Bioelectronic Nose Based on Single-Stranded DNA And

nanomaterials

Article Bioelectronic Nose Based on Single-Stranded DNA and Single-Walled Carbon Nanotube to Identify a Major Plant Volatile Organic Compound (p-Ethylphenol) Released by Phytophthora Cactorum Infected Strawberries

Hui Wang 1,2,* , Yue Wang 1, Xiaopeng Hou 2 and Benhai Xiong 1,* 1 State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; [email protected] 2 Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China; [email protected] * Correspondence: [email protected] or [email protected] (H.W.); [email protected] (B.X.); Tel.: +86-010-62811680 (B.X.) Received: 5 February 2020; Accepted: 3 March 2020; Published: 7 March 2020

Abstract: The metabolic activity in plants or fruits is associated with volatile organic compounds (VOCs), which can help identify the different diseases. P-ethylphenol has been demonstrated as one of the most important VOCs released by the Phytophthora cactorum (P. cactorum) infected strawberries. In this study, a bioelectronic nose based on a gas biosensor array and signal processing model was developed for the noninvasive diagnostics of the P.cactorum infected strawberries, which could overcome the limitations of the traditional spectral analysis methods. The gas biosensor array was fabricated using the single-wall carbon nanotubes (SWNTs) immobilized on the surface of field-effect transistor, and then non-covalently functionalized with different single-strand DNAs (ssDNA) through π–π interaction. The characteristics of ssDNA-SWNTs were investigated using scanning electron microscope, atomic force microscopy, Raman, UV spectroscopy, and electrical measurements, indicating that ssDNA-SWNTs revealed excellent stability and repeatability. By comparing the responses of different ssDNA-SWNTs, the sensitivity to P-ethylphenol was significantly higher for the s6DNA-SWNTs than other ssDNA-SWNTs, in which the limit of detection reached 0.13% saturated vapor of P-ethylphenol. However, s6DNA-SWNTs can still be interfered with by other VOCs emitted by the strawberries in the view of poor selectivity. The bioelectronic nose took advantage of the different sensitivities of different gas biosensors to different VOCs. To improve measure precision, all ssDNA-SWNTs as a gas biosensor array were applied to monitor the different VOCs released by the strawberries, and the detecting data were processed by neural network fitting (NNF) and Gaussian process regression (GPR) with high accuracy.

Keywords: bioelectronic nose; FET; ssDNA; SWNT; gas sensor; Phytophthora cactorum; volatile organic compounds; strawberry

1. Introduction Electronic or bioelectronic noses are securing tremendous applications in food and environment safety, clinical diagnosis, and anti-bioterrorism [1,2], which mainly consists of a gas sensor/biosensor array and signal processing model. The development of nanotechnology has created vast potential to build highly sensitive, low-cost, portable electronic or bioelectronic noses with low power consumption [3,4]. Owing to the excellent electrical, optical, and mechanical properties, carbon materials, including fullerene,

Nanomaterials 2020, 10, 479; doi:10.3390/nano10030479 www.mdpi.com/journal/nanomaterials Nanomaterials 2020, 10, 479 2 of 16 carbon black, carbon nanofiber, carbon nanotubes, and graphene, are ideal materials for developing the new generation of miniaturized, low-power, ubiquitous electronic or bioelectronic noses [5–7]. As one type of carbon nanotube, single-walled carbon nanotube (SWNT) is a one-atom-thick layer of graphite rolled up into a seamless cylinder with a diameter of several nanometers, and length on the order of 1–100 microns [8], which offers a high specific surface area to enhance the sensitivity. Based on electrical conductivity, there are two different types: semiconducting SWNT and metallic SWNT. The semiconducting SWNT is a hole-doped semiconductor, where the conductance of the SWNTs is observed to decrease by three orders of magnitude under positive gate voltages [9]. Even though SWNT-based sensors have been applied in the fields of trace gas molecules, their drawbacks are low sensitivity and selectivity. To enhance the performance of SWNT-based sensors towards gas molecules, the surface of SWNT can be decorated with inorganic molecules (CuO, ZnO, TiO2, SnO2)[10–12], organic macromolecules (polyaniline, porphyrin, polycyclic aromatic hydrocarbons) [13–15] or biomolecules. Compared to inorganic and organic molecules, there are little types of biomolecules applied to modify SWNT to measure gas molecules. In the recent years, different single-strand DNAs (ssDNA) as a naturally occurring polymer has been explored to decorate SWNT through π–π interaction [16] that can respond to the particular odorants, which provides a novel material to develop biomimetic smell sensors for the chemical compounds detection [17]. Previous studies have demonstrated that the ssDNA decoration can change the response profiles of SWNTs to the volatile compounds due to the difference of the base number and sequence [18]. Phytophthora cactorum (P. cactorum) is a common phytopathogenic fungus, which can cause leather rot or crown rot diseases [19]. This disease in strawberries was firstly reported in 1924 in southern states of the USA and then spread from Asia to Europe subsequently over the next few decades. If strawberry plants are infected by P. cactorum, fruit losses of up to 20–30% or even 50% were reported according to the statistics [20]. There is an increasing need for early diagnosis of the P. cactorum infection to enable timely action to cut down fruit losses. Early diagnosis of P. cactorum infection in strawberry can be realized through direct molecular methods such as polymerase chain reaction (PCR) [21], illumination fluorescence (IF), fluorescence in situ hybridization (FISH) [22], electronic tongue (ET), and enzyme-linked immunosorbent assay (ELISA) [23]. Although the low limit of detection (LOD), low limit of quantification (LOQ), and high specificity can be achieved through the mentioned techniques, they are usually used for confirming the diseases after visual symptoms appear. However, due to the low concentration and non-uniform distribution of fungi in the tissues of infected strawberries, compounded by complex sample preparation and long assay times, these techniques are time-consuming, labor-intensive, and hence impractical for rapid detection of P. cactorum required in case of an epidemic. Jelen and coworker [20] was studied that volatile compounds present in strawberries infected with P. cactorum. Among the hundreds of volatile organic compounds (VOCs), two compounds were found to be responsible for the characteristic off-odour of strawberries infected by P. cactorum: 4-ethyl phenol (vapor pressure: 0.13 mmHg at 20 ◦C) and 4-ethyl-2-metoxyphenol (4-ethyl guaiacol) (vapor pressure: 0.017000 mmHg at 25 ◦C). The amount of these two compounds emitted by the infected strawberries ranged from 1.12 to 22.56 mg/kg and 0.14 to 1.05 mg/kg, respectively [24]. In contrast with 4-ethyl guaiacol, the amount of gas molecules of 4-ethyl phenol is much higher that can be responsible for the characteristic off-odor of P. cactorum infection. Therefore, early detection of P.cactorum in strawberries could be achieved through the volatile organic compound (VOC) of 4-ethyl phenol. The odors of the infected plants or fruits are measured using spectrum based analytical instrument. The current state of the art techniques to assess the quality of products through odor evaluation and identification typically utilize gas chromatograph (GC) in combination with various detectors including flame ionization (GC-FID) [25], differential mobility spectrometry (GC-DMS) [26], headspace solid-phase microextraction (HS-SPME), high-performance liquid chromatography (HPLC), or mass spectrometry (GC-MS) [27]. They are not used for early detection, as these techniques require Nanomaterials 2020, 10, 479 3 of 16 a laboratory setup, expensive instrumentation, and skilled personnel. However, these tools could not be used without a skilled analyst and are often unsuitable for in-field tests. Electronic nose based on electrochemical gas sensor, which can be made portable, easy-to-use, and inexpensive, provides a solution to the shortages of spectrum devices. In this study, we propose a (bio)electronic nose using ssDNA decorated SWNTs coupling with a FET device to improve the sensitivity of bare SWNTs, which was applied to determine 4-ethyl phenol, the emissions of which are altered by the P. cactorum infected strawberry. Due to its non-invasive nature, it is amenable for in-field use when compared to other methods. To enhance the accuracy, the detecting data recorded by the ssDNA-SWNTs was processed using neural network fitting (NNF) and Gaussian process regression (GPR).

2. Materials and Methods

2.1. Chemicals and Reagents Six diﬀerent types of single-stranded DNA (ssDNA) [28,29] were synthesized and puriﬁed using standard solid-phase techniques, which were bought from Beijing Sunbiotech Co., Ltd. (Beijing, China). The ssDNA was dissolved in the sterilized Milli-Q water (100 µmol/L) and stored in refrigerator at 4 C. The ssDNA sequences were listed below: − ◦ s1-DNA: 50-CTT CTG TCT TGA TGT TTG TCA AAC-30 s2-DNA: 50-AAA ACC CCC GGG GTT TTT TTT TTT-30 s3-DNA: 50-TTT TTT TTT TTT TTT-30 s4-DNA: 50-GAG TCT GTG GAG GAG GTA GTC-30 s5-DNA: 50-GTT GTT GGT TGT TG-30 s6-DNA: 50-GTG TGT GTG TGT GTG TGT GTG TGT-30

The semiconducting single-walled carbon nanotubes (SWNTs) were purchased from Nano-Integris Inc. (Beijing, China), which was homogeneously dispersed in Milli-Q water with a concentration of 0.01 mg/mL. Acetone, propanol, and ammonium hydroxide were acquired from Fisher Scientific Company (Pennsylvania, CP,USA). 3-Aminopropyltriethoxysilane (APTES), 2-pentanone, 2-heptanone, methyl butanoate, ethanol, methyl hexanoate, and ethyl butanoate were provided by Sigma-Aldrich (Beijing, China). Milli-Q water was used throughout the experiments. 2-Heptanol was offered by Acros Organics (Beijing, China). 4-Ethyl phenol was obtained from Sigma-Aldrich (Beijing, China). The All chemical reagents were analytical purity without purification. All the solutions were prepared using Milli-Q water (Billerica, MA, USA) with an electric resistance of 18.2 MΩ, which was further treated through high-temperature sterilization.

