Yarrowia lipolytica cells. Our treatment process can be integrated with the growth procedure and achieved the release of 50% of total cellular lipids from Y. lipolytica cells. Based on this result, we propose that nanoparticles coated with axially oriented graphene could pave efficient, environmentally friendly, and cost-effective way to release intracellular lipids from yeast cell factories.Engineering of microbial cells to produce high value chemicals is rapidly advancing. Yeast, bacteria and microalgae are being used to produce high value chemicals by utilizing widely available carbon sources. However, current extraction processes of many high value products from these cells are time- and labor-consuming and require toxic chemicals. This makes the extraction processes detrimental to the environment and not economically feasible. Hence, there is a demand for the development of simple, effective, and environmentally friendly method for the extraction of high value chemicals from these cell factories. Herein, we hypothesized that atomically thin edges of graphene having ability to interact with hydrophobic materials, could be used to extract high value lipids from cell factories. To achieve this, array of axially oriented graphene was deposited on iron nanoparticles. These coated nanoparticles were used to facilitate the release of intracellular lipids from  Recently, environmental concerns regarding the use of fossil fuels has increased, due to their contribution to the greenhouse effect, acid rain, ozone depletion and climate change2. Hence, the interest towards the production and utilization of biofuel is growing. Different species of yeast, bacteria and microalgae are being genetically engineered to enhance the production of various biofuels and oleochemicals5. Lipids produced by these microorganisms can be used as an alternative for fossil fuels, both as a sustainable source of energy or chemicals otherwise derived from oil. This production is sustainable, because cheap and widely available carbon sources, even including a variety of industrial waste and biowaste, can be used to grow such microorganisms producing biofuels and oleochemicals7.Microbial cell factories provide a sustainable source of biofuels and olechemicals4. Yarrowia lipolytica is one of the most promising yeast cell factories for production of oleochemicals and biofuels, because of its extremely high yield of lipids4, high levels of lipid accumulation8 and due to its metabolic diversity, which allows for production of a broad spectrum of unusual lipids9. However, Y. lipolytica and other cell factories do not actively secrete the produced lipids10. Instead, the lipids remain trapped inside the cells. Hence, different approaches for release of intracellular lipids from Y. lipolytica and other cell factories have been attempted, including microwave, ultrasound, thermal and mechanical treatments11, bio-based solvents12 and detergents13. However, most of these established methods are not selective for a specific product, they require high anounts of energy and require additional steps for lipid recovery14. Thus, these efforts failed in making biofuel and oleochemical production in yeasts and other cell factories economically feasible. The extraction of lipids from microbial cell factories remains a major bottleneck.Yeast cell factories are particularly promising in this respect, due to their excellent ability to synthesize and accumulation of lipids16. The contact of graphene edges with microbial cell surfaces leads to penetration of the cell membrane and displacement of phospholipids onto the graphene surface19. In extreme cases, the interaction can lead to irreparable membrane damage and leakage of intracellular content20. The key parameter for effective membrane penetration, identified by our group and others, is the contact angle between graphene and the cell surface, which must be perpendicular to each other25.Nanosheets of graphene have been previously shown to interact with microbial membranes via computational simulations and experimentallyY. lipolytica that has been genetically engineered for high-level production of lipids26. The interaction between graphene and membrane lipids is known to be mainly based on short-range van der Waals attractions17. These attractions promote the movement of lipid molecules from the biological membrane onto the graphene surface. For displacement of lipids from the membrane onto the graphene surface, the optimal contact angle between the graphene sheet and the biological membrane surface is 90º18.In this study, we hypothesized that the exposed sharp edges of graphene spikes, deposited axially on magnetic nanoparticles, could interact with the membranes of yeast cells and thereby facilitate the release of valuable lipids  produced by these cell factories. To test this hypothesis, we decided to work with a strain of Y. lipolytica. The treatment can be performed directly in the growth culture, without any interruption, and it can therefore be compatible with continuous release of intracellular lipids in fermenters. Our approach is based on metallic (iron oxide) nanoparticles coated with axially oriented graphene29, which show a considerable potential to release intracellularly accumulated lipids from yeast cell factories.Herein, we demonstrate a novel strategy to mechanically compromise the cell envelope in order to release intracellular lipids from 24. Briefly, iron oxide nanoparticles dispersed in water were drop-casted onto a SiO2 surface and heated at 80 °C for 20 min to remove water. After that, samples were kept at 350 °C for 10 min in the presence of H2 and Ar, and the temperature was increased to 450 °C for 10 min. The SiO2 surface with deposited nanoparticles was then loaded into a cold wall CVD system . There the samples were heated rapidly (~ 300 °C/min) to the growth temperature of 775 °C and annealed in H2 and Ar atmosphere. A 75 W DC glow discharge plasma was ignited, and the graphene growth was initiated by introducing 15 sccm C2H2, 15 sccm H2 and 1000 sccm Ar. The density and orientation of coatings was confirmed by SEM . The samples for SEM were sputter coated with gold (5 nm) before imaging. Raman spectra were measured by a WITec alpha300 R confocal Raman spectrometer equipped with a 100 × objective and spectrometer spectral range from 420 to 830 nm. Each spectrum was recorded in the range 500–3000 cm−1 with 10 min accumulation time and approximately 4 cm−1 resolution. The graphene-coated nanoparticles were detached from the SiO2 substrate carefully by using a surgical scalpel and used in all subsequent experiments.Axial graphene coating was deposited on iron oxide  magnetic nanoparticles using PE-CVD, as described previouslyλ = 1.5418 Å) in the range 5° < 2θ < 70°. Bragg’s Law, λ = 2d sin(Ɵ), was used to calculate the distance d between adjacent graphene layers, where λ is the wavelength of the incident X-Ray beam and Ɵ is the diffraction angle. For FT-IR spectroscopy measurements, graphene powder was dispersed in lipids for 30 min, rinsed with water and dried. The absorption of lipid by graphene was examined by FT-IR. Infrared spectra of the samples were recorded using an attenuated total reflection (ATR) Alpha FT-IR spectrometer from Bruker, with a diamond crystal as refractive element, in the range 400 to 4000 cm−1 at a resolution of 2 cm−1.Lipid absorption by different graphene coatings  was evaluated by contact angle measurements of lipid droplets on the coated surfaces. Commercially available lipids extracted from bacterial cells  were used in these assays. The purchased lipid solution was diluted in 5% of dimethyl sulfoxide (DMSO). Droplets of DMSO (negative control) and lipids were placed onto graphene-coated surfaces and contact angle was measured for 1 h with an interval of 4 s. To evaluate the lipid absorption ability of graphene using X-Ray diffraction (XRD) and Fourier-transform infrared (FT-IR) spectroscopy, graphene nanoflakes in powder form (purchased from Graphitene) were used. The powder was ground before XRD measurements and mixed with lipids (or Mili-Q water), until visibly saturated. The X-Ray diffractograms were obtained using a Siemens Bruker D5000 powder diffractometer operating at 40 kV and 40 mA with nickel-filtered Cu Kα radiation  were divided into three different fractions for further processing: a control fraction containing only Y. lipolytica culture, a fraction containing Y. lipolytica culture plus iron oxide nanoparticles and a fraction containing Y. lipolytica culture plus graphene-coated iron oxide nanoparticles. All samples were further incubated for 2 h with continuous agitation (1000 rpm) and then examined for release of intracellular lipids and morphological alterations. To examine the release of lipids, a fraction of treated cells was stained with 1 μL of BODIPY 493/503 (Thermo Fisher Scientific) solution (1 mg/mL in ethanol), which binds specifically to lipids and provides a green fluorescent signal. The stained yeast cultures were observed using a fluorescence microscope . The acquired images were further processed to calculate the percentage of intracellular lipids (green stain appearing within cells) and released lipid droplets (stain appearing outside cells) by using Image J. The percentages of intracellular and released droplets were quantified by measuring the image area occupied by the droplets. The cumulative data represent quantification of intra- and extracellular lipid obtained by measuring the area of intracellular and extracellular lipid droplets in microscopy images with 200 cells, for both the control experiments (non-treated cells and cells treated with bare nanoparticles), and cells treated with graphene-coated nanoparticles. The morphological alterations of yeast cells were examined with SEM. A fraction of treated cells was fixed in 3% glutaraldehyde overnight. The fixed cells were dehydrated with graded series of ethanol . The dehydrated cells were transferred to a clean cover glass and dried for 24 h at room temperature. The dried samples were sputter-coated with gold and examined by SEM . Further, the effect of graphene coated nanoparticles on growth pattern of Y. lipolytica was evaluated. Graphene-coated nanoparticles was collected from two substrate plates (each having an area of 1 cm2) and dispersed in 1 ml of medium. This represents the standard working concentration of coated nanoparticles in all our experiments. Other concentrations were obtained by diluting the working concentration in culture medium. In order to evaluate the impact of nanoparticles on the growth profile, Y. lipolytica was grown overnight and inoculated in 150 µl of nitrogen limiting media with various concentrations of graphene coated nanoparticles to a starting OD600 of 0.05 in 96-well plates. The cells were cultivated at 30 °C and 200 rpm for 96 h. The growth was measured every 30 min by the Growth Profiler 960 .24. Considering this phenomenon, initially we chose to compare the lipid absorption capacity of two types of differently oriented graphene coatings: a graphene monolayer parallel to the coated surface  and Fourier-transform infrared (FT-IR) spectroscopy Fig. . Figure −1 (C–H stretching (asymmetry)), 2854 cm−1 (C–H stretching (symmetry)), 1746 cm-1 (C=O stretching), 1465 cm−1 (C–H bending (scissoring)), 1165 cm−1 (C–O stretching and C–H bending), and 722 cm−1 (C–H bending)30. Taking together the findings from contact angle measurements  being higher than the G peak (~ 1590 cm−1) is indicative of axial orientation and high density of flake boundaries. The low intensity of the 2D peak (~ 2650 cm−1) (intensity decreases with increasing number of graphene layers) indicated that the samples are a mixture of monolayer and multilayer graphene. Combining high magnification SEM imaging and Raman data, we concluded that the iron oxide nanoparticles were successfully axially coated with PE-CVD graphene, resulting in a complete and homogenous coating, with a large