Besides the consequences of retrotransposition, long interspersed element 1 (L1) retrotransposons can affect the host genome through their antisense promoter. In addition to the sense promoter, the evolutionarily recent L1 retrotransposons, which are present in several thousand copies, also possess an anti-sense promoter that can produce L1 chimeric transcripts (LCT) composed of the L1 5′ UTR followed by the adjacent genomic sequence. The full extent to which LCT expression occurs in a given tissue and whether disruption of the defense mechanisms that normally control L1 retrotransposons affects their expression and function in cancer cells, remain to be established. By using CLIFinder, a dedicated bioinformatics tool, we found that LCT expression was widespread in normal brain and aggressive glioma samples, and that approximately 17% of recent L1 retrotransposons, from the L1PA1 to L1PA7 subfamilies, were involved in their production. Importantly, the transcriptional activities of the L1 antisense promoters and of their host loci were coupled. Accordingly, we detected LCT-producing L1 retrotransposons mainly in transcriptionally active genes and genomic loci. Moreover, changes in the host genomic locus expression level in glioma were associated with a similar change in LCT expression level, regardless of the L1 promoter methylation status. Our findings support a model in which the host genomic locus transcriptional activity is the main driving force of LCT expression. We hypothesize that this model is more applicable when host gene and LCT are transcribed from the same strand. L1 retrotransposons represent ~17% of the human genome  (Long interspersed nuclear element 1 (LINE-1 or L1) is a class of autonomous transposable elements that is mobilized lements) .. insertional mutagenesis, creation of new alternative splicing sites, promoting sequence transduction from the donor to new insertion sites) (Besides the consequences of retrotransposition (e.gn sites) , L1 retrn sites) , but seqn sites) ,6. L1-ASn sites) . Moreoven sites) . Its tran sites) . This suL1-MET LCT initiates in the second intron of the gene encoding the tyrosine protein kinase MET (c-MET)  in various malignancies  . In variunctions . Similargnancies .. absence of weakly expressed transcripts in EST libraries). Therefore, to gain insights into the LCT expression profile in a given tissue and into its relevance to cancer development and progression, we and others developed tools to identify transposable element-derived chimeric transcripts from RNA-seq data. These tools include the LIONS suite (These observations stress that recent L1s (L1PA1 to PA8) with a full length 5′ UTR represent an abundant source of alternative promoters that can disrupt the expression of nearby genes in cancer. This highlight the need to identify LCTs in a systematic manner in the different cancer types. Until now, LCTs have been characterized mainly using bioinformatics approaches. For instance, dbEST has been queried to identify spliced ESTs in which an L1 5′ UTR sequence in antisense orientation is spliced with a gene exon ,14,17,18NS suite , a tool NS suite , and ChiNS suite .IDHwt)   ,25. IDHwls (GSC) ,27.IDHwt gliomas and GSC cell lines to evaluate whether they warrant future investigations as potential players in aggressive glioma biology. We found that LCTs are expressed in healthy brain and that their transcriptional activity/deregulation in glioma follows that of their host locus.In the present study, we used CLIFinder to explore the LCT landscape in IDHwt glioma samples with CLIFinder. As the L1-ASP is active in the more recent subfamilies  data to analyze the whole transcriptome  of three healthy brain and 8 families ,7,19, wefamilies . Althougfamilies . Finallyfamilies , and obsfamilies .. they initiated from one of these two L1-ASP regions). To this aim, we designed a RT-PCR 5′ L1 walking approach using three primers, located at different positions of the L1 5′ UTR, and one LCT-specific primer (LCT-Unique Sequence = LCT-US), located in the unique adjacent sequence, to delineate the area where each chimera initiated (i.e. +200 or +450 in the 5′ UTR or upstream)  .We tested 36 chimeras  in two glioma samples (GS#1 and #2). We could amplify 19 of them using the two primers located in the first 500 bp of the 5′ UTR, but not with the “upstream” primer, for instance chimera 1087 and 179 . This paIDHwt GSC lines (GSC1 and GSC2). By pooling the data of these two samples, we could identify by CLIFinder 226 chimeras that involved L1 elements from the L1PA1 to L1PA7 subfamilies. Then, to determine the proportion of chimeras that corresponded to LCTs, we mapped their TSS along the L1 sequence using a long-read single molecule real-time (SMRT) sequencing dataset generated from three GSC lines . Among the 226 chimeras identified by CLIFinder, 22 were not detected in this dataset. The remaining 204 had at least one TSS within the L1 sequence, including the L1 coding region. Nevertheless, we observed a TSS hotspot within the 900 bp of the L1 5′ UTR  may correspond to LCTs that initiate at the L1-ASP. For the rest of this study, we will consider only L1PA1 to L1PA7 as recent L1 subfamilies.Previous studies based on EST querying showed that among the 8744 recent L1 elements with a 5′ UTR sequence from the L1PA1 to L1PA7 subfamilies, 161 (1.9%) are associated with LCT transcription in various human normal tissue and cancer types and cell lines ,17,18,20IDHwt glioma and normal brain.In conclusion, in normal brain and glioma samples, our approach identified 40% of the 161 L1 elements previously associated with LCT-EST production. It also identified 1444 new recent L1 elements implicated in LCT transcription, thus implicating 17.25% of human L1PA1 to L1PA7 retrotransposons (with a 5′ UTR) in LCT expression in n = 453) was 306 bp, and 77% of them were smaller than 400 bp. As the L1 antisense 5′ UTR contains two donor splicing sites, we evaluated the splicing status of these transcripts by analyzing three parameters: the genomic distance between the L1 side and the unique sequence for each chimera, the total chimera size, and the position of the splice junctions identified by TopHat around the L1 first nucleotide in the 11 samples (The size of the 1509 LCTs ranged from 132 to 45 574 bp. The median size of the LCTs identified by only 1 read ( samples . We foun. long transcript). Therefore, we incubated the same RNA samples with a poly(U) polymerase to add an artificial polyU tail to their 3′ end, before the reverse transcription step performed using an oligodA oligonucleotide. In this case, we could amplify 22 of the 24 LCTs  status. To determine whether LCTs were polyA transcripts, we tested the presence of 24 LCTs, selected among the 31 we previously validated, in cDNA obtained by reverse transcription of total RNA from two glioma samples (GS#1 and #2) with random hexamers or with an oligodT oligonucleotide. We could amplify all 24 LCTs in cDNA obtained with random hexamers, but only 11 in cDNA reverse transcribed with the oligodT primer . The abs 24 LCTs , suggestn = 226 versus n = 36)  . Specifi n = 36) . The 13-IDHwt glioma samples  or genomic distribution  contained only 1 LCT-producing L1, 92 genes contained 2 LCT-producing L1, and 33 genes contained 3–5 LCT-producing L1 retrotransposons. The latter genes were large genes with a size ranging from 0.16 to 2.3 Mb  were intragenic, compared with 31.5% for all recent L1 in the human genome . Surpriso 2.3 Mb . Gene Onmponents . The hosIDHwt glioma samples did not allow assessing the LCT transcriptional status in these two tissue types. Therefore, we first quantified, by RT-PCR, the expression of the 31 validated LCTs in 10 brain control and 41 IDHwt glioma samples. All 31 LCTs were expressed in both control and glioma samples  (g effect size (Hedges’ ES)   . This suges’ ES) . To estacontrols . This alificity) .g values obtained for the 1509 LCTs predicted that 244 (16.2%) and 88 (5.8%) of them were down- and up-regulated, respectively, in IDHwt glioma versus control samples   . Note thAltogether, these observations indicated that most LCTs are not glioma-specific. They suggested that most LCTs are produced in healthy brain and also aggressive glioma, and that ~20% of them shows tumor-specific down- or up-regulation.L1-MET LCT in cancer cell lines   . HoweverIDHwt samples. IDHwt samples are characterized by chromosome 7 gain and chromosome 10 loss  in the genomic loci of the 31 validated LCTs in 3 controls and 19  10 loss . Accordiectively . Howeverectively .Combined, these observations indicated that aberrant DNA methylation and genome CNV might contribute, but are not the main cause of LCT deregulation in glioma samples.IDHwt glioma samples tend to localize in genes with brain functions suggests that the L1-ASP activity of recent L1 could be influenced by the host locus transcriptional activity, as observed for the sense promoter (n = 433 genes/485 L1) and of genes hosting one or several recent L1 not associated with LCT production (n = 969 genes/1332 L1) (n = 3) and IDHwt glioma (n = 8) samples indicated that the expression level of genes containing LCT-producing L1 retrotransposons was significantly higher  . Compariy higher . This way higher , and alsy higher . Thus, i. H3K36me3) compared with those of recent L1 elements not associated with LCT production  in 9 controls and 39  samples . Six gen samples . Moreove samples . UnivariIDHwt glioma samples. The gene expression in tumor and control samples was similar to that of their associated LCTs  of genes associated with the 64 and 120 intragenic LCTs predicted to be up- and down-regulated in gliomas, respectively, in controls and ted LCTs . Genes aThis suggests that LCT down- and up-regulation in glioma imply a similar transcriptional deregulation of the relevant host locus.n = 1509) of recent L1 retrotransposons are involved in their production. It also revealed that these events are not tumor-specific, because LCT transcription occurred also in healthy brain, as previously shown in normal colon  gene (IDHwt) . This study was approved by the relevant ethics committees and competent authorities, and the study protocols follow the World Medical Association Declaration of Helsinki. Written informed consents were provided by all patients. Samples were isolated as previously described ,35. In t (IDHwt) .n = 8) and frontal cortex (n = 7), correspond to white matter enriched in astrocytes and oligodendrocytes, and are relevant non-cancer controls for gliomas. Before use, tumor and control samples were homogenized by cryogenic grinding, and each sample was aliquoted in at least three vials for genomic DNA, RNA and chromatin extraction. All samples were stored at −80°C until use.Ten control brain samples  were obtained from the Brain and Tissue Bank of Maryland . These samples, identified by the Brain and Tissue Bank of Maryland as corpus callosum  derived from patients with cterized .The coordinates of L1 elements were obtained from the RepeatMasker database deposited in the UCSC Table Browser. Recent L1 elements were selected based on their RepName . This list was then filtered using repLeft and repStart <400 and repEnd >600 to retain only L1 elements with an ASP. The final list is in n = 3 brain control, n = 8 IDHwt, and n = 2 GSC samples: GSC-1 and GSC-2) and also polyA mRNA from the GSC-6 and GSC-2 samples .. not annotated by Repeat Masker) of ≥30 bp.The associated 3′ end read sequence should contain at least one unique sequence .In the 11 RNA-seq datasets, CLIFinder identified 1677 chimeras associated with the L1 subfamilies L1PA1 to L1PA8 . Among t(30) 3′ SMART CDS Primer II A was replaced by the oligodA(30) 3′ SMART CDS Primer II A. The obtained full-length cDNA (1:10 dilution) was then amplified by PCR (14 cycles) using