Chemiresistive and Gravimetric Dual-Mode Gas Sensor toward Target

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Chemiresistive and Gravimetric Dual-mode Gas Sensor toward Target Recognition and Defferentiation Yan Chen, Hao Zhang, Zhihong Feng, Hongxiang Zhang, Rui Zhang, Yuanyuan Yu, Jin Tao, Hongyuan Zhao, Wenlan Guo, Wei Pang, Xuexin Duan, Jing Liu, and Daihua Zhang ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.6b02682 • Publication Date (Web): 26 Jul 2016 Downloaded from http://pubs.acs.org on July 26, 2016

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ACS Applied Materials & Interfaces

Chemiresistive and Gravimetric Dual-mode Gas Sensor toward Target Recognition and Defferentiation Yan Chen, † Hao Zhang, † Zhihong Feng,‡ Hongxiang Zhang, † Rui Zhang, ‡ Yuanyuan Yu, ‡ Jin Tao, † Hongyuan Zhao, † Wenlan Guo, ‡ Wei Pang, † Xuexin Duan, ‡ Jing Liu‡ and Daihua Zhang*, †, ‡

†

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University,

Tianjin 300072, China ‡

College of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin

300072, China KEYWORDS: Gas sensor, Graphene, Solidly Mounted Resonator (SMR), Resonators, Electrical sensing

ABSTRACT: We demonstrate a dual-mode gas sensor for simultaneous and independent acquisition of electrical and mechanical signals from the same gas adsorption event. The device integrates a graphene field-effect transistor (FET) with a piezoelectric resonator in a seamless manner by leveraging multiple structural and functional synergies. Dual signals resulting from independent physical processes, i.e., mass attachment and charge transfer can reflect intrinsic properties of gas molecules and potentially enable target recognition and quantification at the same time. Fabrication of the device is based on standard Integrated Circuit (IC) foundry processes and fully compatible with system-on-a-chip (SoC) integration to achieve extremely small form factors. In addition, the ability of simultaneous measurements of mass adsorption and charge transfer guides us to a more precise understanding of the interactions between graphene and various gas molecules. Besides its practical functions, the device serves as an effective tool to quantitatively investigate the physical processes and sensing mechanisms for a large library of sensing materials and target analytes. 1. INTRODUCTION The development of modern gas sensors is currently driven by two major trends. On one hand,

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research and development efforts are targeting ever smaller form factors to promote sensor integration with wearables and smart phones for enhanced human interfacing and wireless connectivity, aiming to create new application scenarios such as real time health monitoring,1 remote diagnostics,2 environmental sensor networks and others yet to be imagined. Industry leaders including Bosch and Cambridge CMOS Sensors have made successful penetration into cell phone markets with their newly released environmental sensor chips.3-5 On the other hand, diverse real-life applications such as breath analysis6-8 and air quality evaluation9, 10 are calling for extended functionalities including quantitative identification of gas mixers with unknown concentrations. The target recognition and differentiation capabilities are becoming particularly and increasingly important to modern gas sensors. Achieving satisfactory size reduction and functionality enhancement at the same time, however, has proven extremely challenging. Gas chromatography (GC)11,

12

and optical sensors13,

14

offer

excellent recognition capabilities, whereas the space consuming components including GC columns and optical pathways make system-on-chip (SoC) integrations almost impossible. Power efficiency is another concern as the systems need to host active components (e.g., gas pumps, photo diodes and coolers) with continuous power consumption during operation. On the other hand, solid-state sensors based on chemiresistors15,

16

and piezoelectric resonators are ideal in size and power efficiency,17, 18

but the poor selectivity is severely limiting their adoption in a large number of real life applications. Approaches to implementing target recognition solid-state sensors are based upon primarily two mechanisms. The first one integrates the sensing membrane with a micro-heater, and employs temperature transient response to differentiate analytes.19, 20 The approach has been fairly mature but yields limited improvements. The second approach works by functionalizing the sensing interface with molecular groups that can either discriminatively interact with different analytes6 or modulate the property of the sensing materials toward specific targets.15,

