of 0.5 mg ml−1) was loaded on the graphene side of the grid. After 5 min, the protein solution was blotted, and the grid was washed with 5 μl of the same buffer three times . Then the grid was immediately stained with 3 μl of 2% uranyl acetate solution. Images were taken using a JEM-1011 electron microscope (JEOL) at 100 kV.A 3 μl solution of 1% (v/v) epichlorohydrin (ECH) in water was loaded on the graphene side of an oxidized graphene grid held with a tweezer. After 5 min, the ECH solution was blotted with a filter paper, and the grid was washed with 5 μl of ultrapure water (or protein buffer not containing a compound with amino group such as Tris) three times. Then, 3 μl of GroEL solution  and 150 mM NaCl. In the washing step after protein loading, the final (third) washing buffer on the grid was not blotted, and the tweezer holding the grid was placed in a chamber of Vitrobot™ Mark IV (Thermo Fisher Scientific) equilibrated at 4 °C and 100% humidity. For Spike-EG, GAPDH-EG, and HA, the grid was not washed after the protein loading. After a liquid ethane container was placed, the grid was blotted for 1.5 s  or 2.0 s (for Spike-EG) and immediately plunged into liquid ethane. Excessive ethane was manually blotted with a filter paper, and the grid was stored in liquid nitrogen. For comparison, non-oxidized graphene grid  or Quantifoil grids  was hydrophilized by glow discharge using a JEC-3000FC sputter coater (JEOL). To avoid graphene breakage, a loose mask made of a thin aluminum foil was placed above the grids during the glow discharge for the graphene grid. A tweezer holding the grid was placed in a chamber of Vitrobot™ and after the liquid ethane container was placed, 3 μl of 1.5 mg ml−1 (for GroEL-Q and GroEL-Gl) or 1.0 mg ml−1 (for GroEL-Gh) GroEL, or 0.1 mg ml−1 (for Spike-G) or 0.5 mg ml−1 (for Spike-Q) spike protein, or 0.5 mg ml−1 (for GAPDH-Q) GAPDH solution was loaded on the graphene or carbon side. Then the grid was blotted for 1.5 s (for GroEL-Gl and GroEL-Gh) or 2.0 s  or 3.0 s (for GAPDH-Q) and immediately plunged into liquid ethane. Excessive ethane was manually blotted with a filter paper, and the grid was stored in liquid nitrogen.The surface functionalization of oxidized graphene by 1% ECH and the protein loading step were performed as described above for negative staining. Protein concentration was 1.5 mg ml39, yoneoLocr43, and JEM-Z300FSC  operated at 300 kV with a K3 direct electron detector  in the CDS mode. The Ω-type in-column energy filter was operated with a slit width of 20 eV for zero-loss imaging. For the GroEL-EG, GroEL-Gl, GroEL-Gh, GroEL-EG3m, and GAPDH-EG dataset, nominal magnification was 60,000×. Defocus varied between − 0.5 and − 2.0 μm. Each movie was fractionated to 40 frames  with a total dose of 40 e−/Å2 for the GroEL-EG, GroEL-Gl, GroEL-Gh, and GAPDH-EG dataset. For the GroEL-Q, GroEL-EG3m, and GAPDH-Q dataset, three movies were collected from one hole, and each movie was fractionated to 40 frames  with a total dose of 40 e−/Å2. For the Spike-EG, Spike-Q, and HA dataset, nominal magnification was 60,000×. Defocus varied between − 0.5 and − 2.0 μm. Each movie was fractionated to 60 frames  with a total dose of 60 e−/Å2. For the V1-ATPase-EG dataset, nominal magnification was 50,000×. Defocus varied between − 1.0 and − 2.5 μm. Each movie was fractionated to 48 frames  with a total dose of 62 e−/Å2. For the β-gal-EG dataset, nominal magnification was 100,000×. Defocus varied between − 0.5 and − 2.0 μm. Each movie was fractionated to 40 frames  with a total dose of 40 e−/Å2. For the apoferritin-EG dataset, nominal magnification was 120,000×. Defocus varied between − 0.3 and − 1.3 μm. Each movie was fractionated to 64 frames  with a total dose of 40 e−/Å2. More parameters are summarized in Supplementary Table 1.All the cryoEM image datasets were acquired using SerialEM44, and CTFs were estimated using CTFFIND 4.145. All the following steps were performed using RELION 3.146 except GroEL-Q, GroEL-EG3m, and GAPDH-Q. RELION 4.047 was used for processing for GroEL-Q, GroEL-EG3m, and GAPDH-Q. Only the images whose CTF max resolutions were beyond 5 Å were selected, and the resulting number of images are referred to as Initial images. For GroEL-EG3m, 1209 movies were processed, and 500 micrographs were randomly selected after the CTF max resolutions beyond 5 Å selection and processed in the following steps. Laplacian of Gaussian (LoG)-based auto-picking was performed for small number of images to generate the 2D class averages, which were then used as a template for second auto-picking for all images. Picked particles were extracted with 1–4× binning, and the resulting number of particles was referred to as the initial particle number. Several rounds of 2D classification were carried out to remove bad particles. For GroEL-EG3m, one round of 2D classification was performed. Initial 3D model was generated with C1 symmetry, and then D7 (for GroEL) or D2  symmetry was applied to the resulting maps. No symmetry was applied for SARS-CoV-2 spike protein and V1-ATPase. 3D classification (into 4 classes) with C1 symmetry was conducted to exclude bad particles, and the number of remaining particles was referred to as the final particle number except the GAPDH-EG and β-gal-EG datasets. The remaining particles were re-extracted with a larger box size, and 3D auto-refine applying D7 (for GroEL) or D2  symmetry was performed. After a soft mask around the map was generated, the resulting map was post-processed and sharpened. After several rounds of CTF refinement and Bayesian polishing with divided optics groups of 6 (for GroEL-Q), 8 (for GAPDH-EG), 10 , 11 (for GroEL-Gl and V1-ATPase-EG), 12 (for Spike-EG), 13 (for GroEL-Gh), and 16 , 3D auto-refine and post-processing were conducted again. For the GAPDH-EG and β-gal-EG datasets, particles were re-selected with rlnMaxValueProbDistribution = 0.05 criteria , and 3D auto-refine and post-processing were conducted to generate the final map.The movies were motion corrected using MotionCor2For apoferritin, in total 793,398 particles picked by using 2D class averages template were extracted from 7500 images (Supplementary Table 1) with a box of 512 × 512 pixels and divided into 25 groups as optical groups according to the time of data collection. After the particle selection by 2D classification, an initial model was generated, and the selected particles were subjected to several rounds of 3D auto-refine with O symmetry