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Prospects of Graphene-hBN Heterostructure Nanogap for DNA Sequencing Vivekanand Shukla, Naresh K Jena, Anton Grigoriev, and Rajeev Ahuja ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b06827 • Publication Date (Web): 03 Nov 2017 Downloaded from http://pubs.acs.org on November 4, 2017

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ACS Applied Materials & Interfaces

Prospects of Graphene-hBN Heterostructure Nanogap for DNA Sequencing Vivekanand Shukla,a Naresh K. Jena, a,* Anton Grigoriev, a, Rajeev Ahuja a,b* a

Condensed Matter Theory Group, Materials Theory Division, Department of Physics and Astronomy, Uppsala University, Box 516, SE-751 20, Uppsala, Sweden b Applied Materials Physics, Department of Materials and Engineering, Royal Institute of Technology (KTH), SE-100 44, Stockholm, Sweden *Corresponding author email: NKJ([email protected] ; [email protected]); RA([email protected])

ABSTRACT Recent advances in solid state nano device based DNA sequencing are at the helm of the development of a new paradigm, commonly referred to as personalized medicines. Paying heed to a timely need for standardizing robust nano devices for cheap, fast and scalable DNA detection, in this article, nanogap formed by lateral heterostructure of graphene and hexagonal boron nitride (hBN) is explored as a potential architecture. These heterostructures have been realized experimentally, and our study boasts the idea that the passivation of the edge of graphene electrode with hBN will solve many of the practical problems, such as high reactivity of the graphene edge and difficulty in controlled engineering of the graphene edge structure, while retaining nanogap setup as a useful nanodevice for sensing applications. Employing first principle density functional theory based non-equilibrium Green’s function methods we identify that the DNA building blocks, nucleobases, uniquely couple with the states of the nanogap and the resulting induced states can be attributed as leaving a fingerprint of the DNA sequence in the computed current-voltage (IV) characteristic. Two bias windows are put forward: lower (1 -1.2 V) and higher (2.7-3 V), where unique identification of all four bases is possible form the current traces, although higher sensitivity is obtained at the higher voltage window. Our study can be practically guiding for experimentalists towards development of a nanodevice DNA sensor based on graphene-hBN heterostructures.

Keywords: DNA-sequencing, graphene-hBN heterostructure, Non-equilibrium Green’s Function, density functional theory, I-V characteristics

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1. Introduction Low-dimensional (2D) materials, based on their exotic electronic and transport properties, have ushered in a fascinating paradigm of research since the emergence of the celebrated material graphene.1-3 Hexagonal boron nitride (hBN), on the other hand, is an important member of this emerging class of 2D materials which is isostructural to graphene. 4 Graphene is a gap-less semiconductor while hBN is a wide band-gap semiconductor. The semimetal nature of graphene and higher carrier mobility makes it a logical choice for the electrode. Conceptually it is intriguing to think that a combination of these two isostructural 2D materials will lead to many interesting technological applications. Graphene-hBN heterostructures with vertical stacking and in-plane lateral heterojunctions have been realized experimentally and explored for new-generation electronics applications. 5-10 Unique features of graphene such as one atomic layer thickness along with its linear dispersion relation of transmission and DOS (density of states) make graphene an ideal candidate for nanoelectrode with fine spatial resolution. In fact, detection of single molecules with graphene nanopores and other related 2D materials is one of the actively pursued research frontiers. 11 Several reports in the literature explore the possibility of DNA sequencing employing graphene as nanoelectrodes. 12-13 Recent progress in nanotechnology has rendered experimental realization of graphene based nanogaps or nanoelectrodes employed in several state of the art molecular electronics applications. 14-19 Similarly, DNA detection through graphene based nanochannels and nanopores have also been emulated computationally. 20-21 The development of the state of the art of graphene based nanodevices is discussed in some of the recent review articles. 22 It is needless to mention how DNA has always intrigued researchers, and developing the third generation sequencing device is sought for standardizing personalized medicines and transforming our healthcare. 23 In DNA sequencing, the genetic code is read from what we call as the “alphabets of life”: viz. A, T, G and C nucleobases sequence. Reading the genetic information coded in these alphabets is one of the long-cherished dreams of modern day nanotechnology. 24-26 Following the breadth of advancements happening in this field, the optimistic target of “$1000 genome” as set forth by NIH (National Institute of Health) is not a far-fletched dream. 27 Single-stranded DNA, whose bases are ~0.34 nm apart can be threaded between graphene electrodes that were proposed to be a perfect match in size to a nucleotide28. Prasongkit et al. computationally proposed that nanogap formed by armchair graphene edges could be a functional device where positioning of HOMO levels of four nucleobases, their physical sizes and relative orientation in relation to the electrode are deciding factors for their transverse conductance. 29 He et al. have shown that sensitivity of DNA sequencing can be improved through hydrogenated graphene edges-DNA interaction mediated by H-bonding. 30 Similarly, Zhang et al. proposed a device based on zigzag graphene nanoribbon (zGNRs) with non-hydrogenated edges. 31 Again, Prasongkit et al. in a recent report have made a theoretical assessment of DNA sequencing through aligned nanopores in bilayer graphene. 32 In a recent experimental breakthrough, solid-state nanopore integrated with graphene nanoribbon has been put forward as a prototype device for DNA sensing. 33 However, although DNA translocation through graphene nanopores was demonstrated, a major disadvantage of the technology was that the DNA adhered to the nanopore because of high reactivity of the graphene edge causing significantly high background noise. 34-36 This has triggered