21

In this case, multiple sensors with

different surface functionalizations are needed, and because of imperfect specificity of each sensor, signal processing always involves sophisticated pattern recognition algorithms.21,

22

In addition,

surface functionalization introduces complexity into device fabrication, generally incompatible with standard IC foundry processes for SoC integrations. 2


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In this report, we propose a new approach to enabling target recognition in solid-state gas sensors through dual-mode sensing. Specifically, the device is configured to simultaneously capture the electrical and mechanical signals from the same absorption event. As each type of gas molecule possesses a unique set of physical properties (i.e., electron affinity, polarity and mass) and undergoes qualitatively and/or quantitatively different interactions with the sensing material, the combined electrical and mechanical signals are expected to reflect specific and intrinsic information from individual targets. In fact, dual- and multi-mode sensing have been utilized in bio-sensing systems to investigate protein adsorption kinetics23,

24

and to expand dynamic sensing range.25 However, the

concept has not been demonstrated in gas sensors presumably due to challenges in multi-modal and real-time detection of gas molecules, which are significantly smaller and lighter compared to biomolecules. 2. EXPERIMENTAL SECTION Fabrication of SMR: The devices were fabricated on 100 mm undoped Si wafers, starting from deposition of the Bragg reflector. The alternating AlN and SiO2 layers were deposited through PVD and CVD, respectively. Thickness of each AlN/SiO2 pair was set to be 1200/700, 1000/1300, and 1000/650 nm from bottom to top. BE of the SMR was made of 600 nm thick Mo and deposited via PVD on top of the Bragg reflector. The film was then patterned through photolithography and plasma etching into isolated islands. PVD was used again to deposit the piezoelectric layer, a 1000 nm thick AlN film on top of the BE. Orientation of the AlN crystal was along c-axis. X-ray diffraction spectrum indicated a sharp peak at (002) with a Full-Width-at-Half-Maximum (FWHM) of 1.5. In the final step, the SMR was capped with a pair of Au top electrodes deposited using Ebeam evaporation followed by wet etch. Thicknesses of the Au electrodes and the underlying Cr adhesive layer were 300 and 50 nm, respectively. It is worth mentioning that the inner electrode (TIE) was intentionally shaped like a pentagon in order to suppress spurious resonance in the device. The electrode area was configured to be 3.0  104 μm2 so that the SMR has a characteristic impedance of 50 Ω in order to match the impedance of external circuits. The outer electrode (TOE) was separated 15 μm away from the TIE and grounded during operation. The area of TOE was several times larger than that of TIE. 3



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This geometrical arrangement ensures good E-field confinement within the active area under the TIE electrode. Graphene transfer and patterning: The CVD-grown monolayer graphene film was purchased from VIGON Technologies. The film first went through a thorough RCA cleaning process46 to remove surface contaminants. Prior to transfer, the device wafer was pre-treated with O2 plasma (100W, 1 min) to clean the surface and condition it to a hydrophilic interface. The graphene film was then pressed against the wafer with the support of a poly methyl methacrylate (PMMA) stamp and held in position for 2 hours at room temperature. The fixture was then heated to 150°C with a hotplate and kept at the temperature for 15 min. The heating promotes Van der Waals binding at the graphene-SMR interface, leaving an even and strongly adhered graphene film after removal of the PMMA stamp.Trimming of the graphene film was done with Ebeam lithography followed by O2 plama etching. We used a negative Ebeam resist from Allresist, which introduced much less surface contaminants compared to most photo resists. Recipe of the O2 plasma etching was 100 W/70 sccm/50 mTorr. The treatment duration was 60 s. 3. RESULTS AND DISCUSSION Our device consists of a thin-film piezoelectric resonator and a graphene field effect transistor (FET) seamlessly integrated with each other. The resonator is made of ultra-light microstructures and operates at gigahertz frequency, which is significantly higher than those of the multi-mode biosensors.23, 26 This enables the device to develop detectable frequency shift upon attachment of very tiny masses like gas molecules. At the same time, the graphene FET senses charge transfers at the sensing interface and provides independent signals from the same adsorption event. Due to its atomically thin nature, the graphene induces negligible perturbation to the underlying piezoelectric resonator. Instead, it considerably enhances the sensitivity of the resonator by providing a highly active surface for gas adsorption.27 We also note that the device fabrication is entirely compatible with IC processes and system-level integration. The simple structure also supports integration with microheaters and surface functionalizations to further expand the dimensionality of parameter space. In our studies, we used two common air pollutants, nitrogen dioxide (NO2) and ammonia (NH3), and two 4