and post-processing with CTF refinement and Bayesian polishing. Then the particles were regrouped into 12 by the range of beam tilt X, Y (0.1 milli-radian increments) and further regrouped into 300 (= 12 × 25:5 × 5 multi-hole shots) by image shift. Regrouped particles were subjected to another round of CTF refinement, 3D auto-refine, and post-processing. Then the particles were further selected with rlnMaxValueProbDistribution = 0.04 criteria, and the selected 527,261 particles were subjected to another rounds of Bayesian polishing with a larger box size (600 × 600), 3D auto-refine, and post-processing. Finally, the Ewald sphere correction and another round of post-processing was applied to generate the final map.30 . The efficiencies in particle orientational distributions were calculated using cryoEF31. The Rosenthal-Henderson B-factors29 were calculated and plotted using bfactor_plot.py script in RELION34. Structural analysis and figure preparation were performed using UCSF Chimera48 and ChimeraX49.The map resolutions were estimated by the gold-standard Fourier shell correlation (FSC = 0.143) criterion. The FSC curves and sphericities were calculated using 3DFSC server39, and the order of tilt series was as the following tilt angle in degree:  ->  ->  ->  ->  ->  ->  ->  ->  ->  -> . Each movie was fractionated to 5 frames  with a total dose of 98 e−/Å2. For GroEL-Gh, the nominal magnification was 20,000×, corresponding to 2.37 Å/pixel. Defocus was set to − 3.0 μm. The tilt angle varied from − 60º to 60º with 3º increment to acquire 41 movies using SerialEM39, and the order of tilt series was as the following tilt angle in degree:  ->  ->  ->  ->  ->  ->  ->  ->  ->  ->  ->  ->  ->  ->  -> . Each movie was fractionated to 5 frames  with a total dose of 40 e−/Å2. Tomograms were reconstructed using IMOD50. Images were aligned without using gold nanoparticles by patch tracking (for GroEL-EG) or by tracking ice contaminations as seeds (for GroEL-Gh).The datasets for tomography were acquired for the same grid as the GroEL-EG or GroEL-Gh dataset was collected using JEM-Z300FSC  operated at 300 kV with a K3 direct electron detector  in CDS mode. The Ω-type in-column energy filter was operated with a slit width of 20 eV for zero-loss imaging. For GroEL-EG, the nominal magnification was 40,000×, corresponding to 1.25 Å/pixel. Defocus was set to − 3.0 μm. The tilt angle varied from − 60º to 60º with 3º increment to acquire 41 movies using SerialEMSupplementary Figures.Supplementary Video 1.Supplementary Video 2.Supplementary Video 3.


An airplane is statistically struck by lightning every year. The need for lightweight aircraft to reduce the production of carbon dioxide has significantly reduced the presence of metals in favour of composites, resulting in lower lightning strike protection efficiency. In this perspective, we critically review the state of technologies in lightning strike protection solutions based on carbon materials, graphene, and MXenes. Furthermore, we comment on possible future research directions in the field. The strike of global climate crisis is driving research on novel solutions to reduce the impact of human industries on the planet. For instance, the CORSIA  program sets a scheme towards a sustainable aeronautical industry increasing the demand for novel efficient composite structures  with theIn the last 50 years, the percentage of composite materials in structural parts of modern passenger aircraft increased from 5 to 50 % with a substantial optimization of the aircraft weight see a 3,4]. ,4. 3,4].Thus, in recent decades efforts have been made to clarify, on the one hand, how lightning strikes and the damage evolves, and on the other hand, how to optimize coatings able to sustain such a short transfer of electrical current along the aircraft surface. To test the LSP capability of aeronautical structures, SAE ARP 5412 defines typical lightning flash current waveforms see a. ComponStill, this task is challenging because the coating needs to express multifunctionalities such as light weight and reduced thickness, resistance to erosion, adhesion to the composite structure, great mechanical stability to avoid crack propagation, and stability in a large range of temperature that would allow an aircraft to lift off from an airport with 40 °C, operate at temperatures down to −40 °C in-flight, and land in one with temperatures of 0 °C.Herein, we report the current solutions proposed in terms of LSP. Then, we critically discuss the possible future trends in this field and materials that could make a breakthrough in this research field.2) of the surface without reasonable depth penetration. Commercial LSP systems use copper meshes with a thickness of about 0.10–0.20 mm [2) to the structure, dampening the positive weight-saving effect of composite usage. Moreover, metallic meshes  are prone to corrosion deteriorating their performance within the composite system. Therefore, careful design avoiding galvanic corrosion  or corrosion-resistive coating of the mesh is necessary. Further, delamination problems can be tackled with adhesion-enhancing coatings on the copper meshes [Preferential solutions for effective LSP are metallic meshes  integrated in the outer layers of the composite structure via impregnation with adhesives or sandwiching between two adhesive films. Providing reasonable conductivity, the copper meshes channel electrical currents and reduce thermal stress on the composite. By tuning the mesh thickness and open hole area, performance of the metallic mesh can be adjusted to the LSP requirements. If correctly applied, metallic meshes can limit the damage to a small area few 100 mm0 mm2 of –0.20 mm ,12. Clear meshes .Alternatively to metallic meshes, the integration of metallic fibres into the fibre ply or the application of metallic coatings onto the fibres can provide a similar protection mechanism, although corrosion and fibre/matrix adhesion challenges remain to be solved . MetalliIn recent decades, conductive carbon materials emerged as an alternative to metallic systems offering good conductivity and easy processing for lightweight composites. The use of carbon nanofillers offers the possibility to introduce lightning strike protection into the matrix structure and is therefore favourable in saving process steps during production . Carbon ntration . Using cntration . Similarntration . To imprntration . With CN3, single-layer graphene sheets appear