Sensitivity Limits and Scaling of Bioelectronic Graphene Transducers

May 2, 2013 - Semiconducting nanomaterials are being intensively studied as active elements in bioelectronic devices, with the aim of improving spatia...
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Sensitivity Limits and Scaling of Bioelectronic Graphene Transducers Zengguang Cheng,† Junfeng Hou,† Qiaoyu Zhou,† Tianyi Li,‡ Hongbian Li,† Long Yang,† Kaili Jiang,‡ Chen Wang,† Yuanchang Li,*,† and Ying Fang*,† †

National Center for Nanoscience and Technology, 11 Beiyitiao Street, Zhongguancun, Beijing 100190, P. R. China Department of Physics & Tsinghua-Foxconn Nanotechnology Research Center, Tsinghua University, Beijing 100084, P. R. China

‡

S Supporting Information *

ABSTRACT: Semiconducting nanomaterials are being intensively studied as active elements in bioelectronic devices, with the aim of improving spatial resolution. Yet, the consequences of size-reduction on fundamental noise limits, or minimum resolvable signals, and their impact on device design considerations have not been defined. Here, we address these key issues by quantifying the size-dependent performance and limiting factors of graphene (Gra) transducers under physiological conditions. We show that suspended Gra devices represent the optimal configuration for cardiac extracellular electrophysiology in terms of both transducer sensitivity, systematically ∼5× higher than substrate-supported devices, and forming tight bioelectronic interfaces. Significantly, noise measurements on freestanding Gra together with theoretical calculations yield a direct relationship between low-frequency 1/f noise and water dipole-induced disorders, which sets fundamental sensitivity limits for Gra devices in physiological media. As a consequence, a square-root-of-area scaling of Gra transducer sensitivity was experimentally revealed to provide a critical design rule for their implementation in bioelectronics. KEYWORDS: Graphene, electrophysiology, noise, scaling, sensor

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tronics.11,12 Here we investigate the optimal transducer configuration of graphene transistors with channel width from 10 μm down to 200 nm, as well as single-wall carbon nanotube (SWNT) transistors with a diameter of 1−2 nm, interfaced to spontaneously beating hearts from newborn rats for recording of extracellular local field potentials (eLFPs) (Figure 1a) (Supporting Information). Mammalian hearts were chosen as the biological system to interface with GraFETs (graphene field-effect transistors) because hearts offer repetitive cardiac cycles with a small variation of action potential amplitude over a time frame up to ten minutes and thus facilitate direct evaluation of transducer performance. Planar graphene transistors configured on silicon/silicon dioxide (Si/SiO2) substrates (planar GraFETs) were fabricated as described previously11 (Supporting Information). The chip was then mounted on a heated stage integrated with an upright microscopy, and a home-built perfusion chamber with a Ag/ AgCl bath electrode in extracellular medium was assembled on top of the chip (Figure S1). A freshly isolated heart from a postnatal day-1 rat was then placed with its ventricular surface in direct contact with the active graphene region. After a brief period of equilibration with perfusing extracellular medium, the heart beats spontaneously at a typical frequency of ∼1 Hz and simultaneously generates electrical signals at the heart−chip

he central importance of electrophysiological recordings to areas from cell biophysics to neuroprosthetics has driven the search for detection probes with higher sensitivity and spatial resolution.1−4 Currently, planar metal microelectrode arrays (MEA) represent the most common tool for noninvasive extracellular recordings of network activity from electrogenic cells and tissues. The typical area of their electrode elements, ca. 700 μm2, however is sufficiently large to make subcellular resolution recording difficult. Moreover, reducing the electrode size has been hampered by the increased thermal noise at metal−electrolyte interfaces.5 Alternatively, electrolyte−oxide−silicon field-effect transistors (EOS-FETs) and their arrays have been investigated for a further improvement in the signal detection capability of extracellular recordings.4,6 More recently, aggressive scaling of FET-based bioelectronic devices has been pursued in studies based on one-dimensional (1D) and two-dimensional (2D) semiconducting nanomaterials.7−12 However, underlying mechanisms that control transducer sensitivity of semiconducting nanomaterials under physiological conditions and their interfaces with cells and tissues are not well-understood, although such information is critical to guide the rational development and implementation of nanoscale transducers. In this regard, graphene,13−17 which bridges between 1D nanostructures and conventional microelectronics and can have an ideal defect free surface, is especially interesting for quantitative evaluation of these questions for bioelec© XXXX American Chemical Society