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volatile organic compounds (VOCs), ethanol (C2H5OH) and n-hexane (C6H14) as target gases to demonstrate the operation and performance of the dual-mode sensor. Figure 1a shows the schematic of the dual-mode device, consisting of a piezoelectric solidly mounted resonator (SMR) and a monolayer graphene film attached on top of it. The inset of Figure 1a depicts the profile of the strain waves across different layers. Thickness of each AlN/SiO2 layer is set to be one forth of the wavelength. Sharp acoustic impedance mismatch between AlN and SiO2 results in quick attenuation of the strain wave across the Bragg reflector, and effectively minimizes energy dissipation to the underlying substrate. We chose Bragg reflector over air cavity as the impedance mismatch layer for the consideration of process stability. Compared to a suspended membrane, a rigid structure can effectively mitigate stress issues commonly present in AlN films, and form a sturdy base for subsequent processes and functional structures built on the top.

a

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TIE

2 3

Piezoelectric layer 5

4

BE

c

Bragg reflector

100 mm

Substrate

Mo

AlN

Au

SiO2

Si

Graphene

Figure 1. (a) Cross-sectional illustration of the multi-layered device structure. From bottom to top are: High-resistivity silicon substrate, Bragg reflector (containing 6 AlN and SiO2 layers to attenuate acoustic waves leaking to the Si substrate (inset)), Bottom electrode (BE), Piezoelectric layer, Top electrodes (TIE and TOE), and monolayer graphene film. (b) The entire process flow includes Bragg reflector deposition (1), BE deposition and patterning (2), piezoelectric layer deposition (3), TIE/TOE deposition and wet etch (4), and graphene transfer and patterning (5). (c) SEM image of a completed device. Scale bar is 100 mm.

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The active layers of the SMR, including the bottom electrode (BE), piezoeletric AlN layer and two top electrodes (denoted as TIE and TOE for inner and outer electrodes) were sequentially deposited on the Bragg reflector. Thickness and quality of the films determine key parameters of the SMR including its impedance, resonant frequcies and effective coupling coefficient. Deposition of the AlN layer was under strict process control with well-optimized parameters to ensure good crystallinity. The film exhibited excellent piezoelectric properties as demonstrated in our previous studies on Bulk Acoustic Wave (BAW)28 and Contour Mode Resonators (CMRs).29, 30 After completion of the SMR, a monolayer graphene film was then transferred onto the surface to form a semiconducting channel between the two top electrodes. The graphene film was then trimmed to within the active area of the SMR to avoid undesired coupling with neighboring devices. The entire process flow is summarized in Figure 1b. The scanning electron microscopy (SEM) image of Figure 1c shows a completed device. Contours of the TIE and TOE electrodes are clearly visible through the semitransparent graphene film. The bottom electrode is buried under the AlN layer therefore unseen from this image. It can be seen that the device architecture leverages several synergies between the SMR and the graphene FET. The TIE and TOE of the SMR serve as the source and drain electrodes of the FET at the same time; The BE is used to create an equipotential surface to shape the RF field inside the piezoelectric layer. The same electrode can also be biased to gate the graphene film to modulate its carrier concentration. The sandwiched AlN film generates and hosts electro-mechanical resonance within the SMR, and simultaneously works as the gate dielectric of the graphene FET. As mentioned above, the graphene film functions not only as a FET channel, but also as an effective surface coating to promote surface adsorption of gas molecules and boost mass detection sensitivity of the SMR.27

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Figure 2. (a) Test configuration and picture of the evaluation board (inset). The Bias-Tee separates RF and DC signals from the same port (TIE) and feeds them to network analyzer and semiconductor parameter analyzer, respectively. (b) RF output of the device. Red and blue curves are the magnitude and phase of electrical impedance at various frequencies, exhibiting sharp transitions at series (1.660 GHz) and parallel (1.693 GHz) resonance frequencies. (c) DC output of the device showing the transfer and output (inset) characteristics of the graphene FET. Current through the drain and source electrodes (Ids) is conducted by holes under zero gate-source bias (Vgs) according to the Ids- Vgs curve. The inset shows the Ids- Vds curves under different Vgs (10, 20, 30, and 40 V).

