Three-Dimensional Crumpled Graphene-Based Nanosheets with

Subscriber access provided by HACETTEPE UNIVERSITESI KUTUPHANESI

Article

Three-Dimensional Crumpled Graphene-Based Nanosheets with Ultrahigh NO2 Gas Sensibility Zhuo Chen, Jinrong Wang, Ahmad Umar, Yao Wang, Hao Li, and Guofu Zhou ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b01229 • Publication Date (Web): 16 Mar 2017 Downloaded from http://pubs.acs.org on March 19, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

ACS Applied Materials & Interfaces is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 28

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

ACS Applied Materials & Interfaces

Three-Dimensional Crumpled Graphene-Based Nanosheets with Ultrahigh NO2 Gas Sensibility Zhuo Chen1, Jinrong Wang1, Ahmad Umar2, Yao Wang*,1, Hao Li3 and Guofu Zhou*,3,4,5 1

Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of

Education, School of Chemistry and Environment, Beihang University, Beijing 100191, P. R. China. 2

Department of Chemistry, Faculty of Science and Arts and Promising Centre for Sensors and

Electronic Devices, Najran University, Najran 11001, Kingdom of Saudi Arabia. 3

Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics,

South China Normal University, Guangzhou 510006, P. R. China. 4

Shenzhen Guohua Optoelectronics Tech. Co. Ltd., Shenzhen 518110, P. R. China.

5

Academy of Shenzhen Guohua Optoelectronics, Shenzhen 518110, P. R. China.

*Address correspondence to [email protected], [email protected]

ACS Paragon Plus Environment

1


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 2 of 28

ABSTRACT: It is well established that the structures dominate the properties. Inspired by the highly contorted and crumpled maxilloturbinate inside dog nose, herein an artificial nanostructure, i.e. 3D crumpled graphene-based nanosheets, is reported with the simple fabrication, detailed characterizations and efficient gas sensing applications. A facile supramolecular non-covalent assembly is introduced to modify graphene with functional molecules, followed with a lyophilization process to massively transform 2D plane graphenebased nanosheets to 3D crumpled structure. The detailed morphological characterizations reveal that the bio-inspired nanosheets exhibit full consistency with maxilloturbinate. The fabricated 3D crumpled graphene-based sensors exhibit ultrahigh response (Ra/Rg=3.8) towards 10 ppm NO2, which is mainly attributed to the specific maxilloturbinate-mimic structure. The sensors also exhibit excellent selectivity and sensing linearity, reliable repeatability and stability. Interestingly, it is observed that only 4mg graphene oxide (GO) raw materials can produce more than 1000 gas sensors, which provides a new insight for developing novel 3D biomimetic materials in large-scale gas sensor production.

KEYWORDS: dog noses, crumpled graphene nanosheets, supramolecular modification, lyophilization, NO2 sensors


2

Page 3 of 28

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


INTRODUCTION “As we see the world, the dog smells it.” The ultrasensitive detectability to various odor of dogs is originated from the complex maxilloturbinate inside dog nose which is highly contorted, crumpled and possesses large surface area, thus enabling dogs the capability of sensing moisture and odorant with ultrahigh response.1,2 Therefore, inspired by the laminar, highly contorted and crumpled maxilloturbinate in dog nose, we present an effective and simple way to develop similar 3D crumpled biomimetic structure for ultrahigh sensitive NO2 gas sensors. NO2 is produced by combustion in power plants or engines, and considered as one of the most toxic gases which severely pollute the air and show adverse effects to human beings.3-5 The exposure of NO2 even at low dose can cause eye irritations, nausea, headache and loss of strength.6 Thus, it is really needed to fabricate highly sensitive gas sensors for efficient NO2 detection. Graphene is well-known for its perfect honeycomb-like lattice structure, however, the perfect flat nanosheet structure is not optimal for the applications in some specific realms, which is different from the metal oxides such as SnO2 nanosheet.7 Besides, reduced graphene oxide (rGO) sheets prefer to stack and aggregate irreversibly due to the strong π-π interactions during the dispersion preparation process,8,9 resulting in a significant reduction in surface area than theoretical value.10-13 Recently, several methods have been exploited to convert the perfect 2D graphene nanosheets to distorted 3D structure.14 The intentional deformation of graphene nanosheets not only preserves the inherent excellent electrical conductivity,15,16 but also offers remarkable high specific surface area.17-19 3D graphene materials are prepared by several techniques, to name a few, aerosol spray drying method, hydrothermal process, thermal reduction method, mechanical process, fast cooling method and so on.20 Even though used but these methods are limited by restricted temperature, requirement of special equipment, and


3


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 4 of 28

difficulty in mass production. Therefore, it remains a challenge to massively prepare 3D graphene-based nanosheets. Recently, very few applications of 3D graphene nanosheets such as energy storage,21,22 supercapacitor23 and lithium-ion batteries16,24 are reported, but to our best knowledge, the fabrication and utilization of 3D crumpled graphene nanosheets for gas sensing applications have never been reported. Herein, inspired from dog nose, we present a simple and effective lyophilization-induced method to transform 2D plane graphene-based nanosheets to 3D crumpled structure based on non-covalent bonding modification. Gas sensors fabricated by these crumpled nanosheets show a high NO2 response at room temperature. It is worth to mention that the lyophilization-induced method to prepare 3D crumpled graphene-based nanosheets is facile, low-cost, and has the potential for industrial mass production of gas sensing materials. The dog nose-inspired nanosheets also provide a new strategy to design new biomimetic materials for gas sensors and chemical trace detectors.