2.2. Apparatus The micro-topography of SWNTs before and after non-covalently functionalized with ssDNA was investigated by Raman spectra (Thermo Scientific DXR 2xi, Shanghai, China), ultraviolet-visible spectra (UV–VIS; UV-1700, Shimadzu, Japan), scanning electron microscope (SEM; JEOL-6460, JSM-750, Beijing, China), and atomic force microscope (AFM; Bruker Bioscope Resolve, Massachusetts, MA, USA). Raman spectra were recorded using Dior XY Laser Raman (514 nm Diode and Ar ion lasers). The UV spectrum was collected by Beckman DU800 UV–VIS spectrophotometer (Beckman Coulter, Inc., Brea, CA, USA). SEM images were obtained by a Zeiss Leo SUPRA 55 with a beam energy of 10 kV. AFM images were taken using a Veeco Innova AFM. The electrochemical measurements, including current-voltage (I–V), field-effect transistor (FET), and current-time (I–T), were analyzed by a semiconductor parameter analyzer (Keithley 2636, Beijing, China). Nanomaterials 2020, 10, 479 4 of 16

2.3. Fabrication of Biosensor Arrays The single gap electrode was fabricated on a high doped p-type silicon wafer, which covered with a 100 nm thick thermal oxide (SiO2). The wafer surface was spanned with photoresist and then written the single gap structure using standard lithographic patterning. After baking, a 20 nm Cr layer and a 180 nm Au layer were deposited on the surface via e-beam evaporation. Finally, the deposited waferNanomaterials was immersed 2020, 10, x FOR in acetone PEER REVIEW overnight to remove the unwritten parts, in which the size of the single4 of 18 gap was 10 µm wide by 10 µm long in Figure1. 143 SWNTs based biosensor arrays were fabricated using the protocol as follows. The single gap SWNTs based biosensor arrays were fabricated using the protocol as follows. The single gap 144 electrode was cleaned sequentially with acetone and isopropanol, and then incubated in ammonium electrode was cleaned sequentially with acetone and isopropanol, and then incubated in ammonium 145 hydroxide for 30 min, which can eliminate the organic and inorganic substances on the surface. After hydroxide for 30 min, which can eliminate the organic and inorganic substances on the surface. 146 that, the electrode was immersed in 0.5 mL APTES for 30 min, washed with Milli-Q water, and blow- After that, the electrode was immersed in 0.5 mL APTES for 30 min, washed with Milli-Q water, 147 dried with nitrogen. Moreover, 50 μL SWNTs solution was covered on the area between the source and blow-dried with nitrogen. Moreover, 50 µL SWNTs solution was covered on the area between 148 and drain with high humidity conditions in the dark for 60 min, followed by rinsing the SWNTs the source and drain with high humidity conditions in the dark for 60 min, followed by rinsing 149 residue. The rinsed electrodes were annealed in CVD at 250 °C. Finally, SWNTs were functionalized the SWNTs residue. The rinsed electrodes were annealed in CVD at 250 ◦C. Finally, SWNTs were 150 with ssDNA by coating a 10 μL ssDNA solution on the electrode’s surface for 4 h under high humidity functionalized with ssDNA by coating a 10 µL ssDNA solution on the electrode’s surface for 4 h under 151 condition, and the residue was cleaned using Milli-Q water. high humidity condition, and the residue was cleaned using Milli-Q water.

152 Figure 1. Single gap electrode decorated with SWNTs and ssDNA: (a) FET, (b) FET incubated in APTES, 153 Figure 1. Single gap electrode decorated with SWNTs and ssDNA: (a) FET, (b) FET incubated in (c) SWNTs modified on the surface of FET and (d) ssDNA decorated on SWNTs. 154 APTES, (c) SWNTs modified on the surface of FET and (d) ssDNA decorated on SWNTs. 2.4. Gas Generator 155 2.4. Gas Generator The gas generator in Figure2 was designed and integrated by our group, which was consisted 156 of twoThe mass gas flowgenerator controllers in Figure (MFCs), 2 was one designed bubbler, and one integrated air cylinder, by andour group, control which software was developed consisted 157 usingof two LabView. mass flow The controllers inlet ports (MFCs), of two one MFCs bubbler, were connectedone air cylinder, to the airand cylinder control directly,software which developed were 158 controlledusing LabView. by the The control inlet software. ports of two One MFCs of MFC were was connected linked with to the bubblerair cylinder to generate directly, the which saturated were 159 vaporcontrolled of VOC, by the and control the other software. was employed One of MFC to control was linked the flow with rate the of bubbler dry air. to Di generatefferent concentrations the saturated 160 ofvapor VOCs of VOC, were achievedand the other by mixing was employed the different to control ratio of the saturated flow rate VOCs of dry and air. dryDifferent air. The concentratio sensing areans 161 of VOCs microelectrode were achieved was covered by mixing with the a different 1.2 cm3 sealed ratio of glass saturated dome, VOCs which and the dry gas air. molecules The sensing can passarea 162 throughof microelectrode the glass dome.was covered with a 1.2 cm3 sealed glass dome, which the gas molecules can pass 163 through the glass dome. 2.5. Sensing Protocol The relative resistance of ssDNA-SWNTs was defined as the following equation:

∆R R R = − 0 100% (1) R R0 ×

where R0 was the original resistance of ssDNA-SWNTs exposed to dry air, and R was the resistance of ssDNA-SWNTs exposed to VOCs.

164 165 Figure 2. The schematic diagram of the gas generator.

166 2.5. Sensing Protocol 167 The relative resistance of ssDNA-SWNTs was defined as the following equation: Nanomaterials 2020, 10, x FOR PEER REVIEW 4 of 18

143 SWNTs based biosensor arrays were fabricated using the protocol as follows. The single gap 144 electrode was cleaned sequentially with acetone and isopropanol, and then incubated in ammonium 145 hydroxide for 30 min, which can eliminate the organic and inorganic substances on the surface. After 146 that, the electrode was immersed in 0.5 mL APTES for 30 min, washed with Milli-Q water, and blow- 147 dried with nitrogen. Moreover, 50 μL SWNTs solution was covered on the area between the source 148 and drain with high humidity conditions in the dark for 60 min, followed by rinsing the SWNTs 149 residue. The rinsed electrodes were annealed in CVD at 250 °C. Finally, SWNTs were functionalized 150 with ssDNA by coating a 10 μL ssDNA solution on the electrode’s surface for 4 h under high humidity 151 condition, and the residue was cleaned using Milli-Q water.

152 153 Figure 1. Single gap electrode decorated with SWNTs and ssDNA: (a) FET, (b) FET incubated in 154 APTES, (c) SWNTs modified on the surface of FET and (d) ssDNA decorated on SWNTs.

155 2.4. Gas Generator 156 The gas generator in Figure 2 was designed and integrated by our group, which was consisted 157 of two mass flow controllers (MFCs), one bubbler, one air cylinder, and control software developed 158 using LabView. The inlet ports of two MFCs were connected to the air cylinder directly, which were 159 controlled by the control software. One of MFC was linked with the bubbler to generate the saturated 160 vapor of VOC, and the other was employed to control the flow rate of dry air. Different concentrations 161 of VOCs were achieved by mixing the different ratio of saturated VOCs and dry air. The sensing area 162 3 Nanomaterialsof microelectrode2020, 10, 479was covered with a 1.2 cm sealed glass dome, which the gas molecules can5 pass of 16 163 through the glass dome.