proportion of exposed sharp edges, containing on average < 10 layers of graphene.Iron oxide nanoparticles (average diameter of 25 nm) were axially coated with graphene, using the method of plasma-enhanced chemical vapor deposition (PE-CVD) Fig. a. As a r32. According to this mechanism, the graphene nanosheet cuts through the lipid membrane, with the edge of the nanosheet sinking into the lipid bilayer. Thereafter, the lipid extraction mode commences, where the nanosheet extracts the phospholipid molecules from the lipid bilayer onto its surface. This massive draining of lipids from the membrane onto the graphene sheet results in membrane deformation, and ultimately in loss of cell membrane integrity34. Following this extraction of phospholipids, strong hydrophobic interactions between the graphene and the lipids have been speculated to play an important role through nanoscale ‘dewetting’. This strong hydrophobic interaction can be compared to the hydrophobic collapse of many biomolecular self-assemblies, such as cell membrane formation and protein folding36. Hence, it has been suggested that nanoscale dewetting can provide the driving forces required for complete cellular collapse, leading to release of all intracellular content37. However, all available mechanistic simulations and experimental data showing this mechanism is based on the interaction of graphene with bacterial cell membranes. To the best of our knowledge, there are no reports showing the interaction of graphene nanosheets with yeast cells.Next, we asked whether these coated nanoparticles can effectively compromise the integrity of yeast cells to release intracellular lipids. Computational simulations of graphene interaction with cellular membranes describe the insertion of edges of free graphene nanosheets into the membrane, driven by strong van der Waals attractions and hydrophobic interactions with membrane lipidsY. lipolytica culture grown for 5 days were treated with bare nanoparticles and graphene coated nanoparticles for 2 h with continuous agitation (1000 rpm). In order to assess if the release of lipid would be compatible with continuous growth in a fermentation process, no other treatment was applied. After 2 h of treatment with bare nanoparticles and graphene coated nanoparticles, yeast cells were examined by brightfield and fluorescent microscopy. The latter was combined with BODIPY staining (specific to lipid drops) that allows for visualization of lipids (including triglycerides), stained bright green  iron oxide nanoparticles were examined, and their lipid content was fully comparable to that of untreated cells, with only about 4.3% of total lipid present in the extracellular space. Thus, we concluded that the graphene coating is the key factor for release of lipids from cells. To more closely examine the morphology of Y. lipolytica cells exposed to graphene-coated nanoparticles, we further examined them by SEM (Fig. A direct interaction between graphene-coated nanoparticles and smaller lipid droplets can be seen in Fig. SEM Fig. . The morSEM Fig. b was indSEM Fig. a. This wY. lipolytica cells were clustered, apparently held together by a lipid matrix presumably formed by lipids released from the damaged cells (Fig. Y. lipolytica was examined. As shown in Fig. 600 = 3. However, the impact of nanoparticles on the final OD was minimal. This finding is very promising, since it indicates that lipid release from yeast cells can be achieved continuously without inhibiting growth, and therefore without compromising the metabolic yield of lipids. This technology could provide a significant improvement over existing methods to extract biofuels from microbial cell factories, thereby reducing energy consumption, extraction time and the use of hazardous chemicals. This encouraging finding is of course preliminary, and further optimization in scaled-up production setup should be performed in follow-up studies, to achieve an optimal balance between lipid production and lipid release.By contrast, cells treated with graphene-coated nanoparticles exhibited various morphological defects Fig. c, indicalls Fig. d. FinallY. lipolytica (Fig. 24. Hence, we propose that our graphene-coated magnetic nanoparticles could be developed into a promising tool to break yeast cells to release intracellular lipid. The magnetic properties of the coated nanoparticles can be used to collect graphene a side while collecting the released lipid and could be compatible with an uninterrupted fermentation process. We propose that this simple technology could be used to resolve the major bottleneck for opening of yeast cells and release of intracellular lipid from yeast cell factories, thus paving the way for commercial and sustainable production of oleochemicals and biofuels. However, future studies should focus on finding the optimal size of graphene coating and concentration of coated nanoparticles which maximize the release of intracellular lipid while minimizing damage to yeast cells.In conclusion, our results demonstrate that nanoparticles that are coated with sharp graphene spikes can be used to effectively break the cells and release intracellular lipids from ica Fig. . Graphenica Fig. , and by ica Fig. , 2. In o


The role of flowers as environmental filters for bacterial communities and the provenance of bacteria in the phyllosphere are currently poorly understood. We experimentally tested the effect of induced variation in soil communities on the microbiota of plant organs. We identified soil-derived bacteria in the phyllosphere and show a strong convergence of floral communities with an enrichment of members of the Burkholderiaceae family. This finding highlights a potential role of the flower in shaping the interaction between plants and a bacterial family known to harbor both plant pathogens and growth-promoting strains. Because the flower involves host–symbiont feedback, the selection of specific bacteria by the reproductive organs of angiosperms could be relevant for the modulation of fruit and seed production. Arabidopsis thaliana in a soil substitute and added two microbial communities from natural soils. We estimated that at least 25% of the phyllosphere bacteria collected from the plants grown in the open environment were also detected in the controlled conditions, in which bacteria could reach leaves and flowers only from the soil. These taxa represented more than 40% of the communities based on amplicon sequencing. Unsupervised hierarchical clustering approaches supported the convergence of all floral microbiota, and 24 of the 28 bacteria responsible for this pattern belonged to the Burkholderiaceae family, which includes known plant pathogens and plant growth-promoting members. We anticipate that our study will foster future investigations regarding the routes used by soil microbes to reach leaves and flowers, the ubiquity of the environmental filtering of Burkholderiaceae across plant species and environments, and the potential functional effects of the accumulation of these bacteria in the reproductive organs of flowering plants.Leaves and flowers are colonized by diverse bacteria that impact plant fitness and evolution. Although the structure of these microbial communities is becoming well-characterized, various aspects of their environmental origin and selection by plants remain uncertain, such as the relative proportion of soilborne bacteria in phyllosphere communities. Here, to address this issue and to provide experimental support for bacteria being filtered by flowers, we conducted common-garden experiments outside and under gnotobiotic conditions. We grew  All plants are colonized by diverse prokaryotic and eukaryotic microorganisms. While root microbiota affect plant development, leaf and floral microorganisms can also provide services, such as protection against pathogens or interference with plant–insect interactions –4. These51318Another important aspect in our understanding of plant microbiota concerns the observed differences between the bacterial communities of roots, leaves, and flowers, not only among each other but also with respect to the surrounding microbial context , 21–23. 21In the present study, we conducted an experiment to address the origin of microorganisms in the phyllosphere. The underlying rationale was that if the phyllosphere organs filter for specific bacterial groups, differences in surrounding microbial communities should not prevent the convergence of the communities. The experimental design was such that it also allowed us to evaluate the proportion of phyllosphere bacteria that can reach leaves and flowers from the soil. We asked the following questions. 1) What is the proportion of bacterial taxa detected on leaves and flowers that have the ability to reach the phyllosphere from soil? 2) Does variation in the soil microbiota alone drive bacterial community divergence in leaves? 3) Does variation in the soil microbiota alone drive bacterial community divergence in flowers? 4) Do specific organ communities converge despite microbial variations in the microbial context surrounding the plant? 5) Which taxa support these potential convergent organ communities? We conducted common-garden experiments in both gnotobiotic and open conditions . In eachArabidopsis thaliana Col-0 and an experimental treatment consisting of growing these plants in different microbial communities of soil .We detected 6,010 different ASVs at a 100% identity threshold, with 5,617 of these taxa being detected at the outside location and 5,239 detected in the growth chamber. An estimation of the possible contamination from seeds despite sterilization revealed that this fraction was small, with four ASVs potentially originating from this contamination source . When we restricted this analysis to the most abundant taxa in the communities , more than 75% of the bacteria detected outside were also detected inside for the AG and FO communities . When we restricted this analysis to the most abundant taxa in the communities , more than 59 and 53% of the bacteria detected outside were also detected inside for the AG and FO communities, respectively . In bacterial communities found on adult rosettes, an average of 51 and 40% of the reads belonged to soil ASVs that can reach these organs grown in AG and FO communities, respectively . At the flowering stage, these proportions were 54 and 61% in AG and FO rosettes, respectively. The soil ASVs able to reach the leaves away from soil represented 60 and 41% of the total number of reads of AG and FO leaf communities, respectively . Finally, soil bacteria able to reach flowers dominated floral communities in plants grown in AG and FO communities .To account for spurious presence generated by potential sample cross-contaminations and index-switching events, we repeated the comparison procedure with a presence threshold of three reads in nonrarefied data . We defined this threshold from an exploration of the Zymo mock community to estimate that ASVs not expected to be part of this community had an average of two reads . To provide statistical support for the clusters, we bootstrapped this rarefied dataset 1,000 times and repeated the cluster analyses with each resulting dataset. This approach allowed us to provide the frequency of recovery of each cluster across these 1,000 analyses. Because rarefaction also involves random sampling of sequences that could lead to clusters supported by chance, we also conducted 1,000 random rarefaction and cluster analyses and calculated the frequencies of recovery of each cluster across these analyses. The communities in the original soils clustered according to their origin, with high statistical support . The communities in the soil inoculated with the AG community formed two well-supported clusters. One included all the communities from the growth chamber , and one included all the communities from the outside location . The communities in the soil inoculated with the FO microbiota also