RESULTS AND DISCUSSION The characteristic π-conjugated structure of GO makes it suitable for non-covalent functionalization with aromatic molecules such as benzene, naphthalene and anthracene via π-π interactions, without disrupting the electronic conjugation of graphene.25-27 Herein, a small aromatic molecule, sodium1-naphthalenesulfonate (NA) with planar conjugated structure was selected to π-π interact with GO, after the reduction by hydrazine hydrate and then the lyophilization process, NA-rGO with lyophilization (i.e. 3D crumpled NA-rGO nanosheets (3DCNN)) that mimic to the maxilloturbinate inside dog nose were finally obtained. As control


4

Page 5 of 28

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


experiments, NA-rGO without lyophilization (NA-rGO w/o L), rGO with lyophilization (rGO w/ L) and rGO without lyophilization (rGO w/o L) were also investigated respectively. As shown in Figure 1, the schematic and scanning electron microscope (SEM) images illustrate the morphology variation of rGO and NA-rGO with or without the lyophilization process. Initially, irreversible aggregation at the bottom of rGO dispersion is observed in the physical picture due to the strong π-π interaction among 2D plane rGO nanosheets (Figure 1a). After lyophilization, the stacked 2D plane rGO nanosheets are even more tight and inseparable to each other since the great vacuum force and the dehydration of interlayers (Figure 1b). As clearly shown in the SEM image of rGO w/ L (Figure 1c and d), the blocky structure is composed of stacked 2D plane rGO nanosheets, causing a waste of interlaminar surface area, which is not desirable as an ideal gas sensing material. Whereas, NA-rGO is well dispersed in aqueous solution and exhibits homogeneous dark (Figure 1e). Such phenomenon relies on the fact that the hydrophilic sulfonic groups (Ph-SO3¯) of NA contribute to the excellent dispersibility of NA-rGO nanosheets. Moreover, the aromatic NA molecules with extra negative charges are non-covalent assembled on rGO nanosheets via strong π-π interactions, which enhances the interlaminar static-repulsion forces and thus prevents the nanosheets from aggregation.25,28 Unlike the aggregated rGO nanosheets, 2D plane NA-rGO nanosheets are well separated in aqueous solution, revealing a low bending rigidity, thus facilitating the shape transformation into 3D crumpled NA-rGO nanosheets (3D-CNN). During the lyophilization process, ice sublimation removes water molecules around the nanosheets, resulting in the twisting and bending of NA-rGO nanosheets (Figure 1f). As shown in the SEM images (Figure 1g and h), the laminar and separated 3D-CNN with significant


5


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 6 of 28

contortions and crumples, resemble the maxilloturbinate of dog nose as shown in Figure 1h inset. Moreover, due to the separated and crumpled conformation rather than the blocky structure in microcosmic level, 3D-CNN powders are apparently larger in volume and more fluffy than rGO w/ L in macroscopical level, implying that 3D-CNN are promising as gas sensing materials. To examine the surface properties, 3D-CNN were characterized by X-ray photoelectron spectroscopy (XPS) as shown in Figure 2a. Compared with rGO w/ L, the spectrum of 3D-CNN shows an obvious S 2p peak at 168.4 eV, indicating the successful modification of NA moieties; The calculated C/S atomic ratio is 32.39, suggesting that every 32 carbon atoms of rGO were non-covalent bonding modified with one sulfonic group (Table S1, Supporting Information). Figure 2b shows the Fourier transform infrared (FTIR) spectroscopy analysis of GO, rGO w/ L and 3D-CNN. Two characteristic peaks assigned to hydroxyl and carboxyl groups of GO are clearly observed at 3410cm-1 and 1722cm-1 respectively, while the peaks are significantly attenuated in the spectra of rGO w/ L and 3D-CNN, indicating that most oxygen-containing functional groups have been effectively vanished in the reduction by hydrazine hydrate. Moreover, the characteristic S=O stretching bands appearing at 1196-1032 cm-1 and naphthyl ring absorption peak at 1576 cm-1 are observed in 3D-CNN spectra, verifying the successful assembly of NA molecules. A drop & dry method was used to facilely fabricate gas sensors. Firstly, 20µL dispersion of the sensing materials was dropped on the Ag-Pd interdigitated electrodes (IEs) supported by a ceramic substrate. After drying step, a thin sensing film was formed and the gas sensor was ready for test. The schematic diagram, optical and SEM image of a typical fabricated gas sensor were displayed in Figure 3a. Besides, the current versus voltage (I-V) relationship was linear between -7V and 7V, exhibiting good ohmic contact between the 3D crumpled nanosheets and the sensor


6

Page 7 of 28

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


electrode (Figure S1, Supporting Information). In other words, the result indicated that the Schottky barriers were absent between 3D-CNN and IEs,29 which ensures the accuracy of the gas sensing measurement in our work. The gas sensing system was employed to record the resistance variation of fabricated sensors, and the response is calculated by the ratio of resistance which captured in an atmosphere of air (Ra) and NO2 (Rg) respectively, i.e. Response=Ra/Rg. Figure 3b depicted the ideal flow chart of the gas sensing test. Details of the sensor fabrication process and gas sensing test were described in the Experimental Section. The detailed gas sensing measurements revealed that the 3D-CNN sensors exhibited the highest response (Ra/Rg=3.8) towards 10 ppm NO2 gas compared to other graphene-based materials, i.e. NA-rGO w/o L (Ra/Rg=2.0), rGO w/ L (Ra/Rg=1.3) and rGO w/o L (Ra/Rg=1.4) as shown in Figure 4a. The high response of 3D-CNN was mainly attributed to the maxilloturbinate-mimic structure, which would be discussed in the next section together with a specific mathematic model analysis. The 3D-CNN sensors also exhibited excellent selectivity to NO2 gas, as shown in Figure 4b. For the convenience of comparison, the evaluation of response was converted as: Response =