Nanomaterials 2020, 10, x FOR PEER REVIEW 5 of 18

∆ − = × 100% (1)

168 0 164 where R was the original resistance of ssDNA-SWNTs exposed to dry air, and R was the 169 resistance of ssDNA-SWNTs exposed to VOCs. 165 Figure 2. The schematic diagram of the gas generator.generator. 170 3. Results Results and and Discussion 166 2.5. Sensing Protocol 171 167 3.1. The The C Characteristic relativeharacteristic resistance of ssDNA ssDNA-SWNTs of -ssDNASWNTs-SWNTs was defined as the following equation: 172 The characteristics of SWNTs before and after functionalized with ssDNA were investigated by 173 differentdiﬀerent spectrum technologies, suchsuch asas SEM,SEM, AFM,AFM, Raman,Raman, andand UV–VIS.UV–VIS. 174 Figure 3 3 shows shows the the SEM SEM images images of of bare bare SWNTs SWNTs and and ssDNA-SWNTs. ssDNA-SWNTs. It It was was clear clear that that bare bare 175 SWNTs were were immobilized immobilized uniformly uniformly on theon areathe areaof the of microelectrode, the microelectrode, which exhibited which exhibited a net structure. a net 176 Afterstructure. SWNTs After modiﬁed SWNTs withmodified ssDNA, with the ssDNA, image the of ssDNA-SWNTsimage of ssDNA became-SWNTs blurred, became and blurred, the diameter and the 177 wasdiameter slightly was larger slightly than larger the bare than SWNTs. the bare Due SWNTs. to the Due poor to electrical the poor conductivity electrical conductivity of ssDNA, itof indicated ssDNA, 178 itthat indicated ssDNA that existed ssDNA on the existed SWNTs’ on the surface. SWNTs’ surface.

179 Figure 3. SEM images of (A) bare SWNTs and (B) ssDNA-SWNTs. 180 Figure 3. SEM images of (A) bare SWNTs and (B) ssDNA-SWNTs. The line-scan proﬁles of bare SWNTs before and after functionalized with ssDNA were analyzed 181 The line-scan profiles of bare SWNTs before and after functionalized with ssDNA were analyzed by AFM, which was shown in Figure4. The height of the bare SWNT was about 1.7 nm, which matched 182 by AFM, which was shown in Figure 4. The height of the bare SWNT was about 1.7 nm, which with the diameter of SWNTs reported by the vendor. The height of ssDNA-SWNT reached ~4 nm, 183 matched with the diameter of SWNTs reported by the vendor. The height of ssDNA-SWNT reached demonstrating that ssDNA had been functionalized on the SWNT surface to form a uniform layer. 184 ~4 nm, demonstrating that ssDNA had been functionalized on the SWNT surface to form a uniform Raman spectra of bare SWNTs and ssDNA-SWNTs were shown in Figure5. There were several 185 layer. 1 peaks existed on the bare SWNTs, the D band, G band, and G’ band, which were located at 178 cm− , 186 1 1 1 1345 cm− , 1592 cm− , and 2682 cm− . After ssDNA modiﬁed on the SWNTs, the relative intensity + of ssDNA-SWNTs was decreased greatly (The relative intensity of G− to G band was reduced 9% + 6 1 relative to bare SWNTs [30]), and the G band was downshifted by 8 cm− . The phenomenon might be C Bare SWNTs attributed to ssDNA wrapping on SWNTs’ surface that blocked the ssDNA-SWNTs SWNTs to contact with Raman or

changed the charge transfer between the4 ssDNA and SWNTs.

A 2 Height (nm) Height

0 20 40 60 80 100 120 B Length (nm) 187

188 Figure 4. AFM image of (A) bare SWNTs; (B) ssDNA-SWNTs; (C) the height profile of bare SWNTs 189 and ssDNA-SWNT. Nanomaterials 2020, 10, x FOR PEER REVIEW 5 of 18

∆ − = × 100% (1)

168 where R0 was the original resistance of ssDNA-SWNTs exposed to dry air, and R was the 169 resistance of ssDNA-SWNTs exposed to VOCs.

170 3. Results and Discussion

171 3.1. The Characteristic of ssDNA-SWNTs 172 The characteristics of SWNTs before and after functionalized with ssDNA were investigated by 173 different spectrum technologies, such as SEM, AFM, Raman, and UV–VIS. 174 Figure 3 shows the SEM images of bare SWNTs and ssDNA-SWNTs. It was clear that bare 175 SWNTs were immobilized uniformly on the area of the microelectrode, which exhibited a net 176 structure. After SWNTs modified with ssDNA, the image of ssDNA-SWNTs became blurred, and the 177 diameter was slightly larger than the bare SWNTs. Due to the poor electrical conductivity of ssDNA, 178 it indicated that ssDNA existed on the SWNTs’ surface.

179

180 Figure 3. SEM images of (A) bare SWNTs and (B) ssDNA-SWNTs.

181 The line-scan profiles of bare SWNTs before and after functionalized with ssDNA were analyzed 182 by AFM, which was shown in Figure 4. The height of the bare SWNT was about 1.7 nm, which 183 matched with the diameter of SWNTs reported by the vendor. The height of ssDNA-SWNT reached 184 ~4 nm, demonstrating that ssDNA had been functionalized on the SWNT surface to form a uniform 185 layer. Nanomaterials 2020, 10, 479 6 of 16 186

6 C Bare SWNTs ssDNA-SWNTs

4 Nanomaterials 2020, 10, x FOR PEER REVIEW 6 of 18 A 190 Raman spectra of bare SWNTs and ssDNA2 -SWNTs were shown in Figure 5. There were several 191 peaks existed on the bare SWNTs, the D (nm) Height band, G band, and G’ band, which were located at 178 cm−1, 192 1345 cm−1, 1592 cm−1, and 2682 cm−1. After ssDNA modified on the SWNTs, the relative intensity of 193 ssDNA-SWNTs was decreased greatly (The0 relative intensity of G− to G+ band was reduced 9% + −1 194 relative to bare SWNTs [30]), and the G band0 was20 downshifted40 60 80by 8 100cm . 120The phenomenon might 195 be attributed to ssDNA wrappingB on SWNTs’ surface thatLength blocked (nm) the SWNTs to contact with Raman 196187 or changed the charge transfer between the ssDNA and SWNTs. Figure 4. AFM image of (A) bare SWNTs; (B) ssDNA-SWNTs; (C) the height proﬁle of bare SWNTs 188 andFigure ssDNA-SWNT. 4. AFM image of (A) bare SWNTs; (B) ssDNA-SWNTs; (C) the height profile of bare SWNTs 189 and ssDNA-SWNT.

1.0 + G Bare SWNTs s6DNA-SWNTs 0.8

0.6

0.4 G-

Relative Intensity (A.U.) Intensity Relative 0.2 D G'

0.0

1200 1600 2000 2400 2800 Raman Shift (cm-1) 197 Figure 5. Raman spectrum of bare SWNTs (Black) and ssDNA-SWNTs (Red). 198 Figure 5. Raman spectrum of bare SWNTs (Black) and ssDNA-SWNTs (Red). To confirm the formation of ssDNA-SWNTs using the UV–VIS spectra, the SiO2/Si substrate was 199 substitutedTo confirm by a quartzthe formation plate due of ssDNA to the poor-SWNTs optical using transmittance. the UV–VIS spectra, The UV the absorption SiO2/Si substrate spectra was of 200 blanksubstituted quartz, by SWNTs a quartz/quartz, plate and due ssDNA-SWNTs to the poor optical/quartz transmittance. were shown inThe Figure UV absorption6. The spectrum spectra of of 201 blankblank quartz quartz was, SWNTs/ set asq theuartz background, and ssDNA so- thatSWNTs/ no peaksquartz appeared were shown in the in entire Figure wavelength 6. The spectrum region. of 202 Whenblank SWNTs quartz were was immobilizedset as the background on the quartz’ so that surface, no peaks an absorption appeared peak in the was entire found wavelength in the range region. from 203 200When nm toSWNTs 300 nm. were For theimmobilized ssDNA-SWNTs on the/quartz, quartz’ the surface, absorbance an absorption peak located peak at was 260 nm,found but in was the much range 204 higherfrom than200 nm SWNTs to 300/quartz. nm. F Theor the reason ssDNA was-SWNTs/ that the characteristicquartz, the absorbance absorption peak oflocated SWNTs at and260 ssDNAnm, but 205 waswas located much at higher 260 nm than that SWNTs/ can generatequartz the. The composite reason was effect that on ssDNA-SWNTsthe characteristic/quartz absorption [31,32]. peak of 206 SWNTsFigure and7 shows ssDNA the I wasDS-V DS locatedof bare at SWNTs 260 nm before that and can after generate decorated the with composite ssDNA. effect In the on Figure ssDNA7A, - 207 I SWNTs/exhibitedquartz a linear [31,32 relationship]. with the voltage ranging from 0.1 V to +0.1 V, and the slope of DS − 208 the IDS -VDS curve was decreased after ssDNA modification. The resistance of the ssDNA-SWNTs 209 increased ~10.5-fold from 8.5 kΩ to 94.0 kΩ compared to bare SWNTs, resulting in a decrease of the charge carrier concentration of the p-type SWNTs and electron scattering from these molecules [33]. The ssDNA-SWNTs was further confirmed by obtaining FET transfer characteristics in Figure7B. The threshold gate voltage (VTH) of bare SWNTs was 4.34 V. After SWNTs immobilized with ssDNA, the transfer curve shifted to the negative direction that V reached 6.28 V. Additionally, the mobility TH − decreased from 140.51 cm2/Vs for bare SWNTs to 41.03 cm2/Vs for ssDNA-SWNTs. The shift in threshold voltage and a decrease in hole mobility can be explained by the reduction in hole concentration of p-type SWNTs due to charge (electron) transfer from the negatively-charged phosphate backbone of ssDNA. Nanomaterials 2020, 10, x FOR PEER REVIEW 7 of 18

0.020 Blank Quartz SWNTs-Quartz s6DNA-SWNTs-Quartz 0.015

0.010 Absorption (%) 0.005

0.000

200 300 400 500 600 Wavelength (nm) Nanomaterials 2020, 10, x FOR PEER REVIEW 7 of 18 210 Nanomaterials 2020, 10, 479 7 of 16 211 Figure 6. The UV spectrum of blank quartz (Black), SWNTs-quartz (Red) and s6DNA-SWNTs- 212 quartz (Blue).