an ideal solution for LSP. Although these properties are promising, technical realisation of graphene-based LSP systems appears challenging.The need for higher conductivity in composites drives the application of two-dimensional materials (2DM) in aeronautical coating research. Since its discovery in 2004 . Due to 106 S/m , a therm103 W/mK , and a dTo form a conductive network within the composite, implementation of the graphene filler is the most important step. A simple form of implementation is mixing it into a polymer matrix by high-shear mixing ,29, caleTo use the full potential of graphene sheets, confined systems are proposed for LSP concentrating graphene in thin layers within the composite.A simple approach to produce concentrated graphene films represents vacuum filtration of previously dispersed graphene sheets. Following filtration, a compression step increases film stability and uniformity producing standalone films with thicknesses of about 25 µm to 150 µm. Electrical conductivity of these compacted graphene sheets are in the range of 1.32 ××105 S/m . The lat2. Further increase in rGO concentration is not possible due to flakes blocking the resin flow through the filter paper at higher amounts. Surpassing the percolation threshold at 2 g/m2, the layers sheet resistance is less than 0.55 Alternatively, production of concentrated graphene layers during vacuum bagging is achieved via resin film infusion (RFI) . By mixi2 hindering performance improvements with higher concentrations. Deteriorating flow dynamics within fibre impregnation, potentially leading to irregularities in matrix distribution, could also be a problematic factor during industrial processing. Nonetheless, vacuum-assisted techniques appear superior to mechanical mixing in terms of LSP application.Compared to ex-situ vacuum-filtration-based film production, in-situ RFI produces thinner concentrated layers with superior conductivity and avoids delamination problems by ensuring impregnation of the rGO layer during composite production. On the downside, the technique is limited to loadings up to 9 g/mBeside vacuum-assisted film production, production in combination with additives can be used for producing conductive graphene films. Commonly, polymers, such as polyvinyl alcohol (PVA) , polyani for LSP . Similar for LSP . GraphenNonetheless, addition of polymers to graphene films deteriorates the electrical conductivity, being about two magnitudes lower than the conductivity of pure graphene films. Conductive enhancement of polymer/graphene films can be achieved via incorporation of metallised polymer veils . The siltion see a was emption see b. The reA summary of the mentioned conductive graphene-based composites is shown in To achieve conductivity comparable to currently used copper mesh, implementation techniques producing confined layers of graphene are necessary. Vacuum-assisted infusion  or film For instance, large scale films are often fragile due to a lack of stabilizers. Adding stabilizing polymers is not an ideal solution as they will hamper the conductivity of the film . Additio2 for an aeronautical LSP copper mesh [2 [Another element that goes against the substitution of copper mesh with graphene-based materials is the production cost of high-quality graphene. Prices are about 94 USD/mper mesh , whereasper mesh  costs ab mesh [2 . Still, The mentioned challenges of performance enhancement, delamination, and improvement of integration and material costs need to be solved to achieve industrialisation of graphene-based LSP systems. This goal can be reached by acting directly on the graphene preparation or by combining it with metals such as silver nanoparticles or other 2DM.The aspect ratio is a crucial factor on the conductive performance of graphene nanofillers in composites. Higher aspect ratios improve the performance, significantly lowering the percolation threshold and reducing contact resistance between particles ,41. PossIn principle, single-layer graphene sheets would be the most suitable option concerning LSP, but the production of defect-free single-layer graphene via CVD is costly, wherefore exfoliation of graphite is a preferred route for industrial application. Graphite is intercalated in aqueous solution by various agents , leading to exfoliation. This often produces defects within the aromatic structures, such as oxidic groups or crystal defects deteriorating the conductivity of the flakes.+-ions prior to the reduction process, graphene oxide and Ag+-ions are simultaneously reduced to rGO and metallic silver by L-Ascorbic acid  [3 [An emerging field of 2DM is MXenes  with their currently most prominent material Ti3C2Tx . Recent 3C2Tx  were abl×106 S/m ) and exh8 V/cm2) . In addi/cm2) [3 ) compareures See a show a Additionally, the combination of rGO and MXenes offers the possibility of layer self-assembly to generate an oriented network, i.e., heterostructures . It has Direct application of MXenes in lightning strike tests comes as follow. MXene-doped thermosetting coatings with a conductivity of about 100 S/m showed reduction in lightning-induced surface damage of a CFRP panel by 67% see c, while The demand for lightweight aircraft to reduce the production of carbon dioxide reveals the need for advanced lightning strike protection (LSP) that does not rely on metal materials. Two-dimensional-material-based LSP systems show promising results to substitute currently used copper mesh in LSP Zone 2. Specifically, the utilization of graphene, MXenes, or a combination of both, has provided inspiring results to address LSP. Still, there are many aspects to keep in consideration while researching 2DM-LSP.First, the fusion of 2DM films and composites is not optimized to the prototype or higher technological level of readiness yet. This implies fundamental investigation on the functionalisation of 2DM to improve the contact points between 2DM flakes dispersed in a composite. Surface analysis will be crucial in this effort, comparing techniques to verify active functional groups and quantify elements at the first atomic layer . Moreover, further improvement in 2DM research will need to pay attention to the environmental impact of the chemistry in use. In this sense, green chemicals are essential for industries to comply with the goals set in the European Green Deal, as discussed in our previous work .Second, instead of pursuing a no-metal strategy, it could be beneficial to approach the challenge as a minimization problem. For instance, the presence of copper in a mesh could be reduced to particle distribution in 2DM composites. As shown in Third, MXenes are undoubtedly one of the most promising 2DMs in terms of conductivity and versatility . The mosFourth, higher conductivity might not necessarily mean a better LSP performance. As discussed in the work of Zhao et al. , despiteIn conclusion, although graphene-based LSP systems could reduce weight up to two order of magnitude to the currently used copper meshes, further investigation is essential to obtain a light composite material able to sustain a lightning strike. Thus, 2DMs are probably the key area that as scientists we must exploit in order to unlock this technology and fly on safe and environmentally friendly aeroplanes.