∆R Ra − Rg = (%) . By Ra Ra

calculation, the 3D-CNN sensors exhibited the highest response

to NO2 (∆R/Ra=74% under 10 ppm) among several interferential gases, including NH3 (∆R/Ra=22%), Acetic acid (∆R/Ra=19%), Ethanol (∆R/Ra=6%), Acetone (∆R/Ra=4%), and Methanol (∆R/Ra=3%), even though the concentration of them were 100 times higher than NO2. The results indicated that 3D-CNN sensors were extremely sensitive to NO2. Same as the previously reported mechanism,29-31 the high selectivity is probably resulting from two facts: (1) The sulfonic groups (-SO3-) of NA possess strong absorbability specially towards NO2 molecules.


7


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 8 of 28

(2) NO2 is a typical electron-withdrawing gas molecule, but the interferential gases have weak electron-withdrawing or donating ability and thus disable to make significant change of sensor resistance as NO2 molecules. The above two reasons should together be responsible for the eventual high NO2 selectivity of 3D-CNN sensors. In practical gas sensing application, linearity of response over gas concentration is also of important significance, hence a successive response and recovery curve towards different NO2 concentrations was measured in Figure 4c. As we expected, the response was gradually increasing along with the rising of NO2 concentration, and the response and recovery time were uniform under different concentrations. The 3D-CNN sensors revealed an excellent linear detection ranged from 1 ppm to 10 ppm with corresponding response (Ra/Rg) measured from 1.5 to 3.8 (Figure 4d). In addition, the 3D-CNN sensors showed reliable repeatability and high stability, as indicated by the three-cycle response and recovery curve (Figure 4e). The sensors revealed an excellent repeatability with a stable response (Ra/Rg=3.78 with a standard deviation of 1.6%) towards 10 ppm NO2 for three successive cycles. Furthermore, an aging test was carried out every month for a half year (Figure 4f) and the observed results exhibited a high stable response towards 10 ppm NO2 within 4.2% standard deviation of its initial value. Moreover, the response and recovery time also maintained around 8 and 53 min, respectively. A comparison of typical graphene-based sensors for NO2 sensing in our work and literature reports was summarized in Table 1. Herein, it is worth noting that only room temperature is required for 3D-CNN sensors to exhibit ultrahigh NO2 response, which make it more versatile and lower power consuming for practical gas sensing application.32-34


8

Page 9 of 28

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


To verify the specific mechanism of response enhancement dominated by 3D crumpled structure, a comparison to typical 2D plane structure by SEM images and schematics was shown in Figure 5. A mathematic model analysis (Equation 1 and 2) was also discussed in detail to further confirm the result. It is generally known that most graphene-based materials exhibit ptype semiconductor behavior under atmospheric ambient with holes as the main carriers, owing to graphene tends to be heavily p-doped by adsorbing H2O and O2 molecules in ambient air.35-39 Without exception, all of the graphene-based sensing materials in our work exhibit p-type semiconductor behavior, which provides the foundation of the mathematic model in this paper. R0

+ Rc dp dζ − q × ∑ ∫∫ ( pair × µ p × + Dp )d x d y dχ dχ Ra xy = Response = R0 Rg + Rc dζ dp − q × ∑ ∫∫ ( pgas × µ p × + D p )d x d y dχ dχ xy

pgas =

ni2  E − EF  = ni exp  i  n  KT 

(1)

(2)

The specific mathematic model of response for graphene-based sensors. Where R0 is a constant, q is the quantity of electric charge, pair and pgas are the hole concentrations in air and NO2 respectively,

∑ ∫∫

is the total effective area to NO2, µp is the mobility of hole, ζ is the

xy

electric field intensity, Dp is the hole-diffusion coefficient, χ is the plate distance, Rc is the resistance of non-sensing region, ni is a constant, n is the electron concentrations, Ei and EF are the energy of the Fermi and the current material respectively, K is the Boltzmann’s constant, and T is the sensing temperature.


9


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 10 of 28

2D Plane Structure: The SEM image of rGO w/ L confirms that the multilayer block is actually formed by pieces of stacked 2D plane nanosheets (Figure 1c). Besides, rGO w/o L and NA-rGO w/o L are also defined as 2D plane structure due to their similar flat morphology from SEM images (Figure S2a and b, Supporting Information). Herein, rGO w/ L is taken as a representative to illustrate the NO2 adsorption schematic of 2D plane structure. As shown in SEM image (Figure 5a), each nanosheet remains flat and a tightly aggregated structure is formed due to strong π-π interaction (Figure 5b). Apparently, the aggregated blocks hinder the effective absorption of NO2 molecules (Figure 5c), resulting in the reduction in NO2 response, which is further confirmed by the following mathematic model analysis. For rGO w/o L, a low response (Ra/Rg=1.4) was measured. After lyophilization, the sheets aggregated tighter to each other and the decrease of interlayer spacing caused the enlargement of plate distance ( χ ). According to Equation 1, the rise of χ induces the decrease of electric field intensity change rate (

dζ dχ

), leading to the diminution of total effective area (∑ ∫∫ ) , resulting in xy

the decrease of final Response. This is the main reason why an even lower response (Ra/Rg=1.3) of rGO w/ L is obtained compared to that without lyophilization. For NA-rGO w/o L, the sulfonic groups with electron affinity of NA can induce the separation of electrons and holes on graphene, and further take away the electrons.29,30 Similarly, the oxidizing gas NO2 also has electron-withdrawing ability and tends to absorb electrons on electron-rich sites such as sulfonic groups.40 Finally, the electrons attracted by NA are taken away by NO2, and extra electrons on graphene are attracted to NA as supplements. Namely, NA acts as an electron-transporting bridge and thus continuous electron-input and output induce the further decrease of electron concentrations (n) on graphene. According to Equation 2, the hole


