Chromatin accessibility mapping is a powerful approach to identify potential regulatory elements. A popular example is ATAC-seq, whereby Tn5 transposase inserts sequencing adapters into accessible DNA (‘tagmentation’). CUT&Tag is a tagmentation-based epigenomic profiling method in which antibody tethering of Tn5 to a chromatin epitope of interest profiles specific chromatin features in small samples and single cells. Here, we show that by simply modifying the tagmentation conditions for histone H3K4me2 or H3K4me3 CUT&Tag, antibody-tethered tagmentation of accessible DNA sites is redirected to produce chromatin accessibility maps that are indistinguishable from the best ATAC-seq maps. Thus, chromatin accessibility maps can be produced in parallel with CUT&Tag maps of other epitopes with all steps from nuclei to amplified sequencing-ready libraries performed in single PCR tubes in the laboratory or on a home workbench. As H3K4 methylation is produced by transcription at promoters and enhancers, our method identifies transcription-coupled accessible regulatory sites. Cells keep their DNA tidy by wrapping it into structures called nucleosomes. Each of these structures contains a short section of DNA wound around a cluster of proteins called histones. Not only do nucleosomes keep the genetic code organized, they also control whether the proteins that can switch genes on or off have access to the DNA. When genes turn on, the nucleosomes unwrap, exposing sections of genetic code called 'gene regulatory elements'. These elements attract the proteins that help read and copy nearby genes so the cell can make new proteins. Determining which regulatory elements are exposed at any given time can provide useful information about what is happening inside a cell, but the procedure can be expensive.The most popular way to map which regulatory elements are exposed is using a technique called Assay for Transposase-Accessible Chromatin using sequencing, or ATAC-seq for short. The 'transposase' in the acronym is an enzyme that cuts areas of DNA that are not wound around histones and prepares them for detection by DNA sequencing. Unfortunately, the data from ATAC-seq are often noisy , so more sequencing is required to differentiate between real signal and noise, increasing the expense of ATAC-seq experiments. Furthermore, although ATAC-seq can identify unspooled sections of DNA, it cannot provide a direct connection between active genes and unwrapped DNA.To find the link between unspooled DNA and active genes, Henikoff et al. adapted a technique called CUT&Tag. Like ATAC-seq, it also uses transposases to cut the genome, but it allows more control over where the cuts occur. When genes are switched on, the proteins reading them leave chemical marks on the histones they pass. CUT&Tag attaches a transposase to a molecule that recognizes and binds to those marks. This allowed Henikoff et al. to guide the transposases to unspooled regions of DNA bordering active genes. The maps of gene regulatory elements produced using this method were the same as the best ATAC-seq maps. And, because the transposases could only access gaps near active genes, the data provided evidence that genes switching on leads to regulatory elements in the genome unwrapping.This new technique is simple enough that Henikoff et al. were able to perform it from home on the countertop of a laundry room. By tethering the transposases to histone marks it was possible to detect unspooled DNA that was active more efficiently than with ATAC-seq. This lowers laboratory costs by reducing the cost of DNA sequencing, and may also improve the detection of gaps between nucleosomes in single cells. Identification of DNA accessibility in the chromatin landscape has been used to infer active transcription ever since the seminal description of DNaseI hypersensitivity by Weintraub and Groudine more than 40 years ago . BecauseFor all of these DNA accessibility mapping strategies, it is generally unknown what process is responsible for creating any particular accessible sites within the chromatin landscape. Furthermore accessibility is not all-or-none, with the median difference between an accessible and a non-accessible site in DNA estimated to be only ~20%, with no sites completely accessible or inaccessible in a population of cells . DespiteA popular alternative to DNA accessibility mapping for regulatory element identification is to map nucleosomes that border NDRs, typically by histone marks, including ‘active’ histone modifications, such as H3K4 methylation and H3K27 acetylation, or histone variants incorporated during transcription, such as H2A.Z and H3.3. The rationale for this mapping strategy is that the enzymes that modify histone tails and the chaperones that deposit nucleosome subunits are most active close to the sites of initiation of transcription, which typically occurs bidirectionally at both gene promoters and enhancers to produce stable mRNAs and unstable enhancer RNAs. Although the marks left behind by active transcriptional initiation ‘point back’ to the NDR, this cause-effect connection between the NDR and the histone marks is only by inference , and dirCleavage Under Targets and Tagmentation (CUT&Tag) method for antibody-tethered in situ tagmentation can identify NDRs genome-wide at regulatory elements adjacent to transcription-associated histone marks in human cells. We provide evidence that reducing the ionic concentration during tagmentation preferentially attracts Tn5 tethered to the H3K4me2 histone modification via a Protein A/G fusion to the nearby NDR, shifting the site of tagmentation from nucleosomes bordering the NDR to the NDR itself. Almost all transcription-coupled accessible sites correspond to ATAC-seq sites and vice-versa, and lie upstream of paused RNA Polymerase II (RNAPII). ‘CUTAC’ (Cleavage Under Targeted Accessible Chromatin) is conveniently performed in parallel with ordinary CUT&Tag, producing accessible site maps from low cell numbers with signal-to-noise as good as or better than the best ATAC-seq datasets.Here, we show that a simple modification of our ++ results in targeted cleavage, releasing the antibody-bound fragment into the supernatant for paired-end DNA sequencing. More recently, we substituted the Tn5 transposase for MNase in a modified CUT&RUN protocol, such that addition of Mg++ results in a cut-and-paste ‘tagmentation’ reaction, in which sequencing adapters are integrated around sites of antibody binding  method , in whic binding . In CUT&We have developed a streamlined version of CUT&Tag that eliminates tube transfers, so that all steps can be efficiently performed in a single PCR tube . HoweverTapestation profiles of libraries produced at home detected nucleosomal ladders down to 200 cells for H3K27me3 and nucleosomal and subnucleosomal fragments down to 2000 cells for H3K4me3 . SequencBecause the Tn5 domain of pA-Tn5 binds avidly to DNA, it is necessary to use elevated salt conditions to avoid tagmenting accessible DNA during CUT&Tag. High-salt buffers included 300 mM NaCl for pA-Tn5 binding, washing to remove excess protein, and tagmentation at 37°C. We have found that other protocols based on the same principle but that do not include a high-salt wash step result in chromatin profiles that are dominated by accessible site tagmentation .2 or removal of the pAG-Tn5 incubation solution and addition of 50 µL 10 mM TAPS pH8.5, 5 mM MgCl2. All other steps in the protocol followed our CUT&Tag-direct protocol  , we reasCleavage Under Targeted Accessible Chromatin (CUTAC).Using CUT&Tag, we previously showed that most ATAC-seq sites are flanked by H3K4me2-marked nucleosomes in K562 cells . HoweverWe confirmed the similarity between CUTAC and ATAC-seq by aligning H3K4me2 CUT&Tag and CUTAC datasets over peaks called from Omni-ATAC data . In a scTo further evaluate the degree of similarity between CUTAC and ATAC-seq, we aligned the ENCODE ATAC-seq dataset over peaks called using Omni-ATAC and CUTAC, where all datasets were sampled down to 3.2 million mapped fragments with mitochondrial fragments removed. Remarkably, heatmaps produced using either Omni-ATAC or CUTAC peak calls for the same ENCODE ATAC-seq data showed occupancy of ~95% for both sets of peaks  motifs scored as peaks of DNaseI sensitivity in K562 and HeLa cells. We excluded nucleosomal fragments by using only ≤120 bp fragments. We observed that 86% of the DNaseI hypersensitive CTCF sites are occupied by CUTAC signal relative to flanking regions , which sFraction of Reads in Peaks (FRiP), a data quality metric introduced by the ENCODE project  with similar values for tagmentation by removal . In add-back experiments, we measured lower FRiP values after stringent washing conditions, suggesting increased background.To evaluate signal-to-noise genome-wide, we called peaks using MACS2 and calculated the  project . For bot project . Using CWe also compared the number of peaks and FRiP values for CUTAC to those for ATAC-seq for K562 cells and observed that CUTAC data quality was similar to that for the Omni-ATAC method , better If low-salt tagmentation sharpens peaks of DNA accessibility because tethering to neighboring nucleosomes increases the probability of tagmentation in small holes in the chromatin landscape, then we would expect smaller fragments to dominate CUTAC peaks. Indeed this is exactly what we observe for heatmaps , tracks H3K4me2/3 methylation marks active transcription at promoters , which rTo determine whether CUTAC sites are also sites of transcription initiation in general, we aligned CUT&Tag RNA Polymerase II (RNAPII) Serine-5 phosphate (RNAPIIS5P) CUT&Tag data over H3K4me2 CUT&Tag and CUTAC and Omni-ATAC peaks ordered by RNAPIIS5P peak intensity. When displayed as heatmaps or average plots, CUTAC datasets show a conspicuous shift into the NDR from flanking nucleosomes .Mammalian transcription also initiates at many enhancers, as shown by transcriptional run-on sequencing, which identifies sites of RNAPII pausing whether or not a stable RNA product is normally produced . AccordiThe correlation between sites of high chromatin accessibility and transcriptional regulatory elements, including enhancers and promoters, has driven the development of several distinct methods for genome-wide mapping of DNA accessibility for nearly two decades . HoweverThe mechanistic basis for asserting that H3K4 methylation is a transcription-coupled event is well-established . In all CUTAC also provides practical advantages over other chromatin accessibility mapping methods. Like CUT&Tag-direct, all steps from frozen nuclei to purified sequencing-ready libraries for the data presented here were performed in a day in single PCR tubes on a home workbench. As it requires only a simple modification of one step in the CUT&Tag protocol, CUTAC can be performed in parallel with an H3K4me2 CUT&Tag positive control and other antibodies using multiple aliquots from each population of cells to be profiled. We have shown that three distinct protocol modifications, dilution, removal and post-wash tagmentation yield high-quality results, providing flexibility that might be important for adapting CUTAC to nuclei from diverse cell types and tissues.Although a CUT&Tag-direct experiment requires a day to perform, and ATAC-seq can be performed in a few hours, this disadvantage of CUTAC is offset by the better control of data quality with CUTAC as is evident from the large variation in ATAC-seq data