Sensor arrays are currently attracting the interest of researchers due to their potential of overcoming the limitations of single sensors regarding selectivity, required by specific applications. Among the materials used to develop sensor arrays, graphene has not been so far extensively exploited, despite its remarkable sensing capability. Here we present the development of a graphene-based sensor array prepared by dropcasting nanostructure and nanocomposite graphene solution on interdigitated substrates, with the aim to investigate the capability of the array to discriminate several gases related to specific applications, including environmental monitoring, food quality tracking, and breathomics. This goal is achieved in two steps: at first the sensing properties of the array have been assessed through ammonia exposures, drawing the calibration curves, estimating the limit of detection, which has been found in the ppb range for all sensors, and investigating stability and sensitivity; then, after performing exposures to acetone, ethanol, 2-propanol, sodium hypochlorite, and water vapour, chemometric tools have been exploited to investigate the discrimination capability of the array, including principal component analysis (PCA), linear discriminant analysis (LDA), and Mahalanobis distance. PCA shows that the array was able to discriminate all the tested gases with an explained variance around 95%, while with an LDA approach the array can be trained to accurately recognize unknown gas contribution, with an accuracy higher than 94%. In the last few decades, being set at front-end in IoT (Internet of Things), gas sensors have become quite important in everyday life and therefore, the interest of researchers in their development has noticeably increased. Market demands have pushed scientists to put their efforts in the development of extremely sensitive, low cost, reliable, stable, highly selective, and with rapid response and recovery, single sensors ,2, exploFor several applications, including breathomics, food quality tracking, or environmental monitoring, a single sensor’s high selectivity is particularly important, since the analyte under investigation should be recognized and detected among many interfering gases ,18. AddiFor these application fields, the development of a single chemiresistor sensor may not be the ultimate solution, on one hand due to the difficulties to maintain a high selectivity in presence of many interfering gases ,18,20, oAs mentioned, among the materials exploited to develop gas sensors and sensor arrays, carbon-based materials are of particular interest due to their outstanding electrical properties ,25. CarbFurthermore, such studies often focus mainly on the sensing performances and disclosure of the sensing mechanism, neglecting a more in dept chemometric analysis on the data provided by the array.The main scope of this work is to develop a chemiresistor graphene-based sensor array to investigate as a proof of concepts the possibility to exploit not only PCA for data discrimination, but to explore quantitative chemometric tools such as linear discriminant analysis (LDA) and LDA coupled with a Mahalanobis distance algorithm.3O4/graphene nanocomposite (Gr_Fe3O4), CoPt/graphene nanocomposite (Gr_CoPt), and TiO2/graphene nanocomposite (Gr_TiO2). These materials have been selected with the aim of exploring and comparing their sensing properties when used in a chemiresistor configuration, and to check their capability to work in an array. All sensing layers are related to a common platform material, i.e., graphene, which allowed to diversify the single layers by morphological properties, in the case of graphene dispersion and graphene nanoplatelets, or through the nanoparticles used for graphene decoration .In this regard, an array composed of 5 graphene-based sensors on alumina/silver interdigitated substrates has been developed exploiting a very simple, easy, and low-cost technique, i.e., dropcasting from solution. In particular, solutions of graphene nanostructures and graphene nanocomposites have been considered: graphene dispersion (hereafter Gr dispersion), graphene nanoplatelets (hereafter Gr_nanoplatelets), FeAfter characterization through Raman spectroscopy, the prepared samples have been put on a properly designed platform, which is able to simultaneously monitor the response from all sensors and allows for a direct comparison of the sensor performances under the same working conditions. Since ammonia is often considered as a benchmarking molecule for novel nanocarbon-based sensors ,43, expoThe discrimination capability of the array against other gases has then been tested through exposures to acetone, ethanol, 2-propanol, water, and sodium hypochlorite, producing a dataset for chemometrics analysis. PCA performed on the dataset shows that the developed e-nose is able to discriminate all the tested gases. Furthermore, the contribution of each sensor in the discrimination is investigated and discussed, supporting the claims with experimental evidence. Finally, we demonstrated that with an LDA approach the array can be trained to accurately recognize each gas contribution, despite the limited dataset. Additionally, the Mahalanobis distance has been evaluated to offer an alternative approach for the identification of unknown data.Incidentally, it is worth mentioning that all the tested gases are relevant for several applications, including breathomics, environmental monitoring, safety, and food quality tracking. Indeed, ammonia is recognized as one of the precursors of fine particulate matter, whose inhalation can provoke severe damage to human and animal respiratory systems . Spoiled3O4/graphene nanocomposite , CoPt/graphene nanocomposite , and TiO2/graphene nanocomposite  have been purchased from Sigma Aldrich and used as received.Graphene dispersion (1 mg/mL in DMF), graphene nanoplatelets , FeThe size of silver interdigitates electrodes on alumina substrate was 7 mm × 13.4 mm. The interdigitates contacts consisted of 7 + 7 branches and each silver line has a 210 μm width. 