Figure 2a shows the measurement setup to characterize the dual-mode device. The chip was mounted onto an evaluation board with the TIE and TOE connected to the center pin and outer shell of a co-axial small-A-type (SMA) connector (Figure 2a inset). The board was then connected to a BiasTee to split radio frequency (RF) and direct-current (DC) signals from each other. The configuration allows the dual modes to operate independently and simultaneously at distinct frequencies (gigahertz vs. DC) with negligible interference. The two output terminals of the Bias-Tee were separately hooked up to a network analyzer (Agilent E5071B) and a source meter (Agilent B1500) to characterize the RF and DC responses. We note that signals from both channels can be electrically read out, which gives the device great advantage over other multi-mode sensors that rely on optical means.24,

25

The

capability of electrical readout eliminates the need of complex and expensive optical components, making the sensor chip fully compatible with system-level integrations in wearable and mobile platforms. The measurement of Figure 2b was taken from the RF port of the Bias-Tee. The red and blue curves plot the magnitude and phase of the device impedance as a function of frequency from 1.55 to 7



1.80 GHz. The dual-mode device behaves essentially the same as a bare SMR despite a moderate decrease in Q factor from 480 to 260. The additional energy loss is primarily due to finite resistance of the graphene film, which adds an extra energy dissipation channel in parallel with the SMR. The valley and peak of the red curve at 1.660 and 1.693 GHz correspond to series and parallel resonance, respectively. Both frequencies (denoted as fs and fp) are sensitively dependent on mass load on the device, and can therefore be used to precisely quantify molecular adsorptions upon gas exposures. The phase change (blue curve) of the SMR indicates transitions between capacitive (negative phase) and inductive (positive phase) reactance occurring at the two resonance frequencies, a characteristic feature of BAW resonators operating in thickness extension (TE) mode.31 Output from the DC port of the Bias-Tee reflects DC characteristics of the dual mode device. Due to nearly infinite DC impedance of the SMR, the device behaves the same as a standard graphene FET. Figure 2c and the inset present the transfer and output characteristics of the bipolar FET. Surface doping of oxygen and moisture from ambient air leads to a p-type channel and shifts the charge neutrality point (CNP) toward positive gate bias (~ 30V). Hole mobility of the device is derived to be ~87.4 cm2/Vs,32 which is comparatively low among CVD-grown graphene films (100-2000 cm2/Vs).33 This implies a relatively large number of intrinsic defects/impurities as scattering centers. Despite the resultant reduction in carrier mobility, these local defects provide active bonding sites for gas molecules and are favorable to sensing applications.34, 35

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Figure 3. Shift in series resonance frequency (fs) during exposure to different concentrations of NH3 (a) and C2H5OH (b). (c) Magnitude of frequency shift (|fs|) as a function of gas concentration (Ce) of NH3 (red), NO2 (blue), C2H5OH (green), and C6H14 (black). The red and blue lines are fittings based on Langmuir Isotherm. Source-drain bias was 5 mV. (d) and (e) are real-time change in FET current recorded simultaneously with the fs data in (a) and (b). (f) Magnitude of current change (|I|) at various gas concentrations when system reaches equilibrium. Data points of NH3 (red) and NO2 (blue) use the scales on the left axis, while C2H5OH and C6H14 share the scales on the right. The measurements in Figures 3a and b characterize the mechanical (RF) response of the sensor to mass adsorption in diluted NH3 and C2H5OH. High purity (99.999%) N2 was used as the carrier gas with a constant flow rate of 1000 sccm. The two figures plot the shift in series resonance frequency (fs) of the SMR during exposure to three different concentrations. The resonance frequency (fs) drops as the gas molecules attach onto the graphene film and plateaus when surface adsorption and desorption reach equilibrium. The following equation allows us to correlate the frequency shift with the amount of mass adsorption on the graphene surface:31 ∆𝑓s 𝑓s

