Electronic Structure of G4-DNA by Scanning Tunneling Spectroscopy

Nov 30, 2010 - G4-DNA molecules, deposited on a gold substrate, is resolved by using cryogenic scanning tunneling spectroscopy in ultra-high-vacuum ...
0 downloads 0 Views 3MB Size
Download PDF
J. Phys. Chem. C 2010, 114, 22079–22084

22079

Electronic Structure of G4-DNA by Scanning Tunneling Spectroscopy Errez Shapir,† Lior Sagiv,† Tatiana Molotsky,‡ Alexander B. Kotlyar,*,‡ Rosa Di Felice,*,§ and Danny Porath*,† Physical Chemistry Department and Center for Nanoscience and Nanotechnology, The Hebrew UniVersity, Jerusalem 91904, Israel, Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel AViV UniVersity, Ramat AViV, 69978 Israel and Tel AViV UniVersity Nanotechnology Center, and Centro S3, CNR Istituto di Nanoscienze, Via Campi 213/A, 41125 Modena, Italy ReceiVed: August 22, 2010; ReVised Manuscript ReceiVed: October 10, 2010

G4-DNA molecules were recently reported as candidates for molecular electronics because of their higher stiffness and polarizability with respect to double-stranded DNA. Short G4-DNA structures have been traditionally studied in the past because of their possible presence in the human genome. The electronic structure of these molecules, unraveled in terms of the electron energy levels, constitutes crucial information in both technology and biology contexts. The discrete electron energy-level spectrum of isolated long single G4-DNA molecules, deposited on a gold substrate, is resolved by using cryogenic scanning tunneling spectroscopy in ultra-high-vacuum conditions. The patterns of the current-voltage curves manifest a considerable, although unexpected, variability, which required the development and implementation of a dataclustering methodology in order to resolve the reproducible curve patterns. On the basis of the sorted curves, we present a statistical analysis of the electronic levels and of the width of the fundamental gap. Introduction The electrical properties of various types of DNA molecules were in the focus of an intensive study over the past two decades,1-4 including a range of experiments that measured electronic transport through single DNA molecules.5-8 Most of these experiments addressed electrical transport along DNA molecules attached to metal electrodes at the molecule ends. In contrast, scanning tunneling spectroscopy (STS) measures the transverse conductance of the molecule to give its electronic density of states (DOS).9,10 The efficacy of this measurement technique was demonstrated for various carbon nanostructures,11,12 molecular objects,13 and inorganic nano particles14 deposited on substrates. STS was also successfully used to reveal the electronic structure of poly(dG)-poly(dC) DNA molecules.15 We have recently reported the synthesis of novel long monomolecular G4-DNA wires.16,17 G4-DNA is a very appealing molecular species, both in the context of molecular electronics18 and in the biology of the human genome.19 Scanning Tunneling Microscopy (STM) is a powerful tool for obtaining highresolution morphological characterization and electrical information on the sub-molecular level.20,21 STM was used to investigate long G4-DNA molecules deposited on a gold substrate.22 The apparent morphology of the molecules and their periodic structure were revealed (see Figure 1). The electroniclevel spectrum of these molecules has never been measured so far. Some knowledge on the electronic structure of G4-DNA molecules emerges from density functional theory (DFT) calculations.23,24 In particular, it was shown that wide peaks in the DOS, separated by an energy gap, are related to the π-like highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of the guanine molecule. The * Corresponding authors. E-mail:[email protected], s2shak@ post.tau.ac.il, and [email protected]. † The Hebrew University. ‡ Tel Aviv University and Tel Aviv University Nanotechnology Center. § CNR Istituto di Nanoscienze.