3O4, Gr_CoPt, and Gr_TiO2.In order to prepare the sensors, 15 droplets of each graphene-based solution have been drop-casted on 5 interdigitated alumina substrates and let dry upon solvent evaporation at room temperature. The prepared samples will be labelled as Gr dispersion, Gr_nanoplatelets, Gr_FeRaman spectra have been collected on the CGS device with a Renishaw-Invia system, equipped with a 532 nm laser source. The laser light has always been focused onto the sample with a 100× objective. An 1800 lines/mm grating and a laser power of 1 mW have been used for the measurements.The five prepared samples have been mounted on a properly designed platform and they can work simultaneously (scheme in L) in series with the sensor and by applying a constant voltage (V = 5 V) and monitoring the output voltage across the sample (VOUT), it is possible to track the resistance R of the sensor. The response of the sensor is then defined as ΔR/R0 = (R − R0)/R0, where R0 is the baseline sensor resistance before the gas exposure, and ΔR= R − R0 is the resistance variation due to the interaction with the gas molecules.The developed graphene-based array works in a chemiresistor configuration b: the gaGas exposures have been performed at room temperature (T = (25 ± 4) °C) and with a relative humidity (RH) value around 50 ± 5%, in order to investigate the sensing behaviour of the array in a working condition close to the application environment. Indeed, both the temperature and humidity tested range could be considered suitable for application in environmental monitoring, safety, and food quality tracking, where the array would work in STP conditions.0 versus the gas concentration.The recovery is always achieved in the same condition, fluxing humid air on the array. A scheme of the set up for the gas exposure is reported in A commercial sensor  has been exploited to monitor and evaluate ammonia concentration. The reliability of this sensor calibration curve has been cross-checked by exposing the sensor in a testing chamber with calibrated mass flow controllers (MFCs) and certified ammonia and air cylinders.To assess the discrimination capability of the array, the data collected from exposures to six different gases have been analyzed at first with principal component analysis (PCA) and then with linear discriminant analysis (LDA) and Mahalanobis distance, all implemented into the R software.PCA is a chemometric method, which is unsupervised and can reduce the problem dimensionality while maximizing the variance of the data; it reorganizes the data into a set of principal orthogonal components (PCs), where PC1 is the component that has the greatest variance, PC2 represents the component with the second greatest variance, and so on. The aim of this statistical tool is to obtain a 2-dimensional or 3-dimensional space where a clear discrimination of the tested gases could be identified ,59.LDA is a supervised chemometric tool, therefore it takes into account the class properties of each sample; it assumes that each class has an identical covariance, and unlike the PCA, it deals only with the covariance of the data matrix. The optimal clustering among different classes  is obtained by maximizing the distance between classes and minimizing the scattering of the data inside each class, resulting in a reduction of the problem dimensionality, and obtaining a new space generated by new coordinates, called discriminant factors . LDA givThe Mahalanobis distance has been selected to evaluate the distance between a gas contribution and each cluster in an LDA space, with the aim to present an alternative approach for the identification of unknown data. Indeed, the Mahalanobis distance is the lowest for the class which a datum belongs to. The algorithm has been written in R as in ref. .The data collected from the gas exposures have been pre-treated only by column mean-centering to feed the PCA, LDA, and Mahalanobis distance algorithm and a set of 32 exposures to six different gases has been considered.−1 and related to the C-C stretching of the sp2 atoms in the honeycomb lattice of graphene pow) has been selected to interpolate the data and the fitting parameters, reported in 0 = y + A [LOD]pow pow ,67, wherThe calibration curves present a sublinear behaviour, which is quite common in the field of gas sensor based on carbon nanomaterials ,68,69.3O4 (response range: 0–0.025), and finally, the lowest response is detected by Gr_TiO2 (response range: 0–0.008).Looking at the calibration curve, it is clear that the highest responses are obtained for the Gr_CoPt sensor (response range: 0–0.09), followed by Gr dispersion (response range: 0–0.05), Gr_nanoplatelets (response range: 0–0.04), Gr_FeThe stability and reproducibility of the sensor response have been proven for ammonia detection and the results clearly demonstrated a good stability and reproducibility up to 3 months after the samples preparation see .Moreover, the stability of the sensors’ response to ammonia exposures in a working temperature range suitable for application in environmental monitoring, breathomics, and also food quality tracking  has been assessed see .As for the sensing mechanisms, the resistance increase upon ammonia exposure indicates that the all layers exhibit an overall p-type nature, consistently with the fact that the electron injection from ammonia reduces the density of holes in these layers.Regarding Gr dispersion and Gr_nanoplatelets layers, the sensing mechanism is well known in literature: an electron transfer from ammonia to graphene occurs when the N atom of ammonia faces the graphene lattice, as shown in 3O4 and Gr_TiO2 layers, although both Fe3O4 and TiO2 nanoparticles act as catalytic active centers, while graphene provides the conductive pathway, in addition to being itself a site for the interaction, the sensing mechanism is slightly different. In the case of Fe3O4, as reported in . Of note, only papers based on graphene and clearly reporting gas concentration and sensor response/sensitivity, operating at