Received: April 10, 2013

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Figure 1. Graphene transducer for extracellular recordings. (a) The schematic of a 3D graphene−biology interface. (b) The circuitry model of graphene−biology interface. (c) The recorded current signals from a heart by a graphene transducer, with an active area of 17.5 μm2, before (black) and after suspension (red), respectively. The right panels are the zoom-in views of single eLFPs at the time indicated by the stars on the current traces of the corresponding left panels.

Figure 2. Improved signal amplitude by suspension of graphene. (a) The transfer curves (source−drain current vs bath-gate voltage) of a GraFET, with an active area of 3 μm2, on substrate (black), on substrate with a heart (gray), after suspension (red), and after suspension with the same heart (crimson), respectively. (b) Zoomed-in are of current signals indicated by the stars on the transfer curve of the suspended graphene in a. (c) Calibrated eLFPs at variant bath-gate voltages for the GraFET on substrate (black) and after suspension (red), respectively.

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Figure 3. Noise of graphene transistors and water-dipole induced disorders. (a) Bath-gate voltage dependence of SI(10 Hz)/I2 for a graphene transducer, with an active area of 6.5 μm2, before (black) and after suspension (red) in extracellular medium at 37 °C. (b) Back-gate voltage dependence of SI(10 Hz)/I2 for a suspended graphene transistor, with an active area of 9 μm2, sequentially measured in vacuum (blue) and in 0.03 atm water vapor (crimson circles) at room temperature. Bath-gate voltage dependence of SI(10 Hz)/I2 was also characterized in liquid deionized water (red crosses) at room temperature. (c) Side view of adsorbed water molecules on a graphene sheet (top) and top view of water-dipole induced potential map on the graphene (bottom), without taking into account of screening by carriers. (d) Energy vs perpendicular separation between a water molecule and a graphene sheet.

junction by transmembrane ionic flows, IM (Figure 1b). Representative real-time measurement of the current through a planar GraFET yields regularly spaced eLFP peaks synchronized with the beating frequency of the heart, and an examination of individual GraFET signal reveals a fast biphasic current spike lasting milliseconds (Figure 1c, black line). The heart was then temporarily removed from the perfusion chamber for in situ etching of SiO2 underneath graphene.18 Immediately after suspension of graphene, the same heart was replaced on the chip and visually aligned at the same position with respect to graphene (Figure 1a). Significantly, the current signal amplitude recorded from the same heart by the same GraFET increased more than 3-fold, and the noise level simultaneously decreased about 2-fold after suspension compared with that in planar configuration on SiO2 (Figure 1c, red line). To understand these results we have modeled GraFET for extracellular recordings as depicted in Figure 1b. The electrical

excitation of electrogenic cells gives rise to an eLFP in the junction between heart and chip which modulates the Fermi level of the semiconducting graphene by a field-effect,4 and this in turn modulates the current through the graphene transducer. Thus the detection signal-to-noise-ratio (SNR) of a graphene transducer for extracellular recordings of LFPs is determined by two factors: (i) sensitivity of the transducer in physiological media; and (ii) strength of eLFPs, which is determined by the sealing between transistor and heart. This statement is written in equation form as SNRgraphene−hear = (ΔI/SI1/2) = (gm/SI1/2) × VJ, where gm = (∂IGra/∂Vbath)|Vsd is the transconductance of the transistor in response to the variation of bath-gate voltage (Vbath) at the applied bias voltage (Vsd), VJ is the eLFP during electrical excitation of heart, and SI is the noise power of the source-drain current (I) through the transistor. We discuss below the roles of these three parameters to the improved SNR in suspended graphene transducers during extracellular recordings. We note that, in a traditional MEA, the resolution of a C