Figure 1. High-resolution STM image of a G4-DNA molecule deposited on a gold (111) surface. The apparent molecule morphology and the triangles of the gold (111) monolayers are clearly demonstrated. Right inset: structure of the guanine tetrad, which defines each stacking plane of the G4-DNA quadruple helical motif.21

width of these peaks is governed by both intraplane (H-bonding) and interplane (π-stacking) interactions. This theoretical background can be used to understand our measured STS spectra. Here, we present data obtained from STS measurements on these molecules. These results enable us to resolve the characteristic shapes of the conductance curves and identify clear peaks that should be related to the electronic levels of these new derivatives of the nucleic acids. The main advantages of cryogenic measurements are the stability of the system and the lack of thermal broadening at low temperature. These were major hurdles for the stability and interpretation of previous STS measurements on various dsDNA molecules performed at room temperature (RT).25-28 Working at cryogenic temperature was apparently an advantage that enabled us to extract the energy levels of poly(dG)-poly(dC).15 Methods G4-DNA molecules were deposited from a solution of 2 mM tris-acetate (pH 7.0) onto a highly smoothed gold (111) surface

10.1021/jp107952y  2010 American Chemical Society Published on Web 11/30/2010

22080

J. Phys. Chem. C, Vol. 114, No. 50, 2010

Shapir et al.

Figure 2. A data set of 400 curves, measured at 4.5 K (a) and its clustering to reproducible groups (b-m). The application of a sorting algorithm to the curve set of panel a results in its splitting to ∼15 groups, each containing at least three curves. Twelve of the extracted groups are displayed. The exact number of groups and curves in each subgroup depends on the allowed standard deviation, as described in the text. More nearly identical curves and different numbers of groups are found in other sets measured on other molecules. The inset in panel a shows a group of curves measured on the bare substrate for comparison.

achieved by flame annealing. Each molecule is composed of ∼750 tetrads, that is, ∼250 nm in length, and includes 40 phosphorothioated G nucleotides at the 5′ end of the four-folded G4-DNA.16,17 The phosphorothioated nucleotides form coordinative bonds with the gold substrate, leading to considerable enhancement of the molecule attachment to the substrate. Figure 1 shows high-resolution images of G4-DNA molecules. The tip-molecule-gold system forms an asymmetric double-barriertunnel-junction (DBTJ) configuration, where the tip-molecule junction is higher in resistance and lower in capacitance, whereas the backbone of the molecule together with the residual hydration layer forms the other, faster junction with the substrate. STS measurements were performed with a commercial Omicron low-temperature STM (LTSTM) system in ultra-high vacuum (UHV) conditions (∼5 × 10-11 mbar). The entire set of data was measured with the same sample and setup. The tip was always positioned above the highest observed point in the pitch of the imaged molecule. The set-point parameters were V ) 2.5 V and I ) 0.5 nA. These parameters for the tunneling spectra on DNA were chosen to get the optimal I-V quality on this system and yet not damage the molecule. This is a very delicate balance. Other parameters usually gave noisier results and damaged the molecules. About 18 000 I-V curves were measured, about 55% of them at 4.5 K and the others at ∼78 K (27%) and at RT. The data comprises 44 series of 400 consecutive I-V curves: one such series, measured at 4.5 K, is shown in Figure 2a. Each curve is composed of 300 measurement points (voltage values) between -2.5 and 2.5 V. To check the reliability of the measurements, we took an image of the measured molecule segment just before and right after the spectroscopic measurement in a small frame (∼15 nm) and at the same position, to verify their equivalence, namely, that there is no drift and that the molecule is intact. The drift during the measurement was negligible at 4.5 K and very small, if any, at 78 K. Every measurement series was followed by a control

spectroscopic measurement taken on the bare gold in the vicinity of the molecule, to verify the tip cleanliness by achieving a typical metallic I-V behavior. Technically, the I-V curves of these control measurements show that the measuring system has no problem to continuously acquire data over the whole spectral range even under the conditions of the relatively largebias set point (inset of Figure 2a). STS8,9 is an electrical measurement through a tunnel junction, in which the current I between the STM tip and the sample is characterized as a function of the voltage bias V. The differential conductance G(V), which is the derivative of the tunnel current with respect to the bias voltage, is proportional to the electronic DOS of the sample, Fs, in the vicinity of the Fermi level, EF; that is, G(V) ≡ (dI(V’)/dV’)V’)V ∝ Fs(EF + eV). This relation applies if the voltage dependence of the transmission function is weak. Because the measured sets of current-voltage (I-V) and conductance-voltage dI/dV-V curves exhibit diverse shapes and evolutions, we devised and implemented a simple clustering algorithm to sort the measured I-V curves into groups that have a limited spread. The sorting procedure rests on treating any curve as a point in an N-dimensional Euclidean space, where N is the number of measurement points for each curve, namely, 300 points, that cover the entire voltage scale. The Euclidean distance, d, between any couple of points in this space can serve as a measure for the difference between the represented curves. Each group of reproducible curves is represented by a cluster of points in this Euclidean space. For the clustering, we set a cutoff distance dc: any two points that are distant from each other by a distance less than dc are regarded as neighbors. A cluster of points is defined as a collection of points that have at least one neighbor in the cluster. Gradual reduction of the cutoff distance, dc, enables a division of the initial curve groups into smaller and smaller groups. By inspecting the sorted behavior for several values of dc, we managed to minimize the spread of the sorted groups in such a way that they contain fewer and