formed two well-supported clusters. One included all the communities collected in the growth chamber , and one included all the communities collected at the outside location . In all these analyses, the soil communities did not cluster according to the time of collection .The original soils and inoculated soil substitutes differed in their community composition and structure, as we showed by unsupervised clustering analysis with the Bray–Curtis distance metric after rarefaction according to the depth of the smallest sample  . This result suggests the efficient removal of most living bacterial cells from these filtrates. This is further supported by the finding that all the communities in the soils inoculated with the filtrates of AG and FO microbiota and collected from the outside location clustered together in a well-supported group and were not separated according to the filtered inoculum .Most likely due to the release of environmental DNA and potentially because cells were not all excluded by the filtration procedure, the soil inoculated with the filtrates of the AG and FO inocula was not free of DNA. However, the communities in the soils inoculated with the filtrates of the wild communities and collected in the growth chamber did not cluster consistently together and had unstable positions across the bootstrapped and sequential rarefaction analyses . Increasing the number of reads to the size of the smallest sample in each organ and developmental stage combination led to results consistent with the 1,084 rarefied samples .To measure whether soil microbiota variations led to detectable divergences in the outside AG and FO phyllosphere communities, we performed unsupervised clustering analyses of the communities on each plant organ separately . If the organ microbiota diverged according to the soil microbiota, we would expect the communities of plants grown in one soil type to cluster together, separate from those grown in the other soil types. To compare the results obtained with the different organs, we rarefied all the samples at 1,084 reads . Only rosettes clustered according to the soil communities in which they grew at the adult stage . We repeated this analysis with the Bray–Curtis distance metric, all the blooming plant samples collected outside, and all the blooming plant samples collected in the growth chamber and grown in the AG and FO communities. With the exception of one, all the communities from flowers grown in the growth chamber were nested in the cluster of floral communities collected outside . Two candidates were Rhizobiaceae from the Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium group. One was Enterobacteriaceae, which belongs to the genus Arsenophonus. One was a Beijerinckiaceae from the genus Methylobacterium. Apart from one Rhizobiaceae (ASV 1019), all the candidates were detected in floral samples not only from the plants grown under environmental conditions but also from the plants cultivated in the growth chamber. Because these 28 ASVs were repeatedly detected across samples, it is unlikely that sequencing errors generated these results . Furthermore, a thorough examination of the dataset suggests that these bacteria originated in the environment rather than from seeds or technical origins . Finally, none of the candidate ASVs should stem from several 16S rRNA copies of the same bacterial taxa based on the observation that known 16S rRNA copies in genomes of plant-associated strains of Burkholderiaceae share 100% similarity within each strain and in the region of our amplicon .Because the plants in the growth chamber were not exposed to natural biotic and abiotic conditions , the relative abundance of some of their bacterial taxa might not reflect natural conditions. For this reason, we identified the bacterial taxa in the communities from the outside samples that supported the floral community cluster. We generated 1,000 datasets rarefied at 1,084 reads. We conducted independent unsupervised hierarchical clustering analyses across these 1,000 datasets but with the data of each taxon separate rather than the entire communities as above. In total, 28 taxa were identified as being responsible for the convergence of the floral communities, as their distribution of relative abundances across the samples supported the clustering of floral communities in all 1,000 rarefied datasets. This conservative approach, which consisted of excluding any bacteria not supporting floral clustering in all the rarefied datasets, allowed us to limit two types of biases. First, it allows the exclusion of false positives generated by bacteria at the limit of detection. Second, it limits the number of false positives generated by the random process of read selection during the rarefaction process. To test whether these bacteria indeed supported the floral cluster, we conducted an unsupervised hierarchical clustering analysis after excluding these 28 taxa. With this reduced dataset, the floral communities were no longer clustered C. Secondamplicon  . These bacteria responsible for the convergence of the floral community were significantly more abundant (24 out of 25) and significantly more frequently detected in floral communities . Among the 15 remaining ASVs identified as being characteristic of flowers there were 10 Burkholderiaceae, 2 Rhizobiaceae, 1 Acetobacteriaceae, 1 Rhodanobacteraceae, and 1 Xanthomonadaceae .To identify ASVs typical of the floral communities, we compared the IndVal metric for ASVs of communities of rosettes, leaves not in contact with soil, and flowers collected outside at the flowering stage . With up to 73% of reads belonging to these taxa in the phyllosphere communities, bacteria able to reach the phyllosphere from soil can dominate this environment . Even if soil might not be the sole environmental origin for these taxa at the outside location, our results support that they can migrate without the help of the previously suggested dispersal factors wind, insect visits, and water splashing, which were not present for the plants cultivated in the growth chamber . Interestingly, the communities of leaves tended to form two clusters . Furthermore, this resemblance between outside and inside communities excludes convergence due to insect visits, which are rare in A. thaliana . This result is in accordance with the previous identification of phylogenetic conservatism in bacterial communities on leaves and flowers .Interestingly, Burkholderiaceae are enriched in disease-suppressive soils  and are 33bundance , 42–45. ndance 42, 47. TheA. thaliana without the need for dispersal factors such as wind, insects, or water. Future experiments to investigate the capacity of bacteria to grow after reaching leaves and flowers from soil will help to better characterize the colonization of these organs from soil. The development of experimental systems closer to natural conditions regarding humidity, wind, or competition with bacteria coming from alternative sources will be particularly relevant to confirm the findings of the present study. In addition, it will be interesting to challenge our results with plant species whose leaves and flowers are farther from soil. Finally, synthetic communities will help to identify the routes used by these microorganisms. Our results also indicate that members of Burkholderiaceae, a plant-associated bacterial family, are part of a core floral community and are selected from the surrounding environment. It opens interesting perspectives to further investigate the causes leading to an enrichment of this bacterial family by the floral environment. Furthermore, the environmental filtering and enrichment of a specific family by the flowers of Arabidopsis call for similar experiments to determine if similar bacterial taxa are filtered at other locations and in other plant species. Additionally, the floral enrichment of this family makes them good candidates for intergenerational interactions with their hosts . This mechanism is a strong driver for coevolution between hosts and symbionts and might explain many of the known positive and negative interactions between plants and Burkholderia relatives. Finally, reproducing the present experiment with different microbial variations  will help to further identify the most promising candidates with which to investigate plant–bacteria interactions.The present study suggests that many soil bacteria could reach leaves and flowers of A. thaliana Col-0 at three different locations: one in a growth chamber at the Institute of Microbiology of Eidgenössische Technische Hochschule (ETH) Zurich and two in outside locations in the common-garden experimental facilities of ETH located at 47°22′47.6″N 8°32′54.3″E (Zurich location) and 47°27′01.2″N 8°40′56.3″E  medium. The resulting solutions for the AG and FO communities were adjusted to pH 7 and used to inoculate our soil substitute. This soil substitute consisted of half washed and heat-sterilized calcined clay  and half autoclaved vermiculite. Vermiculite was used to increase water retention in our soil substitute. The plant containers consisted of autoclaved plastic pots (130 × 130 mm) at the outside locations and gamma-irradiated microboxes  in the growth chamber. Under sterile conditions, each container was filled with 200 mL of the soil substitute and inoculated with 85.7 mL of the MS medium containing the AG community or its filtrate or the FO community or its filtrate. We introduced the filtrates of the two inoculated MS media (using 0.22-μm filters) as soil treatments in each experiment to control for the potential impact of residual soil chemicals extracted during the procedure described above. Each container was planted with surface-sterilized full-sibling seeds of A. thaliana Col-0, which had been stratified for 7 d at 4 °C. Because the survival rates were expected to be lower at the outside locations, we placed several seeds at three separate spots in each pot and microbox . The sowing of seeds in the microboxes was conducted under sterile conditions in the laboratory, and the closed boxes were placed directly into the growth chamber. The sowing of the pots used at the outside locations was conducted at the experimental site, where they had been transported wrapped in sterile aluminum foil. In each common-garden experiment, each soil treatment included five replicates and were as follows: 1) soil inoculated with the AG community; 2) soil inoculated with the filtrate of the AG community; 3) soil inoculated with the FO community; and 4) soil inoculated with the filtrate of the FO community. Because we did not have the benefit of hindsight from previous comparable experiments, we could not exclude a homogenization of soil communities leading to the same soil communities in the AG and FO treatments at the end of the experiment. This might happen, for instance, if plants strongly select for specific soil communities or if the aerial communities at experimental sites erase initial AG and FO differences. To enable the interpretation of this potential failure, we added 10 pots without plants at each outside location. Five of the pots were inoculated with the AG community, and five were inoculated with the FO community. To verify the sterility of the seeds, we added three microboxes containing sterile 0.5× MS agar to the growth chamber, each of which was planted with several of our surface-sterilized seeds.We set up common-garden experiments with crobiome –51. It acrobiome , 52. In crobiome . They coSI Appendix, Fig. S9). At the outside locations, it was not possible to collect leaves that were not in contact with the soil because the plants were much smaller. We collected plant samples from only one of the three planted spots and left the remaining plants for the rest of the experiment. We also collected soil samples away from the plants. At the blooming stage, we collected rosettes, leaves not in contact with the soil, and inflorescences at both locations. At this stage, we collected