room temperature and in a chemiresistor configuration have been taken into account for this benchmarking.Finally, a benchmarking with literature data has been performed considering the sensitivity parameter, defined as S = 100 × , in order to become predictive on the probability of a point to belong to a specific cluster .On the contrary, LDA is a chemometric analysis that can be considered predictive and quantitative. In general, a predictive algorithm should be trained with an initial dataset , which should provide the optimal linear combination of sensor results and the best separation between different clusters. In this context, LDA can be used to better separate the clusters related to the different gases, since it works maximizing the distance between classes and minimizing the scattering of the data inside each class.After the training, a test dataset can be projected on the newly built model; in this way, unknown data can be recognized.Performing LDA on the 32 exposures dataset, we also perform internal cross validation (CV) of the model with CV segment equal to 6; this mean that to build the model, the LDA algorithm randomly removes 6 datapoints and uses these data to test the newly built model, providing an accuracy of the model itself. After using the whole dataset to investigate the capability of the array to identify unknown data through LDA, we randomly split the dataset into two subgroups: a training and a test dataset, both containing data from the all the 6 tested gases, and we perform LDA on the training set only. The test set is then projected on the LDA direction to assess the capability of the array to correctly identify the nature of an unknown.As an example, After the LDA model has been drawn, the test dataset composed of 6 unidentified data (in this particular case one point from each gas class has been randomly selected to be part of the test dataset) has been used to validate the model. The model has been able to recognize and correctly assign each point to the correct class, as reported in Moreover, after external validation it is possible to investigate the distance of each point in the test dataset from the different gas clusters, generated by the LDA model, and recognize the belonging class as function of the distance: the smallest distance identifies the class which the point belongs to. To carry out this task, Mahalanobis distance algorithm, being the most exploited for classification, has been considered and the results are reported in In this example, it is clear that point 1 belongs to the ammonia cluster, point 2 to acetone class, point 3 to the 2-propanol class, and point 6 to water class. Regarding points 4 and 5, the smallest distances are for ethanol and sodium hypochlorite, respectively, but those points are quite close also to sodium hypochlorite and ethanol groups, respectively. This result is in agreement also with the LDA model presented in Of note is also that the highest distance values are obtained considering point 1 and water and point 6 and ammonia; also in this case, this result is quite expected since point 1 belongs to ammonia cluster and point 6 to water group and ammonia and water contributions are quite distant in the LDA model b.Finally, The present results then support the application potential for this sensor array, even with a relatively small dataset, such as the one reported in this study. While PCA performs remarkably well in the discrimination of each gas contribution, for a practical application where prediction is required with specific accuracy, LDA should be preferred.2 are required to discriminate acetone, and Gr_nanoplatelets is essential for sodium hypochlorite discrimination. The capability of the array to recognize unknown data with a high accuracy has been demonstrated by LDA, also investigating different training and test dataset dimensions; specifically, the prediction accuracy is always above 94%, when also halving the training dataset dimension.The goal of the present work was to develop a graphene-based sensor array in a chemiresistor configuration and to explore the discrimination capability of this array in the identification of different gases, related to specific applications, such as breathomics, environmental monitoring, and food quality tracking, exploiting few chemometric tools. The array has been prepared by dropcasting graphene nanostructures and graphene nanocomposites solutions on interdigitate alumina/silver substrates and after characterization, five different samples have been put on a properly designed platform. The goal has then been achieved in two steps: (i) ammonia exposures have been performed, the calibration curve of each sensor has been drawn, the limit of detection has been evaluated and it is in the low ppb range for all the sensors, a remarkable stability up to 3 months of all sensors response has been found and finally, the obtained results have been compared to literature, disclosing a good sensitivity for the present chemiresistors, which is in line with what has been so far published; (ii) exposures to acetone, 2-propanol, ethanol, sodium hypochlorite, and water have been carried out with the aim to prepare a dataset for chemometric analyses. These gases have been selected because of their interfering nature in many applications, including the ones for which the presented array is aimed to. In detail, at first it has been proven that the array can clearly discriminate the tested gases in a PCA space, considering both PC1-PC2 and PC1-PC-3 2D subspaces. Moreover, the role of each sensor in the discrimination is investigated and discussed, supporting the claims with experimental evidence. In particular, thanks to the loading plots, it has been found that all sensors are responsible for the discrimination of ammonia and water vapour, while Gr dispersion and Gr_TiOFinally, an approach to identify unknown data has been suggested and tested by exploiting the Mahalanobis distance algorithm.0 response could be expected from the optimization of each layer thickness, as observed for instance in CNTs chemiresistors [Further improvements in the ΔR/Resistors . This isThe low-cost and simple preparation technique, as well as the easiness of use and simplicity of the sensor array itself, coupled with its remarkable discrimination and predictive capability, make the presented array suitable and promising for environmental monitoring, food quality tracking, and breathomics applications.