Electronic Structure of G4-DNA by STS

J. Phys. Chem. C, Vol. 114, No. 50, 2010 22081

Figure 3. Examples of conductance curves obtained at 4.5 K. (a) The variation of the differential conductance (dI/dV-V) curves in time: peak movement on the energy axis. (b) Abrupt peak appearance and movement. The arrow above panels a and b indicate the moving peaks. (c,d) Three individual I-V curves (c) and their derivative curves (d). A clear transition between two distinct patterns appears as a considerable current change in the red curve and results in a sharp peak in the corresponding dI/dV-V curve. (e,f) A subgroup of almost identical I-V curves (e) and the average of their derivative curves (f).

quite similar curves, yet a significant number of them. The spread of a certain group of curves can be evaluated by using the integrated normalized standard deviation, given by

σ)



N

∑ Varj/d2

(1)

j)1

where N is the number of measurement points in each curve, Varj stands for the current variance in the jth measurement point (bias voltage), and d is the Euclidean distance between the average curve of the group and the zero-current curve. d is given by d ) (∑jN) 1〈Ij〉2)1/2, where 〈Ij〉 is the average current in the jth measurement point. The division process is truncated when the desired spread is achieved. For example, the groups shown in Figure 2b-m were obtained from the original measured set of Figure 2a by fixing a convergence criterion of σ < 0.2. Results and Discussion Despite the low measurement temperature, the series of measured STS curves are characterized by a considerable variability in the I-V curve patterns, as can be seen in Figure 2a. A careful examination of the data, however, showed several reproducible curve patterns within each data set. Panels b-m in Figure 2 show some subgroups of nearly identical curves that are sorted out from the whole data set of 400 curves shown in Figure 2a (the sorting procedure is explained above). The appearance of curves with a given shape and numerical values (nearly identical curves) is not necessarily consecutive in a data set of 400 curves, and usually, each group of similar curves comprises several nonconsecutive subgroups. Some noisy curves that do not belong to any subgroup are also measured. Similar patterns appear in different sets, which were taken on different molecules. The origin of this switching is not conclusively clear. Nevertheless, we speculate that these changes in the curves may reflect transitions of the molecule between its structural equilibrium states or changes in its energy state or ionization state, stimulated by various possible energy sources. Similar spectroscopic measurements on bare gold provided variable but rather featureless curves, similar to those reported by us in