0.020 213 Figure 7 shows the IDS-VDS of bare SWNTs before Blankand Quartzafter decorated with ssDNA. In the Figure SWNTs-Quartz 214 7A, IDS exhibited a linear relationship with the voltage s6DNA-SWNTs-Quartz ranging from −0.1 V to +0.1 V, and the slope of 215 the IDS-VDS curve was decreased0.015 after ssDNA modification. The resistance of the ssDNA-SWNTs 216 increased ~10.5-fold from 8.5 kΩ to 94.0 kΩ compared to bare SWNTs, resulting in a decrease of the

217 charge carrier concentration0.010 of the p-type SWNTs and electron scattering from these molecules [33]. 218 The ssDNA-SWNTs was further confirmed by obtaining FET transfer characteristics in Figure 7B.

219 The threshold gate voltageAbsorption (%) (VTH) of bare SWNTs was 4.34 V. After SWNTs immobilized with ssDNA, 0.005 220 the transfer curve shifted to the negative direction that VTH reached −6.28 V. Additionally, the mobility 221 decreased from 140.51 cm2/Vs for bare SWNTs to 41.03 cm2/Vs for ssDNA-SWNTs. The shift in 222 threshold voltage and a decrease0.000 in hole mobility can be explained by the reduction in hole 223 concentration of p-type SWNTs200 due to300 charge 400 (electron) 500 transfer from600 the negatively-charged 224 phosphate backbone of ssDNA. Wavelength (nm) 210 Figure 6. The UV spectrum of blank quartz (Black), SWNTs-quartz (Red) and s6DNA-SWNTs-quartz (Blue). 211 Figure 6. The UV spectrum of blank quartz (Black), SWNTs-quartz (Red) and s6DNA-SWNTs- 212 18.0k quartz (Blue). Bare SWNTs A 8000 B Bare SWNTs 12.0k ssDNA-SWNTs ssDNA-SWNTs 213 Figure 7 shows the IDS-VDS of bare SWNTs before and after decorated with ssDNA. In the Figure 6000 6.0k 214 7A, IDS exhibited a linear relationship with the voltage ranging from −0.1 V to +0.1 V, and the slope of

215 the IDS-0.0VDS curve was decreased after ssDNA modification (nA) 4000 . The resistance of the ssDNA-SWNTs (nA) DS I DS 216 increasedI ~10.5-fold from 8.5 kΩ to 94.0 kΩ compared to bare SWNTs, resulting in a decrease of the -6.0k 217 charge carrier concentration of the p-type SWNTs and 2000electron scattering from these molecules [33].

218 The ssDNA-12.0k -SWNTs was further confirmed by obtaining FET transfer characteristics in Figure 7B. 0 219 The threshold gate voltage (VTH) of bare SWNTs was 4.34 V. After SWNTs immobilized with ssDNA, -18.0k 220 the transfer-0.10 curve shifted-0.05 0.00to the negative0.05 0.10 direction that VTH-40 reached-30 -20−6.28-10 V. Additionally0 10 20 30, the40 mobility V (V) V (V) 221 decreased from 140.51 cmDS 2/Vs for bare SWNTs to 41.03 cm2/Vs for ssDNAG -SWNTs. The shift in 225 222 threshold voltage and a decrease in hole mobility can be explained by the reduction in hole Figure 7. (A) IDS-VDS characteristics of bare SWNTs before and after functionalization with ssDNA 226223 concentrationatFigureVG = 07.V; (A ( of)B I) DS transferp-V-typeDS characteristics characteristicsSWNTs due of bare curve to chargeSWNTs for bare before (electron) SWNTs and after and transfer ssDNA-SWNTsfunctionalization from the at negativelywithVDS ssDNA= 0.1- Vcharged at 227224 phosphateandVG V= G0 rangingV; backbone (B) transfer from of characteristics40ssDNA. V to + 40 V. curve for bare SWNTs and ssDNA-SWNTs at VDS = 0.1 V and VG − 228 ranging from −40 V to +40 V. 3.2. Optimization

229 3.2.To Optimization simplify18.0k the experimental conditions, some parameters were optimized by detecting diﬀerent

Bare SWNTs A 8000 B concentrations of 4-ethyl phenol. Bare SWNTs 230 To12.0k simplify ssDNA-SWNTs the experimental conditions, some parameters were optimized by detecting different Figure8 shows the relative resistance after di ﬀerent ssDNA-SWNTs exposed to thessDNA-SWNTs 100% saturated 231 concentrations of 4-ethyl phenol. 6000 vapor of6.0k 4-ethyl phenol for 5 min. The relative resistance of bare SWNTs was about 1.26. However, SWNTs functionalized with diﬀerent ssDNA displayed a decrease in resistance, so that the relative 0.0 (nA) 4000 (nA) DS I DS

resistancesI showed negative values. There were only three absolute values of relative resistance of

-6.0k ssDNA-SWNTs were higher than bare SWNTs. Further, the2000 sensitivity of the six gas biosensors were in the order of s6DNA-SWNTs > s4DNA-SWNTs > s1DNA-SWNTs > s2DNA-SWNTs > s5DNA-SWNTs -12.0k > s3DNA-SWNTs, which was ascribed to the types and numbers0 of bases in different ssDNA. Different ssDNA functionalized-18.0k with SWNTs might be formed different complex, sequence-specific set of binding -0.10 -0.05 0.00 0.05 0.10 -40 -30 -20 -10 0 10 20 30 40 V (V) pockets that had different combiningDS abilities with 4-ethyl phenol [34]. TheVG (V) relative resistance of 225 s6DNA-SWNTs reached 14.51, which was chosen to monitor the level of 4-ethyl phenol. − Figure9 shows the relative resistances of s6DNA-SWNTs exposed to 5%, 10%, 40%, and 100% 226 Figure 7. (A) IDS-VDS characteristics of bare SWNTs before and after functionalization with ssDNA at saturated vapor of 4-ethyl phenol for 7 min, and the resistances were recorded every 1 min. It was 227 VG = 0 V; (B) transfer characteristics curve for bare SWNTs and ssDNA-SWNTs at VDS = 0.1 V and VG obvious that the relative resistance of s6DNA-SWNTs was positively related to the exposure time, 228 ranging from −40 V to +40 V. especially for the low concentrations of 4-ethyl phenol. When the relative resistance of s6DNA-SWNTs 229 reached3.2. Optimization the maximum upper the 10% saturated vapor of 4-ethyl phenol, the exposure time needed 4 min, 3 min, and 2 min for 10%, 40%, and 100% saturated vapor of 4-ethyl phenol, respectively. The gas 230 moleculesTo simplify were absorbed the experimental by the gas sensing conditions, material some on parameters the s6DNA-SWNTs were optimized that will by achieve detecting a dynamic different 231 concentrations of 4-ethyl phenol. Nanomaterials 2020, 10, x FOR PEER REVIEW 8 of 18

232 Figure 8 shows the relative resistance after different ssDNA-SWNTs exposed to the 100% 233 saturated vapor of 4-ethyl phenol for 5 min. The relative resistance of bare SWNTs was about 1.26. 234 However, SWNTs functionalized with different ssDNA displayed a decrease in resistance, so that the 235 relative resistances showed negative values. There were only three absolute values of relative 236 resistance of ssDNA-SWNTs were higher than bare SWNTs. Further, the sensitivity of the six gas 237 biosensors were in the order of s6DNA-SWNTs > s4DNA-SWNTs > s1DNA-SWNTs > s2DNA- 238 SWNTs > s5DNA-SWNTs > s3DNA-SWNTs, which was ascribed to the types and numbers of bases 239 in different ssDNA. Different ssDNA functionalized with SWNTs might be formed different complex, Nanomaterials 2020, 10, 479 8 of 16 240 sequence-speciﬁc set of binding pockets that had different combining abilities with 4-ethyl phenol 241 [34]. The relative resistance of s6DNA-SWNTs reached −14.51, which was chosen to monitor the level 242 ofbalance. 4-ethyl The phenol. higher was concentration of 4-ethyl phenol, the shorter was its equilibrium time. Thus, 5 min was selected to identify the lower concentration of 4-ethyl phenol with high sensitivity.

