This study aims at assessing how bilinguals select words in the appropriate language in production and recognition while minimizing interference from the non-appropriate language. Two prominent models are considered which assume that when one language is in use, the other is suppressed. The Inhibitory Control (IC) model suggests that, in both production and recognition, the amount of inhibition on the non-target language is greater for the stronger compared to the weaker language. In contrast, the Bilingual Interactive Activation (BIA) model proposes that, in language recognition, the amount of inhibition on the weaker language is stronger than otherwise. To investigate whether bilingual language production and recognition can be accounted for by a single model of bilingual processing, we tested a group of native speakers of Dutch (L1), advanced speakers of English (L2) in a bilingual recognition and production task. Specifically, language switching costs were measured while participants performed a lexical decision (recognition) and a picture naming (production) task involving language switching. Results suggest that while in language recognition the amount of inhibition applied to the non-appropriate language increases along with its dominance as predicted by the IC model, in production the amount of inhibition applied to the non-relevant language is not related to language dominance, but rather it may be modulated by speakers' unconscious strategies to foster the weaker language. This difference indicates that bilingual language recognition and production might rely on different processing mechanisms and cannot be accounted within one of the existing models of bilingual language processing. When a speaker of more than one language  processes a language, words from the non-relevant language might be activated and interfere. This can happen while speaking, but also during writing, listening and reading. The ability to confine processing to the relevant language is called “language control” and is essential for successful communication. Despite the importance of this phenomenon, research on language control has predominantly concentrated on language production  word production and visual (but not spoken) word recognition. In spoken word production, language control refers to the capacity of a bilingual person to speak in the intended language, while avoiding interferences from the non-intended language. In visual word recognition, language control indicates the ability to understand the meaning of written words belonging to a certain language, while reducing interference from the non-target language. These processes are far from being effortless. Indeed, it is generally agreed that when a word from the intended language is processed, also words from the irrelevant language are coactivated and might interfere during processing  in the former and on the activation level of the language node of the relevant language (language node of the stronger language inhibits words of the weaker language to a greater extent than vice versa) in the latter.Briefly, the different predictions with regard to the IC and the BIA model described in this paper derive from the fact that the amount of inhibition depends on the activation level of the words of the repetition trials  and switch trials . Responses on switch trials are usually less accurate and slower compared to repetition trials, and this difference is known as language “switching costs”  language control in recognition is dominance-related (as the IC model predicts) or dominance-reversed (as the BIA model predicts) and from this at assessing, more generally, whether (2) language control in bilingual recognition and production rely on the same mechanisms. To do it, we tested one group of unbalanced bilinguals  in both a recognition and a production task involving language switching. As to recognition, participants were administered a bilingual lexical decision task; regarding production, participants performed a bilingual picture naming task. In both tasks, switching costs in L1 and L2 were measured. Moreover, to assess languages' baseline strength of activation (believed to be a reflection of language dominance), a “single-language” block was included in the picture naming task, in which pictures had to be named in either L1 or L2 separately.Concerning the predictions, we expect one of two possible outcomes: (1) If the amount of inhibition of the non-relevant language strongly depends on language dominance, we expect to find asymmetrical switching costs in both the lexical decision and the picture naming task. In this framework, if the magnitude of inhibition is dominance-related (as the IC model predicts), then switching costs are expected to be larger for switching to the stronger than for switching to the weaker language in both tasks. If, however, language inhibition in recognition is dominance-reversed (as predicted by the BIA model), switching costs in the lexical decision tasks are expected to be larger for the weaker than for the stronger language. (2) Alternatively, if asymmetric inhibition is applied only in cases of extreme dominance difference between two languages (such in the case of low proficient L2 speakers), then switching costs are expected to be symmetrical in both the recognition and the production task.For the present study, we recruited 32 native speakers of Dutch  from the student population of the University of Groningen . Participants were tested in Dutch and English and were paid for their participation. All participants were right-handed, had normal or corrected to normal vision and had never been diagnosed with reading, learning or language disability. Before the experiment, participants gave their written consent and filled out the Language Experience and Proficiency Questionnaire  and English at school as a second language (L2) for a minimum of 6 years . On a daily basis, speakers were mostly exposed more to Dutch (57%) than to English (34%) or to other languages (9%). To screen for language skills, participants were asked to self-rate their ability to speak, understand and read in Dutch and English based on a ten-point scale, where 0 = no knowledge and 10 = perfect knowledge. Overall, results revealed that participants considered themselves excellent users of Dutch  and good users of English . Their L2 proficiency level was tested using the grammar part of the paper-based Oxford Placement Test Allan, . The teshttp://celex.mpi.nl/), words were matched for word form and lemma frequency  as well as for letter orthographic length . Words were also matched according to their orthographic neighborhood density within each language , between the two languages  and across other languages . The values for the orthographic neighborhood density were taken from the Cross Linguistic Easy Access Resource for Phonological and Orthographic Neighborhood Densities database . Half of the pseudowords were created starting from the selected Dutch words and the other half from the selected English words by using the multilingual pseudoword generator Wuggy . The neighborhood size difference between pseudowords and words was kept as minimal as possible . Finally, words and pseudowords had the same orthographic length.With concern to pseudowords, they were created by modifying the first or the last subsyllabic elements of the words selected for the lexical decision task. Apart from this change, pseudowords maintained the subsyllabic structure of the words they were generated from  against a black background. Pictures had a size of 197 × 281 pixel and were presented against a colored (green or blue) background. Stimuli were presented using the software E-Prime Professional version 2.0  and 2/3 of words . Half of the words belonged to the L1 and the other half to the L2. Participants were told that words' language membership was irrelevant for the task.L2-L1-L1-L2 and L1-L1-L1-L2) and to exclude effects of backwards inhibition on switch trials (note the difference between L1-L2-L1-L2 and L1-L1-L1-L2), for a review on backward inhibition see Koch et al. . For example, in the chunk L1-L1-L1-L2, the first three members belonged to the same language (L1) and the last one to the other language (L2). Each of the four elements had to be classified as existing words, irrespective of language membership. Only the last two elements of the chunk were included in the analysis . We used this system to make sure that every repetition trial was coming from a “pure” repetition trial , while the fourth element was in a different category. For example, in a word-word-word-pseudoword chunk, the first three elements had to be classified as existing words and the fourth one as a non-existing word. If the first three components were words, they always belonged to the same language , while the following pseudoword was generated either from the same language  or from a different one . If the first three components of the chunk were pseudowords, they could have been generated from both the L1 and the L2 and the following word was either in the L1 or in the L2. Only the last two elements of the chunk were included in the analysis .If two items  had occurred together in a specific chunk, this items' combination was not repeated twice; additionally, their translations  and their derived pseudowords  were never presented together within a chunk. To avoid orthographic priming, items sharing more similar orthographic patterns (such as the pseudowords derived from the item words) never occurred in the same chunk. Moreover, a given item was never seen within the next 5 trials and the same type of chunk never occurred more than twice in a row. Because of these constraints, the order of the trials was unpredictable. Four lists of the task were created, two starting with a word-word-word-pseudoword and the other two with a pseudoword-pseudoword-pseudoword-word chunk type. Each participant was administered with one list only. The language of instructions for the lexical decision task was Dutch. Before the main experiment, participants were given a practice session of 24 trials. Practice items were not included in the main experiment.In the picture naming task, participants had to name a presented picture either in their L1 (Dutch) or in their L2 (English). The language to be used was signaled by the background color of the screen . The assignment of the color cue to the response language was counterbalanced across participants. The picture naming task consisted of a single-language followed by a mixed- language block. In the single-language block, pictures had to be named in either L1 or L2 separately. It included a total of 56 trials: Half of the items had to be named in the L1 and the other half in the L2. For each language, the 14 pictures were randomly presented for two times consecutively. The order of languages' presentation was counterbalanced across participants. The language of instruction corresponded to the language in which the upcoming task had to be performed (English for the upcoming L2 part and Dutch for the following L1 part).The mixed-language block involved two kinds of trials, repetition and switch trials. In a repetition trial, a given picture had to be named in the same language as in the trial before . In a switch trial, a presented picture had to be named in a different language compared to the trial before . The mixed-language block was composed by 112 trials: Half of them had to be named in the L1 and the other half in the L2. Trials were organized in pseudo-randomized language chunks composed of 75% repetition and 25% switch trials . For example, the language chunk L1-L1-L1-L2 implied that the three consecutive pictures had to be named in the L1 and the fourth one in the L2. For the analysis, only the second part of the language chunk was used (X-X-L1-L2). The same chunk type was never displayed more than two times in a row. Each picture was seen eight times within the mixed-language block, which is once in each position of the language chunk  of the two language chunks (L1-L1-L1-L2 and L2-L2-L2-L1). The same picture was not seen within 5 trials. Again the order of the items was unpredictable. Four lists of mixed-language block were created: two starting with a L1-L1-L1-L2 and the other two with a L2-L2-L2-L1 chunk type. Each participant saw only one list. In the mixed language block, the language of instructions was Dutch.Participants were given one practice sessions before each language part of the single-language block  and one practice session before the mixed-language block, in which the two chunk types were trained . Practice trials did not appear in the experimental sessions. On average, the experiment (including instructions and breaks) lasted 30 min.The dependent variables of the present study were accuracy rates and reaction times. Before the statistical analyses, we cleaned the dataset based on participants' and items' error rates. Because of low accuracy scores (<66%), one participant and two items were removed from subsequent analyses . Accuracy rates for all other participants and items ranged, respectively from 73 to 100% and from 79 to 100%.Both correct and incorrect responses were included in the accuracy rates' analyses, whereas only correct responses were used to analyse reaction times. We deemed a response as incorrect in the case of wrong button pressing for the lexical decision task (4.5% of the data) and in the case of microphone miss-triggering  and selection of the wrong word and/or language for the picture naming task (9.8% of the data). In both tasks, missing responses were classified as incorrect.After exclusion of the incorrect responses, we screened reaction times for extreme values. Extremely fast (<150 ms) or particularly slow  reaction times were removed from the dataset (0.03% of the data). All the analyses were carried out in GNU-R version 3.2.2  in the lexical decision and in the picture naming tasks. Consider first the lexical decision task. Mean accuracy rates and reaction times are illustrated in Table SE = 0.10, z = 3.35, p < 0.01). A response change  yielded significantly less accurate responses compared to when the same response was repeated . The significant interaction of Language by Category , indicated that compared to pseudowords, words were responded more accurately in the L1 than in the L2. Specifically, while accuracy rates did not differ significantly for pseudowords generated from the L1 and those generated from the L2 (p > 0.05), L1 words were responded more accurately than L2 words . Finally, we found a significant interaction between Condition and Response repetition , indexing that a change of response affected