quality between laboratories . In contNIH 4D Nucleome guidelines. All tested negative for mycoplasma contamination using a MycoProbe kit.Human K562 cells were purchased from ATCC (CCL-243) and cultured following the supplier’s protocol. H1 ES cells were obtained from WiCell (WA01-lot#WB35186) and cultured following protocols.io.Log-phase human K562 or H1 embryonic stem cells were harvested and prepared for nuclei in a hypotonic buffer with 0.1% Triton-X100 essentially as described . A detaiprotocols.io. Briefly, nuclei were thawed, mixed with activated Concanavalin A beads and magnetized to remove the liquid with a pipettor and resuspended in Wash buffer . After successive incubations with primary antibody (1–2 hr) and secondary antibody (0.5–1 hr) in Wash buffer, the beads were washed and resuspended in pA(G)-Tn5 at 12.5 nM in 300-Wash buffer (Wash buffer containing 300 mM NaCl) for 1 hr. Incubations were performed at room temperature either in bulk or in volumes of 25–50 µL in low-retention PCR tubes. For CUT&Tag, tagmentation was performed for 1 hr in 300-Wash buffer supplemented with 10 mM MgCl2 in a 50 µL volume. For CUTAC, tagmentation was performed in low-salt buffer with varying components, volumes and temperatures as described for each experiment in the figure legends. In ‘dilution’ tagmentation, tubes containing 25 µL of pA(G)-Tn5 incubation solution and 2 mM or 5 mM MgCl2 solutions were preheated to 37°C. Tagmentation solution (475 µL) was rapidly added to the tubes and incubated for times and temperatures as indicated. In ‘removal’ tagmentation, tubes were magnetized, liquid was removed, and 50 µL of ice-cold 10 mM TAPS pH 8.5, 5 mM MgCl2 was added, followed by incubation for times and temperatures as indicated. The ‘post-wash’ protocol is identical to the CUT&Tag-direct protocol except that tagmentation was performed in 10 mM TAPS pH 8.5, 5 mM MgCl2 at 37°C as indicated. In ‘add-back’ tagmentation, the post-wash protocol was used with 10 mM TAPS pH 8.5, 5 mM MgCl2 supplemented with pA(G)-Tn5 and incubated at 37°C as indicated.CUT&Tag-direct was performed as described , except Following tagmentation, CUT&Tag and CUTAC samples were chilled and magnetized, liquid was removed, and beads were washed in 50 µL 10 mM TAPS pH 8.5, 0.2 mM EDTA then resuspended in 5 µL 0.1% SDS, 10 µL TAPS pH 8.5. Following incubation at 58°C, SDS was neutralized with 15 µL of 0.67% Triton-X100, and 2 µL of 10 mM indexed P5 and P7 primer solutions were added. Tubes were chilled and 25 µL of NEBNext 2x Master mix was added and vortexed. Gap-filling and 12 cycles of PCR were performed using an MJ PTC-200 Thermocycler. Clean-up was performed by addition of 65 µL SPRI bead slurry following the manufacturer’s instructions, eluted with 20 µL 1 mM Tris-HCl pH 8, 0.1 mM EDTA and 2 µL was used for Agilent 4200 Tapestation analysis. The barcoded libraries were mixed to achieve equimolar representation as desired aiming for a final concentration as recommended by the manufacturer for sequencing on an Illumina HiSeq 2500 2-lane Turbo flow cell.cutadapt 2.9 with parameters -q 20 -a AGATCGGAAGAGC -A AGATCGGAAGAGC. Paired-end reads were aligned to hg19 using Bowtie2 version 2.3.4.3 with options: --end-to-end--very-sensitive--no-unal--no-mixed--no-discordant--phred33 -I 10 - X 700. Tracks were made as bedgraph files of normalized counts, which are the fraction of total counts at each basepair scaled by the size of the hg19 genome. Peaks were called using MACS2 version 2.2.6 callpeak -f BEDPE -g hs -p le-5 –keep-dup all –SPMR. Heatmaps were produced using deepTools 3.3.1.For datasets from GEO with fragment read lengths ≥60 bp we ran th percentile of normalized count sums .To produce the scatterplot  and corrprotocols.io.A detailed step-by-step Data Processing and Analysis Tutorial can be found at  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jessica Tyler as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Charles G Danko (Reviewer #1); Junyue Cao (Reviewer #3).Thank you for submitting your article "Efficient transcription-coupled chromatin accessibility mapping in situ" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Essential revisions1) Reviewers #1 and #3 raise questions regarding the possibility of some accessible regions not being profiled by CUTAC and reviewer #1 suggests to analyze regions bound by CTCF to explore this possibility. Please include these or other analyses to address this point.2) Reviewers #1 and #3 wonder about the quantitative correlation in addition to the spatial overlap shown. Please include these analyses.3) While the experiments presented indicate a correlation between transcription and chromatin accessibility, the term "transcription-coupled" in the title implies that a causal link has been demonstrated, but this would require manipulations. We advise you to change the title to better reflect the content of the manuscript.Additional pointsWe also think that several of the other points made by the reviewers might help you strengthen this manuscript and encourage you to consider addressing them if possible. The full reviews are included below.Reviewer #1:This paper by the Henikoff lab introduces CUTAC, a molecular tool that allows users to sequence DNA inside nucleosome depleted regions accessible to transposition by a protein A (pA)-Tn5 fusion protein. CUTAC builds on the Henikoff lab's exciting new CUT&TAG method. Unlike the CUT&TAG protocol published recently, however, this new work uses low-salt conditions during tagmentation, which appears to promote Tn5 transposition in nucleosome depleted regions adjacent to the primary antibody. The data demonstrating that CUTAC favors transposition inside of nucleosome depleted regions is compelling and clearly shown. Moreover, the new method affords a substantial improvement in the resolution for active regulatory regions compared with CUT&TAG for histone modifications, comparable to that of high-quality ATAC-seq data. Compared with ATAC-seq, there are potentially several compelling advantages of CUTAC, including reproducibility, side-by-side library prep with CUT&TAG, and the possibility of being selective about which open chromatin regions are sequenced (see below). Between these advantages and the authors' past success and broad community interest in the CUT&RUN and CUT&TAG family of methods, I am in favor of publication. I have several comments for the authors:1) The authors' model is that the primary antibody recruits pA-Tn5 fusion, which then transposes DNA in adjacent accessible regions. However, not all nuclease accessible chromatin is marked by H3K4me2/3. Several lines of evidence suggest that, at least CTCF binding sites have a high level of DNase-I accessibility, but many lack histone modifications indicative of active enhancers/ promoters. Most of the previous work on this subject was done using DNase-I-seq, however presumably the same signal is true for ATAC-seq?! Assuming ATAC-seq shows the same signal, I am curious to know whether CUTAC data collected using K4me2/3 antibodies shows accessibility near CTCF binding sites. An easy way to get at this would be to center on CTCF sites, and break them into classes which do/ do not contain evidence of either K4me2/3 or transcription using ENCODE data. If a heatmap shows similar signal between ATAC and CUTAC at CTCF sites associated with K3me2/3, but only ATAC shows signal at CTCF sites not associated with these marks, then it implies a degree of specificity for open chromatin near the primary antibody as would be expected from the author's model.2) Selecting which open chromatin regions to measure could be an additional, compelling advantage of CUTAC over ATAC-seq. One could imagine, for instance, using CUTAC to find open chromatin near specific kinds of transcriptional co-activators or co-repressors, Pol II, or (possibly) transcription factors. I would imagine there are a range of applications that would benefit from something like this. Is it worth saying more about this? Or do the authors think that more exploration would be required before this could be stated with any certainty ?3) To what extent does CUTAC recover the quantitative amount of chromatin accessibility measured by ATAC-seq? Heatmaps suggest the two are highly correlated, as would be expected. It might be useful for readers to see scatterplots that show the correlation in integrated signal near peaks. Note that I would not necessarily expect the correlation to be perfect if there is some specificity for accessible chromatin near H3K4me2/3.4) In some parts of the text and Abstract, I came away with the impression that CUTAC involves both H3K4me2 and H3K4me3 primary antibodies in the same sample. Based on the main text, however, I think the authors are only using one of these two marks at a time. Please clarify.5) In Figure 5, please clarify aspects of the CUTAC experiment that were explored in earlier figures were used. Was it me2 or me3? With or without hexanediol or dimethylformamide?Reviewer #2:Life.In this manuscript Henikoff et al. present a modification of CUT&Tag, a method that they developed previously to profile chromatin epitopes genome-wide. Here, they show that CUT&Tag can be applied to profile transcription-couple chromatin accessibility sites by simply altering the salt concentration during tagmentation that changes the biochemical binding preferences of the pA-Tn5 transposase. The authors have creatively shown that this method can be performed at home to yield the same results as when performed in the lab, an interesting feature given the current restrictions on laboratory occupancy. The authors claim that while CUTAC takes longer to perform that ATAC-Seq, it gives better quality data than ATAC-seq based on the variation of ATAC-seq data quality between laboratories. The authors assume that all laboratories that perform ATAC-Seq are equally proficient in the technique and that variation is simply due to the technique itself and not the experimentalist. Therefore, it is unclear if CUTAC is indeed superior to ATAC-Seq. Together with the fact that CUTAC is a very minor modification of CUT&Tag, I am not convinced that it is a sufficient advance to warrant publication as a research tool in eReviewer #3:eLife, this work only has several points to be clarified as shown below:In this manuscript, Henikoff et al. developed a novel approach for in-situ mapping of transcription-coupled chromatin accessibility. Compared with conventional ATAC-seq, this method displays several unique advantages, including high sensitivity and compatibility with parallel Cut&tag profiling. I am rather enthusiastic about the release of this work. Also it is highly appreciated that the authors already uploaded the detailed protocol to protocol.io. For publication in 1) In Figure 1AB, CUT&Tag-direct with different starting nuclei numbers gave very different fragment size distributions. Is there a specific reason for this? How does the input nuclei/cell number affect the genome-wide signals?2) For the divergent outputs from CUT&Tag and CUTAC, the manuscript implies that this is due to different Tn5-DNA binding affinities between low and high salt conditions. Is it also possibly due to that the high salt simply broad the space between nearby nucleosomes for more efficient tagmentation?3) My major concern for using this approach as a substitution of ATAC-seq is that this method may introduce bias with the use of antibody linked Tn5. Are there enriched H3K4me2 