Integrating fleets of mobile service robots into the operating room wing (OR wing) has the potential to help overcome staff shortages and reduce the amount of dull or unhealthy tasks for humans. However, the OR wing has been little studied in this regard and the requirements for realizing this vision have not yet been fully identified. This includes fundamental aspects such as fleet size and composition, which we have now studied comprehensively for the first time.Using simulation, 150 different scenarios with varying fleet compositions, robot speeds and workloads were studied for a setup based on a real-life OR wing. The simulation included battery recharging cycles and queueing due to shared resources.For all simulated scenarios we report results regarding total duration of execution, average task response times and fleet utilization. The relationship between these performance measures and global scenario parameters—such as fleet size, fleet composition, robot velocity and the number of operating rooms to be served—is visualized.Our simulation-based studies have proven to be a valuable tool for individualized dimensioning of mobile robotic fleets, based on realistic workflows and environmental models. Thereby, important implications for future developments of mobile robots have been identified and a basis of decision-making regarding fleet size, fleet composition, robot capabilities and robot velocities can be provided. Due to costs, space limitations and safety requirements, these aspects must be carefully considered to successfully integrate mobile robotic technology into real-world OR wing environments. Shortage of personnel is among the most pressing problems of modern healthcare systems around the world . This isTechnologic means for overcoming the shortage of healthcare staff are widely explored in academia and industry and mainly draw from the fields of artificial intelligence, digitalization and robotics. This includes the application of mobile robotic assistance systems, which are very promising due to their high level of autonomy and the ability to traverse the environment. While mobile robotics has been explored for various use cases within the hospital –9, appliAutonomous Self-Navigating Robotic OR Assistance (AURORA) aims at designing and implementing a robotic circulating nurse for executing tasks within non-sterile areas of the OR wing. While a single such system operating independently can already be useful for supporting surgical interventions, the possibilities regarding workforce and intelligent workload distribution are limited. To use mobile robotic technology to its fullest potential, the formation of mobile robotic fleets for the OR wing is envisioned by the authors. This would allow for handling higher workloads and for implementing a context-dependent management of robotic resources, offering support where it is currently needed most.Motivated by this vision, the current research project Clearly, a successful clinical translation of this vision to real OR wing environments is not straight-forward and requires careful considerations regarding safety, hygiene and available space. One fundamental question is how large a robotic fleet should be to robustly handle the imposed workload, while limiting the required space and cost of operation to an acceptable level. We argue that this aspect is an important corner stone for demonstrating the general applicability of mobile robotics to OR environments and also sparks important requirements regarding the design of individual robots forming a fleet . This includes the necessity of reaching performance levels comparable to human-only operation, or even surpassing it.Similar investigations have been made by Jeon et al. regarding other hospital environments . The autTo the best of our knowledge, the unique characteristics and requirements of the OR wing  have not yet been studied comprehensively in the context of mobile robotics. To bridge this knowledge gap and provide valuable insights for the AURORA project, we herein present the results of a simulation-based study and draw conclusions regarding optimal fleet sizes and compositions for a variety of different scenarios.The results presented herein have been collected by means of simulation. Since commercially available or open-source tools failed to meet our specific requirements regarding flexibility and extensibility, we chose to implement a dedicated simulation software to be able to control every step of the process. Our ROSThe environment has been modeled closely to the real-life OR wing of a German university hospital. The layout is schematically shown in Fig. Prior to our simulation-based study, we have recorded the tasks of the circulating nurse during 20 laparoscopic cholecystectomies. We have selected this type of procedure since it is highly standardized  and thuTask Scheduling). After task execution is finished, the workflow module is notified and continues to process the recorded surgery workflow.Using the workflow module of our simulation tool, an intervention schedule can be assembled for each operating room of our modeled environment based on recorded interventions. According to this schedule, the workflow module then simulates the workflow of the OR circulator by replaying the recorded data. This is achieved by spawning task requests according to the recorded request time and waiting until task execution has finished. The task requests are sent to the scheduler and then dispatched to the simulated robotic fleet for execution . All different task types considered in our simulated scenarios are summarized in Table For pre- and postoperative phases, the entire collections of tasks are requested at once and the workflow module monitors, whether all tasks are completed within The fleet module of our simulation tool implements the behavior of different types of mobile robots and provides means for assembling a mobile robotic fleet based on these models. Table driving speedEach simulated robotic fleet member provides an interface for receiving task requests dispatched by the scheduler. The execution of a task request is simulated by running a succession of the following three types of activities: driving, manipulating and waiting. For example, to simulate task type T1 (transportation of sterile goods), the robot first needs to navigate to the nearest storage containing the desired article (driving), then retrieve the article from the storage (manipulating), navigate to the operating table (driving) and hand over the article to the scrub nurse (manipulating). In case that one or multiple robots are currently blocking the storage or the hand-over position at the sterile table, the robot is enqueued and needs to wait for its turn (waiting). Driving is simulated by awaiting the travel duration, which is calculated based on the robot’s Since the battery capacity is an important factor limiting the operation of robotic resources, a battery simulation has been implemented as part of the robot models. While—depending on the power demand and battery capacity—overnight charging may suffice for some robotic systems, it may be a considerable limitation