10

Page 11 of 28

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


concentrations (pgas) further increase and enhance the Response (Ra/Rg=2) ultimately (Equation 1). However, the response enhancement induced by NA is limited after all due to the tightly attachment between the sheets and substrate (Figure S2b, Supporting Information), resulting in the waste of effective sensing area of sheets reverse sides. 3D Crumpled Structure: For 3D-CNN, as shown in the SEM image (Figure 5d), irregularity contortions and crumples transform each single nanosheet from the 2D plane into 3D structure (Figure 5e). The resulting large space among neighboring crumped sheets significantly enhances the absorption of more NO2 molecules both in internal and outer surfaces (Figure 5f). The presented schematic can be explained that 3D-CNN keep larger distance to each other due to the formation of numerous contortions and crumples, which causes a decrease in the plate distance χ , an increase in electric field intensity change rate ( area

∑ ∫∫ .

dζ dχ

) along with the total effective

On account of the enhancement of effective adsorption area of extra NO2 molecules,

xy

the doping level (pgas) further increases while non-sensing region (Rc) significantly reduces, which synergistically contributes to the notably rise of response. Thus, the schematic of response enhancement for 3D crumpled structure can be confirmed by the model analysis. Eventually, the observed results have testified clearly that the 3D crumpled structures exhibit much higher response compared to 2D plane structures.

CONCLUSIONS In summary, inspired from dog nose, the 3D crumpled graphene-based nanosheets, mimic to the crumpled maxilloturbinate of dog nose, have been developed for the application of highly


11


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 12 of 28

sensitive NO2 gas sensors. A facile and massively producible lyophilization method was introduced in this work to transform 2D plane graphene-based nanosheets to 3D crumpled structure based on non-covalent bonding modification. Such 3D structures based sensors exhibited comprehensive NO2 gas sensing properties including high response (Ra/Rg=3.8), superior selectivity and sensing linearity, reliable repeatability and stability. Furthermore, a specific mechanism was conducted to illustrate the response enhancement, proving that it is the biomimetic 3D crumpled structure dominates the high response. Based on the obtained outstanding sensing results, we believe that the dog nose-inspired 3D crumpled graphene-based nanosheets represent a new platform for developing novel functional materials in bionic sensors and other industrial applications.

METHODS Preparation of GO Dispersion: For the dispersion of GO (purchased from XianFeng NANO Co., Ltd), GO flakes were ultrasoniated in deionized (DI) water for 30 min and then mild sonicated for 40 min to obtain 1mg/mL GO dispersion.

Preparation of rGO Dispersion: To prepare rGO dispersion, 4 mL of GO dispersion (1mg/mL) was diluted with 16 mL of deionized water, then 75 µL of ammonia (30%) and 10 mL of hydrazine hydrate (1 µL/mL) were added with a mild stir. Finally, the dispersion of rGO was prepared after the reduction in oil bath at 95°C for 1h.

Preparation of NA-rGO Dispersion: In the preparation of NA-rGO dispersion, typically 4 mL of GO dispersion (1mg/mL) was diluted with 10 mL of DI water, followed by the addition of 92


12

Page 13 of 28

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


mg of NA (Purchased from Alfa Aesar). Afterwards, 5 mL of NaOH solution (4 mg/mL) and 10 mL of hydrazine hydrate (1 µL/mL) were dropwise added in succession. The resultant dispersion was heated at 80°C for 1h in oil bath, which was then rinsed thrice by vacuum filtration with DI water. Finally, the rinsed NA-rGO was re-dispersed in 20 mL of DI water under mild sonication.

Lyophilization of rGO and NA-rGO Dispersion: The as-prepared rGO and NA-rGO aqueous dispersions were poured into 10mL glass bottles respectively, which were soon immersed in liquid nitrogen for 5min to be solid frozen. The bottles were then transferred into lyophilizer immediately and freeze-dried at -40°C under 40 Pa for 36h.

Fabrication of Gas Sensors: A Drop and Dry method was applied in the gas sensor fabrication. Firstly, the lyophilized rGO and NA-rGO powders were re-dispersed in DI water to prepare 0.2 mg/mL dispersions respectively. Subsequently, 20 µL of each dispersion (rGO w/ L, rGO w/o L, NA-rGO w/o L and 3D-CNN) was dropped on the surface of interdigitated electrodes (IEs) which were fabricated by jetting Ag-Pd paste on ceramic plates through a metal-jetting system (Figure S3, Supporting Information), and then the dropped IEs were dried in air on a heating holder at 50°C for 10 min. Finally, the gas sensors were available for testing. By the calculation in the flow chart of gas sensors fabrication (Figure S4, Supporting Information), 4mg GO raw materials can produce 1000 gas sensors eventually, which is very promising for commercial production.

Gas Sensing Tests: All the gas sensing tests were conducted by a gas sensing system (CGS-1TP Intelligent Gas Sensing Analysis System, ELITE TECH.), and the automatic system was designed to monitor the resistance changes of fabricated sensors. Prior to the gas sensing measurements, pure air gas was aerated in the gas cylinder to create a stable air environment,


13


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 14 of 28

quantitative NO2 was followed to be injected as the test started, and the gas cylinder should be opened when the test ended. All the gas sensing measurements were completed at roomtemperature (25°C) in the range of 45-65% relative humidity (RH).