signals in the peaks detected only by H3K4me2 CUTAC compared with peaks detected only by Omni-ATACseq?4) Figure 5 is helpful for evaluating technique efficiency and qualities. However, the number of peaks per mapped fragments is affected by the input cell/nuclei number and the library's complexity. It would be great if these comparisons are made based on the same number of input nuclei. This is also helpful for comparing the efficiencies of different approaches. Also, how similar is the CUTAC dataset compared with all other ATAC-seq datasets by correlation analysis?5) For the broad application of the technique, it would be great if the authors can compare the library preparation cost per sample between this technique and conventional ATAC-seq.eLife. Your revised article has been evaluated by Jessica Tyler (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Efficient chromatin accessibility mapping in situ by nucleosome-tethered tagmentation " for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:1) Regarding H3K4me2 and ATAC-seq, your response and the revised text state "Using an interval equal to average peak width at half-height, 51.3% of CUTAC and 50.0% of Omni-ATAC sites overlap ATAC_ENCODE peaks." As this was one of the three key requests of the reviewers, the analysis supporting these numbers should be shown, perhaps as additional panel to Figure 4 (ATAC encode peaks) or as a supplementary figure.2) In Figure 4F, the estimate of 90% coverage of these sites with CUTAC seems generous based on the heatmap. Could you add a horizontal line to clearly indicate where you believe the cutoff is? Also your response mention that you aligned Omni-seq to these sites but it's not shown in the new figure. The reviewer had specifically asked to compare H3K4me2 positive and negative in Omni-seq and CUT&tag.3) Did you submit Table 1? I could not find it. Essential revisions1) Reviewers #1 and #3 raise questions regarding the possibility of some accessible regions not being profiled by CUTAC and reviewer #1 suggests to analyze regions bound by CTCF to explore this possibility. Please include these or other analyses to address this point.We address these points with additional analyses detailed below. For CTCF, we show that CUTAC detects ~90% of CTCF DNaseI hypersensitive sites .2) Reviewers #1 and #3 wonder about the quantitative correlation in addition to the spatial overlap shown. Please include these analyses.We now provide these analyses, which are described below, including additional data .3) While the experiments presented indicate a correlation between transcription and chromatin accessibility, the term "transcription-coupled" in the title implies that a causal link has been demonstrated, but this would require manipulations. We advise you to change the title to better reflect the content of the manuscript.mapping is transcription-coupled, not chromatin accessibility itself. But we agree that the title can be misinterpreted in that way, and so we have changed it to “Efficient chromatin accessibility mapping in situ by nucleosome-tethered tagmentation”.What we meant by the title was that the Additional pointsWe also think that several of the other points made by the reviewers might help you strengthen this manuscript and encourage you to consider addressing them if possible. The full reviews are included below.We thank the reviewers for their many thoughtful comments and we have addressed each of them with new data and analyses together with textual changes as requested.Reviewer #1:This paper by the Henikoff lab introduces CUTAC, a molecular tool that allows users to sequence DNA inside nucleosome depleted regions accessible to transposition by a protein A (pA)-Tn5 fusion protein. CUTAC builds on the Henikoff lab's exciting new CUT&TAG method. Unlike the CUT&TAG protocol published recently, however, this new work uses low-salt conditions during tagmentation, which appears to promote Tn5 transposition in nucleosome depleted regions adjacent to the primary antibody. The data demonstrating that CUTAC favors transposition inside of nucleosome depleted regions is compelling and clearly shown. Moreover, the new method affords a substantial improvement in the resolution for active regulatory regions compared with CUT&TAG for histone modifications, comparable to that of high-quality ATAC-seq data. Compared with ATAC-seq, there are potentially several compelling advantages of CUTAC, including reproducibility, side-by-side library prep with CUT&TAG, and the possibility of being selective about which open chromatin regions are sequenced (see below). Between these advantages and the authors' past success and broad community interest in the CUT&RUN and CUT&TAG family of methods, I am in favor of publication. I have several comments for the authors:1) The authors' model is that the primary antibody recruits pA-Tn5 fusion, which then transposes DNA in adjacent accessible regions. However, not all nuclease accessible chromatin is marked by H3K4me2/3. Several lines of evidence suggest that, at least CTCF binding sites have a high level of DNase-I accessibility, but many lack histone modifications indicative of active enhancers/ promoters.2 = 0.53, see below), it is likely that technical variation alone can account for the degree of peak overlap we observed.The evidence that I am familiar with is based on H3K4me2 and H3K4me3 ChIP-seq signal, which is smeared-out relative to DNAseI or ATAC-seq signals, and so weak positives for ATAC-seq may be below the background level for ChIP-seq. The same relationship is seen when comparing CUTAC with CUT&Tag for H3K4me2, where the only difference is in the tagmentation buffer . To test whether there is a class of nuclease-accessible chromatin not detected by CUTAC we aligned H3K4me2 CUTAC and Omni-ATAC data over ATAC_ENCODE peaks. Using an interval equal to average peak width at half-height, 51.3% of CUTAC and 50.0% of Omni-ATAC sites overlap ATAC_ENCODE peaks. If there were a class of ATAC_ENCODE peaks not adjacent to H3K4me2, then these would show up as a lower percentage of overlap with CUTAC than with Omni-ATAC. In light of the moderate correlation between CUTAC and Omni-ATAC , and aligned Omni-ATAC and H3K4me2 CUTAC and CUT&Tag fragments in heatmaps. We excluded nucleosomal fragments by using only ≤120 bp fragments. We observed that ~90% of the DNaseI hypersensitive CTCF sites were enriched for CUTAC signal relative to flanking regions. This suggests equivalence of the CUTAC and DNaseI hypersensitive CTCF sites, and we have added a new paragraph and new figure panel  to this section. We also found that the H3K4me2 CUT&Tag sample showed detectable signal at only ~50% of the CTCF sites. This improvement in detection of CTCF sites by H3K4me2 CUTAC over H3K4me2 CUT&Tag is further evidence that the H3K4me2 mark on nucleosomes around hypersensitive sites, but is probably too weak to be detected above background in ChIP-seq experiments.We thank Reviewer 1 for suggesting this interesting analysis. To rigorously test the assertion that there are CTCF sites that show DNaseI accessibility but lack active histone marks, we used the set of 9403 19-bp CTCF motifs with a DNaseI hypersensitive site in both K562 and HeLa cells collected in an earlier study  Selecting which open chromatin regions to measure could be an additional, compelling advantage of CUTAC over ATAC-seq. One could imagine, for instance, using CUTAC to find open chromatin near specific kinds of transcriptional co-activators or co-repressors, Pol II, or (possibly) transcription factors. I would imagine there are a range of applications that would benefit from something like this. Is it worth saying more about this? Or do the authors think that more exploration would be required before this could be stated with any certainty ?We agree, and now mention this possibility to close the new paragraph describing the CTCF site comparisons: “This improvement in detection of CTCF sites by H3K4me2 CUTAC over H3K4me2 CUT&Tag illustrates the potential of using ≤120-bp CUTAC fragment data to improve the resolution and sensitivity of transcription factor binding site motif detection.”3) To what extent does CUTAC recover the quantitative amount of chromatin accessibility measured by ATAC-seq? Heatmaps suggest the two are highly correlated, as would be expected. It might be useful for readers to see scatterplots that show the correlation in integrated signal near peaks. Note that I would not necessarily expect the correlation to be perfect if there is some specificity for accessible chromatin near H3K4me2/3.2 = 0.53) on a log10 scale to capture the 5 order-of-magnitude dynamic range . We did not detect off-diagonal clusters that would indicate a subset of peaks found by one but not the other dataset. We now include a correlation matrix to more quantitatively illustrate the relationships between the H3K4me2 and H3K4me3 CUTAC and ATAC-seq datasets as requested by reviewer 3 .This is an excellent suggestion. Below is the requested scatterplot (R4) In some parts of the text and Abstract, I came away with the impression that CUTAC involves both H3K4me2 and H3K4me3 primary antibodies in the same sample. Based on the main text, however, I think the authors are only using one of these two marks at a time. Please clarify.Fixed (one at a time).5) In Figure 5, please clarify aspects of the CUTAC experiment that were explored in earlier figures were used. Was it me2 or me3? With or without hexanediol or dimethylformamide?It was H3K4me2 (fixed), but we now provide equivalent data for H3K4me3 showing the peak-narrowing effect of 1,6-hexanediol and corresponding improvements in peak-calling .Reviewer #2:In this manuscript Henikoff et al. present a modification of CUT&Tag, a method that they developed previously to profile chromatin epitopes genome-wide. Here, they show that CUT&Tag can be applied to profile transcription-couple chromatin accessibility sites by simply altering the salt concentration during tagmentation that changes the biochemical binding preferences of the pA-Tn5 transposase. The authors have creatively shown that this method can be performed at home to yield the same results as when performed in the lab, an interesting feature given the current restrictions on laboratory occupancy. The authors claim that while CUTAC takes longer to perform that ATAC-Seq, it gives better quality data than ATAC-seq based on the variation of ATAC-seq data quality between laboratories. The authors assume that all laboratories that perform ATAC-Seq are equally proficient in the technique and that variation is simply due to the technique itself and not the experimentalist. Therefore, it is unclear if CUTAC is indeed superior to ATAC-Seq. Together with the fact that CUTAC is a very minor modification of CUT&Tag, I am not convinced that it is a sufficient advance to warrant publication as a research tool in eLife.Nature consortium publication . The poorest K562 ATAC-seq dataset is from another recent Nature paper  performed by the same lab that is listed for generating the ENCODE data . It is hard to attribute this extreme variation in ATAC-seq data quality to experimentalist skill when both the excellent and the poor datasets are from the same expert group published a month apart. To further support our assertion that nucleosome tethering of Tn5 is advantageous over free Tn5 tagmentation, our revision now includes a comparison between CUTAC, an ATAC-seq dataset from my own lab (PMID: 31253573) and one from PMID: 32728249 for human H1 ES cells. We find that CUTAC (FRiP = 0.28) is better than our own ATAC-seq dataset (FRiP = 0.16) and much better than the H1 ATAC-seq dataset from PMID: 32728249 (FRiP = 0.014). To reduce the possibility that readers will overlook our comparisons between CUTAC and ATAC-seq from expert groups, we have promoted the table reporting these statistics from part of a supplement to Table 1 in the main body of the paper.From a user’s perspective, the fact that a minor modification converts a method for mapping specific chromatin features into one for precisely mapping accessibility in the same experiment is a major advantage. As for the question of experimentalist proficiency, we also showed that CUTAC at home outperforms ATAC-seq generated by the ENCODE project their recent Reviewer #3:In this manuscript, Henikoff et al. developed a novel approach for in-situ mapping of transcription-coupled chromatin accessibility. Compared with conventional ATAC-seq, this method displays several unique advantages, including high sensitivity and compatibility with parallel Cut&tag profiling. I am rather enthusiastic about the release of this work. Also it is highly appreciated that the authors already uploaded the detailed protocol to protocol.io. For publication in eLife, this work only has several points to be clarified as shown below:1) In Figure 1AB, CUT&Tag-direct with different starting nuclei numbers gave very different fragment size distributions. Is there a specific reason for this? How does the input nuclei/cell number affect the genome-wide signals?To explain this difference, we added the following sentence to the Figure 1 legend: “The higher yield of smaller fragments with decreasing cell number suggests that reducing the total available binding sites increases the binding of antibody and/or pAG-Tn5 in limiting amounts.”2) For the divergent outputs from CUT&Tag and CUTAC, the manuscript implies that this is due to different Tn5-DNA binding affinities between low and high salt conditions. Is it also possibly due to that the high salt simply broad the space between nearby nucleosomes for more efficient tagmentation?No, because the CUTAC signal is centered over annotated NDRs, not over nucleosomes marked by H3K4me2. To better illustrate that the shift is due to low-salt tagmentation and not to mobility of marked nucleosomes with 300 mM NaCl, we show that the mapping of H3K4me2 by CUT&Tag is similar to mapping by ENCODE ChIP-seq where no such mobility is possible. We now make this point in the text and in the new Figure 6—figure supplement 1.3) My major concern for using this approach as a substitution of ATAC-seq is that this method may introduce bias with the use of antibody linked Tn5. Are there enriched H3K4me2 signals in the peaks detected only by H3K4me2 CUTAC compared with peaks detected only by Omni-ATACseq?This point was raised by reviewer 1 and clarified above, including the new analysis showing CUTAC detection of ~90% of annotated DNaseI hypersensitive CTCF sites .4) Figure 5 is helpful for evaluating technique efficiency and qualities. However, the number of peaks per mapped fragments is affected by the input cell/nuclei number and the library's complexity. It would be great if these comparisons are made based on the same number of input nuclei. This is also helpful for comparing the efficiencies of different approaches.This issue is addressed in the expanded Figure 3—figure supplement 1, where we have compared nuclei from 30,000 cells for CUTUC using each of the three tagmentation variations to ATAC-seq with 50,000 nuclei and show the estimated size of each CUTAC library produced.Also, how similar is the CUTAC dataset compared with all other ATAC-seq datasets by correlation analysis?As described in response to reviewer 1, we now include a representative scatterplot and a correlation matrix (shown above) to more quantitatively illustrate the relationships between the H3K4me2 and H3K4me3 CUTAC and ATAC-seq datasets.5) For the broad application of the technique, it would be great if the authors can compare the library preparation cost per sample between this technique and conventional ATAC-seq.For each CUTAC sample in a 32-sample experiment over an 8 hour day  we used 0.25 µL primary and 0.25 µL secondary antibody (at 1:100) and ~$10 for Epicypher pAG-Tn5 per 25 µL incubation volume, twice that for 50 µL sample volumes. Other materials, such as Concanavalin A and SPRI paramagnetic beads (~$1 per sample for both), pipette tips (~$1 per sample) and reagents increase the cost of library preparation to perhaps $15 per sample. Based on an ATAC-seq experiment from my lab , the cost of materials is similar, and both procedures would take about a full day if on the same scale starting with nuclei and finishing with sequencing-ready libraries . The 32-sample scale is typical for several antibodies multiplied by the number of CUT&Tag samples run in parallel with CUTAC, but is probably too high for ATAC-seq. However, the most important cost-differential is the number of sequencing reads that are required to call peaks, and this is where the number of peaks called (sensitivity) and the fraction of reads in peaks  make a big difference. For example, we sequenced to a depth of 4.5 million paired-end 25-bp reads for our published H1 ES cell ATAC-seq data , whereas for the H1 ES cell ATAC-seq data from PMID: 32728247 (~450 peaks with FRiP = 0.014 for 3.2 million fragments) the authors sequenced to a depth of 45 million paired-end 100-bp reads. Our in-house sequencing cost per sample was ~$25 per sample, which exceeds the estimated cost of library preparation.The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:1) Regarding H3K4me2 and ATAC-seq, your response and the revised text state "Using an interval equal to average peak width at half-height, 51.3% of CUTAC and 50.0% of Omni-ATAC sites overlap ATAC_ENCODE peaks." As this was one of the three key requests of the reviewers, the analysis supporting these numbers should be shown, perhaps as additional panel to Figure 4 (ATAC encode peaks) or as a supplementary figure.We have added Figure 4—figure supplement 2 with heatmaps centered over the ATAC_ENCODE peaks made from each of the data files used for the Figure 4 heatmaps.2) In Figure 4F, the estimate of 90% coverage of these sites with CUTAC seems generous based on the heatmap. Could you add a horizontal line to clearly indicate where you believe the cutoff is? Also your response mention that you aligned Omni-seq to these sites but it's not shown in the new figure. The reviewer had specifically asked to compare H3K4me2 positive and negative in Omni-seq and CUT&tag.We have inserted arrowheads on the left of each heatmap to indicate the cutoffs. We have also added the corresponding ≤120 bp heatmaps for Omni-ATAC and ATAC_ENCODE in Figure 4F. The precise cutoffs are: H3K4me2 CUT&Tag (53%), CUTAC (86%), Omni-ATAC (82%) and ATAC_ENCODE (55%), and we have modified the article file and the response accordingly. To avoid confusion, we removed the >120 bp heatmaps, which had been excluded from this analysis, as pointed out in the text.3) Did you submit Table 1? I could not find it.We apologize for the inadvertent omission. Table 1 is now in the article file.


The localized surface plasmons (LSPs) on the NPG sheet, partially hybridized with graphene phonons and surface phonons of the neighboring materials, allow for the control and tuning of the thermal emission spectrum in the wavelength regime from We present a proof of concept for a spectrally selective thermal mid-IR source based on nanopatterned graphene (NPG) with a typical mobility of CVD-grown graphene (up to 3000  Planck’s law describes quantitatively the energy density c is the speed of light in vacuum, u can then be obtained by integrating over all frequencies and angles over the half-sphere, leading to the Stefan-Boltzmann law for the energy density of black-body radiation,P/A of a black-body isAn object that is kept in equilibrium at a given temperature 1. Another method is to use structural optical resonances to enhance and/or suppress the emittance. Recently, photonic crystal structures have been used to implement passive pass band filters that reflect the thermal emission at wavelengths that match the photonic bandgap3. Alternatively, a truncated photonic crystal can be used to enhance the emittance at resonant frequencies5.In recent years, several methods have been implemented for achieving a spectrally selective emittance, in particular narrowband emittance, which increases the coherence of the emitted photons. One possibility is to use a material that exhibits optical resonances due to the band structure or due to confinement of the charge carriers7. In order to avoid the breakdown of the graphene sheet at around 9. In this way, the graphene sheet can be heated up to 9, or even above 10. Kim et al. and Luo et al. demonstrated broadband visible emission peaked around a wavelength of 9. By using a photonic crystal substrate made of Si, Shiue et al. demonstrated narrowband near-IR emission peaked at around 10. To the best of our knowledge, there are neither theoretical nor experimental studies on spectrally selective thermal emission from graphene in the mid-IR range.Recent experiments have shown that it is possible to generate infrared (IR) emission by means of Joule heating created by means of a bias voltage applied to graphene on a a in a graphene sheet. Therefore let us focus first on the optoelectronic properties of a single hole.Here, we present the proof of concept of a method to tune the spectrally selective thermal emission from nanopatterned graphene (NPG) by means of a gate voltage that varies the resonance wavelength of localized surface plasmons (LSPs) around the circular holes that are arranged in a hexagonal or square lattice pattern in a single graphene sheet in the wavelength regime between 3 and 12 x and y of the elliptic hole, and x- and y-direction readxy-plane. a and b are the length and the width of the elliptical wormhole, as shown in Fig. d of the graphene sheet is much smaller than the size of the elliptic hole. The geometrical factors in this limit area the polarizability simplifies toThe frequency-dependent dipole moment of the hole is[see Eq. ]. The po12Using the linear dispersion relation, the intraband optical conductivity is14,k-dependent and nonlocal. Also, below we will conjecture that the diffusion length D must be frequency-dependent. The effect of the hydrodynamic correction on the LSP resonances at around It is well-known by now that hydrodynamic effects play an important role in graphene because the Coulomb interaction collision rate is dominant, i.e. 12d is the thickness of graphene. Inserting this formula into Eq. . Consequently, we obtain the same reflection and transmission amplitudes as in Ref.15, i.e.1815Let us now consider the 2D array of circular holes in a graphene sheet. Since the dipole moments L. A including cavity, i.e.If we include also the whole substrate including cavity and Au mirror, we need to sum over all possible optical paths in the Fabry-Perot cavity, yielding19. The standard  fluctuation-dissipation theorem for fluctuating currents Using these results, let us consider the excitation of the graphene sheet near the hole by means of thermal fluctuations, which give rise to a fluctuating EM field of a localized surface plasmon (LSP). This can be best understood