language repetition and switch trials differently. In particular, compared to a situation in which the same response is repeated , a change of response  yielded significantly lower accuracy rates for repetition trials , but not so for switch trials (p > 0.05).The analysis of the accuracy rates revealed that responses were significantly more accurate in words than in pseudowords  and that response change yielded slower responses compared to response repetition . The main effects of Language and Condition were not significant (p > 0.05). However, the significant interactions of Language by Category  and of Condition by Category  revealed that compared to pseudowords, words were responded faster in the L1 than in the L2 and in repetition compared to switch trials. Both the interactions of Category by Response repetition and of Condition by Response repetition were also significant , indicating that a response change was more costly for repetition compared to switch trials and for words than for pseudowords. Both the three-way interactions between Language, Condition and Category and between Condition, Category and Response repetition were significant . All the other interactions were not significant (p > 0.05).As expected, the analysis of the reaction times showed that pseudowords were responded significantly slower than words  and in repetition compared to switch trials . The effect of Response repetition was also significant , indicating that responses were significantly slower if the preceding trial was a pseudoword compared to a word. This effect was smaller for switch compared to repetition trials . The three-way interaction between Language, Condition and Response repetition was also significant . No other interaction was significant (p > 0.05). Words were further divided based on the category of the preceding trial (Response repetition: yes vs. no).To investigate these interactions, we split the data into words and pseudowords. With regard to words, we found that responses were faster in the L1 than in the L2  and in repetition compared to switch trials . The difference between repetition and switch trials was larger for L1 compared to L2 words , that is asymmetrical switching costs. In particular, the effect of language switching was significant for the L1 , but not for the L2 (p > 0.05). With reference to words preceded by pseudowords , responses were faster in the L1 compared to the L2 . No other main effect or interaction was significant (p > 0.05) in this condition. Concerning pseudowords, items generated from the L1 were responded equally fast as those generated from the L2 (p < 0.05). All the other main effects and interactions were also not significant (p > 0.05).When the same response was repeated , words were responded faster in the L1 relative to L2  compared to response change . Changing response was particularly costly for words and repetition trials than for pseudowords and switch trials, respectively. The effect of Language was influential for words, in that L1 words were responded faster and more accurately than L2 words, but not for pseudowords. The effect of Condition also affected words only, with responses on switch trials being slower and less accurate than on repetition trials. This difference was significant for the L1 and not significant for the L2, i.e., asymmetrical switching costs.Consider now the picture naming task. Mean accuracy rates and reaction times are presented in Table p > 0.05). In the mixed-language block, responses were significantly more accurate in the L2 than in the L1  and in repetition compared to switch trials . The marginal significant interaction of Language by Condition  indicates that compared to repetition trials, switch trials tended to be less accurate in the L1 than in the L2. The accuracy rates' difference between the single- and the mixed-language blocks was not significant (p > 0.05). However, there was a significant interaction between Language and Block , indicating that compared to the single-language block, mixed-language block responses were more accurate for the L2 than for the L1. Specifically, while the L2 was responded to significantly better in the mixed- compared to the single-language block , L1 accuracy rates was not influenced by the type of block (p > 0.05).The analysis of the accuracy rates showed that in the single-language block responses in L1 and L2 were equally accurate (p > 0.05) and that in the mixed-language block, responses were faster in the L2 than in the L1 . Overall, participants responded slower on repetition trials of the mixed-language block compared to trials in the single-language block , i.e., mixing costs. This difference was greater for the L1 than for the L2, as indicated by the significant interaction of Language and Block . In particular, while L1 responses were significantly slower in the mixed- compared to the single-language block , L2 responses were not affected by the type of block (p > 0.05). In the mixed language block, switch trials were responded to slower than repetition trials , and this effect was comparable for the L1 and the L2 (p > 0.05), which means that we found symmetrical switching costs. However, given the relatively small number of trials used in this task, we performed an additional analysis in order to assess whether the observed data might have suffered from unwanted biases. To do so, we used Bayesian hypothesis testing that allows for rigorous quantification of statistical evidence  language inhibition in recognition is dominance-related (as the IC predicts) or dominance-reversed (as the BIA predicts) and from this at investigating whether (2) language control in bilingual recognition and production rely on the same or different mechanisms. To address these issues, we measured language switching costs in a group of native speakers of Dutch (L1)-proficient learners of English (L2) performing a bilingual lexical decision and a bilingual picture naming task.We regard to the main research question, we found that language switching costs for words were larger for the L1 compared to the L2, i.e., asymmetrical switching costs. More precisely, responses were significantly slower on switch than on repetition trials for the L1, but not for the L2 where reaction times in switch and repetition trials were comparable. This result replicates Jackson et al.  study, wWhy did we find language switching costly for the L1, but not for the L2? Before answering this question, it is important to understand the role of language strength of activation in word recognition. As proposed by the BIA model the speed with which a word is recognized depends on its baseline activation level, with more frequent words (such as L1 words) being recognized faster than less frequent words (such as L2 words). Additionally, the speed with which a word is recognized also depends on its relation to other words in the lexicon . This assumption is line with the IC model predictions, according to which the magnitude of inhibition depends on languages' activation strength. Yet, this is in contrast with the BIA model proposal that the L1 language node inhibits L2 words to a greater extent than vice versa  replicates previous studies on bilingual recognition . In particular, prior practice of the items in the lexical decision task might have strengthened the weaker language to a greater extent than the stronger language, leading to a change in their dominance relation in the picture naming task. However, a comparison with a similar study suggests that the results obtained in the picture naming task are likely to be due to the nature of the task rather than to different practice effects for the two languages. Specifically, in a recent study by Mosca and Clahsen , languagThus, why did we find ceiling performance in the production, but not in the recognition task? The reason might lie in the difference between the processes supporting language production and recognition. Indeed, while language recognition is mostly supported by a bottom-up mechanism, language control in production is a mainly top-down process (see next session for further discussion on bottom-up vs. top-down control). As Yeung and Monsell  observedFurthermore, Monsell et al.  showed tIn the single-language block, we found that responses in the L1 were on average faster than in the L2, but that the difference was not significant. Comparable performance in the two languages might indicate that our participants were fairly balanced bilinguals. However, this assumption is in contrast to their performance in the lexical decision task, where participants responded faster to L1 words than L2 ones.Similar performances in L1 and L2 might be due to the order in which languages were presented in the single-language block. Recall that half of the participants named pictures first in the L1 and then in the L2 and the other half named pictures in the opposite order. Several studies investigating the effect of language order in language production have shown that, presented two separate language blocks, L1 naming is hampered if preceded by L2 naming. However, this negative effect is not found in the L2, where performance seems to benefit from the previous naming in the L1  do not report faster responses in the weaker than in the stronger language , language recognition is a predominantly bottom-up process . This difference was already captured in a paper describing the developmental Bilingual Interactive Activation (BIA-d) model by Grainger et al. . AccordiA way to understand the processes underpinning language control in production and recognition is by considering that top-down and bottom-up attentions are commonly deemed as two different types of information processing  in the production, but not in the recognition task.Similarly, the different language switching pattern found in the recognition and the production task could be ascribed to the difference between top-down and bottom-up mechanisms. In the recognition task, asymmetrical switching costs might be attributed to the fact that since words are more activated in L1 than in L2, L1 words need to be inhibited more than L2 words, leading to larger reactivation costs for the L1 relative to the L2. In the picture naming task, symmetrical switching costs for the stronger and the weaker language could be explained either by a strategic process of costs' minimization  or by a modulation of language inhibition over time . However, more studies are needed to assess whether the difference between language control in production and recognition are to be attributed to the fact that they are mainly supported by two different mechanisms of information processing. A more systematic way to address this issue would be, for example, by including a single-language block for the recognition task, so to compare language mixing costs in recognition and production. Alternatively, it could be useful to use a language-specific recognition task, in which only words are used and where speakers have to decide whether a given word belongs to either L1 or L2. This type of task would be more comparable to a picture naming task since only existing words are used and language membership is relevant for the task.The aim of this study was to investigate bilingual language control in reception and production. In particular, we asked whether language control in bilingual perception relies on the same mechanism as in bilingual production and whether these two processes can be incorporated into a single bilingual language control model. To investigate this issue, we considered two prominent models of bilinguals language control, according to which, when one language is in use, the other is suppressed. The (IC) model Green, , 1998 suTo investigate the mechanisms underpinning language control in production and recognition, we tested native speakers of Dutch (L1)-highly proficient speakers of English (L2) in a lexical decision and a picture naming task with language switching. Participants reported being more proficient and more exposed to the L1 compared to the L2. Results from the bilingual lexical decision task confirmed that L1 and L2 were processed differently. Specifically, we found that language switching was costly only for the L1 but not for the L2. We suggested that when L2 words are presented, L1 competitors need to inhibited. If during L2 processing, L1 words are suppressed, reactivating the L1 on a switch trial will lead to L1 switching costs. However, when L1 words are presented, the target word is fast recognized and words from the weaker L2 will not have enough time/strength to compete. In this case, the inhibition applied to the weaker L2 competitors will be relatively small or absent, yielding to undetectable L2 switching costs when the L2 becomes relevant again.These results are in contrast with the BIA model, according to which switching costs are dominance-reversed, but they are in line with the dominance-related IC model, in that we measured switching costs in the stronger L1, and only a tendency toward switching cost in the L2.In contrast to the lexical decision task, in the picture naming task we measured the same amount of switching costs for the L1 and the L2. Symmetrical switching costs between a stronger and a weaker language cannot be accounted for by a conservative dominance-related account of language control. We suggested that symmetrical switching costs in bilinguals with a relatively small difference between L1 and L2 proficiency level might be related to speakers' temporary adjustment to the task. Specifically, we assume that in the production task, the language control system strategically adapts to the task either by minimizing the effort of switching between languages  or by adjusting the amount of language inhibition during the task . Importantly, whilst it remains unclear whether language switching costs can be considered a direct test toward or against a specific model (e.g., Bobb and Wodniecka, This study was carried out in accordance with the recommendations of the Ethics Committee of the University of Potsdam with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of the University of Potsdam.MM: Conception, design, and programming of the experiment. Acquisition, analysis, and interpretation of the data. Draft and revision of the manuscript. Final approval of the version to be published and of the content and the accuracy or integrity of any part of the work. KdB: Conception of the experiment. Draft and revision of the manuscript. Final approval of the version to be published as well as of the content and the accuracy or integrity of any part of the work.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.