GXL PrimeSTAR DNA polymerase (Takara) in 20 PCR reactions. PCR products were purified and concentrated (in 21 μl) with two rounds of 1× AMPurePB beads (PacBio) treatment. cDNA size fractionation was then performed on 0.75% agarose gels (without ethidium bromide) to individualize three cDNA fractions: [0.3–2 kb], [2–5 kb] and [>5 kb]. Dedicated biotinylated riboprobes, designed against the 5′ 400 bp sequences of the 8744 recent L1 , were used to perform three independent captures of each cDNA fraction according to the supplier’s recommendations (except for the addition of Block C solution = human Cot-1 DNA). Captured cDNAs were then amplified by PCR using Prime StarGXL DNA polymerase (Takara) with 10, 12 and 14 cycles respectively for the <2, 2–5 and >5 kb cDNA fractions. PCR products were pooled and purified using 0.55× AMPurePB beads (PacBio). The three samples were pooled together and handled for SMRTbell template preparation and sequencing . The obtained sequences were treated as follows: (1) consensus sequence establishment using the Iso-seq3 CCS algorithm; (2) selection of the full-length cDNA sequences including the SMARter IIA adapter at both their 3′ and 5′ ends using the Iso-seq3 LIMA tool; (3) trimming of the polyT tail at the 3′ end of each sequence  and finally (4) alignment of the obtained sequences to the hg19 reference human genome using the minimap2 aligner. Only good quality (MapQ ≥ 30) aligned reads were retained in a SAM file . The genomic coordinates of PacBio read alignments were extracted using the bamtobed tools from the bedtools suite V2.30.0 and the SAM file. Using the GenomicRanges R library, these coordinates were filtered to keep only reads that overlapped with the 226 L1-producing LCTs identified by CLIFinder in GSC Illumina RNA-seq (GSC1 & GSC2) data. Only PacBio reads with potential LCT characteristics were selected: reads containing a 5′ sequence antisense to one of the 226 L1 and a unique genome sequence at its 3′ end. To identify the TSS of the PacBio reads within the L1s, the starting position of each read was repositioned using the L1 coordinates as reference. These data were used to determine the number of L1 that were associated with a PacBio read that initiated in the ASP region or the 5′ UTR of the L1 (i.e. position 0–500 and 0–900 of a L1). To visualize the TSS position of all reads inside each of the 226 L1 elements, the 5′ initiation position of all reads was considered to compute the percentage of reads that initiated along each L1.To sequence full-length polyA and non-polyA LCT transcripts, a modified Iso-seq protocol (PacBio) was used. For each sample, 3 μg of total RNA was poly-uridinylated using a polyU polymerase (New England Biolabs) following the manufacturer’s recommendations. After poly-uridinylated (polyU) RNA purification with the RNeasy kit (Qiagen) and ethanol precipitation, two reverse transcription reactions were performed (each with 1 μg of polyU RNA) using the SMARTer Kit (Takara) in which the oligodTIDHwt glioma samples were used to determine the normalized gene expression (fpkm), as previously described  and 3 controls and 19 escribed . To detehttps://www.ncbi.nlm.nih.gov/geo/) under the following accession numbers: GSE123892 for the stranded total RNA-seq of control samples and IDHwt gliomas; GSE161438 for the stranded total RNA-seq of GSCs; GSM4907328 and GSM180209 for the stranded polyA RNA-seq of GSCs; GSE190930 for the PacBio sequencing of GSCs; and GSE123682 and GSE161275 for the Cytoscan HD data.Data are available at the NCBI Gene Expression Omnibus  were designed for the 36 chimeras selected according to their detection by CLIFinder using high or low reads number, they tumor-specificity or not, and their localization (intragenic or intergenic). This primer was combined with a L1 common primer . Due to sequence homology, a common primer could be designed for the L1PA1 to L1PA7 subfamilies and a specific L1 common primer for the L1PA8 subfamily. Then, chimera detection was validated by RT-PCR using RNA from two glioma samples (GS#1 and #2) already used for RNA-seq. Briefly, 1 μg of total RNA was treated with DNase I (Promega) according to the manufacturer’s recommendations, and then reverse transcribed using Random Hexamers (Invitrogen) and the SuperScript III Reverse Transcriptase Kit (Invitrogen). After dilution (1:5), 2 μl of cDNA solution was used for PCR amplification with the relevant LCT specific primer, LCT-US and L1 common primers. Primer sequences, PCR conditions, and PCR product amplification size are given in The LCT-Unique Sequence (LCT-US) primers  from two glioma samples was used for these experiments. Additional L1 primers, downstream and upstream of the L1-ASP +450 location, were designed using the Primer3 software. Due to the high sequence homology displayed by the 5′ UTR sequence of the L1PA1 to L1PA6 subfamilies, the same L1 downstream and upstream primers were used for chimeras associated with L1 elements from these six subfamilies. However, as some mismatches could impair PCR amplification (when too numerous or when occurring at the 3′ end of the primer), three different L1 downstream primers (PA1 to PA6) were designed and alternatively used. Specific L1 downstream and upstream primers were designed for the L1PA7 and L1PA8 subfamilies. Primer sequences, mismatch number between L1 primer/L1 sequence, and PCR product amplification sizes and results are given in Three parameters were taken into account to identify splicing events in chimeras: (1) the distance between the start position of the unique sequence and the end position of the L1 sequence of each chimera after alignment (R1–R2 distance). A negative or null distance indicates an overlap or a juxtaposition of both sequences, and implies that these chimeras have been identified in multiple reads (either in one or different samples). A distance between 1 and 200 bp is concordant with the size selection made during the library construction (i.e. 280–340 bp) and often relevant of chimeras identified in few reads in all samples. A distance >500 bp may suggest at least one splicing event between the L1 and unique sequence; (2) the LCT size. Chimeras with a size >1000 bp, even if their R1–R2 distance is negative, can present evidences of splicing event(s), because the unique sequence is very large (>700 bp) due to concatenation of multiple reads by CLIFinder; (3) the occurrence of splice junctions identified by TopHat in the RNA-seq data in the ±200 bp region around the L1 start position. One of these parameters was considered sufficient to conclude that a LCT was spliced. LCT with R1-R2 distance between 200 and 499 bp and a size between 600 and 999 bp were considered to have an undetermined spliced status.m oligod(T)n primer (Invitrogen) to specifically target polyA mRNA transcripts. Then, 2 μl of diluted (1:10) cDNA was used for PCR amplification using the LCT-US and L1 common primers.Total RNA from the glioma samples GS#1 and GS#2 was treated with DNase I and reverse transcribed with SuperScript III using 2.5 μm UTP, 1X Buffer and 40 U RNase inhibitor. The resulting poly-uridinylated RNA was purified on RNeasy columns (Qiagen), concentrated after ethanol precipitation, and 1 μg of PolyU-RNA was reverse transcribed using 0.6 μm of oligod(A)n primer (Eurogentec) and Superscript III (Invitrogen). Then, 2 μl of diluted (1:10) cDNAs was PCR amplified with the LCT-US and L1 common primers.Alternatively, 3 μg of total RNA was treated with DNase I and then with 2 U of PolyU polymerase in the presence of 0.5 mR = (ETOI–CtTOI)/(geometric mean EHK—CtHK) with E = PCR efficiency of each primer pair, TOI = Transcript Of Interest (i.e. chimera or gene to be quantified), and HK = housekeeping genes  used to normalize transcript expression. For each sample, experiments were done in duplicate using the two independent RT reactions.500 ng of total RNA was treated with DNase I (Promega) and divided in two independent aliquots (250 ng RNA/each) for reverse transcription using a random hexamer primer (Invitrogen). Then, first strand cDNA was pre-amplified (14 cycles) with the pool of primers used for the microfluidic PCR assays. Primer sequences and qPCR Efficiency (E) are given in P-values were adjusted with the Bonferroni correction. Correlation analyses between intragenic LCT and the associated gene expression levels were performed using Spearman’s correlation.Differences in the expression levels of the LCT or associated gene between tumor and control samples were assessed with the one-way ANOVA and post-hoc Mann–Whitney test, if applicable. Significant L1 positions and classifications were retrieved from the CLIFinder output. Based on these coordinates, chromatin analyses were done with the computeMatrix scale-regions tools from the deeptools suite (Version 3.1.3)  and chromatin data coverages extracted from GeoDataset in WIG format . These WIG files were converted into binary format with the wigToBigWig program from the UCSC server, to be compatible with the computeMatrix program prerequisites. Chromatin profiles surrounding the L1 start and end positions (±1 kb) that corresponded to the mean chromatin coverage over the set of genomic regions were generated using the compute Matrix output and R program.HhaI or HpaII) and methylation-dependent (e.g. McrBC) restriction enzymes. DNA was extracted from frozen samples (21 IDHwt glioma and 7 control brain samples) using the QIAamp DNA Mini Kit (Qiagen) according to the manufacturer’s recommendations. For each DNA sample, four 200 ng DNA aliquots were prepared. Three were incubated with 5 U of HhaI, HpaII, or McrBC (NEB) at 37°C for 2 h. The fourth aliquot was incubated only with buffer (negative control). The published qAMP protocol was slightly modified by adding a restriction enzyme inactivation step by incubation with proteinase K (0.4 mg/μl) at 40°C for 30 min, followed by its inactivation at 95°C for 10 min. The obtained PCR templates were 8-fold diluted in water and stored at −20°C. PCR primers covered approximately the first 300 bp of each L1 promoter in which the restriction sites for each of the three enzymes used are present. L1 primers were designed using the Primer 3 software and combined to the already described LCT US primer. Additionally, primers for control regions with different DNA methylation levels were designed: (1) a control region that does not contain restriction sites for the three enzymes; (2) the promoter of the imprinted gene PEG10 that is hemi-methylated and (3) the GAPDH promoter region that in our samples, has a methylation level <10%. All primers used for the qAMP experiments are given in SssI enzyme (NEB). These two DNA templates were mixed to obtain a standard curve with theoretical DNA methylation levels of 0, 30, 70 and 100%. The efficiency of each primer pair was evaluated using 3 log10 gDNA concentrations. Only primer pairs with an efficiency ≥1.85 and a standard curve corresponding to the expected theoretical DNA methylation percentages were retained. The DNA methylation index of each sample was then evaluated by qAMP on a LightCyclerR©480II (Roche). Four μl of each DNA  was mixed with 1× SYBR Green Master Mix (Roche) and 0.5 μm forward and reverse primers in a final volume of 10 μl. The PCR program was: 95°C for 5 min, and then 40 cycles , and 72°C for 5 min. The DNA methylation index of each sample was calculated using the amplification data obtained for the three DNA samples digested with the restriction enzymes and as previously described using the ∆Ct values (Ct enzyme—Ct sham)  . DifferePinson_et_al_Supp_Fig_ddac056Click here for additional data file.Pinson_et_al_Table_S1_ddac056Click here for additional data file.Pinson_et_al_Table_S2_ddac056Click here for additional data file.Pinson_et_al_Table_S3_ddac056Click here for additional data file.