by means of the fluctuation-dissipation theorem, which provides a relation between the rate of energy dissipation in a non-equilibrium system and the quantum and thermal fluctuations occuring spontaneously at different times in an equilibrium systemUsing the method of dyadic Green’s functions, it is possible to express the fluctuating electric field generated by the fluctuating current density byConsequently, we haveThen, the fluctuation-dissipation theorem can be recast into the formN is the number of holes. In order to obtain the spectral radiance in the far field, we need to integrate over the spherical angle. Using the results from the Supplementary Information, we obtain20Since the energy density of the emitted electric field at the point mentclass2pt{minimUsing FDTD to calculate the emittance g to Eq.  for the mentclass2pt{minimmentclass2pt{minimLet us consider the dependence of the thermal emission of NPG on the angle 21. After theoretical calculations predicted that long-range coherence may exist for thermal emission in the case of resonant surface waves, either plasmonic or phononic in nature23, experiments showed that a periodic microstructure in the polar material SiC exhibits coherence over many wavelengths and radiates in well-defined and controlled directions24. Here we show that the coherence length of a graphene sheet patterned with circular holes can be as large as 150 However, the assumption that thermal emission of radiation is incoherent is not always true. Since Kirchhoff’s law is valid, thermal sources can be coherent25. First, we choose the nonlocal hydrodynamic response function in Eq. (D, which according to Ref.13 would be 27. Thus, we conclude that the hydrodynamic diffusion length must be frequency-dependent with D into the hydrdynamic regime when the frequency is reduced below around The coherence of thermal emission can be best understood by means of a nonlocal response functionn in Eq. . Using tn in Eq. , we obta27. This coherence length would allow to preserve coherence for a linear array of period Next, let us consider the coherence of thermal emission by means of the nonlocal optical conductivity in the RPA approximation. Using the general formulamentclasspt{minimaIn the case of the LSP resonance for a square lattice of holes at 21, we can extract the coherence length The coherence length and time of thermally emitted photons is larger because the photons travel mostly in vacuum. Taking advantage of the Wiener-Kinchine theoremThus, the latter large coherence length allows for the coherent control of a 150x150 square array of holes with period The temporal control of the individual phases of the holes requires an extraordinary fast switching time of around 1 ps, which is not feasible with current electronics. However, the nonlocal response function reveals a spatial phase shift determined by the coherence wavenumber Due to the full control of directivity with angle of emission between 27, we are going to develop a mid-IR spectroscopy and detection platform based on patterned graphene that will be able to detect a variety of molecules that have mid-IR vibrational resonances, such as CO, 28. Therefore, we will make great effort to demonstrate that our platform will be able to detect with high sensitivity and selectivity the COVID-19 virus and other viruses that pose a threat to humanity.In conclusion, we have demonstrated in our theoretical study that NPG can be used to develop a plasmonically enhanced mid-IR light source with spectrally tunable selective thermal emission. Most importantly, the LSPs along with an optical cavity increase substantially the emittance of graphene from about 2% for pristine graphene to 80% for NPG, thereby outperforming state-of-the-art graphene light sources working in the visible and NIR by at least a factor of 100. Combining our proposed mid-IR light source based on patterned graphene with our demonstrated mid-IR detector based on NPG


Recently, the development of nanotechnology has bloomed in numerous industries. In this regard, graphene, a two-dimensional carbon nanomaterial, has been extensively researched, due to its high academic interests and commercial potential. Due to its unique structure and outstanding material properties, the research on graphene has enabled substantial progress in the development of electronics devices, thermoelectric appliances, optical devices, sensors, and energy harvesting applications. In addition, the mechanical properties, such as young modulus 1TPa), intrinsic strength (130 GPa), high stiffness, and fracture strain, make graphene a promising candidate for industries like nanocomposite coatings, paints, and bioapplications TPa, intr. This spGraphene shows numerous novel and inimitable physical properties, such as the anomalous quantum hall effect, the ambipolar electric field effect, Klein tunneling, and ballistic transport, due to its exclusive crystal and electronic structure. However, the band gap characteristics of graphene limits its applications and commercial interests. The properties of graphene can be tuned by altering its nanostructure in terms of boundary configuration, structural defects, chemical doping, and the formation of heterogeneous structures to meet the demands of the electronic, photo thermal, thermoelectric and photoelectric fields . High enThe special issue is completed with review papers ,3,8,9, w“Electronic and Thermal Properties of Graphene” compiles a series of original research articles and recent review papers to provide depth information on the development of graphene based next generation devices. We hope that this special issue will provide an interesting insight into the latest perspectives in this rapidly evolving industry.In summary, this special issue of nanomaterials entitled


This study investigated the effect of discontinuous, running-based RSH, on repeated-sprint treadmill performance in hypoxia in a team sport cohort, prior to international competition. Over a 6-week “in-season” period, 11 elite male players  completed eight multi-set RSH sessions on a non-motorized treadmill in a normobaric hypoxic chamber (FiO2 = 13.8%). Three testing sessions , involved three sets of 5 × 8-s sprints, with 52-s recovery between sprints and 4–5 min between sets. Training sessions  consisted of four to five sets of 4–5 × 8-s sprints. During testing sessions, maximum sprinting speed was recorded for each sprint with values averaged for each set. For each set, a peak speed and fatigue index were calculated. Data were compared using two-way repeated measures ANOVA (sessions × sets). Average speed per set increased between testing sessions , but not Session 5 , vs. Session 1 (24.2 ± 1.5 km.h−1). Peak sprinting speed in each set also increased across testing sessions  higher than Session 5  and Session 1 . Fatigue index differed between sessions  studies conducted “ The training group comprised four defenders, four midfielders, and three forwards. All players followed the same training/recovery program and had been training full time with the Malaysia national team for >9 months (world ranking #11). This protocol was conducted in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Ethical approval was not sought as these training sessions occurred as part of player's routine training with the national team, as part of their employment, with data retrospectively analyzed  and thermostatically controlled at an ambient temperature of 21°C and 45% relative humidity. Session 1 was replicated during Session 5 and Session 8, in order to assess training effectiveness.Over a 6-week “in-season” period, players completed eight repeated-sprint training sessions on an NMT  in a normobaric hypoxic chamber . Where possible, two training sessions per week were scheduled, integrated with other training requirements, competitive matches and travel. For all RSH sessions, environmental conditions were set to simulate an altitude of 3,000 m  harnessed between the scapulae in a customized sports vest  with statistical significance set at p < 0.05 and data presented as mean ± SD. Effect sizes for main effects and interaction effects are presented as partial eta squared .All outcome variables were assessed for normality and sphericity prior to further analysis. Performance outcomes such as average speed, peak speed, and fatigue index during sessions 1, 5, and 8 were analyzed using two-way repeated measures ANOVA. The average of each set of five sprints was entered into the ANOVA to identify differences between sets (within session) and between testing sessions. In light of the discontinuous prescription of RSH around international fixtures, a further two-way repeated measures ANOVA was completed on four sprints from the first three sets of sessions 1, 3, 6, and 8. Average speed per set and the fatigue index for each set were similarly entered into the ANOVA. Bonferroni correction was applied during Playerload (139 ± 24 a.u.) represented 31% of the average Match Playerload (452 ± 20 a.u.) and 32% of the average Hockey training Playerload (429 ± 124 a.u.), within this training period .The average RSH g period . Across p = 0.001, −1, +4 ± 3%, p = 0.005), but not during Session 5 , compared to Session 1 (24.2 ± 1.5 km.h−1). A statistically significant difference was not observed between Session 1 and Session 5 (p = 0.086). A main effect was also observed for average set speed between sets within testing sessions  to Set 2 , but not between Set 1 and Set 3  or between Set 2 and Set 3 (p = 1.000). No interaction effect (sessions × sets) was observed  was higher than during Session 1  and higher than Session 5 . A main effect was also observed for peak speed between sets within testing sessions , Set 2 (25.8 ± 1.4 km.h−1), or Set 3 (25.8 ± 1.2 km.h−1). No interaction (sessions × sets) was observed , and the smallest value in session 5 . Fatigue index did not differ between sessions 5 and 8 (p = 0.133). There was no main effect for fatigue index across sets within testing sessions , Session 5 (111 ± 12 a.u.), and Session 8 , session 6 , and session 8 , compared with session 1 (24.1 ± 1.6 km.h−1). No other differences between sessions were observed . A main effect was also observed for differences in average speed across sets, within sessions  to set 2  and from set 1 to set 3 , but not between set 2 and set 3 (p = 0.763). The sessions × sets interaction effect for average set speed was not significant . There was no main effect of fatigue index across sets  of weekly total training load.Our data support an emerging body of research pertaining to RSH as an effective training modality for team sports  in average speed during Session 8. This would appear a practically meaningful effect, as all completed sprints exceeded 19 km.h−1, which is commonly used as a sprinting threshold within field hockey  speeds . Ethical approval was not sought as these training sessions occurred as part of player's routine training with the national team, as part of their employment, with data retrospectively analyzed (Winter and Maughan, CJ and OG conceived and designed the study, analyzed and interpreted the data, drafted and revised the manuscript, and approved the final version of the manuscript. CJ collected the data. All authors contributed to the article and approved the submitted version.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. The reviewer MH declared a past co-authorship with one of the authors CJ to the handling editor.