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exploration of other potential 2D materials such as hBN 37 and MoS2 23, 38. Another important issue associated with graphene is getting an edge with desired structure is a problem. 39 In a related study, Prasongkit et al. have attempted enhancing the sensitivity by functionalizing graphene electrode with guanidinium ion on one side which facilitates interaction with phosphate group and the other side, the electrode is functionalized with a nucleobase acting as reader molecule to enhance H-bonding with incoming nucleobase. 40 In a very recent report by Amorim et al., they have shown enhancement of DNA recognition sensitivity in graphene nanogap by nitrogen edge functionalization. 41 It follows from all these discussions that simple hydrogen termination of graphene edges is not optimal for better sequencing and by manipulating the chemical features of the edges the current carrying capacity can be enhanced and selectivity of the device can be significantly improved. Nanodevice architectures for DNA sequencing broadly fall into two classes based on characteristics molecular recognition via major or perturbing current signals as the DNA passes through the device. 12 Configurations based on tunneling electrode gap or scanning tunneling microscopy (STM) belong to the first category. On the other hand, nanochannels represent the second type of devices. Computer simulation has proved to be an invaluable tool to predict exciting functional nanodevices with potential applications for DNA sequencing. Screening several nanodevice architectures, understanding the electronic structure of the device and suggesting further advancements to optimize their performance has become routine within the realm of computational modeling. In this study we envision to assess DNA sequencing through a nanogap formed by lateral heterostructure of graphene and hBN employing density functional theory (DFT) based non-equilibrium Green’s function methods. The motivation comes from two factors; firstly, the demonstration of graphene based nanogaps/nanoelectrodes in several experiments as discussed previously. Secondly, graphene-hBN heterostructures have also been obtained in several experiments and these heterostructures provide ample scope to tune the electronic properties and diversify their applications. 7, 42-44 It was observed by Zhou et al. that significant DNA translocation events were observed through a hydrophilic hBN nanopore where there is enhanced DNA-nanopore interaction and reduced 1/f noise level for smaller diameter pores (less than 10 nm). 45 Sun et al. from their DFT based first principle calculations have shown that significant tuning of band gap is possible for a lateral heterostructure of zig-zag graphene-hBN 2D sheet. 46 On the basis of these reports, we conceived the idea that such an heterostructure can be a suitable nanodevice for DNA sequencing. We have chosen dAMP, dTMP, dGMP and dCMP (deoxy adenosine monophosphate, deoxy thymidine monophosphate, deoxy guanidine monophosphate and deoxy cytosine monophosphate respectively) as our target nucleotides. As we probe the transverse current for the four target nucleobases, we indeed find clear distinction of the above bases at certain bias windows. Interestingly, as in the case of pure graphene electrodes, where molecular states of nucleobases (for example HOMO levels) were responsible for electronic transport, 29 we find a key difference in our results where induced states are responsible for the transmission and which consequently results in better sensitivity. Furthermore, a key objective of our work is to model a nanogap that is not as reactive as the pristine graphene edges towards the DNA molecule. From a comparative study of pristine graphene edge versus graphene-hBN heterostructure with a DNA strand (poly-Adenine), we have shown that indeed the heterostructure affords a smaller interaction energy with DNA compared to pristine graphene. Based on all these findings, we hope that our study can be useful in guiding experimental realization of such a working device.