≈

−𝑚s 𝜌0 𝑑0

(1)

where ms is the surface density of the adsorbed mass, in kg/m2. The minus sign indicates that fs drops with increasing ms. The ρ0 (in kg/m3) and d0 (in m) are the density and thickness of the SMR. In this particular case, the product of ρ0 and d0 is the sum of three components contributed by the BE (Mo), TIE (Au), and the piezoelectric layer (AlN), respectively: 𝜌0 𝑑0 = 𝜌Mo 𝑑Mo + 𝜌Au 𝑑Au + 𝜌AlN 𝑑AlN

(2)

plugging the numbers (Mo = 10.2 g/cm3, dMo = 0.6 µm, Au = 19.3 g/cm3, dAu = 0.3 µm, AlN = 3.3 g/cm3, dAlN =1 µm) in to eq 2 results in 0d0 =1.52  10-2 kg/m3. We carried out measurements with all four gases and plotted the frequency shifts at equilibrium as a function of gas concentration in Figure 3c. The lowest detectable concentrations were 10, 100, 500, and 1000 ppm for NO2, NH3, C2H5OH, and C6H14, respectively. For the two inorganic gases NH3 and NO2, fs shows a clear trend of saturation at high concentrations (red and blue curves), whereas the trend is absent with the two VOC gases, C2H5OH, and C6H14 (green and black curves), which

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result in linear response of fs across the entire concentration range. For NH3 and NO2, the data can be modeled with Langmuir Isotherm: 𝑐s 𝑐max

=

𝑎L 𝐶e 1+𝑎L 𝐶e

（3）

cs is defined as the surface adsorption density of gas molecules (in mol/m3), which relates to ms as cs = ms/M, with M being the molar mass in kg/mol. cmax is a constant denoting the maximum value of cs when adsorption saturates the surface. Ce (in ppm) is the volume concentration of target gas, and aL (in 1/ppm) the ratio between adsorption and desorption constants. Since the change in resonance frequency, as discussed above, is proportional to surface adsorption density, we can equate cs/cmax with fs/fmax, with fmax being the maximum frequency shift when all adsorption sites on graphene are fully occupied. This way we relate fs directly to Ce: ∆𝑓s =

∆𝑓max 𝑎L 𝐶e 1+𝑎L 𝐶e

（4）

The two coefficients, fmax and aL can then be derived from the intercept and slope of the linear fitting between 1/fs and 1/Ce. The fmax of NH3 and NO2 are calculated to be 21.4 and 112 kHz, respectively, which correspond to cmax = 11.6×10-6 mol/m2 (NH3) and 22.2×10-6 mol/m2 (NO2) according to eq 1. We can then use the length of N-H and N-O bonds to estimate the area each adsorbed molecule occupies on the graphene surface, and obtain a rough estimation of the maximum coverage ratio at saturation adsorption. The numbers are derived to be 8% for NH3 and 20% for NO2. The results suggest that only a small portion of the graphene surface is attached with NH3 or NO2 molecules at saturation. This is presumably due to the fact that the molecular attachment is through chemisorption toward preferential sites, which is more selective compared to physisorption that primarily relies on Van der Waals force. In fact, we believe that physisorption dominates the case of C2H5OH, and C6H14 despite weak charge transfers (as will be discussed in the next section). Using eq 1 and assuming single layer adsorption, the coverage ratios of C2H5OH, and C6H14 are estimated to be 0.2 and 0.73 at Ce = 3000 ppm. The adsorption kinetics is unable to be fitted with Langmuir Isotherm in this case, and multi-layer adsorption will likely occur after the first layer saturates. We note that direct measurement of surface adsorption density is a unique and important advantage of gravimetric 10