Electronic devices based on two-dimensional semiconductors suffer from limited electrical stability because charge carriers originating from the semiconductors interact with defects in the surrounding insulators. In field-effect transistors, the resulting trapped charges can lead to large hysteresis and device drifts, particularly when common amorphous gate oxides (such as silicon or hafnium dioxide) are used, hindering stable circuit operation. Here, we show that device stability in graphene-based field-effect transistors with amorphous gate oxides can be improved by Fermi-level tuning. We deliberately tune the Fermi level of the channel to maximize the energy distance between the charge carriers in the channel and the defect bands in the amorphous aluminium gate oxide. Charge trapping is highly sensitive to the energetic alignment of the Fermi level of the channel with the defect band in the insulator, and thus, our approach minimizes the amount of electrically active border traps without the need to reduce the total number of traps in the insulator. The stability of graphene-based field-effect transistors with amorphous aluminium oxide serving as the top-gate oxide can be improved by tuning the Fermilevel of the two-dimensional channel material such that it maximizes the energy distance between the charge carriers in the channel and the defect bands in the gate oxide. In contrast to silicon, 2D semiconductors retain sizable mobilities at atomic layer thicknesses below 1 nm, a thickness that also helps to suppress short-channel effects in FETs and thus allows for physical channel lengths below 5 nm (ref. 2). Furthermore, the integration of 2D materials in van der Waals heterostructures provides design options for energy-efficient transistors that can overcome the limitations of thermal charge-carrier injection. In addition, 2D materials are of potential use in a broad range of applications, including photonics and optoelectronics3, neuromorphic computing4, nanoelectromechanical systems5, and gas and biological sensors6.Two-dimensional (2D) semiconductors are a potential channel material for ultimately scaled field-effect transistors (FETs)7. In addition, there is a lack of suitable gate insulators8 that can ensure high interface quality, scalability9 and a minimum of electrically active border traps, the presence of which in the insulator close to the channel limits device stability. These two challenges are major obstacles for the industrial applications of 2D-based nanoelectronics and, being independent from each other, need to be separately addressed. There has been recent progress regarding the formation of low-barrier contacts for 2D semiconductors using contact gating10 or semimetallic bismuth contacts to achieve ultralow contact resistances11. However, the need to find a suitable insulator with a minimum number of electrically active traps remains. Stability studies of 2D FETs typically show a stability that is at least two orders of magnitude worse13 compared with silicon-based FETs15.There is, however, currently a lack of low-resistive contacts to 2D semiconductors, which minimize the prevalent Schottky barriers16 and stability of threshold voltage under prolonged periods of applied elevated gate biases and temperatures (bias temperature instability (BTI))14. Charge trapping inside the gate oxide has been identified as the root cause of BTI17. At elevated gate biases and temperatures, charges are transferred between the channel and gate oxide in a phonon-mediated transition18, with charging time constants spanning a wide range, from picoseconds to years19. Border traps in the gate oxide close to the channel determine the long-term stability and reliability of silicon FETs15, whereas in 2D-material-based FETs, they typically limit device stability on much shorter timescales20.Measurements of FET stability typically evaluate the hysteresis in the transfer characteristics18. In the first approximation, the energy levels of defects follow a normal distribution around the average defect levels, forming defect bands21. Consequently, the overall density of border traps and the widths of the corresponding defect bands can be considerably reduced by using crystalline insulators, such as hexagonal boron nitride (hBN) or calcium fluoride8 (CaF2). However, these insulators are difficult to synthesize and have several technological challenges. For example, current state-of-the-art crystalline hBN can be grown only at temperatures above 1,200 °C (ref. 22) and CaF2 requires a crystalline silicon(111) substrate for growth, allowing only back-gated configurations23. In addition, hBN is unsuitable for use as a scaled gate insulator because of its small dielectric constant9.In amorphous oxides, the defect trap levels vary due to the differing surroundings of every instance of atomic defectsEF) of the 2D channel material such that it maximizes the energy distance between the charge carriers in the channel and the defect bands in the gate insulator during device operation. This can be achieved via a careful selection of the 2D material and amorphous gate oxide, as well as by doping the 2D layer to shift EF away from the defect bands in the gate insulator. Graphene FETs (GFETs) with aluminium oxide (Al2O3) as the top-gate oxide are measured and compared, where one of the device batches uses a p-doped graphene layer. The GFET batch where EF is tuned away from the Al2O3 defect band edge shows reduced hysteresis and BTI. We further verify the approach with technology computer-aided design (TCAD) simulations24. In addition, double-gated GFETs are fabricated, where the back gate is used to directly tune the Fermi level in the graphene monolayer via electrostatic doping. This shows that the stability observed for top-gate operation strongly depends on the location of EF. Our approach aims to design a metal–oxide–semiconductor (MOS) system with a minimal amount of electrically active border traps without the need to reduce the total number of traps in the insulator.In this Article, we show that the stability and reliability of 2D-material-based FETs with amorphous gate oxides can be improved by tuning the Fermi level (2O3 (oxide) and graphene (semiconductor). The corresponding band diagram of a cut through the MOS stack , HfO2 (refs. 31) and Al2O3 (ref. 28) insulators. Besides the location of the defect bands, another essential property of insulator traps is their extremely broad distribution of time constants, ranging from the picoseconds regime up to years19. As these insulator traps lead to noise, hysteresis and drifts, it is important to thoroughly characterize both traps’ time constants and their energetic location within the defect bands.The energetic position of defect bands in amorphous oxides is an intrinsic material property2O3, we can predict the electrical stability of the threshold voltage in these FETs. In Fig. 33. This graphene layer’s EF lies in the middle of the Al2O3 defect band. Due to this alignment within the defect band, charge traps in the oxide capture and frequently emit charges. As the applied gate voltage modifies the charging probabilities of the defects according to the electric field19, VTH depends on the biasing history and a pronounced hysteresis is visible. In addition, VTH drifts considerably during prolonged periods of applied gate biases.Based on the band alignment of the graphene work function to the defect bands in AlEF down by p doping the graphene layer. Here the EF value of graphene of 5.1 eV can be achieved through p doping34. As the graphene Fermi level is located below the Al2O3 defect band, charge transfer is unlikely. Therefore, the oxide defects are electrically inactive, resulting in stable VTH throughout device operation, independent of the applied biases. In graphene, doping with different adsorbates and substrates results in a quasi-continuous variation in the Fermi level between 3.4 and 5.1 eV (refs. 36), which can be used to tune the Fermi level during device design to minimize the impact of oxide defect bands.However, the theoretical considerations of Fig. 2 FET with HfO2 top-gate oxide. If the Fermi level is aligned close to the conduction band, electrons within WS2 are the majority charge carriers dominating the current flow in Schottky-barrier FETs7. As the conduction band edge of WS2 is aligned with the electron-trapping defect band of HfO2, charge transfer to oxide defects is frequent. If WS2 was p doped instead of n doped, holes at the valence band edge would be the majority , WS2/SiO2 (ref. 39) or graphene/SiO2 (ref. 40). This evidence suggests that there might be a loss of charge neutrality at the ill-defined interfaces between van der Waals–bonded 2D layers and amorphous oxides, causing deviations from the Schottky–Mott rule25. These deviations result in offsets to the band alignments that can be determined, for example, with internal photoemission measurements41 or scanning probe techniques42. Although these offsets would need to be taken into account for optimum matching of the Fermi level at a maximum distance to the oxide defect bands, they are neglected for the proof-of-concept study presented here.It is worth noting that several studies have reported high densities of fixed charges at the interfaces of 2D materials with amorphous oxides, for example, MoS43. However, fixed charges at the interfaces would also degrade the mobility in the semiconducting 2D channel25. This could be avoided by using more complex gate stacks with electric dipoles at the interfaces between different oxides. Such a dipole engineering approach has been successfully used to improve the reliability of silicon FETs with an HfO2/SiO2 gate stack44.At the same time, the intentional placement of charges at the interface could be used to shift the band edges away from the oxide defect bands using, for example, surface charge transfer dopingECB. For simulations, we used the previously developed drift-diffusion-based TCAD methodology45 coupled to a non-radiative multiphonon model18 (Supplementary Section 2 with a back-gate oxide of SiO2 (ref. 45). We evaluated the hysteresis width at VTH, defined here as the voltage where the Fermi level is located –0.05 eV below the conduction band edge . Thus, we would expect that n-type WS2 FETs with an HfO2 gate oxide are more stable when using bulk WS2 as a channel compared with thinner WS2 layers. In cases where an ultimately thin monolayer channel is