soil samples from the surface as well as after mixing to better capture the bacterial richness in the pots, which is often spatially structured. For the growth chamber, we opened the containers and collected samples under sterile conditions to avoid contamination from the laboratory environment. All the samples were collected with gloves, flamed scissors, and forceps in 2-mL tubes. The samples were flash-frozen in liquid nitrogen immediately after collection and maintained at −80 °C until further processing.At the outside locations, we randomly shuffled the plant container positions twice a week to avoid spatial confounding effects in the analyses. Because growth chamber conditions were relatively stable through space and we wanted to limit the manipulation of the containers, which could generate cross-contamination between soil and aerial plant organs, we randomized the box positions once per week. The conditions in the growth chamber were set to favor plant flowering and consisted of a 16-h photoperiod at 22 °C and nighttime temperatures of 18 °C (both at 50% humidity). However, the humidity inside the microboxes was above 90%. Therefore, it was not necessary to water the plants in the growth chamber because the soil substitute remained wet throughout the experiment. At the outside locations, the pots were regularly watered when the soil substitute dried out. To avoid cross-contamination between the soil, leaves, and flowers, we filled the plant saucers with water and allowed the humidity to infiltrate the soil from the bottom. We first collected leaf and soil samples when the growth of rosettes was complete (the adult stage was identified as the start of inflorescence emergence). At this stage, we collected entire rosettes in both the growth chamber and Zurich location. Because of the large size of the plants in the growth chamber, we could also collect leaves that were not in contact with the soil . All the samples with fewer than 1,000 reads were discarded before statistical analyses.The bacterial communities of all the samples collected from the common-garden experiments, five replicates of each original natural soil, and soil substitute collected just after their inoculation (starting points) were characterized by their 16S rRNA amplicon profiles (variable regions V5 to V7) sequenced with one 2 × 300 paired-end run on an Illumina MiSeq System. Because several processing steps involved treating the samples in batches before sequencing, we randomized the order in which they were treated. This procedure excluded the possibility of introducing technical biases as potential confounding factors in the statistical analyses . For each library preparation step, one master mix was made and used for all the samples. Sample preparation, genomic DNA extraction, and DNA library preparation were performed as described previously . The sol11.0.667  to filte11.0.667 . The seq11.0.667 . The ASV11.0.667 . Microbi11.0.667 , 59. Lab11.0.667 . The use11.0.667 , tag-swi11.0.667 –64, or e11.0.667 . In the Datasets S2 and S3).To identify the proportion of bacterial taxa that were detected in the phyllosphere of plants collected outside and that have the potential to colonize from the soil, we compared the phyllosphere communities from the outside location with those from the growth chamber. Because the microboxes used in the growth chamber did not allow any external contamination other than that from the soil, air flows were strongly limited (but not gas-exchanged), water splashing did not occur in this environment, and the bacteria detected on the leaves and flowers in these containers passively or actively migrated from the soil via plant surfaces, vessels, or gas movements due to convection. To quantify ASVs shared between outside and inside plants, we combined the reads of plant samples collected outside into one pool and those of plant samples collected in the growth chamber into another. We conducted this pooling approach for AG and FO communities separately. To make the resulting pools comparable and test the robustness of the results, we repeatedly sampled the same number of reads in each pool using an incremental 100-read procedure and repeated absence–presence comparisons. We started from the number of reads in the smallest compared pool (outside or growth chamber) down to 100 reads . This threshold was defined from an exploration of the number of reads of ASVs not expected to be part of the community of the mock community control (Zymo) and still detected in this sample. The reasoning is that these reads were coming from cross-contaminations and index-switching events, and their average number was two reads. Because the sample size of this control was twice larger than the average number of reads per sample in the dataset , we estimated this value as being reasonably stringent.We estimated the number of reads belonging to the ASVs that have the capacity to reach organ communities from soil in samples collected outside. We first rarefied all the sample sizes to the number of reads contained in the smallest plant sample collected outside  and used the soil ASVs identified as being able to reach the phyllosphere after applying the presence threshold defined above.To test whether the soil communities were different in our experiment and whether these differences strongly influenced leaf and flower communities, we conducted unsupervised hierarchical clustering analyses that do not involve a priori assumptions in the definition and number of groups. We used the complete linkage algorithm, and the Bray–Curtis metric as a dissimilarity measure across samples. The goal was to see if the soil substitute communities collected outside were sorted according to the community used for their inoculation  and if the bacterial communities of the plants collected outside were sorted according to the community used to inoculate the soil in which the plants grew. For the plant organs, we conducted analyses with the following sample types separately: rosettes at the adult stage, rosettes at the flowering stage, leaves not in contact with the soil at the flowering stage, and inflorescences. Because the clustering according to soil communities could be influenced by residual chemical differences generated during the extraction of wild soil communities rather than microbial variations, we repeated the clustering analyses but with plant samples cultivated in soil substitutes inoculated with the filtrates of the inocula . The use of ASVs in the present study requires caution due to the presence of multiple 16S rRNA copies in bacterial cells. If these different copies have small differences, they will belong to different ASVs. We ensured that the 16S rRNA copies in Burkholderiaceae shared 100% similarity in the region of our amplicon .To identify the bacteria underlying this pattern , we applied the following procedures. 1) We performed 1,000 rarefactions of the dataset at 1,084 reads; 2) the relative abundances of each bacterial taxon were used separately to conduct unsupervised hierarchical clustering analyses across the 1,000 rarefied datasets; and 3) each taxon was considered a candidate if its data generated a cluster of communities for the organ of interest in all 1,000 rarefied datasets. This conservative approach, which consisted of excluding any potential ASV candidate if its data did not support the cluster of interest in all the rarefied datasets, allowed us to limit two types of biases. First, it allows the exclusion of false positives generated by bacteria at the limit of detection. Second, it limits the number of false positives generated by the random process of read selection during rarefaction. Once the list of candidates was identified with this procedure, we tested whether withdrawing these specific taxa from communities erased the clustering pattern previously observed. Finally, for the latter test, we tested whether it was not the reduction in the size of the dataset which was responsible for the loss of clustering. To do so, we repeated the hierarchical clustering analyses after 1,000 random exclusions of the same number of taxa, not including one of the identified candidates. If the candidate taxa and not the reduction in the size of the dataset were responsible for the clustering, the organ cluster was expected to be supported in these latter analyses. We identified several Burkholderiaceae as being responsible for floral community convergences. Because these bacteria are known to be common extraction kit and laboratory contaminants , 65, 66,The environmental sources of the bacterial taxa that are responsible for the convergence of the organ communities could be the aerial environment, rain, biotic interactions, or soil. To test whether the convergence involves the selection of soil bacteria, we reconducted the hierarchical clustering analysis with plant samples collected outside during the flowering stage in addition to all the plant samples grown in the inoculated soil substitutes in the growth chamber and collected during the flowering stage. If the clustering of communities involves environmental filtering by organs of soil communities, then the growth chamber samples should be nested within the cluster of the same type of samples collected outside.P values and estimated if the IndVal and A and B components were higher than expected under the null hypotheses of the ASV not being indicative of the floral group, not more relatively abundant in floral communities, and not more often detected in floral samples than by chance under the null hypothesis. We conducted these tests for 142 ASVs and considered a P value of 4.0 e−4 to be significant (0.05 significance level divided by 142) to correct for multiple testing.In order to identify the ASVs that characterize floral communities, we used the IndVal statistic, which is the product of the mean relative abundance of a taxon in a group of samples compared with all groups in the study (component A) and the relative frequency of detection of the taxon in the samples of the group of interest (component B) . A taxonSI Appendix, Text and Tables S4–S7 and Dataset S4).For all ASVs identified as being characteristics of floral communities and not already investigated in the clustering analyses, we thoroughly explored the dataset to ensure that these bacteria were not technical or seed contaminations (see details in Supplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary File