Characterizations: The prepared samples were characterized by several techniques. The general morphological characterization was examined by scanning electron microscope (SEM; Quanta 250 FEG, FEI). The surface properties were analyzed by X-ray photoelectron spectra (XPS; ESCALAB 250 photoelectron spectrometer, Thermo Fisher Scientific, USA) while the characteristic functional groups were examined by FT-IR microscope (Thermo Scientific Nicolet iN10, USA). The I-V characteristic curve was measured on a SA6101 electrical analysis system (Sinoagg Co., Ltd, China) and the voltage applied on samples varied from DC -7 to 7 V with a step of 0.1 V. All the sensing measurements were done by Intelligent Gas Sensing Analysis System, (CGS-1TP, ELITE TECH. Beijing).

CONFLICT OF INTEREST The authors declare no competing financial interest.

ACKNOWLEDGEMENTS This work was supported by National Natural Science Foundation of China (Grant No.51373005, 51673007), National Key Basic Research Program of China (2014CB931800), Program for New Century Excellent Talents in University (NCET-10-0035), Fundamental Research Funds for the Central Universities, Leading Talents of Guangdong Province Program, Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13064).


14

Page 15 of 28

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


Supporting Information Available: I-V characteristics of the 3D-CNN sensors; SEM images of rGO w/o L and NA-rGO w/o L; the metal-jetting system and interdigitated electrodes fabrication process; the flow chart of gas sensor fabrication; XPS peak table of 3D-CNN. This material is available free of charge via the Internet at http://pubs.acs.org.

REFERENCES 1.

Onodera, T.; Toko, K. Towards an Electronic Dog Nose: Surface Plasmon Resonance Immunosensor for Security and Safety. Sensors 2014, 14, 16586-16616.

2.

Craven, B. A.; Neuberger, T.; Paterson, E. G.; Webb, A. G.; Josephson, E. M.; Morrison, E. E.; Settles, G. S. Reconstruction and Morphometric Analysis of the Nasal Airway of the Dog (Canis familiaris) and Implications Regarding Olfactory Airflow. The Anatomical Record

2007, 290, 1325-1340. 3.

Ban-Weiss, G. A.; McLaughlin, J. P.; Harley, R. A.; Kean, A. J.; Grosjean, E.; Grosjean, D. Carbonyl and Nitrogen Dioxide Emissions from Gasoline- and Diesel-Powered Motor Vehicles. Environ. Sci. Technol. 2008, 42, 3944-3950.

4.

Cui, S.; Pu, H.; Mattson, E. C.; Wen, Z.; Chang, J.; Hou, Y.; Hirschmugl, C. J.; Chen, J. Ultrasensitive Chemical Sensing through Facile Tuning Defects and Functional Groups in Reduced Graphene Oxide. Anal. Chem. 2014, 86, 7516-7522.

5.

Wultsch, G.; Nersesyan, A.; Mišík, M.; Kundi, M.; Wagner, K.-H.; Szekeres, T.; Zakerska, O.; Atefie, K.; Knasmueller, S. Formation of Micronuclei and Other Nuclear Anomalies in Exfoliated Nasal and Oral Cells: Results of a Human Study with Workers in a Power Plant Processing Poultry Litter. Int. J. Hyg. Environ. Health 2013, 216, 82-87.


15


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

6.

Page 16 of 28

Liu, H.; Li, M.; Voznyy, O.; Hu, L.; Fu, Q.; Zhou, D.; Xia, Z.; Sargent, E. H.; Tang, J. Physically Flexible, Rapid-Response Gas Sensor Based on Colloidal Quantum Dot Solids. Adv. Mater. 2014, 26, 2718-2724.

7.

Masuda, Y.; Itoh, T.; Shin, W.; Kato, K. SnO2 Nanosheet/Nanoparticle Detector for the Sensing of 1-Nonanal Gas Produced by Lung Cancer. Sci. Rep. 2015, 5.

8.

Liu, S.; Zeng, T. H.; Hofmann, M.; Burcombe, E.; Wei, J.; Jiang, R.; Kong, J.; Chen, Y. Antibacterial Activity of Graphite, Graphite Oxide, Graphene Oxide, and Reduced Graphene Oxide: Membrane and Oxidative Stress. ACS Nano 2011, 5, 6971-6980.

9.

Hong, B. J.; Compton, O. C.; An, Z.; Eryazici, I.; Nguyen, S. T. Successful Stabilization of Graphene Oxide in Electrolyte Solutions: Enhancement of Biofunctionalization and Cellular Uptake. ACS Nano 2011, 6, 63-73.

10. Beidaghi, M.; Wang, C. Micro-Supercapacitors Based on Interdigital Electrodes of Reduced Graphene Oxide and Carbon Nanotube Composites with Ultra High Power Handling Performance. Adv. Funct. Mater. 2012, 22, 4501-4510. 11. Yan, J.; Sun, W.; Wei, T.; Zhang, Q.; Fan, Z.; Wei, F. Fabrication and Electrochemical Performances of Hierarchical Porous Ni(OH)2 Nanoflakes Anchored on Graphene Sheets. J. Mater. Chem. 2012, 22, 11494-11502. 12. Wen, Z.; Wang, X.; Mao, S.; Bo, Z.; Kim, H.; Cui, S.; Lu, G.; Feng, X.; Chen, J. Crumpled Nitrogen-Doped Graphene Nanosheets with Ultrahigh Pore Volume for High-Performance Supercapacitor. Adv. Mater. 2012, 24, 5610-5616. 13. Xiao, M.; Kong, T.; Wang, W.; Song, Q.; Zhang, D.; Ma, Q.; Cheng, G. Interconnected Graphene Networks with Uniform Geometry for Flexible Conductors. Adv. Funct. Mater.