Graphene materials have been chosen for their unique properties to be used either as electrocatalyst or as catalyst support  that develop adequate efficiencies for this reaction. This review focuses on comparing experimental and theoretical results of the electrochemical reduction reaction of carbon dioxide (ECO2RR) described in the scientific literature to establish a correlation between them. This work aims to establish the state of the art on the electrochemical reduction of carbon dioxide on graphene-based catalysts.The current environmental situation requires taking actions regarding processes for energy production, thus promoting renewable energies, which must be complemented with the development of routes to reduce pollution, such as the capture and storage of CO Over the last few decades, the drastic increase of carbon dioxide into the atmosphere has caused global warming and the acidification of the oceans, among other environmental problems, thus leading to climate change. The accelerated consumption of fossil fuels has been the priority factor in these problems, but it has not only caused a crisis in the environmental field but also brought an energy crisis. This is a fact that also affects health because air pollution is the sixth-leading cause of death, causing over two million premature deaths worldwide in addition to increasing asthma, respiratory diseases, diseases cardiovascular disease, cancer, etc. . To put 2, which help, together with natural processes , to balance the presence of CO2 in the atmosphere 17], In2O2O3 , and Pd-2O3  materialal steps . 3D-rGO oduction .2RR. For example, 2O supported on N-doped rGO (Cu2O/N-rGO) enhances the FE up to 70%, raises the production of ethylene at −1.4 V (vs. RHE), and increases the catalytic stability. Based on these results, the authors concluded that pyridine nitrogen improves the catalytic stability of Cu2O due to N (pyridine)–Cu interactions [The introduction of heteroatoms into the graphene structure may improve the catalytic activity towards the ECOractions .2RR. Nitrogen doping into graphene sheets results in the strong binding of CuSn NPs to the catalyst support. The strong binding prevents alloy NP detachment and agglomeration, thereby improving the long-term stability. Furthermore, electron transfer from NG to CuSn NPs appears to be beneficial for the adsorption of CO2 and/or hydrogen, thus enhancing the CO2RR performance [CuSn alloy nanoparticles supported on an NG material stand out due to their low cost and unique catalytic activities towards the ECOformance .0.175. This alloy generated C1 products with an FE close to 93% at −1.0 V (vs. RHE), which was the highest of all prepared materials. This led to considerable improvements over Cu and Sn separately, which were found to develop FEs of 32% and 58%, respectively [2 reduction were carried out ) for methanol production  that will help to improve environmental contamination.The conversion of COWith the aim to solve the principal catalytic problem at the cathodes of low temperature electrolyzers, the fundamental study of carbon dioxide, as well as hydrogen evolution on graphene-based materials in a wide pH range, was considered.2. However, in most cases, the activity of graphene not modified by metal species is very low. All the new advances in the fundamental understanding of reactions occurring on graphene-based catalysts presented here may help to improve the fabrication of novel electrodes in order to enhance the performance and decrease the cost of this technology.In general, the results discussed in this manuscript confirm that graphene materials are excellent supports, producing synergistic effects that cause improvements in the efficiency and selectivity of the catalysts used for the electrochemical reduction of CO


The integration of graphene into CMOS compatible Ge technology is in particular attractive for optoelectronic devices in the infrared spectral range. Since graphene transfer from metal substrates has detrimental effects on the electrical properties of the graphene film and moreover, leads to severe contamination issues, direct growth of graphene on Ge is highly desirable. In this work, we present recipes for a direct growth of graphene on Ge via thermal chemical vapor deposition (TCVD) and plasma-enhanced chemical vapor deposition (PECVD). We demonstrate that the growth temperature can be reduced by about 200 °C in PECVD with respect to TCVD, where usually growth occurs close to the melting point of Ge. For both, TCVD and PECVD, hexagonal and elongated morphology is observed on Ge(100) and Ge(110), respectively, indicating the dominant role of substrate orientation on the shape of graphene grains. Interestingly, Raman data indicate a compressive strain of ca. − 0.4% of the graphene film fabricated by TCVD, whereas a tensile strain of up to + 1.2% is determined for graphene synthesized via PECVD, regardless the substrate orientation. Supported by Kelvin probe force measurements, we suggest a mechanism that is responsible for graphene formation on Ge and the resulting strain in TCVD and PECVD. Graphene with a charge carrier mobility up to 10,000 cm2 V−1 s−16 and a sheet resistance down to 105 Ω/sq.7 has been demonstrated. In order to implement the graphene film into device technology, it must be transferred from the metal foil onto the desired substrate. This transfer process deteriorates the graphene film quality as a result of contamination from metal substrate8, etching agent9 and supporting polymer film11 and due to wrinkle formation12. This severely limits the use of transferred graphene layers in semiconductor device technology. Hence, direct fabrication of graphene on non-metal substrates like silicon (Si)13, germanium (Ge)17, silicon dioxide (SiO2)19 and sapphire (Al2O3)19 has become an alternative route in the last few years.Fabrication of large area graphene is well established on catalytic metal foils, such as copper (Cu)20 and thus, enables monolayer graphene synthesis21. The graphene flakes were reported to nucleate via covalent bonds at Ge dimer vacancies on a surface with (100) crystal orientation22. Di Gaspare et al.23 found a carbon precursor phase, which evolved to graphene domains through a recrystallization process with increasing growth time. By varying the amount of precursor, the morphology of graphene on Ge(100) can be changed from nanoribbons to round shaped flakes24. Persichetti et al.25 suggested that the elevated growth temperature is the critical parameter for reducing the amount of defects in the graphene film, which in turn increases the compressive strain in the film and induces surface faceting of Ge(100). A defect-free monolayer graphene film was demonstrated on a 200 mm Ge/Si wafer with (100) crystal orientation, on which a sheet resistance of 1,500 ± 100 Ω/sq. and a charge carrier mobility of 400 ± 20 cm2 V−1 s−1 were determined15. Lee et al.14 observed that elongated flakes were formed on Ge(110), which merged into a defect-free (ID/IG < 0.03) graphene film with a charge carrier mobility of 2,560 ± 460 cm2 V−1 s−1 and a sheet resistance between 2 kΩ/sq. and 5 kΩ/sq. The elongated shape of the graphene grains was related to the covalent bonds between edge atoms of Ge(110) and graphene nuclei at the early stage of growth26.Similar to Cu, Ge exhibits a self-limiting effect resulting from the low carbon solubility in Ge29 or passivation layers30. Hereby, different Ge platforms, like Ge on Si32 or Ge on insulator34 are used. It is noteworthy, that until now the synthesis of graphene on Ge has been established close to the melting temperature of Ge (937 °C)36. This leads to fundamental challenges in the process development. At these high temperatures, the doping profile of the functional Ge layer may change and/or Si may diffuse towards the surface in case of Ge on Si, forming SiC during graphene growth38. While PECVD has the potential to significantly reduce the growth temperature for depositing graphene on Ge, this approach has not yet been shown in literature up to now.The attractiveness of graphene integration into CMOS technology arises from a wide variety of potential applications, including photodetectors and modulators for the NIR regimeHere, we report on graphene synthesis on Ge at temperatures down to < 760 °C, i.e., almost 200 °C below the melting point of Ge, in a plasma enhanced CVD process. As a reference, TCVD growth is performed at elevated temperatures in the same reactor. By varying growth time and Ge crystal orientation, graphene flake morphology, grain size and strain properties of the films are studied. Based on our data, a model is suggested for graphene formation on Ge by TCVD and PECVD.4, 100 sccm of H2 and 450 sccm of Ar were introduced into the chamber at a total pressure of 750 mbar. The topography of the fabricated sample was analyzed by AFM  surface to reorganize along the (107) plane in order to minimize the surface free energy40. By a detailed AFM topography analysis, we extracted an average facet angle of 8.5°, which according to McElhinny et al.40 indicates the formation of {107} facets in our samples  undergoes faceting exclusively underneath the graphene area. The observed continuous faceting of the sample surface thus indicates the presence of a fully coalesced graphene film.In a first step, graphene was synthesized on Ge100) with the plasma turned off. The substrate temperature was adjusted close to the melting temperature of Ge. During the growth process 3 sccm of CH00 with tI2D/IG ratio of 3.9 and the full width half maximum (FWHM) of the 2D-peak of 40 cm−1 are ascribed to predominantly monolayer graphene. Thus, we interpret the bright regions on the dark background seen in the SEM image , which was reduced by lowering the amount of the precursor.In Fig. ω2D) Raman peak of the graphene is plotted versus the position of the G (ωG) peak in Fig. 44. The data for the samples fabricated at growth times of 30 min, 45 min, 60 min and 90 min are presented in black, green, blue and red, respectively. Analyzing the Raman peak positions, a compressive strain between − 0.2% and − 0.4% is determined, slightly rising with increasing growth time. The graphene film shows p-type doping with a doping level up to ~ 10∙1012 cm−2, not systematically depending on growth time. These findings are in good agreement with literature, where TCVD grown graphene films on Ge show predominantly compressive strain and p-type doping46. Pasternak et al.46 explained the compressive strain by the different thermal expansion coefficients of graphene (− 6 × 10−6 K−1)47 and Ge (+ 5.75 × 10−6 K−1)48. I.e., upon cooling after the TCVD growth the Ge lattice contracts, leading to a compression of the carbon lattice on top due to a (strong) interaction between graphene and Ge. Kiraly et al.39 evaluated the strain in graphene fabricated on Ge(100) and Ge(110) and obtained a compressive strain up to − 1.0%. Lukosius et al.15 also extracted a compressive strain up to − 0.15% on TCVD graphene fabricated on Ge(100), and estimated a p-type doping level of 2 × 1013 cm−2, agreeing with our findings.In order to access strain and doping properties of the fabricated film, the position of the 2D  and after (in green) the electrochemical transfer onto a Si/SiO2 substrate. The graphene film was fabricated at 927 °C for a growth time of 60 min under CH4:H2:Ar flows of 5:20:450 sccm at a chamber pressure of 750 mbar. By changing the CH4/H2 ratio from 5:20 and 5:100 to 3:100, we obtained similar Raman spectra  are determined as 2.5 kΩ/sq. and 2.8 kΩ/sq., respectively. In literature the graphene sheet resistance was reported as 1.5 ± 0.1 kΩ/sq. for graphene on Ge(100)15 and > 2 kΩ/sq. for graphene on Ge(110)14. The marginal deviation in the sheet resistance with respect to literature is attributed to a larger defect density in the fabricated film, as indicated by the D-peak in the Raman spectrum.The electrical properties of the graphene film are determined by the transfer-length method (TLM) on a Si/SiO24. Resulting from the anisotropy in the growth rate of armchair and zigzag edges of graphene, elongated flakes are formed14.In order to study the effect of substrate orientation on the graphene formation and the resulting strain properties, the TCVD experiments have been extended to Ge(110) substrates. For that purpose, graphene is fabricated until ca. 50% of the Ge surface was covered by graphene, which results in a growth time of 30 min for Ge(100)  and (110) crystal orientations, respectively. However, the 2D-peak intensity is much lower on the Ge(110) substrate. As the 2D-peak intensity was reported to be dependent on structural deformations in the graphene film50, we attribute the small I2D/IG ratio to the larger amount of defects on Ge(110).The averaged Raman spectra of the graphene on Ge(110) (in red) and Ge(100) (in black) are presented in Fig. The extracted 2D- and G-peak positions are summarized in Fig. 4 already in the gas phase. Graphene growth is examined on both substrate orientations at temperatures down to 757 °C. Figure 4 and Ar flows of 100 sccm and 200 sccm at a plasma power of 40 W. From the surface coverage at a given growth time it becomes obvious that the growth rate of graphene is larger on