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2. Computational methods All the computational calculations have been performed with SIESTA code.47 Graphene and hBN heterostructure was first optimized separately, then each nucleotide was placed inside the nano-gap with hydrogen passivated zigzag edges of hBN sheets and optimized further. Spacing of the nanogap was maintained at 12.6 Å (H-H distance) throughout our calculations. Left and right electrodes were periodic in Z direction. Periodic boundary condition applied along the edged and has been confirmed that there are no unphysical interactions. During the initial system setup, the molecular plane of the bases is placed on the plane of the heterostructure. Within in the framework of density functional theory (DFT) we have used generalized gradient approximation (GGA) 48 for the exchange correlation functional and DZP basis sets. The atomic core electrons were modeled with Troullier-Martins49 norm conserving pseudopotentials. Mesh cut off of 200 Ry was used for real space integration and for Brillouin zone sampling a mesh of 1x4x2 have been used. All of these structures were fully relaxed with conjugate gradient (CG) algorithm considering the fact that residual forces in each components of the atoms were smaller than 0.01 eV/ Å. The transport calculations were performed using the Landauer50 approach utilizing the quantum transport code TransSiesta51. This method combines DFT and the non-equilibrium green’s function (NEGF) techniques. The basis sets and the real-space grid employed in the transport calculation are the same as used during the structural optimization processes. We have semi-infinite electrodes on either sides. When a given voltage is applied the current is allowed to flow across the system. This current is obtained from the integration of the transmission curve as;

= , − − −

(1).

In the above equation T(E, Vb) is the transmission probability of the electrons entering at an energy E from left to right electrode under application of the bias voltage Vb, ƒ(E-µL,R) is the Fermi Dirac distribution of electrons in the left and the right electrodes and µL,R is the respective chemical potential where µL = EF+Vb/2 and µR = EF–Vb/2 are shifted up and down with respect to the Fermi energy Ef .

3. Results and discussion Proposed nanogap setup (Figure 1) consists of lateral heterostructure of graphene and hBN. Graphene serves the purpose of electrode and hBN terminated with H forms the nanogap by directly interacting with the incoming nucleobase. We believe that the role of hBN edge is to mediate graphene-to-nucleobase interaction in a favorable manner so as to induce in-gap states which facilitate molecular recognition compared to a pristine graphene edge.29 When a given nucleobase passes through the nanogap setup, it tends to bridge the nanogap and characteristic tunneling current is registered which forms the basis of recognition of the base. In our nanogap architecture, the transport direction is along z-axis as illustrated in Figure 1. Concomitantly, we compute the current-voltage (I-V) characteristics (details in computational method section) for four different nucleobases. We find that the distinct molecular states appear in the zero-bias transmission function for the four target nucleotides between energies -1 to -2.2 eV which are characteristics HOMO peaks for the different nucleotides, as shown in Figure 2. Similarly, at the positive