for others, especially when aiming at a lightweight robot design, which is preferable in the OR wing context. Therefore, we chose to include this aspect in our investigations.During task execution, the robot’s battery is drained as follows, with To better reflect the individual power consumption associated with different parts of task execution, specific drain rates can be defined for driving (Auto-Navigation Task Scheduling for the Operating Room (ANTS-OR) [In previous work, we have proposed the score-based context-adaptive scheduling algorithm ANTS-OR) . This scThe scheduling process is split into two parts: prioritization and dispatching. Prioritization is done by ranking all currently requested tasks with respect to their time of reception. Starting with the highest ranking task, the algorithm then dispatches the tasks to currently available fleet members. During this “match-making” process, the individual capabilities of the robots as well as their current state  are taken into account. Robots with a shorter traveling path during task execution are preferred over such with longer paths. Robots that are currently not needed for task executions or those with a battery level below In order to be able to make a statement regarding optimal fleet size, 150 different scenarios were simulated, which are summarized by Table The parameters of the battery simulation were set to constant values for all simulated scenarios and were identical for each robot class. Based on performance characteristics that we have identified as being favorable in the context of our AURORA robot, the lower battery level threshold was set to The aim of fleet size dimensioning is to keep operational costs as low as possible, while guaranteeing that the number of robots is high enough to sufficiently handle the workload imposed on the fleet. As soon as the fleet is no longer able to keep up with the demand, processing durations increase, which in turn leads to delays that are potentially affecting the entire OR wing operation.total duration of surgical interventions, which ideally should not increase when compared to the human-only status quo. The elongation of surgical interventions results in increased costs  and less time for treating subsequent patients, while the revenue per intervention remains constant.One measure for quantifying this aspect is the task response time is another relevant measure, which refers to the timespan between task request From the perspectives of the surgical team and the patients currently undergoing surgery, Lastly, from an economic perspective, resources are usually meant to be used to their full capacity with as few down time as possible. This also translates to robotic fleet operation, where it is desired to avoid robots that are unoccupied (or in a fault state) for long time spans, while not creating any value. In the following, we quantify this aspect by defining the robot utilization using the active time The total durations of all simulated scenarios are shown in Fig. Average task response times are shown in Fig. Fleet efficiencies are shown in Fig. Figure The results presented in the previous provide a valuable foundation for considerations regarding the dimensioning of mobile robotic fleets for the operating room wing. Based thereon, multiple conclusions can be drawn.Our results demonstrate that robot velocity has a tremendous influence on all considered evaluation parameters. For example, task group G6 . While Our second conclusion is that enhancing the fleet size does not necessarily result in a better performance. While there is an obvious “natural” limit to fleet size due to the space required for driving and parking, other effects take over at an even earlier stage. After an increase of fleet size beyond a certain point, performance cannot be further improved. For scenarios with two operating rooms , we have even observed an inversion of total scenario durations. This effect can be explained with increased driving times, since the two ORs in the corresponding scenarios are located at one side of the OR wing  will still be considered acceptable. Regarding scenarios with all-rounder robots, this results in an optimal fleet size of 1 robot per OR for Workflows in Methods). In this regard, our simulations are quite conservative.When considering the human performance as a reference  human performance levels for more scenarios than currently observed would be the implementation of a task prediction algorithm , which Throughout the simulation results, a superior performance of all-rounder robots compared to specialized robots can be observed. For example, scenario group G6 (all-rounders, 6 ORs, A limitation of our simulation-based study is that, as of yet, only one architectural layout has been considered. However, to ensure a high level of realism, the layout was closely modelled after the floorplan of an existing OR wing. In future work, we plan to investigate further existing and speculative layouts, which could be the foundation for formulating recommendations regarding optimal layouts for the integration of mobile robots. Furthermore, acceleration phases and collision avoidance between robots and obstacles were not considered by the simulation and would lead to elongated driving times in practice. It must also be mentioned that the recorded interventions were limited to cholecystectomies. Other types of interventions could potentially require the execution of more tasks per timespan and thus lead to a higher overall workload per OR that must be handled by the robotic fleet. This will also be further explored in future work.Lastly, there is potential for increasing fleet performance by adopting approaches from related application domains, such as industrial intra-logistics. One example is smart vehicle routing, where robots execute multiple tasks along a planned route . TherebyIn this paper, we have presented a comprehensive simulation-based study regarding the dimensioning of mobile robotic fleets within the OR wing. We conclude that robot velocity is a crucial parameter when it comes to fleet performance and could be a potential hurdle regarding the integration of mobile robotic fleets into the OR wing. We further conclude that increasing the fleet size beyond a certain point does not necessarily lead to better results, especially when aiming for a good balance between fleet size and fleet performance. According recommendations regarding fleet size were given for the studied scenarios. Furthermore, we were able to confirm that a fleet consisting of all-rounder robots is advantageous regarding performance and utilization, as compared to a fleet of specialized robots. Therefore, it is desirable to design mobile robotic assistance systems that are capable of executing more than just a single specialized task—an important insight for future development efforts.While some challenges have been identified, we consider our results to support the general feasibility of mobile robotic fleets for the OR wing. Depending on the studied parameters, scenarios have been identified that show an acceptable level of performance while requiring moderately sized robotic fleets.