Microbial communities are constantly challenged with environmental stressors, such as antimicrobials, pollutants, and global warming. How do they respond to these changes? Answering this question is crucial given that microbial communities perform essential functions for life on Earth. Our research aims to understand and predict communities’ responses to change by addressing the following questions. (i) How do eco-evolutionary feedbacks influence microbial community dynamics? (ii) How do multiple interacting species in a microbial community alter evolutionary processes? (iii) To what extent do microbial communities respond to change by ecological versus evolutionary processes? To answer these questions, we use microbial communities of reduced complexity coupled with experimental evolution, genome sequencing, and mathematical modeling. The overall expectation from this integrative research approach is to generate general concepts that extend beyond specific bacterial species and provide fundamental insights into the consequences of evolution on the functioning of whole microbial communities. Microbial communities perform pivotal functions, from the cycling of elements through Earth’s ecosystems to the digestion of complex foods that shape human health and disease. Many of these functions emerge from the interaction of multiple species working as a collective . For exaAchieving such understanding is extraordinarily difficult given that microbial communities are incredibly diverse in terms of numbers of different microbes, genes, and interactions. Progress on this problem requires both top-down studies that deal with the complexity of natural communities and bottom-up research that uses simpler experimental setups . My reseRapid evolution can impact short-term ecological dynamics, and these altered ecological dynamics can feedback to affect subsequent evolutionary change . Eco-evo4–Acinetobacter johnsonii and Pseudomonas putida, two bacterial species that were originally isolated from a polluted aquifer in Denmark   . Thus, bA. johnsonii and P. putida  in natural communities  and different genotypes (evolutionary responses). What is the relative contribution of ecological versus evolutionary processes to mediating responses to change? We previously discussed how adaptive evolution mediated by settings . What resettings , 20. Filsettings . This exOne important ecosystem function is plant litter decomposition, which is performed by microbial communities . I have recently joined a collaboration, The Loma Ridge Climate Change Experiment, aiming to understand how the plant litter microbiome responds to drought in Southern California , 23. We Using an integrative research approach based on evolution experiments of simple microbial consortia, whole-genome sequencing, and mathematical modeling, we have gained general insights into the mechanisms by which microbial communities respond to environmental change. We are currently working to understand how these responses affect community-level functions important for ecosystem functioning. The next exciting research directions will be (i) to study whether natural selection can act on community-level properties  and (ii)


RKIP gene in human cancers is not well understood. In this study, we suggested a possible transcription mechanism for the regulation of RKIP in human cancer cells. We found that Metadherin (MTDH) significantly repressed the transcriptional activity of RKIP gene. An analysis of publicly available datasets showed that the knockdown of MTDH in breast and endometrial cancer cell lines induced the expression RKIP. In addition, the results obtained from qRT-PCR and ChIP analyses showed that MTDH considerably inhibited RKIP expression. In addition, the RKIP transcript levels in MTDH-knockdown or MTDH-overexpressing MCF-7 cells were likely correlated to the protein levels, suggesting that MTDH regulates RKIP expression. In conclusion, we suggest that MTDH is a novel factor that controls the RKIP transcription, which is essential for cancer progression.Raf kinase inhibitory protein (RKIP), also known as a phosphatidylethanolamine-binding protein 1 (PEBP1), functions as a tumor suppressor and regulates several signaling pathways, including ERK and NF- RKIP gene transcription. To test this possibility, we investigated transcription factors and co-factors that directly or indirectly regulate the expression of the RKIP gene.Raf Kinase Inhibitory Protein  was first elucidated as a binding protein of Raf1, a key regulator in mitogen-activated protein kinase (MAPK) pathways. RKIP is ubiquitous in cellular structures, including cytoplasm, inner periplasmic membrane, and others . Thus, lRKIP promoter and significantly inhibits the expression of RKIP. This suggests that MTDH negatively regulates the transcription of the RKIP gene, which could be a target for the development of new cancer therapy.The oncogene Metadherin  was first identified as a novel transcript in the primary human fetal astrocytes (PHFAs) infected with human immunodeficiency virus (HIV)-1 and is specifically elevated in astrocytes . MTDH is cancers ,8,9. A s cancers . In thisn = 31) obtained from human samples in the cancer genome atlas (TCGA), and the corresponding normal tissues from GEPIA [MTDH was significantly higher in tumor tissues compared to control tissues  in the same set of the TCGA cancer studies. We found that few cancer tissue samples had any genetic alterations (2% of cases) of the MTDH gene  in the three MTDH-knockdown breast cells compared to controls  for ChIP analysis are represented in RKIP promoter region, but not in the case of using negative IgG (RKIP promoter (RKIP. This repression could occur via an indirect association with other transcription factors that bind to a nearby region of the RKIP transcription start site.We determined whether MTDH binds to the RKIP promoter using the chromatin immunoprecipitation (ChIP) analysis. Since MTDH contains no DNA-binding domain as previously described , we usedtive IgG A. Interepromoter . These rRKIP promoter. First, we constructed reporter vectors containing serial deletions of the RKIP promoter DNA  linked to the firefly luciferase gene (pGL4.20 vector), as shown in  led to a significant decrease in the promoter activity . The promoter construct −83/+168 showed the highest activity in the assay. As expected, the RKIP promoter activity highly increased in MTDH-knockdown MCF-7 cells compared to control cells. Conversely, the promoter activity dramatically decreased in MTDH-overexpressing MCF-7 cells , RKIP transcripts were functionally associated with the transcription factor VEZF1. In particular, their expression is inversely correlated with each other [RKIP gene could require the functional connection with VEZF1. To test this possible mechanism, we first examined the binding ability of VEZF1 to the RKIP promoter region using ChIP assay. As expected, the VEZF1 protein bound to the RKIP promoter , the amount of RKIP transcripts was similar to the value obtained from VEZF1-overexpressing cells. This result indicates that MTDH-dependent inhibition of RKIP expression is not observed at the high level of VEZF1 protein.We further examined how the two proteins MTDH and VEZF1 influence the transcriptional activation of nscripts B. HoweveRKIP gene. Additionally, the abundance mRNA transcripts of MTDH inversely correlated  with the RKIP gene according to a systemic analysis of breast tissue samples in The Cancer Genome Atlas (TCGA) data. Based on these results, we suggest that understanding the mechanism underlying the regulatory relationship between these two proteins could provide some key solutions for developing new cancer therapy. MTDH, also known as AEG-1, can act as a transcriptional co-factor in cancer signals. It directly interacts with the p65 subunit of NF-RKIP by cooperating with other transcription factors.Several previous studies suggest that MTDH could contribute to cancer progression, and it is considered a hallmark protein of metastatic cancers. Indeed, MTDH stimulates EMT and cancer metastasis, and it also exhibits chemoresistance to cancer drugs. Furthermore, MTDH stimulates proliferation of cancer cells ,19. The  process ,22. In a process ,24,25. B process . TherefoLow expression of RKIP, known as an anti-metastatic factor, is often reported in aggressive breast cancer tissues ,27. FurtAccording to our previous studies, RKIP showed functional connections with many other autophagy-related proteins and, in fact, it inhibited autophagy in cells under the limited nutrients ,17. AutoRKIP gene. Indeed, MTDH is specifically recruited to the RKIP promoter region, specifically the −83/+168 region. Interestingly, the promoter activity of RKIP gene gradually increased with serial deletions from the tentative promoter (−806 to +168) and reached the maximum value at the −83/+168 region , a transcription factor [RKIP promoter, and it significantly inhibited the promoter activity, similar to MTDH. These observations suggest that direct association of MTDH with VEZF1 or indirect connection with unknown proteins, as shown in RKIP gene and control its intracellular protein level in cancer. VEZF1 is highly expressed in endothelial cells, and it is an important regulator for angiogenesis in cancer [RKIP gene could be a critical mechanism in tumorigenesis. However, we need further experiments to show how the two proteins VEZF1 and MTDH cooperatively regulate the expression of RKIP gene during cancer progression.Based on our recent study, the expression of n factor . Therefon cancer ,30,31,32n cancer ,34. ConsRKIP gene. Thus, perturbing the MTDH–RKIP relation in human cancer might serve as a target developing effective anticancer therapy.In conclusion, MTDH and RKIP proteins are important prognostic markers in human cancers. They exhibited an inverse correlation in expression in malignant cancer cells. MTDH transcriptionally repressed sc-28837) and VEZF1 (sc-365561) from Santa Cruz Biotechnology  and MTDH/LYRIC (D5Y8R) and STAR208P) or mouse (STAR117P) were purchased from Bio-Rad . RPMI-1640 (11875-119), fetal bovine serum , Dulbecco’s modified Eagle’s medium , Lipofectamine 3000 (11668-500), and G418 (10131-035) were purchased from Gibco and Life Technologies . Anti-FLAG M2 affinity gel (A2220) and Poly-L-lysine solution (P8920) were from from Sigma-Aldrich . M2 lysis buffer (85111), protease inhibitor cocktails (78441), Enhanced ChemiLuminescence (ECL) detection system (34080), and Disuccinimidyl glutarate/DSG (20593) were from Thermo Scientific . Trizol reagent (15596026) was from Invitrogen, Carlsbad, CA, USA. QuantiNovaTM SYBR Green RT-PCR Kit (208154) was from QIAGEN Inc., Hilden, Germany.Antibodies used in the study were as follows: PEBP1/RKIP (MTDH-knockdown cells obtained from gene expression omnibus (GEO) using GEOquery  are shown in MTDH-knockdown cells and control cells were normalized and compared using Limma [t-test.Three microarray datasets of ng Limma . Genes wng Limma . Expressn = 31) were obtained from the cancer genome atlas  and corresponding normal tissue samples from GEPIA . Percentages of samples with a given MTDH expression or lower in cancer or normal tissue were calculated as the empirical cumulative distribution function (ECDF). Gene copy numbers alterations  of the MTDH gene from human subjects  in TCGA were downloaded from cBioPortal . Percentages of samples with and without altered MTDH gene were calculated as the empirical cumulative distribution function (ECDF).Gene expression datasets of human cancer tissues  for capturing protein–protein complexes for 30 min at room temperature (RT) at the 80% confluence. Subsequently, formaldehyde was added drop-wise to the media to a final concentration of 0.75% to cross-link proteins to DNA, and the culture dish was incubated with gentle rotation for 10 min at RT. Then, glycine is added to a final concentration of 125 mM into the media to quench the formaldehyde and terminate the cross-linking reaction for 5 min at RT. The crossed-linked cells were lysed in M2 lysis buffer . Cell lysates were sonicated to obtain sheared chromatin DNA at an average fragment size of 200–1000 bp. After sonication, cell debris was removed by centrifugation for 10 min and 4 MCF-7 cells were cultivated in RPMI-1640 containing 10% FBS and cultured at 37 Lentiviral particles were produced using a third-generation lentivirus packaging mix . In brief, the shRNA-MTDH construct and the lentiviral packaging plasmids were simultaneously transfected into A293T cells. After incubation, the supernatant containing viral particles was collected and stored at −80 Cells were lysed in a buffer containing 50 mM Tris-Cl (pH 8.0), 150 mM NaCl, 0.1% SDS, 0.5% sodium deoxycholate, 0.02% sodium azide, and protease inhibitor cocktails. The protein concentration of total cell lysates was determined using the BCA method. Total proteins  vector expressing Renilla luciferase into MCF-7 cells using lipofectamine 3000. The luciferase activity was measured by the Dual-Glo Luciferase assay system as described in the manufacture’s manual (Promega). After 48 h, an equal volume of Dual-Glo reagent to the volume of culture medium was added into each well of the plate. The Firefly luminescence was measured after incubation for 10 min at 20–25 Renilla luminescence was measured by incubating the wells with Dual-Glo Stop and Glo Reagent at 20–25 Firefly: Renilla luminescence in each well was calculated. The values were normalized to the ratio obtained from a control well.Cells were plated at t-test using the Prism software  and R [p values < 0.05 were considered significant.Each experiment was independently conducted at least three times, and the data were expressed as the mean value (±S.D). Statistical significance between two groups was determined by Student A) and R  (Vienna,