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transducer is defined as fluctuations of the input voltage at electrode−solution interface, (SV)1/2. Thus in graphene, we have the relation of: Transducer sensitivity = (gm/SI1/2) = (1/ SV1/2). The current signal of a GraFET, ΔI, is linearly determined by the device transconductance (Figure 1b). It is generally accepted that Coulomb scattering by extrinsic charge impurities at SiO2/graphene interface is dominant in limiting mobility and transconductance in substrate-supported single-layer graphene.18−20 In-situ etching of SiO2 underneath graphene allows us to exclusively quantify effects from the underlying substrate to the transport characteristics of graphene.18 Figure 2a and Figure S7b show source−drain currents of two-terminal GraFETs as a function of bath-gate voltages. Suspending GraFETs leads to linear regime transconductance increases of 1.2−2.5 times for hole and 1.2−3 times for electron carriers, respectively, in extracellular medium. Consequently, the improved transconductance in suspended graphene enables larger current signal amplitude responding to eLFPs from the same heart (Figure 2a and b). Second, the eLFPs measured by the graphene transducer interfaced to the same heart (Figure 2c) show a value of ∼22 mV for the suspended GraFET compared with that of ∼17 mV for the same device in planar configuration. These results demonstrate clearly improved electrical coupling, i.e., large junction resistance, RJ, between suspended graphene and heart (Figure 1b). Tight bioelectronic interfaces are difficult to achieve with conventional electronics due to mechanical mismatch between soft, curvilinear biological tissues and hard, planar chips.21 By suspension, a graphene sheet protrudes ∼300 nm above the chip surface and accordingly presents itself in a three-dimensional (3D) configuration (Figure 1a), which narrows the graphene-heart junction and improves the amplitude of recorded eLFPs by close to 30% (Figure 2c).22,23 Third, the smallest detectable signal by a GraFET is determined by its level of noise in the operating environments. In extracellular medium, the current noise power spectral density (PSD) of graphene transistors, SI(f), exhibits a distinct 1/f characteristic in the low-frequency region (kHz) where electrophysiological measurements are performed (Figure S8). Notably, we have consistently observed 5- to 10-fold reduction of the normalized current noise power, SI/I2, in linear operation regimes of graphene transistors after suspension in extracellular medium due to removal of fluctuations associated with graphene/SiO2 interface (Figure 3a). Thus we conclude that the improved SNR in suspended graphene transducers responding to eLFPs can be attributed to concomitantly increased sensitivity of graphene transducers and tighter bioelectronic interfacing. We have further considered the origin of the low-frequency 1/f noise in suspended graphene transducers which ultimately sets their fundamental sensitivity limit in physiological media. First, fluctuation effects due to metal contact−solution interfaces and salt ions were experimentally ruled out as major sources of low-frequency noise in suspended graphene transistors in extracellular medium (Figure S9).24,25 Hence, we have focused on the interaction of water molecules with graphene that is ubiquitous in graphene-based bioelectronics. To identify the role of water, we systematically characterized the low-frequency noise characteristics of a suspended graphene transistor in vacuum and water vapor (Figure 3b and Figure S10). Several key points can be gleaned from this data. First, suspended graphene exhibits an inverted V-type noise which