This resulted in GNRs with various widths in a highly controllable manner, where the on/off current ratio was inversely proportional to ribbon width. The field-effect mobility decreased with decreasing GNR widths due to carrier scattering at the GNR edges. These results demonstrate the formation of a bandgap in GNRs due to enhanced carrier confinement in the transverse direction and edge effects when the GNR width is reduced.There has been growing interest in developing nanoelectronic devices based on graphene because of its superior electrical properties. In particular, patterning graphene into a nanoribbon can open a bandgap that can be tuned by changing the ribbon width, imparting semiconducting properties. In this study, we report the effect of ribbon width on electrical transport properties of graphene nanoribbons (GNRs). Monolayer graphene sheets and Si nanowires (NWs) were prepared by chemical vapor deposition and a combination of nanosphere lithography and metal-assisted electroless etching from a Si wafer, respectively. Back-gated GNR field-effect transistors were fabricated on a heavily p-doped Si substrate coated with a 300 nm-thick SiO In pa V−1 s−1 . In spit4 in GNR FETs with sub-5 nm widths at room temperature [As graphene is intrinsically a semimetal with zero bandgap, the formation of a bandgap is necessary to achieve a sufficiently high on/off current ratio when used as a channel in a field-effect transistor (FET). Several routes to bandgap tuning in graphene have been reported, such as chemical doping of graphene, application of a vertical electric field in bilayer graphene, and patterning graphene into a narrow ribbon structure –11. Amonperature . The fabperature –15, blocperature , and useperature –19 on grperature –24. In t2 gas flow to increase the Cu grain size and ensure the removal of native oxide and a smooth Cu surface. Graphene was grown on Cu foil at 1000 °C using a mixture of 20 sccm of CH4 and 50 sccm of H2 by low-pressure chemical vapor deposition (CVD) [2 substrate, the surface of the graphene on Cu was coated with poly(methyl methacrylate)  as a transfer medium. The PMMA-coated samples were baked at 60 °C for 5 min. The Cu was wet-etched using a copper etchant , resulting in graphene/PMMA films floating on the etchant. These films were then collected onto Si/SiO2 substrates, and the PMMA layers were removed with acetone, yielding Si/SiO2/graphene.Cu foil (Alfa Aesar) was used as a substrate for graphene growth. The Cu foil was annealed at 1000 °C for 30 min under 10 sccm of Hon (CVD) , 25. To 2O2:H2SO4) at 100 °C. Then, the substrate was rinsed with deionized water and spin-coated with the PS bead suspension at 3000 rpm to form a monolayer of PS beads on the Si substrate. The large-area ordered and close-packed monolayer assembly of PS beads on the Si substrate was transformed into a non-close-packed arrangement by reducing the diameter of the PS spheres using O2 reactive ion etching (RIE). The e-beam evaporator was used to deposit 40 nm-thick Ag films as catalysts to form Si NWs from the substrate, and the substrate was subsequently sonicated in chloroform for 20 min to remove the PS spheres. Wet etching was performed at 50 °C in a solution of 4.5 M HF and 0.5 M H2O2 for 20–30 min, yielding vertically aligned Si NW arrays. Finally, the Ag films were removed by immersion in boiling aqua regia (3:1 of HCl:HNO3) at 100 °C for 20 min.Si NWs were prepared by nanosphere lithography, which is a technique for generating hexagonally close-packed nanoscale patterns of nanometer-sized PS spheres on a Si wafer. PS beads with different diameters of 100, 300, and 460 nm were dispersed in methanol with Triton X-100 as a surfactant. To make the surface of the Si substrate hydrophilic, the substrate was immersed in a piranha solution (1:3 of 30% H2 layer (gate dielectric) was used as a starting material to fabricate GNR FETs. First, the source/drain pads were patterned by photolithography, followed by deposition of Ti/Au as the electrode metal on the graphene by e-beam evaporation. The channel length of the FETs was 8 μm. The Si NWs were raked out from the vertically aligned Si NW arrays prepared on the Si substrate and dissolved in isopropyl alcohol (IPA). This Si NW suspension was sonicated for 10 min and centrifuged at 2500 rpm for 10 min. Then, the Si NWs were aligned on graphene between the two electrodes by a dielectrophoresis method. The assembly of Si NWs was performed by dispensing a dilute suspension of the Si NWs onto the substrates with applied voltages of ± 10 V at frequencies of 1–100 kHz . O2 plasma etching was used to selectively etch an unprotected graphene region away and leave a ribbon structure of graphene underneath the Si NW mask, leading to the formation of GNRs. After that, the NW mask was removed by sonication in IPA, resulting in GNR FETs.A large-area monolayer graphene on a heavily p-doped Si substrate (bottom gate) coated with a 300 nm-thick SiOThe monolayer graphene was characterized by Raman spectroscopy  using a laser with an excitation wavelength of 532 nm. The morphological properties of graphene, Si NWs, and GNRs were investigated by optical microscopy , field-emission scanning electron microscopy , and atomic force microscopy . The electrical characteristics of the GNR FETs were studied with a semiconductor parameter analyzer .−1 with a full width at half maximum of ~ 33 cm−1 was observed. The peak intensity ratio of the 2D and G bands was equal to 2, indicating high-quality monolayer graphene. The weak peak located at 1345 cm−1 corresponded to the D band of graphitic carbon species, the intensity of which was associated with the level of defects in the crystalline structure of the graphene layers. This weak D band confirmed the high quality of the synthesized graphene films. Figure 2 layer.Raman spectroscopy was performed to confirm the formation of graphene and to obtain information about the quality and the number of graphene layers. Figure 2 RIE process, which results in the formation of PS bead arrays that are no longer close-packed  of GNRs has an exponential relationship with the on/off current ratio of GNR FETs (Ion/Ioff  =  C exp(Eg/kT)) [Eg = α/W), where α is between 0.3 and 1.5 [The electrical characteristics of GNR FETs with various widths were measured at room temperature. As shown in Fig. (Eg/kT)) , 33. The and 1.5 , 33. As gm is the transconductance, L is the channel length, W is the channel width, and C is the gate capacitance per unit channel length of a GNR. Since the fringe effect on capacitance at the ribbon edges of the GNRs must be considered due to the narrow ribbon widths of the GNRs, a simulation model based on finite element analysis reported previously was employed to obtain the capacitance [2 V−1 s−1, respectively, at room temperature, due to the carrier scattering at GNR edges. These mobilities are comparable to those reported previously [acitance , 34, 35.eviously –38.kT << Eg, where k is the Boltzmann constant and T is absolute temperature. This confirms that structuring a graphene sheet into a ribbon can open the bandgap.Figure IDS–VGS curves on ribbon width; (3) the field-effect mobility decreased with decreasing width of the GNR; and (4) the bandgap increased with increasing on/off current ratio by narrowing the width of the GNR. These results demonstrate the opening of the bandgap in graphene due to enhanced carrier confinement in the transverse direction and the edge effect in the ribbon structure.In summary, GNRs with different widths were fabricated in a highly controllable manner, and their electrical transport properties were investigated. The GNRs were prepared from CVD-grown graphene sheets by using etch masks based on Si NWs synthesized from Si substrates by a combination of nanosphere lithography and metal-assisted chemical etching, and aligned between two electrodes by an electric-field-assisted alignment method, producing GNR FETs. The ribbon widths of the GNRs were controlled by changing the diameters of the Si NWs and the graphene etching times. The electrical transport characteristics of GNRs were found to be highly dependent on the GNR width: (1) the on/off current ratio was inversely proportional to GNR width; (2) the electrical properties of the GNR FETs showed a strong temperature dependence of the


Upon re-submersion, an additional rapid rise in DMSgas was observed, reflecting increased production by the coral and/or dissolution of DMS-rich mucus formed by the coral during air exposure. Depletion in DMS following re-submersion was likely due to biologically-driven conversion of DMS to dimethylsulfoxide (DMSO). Fast Repetition Rate fluorometry showed downregulated photosynthesis during air exposure but rapid recovery upon re-submersion, suggesting that DMS enhances coral tolerance to oxidative stress during a process that can induce photoinhibition. We estimate that DMS emission from exposed coral reefs may be comparable in magnitude to emissions from other marine DMS hotspots. Coral DMS emission likely comprises a regular and significant source of sulfur to the tropical marine atmosphere, which is currently unrecognised in global DMS emission estimates and Earth System Models.Corals are prolific producers of dimethylsulfoniopropionate (DMSP). High atmospheric concentrations of the DMSP breakdown product dimethylsulfide (DMS) have been linked to coral reefs during low tides. DMS is a potentially key sulfur source to the tropical atmosphere, but DMS emission from corals during tidal exposure is not well quantified. Here we show that gas phase DMS concentrations (DMS Symbiodinium47896Reef-building corals are prolific producers of dimethylsulfoniopropionate (DMSP), an important precursor to the climate-relevant gas dimethylsulfide, DMS12Symbiodinum sppCO2) and elevated DMSP, DMS and DMSO in pelagic microalgae12131516178202022Dimethylated sulfur compounds, particularly DMS and DMSO, readily scavenge harmful hydroxyl radicals and other reactive oxygen species8Acropora cf. horrida, Seriatopora hystrix, Porites cylindrica) to air for short periods and subsequent re-submersion. Measurements of atmospheric DMS (DMSgas), seawater DMS, DMSP, DMSO and Fast Repetition Rate fluorometry (FRRf) were made before, during and after each exposure. Together, our results revealed a marked response in coral sulfur cycling and photophysiological condition that carry important implications both for estimates of sulfur emissions to the tropical/sub-tropical atmosphere and for our understanding of how corals respond to environmental fluctuations.Air exposure experiments are a potentially useful tool to evaluate how reef-forming corals contribute to local atmospheric DMS concentrations and regulate dimethylated sulfur compounds during environmental stress. We describe a series of novel laboratory experiments under controlled temperature and light conditions exposing three common Indo-Pacific scleractinian coral species , originally from the same parent colony from the Indo-Pacific, were used to generate between 1 and 25 (mean 11 ± 9) equal-sized nubbins (ramets), 2–5 