Figure 1c of ref 15 with no clear subgrouping. An example of a group of I-V curves measured on the bare gold is shown in the inset of Figure 2a. Figures 3 and 4 display sets of dI/dV-V curves that represent the conductance characteristics. The peak structures in these curves should correspond to the electronic energy levels of G4DNA molecules. STS dI/dV-V conductance curves are sometimes directly measured by lock-in methods,20 especially at low temperature. However, the approach adopted by us, that is, measuring the current-voltage, is much faster yet reliable and with a sufficient precision, as was recently demonstrated with STS measurements15,20 of double-stranded DNA. The large number of curves measured for this analysis indeed required the application of such faster-measurement method. Figure 3 illustrates a selection of odd behaviors that reflect the time evolution of the differential conductance peaks and indicate that the complexity of the data is further increased in the dI/dV-V curves relative to the I-V curves. The time evolution of the peaks is manifested, for example, in a movement of the peaks on the energy axis or in an abrupt appearance of a peak, as demonstrated in Figure 3a,b. The STS measurements sometimes manifest an abrupt switching between two or more curve patterns during the curve acquisition, as illustrated in Figure 3c-d. This phenomenon emerges either in the molecule measurements or in controls taken on the bare gold substrate. It may originate from the occupation or depletion of electronic states trapped between the gold surface and the insulating residual hydration layer29 or from another abrupt change of state. It leads to pseudopeaks in the dI/dV-V curves, which should be distinguished from the characteristic G4-DNA electroniclevel peaks. A clear example of a pseudopeak doublet is displayed in Figure 3e,f: the conductance doublet of Figure 3f is the feature associated to the reproducible transition in the measured current curves observed in Figure 3e. Note that other authors reported on the data spread in transport measurement with the STM30,31 or in break junctions.32 However, the statistics in those works was inherent to the measurement of several molecules, whereas we refer here to the variability of the electrical signal in a single molecule. Resolving the energy-level structure of the G4-DNA molecules required extracting the reproducible curve patterns hidden

22082

J. Phys. Chem. C, Vol. 114, No. 50, 2010

Shapir et al.

Figure 4. The conductance curves obtained by differentiating average I-V curves from sorted groups are shown shifted along the vertical axis. (a-g) Most frequent patterns found in the negative-bias regime, measured at 4.5, 78 K, and RT (all temperatures are combined here). Each of the displayed curves is the derivative of an average of 5-20 reproducible I-V curves, characterized by a standard deviation σ < 0.05, for both voltage ranges. Some peaks are characterized by a considerable movement on the energy axis. (h-j) Characteristic conductance patterns in the positive-bias range. (k) Isosurface plot of the HOMO of an isolated guanine molecule. (l) Isosurface plot of the highest-energy electron state in the multiplet of four states originated in the guanine tetrad by the HOMO of the individual guanine. The electron states were derived from DFT calculations with a technique described elsewhere.23,24

in these quite dispersed data sets, and we did so according to the clustering algorithm explained in the methods. From a careful inspection, we noted that the spread of the data in the negative and the positive-bias ranges is, in many cases, approximately independent. Namely, a group of curves with similar patterns in the negative-/positive-bias range may have very diverse patterns in the other branch. This observation prompted us to sort the measured curves in the negative- and positive-bias ranges separately. The G4-DNA characteristic curve patterns were extracted from those groups of current curves that include at least five individual curves with σ < 0.05, separately for V < 0 and V > 0. This choice allows us to take into account most of the measured curve patterns and still verify the reproducibility of the patterns. Each of these groups was averaged, differentiated, and smoothed uniformly in order to extract the characteristic peak structure of the molecule. The derivative curves are then gathered in sets of lines with an overall similar shape and displayed in Figure 4. Figure 4a-g exemplifies the seven most reproducible patterns found in the negative-bias range, which corresponds to the