Ge(110) compared to Ge(100). A continuous increase in the surface coverage, nucleation density and grain size with growth time was observed for both orientations  the Raman spectrum indicates a defective film with a very small 2D-peak. With increasing growth time, the 2D-peak intensity becomes more and more dominant, indicating an emerging long-range order in the film. The I2D/IG ratio is calculated as 0.6 with an FWHM of 50 cm−1 for the 2D-peak, and a ratio ID/IG ~ 1.3 is obtained for the sample with a growth time of 6 h.To get information about quality, strain and doping levels of graphene that is synthesized via PECVD on Ge(100), Raman spectra averaged over four to six randomly chosen measurement points on the samples with growth times of 40 min (in blue), 4 h (in green) and 6 h (in red) are depicted in Fig. I2D/IG ratio is derived as 0.6 (0.8), and the FWHM of the 2D-peak as 39 cm−1 (35 cm−1). Although the I2D/IG ratio is slightly lower than that of graphene fabricated by TCVD, a much sharper 2D peak is observed for graphene grown via PECVD. Therefore, it is concluded that monolayer graphene is fabricated successfully on both substrates in presence of a plasma. The ID/IG ratio is determined to be 1.2 (1.2), which is almost two times higher than for the reference samples synthesized by TCVD. The increased number of defects is attributed to the reduced migration of carbon species on the Ge surface at low temperatures. Since on these samples also regions with a lower 2D-peak intensity are observed, we conclude that there are regions with long- (large 2D-peak intensity) and short-range order . Hence, the broadening of the 2D-peak in the average Raman spectra is attributed to the overlapping signal of regions with different long-range order.In Fig. By extracting the energetic positions of the 2D- and G-peak from the Raman data, strain and doping levels of the PECVD grown graphene can be accessed. Figure 51 for graphene grown on Ge(100) via TCVD. Dabrowski et al.22 reported on a tensile strain of + 0.4% for TCVD graphene grown on Ge(100), which is attributed to the coalescence of small graphene domains, while its detailed origin is not described further. In our work we observed compressive and tensile strain for TCVD and PECVD grown samples, respectively, regardless the growth parameters and substrate orientation. Our findings may act as a first hint of a different growth mechanism of graphene by PECVD as compared to TCVD.The observation of tensile strained graphene on Ge in case of PECVD growth is surprising, having in mind the compressive strain found in our work see Fig. c as well52. This allows us to differentiate the work function of the graphene flakes and their surroundings. Note that both the tip and the measurement conditions are kept constant during the following measurements and are therefore directly comparable to each other.Since the graphene flake size is around 100 nm, it is not possible to extract spatially resolved information via Raman spectroscopy with a laser spot size of 0.5 µm. Therefore, the data base that is required for the development of a suitable growth model is expanded and the surface topography and the electronic properties are examined by Kelvin probe force microscopy. During the measurements, topography and contact potential difference (CPD) are collected simultaneously. The CPD is proportional to the work function difference between the tip and the sample. For a given tip, the CPD along the sample surface can be extracted with a spatial resolution of < 20 nm and a sensitivity of < 20 mV4/H2 flux ratio of 3 sccm/100 sccm and a growth time of 2 h at a plasma power of 100 W is presented in Fig. The atomic force microscopy (AFM) topography map of a sample fabricated without plasma on Ge(110) is shown in Fig. 53. The height of the PECVD graphene regions is determined as 0.9 ± 0.3 nm by averaging over four grains with reference to the lowest measured point. Around the flakes on the other hand, the height increases by about 2 ± 0.2 nm.In order to quantify the height profile, line scans across selected graphene flakes are extracted, indicated by the black and red lines in Fig. VCPD = VCPD,graphene − VCPD,GeRAW). In Fig. VCPD map is shown for the graphene grown by TCVD. The graphene areas are represented by the blue/green colours, indicating the elongated shape of the graphene flakes. The Ge substrate surface, on the other hand, is characterized by the red coloured areas, i.e., by significantly reduced values of ΔVCPD with respect to the raw Ge(110) wafer.Simultaneously to the topography measurements, the CPD voltage is collected over the sample for getting access to the local work function. As a reference, the raw Ge(110) substrate is measured by the same tip prior to each measurement, and the contact potential difference between the graphene covered samples and the raw Ge substrate is extracted , the work function of the raw Ge(110) is estimated as ΦGe,raw = ΦTip − e∙VCPD = 4.65 eV, which agrees well with the literature value of 4.67 eV55. The work function of the graphene grains is determined as Φgraphene = ΦGe,raw − e∙ΔVCPD ≈ 5.1 eV, regardless the fabrication method. The work function of pristine graphene on Si/SiO2 is reported between 4.2 eV56 up to 5.11 eV57 in literature, and is shown to increase in presence of p-type doping58. The relatively large work function of our graphene thus indicates p-type doping, agreeing with our Raman data.In order to quantify the corresponding work functions, a reference gold film is measured with the same tip and a VCPD of ~  − 0.1 V … − 0.2 V is derived for the substrate area around the graphene flakes for TCVD  surface. In contrast, the graphene flakes grown by PECVD are surrounded by a 2 ± 0.2 nm thick defective carbon film covering the Ge surface. The same trend is also observed for samples that are fabricated on Ge(100)  and PECVD (red) on Ge(100) and Ge(110) as indicated by squares and triangles, respectively. Independent of the substrate orientation or the growth parameters, a quite homogenous, compressive strain is observed for graphene fabricated by TCVD within our set of experiments. The samples in addition show p-type doping agreeing with most literature51. Graphene films, which are synthesized at reduced temperatures in presence of a plasma, on the other hand, exhibit a pronounced tensile strain up to + 1.2%. Considering the scattering of the data (size of the shaded areas), the PECVD samples apparently vary regarding strain and doping properties, indicating deviations in the long-range order and/or defect formation across the sample.Combining these results with the Raman data discussed above, a mechanism is suggested to explain the graphene formation and the related origin of the observed strain. Figure Our analysis finally allows us to propose a model idea for the graphene growth on Ge, explaining the different strain states found in TCVD and PECVD. In Fig. 2 substrate. In PECVD, in contrast, a defective carbon film is located around the crystalline flakes and probably also beneath them. As a result, the interaction between the graphene and Ge may be weakened. During the cooling process, the graphene areas expand, and the edge carbon atoms from the crystalline flakes and the surrounding defective carbon layer become closer, potentially forming bonds in order to saturate the existing dangling bonds. The attractive forces between the graphene and the defective regions would thus induce the tensile strain in the graphene areas, in agreement with our findings.In TCVD, the Ge wafer is expected to contract during cool down, while the graphene layer tends to expand as indicated by the different signs of the thermal expansion coefficients. As in TCVD the strain of the graphene is compressive, we assume a strong interaction between the graphene and Ge wafer. This interaction forces graphene to shrink during cooling. After delamination, this graphene–Ge interaction disappears, and the graphene film relaxes. This leads to a reduced strain in graphene transferred on a Si/SiOI2D/IG ratio of 3.9 is synthesized at temperatures close to the melting point of Ge, and a sheet resistance of 2.5 kΩ/sq. is determined. The resulting film exhibits a compressive strain of − 0.4% regardless the substrate orientation and faceting, which is related to the strong interaction between graphene and Ge substrate during the cooling after growth. In presence of a plasma, the growth temperature is reduced to 757 °C—the lowest growth temperature for graphene on Ge hitherto—and round to hexagonal and elongated flakes are observed on Ge(100) and Ge(110), respectively. With increasing growth time the grain size of graphene increases and a pronounced 2D-peak emerges indicating the long-range order in the film. A tensile strain is monitored for the PECVD grown films. Combining our findings from Raman spectroscopy with Kelvin probe force measurements we demonstrate that in case of TCVD, isolated graphene grains are formed on the substrate, merging to a film with growth time, whereas in case of PECVD, graphene flakes are surrounded by a defective carbon film.We demonstrate graphene fabrication on Ge wafers with (100) and (110) crystal orientation by plasma-enhanced CVD at reduced temperatures. In reference experiments, where the plasma is switched off, monolayer graphene with a 2 in size and cleaned in boiling acetone and ethyl alcohol for 2 min. The substrates are then flushed by isopropyl alcohol and blown dry under a stream of nitrogen.The Ge(100) wafer is grown epitaxially on a Si wafer with a film thickness of 2 µm in order to avoid any Si diffusion to the surface during graphene growth. The Ge(110) wafer, on the other hand, is a pure Ge wafer. Prior to the process, the wafers are cut into pieces with one cmBlack Magic system from Aixtron Ltd. Prior to the graphene synthesis, the wafer was loaded to the chamber and the temperature was increased until the sample surface begins to melt at a chamber pressure of 750 mbar. The processing temperature was adjusted roughly 10 K for Ge(100) or 30 K for Ge(110) below the noted thermocouple temperature, at which the substrate started to melt. Note that since the melting temperature is defined by visual analysis, there is an inevitable error in the determination of the substrate temperature. The given temperature values in the manuscript were calculated from the melting temperature of 937 °C.CVD growth of graphene on Ge is established in a 4″ 60. The chamber pressure was adjusted to 4 mbar at a constant temperature of 757 °C. As precursor gas CH4 is used at varied flow rate between 3 and 100 sccm. The plasma atmosphere is realized by either H2 or Ar flow. The process parameters of the individual samples are indicated in the manuscript.In the plasma enhanced method, a pulsed-DC plasma with a pulse frequency of 10 kHz and 1 µs of reverse time is ignited in the same chamber during the growth process. The plasma power is varied between 40 and 100 W and the external voltage is regulated dependent on the current flowing to the electrodes. To shield the electrical field between the plasma sheath and the substrate, a Ni foil was folded into an inverted cup, which served as a Faraday cage. Note that for each process a fresh foil was used. Details of the process and the sacrificial foil are discussed in more detail elsewhereNTEGRA Spectra System from NT-MDT. In order to transfer the graphene film, Poly(methyl methacrylate) (PMMA) is spin-coated to protect the film during the standard electrochemical delamination method61 in a NaOH bath against a platinum electrode at an external voltage between 3 and 4 V. The graphene film is then separated by the evolving H2 gas between graphene and Ge and fished out of the H2O bath by a Si/SiO2 substrate (285 nm of SiO2). The PMMA was removed by leaving the sample ca 1.5 h in the acetone bath. For TLM measurements, electrodes with 15 nm Ti and 85 nm Au were evaporated onto the graphene film by a shadow mask method. The spacing between the contact pads (d) are 20 µm, 40 µm, 60 µm, 80 µm, 100 µm and 120 µm for the first and 20 µm, 80 µm, 140 µm, 200 µm, 260 µm and 320 µm for the second structure with a width (W) of 400 µm for one contact pad. Electrical properties of the film were determined from the slope of the total resistance at a source-drain voltage of 0.1 V vs. d/W.The Raman spectra were taken with a 532 nm incident laser wavelength in a Veeco/Bruker Innova Atomic Force Microscope directly on the Ge wafer. The Ge wafer was grounded and the contact potential difference (VCPD) between the substrate and the ElektriMulti75-G tip with a Pt (95%)/Ir (5%) coating is measured and the work function is calculated with the relation VCPD = (ΦTip − ΦSample)/e. Prior to and after each measurement the raw Ge wafer of the corresponding orientation was measured as a reference and the ∆VCPD is extracted relative to the reference.AFM and Kelvin probe force microscopy measurements were performed by a The morphology of the grown samples was characterized by scanning electron microscopy (ZEISS Supra 25) with an in-lens and an Everhart–Thornley detector. The operation parameters were a primary electron energy of 1 keV and working distance of 4–5 mm.Supplementary Information.


