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Chemosensors 2018, 6(4), 60; https://doi.org/10.3390/chemosensors6040060
Carbon Nanostructures as a Multi-Functional Platform for Sensing Applications
Leibniz Institute for Solid State and Materials Research Dresden; Helmholtzstr. 20, 01069 Dresden, Germany
Centre of Polymer and Carbon Materials Polish Academy of Sciences; Marie Curie-Skłodowskiej 34 Str., 41-819 Zabrze, Poland
Soochow Institute for Energy and Materials Innovations, College of Physics, Optoelectronics and Energy, Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou 215006, China
Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, Soochow University, Suzhou 215006, China
Center for Nanochemistry, Beijing Science and Engineering Centre for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
Authors to whom correspondence should be addressed.
Received: 30 October 2018 / Accepted: 28 November 2018 / Published: 5 December 2018
The various forms of carbon nanostructures are providing extraordinary new opportunities that can revolutionize the way gas sensors, electrochemical sensors and biosensors are engineered. The great potential of carbon nanostructures as a sensing platform is exciting due to their unique electrical and chemical properties, highly scalable, biocompatible and particularly interesting due to the almost infinite possibility of functionalization with a wide variety of inorganic nanostructured materials and biomolecules. This opens a whole new pallet of specificity into sensors that can be extremely sensitive, durable and that can be incorporated into the ongoing new generation of wearable technology. Within this context, carbon-based nanostructures are amongst the most promising structures to be incorporated in a multi-functional platform for sensing. The present review discusses the various 1D, 2D and 3D carbon nanostructure forms incorporated into different sensor types as well as the novel functionalization approaches that allow such multi-functionality.
Keywords:carbon nanostructures; graphene; carbon nanotubes; graphene foam; sensors
Carbon can present itself in many different natural and artificial allotropes [1,2,3,4]. These structures pursue unique electronic, mechanical, physical and chemical properties [5,6,7], which allow applications in a number of fields, especially in materials science and molecular electronics . A few years ago we reviewed these nanostructures and their potential use as a drug delivery and diagnostics platform . Currently we explore these versatile structures as multi-functional platforms for sensing. To this end, we explore the one-dimensional (1D), two-dimensional (2D) and three-dimensional (3D) carbon nanostructures, represented by carbon nanotubes (CNTs), graphene and graphene foam, respectively illustrated in Figure 1.
Sensing is an important area in modern society, because the use of sensors is a widespread reality, for example in the gas emission control industry, household security, automotive emission control, environmental monitoring and health care diagnosis. Most investigations have focused on detecting oxygen (O2) [9,10], ozone (O3) , carbon monoxide (CO) [12,13], carbon dioxide (CO2) , ammonia (N3) , sulfur dioxide (SO2)  and hydrogen (H2) [16,17]. Other organic gases such as ethanol , benzene , isopropanol , acetone [21,22] and methanol  have also called attention to be detected. Different biological electroactive species and other biomolecules as glucose [24,25], neurotransmitters , ascorbic acid [27,28], uric acid , hydrogen peroxide (H2O2)  among others are also of great interest to be monitored due to the need of diagnosis of diseases . There is a high demand for sensors that have a high selectivity, sensitivity and reversibility as well as high scalability, low costs and compact sizes to be incorporated in wearable devices.
To achieve these properties, the active sensing spot must ideally pursue a high surface area/volume ratio to increases the probability of adsorption of molecules and increase the sensitivity. The selectivity of the sensor is mostly achieved by functionalization with specific molecules that have a high affinity to the analytes of interest. Carbon nanostructures pursue all the ideal properties such as high surface area and can be easily functionalized. Currently, their relative cheap and simple production steps as well as advanced manipulation methods of a wide variety of carbon nanostructures makes them perfect candidates to be used in different types of sensors.
A key attention to the realization of sensors in general is directed to the integration of the recognition elements with the electronic elements, which can be classified into many different types, such as amperometric sensors, electrochemical impedance sensors, luminescence sensors and photoelectrochemical sensors . In this way, the detection of chemical and biological targets is done by measuring electrochemical changes of the electrode that interacts with the analyte . This highlights that the construction of high performance sensor platforms must not only consider the material used in the sensing area, but also the electrode materials, which will determine how well the signal can be transduced, transmitted and amplified.
The field of sensing using carbon nanostructures is very broad and diverse due to the variety of nanostructures and type of functionalization available. Therefore, most of the reviews in the field focus on a specific nanostructure with special attention to carbon nanotubes and graphene [34,35,36,37] or type of application . More recently, research on 3D carbon nanostructures for a wide variety of applications has been rising and is mostly focused on synthesis approaches and battery applications [39,40,41,42] but to our knowledge this class of carbon nanostructures as sensors has not been covered in reviews. In this work we focus primarily on novel approaches used to build and functionalize carbon nanostructures as a multi-functional platform for sensing. Mostly of the presented strategies are applicable to a variety of gas, electrochemical and biosensors. This review is divided into 1D, 2D and 3D carbon nanostructures and subdivided into the various types of sensors where these nanostructures are applied. The specific functionalization of each nanostructures and their state-of-the-art application is addressed. Finally, their future applications perspectives are briefly discussed.