cm tall, and distributed equally on holding racks receiving one of two light acclimation levels  for 8–10 weeks before experiments commenced. Ramets were followed throughout experimental design and selected from different genets to ensure biological replication for each treatment. At the beginning of experiments, coral nubbins (ramets) were transferred within a few seconds from the acclimation tank to 3 × 870 mL glass flasks (n = 3 or 4 nubbins per flask) with gas-tight lids and fully covered  in 500 mL seawater from a 1000 L tropical aquarium. The number of nubbins used was dependent on the number that could be placed inside the flask without the nubbins touching each other or the flask walls. Each flask was positioned within a temperature-controlled water bath at 26 °C illuminated by 54 W fluorescent tubes  delivering ~300 μmol photons/m2/s within each flask.Full details of the analytical methods and coral experimental facility, including coral husbandry, are given in the air exposure; Stage II) by removing seawater from the flasks. The mean exposure time for all experiments (32.7 ± 11.7 min) falls within the range of tidal exposure times of 15–105 min experienced in a natural reef environmentre-submersed; Stage III). When the coral nubbins were (re-)submersed, rising bubbles fully equilibrated with seawater DMS (see Discussion) and exited in the waste air stream. During the air exposure phase the air flow from the base directly displaced the headspace in the flask.Seawater in the flasks was continuously and gently bubbled with compressed air from tubing inserted to the base of the flask. Set using metering valves and digitally measured , the mean (±SD) flow for all experiments through the flasks was 59.3 ± 6.5 mL/min. It was ensured that neither the tubing nor the bubble stream came into contact with the coral nubbins. Flasks were left for 1 h to approach constant DMS concentrations where net biological production by the coral holobiont was balanced by removal of DMS due to bubbling. Coral nubbins were subsequently exposed to air (gas) were monitored in the waste air stream from the flasks before, during and after each period of exposure. In experiments with A. cf. horrida, DMSgas was quantified every 4 s using atmospheric pressure ionisation–chemical ionisation mass spectrometry, API-CIMS  to make up the total flow required by the API-CIMS instrumentgas measurements from API-CIMS during the first 2 minutes after each stage change  are not completely representative of the gas phase DMS within the flasks, and are excluded from analyses. The API-CIMS D.L. within this experimental setup was calculated as 3 standard deviations above the mean of the control ‘no coral’ flasks (0.03 ppm for 5-minute averaged data).During replicate incubations , the was−1. The trap was heated rapidly to inject onto the GC column and DMS quantifiedWaste gas outflow from experimental flasks was collected for 10 min in 5 L Tedlar bags pre-rinsed with oxygen-free nitrogen . The bag contents were pre-concentrated on a cryogenic trap at 60 mL mint) and dissolved DMSO (DMSOd) samples were taken before and after coral exposure. For DMS, 5 mL samples of pre-filtered seawater media were analysed via purge and cryogenic trap followed by GC-FPD54 to seawater samples, and purge and cryogenic trap followed by GC-FPD29In a separate experiment without bubbling with compressed air, seawater DMS, total aqueous phase DMSP  data was collected from coral nubbins during parallel air exposure experiments. FRRf inductions (0.2 Hz) parameterised the photosynthetic efficiency , effective absorption cross sectional area , whilst the other species were within a lower range of 0.05–0.2 ppm (0.01-10.4 pmol/cm2)  was 900–1600 nmol/cm2 for A. cf. horrida but 40–80 nmol/cm2 for the remaining three species 13819During initial submersion, DMSmol/cm2) . It is icies see . Our A. gas from a flask containing a colony of A. cf. horrida increased by about an order of magnitude from 0.3 ± 0.2 ppm (mean ± SE) during submersion up to a maximum of 5.8 ppm within the first 2 minutes after air exposure  is 11.4 at 26 °Cgas within the first minutes after air exposure. After the initial spike, DMSgas steadily decreased during the subsequent 12 minutes and returned to near baseline levels. We estimate the exponential decay in DMSgas due to flushing with DMS-free compressed air . We note that this observation does not imply zero DMS production from the coral holobiont upon air exposure. DMS production likely continues leading to the initial spike in DMSgas, but is then trapped by the mucus layer that quickly forms on the coral surface upon exposure (see Discussion).This initial spike in DMSA. cf. horrida , while the other species yielded less than 1 ppm (<100 pmol/cm2). Both raw DMSgas concentrations and zooxanthellae cell density-normalised DMS concentrations yielded the same trends (2/s)A. cf. horrida100 and two flasks holding A. cf. horrida400 (n = 3 nubbins per flask). The same response was seen during air exposure, with DMSgas reaching maxima that ranged from 0.8–4 ppm across four experimental flasks, compared to 0.1–1.5 ppm during submersion  from the coral, we started from the mass balance equation of gas phase DMS:gas = 700 mL, the gas flow Flow = 60 mL/min. To make the coral emissions comparable to other marine DMS flux hotspots, we converted our flux estimates into equivalent sea surface areas (SSA); we take SSA for each fragment in our experiments to be the birds-eye-view water surface area above the coral (estimated as 25 cm2 for each fragment x 3 nubbins = 75 cm2). gas increase (dDMSgas/dt), which we could not accurately quantify due to the measurement system design. For simplicity, we therefore assume the averaged DMSgas shown in A. cf. horrida was 3–11 mmol/m2/day. Emissions from other coral species were lower, at 1.7 mmol/m2/day for P. cylindrica and 2.3 mmol/m2/day for S. hystrix.Here the headspace volume during air exposure VA. cf. horrida at 24 minutes into the experiment resulted in a second rapid increase of DMSgas up to 6 ppm in the outflow of the flask, followed by a near-exponential decrease in concentration , re-submersing the coral in seawater could result in dissolution and liberation of the DMS trapped in mucus. The subsequent decrease in DMSgas is driven by biological processes associated with the presence of coral, with water-to-air emission accounting for only a quarter of the DMS loss (see Discussion).Re-submersion of ntration . Unlike Fq’/Fm’) in conjunction with an increased photosynthetic electron turnover time (τ´)  periods of exposure during spring tides38Acroporids may possess physiological mechanisms that instil tolerance to a range of environmental conditions.Continuous FRRf measurements provided a diagnostic of photosynthetic performance before, during, and after air exposure. Photosynthetic downregulation was identified by a temporary decrease in the effective absorption cross-section (σ´)  and the ime (τ´) . This iso-damage . P. cyliexposure . The fuld) and total aqueous phase DMSP (DMSPt) at regular intervals after re-submersion of A. cf. horrida400 in a separate experiment . Abiotic photochemical loss of DMS, inferred from previous measurements from the Sargasso SeaTo explore the roles of dimethylated sulfur compounds as anti-oxidants, we measured the seawater concentrations of DMS, dissolved DMSO . Simultaneously, DMSOd increased by 175 nM (1.4 nmol/cm2). We see that the net increase in DMSO concentration  was 55 nM higher than the net decrease in DMS concentration. Using a simplistic mass balance where no DMSO is directly released by the coral, 100% of DMS is converted to DMSO, and DMSO loss is negligible, 42After 20 minutes, DMS levels had decreased by 120 nM  in a separate experiment  and monitored the rate of decrease due to bubbling (60 mL/min) in a separate test. The observed reduction , which shows excellent agreement with the modelled DMS equilibrium outgassing (red line). Based on this, we convert the API-CIMS gas phase DMS mixing ratio to a waterside concentration  via the ideal gas law and the Henry’s solubility.We developed a simple box model in order to explore the plausible bounds of biological DMS production and consumption. As an independent measure, we used the biological consumption rate derived from the no-bubbling experiment  to modelperiment . The corperiment . DMS in sw a few minutes after re-submersion. Similar to previous experiments, elevated DMSsw (~1 μM) was observed immediately after the coral was re-submersed followed by a gradual decline in concentration to ~0.01 μM after 60 minutes. The model shows that equilibrium outgassing can only explain about 25% of the total DMS loss within the first 20 minutes after re-submersion. This indicates additional coral-associated loss of DMSsw, similar to sw relative to observations. A consumption-only model run  concentrations that are orders of magnitude higher than in seawaterA. cf. horrida that had been exposed to air for 15 minutes (a separate colony to those used in the main experiments - see SI methods). Concentrations ranged from 279–577 nM in mucus, with a value of 373 nM in a sample of mucus rope It is worth mentioning that even a high DMS production of 2 μM/h see  does notsw] upon dissolution of DMS-rich mucus into the water phase:Using this information, we can estimate the resultant [DMSA. milleporaA. cf. horrida nubbins used in our study is 125.7 cm2 (See mucus) of 0.006 L. Seawater volume in the flask (volwater) was 0.5 L. Taking the maximum value of [DMSmucus] we measured for A. cf. horrida (577 nM) would result in [DMSsw] of only 7.3 nM, far from the ~1 μM we measured . The extent of exposed coral reef during low tide events is not well documented but typically includes the reef crestAcropora colonies and is exposed on average 12 hours per month (~0.5 days per 30 days = ~0.02)252/day (i.e. 3–11 mmol/m2/day *(0.158)*0.02*1000) by assuming that Acropora on the reef rim are the sole source of DMS and only emit to the atmosphere when exposed to air. This simplistic calculation does not account for the complexity of the reef environment . Furthermore, the experimental exposure times used here (32.7 ± 11.7 min) fall short of maximum exposure times in a natural reef environment (up to >100 min)47Our experiments suggest that coral reefs represent an as-yet unquantified all-year-round direct DMS flux into the tropical atmosphere (~23°N to 23°S), which is not currently accounted for in existing climatologies and Earth System models56572/day2/day were measured over a coccolithophore-dominated bloom in the North Atlantic2/day was seen over a dinoflagellate bloom in the Southern OceanNevertheless, DMS emissions from corals may be in the same order of magnitude as other marine DMS hot spots, which tend to be more transient. For example, DMSP-rich surface pack ice in Antarctica is estimated to emit DMS to the atmosphere at a few tens of μmol/mAcropora dominate coral communities. Acropora is a highly successful genus contributing to rich species diversity and productivity on coral reefs via their complex