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energies between 1-2 eV we find analogous distinct peaks, which can be attributed to the LUMO states. To prove this finding, we have presented the zero bias density of states (DOS) of four target nucleotides placed between the electrode setup in Figure 3. This figure gives a pictorial illustration of molecular states that we introduced earlier. The y-axis corresponds to z-direction of system within the range of 5-40 Å representing the scattering region excluding the electrode parts from left and right sides. We can clearly see the spatial localization of molecular states with higher densities (yellow in color) that contribute to the transmission within an energy window from -2 to 2 eV. These observations of the appearance of these molecular peaks at different positions infer that the target nucleotides can be identified at different current ranges. These findings, analogous to the approaches that have been made with the case of graphene electrode40, opens a way to enhance the sensitivity of the device if we can further tune the interaction between the target nucleobase and electrodes by some chemical functionalization of the electrodes, such that these molecular states can be moved along the energy scale. The calculated I-V curves for specific orientations of dAMP, dTMP, dGMP and dCMP presented in Figure 4 shows two distinct trends corresponding to lower and higher bias regimes. We note that the current axis is plotted on a logarithmic scale, hence small difference between current traces for the nucleotides amounts to significant changes in the current values. For instance, at ~3.2 V, the current for dTMP is almost two orders of magnitude higher than dCMP. This underlines the better sensitivity of our proposed setup. Let us turn our attention to higher bias window (> 2 V) first, because it is where we observe significant differences between all four bases for their unique identification. Here in, we find that probing dTMP results in higher current which is appreciably larger than other three bases. On the other hand, current trace for dAMP is smaller than that of dTMP and at ~2.9 V there is noticeable difference between these two bases. So, at this voltage distinct identification of these two bases seems possible. Additionally, looking at the higher bias region we find that current curves for dGMP and dCMP although mostly run parallel to each other, tend to split at ~2.9 V which makes it possible to identify these bases separately. We therefore, put forward that voltage window of 2.7 – 3 V will be optimal for clearer distinction of all four nucleotides. In an attempt to illustrate the sensitivity of detection of four target nucleotides at the higher bias regime, we have presented the current responses of individual bases for bias voltage between 2.8-2.9 V in Figure 5. This figure essentially summarizes the read-outs of current traces for all four bases within a given bias window (2.8-2.9 V). It is very clear from this figure that all four bases impart unique current signatures at the given bias window. To be more specific, dTMP affords higher current and dCMP lower current, whereas dAMP and dGMP can be detected in between the former two nucleobases. Additionally, it is worth mentioning that the bias in our proposed nanogap setup is relatively high and should be dealt with care. In a closely related system involving graphene nanoribbons, in order to prevent the graphene nanoribbons from damage due to high voltage, it has been theoretically shown that by tuning the gate voltage facilitated fingerprinting of nucleobases. 52 We, however, note that we have graphene and hBN lateral junction and hBN is an insulating material with high band gap. We are dealing with 2D heterostructures with graphene electrodes, and it has been reported that graphene starts rupturing at 3.1 V. 53 In our system current is limited by tunneling through the nanogap. At high bias, the applied electrostatic potential drops at the hBN sheet so the bases and electrodes do not feel all the applied potential. To substantiate this, we have presented the potential drop profile for one system (dTMP) in Figure 6. This figure reveals that the actual potential felt by the target molecule is very less (~0.5 V). Another noteworthy feature about the above figure is that we can clearly see two different types of slopes in the potential drop profiles; one corresponding



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to the graphene region where the change in the drop is minimal and second corresponding to the hBN region where there is a drop in the potential. Hence, the layers of hBN in a way screens the higher overall bias applied to the system whereas the actual bias felt by the target molecules are considerably less. However, in the low bias regime of the I-V plot we see that distinction between all four bases is also possible. From Figure 4 it is clear that dAMP yields higher current compared to remaining bases up to 1 V and it can be clearly differentiated from dTMP. Moreover, for characterization of other bases we have to increase the voltage range up to 1.2V and all of the four have different current traces at this voltage window. Hence, keeping both lower and higher bias windows in perspective, it is feasible to detect all four bases uniquely at two voltage regimes although we find better sensitivity at higher bias window. We have made additional efforts to compare the reactivity of DNA towards the nanoelectrode edges. This is important because in a real DNA sensing device, fast detection of bases is highly desirable. If the edges of nanogap tend to interact strongly with the incoming DNA, it might not be an optimal setup. A comparative study of interaction of polyadenine (poly-A; dimer) with pristine graphene edge and graphene-hBN heterostructure edge has been made. The optimized geometries for both the above mentioned cases is presented in figure S1 in the SI. We found an interaction energy of -0.64 eV for graphene-polyA whereas the value for graphene-hBN-polyA is -0.39 eV. The higher strength of interaction for graphene edges indicates the reactive nature of graphene edges. This stronger interaction can be attributed to favorable π-stacking interaction in the case of graphene. On the contrary, relatively lesser interaction energy for the graphene-hBN heterostructure indicate that this can be a better electrode in a nanogap setup. In the context of nanogap electrode device performance, the orientation of the bases with respect to the electrodes can be of importance and requires further attention. 29, 54, 55 In the work of Prasongkit et al., they have considered the effect of rotation and lateral translation of the bases on the transmission functions.29 In a similar spirit, we have also taken into consideration the effect of rotation and translation of the bases and these results are presented in the SI. There are few noteworthy points about the effect of rotations on the transmission functions. We see the transmission functions sensitively depend on the orientations of the bases (Figure S2). Firstly, all the rotated configurations have higher relative energies compared to our original orientation. It is also noted that a rotated configuration corresponding to 1200 manifest the largest deviation from other orientations. However, if we note carefully the relative energies (Table S1 in SI), it observed that this orientation has highest energy and hence less favorable. Moreover, the variations in the transmission functions for these rotated geometries are less compared to the case of pristine graphene edges as discussed by Prasongkit et al.29 These variations happen because of varying degree of coupling strength between the nucleotide and the hBN electrode. Furthermore, we have tested the effect of nanogap width on the transmission function by varying it to 14.7 Å as considered by Prasongkit et al.29 Herein, we observe that increasing the width affects only the intensity of transmission functions, leaving the qualitative feature of the peaks unchanged.29 Additionally, by laterally shifting the base to left or right side by 0.5 Å, we observe some variation to the transmission peaks. These results are summarized in Figure S2 and S3 in the SI. Hence from the foregoing discussions, we conclude that there is some inevitable effect of rotations and translations on the transmission function and this fact has already been addressed by several reports. These effects can also affect the detection voltages. We, on the other hand, made a detailed analysis of a configuration which was relatively more stable (in a static sense) compared to few other orientations.