2015, 25, 6165-6172.


16

Page 17 of 28

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


14. Kim, J. Y.; Lim, J.; Jin, H. M.; Kim, B. H.; Jeong, S. J.; Choi, D. S.; Li, D. J.; Kim, S. O. 3D Tailored Crumpling of Block-Copolymer Lithography on Chemically Modified Graphene. Adv. Mater. 2016, 28, 1591-1596. 15. Novoselov, K. S.; Fal, V.; Colombo, L.; Gellert, P.; Schwab, M.; Kim, K. A Roadmap for Graphene. Nature 2012, 490, 192-200. 16. Huidobro, P. A.; Kraft, M.; Maier, S. A.; Pendry, J. B. Graphene as A Tunable Anisotropic or Isotropic Plasmonic Metasurface. ACS nano 2016, 10, 5499-5506. 17. Song, J.; Yu, Z.; Gordin, M. L.; Wang, D. Advanced Sulfur Cathode Enabled by Highly Crumpled Nitrogen-Doped Graphene Sheets for High-Energy-Density Lithium-Sulfur Batteries. Nano Lett. 2016, 16, 864-870. 18. Yavari, F.; Chen, Z.; Thomas, A. V.; Ren, W.; Cheng, H. -M.; Koratkar, N. High Sensitivity Gas Detection Using a Macroscopic Three-Dimensional Graphene Foam Network. Sci. Rep.

2011, 1, 166. 19. Amiri, A.; Ahmadi, G.; Shanbedi, M.; Savari, M.; Kazi, S.; Chew, B. Microwave-Assisted Synthesis of Highly-Crumpled, Few-Layered Graphene and Nitrogen-Doped Graphene for Use as High-Performance Electrodes in Capacitive Deionization. Sci. Rep. 2015, 5, 1750317515 20. El Rouby, W. M. Crumpled Graphene: Preparation and Applications. RSC Adv. 2015, 5, 66767-66796. 21. Zhao, Y.; Feng, J.; Liu, X.; Wang, F.; Wang, L.; Shi, C.; Huang, L.; Feng, X.; Chen, X.; Xu, L. Self-Adaptive Strain-Relaxation Optimization for High-Energy Lithium Storage Material through Crumpling of Graphene. Nat. Commun. 2014, 5, 4565-4572.


17


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 18 of 28

22. Lee, J. Y.; Lee, K. H.; Kim, Y. J.; Ha, J. S.; Lee, S. S.; Son, J. G. Sea-Urchin-Inspired 3D Crumpled Graphene Balls Using Simultaneous Etching and Reduction Process for HighDensity Capacitive Energy Storage. Adv. Funct. Mater. 2015, 25, 3606-3614. 23. Qian, T.; Yu, C.; Wu, S.; Shen, J. A Facilely Prepared Polypyrrole-Reduced Graphene Oxide Composite with a Crumpled Surface for High Performance Supercapacitor Electrodes. J. Mater. Chem. A 2013, 1, 6539-6542. 24. Xiong, F.; Cai, Z.; Qu, L.; Zhang, P.; Yuan, Z.; Asare, O. K.; Xu, W.; Lin, C.; Mai, L. ThreeDimensional Crumpled Reduced Graphene Oxide/MoS2 Nanoflowers: A Stable Anode for Lithium-Ion Batteries. ACS Appl. Mater. Interfaces 2015, 7, 12625-12630. 25. Su, Q.; Pang, S.; Alijani, V.; Li, C.; Feng, X.; Müllen, K. Composites of Graphene with Large Aromatic Molecules. Adv. Mater. 2009, 21, 3191-3195. 26. Cheedarala, R. K.; Jeon, J. -H.; Kee, C. -D.; Oh, I. -K. Bio-Inspired All-Organic Soft Actuator Based on a π-π Stacked 3D Ionic Network Membrane and Ultra-fast Solution Processing. Adv. Funct. Mater. 2014, 24, 6005-6015. 27. Su, Y. H.; Wu, Y. K.; Tu, S. L.; Chang, S.-J. Electrostatic Studies of π-π Interaction for Benzene Stacking on a Graphene Layer. Appl. Phys. Lett. 2011, 99, 163102-163104. 28. Parviz, D.; Das, S.; Ahmed, H. T.; Irin, F.; Bhattacharia, S.; Green, M. J. Dispersions of Non-Covalently Functionalized Graphene with Minimal Stabilizer. ACS Nano 2012, 6, 8857-8867. 29. Yuan, W.; Liu, A.; Huang, L.; Li, C.; Shi, G. High-Performance NO2 Sensors Based on Chemically Modified Graphene. Adv. Mater. 2013, 25, 766-771.