occupied molecular levels: the first peak found at negative bias should represent the helical feature that derives from the guanine HOMO. Each of the peaks in Figure 4a-g appears in at least several tens of measurements. Figure 4a-d shows a sharp peak, Figure 4e shows a broad and less clear peak, and Figure 4f,g shows a gentle current shoulder in the voltage interval between -2.5 and -2.2 V. The main peak in each curve clearly undergoes energy shifts (see also Table 1). Such shifts may stem from changes in the molecule conformation or simply from changes in the voltage-drop ratio between the tip-molecule and molecule-substrate junctions. In some curves, the main peak is actually composed of several sub peaks. For example, the main peak in the curves of Figure 4a is likely composed of at least three sub peaks that appear intermittently in the various measurements. The multiplicity is apparent in Figure 4a in just two or three of the curves and fewer in the others, also because of the presentation scale, but we could note it clearly in the bare data. The situation in the positive-bias range (Figure 4h-j), which is associated to the unoccupied orbitals, is more complex. It is more difficult to recognize a set of well-defined patterns similar to those found in the negative-bias range. Nevertheless, some general trends can be outlined. In all the curves shown in Figure 4h-j, the current onset is followed by a clear peak that appears between 0.7 (Figure 4h) and 2.15 V (Figure 4j), and this is a quite persistent feature also in the data that we do not show. This peak, namely, the first peak that appears at positive-bias voltage, should represent the helical feature that derives from the guanine LUMO. Generally speaking, our data show that the first unoccupied-level peak appears at three different energies, each exemplified in one of the positive-voltage panels of Figure 4. In most cases, the dominant peak is accompanied by other, less obvious ones. The positions of all of the dominant peaks at negative and positive voltage are summarized in Table 1. The HOMO-related peak in groups c, d, f, and g, which correspond to the curves in the panels of Figure 4 labeled with the same letter, is rather stable at the same energy, though shifts are evident from one panel to the other. Groups a, b, and e have a more pronounced intergroup variability of the HOMO-related peak. The LUMOrelated peak in sets h and I is overall more variable. The statistics of the fundamental energy gap requires an analysis of the curve patterns in the whole voltage range. The reproducibility of the patterns over the whole measured range is lower than over the half-scale range, and therefore, it is based on those curve subgroups that include at least three curves and with a spread σ < 0.1. By taking into account all the measurements at different temperatures, the gap width manifests a wide distribution, with a maximum at 2.55 V, a mean value of 2.66 V, and a standard deviation of 0.89 V (Figure 5). This distribution includes three dominant subdistributions, with a gap peaked at ∼1.75, ∼2.55, and ∼3.55 V. Although the maximum likelihood gap width is growing with increasing temperature, the mean values do not show such dependence: the extracted mean values are 2.73, 2.55, and 2.91 V at 4.5, 78 K, and RT, respectively (see the right insets in Figure 5). It is important to note that the RT analysis is based on merely ∼100 curves, likely because of the temperature smearing and large data spread at this temperature, hence with a poorer statistics compared to the

TABLE 1: Summary of the Energies of the Dominant Peaks, Relative to the Plots of Figure 4 (σ < 0.05) dominant peak position (V)

a

b

c

d

e

f

g

h

i

j

-1.85 -1.55

-1.95 -1.75

-1.45

-1.00 -0.90

-1.75 -1.10

-2.50 -2.35

-2.40 -2.25

0.65 1.15

1.35 1.75

1.95 2.35

Electronic Structure of G4-DNA by STS

J. Phys. Chem. C, Vol. 114, No. 50, 2010 22083

Figure 5. Statistical analysis of the energy gap width. This statistics is based on the data measured at both positive and negative voltage and sorted with a standard deviation σ < 0.1 in the sorting procedure applied to the measured current curves. The gap-width distribution can be divided into three rough Gaussian distributions. The diagrams on the right side show the gap-width distributions for the 4.5, 78 K, and RT measurements.

analysis at 4.5 and 78 K. Figure 5 summarizes the gap width distributions. For a better understanding of the negative-bias range, we recall here some DFT theoretical findings on the formation of extended occupied electron states in G4-DNA, which qualitatively explain the experimental observations reported here. In a GC pair, the unit constituent of a poly(dG)-poly(dC) double helix, there is a unique HOMO that stems from the HOMO of isolated guanine (Figure 4k). Instead, in a G4 tetrad, the unit component of a G4-DNA quadruple helix, the HOMO is not unique: a multiplet of four almost degenerate levels (Figure 4l), with a dispersion of 200 meV, stems from the HOMO of isolated guanine by virtue of H-bonding interactions.24 Because of their H-bonding nature, the electron states in the HOMO multiplet are not bonding orbitals. Yet, they are delocalized in the plane of the tetrad. From these characteristics of the single-constituent plane, we can infer on the origin of the DOS peaks in double and quadruple helices. The highest-energy peak in the DOS of a poly(dG)-poly(dC) double helix is obtained by π-stacking coupling of a unique electron state, the guanine HOMO, which, for 10 planes, yields a multiplet of 10 levels with a width of ∼0.3 eV15 (by increasing the molecule length, the number of levels in the multiplet increases but the width should remain the same as that in the helical unit). For the same length of a G4-DNA molecule, the highest-energy peak in the DOS is instead obtained by π-stacking coupling of four electron states similar to that of Figure 4l, yielding a multiplet of 40 levels with a width of ∼0.7 eV (this width is fixed by the helical unit of three tetrads).24 In both double-stranded and quadruplestranded DNA molecules, the levels in the HOMO multiplet are not necessarily equally spaced in energy: many levels may cluster around a given energy, causing the possible splitting of the multiplet into different subpeaks (see, e.g., Figure 4a) and/ or the shift of the peaks (see, e.g., Figure 4b). The spontaneous structural variations that occur at finite temperature strongly affect such a clustering mechanism.33,34 As a consequence of the larger HOMO-multiplet width and the redundancy of electron states contributing to the same multiplet in G4-DNA quadruplexes relative to DNA duplexes, one can expect a much more pronounced variability of the HOMO-related DOS peak. We speculate that this is a partial explanation of our measure-