required, electrically stable FETs could be designed by choosing a different combination of 2D semiconductor and insulator. For example, increased electrical stability is predicted for BP/HfO2 FETs and for ZrSe2/Al2O3 FETs . Independently, graphene, with its continuous tunability of EF over an interval of nearly 2 eV, provides the largest design freedom. Due to the possibility to tune the Fermi level in graphene by a few 100 meV through moderate doping, we chose graphene/Al2O3 as a model system to experimentally verify our stability-based design approach.It should be noted that for narrower defect bands, the improvement accessible by tuning the semiconductor band edges is much larger. For example, we repeated the calculations shown in Fig. 48. In the first two batches, graphene monolayers form a channel with an area of W × L = 100 μm × 160 μm on top of mechanically flexible polyimide (PI) substrates49  process and layer transfer. Type 1 graphene exhibits a work function that results in a small distance of EF to the Al2O3 trap band  and transfer (ID–VG) characteristics are shown for a representative Type 1 GFET 34. Pristine graphene has a work function of 4.56 eV (ref. 50), which is shifted towards higher values by p doping34 and towards smaller values by n doping35. To calculate the Fermi-level location in the two graphene types, we obtain the charge-carrier concentration (n) based on the analytic expression for n in the MOS capacitor51. At a top-gate bias of 0 V, we extract the charge-carrier concentration (n) caused by the intrinsic doping of graphene samplesCtot is given by the capacitance of Al2O3 (Cox) in series with the capacitance of 0.5 nm van der Waals gap (CvdW) and the quantum capacitance of graphene52 (Cq), amounting to Ctot = 0.16 μF cm−2. This expression gives a p-doping density for Type 1 graphene of n1 = 5.5 × 1010 cm−2 and for Type 2 graphene of n2 = 2.8 × 1012 cm−2. Thus, Type 2 graphene is more p doped by an additional doping density of approximately 2.75 × 1012 cm−2. These hole densities in the graphene layers at 0 V gate voltage determine the work function via42νF = 1.1 × 106 m s−1 (ref. 53). Consequently, we obtain EW1 of Type 1 graphene to be 4.6 eV and EW2 of Type 2 graphene to be 4.8 eV, that is 0.2 eV higher  of Al2O3, which determines the location of the conduction band edge, varies in the literature. Here we use 1.96 eV, as obtained from internal photoemission measurements41. For all the measurement ranges used in our work, we only probe the lower part of a potentially wider defect band further up, as reported using other methods55. This is illustrated in Fig. 55. For Type 1 graphene, EF is aligned within the defect band  Fig. , whereasle) Fig. . Below, VH as a function of the inverse sweep time (tSW), namely, the sweep frequency (f = 1/tSW). In Fig. VH than Type 1 devices, which is linked to the increased variability of VDirac on Type 2  curves after biasing the devices at a static VBG. Subsequently, the top-gate hysteresis was measured at different sweep rates. In Fig. VBG. It is expected that hysteresis can be reduced for high negative back-gate voltages, as the work function of the graphene channel is the highest at 4.3 eV for a higher negative VBG ..ID (VTG)VH over different measurement rounds on two double-gated GFETs, namely, D1 and D2  after static elevated gate voltages  were applied for varying charging times (tcharging). We record the magnitude of the initial ΔVDirac shift and monitor the recovery after the increased gate-biasing period with fast ID (VG) sweeps at logarithmically spaced recovery times. In Fig. ID (VG) sweeps recorded during the recovery from negative gate biasing (negative-bias temperature instability (NBTI)) at –10 V are shown. Such BTI measurements give a complementary perspective on the long-term stability and reliability of FETs compared with the hysteresis measurements discussed earlier. Although during hysteresis measurements, GFETs are subjected to slow up and down gate-voltage sweeps, during a BTI measurement, an elevated gate bias is applied for a certain charging time and the recovery of the Dirac point is recorded. With this well-established measurement scheme, the impact of border traps is studied15. Thus, in a BTI measurement, the observed hysteresis during the probing sweeps is small and not in focus. To avoid measurement artefacts coming from fast traps causing the hysteresis in the probing ID (VG) curves, the down-sweep ID (VG) curves are used to evaluate the VDirac shifts for all NBTI measurements and the up-sweep ID (VG) curves for positive gate biasing (positive-bias temperature instability (PBTI))  sweeps used for the BTI evaluation of Type 2 GFETs  sweeps measured after a positive bias at 10 V are shown, together with the corresponding recovery traces for Type 1 GFETs , have a smaller hysteresis and increased stability of their Dirac voltage when subject to prolonged elevated gate biases. Furthermore, by electrostatic doping of the graphene channel via a back gate, the hysteresis width can be reduced by a factor of up to 4.5. These results suggest that more stable and reliable 2D-material-based FETs using common amorphous gate oxides can be built by minimizing the impact of defect bands in the gate oxides during design.We have reported an approach to improve the electrical stability of FETs based on 2D materials. Charge trapping at the border traps in amorphous oxides is the principal cause of the threshold-voltage drifts and reduced long-term stability in 2D FETs. Therefore, the impact of defect bands in amorphous gate oxides can be reduced by tuning the energy alignment of the Fermi level. We demonstrate our approach using GFETs with Al8. However, future studies will be necessary to clarify what levels of stability can be achieved by Fermi-level tuning in systems based on amorphous oxides and crystalline insulators. In addition, the stability-based design approach relies on prior knowledge about the energy location of the defect bands in the oxide, which at the moment is incomplete.In 2D semiconductors, the design options mainly consist of choosing suitable materials depending on n or p doping, or varying the thickness of the channel material. There is more design freedom with graphene, as the graphene Fermi level can be tuned over a range of up to 2 eV. Moreover, our approach to improve stability may be universally applicable to other insulators, such as crystalline insulators, where the impact of narrow insulator-defect bands can be reduced further than in amorphous oxides59; for Type 2 GFETs, the transfer was performed by vendor 2. The Type 1 graphene flake covered an area of 2 × 2 cm2 and was of higher quality than the Type 2 flake (which covered a six-inch wafer). The different qualities of the graphene layer were confirmed by Raman spectroscopy  of 160 μm and width (W) of 100 μm. In the next step, the source and drain contacts were deposited by sputtering 50 nm Ni, followed by a lift-off process. This step was followed by growing 40 nm Al2O3 with atomic layer deposition (ALD) on top of the devices to form the gate oxide in a top-gated configuration. To finalize the GFETs, the top-gate electrode was fabricated by sputtering 10 nm Ti and 150 nm Al and patterned in a lift-off process. To be able to contact the source and drain pads, vias were opened through the Al2O3 with a wet-buffered oxide etchant. For our double-gated GFETs, we transferred CVD graphene monolayers from vendor 1 using a PMMA-assisted wet transfer method59 to a 90 nm SiO2 on a Si wafer. Subsequently, the graphene layer was patterned in an oxygen-plasma etch step to form channels of L = 80 μm and W = 50 μm and the source and drain contacts were deposited by sputtering 50 nm Ni followed by a lift-off process. After this step, the gate oxide of 40 nm Al2O3 was grown using ALD and the top-gate electrode was deposited with a sputter process.Our top-gated GFETs were fabricated on spin-coated PI substrates using photolithography. First, the flexible substrate was prepared by spin coating PI in the liquid form on a Si wafer and subsequently curing the layer. The thickness of the solidified PI film was about 8 μm. During the fabrication process, a rigid Si substrate was used as a support layer. In the next step, a CVD-grown graphene layer was transferred to the PI substrate. We study two batches of GFETs where the channel is formed by graphene samples purchased from different vendors, namely, vendor 1 (Type 1) and vendor 2 (Type 2). For Type 1 devices, CVD graphene was transferred from the copper growth substrate using a polymethyl methacrylate (PMMA)-assisted wet transfer method2 V−1 s−1 for Type 1 graphene and 1,000 cm2 V−1 s−1 for Type 2 graphene. The Hall mobility of both the samples was found to be slightly higher. The hysteresis was analysed by measuring the double-sweep ID–VG characteristics using different sweep times tSW and sweep ranges of VH was extracted as the difference between the forward- and reverse-sweep VDirac value. As suggested in our previous work20, we expressed the hysteresis dynamics using ΔVH (1/tSW) traces. Finally, the BTI degradation/recovery dynamics were analysed using subsequent degradation/recovery rounds with either fixed stress time tdeg and increasing high-voltage levels VG,high, or fixed VG,high and increasing tdeg. During the recovery period, we applied a constant recovery voltage of VG,recovery = 1 V between the sweeps. This voltage is chosen to be close to the charge-carrier equilibrium at VDirac. To avoid artefacts from fast traps charged during the sweep, the down-sweep ID–VG curve is used to monitor the recovery of NBTI13. The characteristics obtained when using up-sweeps to measure the NBTI recovery are shown in Extended Data Fig. 13; thus, we used ID–VG sweeps from negative to positive voltages for the evaluation of PBTI. As was suggested in our previous study on GFETs13, we expressed the BTI degradation magnitude using a Dirac-point voltage shift ΔVDirac and plotted it versus the relaxation time tr. To gain more statistics, all our measurements were repeated on several devices.Our electrical measurements were performed in a vacuum at room temperature and in complete darkness. The devices were examined with the PI supported on a silicon wafer. From two-probe measurements, we extracted the field-effect mobility of the GFETs and found it to be 4,000 cmSupplementary InformationSupplementary Sections 1–12, Figs. 1–3 and Table 1.