-4

-8

-12 Relative resistanc (%) resistanc Relative -16

-20 s1DNA s2DNA s3DNA s4DNA s5DNA s6DNA SWNTs Different type of ssDNA 243 Figure 8. The relative resistance of SWNTs before and after functionalized with different ssDNA Nanomaterials 2020, 10, x FOR PEER REVIEW 9 of 18 Figure 8. The relative resistance of SWNTs before and after functionalized with different ssDNA 244 exposed to the saturated vapor of 4-ethyl phenol for 5 min (VDS = 0.1 V and VG = 0 V; each point 245 exposedcalculated to by the the saturated average valuevapor ofof four4-ethyl diff erentphenol s6DNA-SWNTs). for 5 min (VDS = 0.1 V and VG = 0 V; each point 246 calculated by the average value of four different s6DNA-SWNTs). 5 247 5 Figure 9 shows the relative resistances of s6DNA-SWNTs exposed 10 to 5%, 10%, 40%, and 100% 248 saturated vapor of 4-ethyl phenol0 for 7 min, and the resistances 40were recorded every 1 min. It was 100 249 obvious that the relative resistance of s6DNA-SWNTs was positively related to the exposure time, 250 especially for the low concentrations-5 of 4-ethyl phenol. When the relative resistance of s6DNA- 251 SWNTs reached the maximum upper the 10% saturated vapor of 4-ethyl phenol, the exposure time 252 needed 4 min, 3 min, and 2-10 min for 10%, 40%, and 100% saturated vapor of 4-ethyl phenol, 253 respectively. The gas molecules were absorbed by the gas sensing material on the s6DNA-SWNTs Relative resistance (%) resistance Relative 254 that will achieve a dynamic balance.-15 The higher was concentration of 4-ethyl phenol, the shorter was 255 its equilibrium time. Thus, 5 min was selected to identify the lower concentration of 4-ethyl phenol 256 with high sensitivity. -20 0 1 2 3 4 5 6 7 Time (min) 257 Figure 9. The relative resistance of s6DNA-SWNTs exposed to 5%, 10%, 40%, and 100% saturated 258 vaporFigure of 9. 4-ethyl The relative phenol forresistance 7 min (V ofDS s6DNA= 0.1 V- andSWNTsVG =exposed0 V; each to point 5%, calculated10%, 40%, byand the 100% average saturated value 259 ofvapor four of di ff4erent-ethyl s6DNA-SWNTs). phenol for 7 min (VDS = 0.1 V and VG = 0 V; each point calculated by the average value 260 of four different s6DNA-SWNTs). The detecting voltage between the source and the drain (VDS) was one of the most important

261 parametersThe detecting for the bioelectronicvoltage between nose. the According source and to thethe voltage-currentdrain (VDS) was curves one of shown the most in Figure important7A, the current increased with voltage in the range from 0.1 V to 0.1 V,which displayed an excellent linearity 262 parameters for the bioelectronic nose. According− to the voltage-current curves shown in Figure 7A, 263 relationship.the current increased The resistance with atvoltage each voltage in the range was calculated from −0.1 using V to the0.1 Ohm’sV, which law, displayed which was an shown excellent in 264 Figurelinearity 10. relationship. It was clear that The the resistance values of at bare each SWNTs voltage and was s6DNA-SWNTs calculated using were the almost Ohm’s equal law, atwhich different was 265 voltage,shown excludingin Figure 010. V. It The was discontinuities clear that the at values 0 V was of mainly bare SWNTs because theand insufficient s6DNA-SWNTs precision were of device.almost 266 Theequal device at different offered an voltage, ultralow excluding voltage, but 0 V cannot. The measurediscontinuities the ultralow at 0 V current was mainly with high because accuracy. the 267 Toinsufficient obtain a higher precision current of device.signal of The bioelectronic device offered noise, a0.1n ultralow V was chosen voltage, as the but detecting cannot voltage. measure the 268 ultralow current with high accuracy. To obtain a higher current signal of bioelectronic noise, 0.1 V 269 was chosen as the detecting voltage.

120000

100000

80000 ) Ω

60000 Bare SWNTs s6DNA-SWNTs 40000 Resistance ( Resistance

20000

-0.10 -0.05 0.00 0.05 0.10 Voltage (V) 270

271 Figure 10. The resistance of SWNTs before and after functionalized with different s6DNA at different 272 voltages.

273 3.3. Sensitivity for 4-ethyl Phenol Detection 274 Under the optimized parameters discussed above, the analytical performances of bare SWNTs 275 and s6DNA-SWNTs were studied through monitoring different concentrations of the saturated 276 vapor of 4-ethyl phenol. Figure 11A shows the dynamic response of bare SWNTs and s6DNA-SWNTs 277 at VDS = 0.1 V and VG = 0 V, which were exposed to 4-ethyl phenol for 5 min and air for 10 min in 278 proper order. The concentrations of the saturated vapor of 4-ethyl phenol were ranging from 0.25% Nanomaterials 2020, 10, x FOR PEER REVIEW 9 of 18

5 5 10 0 40 100

-5

-10 Relative resistance (%) resistance Relative -15

-20 0 1 2 3 4 5 6 7 Time (min) 257

258 Figure 9. The relative resistance of s6DNA-SWNTs exposed to 5%, 10%, 40%, and 100% saturated 259 vapor of 4-ethyl phenol for 7 min (VDS = 0.1 V and VG = 0 V; each point calculated by the average value 260 of four different s6DNA-SWNTs).

261 The detecting voltage between the source and the drain (VDS) was one of the most important 262 parameters for the bioelectronic nose. According to the voltage-current curves shown in Figure 7A, 263 the current increased with voltage in the range from −0.1 V to 0.1 V, which displayed an excellent 264 linearity relationship. The resistance at each voltage was calculated using the Ohm’s law, which was 265 shown in Figure 10. It was clear that the values of bare SWNTs and s6DNA-SWNTs were almost 266 equal at different voltage, excluding 0 V. The discontinuities at 0 V was mainly because the 267 insufficient precision of device. The device offered an ultralow voltage, but cannot measure the 268 ultralow current with high accuracy. To obtain a higher current signal of bioelectronic noise, 0.1 V 269 was chosen as the detecting voltage. Nanomaterials 2020, 10, 479 9 of 16

120000

100000

80000 ) Ω

60000 Bare SWNTs s6DNA-SWNTs 40000 Resistance ( Resistance

20000

-0.10 -0.05 0.00 0.05 0.10 Voltage (V) 270 Figure 10. The resistance of SWNTs before and after functionalized with different s6DNA 271 at diFigurefferent 10. voltages. The resistance of SWNTs before and after functionalized with different s6DNA at different 272 voltages. 3.3. Sensitivity for 4-ethyl Phenol Detection 273 3.3.Under Sensitivity the optimized for 4-ethyl parametersPhenol Detection discussed above, the analytical performances of bare SWNTs Nanomaterials 2020, 10, x FOR PEER REVIEW 10 of 18 274 and s6DNA-SWNTsUnder the optimized were studied parameters through discussed monitoring above, different the analytical concentrations performance of the saturateds of bare vaporSWNTs 275 of 4-ethyl phenol. Figure 11A shows the dynamic response of bare SWNTs and s6DNA-SWNTs 279 toand 100%. s6DNA Compared-SWNTs with were bare studied SWNTs through without monitoring the s6DNA different interlayer, concentrations the sensitivity of the of saturateds6DNA- 276 at vaporVDS = of0.1 4- Vethyl and phenol.VG = 0 Fig V,ure which 11A were show exposeds the dynamic to 4-ethyl response phenol of forbare 5 SWNTs min and and air s6DNA for 10 min-SWNTs in 280 properSWNTs order. was The nearly concentrations one order of of the magnitude saturated vapor higher, of 4-ethyl indicating phenol that were s6DNA ranging was from a promising 0.25% to 277 DS G 281 functionalat V = 0.1 material V and forV the= 0 detectionV, which ofwere 4-ethyl exposed phenol. to 4Based-ethyl on phenol calibration for 5 plotsmin and shown air infor Figure 10 min 7B in, 278 100%.proper Compared order. The with concentrations bare SWNTs withoutof the saturated the s6DNA vapor interlayer, of 4-ethyl the phenol sensitivity were ofranging s6DNA-SWNTs from 0.25% 282 wasthe nearly relative one resistance order ofmagnitude increased higher, gradually indicating in the that low s6DNA concentration was a promising range and functional continued material to rise 283 forsharply the detection in the high of 4-ethyl concentration phenol. range. Based The on calibration relative resistance plots shown of s6DNA in Figure-SWNTs7B, the revealed relative an 284 resistanceexcellent increasedrelationship gradually with the in 4 the-ethyl low phenol concentration concentration range and in the continued range of to 0.25 rise% sharply to 20% inand the 20 high% to 285 concentration100%. The regression range. The equation relative was resistance = −0 of.5335 s6DNA-SWNTs − 0.871 and revealed = −0 an.0892 excellent − 5.6705 relationship with the 286 withlinear the regression 4-ethyl phenol correlation concentration coefficient in theof 0.993 range and of 0.25%0.990, toand 20% the and limit 20% of detection to 100%. Thewas regression0.13 (S/N = 287 equation3). was y = 0.5335x 0.871 and y = 0.0892x 5.6705 with the linear regression correlation 1 − − 2 − − coefficient of 0.993 and 0.990, and the limit of detection was 0.13 (S/N = 3).