architecture and rapid growthAcropora is also particularly susceptible to major environmental and climatic stressors, which are expected to increase in frequency in the futureAcropora cover could impact upon future biodiversity and productivity associated with coral reefsIn situ measurements of atmospheric DMS concentrations and coral-to-air DMS fluxes in reef environments at a range of temporal and spatial scales are required to better quantify the importance of DMS in the tropical marine atmospheric chemistry.Our experiments show that when water is removed and corals are exposed to air, a solubility effect results in an order of magnitude increase in DMS concentrations in the overlying air in the first few minutes after exposure. This is consistent with observations of elevated DMS in the air above coral reefs following low tides222364How to cite this article: Hopkins, F. E. et al. Air exposure of coral is a significant source of dimethylsulfide (DMS) to the atmosphere. Sci. Rep.6, 36031; doi: 10.1038/srep36031 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Surprisingly, the passing ion does not tear nanometre-sized holes into the single layer graphene. We use time-dependent density functional theory to gain insight into the multielectron dynamics.The way conduction electrons respond to ultrafast external perturbations in low dimensional materials is at the core of the design of future devices for (opto)electronics, photodetection and spintronics. Highly charged ions provide a tool for probing the electronic response of solids to extremely strong electric fields localized down to nanometre-sized areas. With ion transmission times in the order of femtoseconds, we can directly probe the local electronic dynamics of an ultrathin foil on this timescale. Here we report on the ability of freestanding single layer graphene to provide tens of electrons for charge neutralization of a slow highly charged ion within a few femtoseconds. With values higher than 10 Graphene has so far demonstrated remarkable properties, making it increasingly interesting for ultrafast electronic applications. Here, the authors show that, when probed by a highly charged ion, freestanding graphene is able to provide dozens of electrons for ion neutralization within a few femtoseconds. A charge state of  fs refs , 33, 34.qin=30 captures ∼25 electrons on a timescale of a few fs. Based on the experimental data, a lower bound for the current densities reached locally within a nm2 area is at least 1012 A cm−2 exceeding the breakdown current densities reported so far567In this contribution, we take the final step and present the results for the ultimately thin carbon target, a freestanding single layer of graphene. Measurements of the charge state and energy of the transmitted ions and time-dependent density functional theory (TDDFT) calculations show that large number of electrons are extracted from a very small surface area, which implies a high local surface current density. We identify a multielectron process and estimate the relevant timescales for charge transfer along the graphene layer. For example, while passing through SLG, the HCI with initial charge state qout and the energy of highly charged Xe ions after transmission through SLG , The amount of charge transferred to the HCI can be estimated by measuring the distribution of exit charge states analyser . Typicalanalyser  show a dτ=deff/v, where v is the ion velocity. The effective interaction length deff corresponds to the projectile-surface distance where the electron transfer processes between the HCI and graphene take place. The values of deff can be obtained from the TDDFT calculations which yield for qin=20 a value of Depending on the initial charge state the HCI captures and stabilizes between 20 and 30 electrons during its transmission through graphene . The expThe number of captured and stabilized electrons (they are not reemitted due to autoionization processes) as function of interaction time is shown in deff=9 Å we obtain qin=35, and qin=20, respectively. The performed TDDFT study  into the SLG, as we have observed for carbon nanomembranes35qin=20 projectile incident at graphene with velocity v=0.87 nm fs−1. Results of the TDDFT calculations are presented for four different HCI–graphene distances zion along the ingoing trajectory path. We use cylindrical  coordinates, with the z axis set along the projectile trajectory assuming normal incidence geometry. Note that because of the cylindrical symmetry in our calculations  can be observed.To illustrate the way the electronic processes take place, in Jz determines the charge exchange and energy loss of the HCI (see the section Energy loss), while the current density along the transverse direction Jρ shows the fast local response of graphene to the strong HCI perturbation. In fact, our calculations reproduce our experimental estimate since the transverse current densities in the proximity of the graphene layer reaches values as high as 1012 A cm−2. This means that, transiently and locally, in the femtosecond and nanometre scale, graphene is able to sustain extremely high current densities. The positive charges created by electron capture and electron emission are spread over the entire layerThe current density along the direction of motion qout=2 and qout=4 increases quadratically with the incident charge state  all the missing electrons in the projectile. Our experiments and TDDFT calculations, both suggest local current densities in the graphene plane exceeding 10q keV corresponding to velocities between 0.13 and 0.5 nm fs−1. The pressure in the experimental chamber is kept below 5 × 10−9 mbar during measurements to prevent charge-exchange processes of the ions before interaction with the target.The measurements are performed at the Ion Beam Center of the Helmholtz-Zentrum Dresden-Rossendorf. Highly charged Xe ions are produced in a room temperature electron beam ion trap, charge state separated by an analysing magnet and then guided by several electrostatic lenses into the target chamber. Due to an electrostatic deceleration system, the kinetic energy of the extracted Xe ions can be varied between 0.1 and 4.4E/E≈1.5 × 10−3. The analyser's maximum operation voltage of 5,000 V limits the range of measurable charge states for projectiles with larger kinetic energies.The freestanding SLG sheets, which span over a regular array of holes in a TEM grid, are produced at the University Duisburg-Essen and transferred without the use of polymer coating see . The eleR=0.26 Å, Coulomb tail Q/r) and a planar jellium disk with the correct work function value (4.6 eV)13r=R. Finite size effects have been checked using jellium disks of different sizes containing 500, 1,004 and 2,000 electrons .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures, Supplementary Notes and Supplementary References.in=20 at v=0.87 nm fs-1. The polarization of the surface of graphene due to the approaching HCI, the charge transfer from graphene to the projectile and the excitation of the graphene layer while and after the crossing of the ion are clearly visible.The video illustrates the induced charge density for an incoming ion with q


Metallic gate electrodes are often employed to control the conductivity of graphene based field effect devices. The lack of transparency of such electrodes in many optical applications is a key limiting factor. We demonstrate a working concept of a double layer graphene field effect device that utilizes a thin film of sputtered aluminum nitride as dielectric gate material. For this system, we show that the graphene resistance can be modified by a voltage between the two graphene layers. We study how a second gate voltage applied to the silicon back gate modifies the measured microwave transport data at around 8.7 GHz. As confirmed by numerical simulations based on the Boltzmann equation, this system resembles a parallel circuit of two graphene layers with different intrinsic doping levels. The obtained experimental results indicate that the graphene-aluminum nitride-graphene device concept presents a promising technology platform for terahertz- to- optical devices as well as radio-frequency acoustic devices where piezoelectricity in aluminum nitride can also be exploited. Flexible and transparent FET devices may lead to novel device integration concepts such as wearable electronics and solar cells, integrated RFID tags in packaging material, and would therefore contribute to the ongoing electronic revolutionGraphene is a promising material for terahertz-to-optical devices, in particular voltage controlled attenuators and modulators for terahertz components to be used for wireless communication with high data rate1569101112132 layer. The device is fabricated in three steps as demonstrated by 2. This is followed by magnetron- sputtering of 50 nm AlN over the entire area at 400 °C substrate temperature  layer sandwiched between two graphene layers. Previous attempts to fabricate double-graphene structures include implementation of poly (methyl methacrylate), PMMA, in between two graphene layersperature  and thenperature . This arperature . As a corature 19. The gra01δ, mode of the dielectric puck is the lowest fundamental mode that is excited via two coupling loop antennas in the cavity and the resulting resonance curve is measured using a Rohde-Schwarz ZVA vector network analyzer (VNA). This mode exhibits only azimuthal electric field component and therefore the field remains continuous on the surface of the ceramic resonator and high confinements of the field within the puck is achieved. The resulting evanescent field leaks through the aperture of the cavity and excites circular high-frequency currents in the graphene layers. As such, it represents an ideal measurement methodology for highly accurate and contact-free electrical measurements of 2D materialsThe transverse electric, TE01δ mode21Q-factor. Prior to presenting the double-gated structure, we first verify this characterization technique on a simpler commonly employed single gated silicon based system. For this system, we transferred graphene on both nSiO2/Si and AlN/Si substrates. This was then followed by the microwave field-effect measurements using the dielectric-loaded cavity method. Q, which determines the sheet resistance via equation (1)The entire GFET device is semi-transparent for the electromagnetic fields of the TEQ-factor values. Here, the characteristic impedance of the cavity, ZC, is determined by a substrate calibration based on the measured shift of the resonant frequency due to the graphene-free substrateQS denotes the measured quality factor with the uncoated HRS substrate and Q(V) is the measured quality factor at a given gate voltage for a graphene covered substrate. The ratio between the resistance R(V) at a given gate voltage V  and the minimum resistance Rmin, (representing maximum field induced n- or p-type doping), given by Qmin represents the smallest measured quality factor corresponding to the minimum sheet resistance. Note that this ratio is independent of the cavity calibration