Chemical vapor deposition synthesis of graphene on copper foil from methane is the most promising technology for industrial production. However, an important problem of the formation of the additional graphene layers during synthesis arises due to the strong roughness of the initial copper foil. In this paper, various approaches are demonstrated to form a smooth copper surface before graphene synthesis to reduce the amount of few layer graphene islands. Six methods of surface processing of copper foils are studied and the decrease of the roughness from 250 to as low as 80 nm is achieved. The correlation between foil roughness and the formation of the additional layer is demonstrated. Under optimized conditions of surface treatment, the content of the additional graphene layer drops from 9 to 2.1%. The quality and the number of layers of synthesized graphene are analyzed by Raman spectroscopy, scanning electron microscopy and measurements of charge mobility. Chemical vapor deposition (CVD) synthesis of graphene on metal substrates is the most promising method for the growing large-area films of high-quality . Copper Copper foil is usually produced by mechanical rolling resulting in scratches along the rolling direction. As reported, the density and uniformity of graphene domains correlate with the surface roughness of the copper substrate ,9. One oIn this work, we compare several methods for smoothing the foil before graphene synthesis, such as etching in nitric acid, ECP in phosphoric acid, long-term annealing in a hydrogen atmosphere for 5 h, as well as a combination of these methods. We investigate the surface roughness of copper foils after various treatments and then grow a graphene film on each type of prepared surface under the same conditions. We analyze the formation of additional graphene layers only inside one copper grain, regardless of its size and orientation. The dependence of the formation of the additional and following graphene layers on the roughness parameter of the prepared foils is established and the optimal conditions of pretreatment are determined. We also compare graphene synthesized on treated copper foil and the smooth surface of a copper film, deposited on a polished sapphire substrate.2 90 nm or 300 nm thickness for best contrast in the optical microscope described in We developed a protocol for the investigation of the effect of copper treatment on graphene formation. Before synthesis, the roughness of copper foil was analyzed by interference profilometry. All graphene samples were synthesized on copper foil using the procedure described in 2O:HNO3= 2:1 for 60 s. ECP was made using copper foil as anode placed in parallel to bulk copper substrate cathode with an interelectrode spacing of 2 cm in phosphoric acid (85% concentration) for 90 s at room temperature. Large irregularities dissolve first due to the effect of electric current. The annealing of the copper sample was performed at 1000 °C in a hydrogen atmosphere under 100 mbar for 5 h.Alfa Aesar copper foil  was used for various processing methods before the graphene synthesis. The chemical etching of the sample in nitric acid (65% concentration) was performed in solution with a concentration of H−5 mbar by passing an electric current through a molybdenum boat with copper metal pieces. The molybdenum boat was heated up to 1700–2000 °C and the copper evaporated and deposited on the target substrate. The thickness of the copper film was 1000 nm and was determined by the mass of metal placed into the boat.The copper film was deposited on a single-crystal sapphire substrate in a UNIVEX 300 vacuum chamber at a pressure of 102:CH4 = 100:1, temperature 1000 °C for 20 min. Before the graphene formation, the copper foil was annealed at 1000 °C for 30 min in a hydrogen atmosphere at 100 mbar to remove natural oxide from the copper foil. This pretreatment changes neither the roughness of the foil nor the average size of the crystallites.The synthesis of graphene on copper foil has been well studied and various scientific groups have presented numerous options for the synthesis of graphene on copper foil under various conditions. The review  presents2/Si substrate using standard “wet” transfer technology [4)2S2O8). Finally, a PMMA/graphene film was washed with DI water and transferred onto the target substrate and PMMA was removed with acetone.After the growth, the graphene was transferred from the copper to a SiOchnology . First, 2/Si substrate had a higher contrast (darker areas) in optical images than the monolayer. This difference was enough to set the monolayer as the base contrast and calculate the number of all darker spots as a percentage of the entire surface area.The quality of graphene films and the number of islands with additional graphene layers were investigated using optical microscopy and Raman scattering. Raman spectra obtained with a 514 nm excitation wavelength using 25% of 20 mW laser. The electrical characteristics of the graphene films were obtained using the four-probe method and Hall effect measurements. The surface morphology of the copper foil was examined by a white-light interference microscope  and a scanning electron microscope . The amount of the additional layer on the surface area was calculated using graphics software for image analysis of transferred graphene films obtained using an optical microscope. The second and subsequent graphene layers transferred from copper foil onto 90 nm or 300 nm SiO2 substrate and analyzed by Raman spectroscopy  and less than 500 Ohm/square, respectively, that indicates the high quality of the synthesized graphene film.The analysis of obtained graphene films by Raman spectroscopy is presented in 2) of graphene transferred onto silicon oxide were taken from each sample and analyzed using optical microscopy. It was confirmed already that combination of the Raman analysis and the optical microscopy is enough for the identification of 1 and 2 layers of graphene [2 are obtained in the area of 100 × 100 μm2, that is, the total area of the few layer graphene film (ρ) is 8.1 ÷ 9.4% of the entire surface (2 (ρ = 2.7–3.5%)  . In the 1 ÷ 5.9% . On a deThe chemical vapor deposition technique is an efficient approach for the synthesis of high-quality graphene on copper foil over large areas. The roughness of the copper surface is the key parameter for the fabrication of the most uniform single-layer graphene film. The correlation between foil roughness and the formation of the additional layer is demonstrated. The use of the original commercial copper foil with Ra = 250 nm leads to the formation of a few layer graphene film on an area of 9% of the total surface. Additional processing of catalytic copper foil causes a decrease in the amount of the additional graphene layers. The lowest roughness of 80 nm is achieved using a combination of annealing, etching and electrochemical polishing, which results in the synthesis of the film with only 2.1% of the additional graphene layers. It is found that the optimal treatment is simply electrochemical polishing, which gives the same amount of a few layer graphene film, but on a copper surface with Ra = 120 nm. A further decrease in roughness even to 4 nm does not lead to a decrease in the amount of the additional graphene layers.


Graphene absorbs 980 nm pump light, and uses the heat generated by ohmic heating to switch on and off the signal light at 1550 nm. The simulation results show that, when the graphene is in the right position, we can obtain the power consumption of 9.5 mW, the propagation loss of 0.01 dB/cm, and the switching time of 127 μs (rise)/125 μs . The switching time can be improved to 106 μs (rise) and 102 μs  with silicon substrate. Compared with an all-fiber switch, our model has lower power consumption and lower propagation loss. The proposed switch is suitable for optically controlled fields with low loss and full polarization. Due to the low cost and easy integration of polymer materials, the device will play an important role in the fields of all-optical signal processing and silicon-based hybrid integrated photonic devices.All-optical devices have a great potential in optical communication systems. As a new material, graphene has attracted great attention in the field of optics due to its unique properties. We propose a graphene-assisted polymer optically controlled thermo-optic switch, based on the E With the rapid growth in demand for information in today’s society, a series of optical components has been proposed and produced. Optical signal control is crucial in the optical transmissions. Signal control can be realized by the manipulation of a metal electrode, in turn achieving thermal optical or electrical optical switching functions ,2,3,4. TAs a two-dimensional (2D) material, graphene has many obvious electrical, optical, physical, and chemical properties. It has been extensively investigated in the field of opto-electronics, such as in optical modulators ,10, photRecently, polymer has been introduced into the photothermal all-optical field. Due to its excellent photo-thermal effects, the integration of polymer waveguide and graphene is more efficient. At the same time, it reduces the switching threshold for all-optical devices. In 2019, it was reported that graphene had been integrated as an electrode heater for a polymer MZI waveguide thermo-optic device. The power consumption of the fabricated device was 2.1 mW, with a rise time of 1.58 ms, and a fall time of 1.57 ms . Therefox01 mode was proposed. We used the finite element method (FEM) for simulation analysis. Due to the advantages of its fast switching speed and its lack of need for photoelectric conversion, this device could play an important role in the fields of all-optical signal processing and silicon-based hybrid integrated photonic devices. In this study, we employed graphene’s excellent optical properties and efficient thermal properties to accomplish an alternative method for an all-optical switch. In order to reduce the absorption loss of graphene, a polymer MZI thermo-optical switch based on the Egε (=gn2) can be calculated from the conductivity σ: [ω is the angular frequency of the light, and ε0 is the vacuum permittivity. The graphene conductivity σ is affected by the temperature, graphene electronic relaxation time, optical angular frequency, and graphene Fermi level. When the number of graphene  layers is 1, the parameters of graphene conductivity, including intraband conductivity and interband conductivity, can be calculated using the Kubo formalism:cμ = 0.1 eV is the chemical potential (after the transfer of a single layer of graphene), ħ is the reduced Planck’s constant, τ = 5 × 10−13 s is the relaxation time, BK is Boltzmann constant, and T is temperature. The calculated refractive indices parallel to the graphene surface are 2.41 + 2.19i @980 nm and 2.98 + 2.79i @1550 nm, respectively.Thermo-photo switch is achieved due to graphene absorption. The graphene effective permittivity ivity σ: (1)εg(ω)−4. The upper cladding adopted Polymethyl Methacrylate (PMMA) , which has a low refractive index. The lower cladding was silica. The related material parameters of the switch are shown in Compared with inorganic materials, polymers have a higher thermo-optical coefficient and thus require lower power. In this paper, SU-8 was used as the core of the waveguide. SU-8  is a polymer material with a thermo-optic coefficient of −1.8 × 10x00 mode into Ex01 mode. The Ex01 mode is output as a new signal light source to realize the thermo-optic modulation function of the symmetric MZI. We embedded monolayer graphene in the center of the waveguide with the