5 -20 5 A B 0 0 0 y1=-0.5335x-0.6671 20 R2=0.987 -5 -5 40 y2=-0.0892x-5.6705 2 60 R =0.9807 -10 -10

Relative resistance (%) resistance Relative 80 Bare SWNTs Relative resistance (%) Bare SWNTs -15 s6DNA-SWNTs -15 100 s6DNA-SWNTs Saturated vapor of 4-ethylphenol (%) of 4-ethylphenol vapor Saturated

-20 120 -20 0 2000 4000 6000 8000 10000 0 20 40 60 80 100 Time (s) Saturated vapor of 4-ethyl phenol (%) 288 Figure 11. (A)The dynamic responses and (B) the relative resistances of bare SWNTs and s6DNA-SWNTs 289 towardsFigure di11ff. erent(A)The concentrations dynamic responses (0%, 0.25%, and 0.5%,(B) the 1.0%, relative 2.5%, resistance 5%, 10%,s 20%, of bare 40, 60%,SWNTs 80%, and and s6DNA 100%) - 290 ofSWNTs the saturated towards vapor different of 4-ethyl concentrations phenol (VDS (0=%0.1V, 0.25 and%, 0.5VG%=, 01.0 V;% each, 2.5 point%, 5% calculated, 10%, 20% by, 40, the 60 average%, 80%, 291 valueand 100 of four%) of di thefferent saturated gas biosensors). vapor of 4-ethyl phenol (VDS = 0.1V and VG = 0 V; each point calculated by 292 the average value of four different gas biosensors). 3.4. Repeatability 293 3.4. ToRepeatability study the repeatability, four different s6DNA-SWNTs were fabricated using the protocol 294 mentionedTo study above. the These repeatability, four s6DNA-SWNTs four different were s6DNA used-SWNTs to determine were fabricated the different using concentrations the protocol 295 ofmentioned the saturated above. vapor These of 4-ethylfour s6DNA phenol.-SWNTs Figure were 12 showsused to the determine dynamic the responses different ofconcentrations four different of 296 the saturated vapor of 4-ethyl phenol. Figure 12 shows the dynamic responses of four different 297 s6DNA-SWNTs exposed to different concentrations of 4-ethyl phenol vapor for 5 min and flow air 298 for 10 min at intervals. It was clear that the relative resistance was positive correlation with the 4- 299 ethyl phenol concentration, and the trend of four curves was consistent. The relative standard 300 deviation (RSD) at each concentration was lower than 7%, indicating that the s6DNA-SWNTs had 301 excellent repeatability. Nanomaterials 2020, 10, 479 10 of 16

s6DNA-SWNTs exposed to diﬀerent concentrations of 4-ethyl phenol vapor for 5 min and ﬂow air for 10 min at intervals. It was clear that the relative resistance was positive correlation with the 4-ethyl phenol concentration, and the trend of four curves was consistent. The relative standard deviation (RSD)

Nanomaterialsat each concentration 2020, 10, x FOR was PEER lower REVIEW than 7%, indicating that the s6DNA-SWNTs had excellent repeatability.11 of 18

302 Figure 12. The dynamic responses of four different s6DNA-SWNTs exposed to different concentrations 303 Figure 12. The dynamic responses of four different s6DNA-SWNTs exposed to different (0, 0.25, 0.5, 1.0, 2.5, 5, 10, 20, 40, 60, 80 and 100) of 4-ethyl phenol vapor for 5 min and flow air for 10 min 304 concentrations (0, 0.25, 0.5, 1.0, 2.5, 5, 10, 20, 40, 60, 80 and 100) of 4-ethyl phenol vapor for 5 min and at intervals. (VDS = 0.1 V and VG = 0 V; each concentration exposed for 5 min and point calculated by 305 flow air for 10 min at intervals. (VDS = 0.1 V and VG = 0 V; each concentration exposed for 5 min and the average value of four different gas biosensors). 306 point calculated by the average value of four different gas biosensors). 3.5. Sensing Mechanism 307 3.5. Sensing Mechanism To identify the sensing mechanism, s6DNA-SWNTs were operated in a field-effect transistor 308 modeTo using identify Si as the the sensing back-gate mechanism, electrode. s6DNA The changes-SWNTs in were the transfer operated characteristic in a field-effect curves transistor (IDS-VG , 309 DS G DS modeVDS = using0.1 V) Si upon as the functionalization back-gate electrode. with The ssDNA changes and in exposurethe transfer to characteristic the VOCs were curves used (I to- explainV , V 310 =the 0.1 electrostatic V) upon functionalization interactions of thewith ssDNA ssDNA and and VOC expo moleculessure to the with VOCs the were SWNTs. used Figure to explain 13 shows the 311 electrostatictransfer characteristics interactions (ofIDS the-V ssDNAG) curves and for VOC s6DNA-SWNTs molecules with exposed the SWNTs. to air Figure and saturated 13 shows vaportransfer of 312 characteristics4-ethyl phenol. (I TheDS-VG transconductance) curves for s6DNA (slope-SWNTs of the exposed transfer characteristics to air and saturated curve) increased vapor of slightly4-ethyl 313 phenol.after exposure The transcond to saturateductance vapor (slope of 4-ethyl of the phenol, transfer and characteristics the threshold gate curve) voltage increased (VTH) also slightly decreased after 314 exposureto 11.02 to V. saturated This negative vapor shift of 4- ofethylV phenol,is attributed and the to threshold adsorption gate of voltage partially (VTH charged) also decreased/polar VOC to − TH 315 TH −molecules11.02 V. Thisthat induce negative a screening shift of chargeV is (doping) attributed of the to SWNTs adsorption and shift of partial the IDSly-V charged/polarG curve to a negative VOC 316 moleculesvoltage. The that mobility induce of a s6DNA-SWNTs screening charge increased (doping) to of 46.16 the cmSWNTs2/Vs in and comparison shift the withIDS-VG bare curve SWNTs. to a 317 negativeThis mechanism voltage. wasThe themobility electrostatic of s6DNA gating-SWNTs [35]. increased to 46.16 cm2/Vs in comparison with bare 318 SWNTs. This mechanism was the electrostatic gating [35]. 319 3.6. Interference In multiple real-world environments, there were over 350 volatile compounds emitted by the strawberry. According to the previous research, the average relative proportion of 14 VOCs (ethyl pentanoate, methyl 3-methyl butanoate, methyl pentanoate, pentyl acetate, ethyl 3-methyl butanoate, 1-methyl ethyl butanoate acetaldehyde, 2-pentanone, 2-heptanone, methyl butanoate, ethanol, acetone, methyl hexanoate, ethyl butanoate) accounted for more than 82%. In comparison, the average relative proportion of the first seven VOCs was much lower than the last seven VOCs, which was only about 3.28%. The last seven VOCs (2-pentanone, 2-heptanone, methyl butanoate, ethanol, acetone, methyl hexanoate, ethyl butanoate) were chosen to evaluate the selectivity Nanomaterials 2020, 10, x FOR PEER REVIEW 12 of 18

2500

Air 2000 4-ethyl phenol

1500

1000 Current (pA) Current

500 Nanomaterials 2020, 10, 479 11 of 16

-60 -40 -20 0 20 40 60 of s6DNA-SWNTs. Under the optimized conditions, Figure 14 shows the relative resistance of Voltage (V) 320 s6DNA-SWNTs were exposed to diﬀerent saturated vapors, respectively. Only methyl hexanoate hadNanomaterials no interference 2020, 10, andx FOR s6DNA-SWNTs PEER REVIEW was diﬃcult to discriminate 4-ethyl phenol from a mixture12 of 18 321 of VOCs.Figure 13. The transfer characteristics curve of s6DNA-SWNTs exposed to air and saturated vapor of 322 4-ethyl phenol for 5 min. (VDS = 0.1 V and VG in the range from −60 to +60 V with the scan rate 1 V).