As the technology revolution and industrialization have flourished in the last few decades, the development of humidity nanosensors has become more important for the detection and control of humidity in the industry production line, food preservation, chemistry, agriculture and environmental monitoring. The new nanostructured materials and fabrication in nanosensors are linked to better sensor performance, especially for superior humidity sensing, following the intensive research into the design and synthesis of nanomaterials in the last few years. Various nanomaterials, such as ceramics, polymers, semiconductor and sulfide, carbon-based, triboelectrical nanogenerator (TENG), and MXene, have been studied for their potential ability to sense humidity with structures of nanowires, nanotubes, nanopores, and monolayers. These nanosensors have been synthesized via a wide range of processes, including solution synthesis, anodization, physical vapor deposition (PVD), or chemical vapor deposition (CVD). The sensing mechanism, process improvement and nanostructure modulation of different types of materials are mostly inexhaustible, but they are all inseparable from the goals of the effective response, high sensitivity and low response–recovery time of humidity sensors. In this review, we focus on the sensing mechanism of direct and indirect sensing, various fabrication methods, nanomaterial geometry and recent advances in humidity nanosensors. Various types of capacitive, resistive and optical humidity nanosensors are introduced, alongside illustration of the properties and nanostructures of various materials. The similarities and differences of the humidity-sensitive mechanisms of different types of materials are summarized. Applications such as IoT, and the environmental and human-body monitoring of nanosensors are the development trends for futures advancements. Relative humidity (RH) is expressed as a percentage, which indicates a present state of absolute humidity relative to the saturation level at a given temperature. As the technology revolution and industrialization have flourished, the measurement of RH has become an important issue in industry, food preservation, chemistry, agriculture, environmental monitoring and our daily life ,2,3,4,5.Some materials and measurement types of humidity nanosensor technologies have been researched in the literature; materials including ceramic ,7,8,9,10The humidity nanosensor system  includesThe solution method ,52,53,543), were prepared. The nanoporous structures were prepared from a commercially available AAO template with the spin-coating process to from the AlPcCl dielectric thin film, the schematic cross-sectional diagram is shown in Another example was proposed by Ahmad et al.: to develop the organic nanostructure sensing layer on an AAO template for humidity sensing by Aluminum 1,8,15,22-tetrakis 29H, 31H phthalocyanine chloride  . Two dif2O3  PbIrication . The sen5 KΩ to 90 KΩ at RH% from 11% to 95%. If the Formula (13) is applied to this case, the response can be higher to 200,000%; however, only 100% was calculated using Formula (11). Therefore, some groups define a response-estimation formula in their papers to present the sensor characteristics. For example, Lu et al. [3/TiO2 composite achieved 15723% using Formula (17).The response value can reach to ~1,000,000% in the RH range from 10% to 100%, which is much larger than other works we mentioned except the SnS nanoflake sensor with current-type measurement from 3% to 99% proposed by Tang et al. . The curu et al.  defined Recently, two-dimensional (2D) nanomaterials have played an important role in humidity nanosensors due to the larger specific surface area and high carrier mobility. The nanostructures of carbon-based materials, such as grapheme oxide (GO) , carbon In terms of the optical sensors made of carbon-based materials, these are mainly proposed with wavelength and power measurement using microfiber knot resonators (MKRs) ,119, whiTo solve the energy crisis and environmental-protection problems of using fossil fuels, energy policies in the new era focus on improving green-energy technology ,125,126.The new triboelectric nanogenerator (TENG) technology is successfully used for harvesting wasted energies from motion, sliding, vibration, hydraulic, or air power and has received much attention for effective harvesting energy in numerous practical applications, including consumer electronics, biosensors, pressure sensors, and portable electronic devices. It is an important and a significant issue for TENG devices to develop a cost-effective and rapid processing, to be environmentally friendly, and to develop high-performance technology. The TENG performance-measurement method is shown in In the TENG humidity-sensing field, the mechanism of electrical-signal formation is totally different from the above. The TENG output voltage is mainly affected by the triboelectricity electron transfer from different materials. For the semiconductor- or polymer-based materials we mentioned above, the water-molecule adsorption reduces the resistance on the surface of the sensing material, resulting in an increase in conductance. However, in terms of TENG, the water molecules will restrict the electron transfer and increase the contact resistance from the tribolayers. Therefore, the TENG output voltage will reduce at higher humidities ,42,44.0 stand for the highest and original value of voltage, respectively:0 is the voltage under the lowest humidity measured, and V is the voltage at the highest RH. The influence of the response value of different formulas was discussed in 2 monolayer from APCVD process for a self-powered TENG. The higher specific surface area of nanofiber can achieve better performance in sensor response.In sensor performance discussions, the poly /MXene nanofber TENG proposed by Zhang et al. achieved a 4000% response and response–recovery time of 0.9/6.3 s. The TENG was synthesized using the solution method for poly /MXene preparation and MoSeExcept the electrical humidity nanosensors mentioned above, a special smart deformable TENG was proposed by Chen et al. . The vapIn recent years, the novel material MXene has been applied to the humidity-sensing field ,134,135.The sensing mechanism of MXene can be divided into two parts. First is the chemisorbed and physisorbed layer of water molecules, which is very similar to ceramic- and polymer-based humidity nanosensors and was discussed in Humidity nanosensors were briefly reviewed and categorized into six types, and the nanosensor performance is affected directly by the material, nanostructure and measurement type and calculation formula. Humidity sensors made of ceramics, polymers, semiconductors, and carbon materials have been developed for a long time, and the research is moving forward with more economical manufacturing processes and better sensor performances. On the other hand, humidity sensors made from TENG and MXene materials have emerged in recent years, and have received more attention. Among the above-mentioned various materials, the semiconductor and carbon humidity sensor can achieve better performance. The sensing mechanism of various materials is related to the adsorption of water vapor and photon hopping, but the adsorption of water molecules significantly changes the electrical properties from semiconductor- and carbon-based sensors. Therefore, in addition to resistance and capacitance, the sensing types also include current and voltage types. The current-type humidity nanosensors are able to achieve a higher response due to a larger electrical conduction change. The response value will increase sharply with the measured humidity range, so some research may be able to reach a higher response from a larger RH range. In addition, the response calculation based on the denominator of the highest value or original data directly affects the response value. For instance, the response calculation based on Formulas (11) and (13) shows significant differences, from 100% to 20,000%, in In addition to the traditional applications mentioned in We briefly reviewed the research progress of the fabrication methods, measurement types, sensing mechanisms and applications of humidity nanosensors in recent years. First, the properties and characteristics of different materials, such as ceramics, polymers, semiconductor, carbon-based, TENG, and MXene humidity nanosensors were summarized. In addition, several fabrication processes, such as solution methods, anodization, PVD and CVD, were described under several conditions for sensor preparation. Dozens of studies are listed in several tables for comparison. The semiconductor- and carbon-based nanosensors synthesized with solution methods are expected to achieve better sensor performance, a higher response of >1,000,000% and shorter response–recovery time, below 1 s.The recent advance of improving the sensor performance has been linked to high-specific-surface-area ,59,107 n


Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a “computational ethology natural history” study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality. Individual animals exhibit idiosyncratic behavior, even when their genetics and rearing environment are held constant. This variability is termed intragenotypic variability  and likeIncreasing behavioral assay throughput via automation is an effective way to attain the sample sizes needed to study variability. This can be achieved through miniaturization and parallelization of imaging platforms in a lab context . While tDrosophila behavior  of the mean, standard deviation, skewness and kurtosis of the behavioral distributions . These wp-value = 0.04 across 10 experimenters; nor in a model with only genotype and experimental condition as the other predictors: lowest p-value = 0.11). In contrast, 47/569 genotypes have significant effects (p < 0.05) in a linear model where genotype is the sole predictor of turn bias . Incidentally, we noticed the effects of the discreteness of number of turns as a measure, and the resulting limited values that turn bias can take on, as a fractal-like , we were also able to investigate the joint distributions and correlations of these measures. We first tested whether there might be a correlation between turn bias and number of turns, specifically a negative correlation arising from higher sampling error in estimating turn bias for flies making fewer turns. Counter to this prediction, we observed a slight positive correlation  partial rescue by the expression of the “mini-white” allele at non-endogenous transgenic insertion sites (white disruption). White genetic disruption was associated with small reductions in variability in turn bias and turn switchiness , 7.5% (p < 0.001), and 1.8% (n.s.) greater than that of females in turn bias, number of turns, and turn switchiness respectively.It has been hypothesized that individuals of the heterogametic sex will exhibit greater trait variability due to noise in gene compensation , though Lastly, we examined the effect of temperature during behavioral testing, with the hypothesis that flies would exhibit higher variability at high temperature (32–33°C) than at room temperature (22–23°C). This would be consistent with a mechanism in which heat pushes neural circuits out of the range in which physiological buffering keeps circuits operating similarly despite latent developmental and genetic variability . We examwhite gene (which encodes a channel that imports serotonin precursor), sex and temperature. Compared to the effects of genotype and the choice of behavior measure, the effects of these manipulations were generally small and context-dependent, underscoring the complexity of relationships between axes of biological regulation and behavioral variability.We gathered Y-maze data collected by lab members back to the origination of this assay 11 years ago. This large data set comprised the behavioral measures of over 180,000 individual flies that made a total of nearly 80 million left-right choices. With it, we were able to estimate the distribution of three measures of individual behavior with unprecedented precision, even out to the 20th standardized statistical moment . In explAdmittedly, a motivation for this study was the desire to explore a very large data set reflecting the work over many years of many lab colleagues. In that spirit, it is fun to think about how throughput might be expanded another order of magnitude in the coming years. One possibility is robotic fly-handling , which hWhile increasing throughput through further automation is an appealing possibility, and perhaps essential for certain classes of experiments , it is not without conceptual consequences. One of these is how to assess small effects that are extremely statistically significant due