same width as the core and a thickness of 0.34 nm. A first-order mode double-layer symmetry MZI optical switch can then be established with a graphene layer in one arm of the MZI branch to change the phase. A coupling waveguide was introduced on one arm of the MZI to couple the pump light at 980 nm. In this paper, two kinds of structures were used, one with silicon substrate and the other without substrate. The switch without substrate can be used in the flexible field. Compared to the switch without substrate, the switch with silicon substrate provides support for cross-section cutting and optical alignment, as well as faster heat dissipation, which can improve response speed.The device designed in this paper is composed of a mode converter and a MZI first-order mode optical switch, as shown in x01 mode, we set the thickness of the waveguide at twice its width. For the signal light of 1550 nm, Matlab was used to calculate the relationship between the effective refractive index of different modes and the thickness of polymer waveguide core. The result is shown in x01 mode was satisfied. Therefore, the width of the waveguide was 1.5 μm and the thickness was 3 μm.To produce EMost mode converters use long-period waveguide gratings or vertical directional coupling structures ,25. In tn0 and n1 are the effective refractive index of the single-order mode and the first-order mode, respectively, and dL is the length of the coupler. When the light couples into another waveguide, I(t) = 1, dL is an odd multiple of the coupling length; the coupling length becomesThe function of the coupler aims to avoid the waste of pump light caused by splitting the beam of the MZI structure. Coupling occurs along the transmission when the two waveguides are close and parallel. In this paper, the proportion of the input light coupled to another waveguide energy could be expressed by Equation (6),The structure of the coupler is shown in N is the effective refractive index difference between the two arms, and L is the length of the heating electrode. The effective refractive index can be calculated by COMSOL 2D model and searched by mode analysis, so as to calculate the effective refractive index of the waveguide before and after heating. The electric field diagram of the first-order mode is shown in In this section, we discuss the research methods of an optically controlled switch. The COMSOL was used to calculate the electric field. First, a three-dimensional (3D) model was built to calculate the electric field generated by the pump at 980 nm. The grid was constructed as a sweep grid, and the effective refractive index was searched using boundary mode analysis, so as to calculate the single-mode at 980 nm. The absorption of graphene to the TE single-mode is particularly large, which limits the length of the modulation arm. Considering power consumption, the final length was selected at 500 μm. In this paper, a thermo-optic switch of MZI structure was used to heat an arm to change the effective refractive index of the waveguide. The phase difference between the two arms can be calculated by Equation (8),k is the thermal conductivity of the material and sQ is the heat source per unit volume generated by graphene absorption [ε0 is the permittivity of vacuum, ω is the angular frequency of light, and rε is the relative permittivity of graphene. The thermal field can therefore be calculated from the above formulas. We used graphene as a surface heat source in our simulation.The thermal field can be calculated from the electric field. To simplify our simulation, as the length of graphene is much larger than the thickness of the waveguide, and the longitudinal temperature gradient along the waveguide resulting from the variation in graphene absorption is small, we omitted the longitudinal dependence of the temperature distribution. With continuous pumping, the temperature distribution in the waveguide T is described by the steady-state heat conduction equation (9)−k∇2Tsorption (10)Qs=12·k) and the heat field distribution of the waveguide can be obtained as shown in According to the principle of heat conduction, the thermal field distribution satisfies the Fourier heat conduction equation for homogeneous solid material. Only the steady-state case is considered as: T is the unknown temperature as a function of time and space, ρ is the mass density of material, c is the heat capacity at constant pressure of material, and k is the thermal conductivity of the material.When calculating the switching time, the transient equation is used to describe (11)ρc∂TThe power consumption of the switch is our concern, so we calculated the relationship between waveguide temperature variation and pump power. The result shows a linear slope of nearly 1.8614 K/mW in Due to the differences in the upper and lower cladding materials for the waveguide structure used in this paper, the refractive index is different. As such, the smallest part of the first-order mode light field is not in the middle of the waveguide. The optimized result is shown in Comparing the two switching properties, we find that when the graphene is at 1.55 μm, the propagation loss and power consumption are reduced. As the pump power decreases, the switching time increases.The above two switches did not have a silicon substrate. In order to improve switching time, a silicon substrate switch was proposed. For a waveguide with a thickness of 3 μm, when the vertical position of graphene was 1.55 μm, the propagation loss is 0.07 dB/cm, and the switching performance of the optimized structure was simulated. As shown in Compared with the traditional optical waveguide switch, the optical switch proposed in this paper does not need photoelectric conversion, so the integration degree is improved; compared to all-fiber optical switch, our switch has lower power consumption and lower propagation loss. This device reduces the loss of TE mode and could play an important role in the field of all-optical signal processing and silicon-based hybrid integrated photonic devices.x01 mode. Graphene absorbed pump light at 980 nm to generate heat, which was used to modulate signal light at 1550 nm. The simulation results by FEM showed that the power consumption of the switch was 9.5 mW, the propagation loss was 0.01 dB/cm, the rise time of the switch was 127 μs, and the fall time was 125 μs. Switching times improved to 106 μs (rise) and 102 μs  with the addition of silicon substrate. Compared to the traditional optical waveguide switch, the optical switch proposed in this paper does not need photoelectric conversion, thus, the degree of integration is improved; compared to all-optical switch of fiber, our switch has lower power consumption and a lower propagation loss. Due to the low cost and easy integration of polymer materials, this device will play an important role in the fields of all-optical signal processing and silicon-based hybrid integrated photonic devices.In this article, we proposed a graphene-assisted polymer optically controlled thermo-optic switch based on E


Despite the presence of the graphene interlayer, the Heusler films have epitaxial registry to the underlying sapphire, as revealed by x-ray diffraction, reflection high energy electron diffraction, and transmission electron microscopy. The weak Van der Waals interactions of graphene enable mechanical exfoliation to yield free-standing GdPtSb membranes, which form ripples when transferred to a flexible polymer handle. Whereas unstrained GdPtSb is antiferromagnetic, measurements on rippled membranes show a spontaneous magnetic moment at room temperature, with a saturation magnetization of 5.2 bohr magneton per Gd. First-principles calculations show that the coupling to homogeneous strain is too small to induce ferromagnetism, suggesting a dominant role for strain gradients. Our membranes provide a novel platform for tuning the magnetic properties of intermetallic compounds via strain (piezomagnetism and magnetostriction) and strain gradients (flexomagnetism).Single-crystalline membranes of functional materials enable the tuning of properties via extreme strain states; however, conventional routes for producing membranes require the use of sacrificial layers and chemical etchants, which can both damage the membrane and limit the ability to make them ultrathin. Here we demonstrate the epitaxial growth of the cubic Heusler compound GdPtSb on graphene-terminated Al Single crystalline membranes enable the tuning of materials properties via strain states that are not accessible to bulk crystals or epitaxially clamped films. Here, the authors demonstrate the synthesis and strain gradient-induced magnetism in membranes of the Heusler compound GdPtSb. In contrast to the uniform strain of epitaxial films, strain in membranes can be applied dynamically, anisotropically, in gradient form, and at larger magnitudes. For example, recent experiments on ultrathin oxide membranes demonstrate the application of extreme uniaxial strain of 8%1, whereas the maximum strain possible in an epitaxial thin film is typically no more than 3% before plastic deformation. In addition, membranes enable the application of strain gradients, which are difficult to control for a film that is rigidly clamped to a substrate  and magnetostriction (M2 ∝ ϵ), is widely used to tune the magnetic properties of thin films11. In contrast, the magnetic coupling to strain gradients, i.e., flexomagnetism (M ∝ ∇ ϵ), has been theoretically predicted13, but to our knowledge has not experimentally demonstrated. This is due, in part, to difficulties in synthesizing and controlling strain gradients in nanostructures and membranes. Conventional techniques for fabricating single-crystalline membranes require etching of a sacrificial layer14, which requires a detailed knowledge of etch chemistry and limits the ability to make ultrathin membranes of air-sensitive materials.Magnetism is particularly attractive for tuning via strain in the membrane form. The coupling between magnetism and strain 15, topological states18, and novel superconductivity20. In contrast with conventional membrane synthesis techniques, which require etching of a sacrificial buffer layer14, our use of a monolayer graphene decoupling layer allows GdPtSb membranes to be mechanically exfoliated, bypassing the need for detailed knowledge of etch chemistries. Our approach is akin to “remote epitaxy,” which has recently been demonstrated for the growth of compound semiconductor22, transition metal oxide7, halide perovskite23, and elemental metal films24. Here, we show that similar approaches apply to intermetallic quantum materials. We find that the large strains and strain gradients in rippled GdPtSb membranes drive an antiferromagnet to ferri- or ferromagnet transition. First-principles calculations show that the coupling to homogeneous strain is too small to induce ferromagnetism, suggesting a dominant role for strain gradients, which would make this system the first experimental example of flexomagnetic coupling. Our work opens a new platform for driving ferroic phase transitions via complex strained geometries.We demonstrate the etch-free epitaxial synthesis and exfoliation of Heusler membranes and show that rippled membranes induce magnetic ordering, turning the antiferromagnetic half Heusler compound GdPtSb into a ferro- or ferrimagnet. Heusler compounds are a broad class of intermetallic compounds with tunable magnetic textures2O3 substrate in (0001) orientation. GdPtSb crystallizes in the cubic half Heusler structure, the same structure as the antiferromagnetic Weyl semimetal GdPtBi26. Since the layer-transferred graphene has randomly oriented polycrystalline domains, if the primary interactions are between GdPtSb film and graphene, then a polycrystalline film is expected. If, on the other hand, the primary interactions are between GdPtSb film and the underlying substrate, then an epitaxial film is expected. Given the recent demonstration of