2500 323 3.6. Interference Air 324 In multiple real-world environments,2000 there were over 4-ethyl 350 phenol volatile compounds emitted by the 325 strawberry. According to the previous research, the average relative proportion of 14 VOCs (ethyl 1500 326 pentanoate, methyl 3-methyl butanoate, methyl pentanoate, pentyl acetate, ethyl 3-methyl butanoate, 327 1-methyl ethyl butanoate acetaldehyde, 2-pentanone, 2-heptanone, methyl butanoate, ethanol, 1000

328 acetone, methyl hexanoate,(pA) Current ethyl butanoate) accounted for more than 82%. In comparison, the 329 average relative proportion of the first seven VOCs was much lower than the last seven VOCs, which 500 330 was only about 3.28%. The last seven VOCs (2-pentanone, 2-heptanone, methyl butanoate, ethanol,

331 acetone, methyl hexanoate, ethyl0 butanoate) were chosen to evaluate the selectivity of s6DNA-

332 SWNTs. Under the optimized co-60nditions,-40 Figure-20 140 shows20 the relative40 60 resistance of s6DNA-SWNTs

333 were exposed to different saturated vapors, respectively.Voltage (V) Only methyl hexanoate had no interference 334320 and s6DNA-SWNTs was difficult to discriminate 4-ethyl phenol from a mixture of VOCs. Figure 13. The transfer characteristics curve of s6DNA-SWNTs exposed to air and saturated vapor of 321 4-ethylFigure phenol 13. The for transfer 5 min. (characteristicsV = 0.1 V and curveV ofin s6DNA the range-SWNTs from exposed60 to +60 to V air with and the saturated scan rate vapor 1 V). of DS G − 322 4-ethyl phenol for 5 min. (VDS = 0.1 V and VG in the range from −60 to +60 V with the scan rate 1 V). 6 323 3.6. Interference

324 In multiple real-world environments,0 there were over 350 volatile compounds emitted by the 325 strawberry. According to the previous research, the average relative proportion of 14 VOCs (ethyl 326 pentanoate, methyl 3-methyl butanoate, methyl pentanoate, pentyl acetate, ethyl 3-methyl butanoate, -6 327 1-methyl ethyl butanoate acetaldehyde, 2-pentanone, 2-heptanone, methyl butanoate, ethanol, 328 acetone, methyl hexanoate, ethyl butanoate) accounted for more than 82%. In comparison, the 329 average relative proportion-12 of the first seven VOCs was much lower than the last seven VOCs, which Relativeresistance (%) 330 was only about 3.28%. The last seven VOCs (2-pentanone, 2-heptanone, methyl butanoate, ethanol, 331 acetone, methyl hexanoate, ethyl butanoate) were chosen to evaluate the selectivity of s6DNA- -18 332 SWNTs. Under the optimized coMethyl nditions,butyrate Methy hexanol Ethyl Figure butyrate Ethnoal 14 Actoneshows2-Pan the2-heptanone relativeEthy phonl resistance of s6DNA-SWNTs 333 were exposed to different saturated vapors, respectively.Different VOCs Only methyl hexanoate had no interference 335 334 andFigure s6DNA 14.-TheSWNTs relative was resistances difficult to of s6DNA-SWNTsdiscriminate 4- exposedethyl phenol to diﬀ fromerent saturateda mixture vapors of VOCs. of VOCs 336 (methylFigure butanoate,14. The relative methyl resistances hexanoate, of s6DNA ethyl butanoate,-SWNTs exposed ethanol, to acetone, different 2-pentanone, saturated vapors 2-heptanone, of VOCs 337 4-ethyl(methyl phenol) butanoate, for 5 min.methyl (V DShexanoate,= 0.1 V and ethylVG butanoate,= 0 V). ethanol, acetone, 2-pentanone, 2-heptanone, 338 4-ethyl phenol) for 5 min. (V6DS = 0.1 V and VG = 0 V). 3.7. Chemometric Analysis 339 To improve the accuracy of the measurement towards 4-ethyl phenol, the seven gas biosensors 0 (s1DNA-SWNTs, s2DNA-SWNTs, s3DNA-SWNTs, s4DNA-SWNTs, s5DNA-SWNTs, s6DNA-SWNTs, bare SWNTs) were employed to the nine VOCs (methyl hexanoate, methyl butanoate, ethyl butanoate, ethanol, acetone, 2-pentanone,-6 2-heptanone, water, and 4-ethyl phenol) released by the infected strawberry, which the relative resistances at each concentration were shown in Figure 15. The dynamic responses for ﬁrst eight VOCs were displayed in Figures S1–S8. -12 Relativeresistance (%)

-18 Methyl butyrate Methy hexanol Ethyl butyrate Ethnoal Actone 2-Pan 2-heptanone Ethy phonl Different VOCs 335 336 Figure 14. The relative resistances of s6DNA-SWNTs exposed to different saturated vapors of VOCs 337 (methyl butanoate, methyl hexanoate, ethyl butanoate, ethanol, acetone, 2-pentanone, 2-heptanone, 338 4-ethyl phenol) for 5 min. (VDS = 0.1 V and VG = 0 V).

339 Nanomaterials 2020, 10, x FOR PEER REVIEW 13 of 18

340 3.7. Chemometric Analysis 341 To improve the accuracy of the measurement towards 4-ethyl phenol, the seven gas biosensors 342 (s1DNA-SWNTs, s2DNA-SWNTs, s3DNA-SWNTs, s4DNA-SWNTs, s5DNA-SWNTs, s6DNA- 343 SWNTs, bare SWNTs) were employed to the nine VOCs (methyl hexanoate, methyl butanoate, ethyl 344 butanoate, ethanol, acetone, 2-pentanone, 2-heptanone, water, and 4-ethyl phenol) released by the 345 infectedNanomaterials strawberry,2020, 10, 479 which were shown in Figure 15. 12 of 16 346

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

347 FigureFigure 15. TheThe relative relative resistance resistance of of different different ssDNA ssDNA-SWNTs-SWNTs (s1DNA (s1DNA-SWNTs,-SWNTs, s2DNA s2DNA-SWNTs,-SWNTs, 348 s3DNAs3DNA-SWNTs,-SWNTs, s4DNA s4DNA-SWNTs,-SWNTs, s5DNA s5DNA-SWNTs,-SWNTs, s6DNA s6DNA-SWNTs,-SWNTs, bare bare SWNTs) SWNTs) towards towards different different 349 concentrationsconcentrations of of different different VOCs ( a–i:i: methyl methyl hexanoate, hexanoate, methyl methyl butanoate, butanoate, ethyl ethyl butanoate, butanoate, ethanol, ethanol, 350 acetone,acetone, 2 2-pentanone,-pentanone, 2-heptanone,2-heptanone, water,water, and and 4-ethyl 4-ethyl phenol) phenol) varying varying from from 0.25% 0.25% to 100%.to 100%. (V DS(VDS= =0.1 0.1 V 351 Vand andV GVG= =0 0 V, V, each each point point calculated calculated by by the the average average value value of of four four di differentfferent ssDNA-SWNTs). ssDNA-SWNTs).

352 3.7.1.3.7.1. Neural Neural N Networketwork F Fittingitting 353 NeuralNeural network network fitting fitting (NNF) (NNF) had had been been found found useful useful and and efficient, efficient, particularly particularly in in problems problems for for 354 which thethe characteristicscharacteristics of of the the processes processes are are diffi difficultcult to describe to describe using using physical physical equations. equations. As the As overall the 355 overalstructurel structure is shown is in shown Figure in9 , Figure the procedure 9, the procedure of NNF is of mainly NNF divided is mainly into divided three parts into containingthree parts 356 containingan input layer, an input hidden layer, layers, hidden and an layers, output and layer. an output In my model,layer. In the my X matrixmodel, is the work X matrix as the inputis work layer, as 357 thewhich input consists layer, of which 100 columns consists and of 100 seven columns rows by and using seven di ffrowserent by concentrations using different (corresponding concentrations to 358 (corresponding0.25%, 0.5%, 1%, to 2.5%,0.25%, 5%, 0.5%, 10%, 1%, 20%, 2.5%, 40%, 5%, 60%,10%, 20%, 80%, 40%, and 100%60%, 80% saturated, and 100% vapors) saturated of nine vapors) VOCs as the columns and ssDNA functionalized SWNTs (s1DNA-SWNTs, s2DNA-SWNTs, s3DNA-SWNTs, s4DNA-SWNTs, s5DNA-SWNTs, s6DNA-SWNTs, bare SWNTs) as rows. The Y matrix is selected as an output layer, which consists of 100 columns and nine rows that four concentrations of each row corresponding to each column of X. The data of the X matrix is divided randomly into three sets: 70% as training samples, 15% of validation samples, and 15% of testing samples. During learning, output values from the NNF are compared to true values, and the coupling weights are adjusted Nanomaterials 2020, 10, x FOR PEER REVIEW 14 of 18

359 Nanomaterialsof nine VOCs 2020 ,as 10 ,the x FOR columns PEER REVIEW and ssDNA functionalized SWNTs (s1DNA-SWNTs, s2DNA-SWNTs,14 of 18 360 s3DNA-SWNTs, s4DNA-SWNTs, s5DNA-SWNTs, s6DNA-SWNTs, bare SWNTs) as rows. The Y 359361 ofmatrix nine VOCs is sele ascted the ascolumns an output and ssDNA layer, whichfunctionalized consists SWNTs of 100 columns(s1DNA -SWNTs, and nine s2DNA rows - thatSWNTs, four 360362 s3DNAconcentrations-SWNTs, of s4DNA each row-SWNTs, corresponding s5DNA-SWNTs, to each column s6DNA -ofSWNTs X. The, baredata of SWNTs) the X matrix as rows. is dividedThe Y 361363 matrixrandomly is seleintocted three as sets: an output70% as layer,training which samples, consists 15% of of 100validation columns samples, and nine and rows 15% thatof testing four 362364 concentrationssamples. During of learning,each row output corresponding values from to each the NNF column are comparedof X. The data to true of values,the X matrix and the is couplingdivided Nanomaterials 2020, 10, 479 13 of 16 363365 randomlyweights are into adjusted three sets:to give 70% a minimum as training sum samples, of square 15% errors. of validation After testing samples, and comparing, and 15% of we testing found 364366 samples.that ten nodesDuring of learning, the hidden output layer values can m fromake the the average NNF are relative compared error to minimum true values, shown and the in Figurecoupling 16. 365367 toweightsAfter give the a are minimum model adjusted was sum toestablished, give of squarea minimum the errors. predicted sum After of valuessquare testing from errors. and the comparing, After NNF testing was wecompared and found comparing, thatto the ten true we nodes values,found of 366368 thethatwhic hidden tenh were nodes layer exhibited of canthe hidden make in Figure the layer average 17. can m relativeake the erroraverage minimum relative shown error minimum in Figure 16shown. After in theFigure model 16. 367 wasAfter established, the model was the established, predicted values the predicted from the values NNF from was comparedthe NNF was to thecompared true values, to the whichtrue values, were 368 exhibitedwhich were in exhibited Figure 17. in Figure 17.