to large sample sizes. Two examples from this study are the apparent slight left turn bias  and the With caveats of big data in mind, we want to consider possible errors that might explain the apparent slight (but highly significant) left mean turn bias. All experimenters who conducted these experiments are right-handed. It is formally possible that chiral manipulation during the experimental set-up imparted a slight chirality to turning in the Y-maze, though we cannot think of a convincing mechanism by which this would happen. We also cannot think of mechanisms by which small, inevitable asymmetries in our behavioral rigs would impart a consistent left bias to behaviors measured across several generations of rigs and tracking software versions. Arguments in favor of the apparent left turn bias being real are previous reports of small mean asymmetries in wing size and shape , possiblWhile we found that our data set allowed the precise estimation of the distribution of individual behavioral scores, we also saw that the stability of higher-order moment estimates depended strongly on the behavioral distribution in question . Thus, twhite locus increasing variability in number of turns, disruption of white decreasing variability of turn bias and turn switchiness, males exhibiting slightly lower variability in turn bias but higher variability in number of turns, and conducting experiments at high temperatures lowering variability in number of turns and turn switchiness , the default culture conditions were cornmeal-dextrose media containing tegosept  and incuExperimental flies receiving drug treatments were reared from egg-laying in drug-supplemented media (or control media). Drug media are indicated in the expCond metadata variable see . To supphttps://github.com/de-Bivort-Lab/dblab-schematics/tree/master/Ymaze. Trays were imaged in opaque enclosures constructed from aluminum extrusion and laser-cut acrylic panels . A variety of USB digital cameras (often made by PointGrey) with resolution exceeding 1 MP were used to capture video of behaving flies for real-time tracking at 6–30 Hz. The default assay length was 2 h. Fly centroids were computed in real time using background subtraction implemented in a variety of custom software environments coded in LabView or MATLAB. The centroid tracking software used in recent experiments was MARGO  using custom functions. 95% confidence intervals estimated by bootstrapping were estimated as ± twice the standard deviation of values across bootstrap replicates. For the analysis of the effect of temperature on variability , the 23°The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.BB oversaw the project and conducted data analysis. All authors collected the data and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Scanning tunneling microscopy (STM) is one of the indispensable tools to characterize surface structures, but the distinction between atomic geometry and electronic effects based on the measured tunneling current is not always straightforward. In particular, for single-atomic-thick materials (graphene or boron nitride) on metallic substrates, counterintuitive phenomena such as a larger tunneling current for insulators than for metal and a topography opposite to the atomic geometry are reported. Using first-principles density functional theory calculations combined with analytical modeling, we reveal the critical role of penetrating states of metallic substrates that surpass 2D material states, hindering the measurement of intrinsic 2D materials states and leading to topography inversion. Our finding should be instrumental in the interpretation of STM topographies of atomic-thick materials and in the development of 2D material for (opto)electronic and various quantum applications. Since the tunneling current is determined by both the electronic structure and the geometry of the surfaces, it is not always straightforward to determine true atomic structure of the surface from the apparent topographic image. One of the most commonly studied nontrivialities is the contrast inversion in the topography as a function of tip-to-sample distance, which is observed for various systems: Fe2N surface7, TiO2 surface8, and oxygen adatoms on metal surfaces11. These phenomena are on account of the multiorbital nature of the sample states, as the tunneling current is sensitive to the tails of the decaying wave functions of the sample in vacuum, whose decay rates are orbital dependent.Scanning tunneling microscopy (STM) is a powerful tool that provides the topographic image and electronic structure of surface at atomic resolution. The topographic images obtained by mapping the tunneling current have provided numerous key data to characterize the details of the surface structures, including the surface reconstruction and the charge density wave13, Ir15, and Rh17 substrates and graphene nanomesh on Ir21 and Au24 substrates. STM topography of the periodic corrugations are inverted when the measuring condition changes as reported for STM27 and atomic force microscopy28 of BN on Rh(111) or graphene on Au(111). For BN monolayers, the inversions were explained by the bias-dependent electronic structure26 but in some case29, two inverted images occur simultaneously at the same bias. For graphene on Au(111), the simultaneous appearance of convex and concave topography is interpreted as true geometric corrugation22 based on the similar energy of both structures but it cannot explain the observed dominance of convex geometry.Complications also arise in the topography of geometrically corrugated atomic monolayer on metal surface, such as BN nanomesh on Ru31.In this paper, we reveal that the STM topography of an atomically thick material on a metal surface can be strongly affected by the substrate that the apparent relative height can be severely flattened or even opposite to the true atomic geometry, i.e., a higher location looks lower and vice versa. The phenomenon is quite general for insulators with one-atom thickness, where penetrating substrate states dominate the tunneling current. Unexpectedly, even in graphene, the penetrating substrate states readily supersede the intrinsic electronic structure of graphene, so cautions should be exercised in interpreting the topography data. Quantitative analysis of the penetration explains counterintuitive effects such as the larger tunneling current on the BN monolayer than on graphene and the general topography inversions for atomic monolayers. This suggests that a systematic study of tip-to-sample distance-dependent measurements is imperative for geometrically corrugated monolayers on metal surfaces or lateral heterostructuresFor the STM simulation, we use Cu(111) surface as a noble metal substrate for atomic-thickness materials. The surface is modeled as a slab geometry consisting of 21 layers of Cu thick enough to avoid any artificial interaction between the top and bottom surface states. Our calculations show that at the 32. Cu(111), graphene and BN are selected as prototypical models for metallic substrate, metallic and insulating 2D materials, respectively. Figure Figure mentclass2pt{minimmentclass2pt{minimNext, we investigate the characteristics of the tunneling current as a function of entclass1pt{minimaentclass1pt{minima33 graphene, respectively. The former is still observable at larger LDOS of graphene on Cu(111) at 30 shows a discrepancy between the STM topography and actual atomic geometry31. Our results shed light on why the measured topography of BN looks much higher (> 0.3 Å) in spite that the calculated 2D material-Cu distances  from the following analysis. The wavefunction in the vacuum is entirely determined by its energy and planar boundary conditions. Explicitly, the Bloch wavefunction s and pz for Cu and graphene states, respectively, that xy plane; the smallest Fourier component becomes a Bloch wavevector This is the decay constant of the incident 1D wave with energy 35 and the charge transfer from Cu(111) to graphene tends to decrease the shift. Graphene on Cu presses the top surface states of the substrate, leading to an increase in the energy of the top surface states by 0.13 eV at -1 and are shown in Fig. Figure mentclass2pt{minimmentclass2pt{minim2 may have similar properties with graphene , but due to the thickness of them (3 atomic layers), the dominant contribution of substrate states seems to be not so drastic.We have shown that the Bloch wavevectors of the surface wavefunctions determine the decay rates into the vacuum and that the large Bloch wavevectors of the graphene states cause their electronic structure buried by Cu states. We can assume that this also occurs on other noble metal surfaces or on other crystallographic orientations without surface states such as Cu(100), given that the decay rate is related only to the energy and Bloch wavevectors of the states. In general, the intrinsic electronic structures of 2D materials on a metal substrate can only be observed if they exhibit small planar Bloch wavevectors at the given bias voltage. For graphene, the states between − 7.5 and − 2 eV from the Fermi level are the case, and they are well reflected in the LDOS as demonstrated in Sect. 2 of Supplementary information. Some semiconducting transition metal dichalcogenides such as monolayer H-MoTeWhile the substrates states dominate in the tunneling current, the STM topography resembles the structure of the 2D material on the substrate. This is due to the underlying potential of the 2D material that leads to the in-plane variations of penetrating states from the substrate. Graphene, as an example, generates a spherical potential that is hexagonally arranged at the atomic sites. This potential modulates the penetrating substrate states of Cu, and amplifies them near the potential minimum generated by the 2D material (graphene). This enhancement of the substrate states (black and blue solid lines) is clearly seen in Fig. d increases slowly, and compares them with the actual changes in the distance profile (red dashed lines). Here, the bias voltage is set to be 0.5 V and the topographies (black solid lines) are drawn for five selected current settings corresponding to –5 e/Å3).Next, we discuss the implication for STM measurements of the substrate states that last longer than the 2D material states as they decay into the vacuum. One of the most noticeable consequences of prolonged substrate states is a topography inversion in STM measurements, where the measured topography of a 2D material on a substrate does not correspond to its actual geometric corrugation and even it is reversed, i.e., a peak looks like a valley and vice versa. As an example, Fig. d are mostly negative, which means that the apparent height is even the opposite of the actual atomic geometry. The integrated LDOS around BN comes entirely from Cu states, so the asymmetry of the two peaks, which is the distinguishing feature between Cu states and For graphene, the apparent height variation is much less than the actual geometric variation. For -1), while the relative magnitudes are material specific. We also confirm that topography inversion occurs for Ar monolayer by comparing the integrated LDOS’s of two Ar monolayers (third column) with Finally, we explain the origin of the large tunneling current of an insulator, which is larger than that of a semimetal as graphene at a large distance  or electronically (Moiré pattern) that the corrugation design has been suggested as a new control knob modifying the electronic and chemical properties of parent materials. For those materials, our findings can not only reconcile apparent contradictions between STM topography and atomic structures obtained with first-principles calculations or atomic force microscopy but also help decoupling of intrinsic 2D material properties from the substrate effects.36 and the projector augmented wave method for ionic potentials37 as implemented in the Vienna Ab Initio Simulation Package38. The energy cutoff of planewave basis is 400 eV. Within the Tersoff-Hamann approximation32, STM topographies are obtained by the real space summation of wavefunctions in the specific energy range and the wavefunctions are densely sampled (192 × 192 points for 5.387 Å2 of 2D cell or denser) in the 2D Brillouin zone. For a standard energy cutoff setting of planewave basis (< 600 eV), the exponentially decaying tails of ~ 3 Å above the 2D material are overwritten by numerical noise. To overcome this technical issue, analytical extrapolations are applied to the wavefunctions to achieve accurate LDOS. Details can be found in the Sect. 3 of Supplementary Information.Our calculations and analysis are based on the first-principles density functional theory (DFT) employing the Perdew–Burke–Ernzerhof exchange–correlation functionalSupplementary Information.