semi-lattice transparency of graphene during GaAs/graphene/GaAs “remote epitaxy”21, we expect epitaxial registry between GdPtSb and the underlying sapphire to dominate. Single-crystalline GdPtSb membranes can then be exfoliated and strained in complex geometries.Our concept relies on the weak van der Waals interactions of monolayer graphene to enable epitaxial growth and exfoliation of a membrane from a graphene-terminated single-crystalline substrate. Figure 2O3 (0001) heterostructures, we first transfer polycrystalline monolayer graphene onto a pre-annealed Al2O3 (0001) substrate using standard wet transfer techniques (“Methods”). The graphene is grown on copper foil by chemical vapor deposition. Raman spectroscopy and atomic force microscopy indicate clean transfers with long-range coverage and minimal point defects or tears in the graphene  to clean the surface. At this stage, the reflection high-energy electron diffraction (RHEED) pattern shows a bright but diffuse specular reflection compared to bare sapphire27, which we attribute to diffuse scattering from the randomly oriented top graphene layer . The streaky RHEED patterns for GdPtSb indicate growth with the epitaxial registry to the underlying sapphire substrate  by a factor of 2O3 [0001] axis with the epitaxial relationship GdPtSb films were grown by molecular beam epitaxy (MBE) on the graphene/Al2O3 1022 and for Cu films growth on monolayer graphene/Al2O3 (0001)24. However, in those cases, the presence of the second domain was attributed to an epitaxial relationship between the film (Cu or GaN) and the 2D monolayer (h-BN or graphene), while the primary domain results from an epitaxial relationship between the film and the substrate. In the present case of ±30° domains of GdPtSb, we rule out a long-range epitaxial relationship to the graphene because our graphene barrier is polycrystalline, and thus if there were an epitaxial relationship between GdPtSb and polycrystalline graphene, a large distribution of GdPtSb domain orientations would result.XRD pole figure and cross-sectional TEM measurements confirm the presence of these two epitaxial domains. Figure ϕ = 0 domain consists of arrays of tilted spindle-shaped dark spots, each of which represents a combination of one Gd atomic column, one Pt atomic column, and one Sb atomic column. For the STEM image of the ±30° rotated GdPtSb, the spindle-shaped spots are aligned along the vertical direction corresponds to clusters of Gd-Pt-Sb atomic columns, as shown by the inset cartoon.Annular bright-field (ABF) scanning transmission electron microscopy (STEM) images in Fig. 2O3 can be mechanically exfoliated to yield free-standing membranes, without the need for a metal stressor release layer. Figure θ − 2θ scans of GdPtSb, before and after exfoliation. Before exfoliation, we observe all of the expected 111, 222, 333, and 444 reflections. The rocking curve width of the 111 reflections before exfoliation is 15.4 arc second, indicating a high-quality film  and for rippled membranes on two different polymer handles (blue and red). The unstrained film shows a weak linear M(H) dependence. Temperature-dependent susceptibility measurements indicate that the films become antiferromagnetic at a Néel temperature of ~12 K  in which local Gd moments are aligned ferromagnetically within (111) planes and the planes are aligned antiferromagnetically with neighboring planes29. Our first-principles calculations are consistent with an antiferromagnetic ground state for GdPtSb  magnetic rippled membranes at room temperature. Figure μB per Gd atom, approaching the ~ 7μB/Gd ferromagnetic limit . Melting the crystalbond on a hot plate releases the membrane/polymer bilayer. Ripples form upon release and cooling  and polyurethane (red) layers show a spontaneous magnetization and hysteresis loops characteristic of ferrimagnetic (FiM) or ferromagnetic (FM) order. The saturation magnetization in the most rippled membrane on polyurethane is 5.2 30 or by bending in polycrystalline membranes on a flexible substrate31. In this study, we observe not a tunable anisotropy, but a strain-induced magnetic ordering. Other studies have investigated magnetoelastic coupling in ferromagnetic shape memory alloys33, where the magnetic phase transition is coupled with a structural martensitic phase transition.Our observation of ripple-induced magnetism in Heusler lies in contrast with previous studies of strain coupling in magnetic Heusler compounds. Previous studies have focused on the strain tuning of magnetic anisotropy, either in epitaxial films35. The RKKY interaction oscillates in sign and magnitude with the distance between magnetic atoms, exhibiting oscillation between ferromagnetic (FM) and antiferromagnetic (AFM) ordering38. However, the atomic-scale mechanism for ripple-induced magnetism in GdPtSb is not fully understood. A key question is whether the AFM to ferro- or ferrimagnetic ordering in our rippled membranes is driven by strain (piezomagnetism and magnetostriction) or strain gradients (flexomagnetism). Both strain and strain gradients are present in our rippled membranes, and both increase with the ripple aspect ratio. While piezomagnetism and magnetostriction have been widely studied, to our knowledge flexomagnetism has only been predicted13, but not experimentally observed.Due to the short-range nature of exchange interactions, subtle changes in bond length and local symmetry are expected to modify the magnetic ground state, consistent with our observed AFM to FM (FiM) transition. For example, long-range and oscillatory Ruderman–Kittel–Kasuya–Yosida (RKKY) interactions were detected in some Mn-based Heuslersϵs along the sinusoidal paths are modest, with peak values of ±0.17%. In our first-principles calculations, ±0.17% is much too small a strain to induce a ferromagnetic or ferrimagnetic state, with a much larger strain of ~ 5% being required  were cut into 1-inch-by-1-inch pieces and soaked in dilute nitric acid (5.7%) for 40 s followed by 3× DI water rinse followed by soaking in acetone and IPA to remove water from the surface. Dilute nitric acid helps remove the oxide and impurity particles from the surface. Foils were then dried under a gentle stream of air. Foils were subsequently loaded into a horizontal quartz tube furnace in which the furnace can slide over the length of the tube. Prior to monolayer graphene synthesis, the CVD chamber was evacuated to < 10−2 Torr using a scroll pump. The system was then back-filled with Ar and H2, and a steady flow  monitored by mass flow controllers was maintained at ambient pressure. The furnace was then slid to surround the samples, and annealed for 30 min. Then 0.3 sccm of P-5 gas (5% CH4 in Ar) was flowed for 45 min so that a monolayer of graphene formed on the surface. To terminate the growth, the furnace was slid away from the samples, and the portion of the quartz tube containing the samples was cooled to room temperature.Graphene was grown using thermal chemical vapor deposition of ultra-high purity CH40. (0001)-oriented Al2O3 substrates were prepared by annealing at 1400 °C for 10 h at atmospheric pressure in order to obtain a smooth, terrace-step morphology. To perform the graphene transfer, the graphene/Cu foils were cut into 6-mm-by-6-mm pieces and flattened using clean glass slides. Approximately 300 nm of 495K C2 PMMA  was spin-coated on the graphene/Cu foil substrate at 2000 RPM for 2 min and left to cure at room temperature for 24 h. Graphene on the backside of the Cu foil was removed via reactive ion etching using 90 W O2 plasma at a pressure of 100 mTorr for 30 s. The Cu foil was then etched by placing the PMMA/graphene/Cu foil on the surface of an etch solution containing 1-part ammonium persulfate  and 3-parts H2O. After 10 h of etching at room temperature, the floating PMMA/graphene membrane was scooped up with a clean glass slide and sequentially transferred into three 30-min water baths to rinse the etch residuals. The PMMA/graphene membrane was then scooped out of the final water bath using the annealed sapphire substrate, to yield a PMMA/graphene/Al2O3 stack.Our graphene transfer procedure is a modified polymer-assisted wet transfer, similar to the transfer recipes studied in previous works2O3 interface, samples were baked in the air at 50 °C for 5 min, then slowly ramped to 150 °C and baked for another 10 min. The PMMA is removed by submerging the sample in an acetone bath at 80 °C for 3 h. This is followed by an isopropanol and water rinse. The sample is indium bonded onto a molybdenum puck and outgassed at 150 °C for 1 h in a loadloack at a pressure P < 5 × 10−7 Torr before introduction to the MBE growth chamber. Finally, the graphene/Al2O3 sample is annealed at 400 °C for 1 h in the MBE chamber to desorb remaining organic residues and then annealed up to 700 °C immediately prior to the growth of GdPtSb.To remove water at the graphene/Al2O3 by molecular beam epitaxy (MBE) at a sample temperature of 600 °C, using conditions similar to ref. 27. Gd flux was supplied by a thermal effusion cell. A mixture of Sb2 and Sb1 was supplied by a thermal cracker cell with a cracker zone operated at 1200 °C. The Pt flux was supplied by an electron beam evaporator. Fluxes were measured in situ using a quartz crystal microbalance (QCM) and calibrated with Rutherford Backscattering Spectroscopy. The Gd to Pt atomic flux ratio was maintained to be 1:1. Due to the high relative volatility of Sb, GdPtSb films were grown in an Sb adsorption-controlled regime with a 30% excess Sb flux. After the growth, films were capped in situ with 75 nm amorphous Ge to protect the surfaces from oxidation.GdPtSb films were grown on graphene/AlGraphene transfer coverage and cleanliness is studied using field emission scanning electron microscopy (SEM) (Zeiss LEO 1530 Gemini). The graphene quality after the transfer is assessed via Raman spectroscopy using a 532 nm wavelength laser (Thermo Scientific DXR Raman Microscope). The laser power is kept below 5 mW in order to prevent damage to the graphene. The terrace-step morphology of the annealed sapphire substrates with and without graphene termination is analyzed by AFM (Bruker Multimode 8 SPM) in tapping mode.2O3 sample was prepared for TEM using a Zeiss Auriga focused ion beam (FIB) with a final FIB polishing step with a 5 kV 100 pA Ga-ion beam. The sample surface was further polished for a higher smoothness in a Fishione 1040 Nanomill with a 900 eV Ar ion beam, to a thickness of ~80 nm. We did not seek to get the thinnest possible sample, as the film layer could peel off from the substrate when the sample is too thin. The TEM sample was kept under vacuum and cleaned in an Ibss GV10x DS Asher plasma cleaner for 10 min under 20 W to remove contaminations before being inserted into the TEM column.The GdPtSb/graphene/AlA Thermo-Fisher Titan STEM equipped with a CEOS probe aberration corrector operated at 200 kV was used to collect STEM data. A 24.5 mrad semi convergence angle and an 18.9 pA current probe were used. A Gatan BF/ABF detector with 5.7 mrad and 22.8 mrad inner and outer collection angles was used to collect the annular bright-field (ABF) images.2O3 substrate. For the membrane samples, we subtract a background measurement of the polymer tape (polyimide or polyurethane) +  Crystalbond.Magnetic properties were measured using a Quantum Design MPMS SQUID (Superconducting Quantum Interference Device) Magnetometer. For the film data, we subtract a background measurement of the Al41 within the local density approximation. Calculations were done on a conventional unit cell (simple cubic with four formula units per cell) with 6 × 6 × 6 Monkhorst-Pak k-point mesh, a plane-wave cutoff of 25 Hartree, with spin–orbit coupling included. The criterion for convergence was a potential residual of <1.0 × 10−8.Density-functional theory (DFT) calculations were carried out with the ABINIT package. The pseudopotentials used were PAW JTH v1.1Supplementary InformationPeer Review File