18

Page 19 of 28

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


30. Chen, Z.; Umar, A.; Wang, S.; Wang, Y.; Tian, T.; Shang, Y.; Fan, Y.; Qi, Q.; Xu, D.; Jiang, L. Supramolecular Fabrication of Multilevel Graphene-Based Gas Sensors with High NO2 Sensibility. Nanoscale 2015, 7, 10259-10266. 31. Huang, L.; Wang, Z.; Zhang, J.; Pu, J.; Lin, Y.; Xu, S.; Shen, L.; Chen, Q.; Shi, W. Fully Printed, Rapid-Response Sensors Based on Chemically Modified Graphene for Detecting NO2 at Room Temperature. ACS Appl. Mater. Interfaces 2014, 6, 7426-7433. 32. Zhou, L.; Shen, F.; Tian, X.; Wang, D.; Zhang, T.; Chen, W. Stable Cu2O Nanocrystals Grown on Functionalized Graphene Sheets and Room Temperature H2S Gas Sensing with Ultrahigh Sensitivity. Nanoscale 2013, 5, 1564-1569. 33. Cui, S.; Wen, Z.; Huang, X.; Chang, J.; Chen, J. Stabilizing MoS2 Nanosheets through SnO2 Nanocrystal Decoration for High-Performance Gas Sensing in Air. Small 2015, 11, 23052313. 34. Karaduman, I.; Er, E.; Celikkan, H.; Acar, S. A New Generation Gas Sensing Material Based on High-Quality Graphene. Sens. Actuators, B 2015, 221, 1188-1194. 35. Ni, Z. H.; Wang, H. M.; Luo, Z. Q.; Wang, Y. Y.; Yu, T.; Wu, Y. H.; Shen, Z. X. The Effect of Vacuum Annealing on Graphene. J. Raman Spectrosc. 2010, 41, 479-483. 36. Levesque, P. L.; Sabri, S. S.; Aguirre, C. M.; Guillemette, J.; Siaj, M.; Desjardins, P.; Szkopek, T.; Martel, R. Probing Charge Transfer at Surfaces Using Graphene Transistors. Nano Lett. 2010, 11, 132-137. 37. Randeniya, L. K.; Shi, H.; Barnard, A. S.; Fang, J.; Martin, P. J.; Ostrikov, K. Harnessing the Influence of Reactive Edges and Defects of Graphene Substrates for Achieving Complete Cycle of Room-Temperature Molecular Sensing. Small 2013, 9, 3993-3999.


19


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 20 of 28

38. Park, H.-Y.; Yoon, J.-S.; Jeon, J.; Kim, J.; Jo, S.-H.; Yu, H.-Y.; Lee, S.; Park, J.-H. Controllable and Air-Stable Graphene n-Type Doping on Phosphosilicate Glass for Intrinsic Graphene. Org. Electron. 2015, 22, 117-121. 39. Shin, D.-W.; Lee, H. M.; Yu, S. M.; Lim, K.-S.; Jung, J. H.; Kim, M.-K.; Kim, S.-W.; Han, J.-H.; Ruoff, R. S.; Yoo, J.-B. A Facile Route to Recover Intrinsic Graphene over Large Scale. ACS Nano 2012, 6, 7781-7788. 40. Deng, S.; Tjoa, V.; Fan, H.; Tan, H. R.; Sayle, D. C.; Olivo, M.; Mhaisalkar, S.; Wei, J.; Sow, C. H. Reduced Graphene Oxide Conjugated Cu2O Nanowire Mesocrystals for HighPerformance NO2 Gas Sensor. J. Am. Chem. Soc. 2012, 134, 4905-4917. 41. Cho, B.; Yoon, J.; Lim, S. K.; Kim, A. R.; Kim, D.-H.; Park, S.-G.; Kwon, J.-D.; Lee, Y.-J.; Lee, K.-H.; Lee, B. H. Chemical Sensing of 2d Graphene/MoS2 Heterostructure Device. ACS Appl. Mater. Interfaces 2015, 7, 16775-16780. 42. JuáYun, Y.; JuáPark, H.; EonáMoon, S.; HoonáKim, B. A 3D Scaffold for Ultra-Sensitive Reduced Graphene Oxide Gas Sensors. Nanoscale 2014, 6, 6511-6514. 43. Niu, F.; Liu, J.-M.; Tao, L.-M.; Wang, W.; Song, W.-G. Nitrogen and Silica Co-Doped Graphene Nanosheets for NO2 Gas Sensing. J. Mater. Chem. A 2013, 1, 6130-6133. 44. Duy, L. T.; Kim, D. J.; Trung, T. Q.; Dang, V. Q.; Kim, B. Y.; Moon, H. K.; Lee, N. E. High Performance Three-Dimensional Chemical Sensor Platform Using Reduced Graphene Oxide Formed on High Aspect-Ratio Micro-Pillars. Adv. Funct. Mater. 2015, 25, 883-890. 45. Choi, H.; Choi, J. S.; Kim, J. S.; Choe, J. H.; Chung, K. H.; Shin, J. W.; Kim, J. T.; Youn, D. H.; Kim, K. C.; Lee, J. I. Flexible and Transparent Gas Molecule Sensor Integrated with Sensing and Heating Graphene Layers. Small 2014, 10, 3685-3691. 46. Yang, Y.; Li, S.; Yang, W.; Yuan, W.; Xu, J.; Jiang, Y. In Situ Polymerization Deposition of


20

Page 21 of 28

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


Porous Conducting Polymer on Reduced Graphene Oxide for Gas Sensor. ACS Appl. Mater. Interfaces 2014, 6, 13807-13814. 47. Yang, W.; Wan, P.; Zhou, X.; Hu, J.; Guan, Y.; Feng, L. Additive-Free Synthesis of In2O3 Cubes Embedded into Graphene Sheets and Their Enhanced NO2 Sensing Performance at Room Temperature. ACS Appl. Mater. Interfaces 2014, 6, 21093-21100. 48. Wang, D.; Hu, Y.; Zhao, J.; Zeng, L.; Tao, X.; Chen, W. Holey Reduced Graphene Oxide Nanosheets for High Performance Room Temperature Gas Sensing. J. Mater. Chem. A,

2014, 2, 17415-17420.