factor ZC. It is important to note that the achieved modulation depth of 2.1 dB is much lower than the maximum achievable modulation depth for a graphene-modulated microwave cavity. This is merely because the silicon substrate, in spite of its high resistivity, contributes significantly to the cavity losses.Here, We compare the voltage dependent conductivity to the theoretical Boltzmann equation results given by equation (3)kB and μ denote the Boltzmann constant and the chemical potential, respectively, T the temperature, and e denotes the electron charge and ħ is reduced Planck’s constant. At microwave frequencies the Boltzmann equation represents an accurate approximation to the more general Kubo formula for the conductivity, because the interband contribution to σ is negligible for frequencies below 100 GHzτ+) and negative (denoted τ−) energies. The chemical potential, μ, is determined by the carrier density, which is controlled via the gate voltage VGS. For a single graphene layer, the charge density is given by n = n0 + CVGS/e, where n0. denotes the intrinsic charge density and C is the areal capacitance associated with the gate dielectric between graphene and Si. For the double layer (see e2/πh), the widely accepted minimum optical conductivity of graphenewhere ayer see  we obtai2 and 5 nm thick AlN respectively with the simulations based on the Boltzmann equation result (τ+ = 17 fs a τ− = 14 fs give the best fits for nSiO2 and τ− = 14.5 fs for AlN as well as n0 = 8.6 × 10111/cm2 for the intrinsic charge density of graphene are used to obtain the fit (see 9252627n result . Electro fit see . We note2) double graphene-FET as a function of the applied voltage between the two graphene layers. The different curves correspond to different voltages applied to the HRS back gate. Note that the partial transparency of graphene and AlN to the electromagnetic cavity fields enables the measurements of the parallel resistance of the two graphene layers. In order to understand the variation of the sheet resistance with the voltage applied to the Si back-gate and the measured asymmetry of the curves, simulations based on the Boltzmann equation were performed. τ+ = τ− = 3.6 fs and n0 = 10.8 × 10111/cm2 for the top graphene sheet and τ+ = 10.9 fs, τ−= 5.8 fs and n0 = −4.0 × 10111/cm2 for the bottom graphene sheet. Areal capacitances of 0.3 mF/m2 and 0.17 mF/m2 were used for the top capacitor (50 nm AlN) and the bottom capacitor (nSiO2), respectively.The simulations reproduce some of the key features of the experimental result, including the shift of the Dirac point of the bottom graphene layer for changes of the back gate voltage and the strong asymmetry of the conductivity curves, which arises from the presence of the top graphene layer whose Dirac point is at a large negative bias voltage which lies outside the range of leakage-free voltages between the two graphene layers. It is important to note that the induced current in the bottom graphene layer is slightly higher than the current in the top graphene layer due to partial reflection of the magnetic field from the bottom graphene layer. The results demonstrate a switching ratio as high as 4.5 for the parallel resistance due to alteration of the gate voltage between the two graphene layers, i.e. without any change of the back-gate voltage and without any metallic top gate. As such, this structure represents an all-graphene voltage-controlled attenuator for microwave frequencies. The attenuation of the microwave signal results from an Ohmic current through the graphene layers which is induced by the electromagnetic wave. Any variation of the sheet-resistance changes the amount of partial reflection and transmission. As a result, the on-off ratio of the sheet resistance is directly proportional to the on-off ratio of the attenuator. Moreover, we find that the minimum conductivity in the theoretical results increases upon increasing the back gate voltage, while its value is almost constant in the experimental measurement. We attribute this discrepancy to the assumption of a constant scattering time in the computed conductivity, which cannot reproduce the experimentally observed saturation of the conductivity at large gate voltages. Furthermore, the spacing between the resistance curves in In conclusion, we fabricated an all graphene field-effect device that operates at microwave frequencies and established a microwave cavity technique as a powerful tool for the characterization of such a field effect device. A structure composed of a 50 nm thick aluminum nitride layer between two graphene layers exhibits variation of the stack sheet resistance with a pronounced Dirac point and an on-off ratio of 4.5 by applying a DC voltage between the two graphene layers only. Use of native oxide gate dielectrics in combination with annealing at moderate temperatures results in reproducible transfer curves with charge neutrality points around zero volts, which are in very good agreement with the expectation from our theoretical model. This structure possesses a great potential for microwave-to-THz applications, such as in voltage-controlled attenuators and modulators for high data-rate wireless communication. Moreover, this technology may be used to build integrated electrodes for piezoelectric devices enabling excitation of mechanical oscillations in AlN based thin film bulk acoustic devices (FBAR) and thin film microbridges at RF and microwave frequencies. It is expected that the outstanding mechanical performance of AlN is not negatively affected by the presence of ultrathin graphene layers, which possess outstanding mechanical properties themselves. The possible combination of electric biosensing by monitoring conductivity changes of functionalized graphene and mass detection by an AlN microbridge at the same time within one device can pave the way to a new generation of silicon-integrated dual-mode biosensors with two orthogonal sensor channels.via a PMMA method, which has been described in our previous work2/CH4 gas flow ratio of 4. The growth temperature is set to 1050 °C for the duration of 30 minutes. In fabrication of the DG-FET structures, graphene films were grown on a 7 mm by 5 mm copper substrates to facilitate the transfer process.Large area single layer graphene was fabricated using a vertical CVD technique and transferred −4 at 10 GHz) and a high relative permittivity (29) is placed inside a copper cavity. The puck is fixed on top of a quartz holder approximately 1 mm below a circular aperture of 5 mm diameter, which is located at the center of the top wall of the cavity and allows the resonant field to interact with the graphene sample ) with a diameter of 7 mm, a very low loss-tangent at microwave frequencies  magnetron sputtering with thickness of 50 nm. The RF sputtering process was conducted in a Mantis combined sputtering/thermal evaporation system with a base pressure of 5 × 10The schematic in How to cite this article: Adabi, M. et al. Microwave Study of Field-Effect Devices Based on Graphene/Aluminum Nitride/Graphene Structures. Sci. Rep.7, 44202; doi: 10.1038/srep44202 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


This barrier can be mitigated by considering graphene-MoS2 heterostructure, as graphene possesses strong mechanical properties. We have developed efficient closed-form expressions for the equivalent elastic properties of such multi-layer hexagonal nano-hetrostructures. Based on these physics-based analytical formulae, mechanical properties are investigated for different heterostructures such as graphene-MoS2, graphene-hBN, graphene-stanene and stanene-MoS2. The proposed formulae will enable efficient characterization of mechanical properties in developing a wide range of application-specific nano-heterostructures.Two-dimensional and quasi-two-dimensional materials are important nanostructures because of their exciting electronic, optical, thermal, chemical and mechanical properties. However, a single-layer nanomaterial may not possess a particular property adequately, or multiple desired properties simultaneously. Recently a new trend has emerged to develop nano-heterostructures by assembling multiple monolayers of different nanostructures to achieve various tunable desired properties simultaneously. For example, transition metal dichalcogenides such as MoS The interest in such hexagonal two-dimensional materials has expanded over the last decade from hBN, BCN, graphene oxides to Chalcogenides like MoS2, MoSe2 and other forms of two-dimensional materials like stanene, silicene, sermanene, phosphorene, borophene etc.6. Among these two-dimensional materials, hexagonal honeycomb-like nano-structure is a prevalent structural form3. Four different classes of single-layer materials with hexagonal nano-structure exist from a geometrical point of view, as shown in Fig. 7 consists of a single type of atom (carbon) to form a honeycomb-like hexagonal lattice structure in a single plane, while there is a different class of materials that possess hexagonal monoplanar nanostructure with different constituent atoms such as hBN8, BCN9 etc. Unlike these monoplanar hexagonal nanostructures, there are plenty of other materials that have the atoms placed in multiple planes to form a hexagonal top view. Such multiplanar hexagonal nanostructures may be consisted of either a single type of atom , or different atoms . Even though these two-dimensional materials show promising electronic, optical, thermal, chemical and mechanical characteristics for exciting future applications, a single nanomaterial may not possess a particular property adequately, or multiple desired properties simultaneously. To mitigate this lacuna, recently a new trend has emerged to develop nano-heterostructures by assembling multiple monolayers of different nanostructures for achieving various tunable desired properties simultaneously.A generalized analytical approach is presented to derive closed-form formulae for the effective in-plane elastic moduli of hexagonal multiplanar nano-structures and heterostructures. Hexagonal nano-structural forms are common in various two-dimensional and quasi-two-dimensional materials. The fascinating properties of graphene2 show exciting electronic and piezoelectric properties, but their low in-plane mechanical strength is a constraint for any practical application. In contrast, graphene possesses strong in-plane mechanical properties. Moreover, graphene is extremely soft in the out-of-plane direction with a very low bending modulus, whereas the bending modulus of MoS2 is comparatively much higher, depending on their respective single-layer thickness19. Having noticed that graphene and MoS2 possess such complementary physical properties, it is a quite rational attempt to combine these two materials in the form of a graphene-MoS2 heterostructure, which could exhibit the desired level of electronic properties and in-plane as well as out-of-plane strengths. Besides intense research on different two dimensional hexagonal nano-structural forms, recently the development of novel application-specific