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sensors over chemiresistors. The latter is more prevalent but unable to correlate sensing signals directly to the amount of surface adsorptions. Figures 3d through 3f discuss the FET sensing signals acquired through the DC channel. Figures 3d and 3e are real time monitoring of the change in source-drain current (I) (at source-drain bias of 5 mV) during NH3 and C2H5OH exposure. The data were simultaneously recorded with the fs measurements in Figures 3a and 3b. The DC current responded to NH3 and C2H5OH in opposite directions. NH3 is a reducing gas and withdraws holes from graphene,36 thus lowering the carrier concentration within the p-type channel. On the contrary, C2H5OH is a weak oxidizing gas and able to raise the hole concentration by withdrawing electrons.37 Similar to Figure 3c, we summarize the magnitude of current change (|I|) at various concentrations (Ce) for all four gases in Figure 3f. We note that the sign of I for NH3 and C6H14 was inverted in the plot for better presentation and comparison. The detection limits to NO2 and NH3 were about 0.5 and 2.5 ppm with the graphene sensor, significantly lower than those of the SMR. NO2 and C6H14 are another pair of oxidizing and reducing gases that result in opposite changes in source-drain current. In addition, charge transfers induced by the two VOC gases, C2H5OH and C6H14 are significantly less than those of NH3 and NO2, as evidenced by the contrast between the scales on left and right axes. It is interesting to note that the profiles of |I| ~ Ce curves considerably differ from the |f |~ Ce curves in Figure 3c. The difference is attributed to changes in carrier mobility in addition to charge transfers during gas adsorption. We will continue the discussion in later sections.

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|I| 20 |fs| 0 6

Figure 4. Correlation between |I| (red), |fs| (blue), and surface adsorption density (cs) of different gas molecules. Reducing gases like NH3 (a) and C6H14 (d) follow concave curves, while oxidizing gases like NO2 (b) and C2H5OH (c) have a convex shape. The simultaneous dual-mode measurements enable a very important analysis, that is, to directly correlate the current change |I| with surface adsorption densities cs. Figure 4 shows the correlation for all four gases. We have also included the fs data for easy comparison, which, by the definition of eq 1, are always proportional to cs in all cases. Since the scales are quite different for various gases, we split the curves into separate figures 4a-4d. Previous studies on FET gas sensors38, 39 generally assumed that changes in source-drain current were proportional to densities of surface adsorption. The assumption holds well for small molecules with strong charge transfer tendency (e.g., NH3 and NO2) at very low concentrations, while Figure 4 gives a more precise idea about how the sensing behavior deviates from this simplest approximation in more generic cases. In general, |I| ~ cs curves are not straight lines. They are either concave (NH3 and C6H14) or convex (NO2 and C2H5OH) depending on direction of charge transfer at the sensing interface. When gas molecules are adsorbed on the graphene surface, they reduce hole mobility by adding extra scattering centers in the conduction channel.40 Since the current density is proportional to the product of carrier mobility and concentration, the net effect of mobility reduction and charge transfer leads to pronounced deviation from a linear |I| ~ cs 12


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relationship. Specifically, for reducing gases that withdraw holes from the channel, the |I| ~ cs curves are concave (Figures 4a and 4d) as the changes in mobility and carrier concentration act in the same direction, leading to “accelerating” decrease in current density; whereas for oxidizing gases, the decrease in mobility counteracts the increase in hole concentration and weakens the sensing response at high concentrations, resulting in convex |I| ~ cs curves in Figures 4b and 4c. a 20

b NH3

c

30

NO2

NH3

140

NO2

15 10 5

1/m(Vs/m2)

|I|(mA)

20

|I|(mA)

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

130 120 110

0

0 0

2

4

6 8 cs (mmol/m2)

10

12

0

2

4

6 8 cs (mmol/m2)

10

12

0

2

4

6 8 cs (mmol/m2)

10

12

Figure 5. |I| - cs correlations of NH3 (a) and NO2 (b). Slope of the linear fitting at low concentrations reflects the amount of charge transfer per gas molecule. (c) Reciprocal of carrier mobility (1/m) as a function of cs with linear fitting lines for data points at high concentrations. We redraw the |I| ~ cs curves of NH3 and NO2 with more details in Figure 5 for quantitative analysis. The source-drain current (I) can be written as (assuming metallic contacts as suggested by the output characteristics management in Figure 2c inset) 𝐼= V