The alloy morphology, composition, and microstructure were characterized via scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDS), and X-ray diffraction (XRD), respectively. Subsequently, the CuxNix1− alloy thin films were used to grow graphene in a CH4-Ar-H2 ambient at atmospheric pressure. The underlying rationale is to adjust the CuxNix1− composition to control the graphene. Energy dispersive x-ray spectroscopy (EDS) analysis revealed that a continuous gradient of CuxNix1− (25 at. % < x < 83 at.%) was initially achieved across the 100 mm diameter substrate (~0.9%/mm composition gradient). The XRD spectra confirmed a solid solution was realized and the face-centered cubic lattice parameter varied from ~3.52 to 3.58 x > 69 at.%, bilayer growth dominates from 48 at.% < x < 69 at.%, and multilayer (≥3) growth occurs for x < 48 at.%, where x is the Cu concentration. Finally, a large area of bi-layer graphene was grown via a CuxNix1− catalyst with optimized catalyst composition and growth temperature.We synthesized a combinatorial library of Cu The exceptional properties of graphene such as its good carrier mobility, optical properties (transparent), thermal conductivity, and carrier-density make it an ideal two-dimensional 2-D) material for various electronic, optical, and sensor applications ,2,3. The-D materi2/CH4 ratio in low-pressure CVD processes. Bilayer graphene flakes produced by mechanical exfoliation and transfer process from bulk crystals yield domain sizes which are generally tens of micrometers [Metal catalysts perform an essential role in governing the graphene growth quality, domain size, rate of production, and number of graphene layers ,8,9,10. rometers ; howeverrometers . xNix1− alloy has been reported [xNix1− alloy of appropriate composition one can regulate the graphene layer numbers during CVD graphene growth. Mechanistically it has been shown that the underlying growth mechanism for CVD graphene includes dehydrogenation reaction of the gaseous source of carbon precursors (CH4), diffusion of molecules (substrate), dissolution in CuxNix1− alloy solid solution, and precipitation of carbon atoms (supersaturation) at defect locations such as grain boundaries on the surface (equilibrium graphene growth) upon cooling [Recently, bi-layer and multi-layer CVD graphene growth on Cureported ,19,20,21reported . Hence,  cooling ,23 due txNix1− alloy composition on the number of layers of graphene by thermal CVD process has been studied including bi-layer Cu/Ni and Cu-Ni solid solutions [While there have been numerous studies using CuNi alloys to grow multilayer graphene, large area coverage of high-quality bi-layer graphene remains a challenge ,19,20,21olutions ,19,20,21olutions . AlthougxNix1− alloy range for achieving predominantly monolayer, bilayer, and multilayer (≥3 L) CVD graphene. RF magnetron sputtering of a thin-film CuxNix1− gradient alloy catalyst (~2 μm thick) on Si/SiO2 substrate was performed. Graphene was subsequently grown on the gradient catalyst by an atmospheric pressure CVD process. By employing Raman spectroscopy, we showed that the gradient catalyst is indeed effective at growing a gradient in graphene thickness during a single CVD growth and therefore can serve as a method to rapid prototype recipe/catalyst combinations to grow graphene of controllable thickness. The resultant graphene grown on the gradient catalyst enabled compositions to be identified which yield predominantly monolayer (>85% Cu), bilayer (~61% Cu), and multilayer coverage (<49% Cu). Finally, we demonstrated a final proof-of-concept that combinatorial sputtering can be used to sputter a catalyst alloy of uniform composition which is optimized to produce large-area bilayer graphene. In this study, we experimented with regulating CVD graphene layers and investigating the appropriate at.% of CuxNix1− alloy gradients were grown on silicon dioxide (SiO2)/silicon (Si) substrates via a combinatorial co-sputtering process [−7 Torr and backfilled with Ar to 5 mTorr. The 2-inch diameter targets were mounted and tilted such that when the substrate was not rotated, a composition gradient was achieved across the 100 mm diameter substrate. To achieve an initial composition CuxNix1− gradient of 25 at.% < x < 83 at.%, elemental Cu and Ni targets were first powered to 115 and 200 W, respectively, and sputtered for 66 min to achieve a ~1 μm thick film. A second, Cu-rich sample where 42 at.% < x < 94 at.% was co-sputtered with Cu and Ni sputtering powers 230 and 200 W, respectively, for 89 min to achieve a ~2 μm thick film. Finally, to investigate large-area bilayer graphene growth, a uniform ~2 μm (rotated substrate to eliminate composition gradient) Cu63Ni37 as catalyst film was co-sputtered for 180 min using Cu and Ni powers of 115 and 120 W, respectively. Thin-film (~1–2 µm thick) Cu process ,21,24. TxNix1− composition as a function of position on the substrate was determined via energy-dispersive X-ray spectroscopy (EDS). EDS was measured using an X-ray detector mounted on a Carl Zeiss EVO scanning electron microscope (SEM)  and spectra were obtained at 10 keV and a magnification of 10 kx. A Zeiss Auriga SEM was used to capture high magnification images of the CuxNix1− alloy catalyst before and after graphene growth. The CuxNix1− alloy catalyst. An Empyrean X’Pert3 materials research diffractometer (MRD) was used with the following instrument parameters: wavelength Cu Kα1 (α2 (X-ray diffraction (XRD)  of the as-deposited catalyst was measured at various positions on the substrate to correlate with the crystal structure of the catalyst and confirm the solid solution of the Cu2/Si wafers were placed into the furnace on a quartz pallet. The ends of the CVD tube were sealed by flanges and cooled by convection of ambient air. Graphene growth was achieved by flowing non-flammable stock gas mixtures including 5% CH4 in Ar and 2.5% H2 in Ar. The growth was conducted with the following steps: the first was a flow of 5 L/min of 2.5% H2 in Ar to purge the quartz tube. While flowing 5 L/min of 2.5% H2 in Ar, all three zones of the furnace were ramped to 1000 °C to anneal the catalyst for 1 h. Next, the carbon precursor was introduced by flowing 0.07 L/min and 0.25 L/min CH4 in Ar for all subsequent growths with the co-flow of 5 L/min of 2.5% H2 in Ar. After 30 min growth in the co-flow of 0.25 L/min CH4 in Ar with 5 L/min of 2.5% H2 in Ar, the heating elements were powered down and the furnace was opened to rapidly cool the samples. Co-flow of H2 and CH4 in Ar was maintained during cooling. All CVD experiments were conducted in a 3-zone furnace equipped with a 6” diameter quartz tube. The combinatorial CuNi catalysts sputtered on SiO2/Si wafer. For transfer, the graphene was spin-coated with a mixture of PMMA/anisole at 2000 rpm. The PMMA was cured at 150 °C on a hot plate. The CuNi catalyst was etched away by suspending the PMMA/Graphene/CuNi/Si samples on the surface of an aqueous (NH4)2S2O8 etchant. Surface tension caused the samples to float on the surface of the etchant and the CuNi etch slowly progressed from the edge of the samples. After the CuNi layer was etched away, the Si wafer fell to the bottom of the etching solution and left the PMMA/graphene floating on the surface. The graphene/PMMA was then rinsed in three water baths to remove residual etchant. The graphene/PMMA was then scooped onto a 300 nm SiO2/Si wafer. After water dried from the graphene/Si wafer interface, the sample was submerged in acetone to dissolve the PMMA film and rinse in IPA.Graphene was grown on the CuNi catalyst which was on a SiOThe growth quality of graphene layers is determined by performing Raman spectroscopy measurements. Raman spectra were obtained on a Renishaw inVia Qontor Raman microscope  using a 532 nm laser for excitation. Raman mapping was constructed with Wire V5.0 software. Optical images were collected in a Keyence VHX 7000 series microscope. xNix1− alloy is a function of the composition of CuxNix1− alloy. The focus here was to determine compositions of this alloy that provides the most uniform and coherent single, bilayer, and multilayer graphene growth. The rationale was to adjust the catalyst alloy’s carbon solubility such that the graphene layer number varies at ~1000 °C growth. The combinatorial co-sputtering process in which a wide composition space of the CuxNix1− alloy is obtained is schematically illustrated  orientation  reflection at higher angles—not shown) and, as expected, the diffraction intensity of the as-deposited films were much lower and broader than the annealed films. The two compositions had the expected peak shifts associated with the CuxNix1− solid solution, where higher Cu led to a shift to lower 2θ. After heat treatment, the peaks narrowed, and the intensity grew consistent with the observed recrystallization and grain growth demonstrated in the SEM images. Another peak emerges in the annealed films at approximately 51 degrees, which are consistent with a CuNi-silicide and/or Ni-silicide which result from reactions with the substrate. Comparing the as-deposited and annealed films, there was also a shift to higher 2θ after annealing, which was consistent with either relief of compressive stress in the films. The as-deposited near equiatomic film had a lower intensity in both the as-deposited and annealed film, which was consistent with a decrease in the melting point at a higher Cu in the solid solution. Consistent with this observation, the observed grain size distribution was also slightly higher in the annealed SEM image of the higher Cu content film.To understand how the temperature ramp/soak before graphene growth affects the composition and microstructure of the film, we measured the composition, microstructure, and crystal structure of the film at two compositions as deposited and after the heat treatment (60 min at 1000 °C). Cu) at 1000 °C which is approximately 1 × 10−13 m2/s [x) was given by x = (4Dt)0.5. Thus, during the total high-temperature exposure, this suggested a mean displacement distance of approximately 465 μm. Thus, as realized by our EDS results in As the graphene growth is at a high temperature and the total high-temperature exposure time is on the order of ninety minutes, the question arises if this exposure affects the induced gradient. As an estimate of the diffusion distance, we used the Cu self-diffusion coefficient  and also increased the 5% CH−1, consistent with monolayer graphene. As the Cu concentration is decreased (<69%), multiple peaks emerge, centered at 2668 and 2710 cm−1, which are consistent with the emergence of bilayer and multi-layer graphene. Clearly, the 2D/G ratio  concentration typically results in large variations in graphene thickness and non-uniformity. Furthermore, graphene is often thicker at grain boundaries because these regions have high carbon mobility and results in more graphene precipitation during cooling ,22. As tTo demonstrate the utility of the rapid materials discovery process, we subsequently grew a full wafer with a copper composition of ~63 at.% and transferred the large-area graphene layer to a silicon wafer. A low magnification optical micrograph of a graphene film grown was shown a, left. xNi1-x alloy on Si/SiO2 substrate. This approach produced a thin-film CuxNix1− alloy catalyst with a gradient in composition from 25 at.% < x < 83 at.% across the 100 mm diameter substrate (~0.87%/mm composition gradient). While keeping growth conditions constant, the graphene synthesized on the gradient catalyst ranged from monolayer graphene on the Cu-rich side, to multilayer graphene on the Ni-rich side. A composition of ~61 at.% Cu yielded predominantly bi-layer coverage. A wafer-scale CuxNix1− alloy was grown with uniform ~63 at.% Cu concentration using the co-sputtering technique. This catalyst yielded wafer-scale CVD graphene with >70% coverage being bi-layer graphene. The combinatorial sputtering approach provides a technique to rapidly study alloy catalyst composition for CVD processes. Additionally, the combinatorial sputtering approach can be used to study catalysts for CVD processes beyond graphene, such as carbon nanotubes and hexagonal boron nitride.The number of graphene layers grown in a CVD process is typically tuned by the growth catalyst as well as growth parameters such as temperature, gases, and duration of the growth process. We were able to fine-tune the number of graphene layers produced by combinatorically sputtering a thin film of Cu