ments, which indeed reveal a much larger variability of the sequential I-V curves than in poly(dG)-oly(dC).15 The existence of three different peaks for the gap statistics in Figure 5, mainly induced by the existence of multiple guanine-LUMO-derived features, can be ascribed to different structural effects: (i) the presence of electron states localized on the backbone and counterions within the HOMO-LUMO gap of guanine, as shown for poly(dG)-poly(dC);15,22 (ii) strained conformations, possibly realized upon adsorption on the metal substrate, which may change the energy and the width of the DOS peaks;35 and (iii) any other trapping of the tunneling charges in defects. Conclusions We identified the reproducible I-V curve patterns and dominant peaks and statistically analyzed the energy gap in the electronic structure of G4-DNA molecules adsorbed on a gold surface by means of STM spectroscopy measurements. The dI/ dV-V curve patterns, and in particular the peak positions in such curves, serve as a fingerprint of the discrete energy spectrum of the molecules. This information is particularly important in the absence of any other energetic characterization of these novel molecules. We remark that the STS data presented here exhibit a much higher variability than those measured in poly(dG)-poly(dC) molecules, at odds with the intuition that stiffer molecules should give more pronounced and clear-cut characteristics. This required the development of a special sorting approach. The variability is likely induced by the interplay between intraplane H-bonding and interplane π-stacking in the determination of each quadruplex DOS peak, whereas only π-stacking is at work in the peaks of duplex DNA. The concurrence of different interactions and the redundancy of energy levels due to multiplets in each tetrad, responsible for significant energy shifts in different dynamical conformations and peak splitting, may in fact render the whole molecular electronic structure more dependent on the mechanical flexibility in G4-DNA. Acknowledgment. We thank Igor Brodsky, Jose Soler, Yosef Bar-David, and Yonatan Horovitz for assistance and fruitful discussions. This work was supported by the European Com-

22084

J. Phys. Chem. C, Vol. 114, No. 50, 2010

mission through FP5/FP6-IST grants for Future & Emerging Technologies “DNA-Based Nanowires” (IST-2001-38951) and “DNA-based Nanodevices” (FP6-029192) and FP7-NMP grant “NanoTools for Ultra Fast DNA Sequencing” (FP7-NMP214840). References and Notes (1) Ratner, M. A. Nature 1999, 397, 480. (2) Taubes, G. Science 1997, 275, 1420. (3) Porath, D.; Cuniberti, G.; Di Felice, R. Top. Curr. Chem. 2004, 237, 183. (4) Endres, R. G.; Cox, D. L.; Singh, R. R. P. ReV. Mod. Phys. 2004, 76, 195. (5) Fink, H. W.; Scho¨nenberger, C. Nature 1999, 398, 407. (6) Porath, D.; Bezryadin, A.; de Vries, S.; Dekker, C. Nature 2000, 403, 635. (7) Yoo, K.-H.; Ha, D. H.; Lee, J.-O.; Park, J. W.; Kim, J.; Kim, J. J.; Lee, H.-Y.; Kawai, T.; Choi, H. Y. Phys. ReV. Lett. 2001, 87, 198102. (8) Guo, X.; Gorodetsky, A. A.; Hone, J.; Barton, J. K.; Nuckools, C. Nature Nanotechnol. 2008, 3, 163. (9) Wiesendanger, R. Scanning Probe Microscopy and Spectroscopy; Cambridge University Press, 1994. (10) Chen, C. J. Introduction to Scanning Tunneling Microscopy, 2nd ed.; Oxford University, 2008. (11) Porath, D.; Millo, O. J. Appl. Phys. 1997, 81, 2241. (12) Porath, D.; Levi, Y.; Tarabiah, M.; Millo, O. Phys. ReV. B 1997, 56, 9829. (13) Huo, J. G.; Wang, K. D. Pure Appl. Chem. 2006, 78, 905. (14) Banin, U.; Millo, O. Annu. ReV. Phys. Chem. 2003, 54, 465. (15) Shapir, E.; Cohen, H.; Calzolari, A.; Cavazzoni, C.; Ryndyk, D. A.; Cuniberti, G.; Kotlyar, A. B.; Di Felice, R.; Porath, D. Nat. Mater. 2008, 7, 68.