𝑊 𝐿

𝑛𝑒𝜇

(5)

where V is the source-drain bias and set to be 5 mV throughout the measurements; W and L are width and length of the FET conduction channel, which are 400 and 15 mm, respectively; e = 1.6  10-19 C is the elementary charge; n and m are hole concentration (in 1/m2) and mobility (in m2/Vs) of the graphene FET. We assume that mobility change at very low gas concentrations is largely negligible, as suggested by previous observations in graphene36 and black phosphorous sensors.40 We can therefore use the initial carrier mobility m0 = 87 cm2/Vs to make a linear fitting of the |I| vs. cs curve at small concentrations, as shown in Figures 5a and 5b. Slope of the linear fitting can be used to derive the amount of charge each gas molecule exchanges with the graphene film. The number is generally a small portion of the elementary charge (e) and reflects very intrinsic property of the target gas

13



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associated with its electron affinity. We define ni as the charge transfer amount by single gas molecule (in C) divided by e. The numbers are calculated to be -0.008 (NH3), 0.049 (NO2), 0.002 (C2H5OH) and -0.003 (C6H14) for the four gases. The plus and minus signs denote hole donating and withdrawing, respectively. The results of NO2 and NH3 agree relatively well with previous studies.39, 41 The data of C2H5OH and C6H14 are, however, hard to find references to compare with due to the lack of studies on this particular topic. At higher gas concentrations, the effect of mobility modulation starts to take a more dominating role. As a reasonable approximation, we assume the change of carrier mobility is governed by Matthiessen's rule:42 −1 −1 −1 𝜇 = (𝜇lattice + 𝜇impurity + 𝜇adsorption )−1

(6)

where mlattice is the carrier mobility that the material would have if only lattice phonon scattering exits;

mimpurity and madsorption are the mobilities when assuming only impurity scattering or surface adsorption scattering, respectively. To the first-order approximation, madsorption is inversely proportional to the density of surface adsorption (cs).42 Therefore, the plot of 1/m ~ cs should display a linear trend when cs is sufficiently large. This has been verified in Figure 5c with both NH3 and NO2. Slope of the linear fitting is an important parameter that characterizes the efficiency of mobility modulation by unit density of surface adsorption. The two fitting lines have similar intercepts around 105 Vs/m2. This is not surprising as the intercept corresponds to µlattice-1 + µimpurity-1, which is an intrinsic value of the graphene FET and independent of the type of target gases. The correlation between I and cs (or equivalently I vs. fs) not only provides valuable insights into the adsorption kinetics and gas-graphene interactions, but also enables a very unique and important feature of the dual mode sensor. In real-life applications such as human breath and auto exhaust analysis, the application environment is much more complex compared to most lab experiments, which commonly assume the onset of target gas to follow perfect step functions with well-defined start/stop time and a steady flow in between.38 In realistic situations, however, concentrations of target gases follow continuous and relatively random waveforms. This introduces

14


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additional variables and makes the measurement and recognition extremely challenging with singlemode sensors. In contrast, the dual mode sensor is theoretically able to discriminate gas species by generating a I - fs curve in real time as the concentration varies. Each specific gas type (or mixture) would correspond to a unique trace on the I - fs plane independent of instantaneous concentration or shape of the waveform. At the same time, the start and end points of the trace mark the minimum and maximum concentrations during the exposure. In the case of dual mode sensing, the variation (or instability) of concentration is in fact beneficial for target recognition. With small portable sensors, users can even “scan” the concentration initiatively by varying the sensor-to-source distance to achieve higher accuracy.