heterostructures has started receiving considerable attention from the scientific community due to the tremendous prospect of combining different single layer materials in intelligent and intuitive ways to achieve several such desired physical and chemical properties26.Although the single-layer of two-dimensional materials have hexagonal lattice nano-structure (top-view) in common, their out-of-plane lattice characteristics are quite different, as discussed in the preceding paragraph. Subsequently, these materials exhibit significantly different mechanical and electronic properties. For example, transition metal dichalcogenides such as MoS27, heterostructure containing both mono-planar and multi-planar nanostructures  and heterostructure containing only multi-planar nanostructures . Recently different forms of multi-layer heterostructures have started receiving immense attention from the scientific community for showing interesting chemical, thermal, optical, electronic and transport properties30. Even though the heterostructures show various exciting physical and chemical characteristics, effective mechanical properties such as Young’s moduli and Poisson’s ratios are of utmost importance for accessing the viability in application of such nano-heterostructures in various nanoelectromechanical systems. The research in this field is still in a very nascent stage and investigations on elastic properties of these built-up structural forms are very scarce to find in literature21.The hexagonal nano-heterostructures can be broadly classified into three categories based on structural configuration, as shown in Fig. 2 etc.33. This approach of mechanical property characterization for single-layer nanostructures is computationally very efficient, yet accurate and physically insightful. However, the analytical models concerning two-dimesional hexagonal nano-structures developed so far are limited to single-layer structural forms; development of efficient analytical approaches has not been attempted yet for nano-heterostructures. Considering the future prospect of research in this field, it is essential to develop computationally efficient closed-form formulae for the elastic moduli of nano-hetrostructures that can serve as a ready reference for the researchers without the need of conducting expensive and time consuming molecular dynamics simulations or laboratory experiments. This will accelerate the process of novel material development based on the application-specific need of achieving multiple tunable properties simultaneously to a desirable extent.The common practises to investigate these nanostructures are first principle studies/ab-initio and molecular dynamics, which can reproduce the results of experimental analysis with the cost of computationally expensive and time consuming supercomputing facilities. Moreover, availability of interatomic potentials can be a practical barrier in carrying out molecular dynamics simulation for nano-heterostructures, which are consisted of multiple materials. The accuracy of molecular dynamics simulation depends on the interatomic potentials and the situation can become worse in case of nano-heterostructures due to the possibility of having lesser accuracy for built-up structural forms. Molecular mechanics based analytical closed form formulae are presented by many researchers for materials having hexagonal nano-structures in a single layer such as graphene, hBN, stanene, MoS2, graphene-stanene and stanene-MoS2), belonging to all the three classes as discussed in the preceding paragraphs, are investigated considering various stacking sequences. The analytical formulae for elastic moduli of heterostructures are applicable to any number of different constituent single-layer materials with multi-planar or mono-planar hexagonal nanostructures.In this article, we aim to address the strong rationale for developing a generalized compact analytical model leading to closed-form and high fidelity expressions for characterizing the mechanical properties of a wide range of hexagonal nano-heterostructures. Elastic properties of four different heterostructures  and thereby presenting new results for various stacking sequence of different nano-heterostructures belonging to the three different classes as described in the preceding section .In this section, the closed-form analytical expressions of elastic moduli for generalized multiplaner hexagonal nano-heterostructures are presented. The molecular mechanics based approach for obtaining the equivalent elastic properties of atomic bonds is well-documented in scientific literature34. Total strain energy (E) is expressed as the sum of energy contributions from bending of bonds (Eb), bond stretching (Es), torsion of bonds (Et) and energies associated with non-bonded terms (Enb) such as the van der Waals attraction, the core repulsions and the coulombic energy , the strain energy caused by bending consists of two components, in-plane component (EbI) and out-of-plane component (EbO). The out-of-plane component becomes zero for monoplanar nanostructures such as graphane and hBN. Thus the total interatomic potential energy (E) can be expressed asl, Δθ and Δα denote the change in bond length, in-plane and out-of-plane angle respectively. The quantities kr and kθ represents the force constants for bond stretching and bending respectively. The molecular mechanics parameters (kr and kθ) and structural mechanics parameters (EA and EI) of a uniform circular beam with cross-sectional area A, length l, Young’s modulus E, and second moment of area I, are related as: 35. Based on this relationship, the closed form expressions for the effective elastic moduli of multilayer hexagonal nano-heterostructures are derived following a multi-stage idealization scheme using force equilibrium and deformation compatibility conditions. The closed form expressions for the two in-plane Young’s moduli of nano-heterostructures are derived asHowever, among all the energy components, effect of bending and stretching are predominant in case of small deformationi in the above expressions indicates the molecular mechanics and geometrical properties , bond length (l), in-plane bond angle (ψ) and out-of-plane angle (α), which are well-documented in the molecular mechanics literature. The analytical formulae are valid for small deformation of the structure (i.e. the linear region of stress-strain curve). The effect of inter-layer stiffness contribution due Lennard-Jones potentials are found to be negligible for the in-plane elastic moduli considered in this study and therefore, neglected in the analytical derivation .Here 2, graphene-hBN, graphene-stanene and stanene-MoS2 (belonging to the three categories as depicted in the introduction section). Though all these four heterostructures have received attention from the concerned scientific community for different physical and chemical properties recently, only the graphene-MoS2 heterostructure has been investigated using molecular dynamics simulation for the Young’s modulus among all other elastic moduli21. Thus we have validated the proposed analytical formulae for Young’s moduli of graphene-MoS2 heterostructure with available results from literature. New results are presented for the two in-plane Poisson’s ratios of graphene-MoS2 heterostructure using the analytical formulae, which are validated by carrying out separate molecular dynamics simulations. Having the developed analytical formulae validated for the two Young’s moduli and Poisson’s ratios, new results are provided for the other three considered heterostructures accounting for the effect of stacking sequence. Moreover, it can be noted that for single layer of the heterostructure (i.e. for n = 1), the proposed analytical formulae can be used to predict the effective elastic moduli of monoplanar (i.e. α = 0) and multiplanar (i.e. α ≠ 0) materials. The analytical predictions for the Young’s moduli and Poisson’s ratios of such single-layer materials are further validated with reference results from literature, as available.Results are presented for the effective elastic moduli of hexagonal multi-layer nano-heterostructures based on the formulae proposed in the preceding section. As investigations on nano-heterostructures is a new and emerging field of research, the results available for the elastic moduli of different forms of heterostructures is very scarce in scientific literature. We have considered four different nano-heterostructures to present the results: graphene-MoSn = 1) belonging to all the four classes as described in the preceding section , the in-plane Young’s moduli obtained using the proposed analytical formulae are in good agreement with reported values in literature for graphene, hBN, stanene and MoS2. These observations corroborate the validity of the proposed analytical formulae in case of a single-layer. However, in case of Poisson’s ratios, the reported values in scientific literature for graphene and hBN show wide range of variability, while the reference values of Poisson’s ratios for stanene and MoS2 are very scarce in available literature. The results predicted by the proposed formulae agree well with most of the reported values for Poisson’s ratios.As shown in Tables 2. The results are compared with the numerical values reported in scientific literature. It can be noted that the difference between E1 and E2 is not recognized in most of the previous investigations and the results presented as E1 = E2. The Young’s moduli E1 and E2 are found to be different for multiplanar single-layer nanostructural forms (such as stanene and MoS2). A similar trend has been reported before by Li41 for MoS2. Thus the effective Young’s moduli of the heterostructures with at least one layer of multiplanar structural form is expected to exhibit different E1 and E2 values. In Table E1 = E2, while for single and bi-layer of MoS2E1 ≠ E2. In case of heterostructures consisting of both graphene and MoS2 the value of E2 is observed to be higher than E1. However, the numerical values of E1 for different stacking sequences are found to be in good agreement with the values of Young’s modulus reported in literature (presumably obtained for direction-1) corroborating the validity of the developed closed-form expressions. We have carried out separate molecular dynamics simulations for graphene – MoS2 heterostructures to validate the analytical predictions of Poisson’s ratios, as Poisson’s ratios have not been reported for graphene–MoS2 heterostructures in literature. The analytical predictions of Poisson’s ratios reported in Table 2 heterostructure, the two in-plane Poisson’s ratios (ν12 and ν21) are found to have different values when at least one multi-planar structural form is present in the heterostructure. Thus having the analytical formulae for all the elastic moduli validated, we have provided new results for three other nano-heterostructures in the following paragraphs based on Equations Table E1 = E2 and ν12 = ν21) in case of graphene-hBN heterostructure as these are consisted of only mono-planar structural