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Moving on to our original case of lateral nanogap setup, a sneak peek into the bias dependent transmission spectra will provide better understanding of the overall I-V characteristics. We, therefore, have presented the transmission functions with varying biases for all four bases in Figure 7, which reveal the states responsible for transmission and their trends with changing bias can be easily perceived. If we look at the transmission function for dAMP, we see that the peak A1 (~ -1.77 eV at 0 bias, attributed to HOMO level of the given nucleotide from eigenvalues analysis) moves inwards with increasing bias. However, this movement is rather slow. On the other hand, states at the right side of the transmission spectra (A2, A3 with energies ~1.2 and ~1.3 eV respectively) move inwards significantly thus contributing to the transmission. From the molecular orbital picture presented in Figure 3, these states can be clearly identified to be induced states on the graphene-hBN heterostructure rather than being purely molecular states. This feature of having induced states contributing to the transmission is a key feature of our setup and it can be contrasted to purely molecular states, which Prasongkit et al. have discussed in their study with pristine graphene nanogap. 29 Likewise, in case of dTMP, we see that such induced state (T1 at E=1.27 eV in 0-bias spectrum) exhibit significant movement and thereby adding up to the transmission function. Moreover, for dTMP we find another state T2 (E=1.47 eV) which fall under our probed bias window. This state is associated with the LUMO level of the base. State T2 makes remarkable contribution to the transmission and the significant enhancement of current for dTMP particularly at higher bias window (2.7- 3 V) can be attributed to the combined effect of the two states (T1 and T2). Moving on to dGMP, we see that the spectral peak at ~ -1.5 V (G1) which corresponds to HOMO level of the base makes significant contribution to the transmission. In particular, this peak enters the bias window of 2.6-3 V results in the hump at ~2.8 V which we see form the I-V characteristics. Additionally, as was the case with other bases, we also find the induced states (G2) on the nanogap which contribute to transmission function. The case with dCMP is rather analogous to dGMP with the exception that state at ~ -1.6 eV (C1, HOMO level) does not move appreciably with changing bias. This is reflected in I-V curve as dGMP shows lowest current among all four bases. The spectral feature at ~1.1 eV (C2) is reminiscent of the induced nanogap states that we have observed in the remaining nucleotides. Hence, from the foregoing discussion we conclude that molecular states of the target nucleotides as well as the induced states on the electrodes play a decisive role on the transmission functions. Contingent upon the variation of transmission functions for all four target nucleotides, the trends in current follow unique trends in the lower and higher bias regimes thereby facilitating unique identification of the bases.

4. Summary In summary, we have investigated application of nanogap setup in the form of a lateral heterostructure of graphene passivated with hBN for DNA sequencing. We observe that graphene performs as a perfect nanoelectrode and H-terminated hBN can mediate the interaction with electrode to an incoming nucleotide in a favorable fashion. This setup proved to be more robust than graphene nanogap from adsorption energy point of view. By computing the transverse transmission and the I-V characteristics of these bases in the proposed nanogap device, we find that it is, in principle, feasible to uniquely identify all four nucleobases present in the DNA. It is also observed that translation and rotation of bases also sensitively govern the transmission functions. We observe significant variation in I-V traces at a lower (1-1.2 V) and higher (2.7-3 V) bias regimes, where we find better sensitivity at the higher bias window. Bias dependent transmission plots reveal the states contributing to I-V characteristics, that play a key role in the detection process. Due to unique coupling of bases



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with these states, the induced density in the detected molecules provides a molecule’s fingerprint as recorded through I-V. We indeed assert that, because these heterostructures have been realized in experiments, developing such a nanodevice for DNA sensing should be feasible within experimental realms and likely to draw further attention.