In the last part, we demonstrate the usage of the dual mode sensor for quantitative analysis of multi-component gas mixtures. For simplicity, we consider a trinary gas mixture containing trace amount of gas A and B diluted in carrier gas C. The types of all components are known but the concentrations of A and B are wanted. We note that this simplified problem setting is actually very representative of a large number of application scenarios, in which the absolute or relative concentrations of the two most important “labels” are substantially decisive. Examples include ethanethiol and methanethiol in human breath for diagnosis of chronic liver disease,43 acetone and ammonia for diabetes/hyperglycemia,44 and pentane and dimethyl selenide for early detection of cystic fibrosis.45 To give specific numbers, we set A = NO2, B = C2H5OH, and C = air, and use the fitting curves of the sensing data from single component to construct the plot in Figure 6a. When all possible concentrations (CA and CB) are sampled, the measured I and fs values form two curved surfaces in the parameter space. Each fixed I (or fs) value corresponds to a series of CA/CB combinations that form a specific contour line in Figure 6b. The intersection point of each pair of the ΔI/Δfs contour lines determines the corresponding CA and CB values definitively. We have selected 16 pairs of CA/CB combinations to experimentally verify the hypothesis. I and fs were recorded under these concentrations and used to derive the CA/CB values. These numbers

15



are also plotted in Figure 6 (green dots with error bars) to be compared with the actual concentrations (red circles in Figure 6b). It can be seen that most data points match reasonably well with the actual values (within ±5~20%). The demonstration gives a good example of gas mixture analysis by leveraging independent readings from both the current and frequency signals. The conclusion should also hold for more practical carrier gases (e.g., breath air) with multiple background components, which would induce a shift in background signal but preserve the systematic trends with varying CA and CB. Finally, we note that the model assumes independent interactions of different components. Cross-talks in terms of screening effect or other interactions were not considered. We believe it is reasonable when the concentrations of the target species are substantially low. b

a I (mA) fs (kHz)

28 24 20

120

16 I

100

12 8

80 60

4

40

0.1

20 0.05

0 0

2 CA (10 3

4 ppm)

6

8

0

C

B

3

0 (1

m pp

120

20

100

16

80

12

60

8

40

4

20

0.08

I (mA)

140

24

0.1 0.09

0.07 CB (103 ppm)

fs

fs (kHz)

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

Page 16 of 20

0.06 0.05 0.04 0.03

)

0.02 0.01 0

0

1

2

3 4 CA (103 ppm)

5

6

7

Figure 6. (a) Calculated I and fs values under all concentration combinations of gas A (NO2) and gas B (C2H5OH) within the range of CA = (0, 100 ppm) and CB = (0, 7500 ppm). (b) Experimental data of the gas mixture. (The red circles indicate actual concentrations and the green dots indicate the values derived from the I /fs measurements). The dashed lines shows the I and fs contour lines projected on CA – CB plane. Coordinates of each intersection point correspond to definitive (CA, CB) values.

4. CONCLUSIONS In this work, we developed a dual-mode gas sensor based on SMR and graphene FET to simultaneously measure the mass adsorption and charge transfer occurring at the sensing interface. Four target gases including NO2, NH3, C2H5OH and C6H14 were used to evaluate the function and performance of the device. The SMR provides precise measurement of the amount of gas molecules

16


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adsorbed on the graphene surface. The surface adsorption density is crucial information for us to understand the adsorption kinetics of various gas molecules, and more importantly, when combined with FET signals, it provides intrinsic information of the target gas such as redox properties and electron affinity. This capability can therefore be tailored to recognize and differentiate targets, or even quantify the concentration of individual component in mixtures without the help of complicated data processing algorithms. Our scheme can also work together with other approaches such as surface functionalization and micro-heaters to further expand the parameter space and eventually achieve accurate target recognition and quantification in complex gaseous environment for real-life applications.

ACKNOWLEDGMENT The authors acknowledge financial support by Tianjin Applied Basic Research and Advanced Technology (13JKYBJC37100) and 111 Project (B07014).

AUTHOR INFORMATION Corresponding Author *[email protected].

Notes The authors declare no competing financial interest.

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Table Of Contents (TOC) graphic Bias-Tee RF DC

0.1

C

0.09

L

0.08 0.07

CB (103 ppm)

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

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0.06 0.05 0.04 0.03 0.02 0.01 0

0

1

2

3 4 CA (103 ppm)

5

6

7

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Chemiresistive and Gravimetric Dual-Mode Gas Sensor toward Target

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