Furthermore, a mechanical strain that increases the electron-tunneling width between graphene nanostructures (~ 38 nm) by only 0.19 Ǻ reduces the electron flux by 1e/s/nm2 (from 18.51 to 19.51 e/s/nm2) through the polylactic acid junctions in the 3D-printed heterostructure. This corresponds to a sensitivity of 2.59 Ω/Ω%, which compares well with other tensile gauges. We envision that the proposed electron-tunneling model for conductive 3D-printed structures with thermal expansion and external strain will lead to an evolution in the design of next-generation of ‘on-demand’ printed electronic and electromechanical devices.Designing 3D printed micro-architectures using electronic materials with well-understood electronic transport within such structures will potentially lead to accessible device fabrication for ‘on-demand’ applications. Here we show controlled nozzle-extrusion based 3D printing of a commercially available nano-composite of graphene/polylactic acid, enabling the fabrication of a tensile gauge functioning via the readjustment of the electron-tunneling barrier width between conductive graphene-centers. The electronic transport in the graphene/polymer 3D printed structure exhibited the Fowler Nordheim mechanism with a tunneling width of 0.79–0.95 nm and graphene centers having a carrier concentration of 2.66 × 10 Compounding on these properties, graphene’s chemical and physical stability has led to numerous applications, including nanoelectronics, biomedical applications such as biosensors, antibacterial, drug delivery, cell imaging, tissue engineering, and energy storage applications in batteries6. Graphene and its derivatives include graphene oxide (oxygenated graphene), graphane (hydrogenated graphene), and fluorographene (fluorinated graphene). Graphene oxide is one of the most widely studied chemical derivatives of graphene because of its water solubility and fast, scalable synthesis. A fast-evolving research-field is the 2D and 3D printing of nanomaterials, including graphene oxide and its composites. 3D printing, which became an important research area in the 1980s, can significantly improve fabrication of microelectronic devices and other constructs for various fields due to the ability of on-demand manufacturing, at reduced cost in comparison to other fabrication techniques.Graphene—a single-atom-thick sheet of carbon atoms arranged in a hexagonal honeycomb lattice8, and sensors13. There is also an increasing number of applications in electrochemical systems such as supercapacitors, electrodes, electrochemical, batteries, and water splitting using 3D printing14. The Black Magic filament (used in this work) has been previously used for photoelectrochemical sensors, supercapacitor, and electrodes17. This PLA based filament has 8 wt% of graphene18. In this work, we show that electronic devices with graphene-composite can be 3D printed for device evaluation and strain gauge application. These electronic devices constructs can potentially have applications in wearable electronics where flexibility is essential21.Further, the applications of 3D printing has evolved into the fields of biomedicineHere, we outline the electron-transport properties  of 3D printed devices made from graphene/PLA filaments, and the effect of temperature and strain on these properties.−1, 1,580 ± 1 cm−1, and 2,692 ± 1 cm−1) bands, and (c) the arrangement of the graphene platelets is random  is maintained at different depths in the printed graphene/PLA structure, (even at 80 m deep)  Fig. . Importa2 area-scale.When the measurements were made at different depths, we confirmed that graphene exists at every depth. However, since graphene is a large nanomaterial , its presence in a composite cannot be uniform in the micron scale. We found that its composition is uniform in the 50 × 50 micron24 relationship, the graphitic size of the samples were calculated using the ID/IG intensity ratio:La is the in-plane correlation length or cluster diameter, I(D), and I(G) are the intensity of D and G peak, respectively. 25, I(D), and I(G) was obtained from the Raman graph (from the integration of the area under the curve), indicating that the ordered graphitic region with sp2 hybridized carbon atoms in the graphene sheets is in the order of 37.75 ± 2.42 nm (La). In addition, using the relationship26:Pos(G) is the position of the G peak (derived from the Lorentz fitting), Pos(G)0 is the position of the G peak without doping, 26), and Pos(G)0 have been reported in the literature (See Supporting Information) with an average value of 1583.67 cm−135. Using Eq. 2 and the values of the G peak positions for the different depths, we found the different values of EF (0.21 eV). The doping concentration was calculated using36:6 m/s), and 12/cm2. This order of magnitude of doping is consistent with that of other graphenic composites. Equations 2 hybridization of carbon atoms (infinite sp2 carbon lattice); however, the relatively sp2 domain size of graphene in this study is ~ 37.75 nm . Therefore, the validity of these equations and the derived charge density is limited.Using the Tuinstra and KoenigI–V) data of device 1 at different temperatures. Figure The electron transport for the percolating network of the 3D printed structure of device 1 was studied using a cryo-probe-station under vacuum (0.75 mTorr), acquiring the current–voltage . Since thermal emission occurs with thermal barriers higher than kBT at room temperature, the mechanism of carrier transportation for this device is electron tunneling38. Electron-tunneling is a phenomena that occurs when the electron potential is below the barrier height and therefore, can occur at low-electrical fields  as shown by Nakatsuji et al.39, Takayanagi et. al.40, Wang et. al.41, and Nakatsuji et al.42.To obtain the overall transport thermal-barrier of the 3D printed devices for electron transfer between graphene platelets, we applied Arrhenius Law to fit the I–V data. This was done to determine the mechanism that most appropriately describes the electron transport  shown in  :4\documentclass1pt{minimaentclass1pt{minima46. Consistently, Connelly et al. also employed the Fowler–Nordheim Model for electron transport within their 3D printed structure47.For electron-tunneling within this thermally-expandable network, we included a thermal-expansion equation with the Fowler Nordheim electron tunneling (FNET) Eq. a0 and a are the average tunneling distance at zero-Temperature and at any other temperature, and is the thermal expansion coefficient.48 Combing with the FNET equation with Eq. m is the mass of an electron, t is the FNET constant, and is the tunneling barrier height. Since the electron must dissociate from graphene before tunneling into the next graphene platelet, the tunneling barrier is assumed to be graphene’s work function: 4.85 eV. Fitting the data, as shown in Fig. In this work, the thermal expansion equation applied on the interparticle polymer layers governs the tunneling distance:entclass1pt{minima3) by a motion controller driver and the strain analysis of the 3D printed graphene/PLA structure was performed. Here, the device was fixed on a lever with the motion-controller driver pushing on the graphene/PLA to generate strain, and two electrodes were connected to the edges of the devices, as shown in Fig. ε is the strain, h is the bending-distance after strain is applied. Details of this calculation and a figure to illustrate it is shown in the The strain was induced on device 2  is kept as the work function of graphene (4.85 eV). Fitting the above equation with the current versus the strain data  within the polymer matrix can be building blocks for on-demand electronic devices.In conclusion, we demonstrate 3D printed structures of graphene/PLA, applicable as components of on-demand electronic devices. We show the operation of a tensile gauge functioning via the modification of electron-tunneling width between graphenic-centers. For the graphene/PLA system, the thermal barrier to electron transport was 150 µeV , and the electron tunneling distance was 0.78 nm at cryo-temperature and 0.95 nm at room temperature. The mechanical strain that increases the electron-tunneling width between graphene nanostructures (~ 38 nm) by an average of 0.19 Angstrom reduces the electron flux from 18.51 to 19.51 e/s/nm3. (Follow the same process for device 2). The structure and spatial distribution of graphene in the printed composite devices were characterized employing confocal Raman spectroscopy (WITEC Alpha-300-RA system with 532 nm incident laser and 100X objective lens). The Raman spectra of the devices also provided information on the doping levels of the graphene as well as the size of the ordered graphitic regions.In this report, we fabricated two different devices for investigating the effects of temperature (device 1) and mechanical strain (device 2) on the electrical conductivity of the 3D printed graphene/PLA nano-composite structures. The devices were designed in Autodesk Inventor and loaded to the CTC Bizer series Dual Nozzle 3D Printer , Printer setting: stage-temperature at 60 °C, and extruder temperature at 190 ° C) for device-fabrication. The schematic of the mechanism to 3D printed devices is shown in Fig. Supplementary file1 (PDF 424 kb)