21


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 22 of 28

Figure 1. The schematic and SEM images of morphology variation for rGO and NA-rGO with or without the lyophilization. Physical picture and schematic of (a) aggregated 2D plane rGO nanosheets without lyophilization, (b) further aggregated 2D plane rGO nanosheets with lyophilization. (c) Low- and (d) high-magnification SEM images of the lyophilized aggregated 2D plane rGO nanosheets. Physical picture and schematic of (e) well-dispersed 2D plane NArGO nanosheets without lyophilization, (f) laminar and separated 3D crumpled NA-rGO nanosheets with lyophilization. (g) Low- and (h) high-magnification SEM images of the lyophilized 3D crumpled NA-rGO nanosheets. Inset: the three-dimensional surface model of the left canine nasal airway with cross-section of maxilloturbinate. Inset figure is reproduced with permission.2 Copyright 2007, Wiley-Liss.


22

Page 23 of 28

Intensity (arbitrary units)

a

3D-CNN

rGO w/ L

164 165 166 167 168 169 170 171 172 173 174

Binding Energy (eV)

b % Transmittance

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


GO 3410

1722

rGO w/L 3D-CNN 1576 1196-1032

3500

3000

2500

2000

1500

1000

-1 )

Wavenumbers (cm

Figure 2. (a) Typical XPS spectra of the 3D-CNN and rGO w/ L. (b) FT-IR spectra of GO, rGO w/ L and 3D-CNN.


23


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 24 of 28

Figure 3. (a) The schematic diagram for fabricating gas sensor with overall dimensions and optical & SEM image of a typical fabricated graphene-based gas sensor. (b) An ideal flow chart of the gas sensing test.


24

Page 25 of 28

b 90

4.5

Air NO2 Air

4.0

3D-CNN NA-rGO w/o L rGO w/o L rGO w/ L

Ra/Rg

3.5 3.0 2.5 2.0

80

10 ppm

70

∆R/Ra (%)

a

60 50 40 30

1000 ppm 1000 ppm

20

1.5

10

1.0

0

1000 ppm 1000 ppm 1000 ppm

0

500 1000 1500 2000 2500 3000 3500 4000

NO2

NH3 Acetic acid Ethanol Acetone Methanol

Time (s) 3D-CNN

5

Ra/Rg

d 4.0

6

10 ppm

Actual measured data points Theoretical fitting line

3.5

8

4

6

Ra/Rg

c

5 4

3

3 2

1 0

3.0 2.5 2.0

2

1.5

1

1.0

5000 10000 15000 20000 25000 30000 35000

0

1

f

e 6

10 ppm

3

4

5

6

5.0

4.0

10 ppm

10 ppm

7

8

9

10 11

Response Response Time Recovery Time

4.5

3D-CNN

5

2

Concentration (ppm)

Time (s)

3

6000 5000 4000

3.5

4

Ra/Rg

Ra/Rg

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


3.0

3000

2.5

2000

2.0

2

1000

1.5

1

1.0

0

2000

4000

6000

8000 10000 12000

Time (s)

0 0

1

2

3

4

5

6

Aging Time (months)

Figure 4. (a) Response comparison of 3D-CNN, NA-rGO w/o L, rGO w/o L and rGO w/ L sensors towards 10 ppm NO2. (b) Selective response of the 3D-CNN sensors towards 10ppm NO2 and 1000ppm interferential gases, including NH3, Acetic acid, Ethanol, Acetone, and Methanol. (c) A successive response and recovery curve of 3D-CNN sensors with NO2 concentrations ranging from 1 to 10 ppm and (d) corresponding linear fitting curve, error bars for the data points lie within the symbols themselves. (e) A three-cycle response and recovery curve of 3D-CNN sensors exposed to 10 ppm NO2. (f) An aging test towards 10 ppm NO2 for 6 months.


25


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 26 of 28

Figure 5. The morphology of typical 2D plane and 3D crumpled structure with corresponding schematics of NO2 absorption on IEs and NO2 response. For 2D plane structure: (a) the SEM image at low-magnification, (b) the schematics with tightly aggregated nanosheets and (c) corresponding NO2 molecules adsorption on IEs. For 3D crumpled structure: (d) the SEM image at low-magnification, (e) the schematics with irregularity crumpled nanosheets and (f) corresponding NO2 molecules adsorption on IEs.


26

Page 27 of 28

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


Table 1. Typical Graphene-based Sensors for NO2 Sensing Materials &Structure 2D graphene/MoS2 heterostructure41 42

Method

NO2 (ppm)

Response(a)

Operating Temperature

5

7%

150°C

Photolithography & ion etching

3D scaffold rGO/polymer

Electrospinning

4.5

18%

100°C

N & Si co-doped graphene nanosheets43

High temperature annealing

21

26%

RT(b)

rGO network covered 3D micro-pillar44

Photoetching

5

28%

RT

CVD

10

28%

100-165°C

In situ polymerization deposition

10

33%

RT

Gravure print

10

45%

RT

Microwave-assisted hydrothermal

10

50%

RT

45

Single-layer graphene channel 46

RGO/porous PEDOT

Flexible Ag−S-RGO31 47

In2O3 cubes/rGO composites 48

Holey rGO nanosheets

Hydrothermal reduction

12.5

54%

RT

29

EDA-rGO

Chemically modification

10

58%

RT

Multilevel Ag-NA-rGO30

Supramolecular modification

10

64%

RT

3D-CNN (This work)

Lyophilization

5

60%

RT

10

74%

RT

(a)

For the convenience of comparison, the evaluation of response is converted as:

Response =

(b) ∆R Ra − Rg (%) ; RT = Ra Ra

represents room temperature.


27


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 28 of 28

TOC figure


28

Three-Dimensional Crumpled Graphene-Based Nanosheets with

Recommend Documents