forms. Table E1 ≠ E2 and ν12 ≠ ν21) when at least one of the constituent layers of the heterostructure is stanene. Table 2 heterostructure considering different stacking sequences. As stanene and MoS2 both have multi-planar structural form, the two Young’s moduli and two in-plane Poisson’s ratios show considerably different values (i.e. E1 ≠ E2 and ν12 ≠ ν21). The results of different elastic moduli corresponding to various stacking sequences are noticed to have an intermediate value between the respective elastic modulus for single layer of the constituent materials, as expected on a logical basis.Table 2 and graphene-stanene heterostructures with little difference in the actual numerical values. The variation of elastic moduli for graphene-hBN heterostructure are presented for E1 and ν12 as the numerical values are exactly same for the two Young’s moduli and two in-plane Poisson’s ratios, respectively. The plots furnished in this section can readily provide an idea about the trend of variation of elastic moduli with stacking sequence of multi-layer nano-heterostructures in a comprehensive manner; exact values of the elastic moduli corresponding to various stacking sequences can be easily obtained using the proposed computationally efficient closed-form formulae.The physics based analytical formulae for nano-heterostructures presented in this article are capable of obtaining the elastic moduli corresponding to any stacking sequence of the constituent layer of nano-materials. However, from the expressions it can be discerned that the numerical values of elastic moduli actually depend on the number of layers of different constituent materials rather than their exact stacking sequences. From a mechanics view-point, this is because of the fact that the in-plane properties are not a function of the distance of individual constituent layers from the neutral plane of the entire heterostructure. Figures α ≠ 0) or monoplanar (i.e. α = 0) configurations. It is interesting to notice that the generalized analytical formulae developed for the Young’s moduli of heterostructures can be reduced to the closed-form expressions provided by Shokrieh and Rafiee31 for graphene considering single-layer (i.e. n = 1), α = 0 and ψ = 30°.We have presented computationally efficient analytical closed-form expressions for the effective elastic moduli of multi-layer nano-heterostructures, wherein individual layers may have multiplanar (i.e. E1 = E2 and ν12 = ν21). However, for single-layer materials with multiplanar nanostructure, the elastic modulus for direction-2 is more than that of direction-1, even though the difference is not significant. Similar observation is found to be reported in literature41. For single-layer of materials, the formulae of elastic moduli deduced from Equations n = 1, perfectly obeys the Reciprocal theorem (i.e. E1ν21 = E2ν12)42. In case of nano-heterostructures, the Young’s moduli and Poisson’s ratios possess different values if at least any one of the layers have a material with multiplanar hexagonal nano-structure (i.e. E1 ≠ E2 and ν12 ≠ ν21). An advantage of the proposed bottom-up approach of considering layer-wise equivalent material property is that it allows us to neglect the effect of lattice mismatch in evaluating the effective elastic moduli for multi-layer heterostructures consisting of different materials. In the derivation for effective elastic moduli of such heterostructues, the deformation compatibility conditions of the adjacent layers are satisfied. This is expected to give rise to some strain energy locally at the interfaces, which is noted in previous studies21. From the derived expressions it can be discerned that the numerical values of elastic moduli actually depend on the number of layers of different constituent materials rather than their stacking sequences. In case of multi-layer nanostructures constituted of the layers of same material , it can be expected from Equations t.It can be noted from the presented results that the single-layer materials having regular monoplanar hexagonal nano-structures (such as graphene and hBN) have equal value of elastic modulus in two perpendicular directions . Even though the results are presented in this article considering only two different constituent materials in a single heterostructure , the proposed formulae can be used for heterostructures containing any number of different materials26. The physics-based analytical formulae are capable of providing a comprehensive in-depth insight on the behaviour of such multilayer heterostructures. Noteworthy feature of the present analytical approach is the computational efficiency and cost-effectiveness compared to conducting nano-scale experiments or molecular dynamics simulations. Thus, besides deterministic analysis of elastic moduli, as presented in this paper, the efficient closed-form formulae could be an attractive option for carrying out uncertainty analysis50 based on a Monte Carlo simulation based approach . The bottom-up approach based concept to develop expressions for hexagonal nano-heterostructures can be extended to other forms of nanostrcutures in future.Effective mechanical properties such as Young’s moduli and Poisson’s ratios are of utmost importance to access the viability for the use of nano-heterostructures in various nanoelectromechanical applications. The major contribution of this work is to develop the generalized closed-form analytical formulae for multi-layer nano-heterostructures. Thses formulae are also applicable to single-layer of materials with monoplanar as well as multiplanar nanostructures. Thus the developed analytical formulae for elastic moduli can be used as an efficient reference for the entire spectrum of materials with lattice-like structural form and the heterostructures obtained by combining multiple layers of different such materials with any stacking sequence. Such generalization in the derived formulae, with the advantage of being computationally efficient and easy to implement, opens up a tremendous potential scope in the field of novel application-specific heterostructure development. We have validated the proposed expressions considering multiple stacking sequences with existing results of literature and separate molecular dynamics simulations for the Young’s moduli and Poisson’s ratios of graphene-MoS23, as the interest in graphene did few years ago. The attentiveness is expected to expand further in coming years with the possibility to consider different tunable nanoelectromechanical properties of the prospective combination (single and multi-layer structures with different stacking sequences) of so many two dimensional materials. This, in turn introduces the possibility of opening a new dimension of application-specific material development that is analogous to metamaterials52 in nano-scale. The present article can contribute significantly in this exciting endeavour.After several years of intensive investigation, research concerning graphene has logically reached to a rather mature stage. Thus investigation of other two dimensional and quasi-two dimensional materials have started receiving the due attention recently. However, the possibility of combining single layers of different two dimensional materials (heterostructures) has expanded this field of research dramatically; well beyond the scope of considering a simple single layer graphene or other 2D material. The interest in such heterostructures is growing very rapidly with the advancement of synthesizing such materials in laboratory2 heterostructures by carrying out separate molecular dynamics simulations and available results from literature. New results are presented for graphene–hBN, graphene–stanene and stanene–MoS2 heterostructures using the developed analytical framework. As the proposed closed-form formulae are general in nature and applicable to wide range of materials and their combinations with hexagonal nano-structures, the present article can serve as a ready reference for characterizing the material properties in future nano-materials development.In summary, we have developed computationally efficient physics-based analytical expressions for predicting the equivalent elastic moduli of multi-layer nano-heterostructures. The proposed expressions are validated for graphene–MoSkr and kθ), bond length and bond angles for different materials, which are used to obtain numerical results based on Equations A concise description of the basic philosophy behind the developed analytical framework is explained in this section . A multi-stage bottom-up idealization scheme is adopted for deriving the closed-form expressions, as depicted in Fig. kr and kθ can be obtained from literature using AMBER force filed53 as kr = 938 kcal mol−1 nm−2 = 6.52 × 10−7 Nnm−1 and kθ = 126 kcal mol−1 rad−2 = 8.76 × 10−10 Nnm rad−2. The out-of-plane angle for graphene is α = 0 and the bond angle is θ = 120° (i.e. ψ = 30°), while bond length and thickness of single-layer graphene can be obtained from literature as 0.142 nm and 0.34 nm respectively7. In case of hBN, the molecular mechanics parameters kr and kθ can be obtained from literature using DREIDING force model54 as kr = 4.865 × 10−7 Nnm−1 and kθ = 6.952 × 10−10 Nnm rad−2 55. The out-of-plane angle for hBN is α = 0 and the bond angle is θ = 120° (i.e. ψ = 30°), while bond length and thickness of single-layer hBN can be obtained from literature as 0.145 nm and 0.098 nm respectively8. In case of stanene, the molecular mechanics parameters kr and kθ can be obtained from literature as kr = 0.85 × 10−7 Nnm−1 and kθ = 1.121 × 10−9 Nnm rad−2 57. The out-of-plane angle for stanene is α = 17.5° and the bond angle is θ = 109° (i.e. ψ = 35.5°), while bond length and thickness of single layer stanene can be obtained from literature as 0.283 nm and 0.172 nm respectively59. In case of MoS2, the molecular mechanics parameters kr and kθ can be obtained from literature as kr = 1.646 × 10−7 Nnm−1 and kθ = 1.677 × 10−9 Nnm rad−2, while the out-of-plane angle, bond angle, bond length and thickness of single layer MoS2 are α = 48.15°, θ = 82.92° (i.e. ψ = 48.54°), 0.242 nm and 0.6033 nm respectively62.The molecular mechanics parameters and geometric properties of the bonds are well-documented in scientific literature. In case of graphene, the molecular mechanics parameters 64 for calculating the Poisson’s ratios of graphene–MoS2 bilayers and heterostructures through molecular dynamics simulation. The interatomic potential used for carbon-carbon, molybdenum-sulfur interactions are the second-generation Brenner interatomic potential66. We have stabilized the heterostructures following the same method as described in literature21. The MoS2 and graphene layers of the heterostructures are coupled by van der Waals interactions, as described by the Lennard-Jones potential. The adopted cut-off is 10.0A° for M/G/M and 5.0A° for G/M/G heterostructures. These cut-off values are determined by stabilizing and minimizing the M/G/M and G/M/G heterostructures21.We have followed a similar method as reported in literatureSupplementary Information