369

369370 Figure 16. Type of architecture of the neural network fitting .

370 Figure 16. Type of architecture of the neural network ﬁtting.fitting.

120

Real 4-Ethyl phenol 100 Pre 4-Ethyl phenol 120 80 Real 4-Ethyl phenol 100 Pre 4-Ethyl phenol 60 80 40 60

Concentration (%) Concentration 20 40 0

Concentration (%) Concentration 20 -20 0 -20 0 20 40 60 80 100 120 Concentration (%) 371 -20 Figure 17. Predicted concentration-20 against0 20 true40 concentration60 80 of100 4-ethyl120 phenol for the NNF model. Concentration (%) 372 Figure 17. Predicted concentration against true concentration of 4-ethyl phenol for the NNF model. 371 3.7.2. Gaussian Process Regression

372373 3.7.2.GaussianFigure Gaussian 17. process Predicted Process regression Regressionconcentration (GPR) against was used true concentration to build the prediction of 4-ethyl phenol model. for The the X NNF matrix model consisted. of 100 rows, and seven columns are work as the workspace variable, and the Y matrix consisted of 374 Gaussian process regression (GPR) was used to build the prediction model. The X matrix 373 1003.7.2.Nanomaterials rows Gaussian and 20 one20 Process, 10 column, x FOR Regression PEER is selected REVIEW as the response. After the model was established, the predicted15 of 18 375 consisted of 100 rows, and seven columns are work as the workspace variable, and the Y matrix values from the GPR are compared to the true values, which were exhibited in Figure 18. 374376 consistedGaussian of 100 process rows and regression one column (GPR) is wasselected used as tothe build response. the prediction After the model model. was The established, X matrix 375377 consistedthe predicted of 100 values rows, from and the seven GPR columns are compared are work to the as truethe workspacevalues, which variable, were exhibitedand the Yin matrixFigure 376 consisted of 100 rows and one column is selected as the response. After the model was established, 378 18. 100 Real 4-Ethyl phenol 377 the predicted values from the GPR are compared Pre 4-Ethyl phenol to the true values, which were exhibited in Figure 378 18. 80

40 Concentration (%) Concentration 20

0 20 40 60 80 100 Concentration (%) 379 Figure 18. Predicted concentration against true concentration of 4-ethyl phenol for the GPR model. 380 Figure 18. Predicted concentration against true concentration of 4-ethyl phenol for the GPR model. NNF and GPR were simple methods that can oﬀer qualitative and quantitative information, 381 whichNNF widely and employed GPR were in stoichiometry.simple methods The that correlation can offer coe qualitativeﬃcient (R 0 and) and quantitative root mean squareinformation, error 382 (RwhichSMT) between widely employed real concentrations in stoichiometry. and predicted The correlation concentrations coefficient were (R shown0) and root in Table mean1. square We found error 383 (RSMT) between real concentrations and predicted concentrations were shown in Table 1. We found 384 that the best results were achieved with the utilization of the GPR model because the R0 for 4-ethyl 385 phenol was higher, and the RSMT was lower in comparison with NNF.

386 Table 1. The correlation coefficient (R0) and root mean square error (RSMT) between real concentrations 387 and predicted concentrations.

R0 RSMT NNF 0.98321 4.82493 GPR 0.99883 1.33108

388 4. Conclusion 389 A novel bioelectronic nose based on a gas biosensor array was fabricated using FET immobilized 390 with SWNTs and ssDNA successively, and the detecting data was processed though the neural 391 network fitting (NNF) and Gaussian process regression (GPR). The bioelectronic nose was used to 392 measure the content of 4-ethyl phenol over a wide range of 0.25% to 100%, which was further 393 diagnosed whether P. cactorum infected in strawberries or not. The ssDNA-SWNTs shows a 394 significant improvement in sensitivity compared to bare SWNTs with excellent recovery and 395 respectability.

396 Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1, Figure S1: The relative 397 resistance of different ssDNA-SWNTs towards different concentrations of 2-heptanone varying from 0.25% to 398 100%, Figure S2: The relative resistance of different ssDNA-SWNTs towards different concentrations of 2- 399 pentanone varying from 0.25% to 100%, Figure S3: The relative resistance of different ssDNA-SWNTs towards 400 different concentrations of actone varying from 0.25% to 100%, Figure S4: The relative resistance of different 401 ssDNA-SWNTs towards different concentrations of ethnoal varying from 0.25% to 100%, Figure S5: The relative 402 resistance of different ssDNA-SWNTs towards different concentrations of ethyl butanoate varying from 0.25% 403 to 100%, Figure S6: The relative resistance of different ssDNA-SWNTs towards different concentrations of 404 methyl hexanoate varying from 0.25% to 100%, Figure S7: The relative resistance of different ssDNA-SWNTs 405 towards different concentrations of methyl butanoate varying from 0.25% to 100%, Figure S8: The relative 406 resistance of different ssDNA-SWNTs towards different concentrations of 4-ethyl phenol varying from 0.25% to 407 100%.

408 Author Contributions: Data curation: H.W. and Y.W.; formal analysis: H.W.; funding acquisition: B.X.; 409 investigation: H.W. and X.H.; software: Y.W.; writing—original draft: H.W.; writing—review and editing: H.W., 410 X.H., and B.X. All authors have read and agreed to the published version of the manuscript. Nanomaterials 2020, 10, 479 14 of 16

that the best results were achieved with the utilization of the GPR model because the R0 for 4-ethyl phenol was higher, and the RSMT was lower in comparison with NNF.

Table 1. The correlation coeﬃcient (R0) and root mean square error (RSMT) between real concentrations and predicted concentrations.

R0 RSMT NNF 0.98321 4.82493 GPR 0.99883 1.33108

4. Conclusion A novel bioelectronic nose based on a gas biosensor array was fabricated using FET immobilized with SWNTs and ssDNA successively, and the detecting data was processed though the neural network ﬁtting (NNF) and Gaussian process regression (GPR). The bioelectronic nose was used to measure the content of 4-ethyl phenol over a wide range of 0.25% to 100%, which was further diagnosed whether P. cactorum infected in strawberries or not. The ssDNA-SWNTs shows a signiﬁcant improvement in sensitivity compared to bare SWNTs with excellent recovery and respectability.

Supplementary Materials: The following are available online at http://www.mdpi.com/2079-4991/10/3/479/s1, Figure S1: The relative resistance of different ssDNA-SWNTs towards different concentrations of 2-heptanone varying from 0.25% to 100%, Figure S2: The relative resistance of different ssDNA-SWNTs towards different concentrations of 2-pentanone varying from 0.25% to 100%, Figure S3: The relative resistance of different ssDNA-SWNTs towards different concentrations of actone varying from 0.25% to 100%, Figure S4: The relative resistance of different ssDNA-SWNTs towards different concentrations of ethnoal varying from 0.25% to 100%, Figure S5: The relative resistance of different ssDNA-SWNTs towards different concentrations of ethyl butanoate varying from 0.25% to 100%, Figure S6: The relative resistance of different ssDNA-SWNTs towards different concentrations of methyl hexanoate varying from 0.25% to 100%, Figure S7: The relative resistance of different ssDNA-SWNTs towards different concentrations of methyl butanoate varying from 0.25% to 100%, Figure S8: The relative resistance of different ssDNA-SWNTs towards different concentrations of 4-ethyl phenol varying from 0.25% to 100%. Author Contributions: Data curation: H.W. and Y.W.; formal analysis: H.W.; funding acquisition: B.X.; investigation: H.W. and X.H.; software: Y.W.; writing—original draft: H.W.; writing—review and editing: H.W., X.H., and B.X. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Key Realm R and D Program of Guangdong Province (no. 2019B020215004), Science and Technology Major Project of Guangxi Province (GUIKE AA17204087-13), and the Chinese National Natural Science Foundation (no. 31671578). Acknowledgments: This work was supported by the Key Realm R and D Program of Guangdong Province (no. 2019B020215004 and 2019B020215002), the Science and Technology Major Project of Guangxi Province (GUIKE AA17204087-13), and the Chinese National Natural Science Foundation (no. 31671578). Conflicts of Interest: The authors declare no conflict of interest.

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