monotonically decreases with its carrier density in vacuum as reported previously.26 Second, the addition of water vapor yields a distinctly different noise characteristic with an M-type dependence on its carrier density. Moreover, the magnitude of the normalized current noise power drastically increases ca. 10fold for the suspended graphene device in linear operation regimes by water vapor. Finally, the suspended graphene transistor was immersed in liquid deionized water where its Mtype noise characteristic was retained. Taken together, these results show that water near the graphene surface represents a dominant noise source to suspended graphene transducers in physiological media. To understand the microscopic mechanism of water-induced 1/f noise in suspended graphene, we first consider McWhorter’s carrier-number-fluctuation model27 that is widely applied to explain 1/f noise in metal−oxide−semiconductor FETs and graphene supported by SiO2.28 Distinctly, our suspended graphene device has few lattice defects and interfacial charge traps. Direct electron transfer between water and graphene is also forbidden at room temperature.29 Thus noise in suspended graphene transducers cannot originate from carrier-number fluctuations by charge trapping−detrapping between graphene and water molecules. Accordingly, here the observed noise is attributed to mobility fluctuations by motions of water molecules near graphene. To unravel it, we have performed ab initio density functional theory (DFT) calculations to explore the binding of water to graphene (Figure 3c). A water molecule binds weakly to graphene with a free-energy minimum of ∼70 meV (Figure 3d), so the physisorbed water can move rather freely near the graphene surface at room temperature. The dipole moment of the water molecule induces a disorder potential in graphene which scatters conduction carriers.30 When the disorder potential varies along with motions of the water molecule, it results in conductance fluctuations in graphene by the fluctuating interference of electron waves.31 Specifically, for a certain dynamical process of the water molecule with a characteristic time τ, the associated voltage noise has a Debye−Lorentzian spectrum SV(ω) ∝ τ/(ω2τ2 + 1). When the motions of the water molecule is thermally activated at temperature T, we then have τ = τ0 exp(E/kBT) and consequently SV(ω,T) ∝ ∫ (τ0 exp(E/kBT))/(ω2τ20 exp(2E/kBT) + 1)D(E) dE, where D(E) is the distribution of activation energies E of nearby water molecules. According to Dutta−Dimon−Horn model,32 when D(E) varies slowly with kBT, the integration is approximated to SV(ω,T) ∝ (kBT)/(ω)D(Ẽ ) with Ẽ = −kBT ln(ωτ0). The rigorous 1/f noise spectrum observed in suspended graphene reveals an almost uniform distribution of activation energies of disorders, which is reasonable given the spatially wide distribution of water molecules with respect to graphene transducer. Last, we have investigated the dependence of sensitivity on graphene transducer size in physiological media (Figure 4). The sensitivity of planar-GraFETs supported on SiO2 increases with increasing area,17 although the data are scattered. In contrast, the suspended graphene transducers follow a rigorous squareroot scaling on the area of graphene. This scaling rule can be understood in terms of disorder potentials by water molecules as discussed above. Generally speaking, the scattering length l can be expressed as l−1 = ∑iσi/A,31 where the sum runs over all disorder cross section σi on the entire graphene area, A. Thermally activated motions of water molecules should be uncorrelated, which directly leads to SV ∝ 1/A and thereby the D

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ACKNOWLEDGMENTS We thank J. Huang and C. M. Lieber for helpful discussions. This work was supported by the National Natural Science Foundation of China (21161120321 and 21173055) and the National Basic Research Program of China (973 Program) (2011CB932700).

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Figure 4. Dependence of transducer sensitivity on active areas of graphene and SWNTs. Black squares were measured with planar GraFETs on SiO2, and red squares were suspended GraFETs in extracellular medium. The fittings for GraFETs on SiO2 and after suspension are: Transducer sensitivity = 670 × A1/2 (solid black) and Transducer sensitivity = 3533 × A1/2 (solid red line), respectively. The black and red circles were measured with SWNTs on SiO2 and after suspension, respectively. The sensitivity of suspended SWNTs was 2− 5 times larger than the extrapolated values of suspended graphene (dashed red line) due to the high curvature effect. Inset, representative SEM image of suspended GraFETs at different lengths after electrical characterization in extracellular medium.

square-root-of-area scaling of graphene transducer sensitivity. The well-defined scaling of suspended graphene transducers in physiological media strongly suggests that our study approaches their fundamental sensitivity limit set by fluctuations of water. Furthermore, a suspended graphene with an active area of 70 μm2 reaches a detection resolution of 34 μV on the input voltage in physiological media (Figure 4), which, combined with its improved bioelectronic coupling capability, satisfies the sensitivity requirements in typical neural electrophysiology. We anticipate that these results, together with advances in graphene based flexible electronics,16 can open new and exciting opportunities for graphene in 3D bioelectronics.

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ASSOCIATED CONTENT

* Supporting Information S

Methods and Figures S1−S14. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected], [email protected]. Author Contributions

Z.C. and J.H. contributed equally to this work. Notes

The authors declare no competing financial interest. E

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