Acknowledgements The authors acknowledge computational resources provided through Swedish National Infrastructure for Computing at NSC-Triolith and HPC2N-Abisko. V.S. acknowledges funding from the European Erasmus fellowship program. A.G. and R.A. acknowledge support from the Swedish Research Council (VR). R.A. also acknowledge StandUP and Carl Tryggers Stiftelse for Vetenskaplig Forskning (CTS) for financial support.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: Graphical representation of possible interaction of a poly-A nucleotide with graphene and graphene-hBN heterostructure, effect of rotation and width variation of nanogap and translation of nucleobases on transmission functions.

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dAMP

dTMP

dGMP

dCMP

Right Left Scattering Figure 1: Schematic of our nanogap setup for four different nucleotides (dAMP, dTMP, dGMP, dCMP) illustrating the electrodes and scattering regions. The transport direction is along z-axis and the electrodes are on y-z plane. (Atom color code: C(gray), N(blue), B(pink), O(red), P(orange), H(white))


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10 0

Transmission

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


dAMP dTMP dGMP dCMP

10 -5

10

-10

10 -15

-2

-1

0 Energy (eV)

1

2

Figure2: Zero-bias transmission function for the four target nucleotides dAMP, dTMP, dGMP, and dCMP.



dAMP

dTMP

dGMP

dCMP

Z (Å)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

E-Ef (eV)

Figure 3: Zero bias DOS for all four target nucleotides dAMP, dTMP, dGMP, dCMP placed between the graphene-hBN heterostructure nanoelectrode set up. The Fermi level is set to zero energy. Presented Z-range of 5-40 Å corresponds to the scattering regions excluding the electrode parts. The density variation from high to low is represented by color transition from yellow to blue to visualize the localization of molecular states within the set up.


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10 -12

dAMP dTMP dGMP dCMP

10 -14

Current(A)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


10 -16

10 -18

0.5

1

1.5

2

2.5

3

Voltage(V)

Figure 4: Current-Voltage characteristics of the four target nucleotides (dAMP, dTMP, dGMP, dCMP). The current axis is presented on a logarithmic scale.

×10 -14 I, A

8 dTMP

6

4 dAMP

2

dGMP

0

dCMP

Figure 5: Current responses (read-outs of current traces from Fig. 4) for four target nucleotides within bias range of 2.8-2.9 V. ACS Paragon Plus Environment


1.6 0.4 V 0.8 V 1.2 V 1.6 V 2.0 V 2.4 V 2.8 V 3.2 V

1.4 1.2 1 0.8 0.6 0.4 ∆VH(V)

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0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 -1.6

X

10

20 Z (Å)

30

Z

40

Figure 6: Electrostatic potential profile showing the voltage drop across graphene-hBN lateral heterostructure junction with dTMP located in nano-gap.


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A1 10

0

10

3.2 2.8 2.4 2 1.6 1.2 0.8 0.4 0

10-2 10

-4

10

-6

10

-8

10

-10

10

-12

10-14

Transmission

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


10

-2

-1.5

10-2 10

-4

10

-6

10

-8

10

-10

10

-12

3.2 2.8 2.4 2 1.6 1.2 0.8 0.4 0

10-14

T2 T1

A3 A2

A1 -2.5

0

-1

-0.5

0

0.5

1

1.5

2

2.5 -2.5

0

10

10-2

3.2 2.8 2.4 2 1.6 1.2 0.8 0.4 0

10-4 10-6 10-8

10-10 10-12 10-14

-2

-1.5

-1

-0.5

0

0.5

G1 -2

-1.5

-1

-0.5

0

0.5

1

1.5

1.5

2.5

2

T2

10-2 10-4

3.2 2.8 2.4 2

10-6 10-8

G1

1.6

10-10

1.2 0.8 0.4 0

10-12 10-14

G2

C2 2

C1

2.5 -2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Energy (eV)

Figure 7: Bias-dependent transmission function for dAMP (black), dTMP (red), dGMP (magenta) and dCMP (blue) with variation of E. The bias window is marked as shaded areas in the respective plot. Labels with letters A, T, G and C correspond to specific transmission peaks and the molecular orbitals responsible for these peaks are presented in the right hand panel of the figure with respective labels.

A3 T1

0

G2 -2.5

1

A2


C1 C2


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

TOC/Graphical Abstract


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