Shapir et al. (16) Kotlyar, A. B.; Borovok, N.; Molotsky, T.; Cohen, H.; Shapir, E.; Porath, D. AdV. Mater. 2005, 17, 1901. (17) Borovok, N.; Molotsky, T.; Ghabboun, G.; Porath, D.; Kotlyar, A. B. Anal. Biochem. 2008, 374, 71. (18) Cohen, H.; Sapir, T.; Borovok, N.; Molotsky, T.; Di Felice, R.; Kotlyar, A. B.; Porath, D. Nano Lett. 2007, 7, 981. (19) Davis, J. T. Angew. Chem., Int. Ed. 2004, 43, 668. (20) Tanaka, H.; Kawai, T. Nature Nanotechnol. 2009, 4, 518. (21) Porath, D. Nature Nanotechnol. 2009, 4, 476. (22) Shapir, E.; Sagiv, L.; Borovok, N.; Molotsky, T.; Kotlyar, A. B.; Porath, D. J. Phys. Chem. B 2008, 112, 9267. (23) Calzolari, A.; Di Felice, R.; Molinari, E.; Garbesi, A. Appl. Phys. Lett. 2002, 80, 3331. (24) (a) Calzolari, A.; Di Felice, R.; Molinari, E.; Garbesi, A. J. Phys. Chem. B 2004, 108, 2509. (b) Calzolari, A.; Di Felice, R.; Molinari, E.; Garbesi, A. J. Phys. Chem. B 2004, 108, 13058. (25) Lindsay, S. M.; Li, Y.; Pan, J.; Thundat, T.; Nagahara, L. A.; Oden, P.; De Rose, J. A.; Knipping, U.; White, J. W. J. Vac. Sci. Technol. B 1991, 9, 1096. (26) Iijima, M.; Kato, T.; Nakanishi, S.; Watanabe, H.; Kimura, K.; Suzuki, K.; Maruyama, Y. Chem. Lett. 2005, 34, 1084. (27) Xu, M. S.; Endres, R. G.; Tsukamoto, S.; Kitamura, M.; Ishida, S.; Arakawa, Y. Small 2005, 1, 1168. (28) Xu, M. S.; Tsukamoto, S.; Ishida, S.; Kitamura, M.; Arakawa, Y.; Endres, R. G.; Shimoda, M. Appl. Phys. Lett. 2005, 87, 083902. (29) Altfeder, I. B.; Chen, D. M. Phys. ReV. Lett. 2000, 84, 1284. (30) He, J.; Lin, L.; Zhang, P.; Lindsay, S. Nano Lett. 2007, 7, 3854. (31) Xu, B.; Zhang, P.; Li, X.; Tao, N. Nano Lett. 2004, 4, 1105. (32) Kang, N.; Erbe, A.; Scheer, E. New J. Phys. 2008, 10, 023030. (33) Troisi, A.; Orlandi, G. J. Phys. Chem. B 2002, 106, 2093. (34) Kubarˇ, T.; Elstner, M. J. Phys. Chem. B 2009, 112, 8788. (35) Di Felice, R.; Calzolari, A.; Garbesi, A.; Alexandre, S. S.; Soler, J. M. J. Phys. Chem. B 2005, 109, 22301.

JP107952Y