However, as the irradiation time increased to 5 min, the friction force increased rapidly with the aging effect and the breakdown of sp3-like bonds between the graphene–substrate interface. This study presents a new method of controlling nanofriction on graphene based on UV irradiation-sensitive posterities in vacuum conditions, which is essential to the application of two-dimensional materials in aerospace equipment, to improve anti-aging properties and wear reduction.The friction characteristics of two-dimensional materials in the ultraviolet (UV) radiation environment are important to the reliability of two-dimensional material nano-structures of space equipment. A novel mechanism of UV light-sensitive nano-friction on graphene was proposed by ultraviolet vacuum irradiation modification using an atomic force microscope (AFM). The surface roughness, adhesion force, and friction of graphene were gradually reduced over a time of irradiation below 3 min. UV185 passes through graphene and causes photochemical reactions between its bottom layer and Si/SiO Electronic components and microelectromechanical systems (MEMS) are important components in the field of aerospace equipment. In the complex space environment, the stability and reliability of their hardware functions are challenged to the extreme. In a vacuum environment, wear and aging cause rapid MEMS actuator failure . GraphenThe frictional properties of functionalized modified graphene have expanded its application in various fields. Among them, after plasma modification and chemical modification, light irradiation modification ,5 is anoIn this paper, the graphene samples were modified via UV vacuum irradiation with different durations. AFM was used to study the effect of UV vacuum irradiation time on the tribological properties of graphene, and to explore the mechanism and method of UV vacuum irradiation regulation on the nanofriction properties of graphene. This study contributes to the further application of graphene in of aerospace field.2) were used as the substrate. First, the silicon substrate was ultrasonically treated for 10~15 min by acetone, ethanol, and deionized water to remove surface impurities, and then placed in a nitrogen environment for natural drying. After that, the graphene was stripped from the single-crystal graphite using a mechanical stripping method and transferred to SiO2/Si substrate, and then transferred to a drying oven at 80 °C for 1 min to weaken the van der Waals force between the graphene sheets and to improve the quality of the mechanical stripping method [2/Si substrate, and the corresponding topography of the red box region measured by AFM tapping mode is shown in In this study, dry oxide N-doped silicon wafers  as the radiation source. The working parameters are shown in An MFP-3D atomic force microscope (AFM) from Asylum Research was used in this study. The probe adopts a silicon probe with a tip of less than 10 nm (Multi75 Al-G Budget Sensors). The normal elastic coefficient of the probe cantilever is calibrated using the Sader thermal noise method, and the lateral sensitivity is determined using the non-contact calibration method. In order to study the effects of UV vacuum irradiation time on the tribological properties of graphene, the experiment must be carried out with the same probe under the same measuring environment conditions .The measurement experiment steps are as follows. First, find and determine the sample area of each experiment in the tapping mode to ensure that each experiment is in the same position. After calibrating the probe normal and lateral sensitivity of probe coefficients in contact mode, the adhesion force, friction–load relationship, and stick–slip characteristics in the 500 nm × 500 nm region were tested to determine the initial tribometric properties of graphene. A UV vacuum irradiation timing experiment was conducted, and an AFM characterization experiment was conducted immediately after irradiation. The specific measurement steps were the same as above. In the adhesive force test, 10 nN loads are taken uniformly, and the adhesive force values are taken from 5 different positions on the same sample and the average value is obtained. The morphology, roughness, adhesion force, friction force, and stick–slip characteristics of graphene under different UV vacuum irradiation durations were compared and analyzed, and the methods and mechanisms of regulating the friction characteristics of graphene were explored.−2 MPa), and modified by irradiation within 2 min in a UV ozone cleaning machine. This study defined the short-term experimental conditions at an ambient temperature of 25 °C ± 2 °C and humidity of 45% ± 5% R.H to pump the tube to vacuum  and scanning speed. The surface of thin graphene shows friction enhancement due to the folding phenomenon [2, forming several suspended bonds, such as hydroxyl and silyl, which increase the surface energy of the substrate and increase with the increase in the treatment time. Moreover, sp3-like bonds are generated in the vertical direction of the interface. Thus, the interface binding between graphene and the substrate is enhanced, and the ability of graphene to deform out of the plane is inhibited, which has a close impact on the surface friction properties of graphene. Other studies have shown that friction force is closely related to the contact mass between two objects, which indicates that friction characteristics can be regulated by changing the contact mass without changing the structural characteristics of the object surface [In order to reveal the effect of UV vacuum irradiation on the bonding strength between graphene and SiOenomenon ,18. Thisenomenon , then th surface ,20.The hydration electron: the REDOX potential is −2.9 V, and φ represents the quantum yield of each reaction .(2)O2→h3 formation is 0.009.In Formula (2), M stands for the remaining energy from other molecules in the environment, and the quantum yield of OGraphene has extremely high resistance to gas permeability and light transmission. So, in UV vacuum irradiation modification, when a portion of UV light is irradiated through graphene on the substrate, the trapped water molecules and oxygen atoms between the interface of the graphene and substrate undergo photochemical reactions (see Formulas (1) and (2)), such as hydrogen bonding, hydroxyl. It is the existence of this part of the hanging key that enhances the interaction between the graphene and basement. To some extent, the folding state of the transferred graphene is slowed down (as shown in Long-time UV vacuum irradiation provides graphene more energy absorption. Under the excitation of high energy, graphene produces partial defects and even layer reduction. As a kind of high-energy ray, ultraviolet ray has enough energy to cut the chemical bonds in most substances. When the ground state of a substance absorbs photons, it forms an excited state, which is followed by photophysical and photochemical reactions, resulting in photoionization, intramolecular rearrangement, free radical formation, and other phenomena . AlthougEtersoff is the potential energy between the graphene substrate and the silicon probe, RV is the repulsion and separation, AV is the attractive part, ijr is the distance between atom i and atom j in the graphene substrate or silicon probe, and ijb is the measure of bond order [LJE is the van der Waals potential between graphene and probe, the potential well depth = 8.0909 meV [ijr is the distance between graphene carbon atom i and probe silicon atom j.Vacuum irradiation is the aging of graphene, that is, the formation of surface defects of graphene after UV vacuum irradiation, as shown in nd order . There i0909 meV , the equThe site where graphene defects are formed has higher activity, which increases the interaction potential between the needle tip and the graphene interface. As a result, the needle tip needs more energy to overcome the high barrier when passing through the defect ,35, resu2 substrate, resulting in hydroxyl, carboxyl, and silanol suspension bonds and sp3-like bonds, which enhances the binding energy of graphene on the substrate, inhibits out-of-plane deformation, reduces the folding effect, and leads to the reduced roughness and improved friction properties of graphene; yet, after long-term UV vacuum irradiation, the surface morphology of the graphene produced an irregular mesh, and the surface was easily destroyed under shear force. At the same time, the surface friction increased obviously with the increase in UV vacuum irradiation time. Because of the strong absorption effect of graphene on UV254, the accumulation of high energy leads to the reduction in the bond energy of the graphene C-C bond until dissociation recombination occurs, resulting in a large number of vacancy defects on the surface of graphene, leading to surface aging and friction increasing.In this study, it was found that the height and surface roughness of thin graphene tended to decrease after short-term UV vacuum irradiation. Then, the friction also decreased with the increase in UV vacuum irradiation time, and the friction enhancement phenomenon in stick–slip behavior was significantly inhibited. The reason is that UV185 passes through graphene and causes photochemical reactions between its bottom layer and Si/SiOThe results of this research present a new method of UV vacuum irradiation modification of graphene. The bidirectional regulation of the tribological properties of graphene was achieved through UV vacuum irradiation. There are more potential oxidation mechanisms under UV irradiation in vacuum/air and more applications of this UV light-sensitive control method in graphene nanofriction as a carrier and lubricant for micro and nanosensors in aerospace equipment.