2. 1D Carbon Nanostructures
It is almost twenty years since the discovery of CNTs by S. Iijima . During these twenty years many papers related with CNTs unique properties  and applications were published. Nowadays CNTs even find their place in commercial applications, which is mostly in composite technology . However, in the field of sensing devices, CNTs still have a wide field to be explored. Due to their very good electrical properties, possibility to functionalize their sp2 backbone and large surface area (higher than graphite) [46,47], making them an interesting active material in sensing devices.
2.1. Carbon Nanotubes as Chemiresistors and Chemical Field-Effect Transistor
As mentioned previously, due to the good electrical properties of CNTs, they are a promising material to be used as a sensing layer in chemoresisting sensing devices. Chemoresistors are based on changing the electrical current flow through the material, which in the presence of a substance impacts the electron state of the active material. The first work related with CNT as a gas sensor is dated at the beginning of twenty first century. P. G. Collins et al.  researched the sensing ability of single-walled CNTs in the presence of oxygen. The experiment was carried out using a four-probe contact configuration at two temperatures 17 °C and 117 °C by flooding the device in cycles by air and evacuating it by vacuum pump. They also carried out alternative experiments by flooding the chamber with N2 to confirm the sensor response to the oxygen molecules. Their research showed a rapid change in conductivity of the CNT-based active material. Moreover, CNT-based sensors were much faster than current ones based on metallic semiconducting devices, which work at high temperatures. The advantage of the device proposed by P. G. Collins et al. lies in the fact that it works at room temperature, which is also a milestone ahead in comparison to commercial ones using semiconducting active materials. Y. Wang et al.  presented chemiresistors based on single-walled CNTs to detect dimethyl methylphosphonate (DMMP) which is a flame retardant additive to polymer composites. The experiment was carried in a homemade chamber with N2 as a carrier gas passing through a bubbler. The DMMP was evacuated after every test cycle. Their sensors were able to detect 5 ppm of DMMP in an experimental atmosphere.
L. Valentini et al.  researched the temperature influence on CNT-based chemiresistors in terms of changes in their resistivity. They concluded that the CNT structure is stable and able to go back to the starting state in the range between 25 and 250 °C. The resistivity of the CNT remains stable at 25 °C before heating. Above 250 °C structural changes can impact the material resistivity. They investigated their CNT-based chemiresistors affinity to NO2 gas and they were able to detect the gas concentrations from 10 ppb and increased it stepwise to 100 ppb in temperature range from 25–250 °C. For the measurement carried out at 165 °C, a second thermal treatment increased the sensitivity of the sensor to NO2 gas by almost 20 times, from approximately 3% to 56% after a second thermal treatment.
T. Ueda et al.  grew CNTs on a SiC surface and used them to prepare NO2 and NO gas sensing devices. The detection level of CNT-based chemiresistors was 2 ppm. In this case they purified their device before the experiment by heating it to 80 °C in H2O2 in a heating bath to remove impurities and amorphous carbon. The experiment was carried out at room temperature for NO and at 100, 150 and 200 °C for NO2. They reported sensitivity of 2% for NO at room temperature and from 0.3% to 2.2% for NO2 depending on the operating temperature. However, at temperatures lower than 200 °C the sensor was not able to return to the ground state. This is one of the reasons why different research groups use UV irradiation to promote a faster desorption of NO2 gas molecules adsorbed at device surface (sensor recovery).
Nowadays there is a possibility to change the gas affinity to the active material in the sensor or its properties by functionalization, which tailors and improves the basic device properties like selectivity or detection range. CNTs are composed by sp2 C=C bonds which take part in many chemical reactions, especially in organic chemistry. Therefore, their chemical structure opens the possibility of functionalization. There are numerous papers reporting the functionalization of CNTs for instance by oxidation [52,53,54], thiolination [55,56,57] and bromination . I. Sayago et al.  prepared chemiresistors from carboxylated CNTs and tested their sensing ability to NO2. Their chemiresistors showed high selectivity to NO2 even when flushing it mixed with NH3. The sensor was still selectively active promoting the detection of NO2. Their work also highlighted the sensor performance when working in temperature higher than room temperature. At temperatures higher than 200 °C the sensitivity achieved for 0.7 ppm of NO2 was almost 3% higher in comparison to the sensor measured at 25 °C. M. Guo et al.  prepared thiolinated CNTs by a number of chemical reactions from carboxylation to final thiolinated CNTs. Their sensors have the ability to detect trace concentration of formaldehyde. H. Xie et al.  propose an amino-functionalized CNT system to detect formaldehyde gas. In their work they discuss the impact in resistivity due to changes of the sensor active material chemical structure. Their study shows clearly that the amino functionalization can improve the sensitivity to the analyte. In other words, unfunctionalized CNTs had marginal change in resistivity during exposure to formaldehyde, but after adding 5% of amino groups to the CNTs there was a clear change in resistivity of the active material when the gas sensor was exposed to formaldehyde concentrations of 20 and 200 ppb. D. Hines et al.  presented the response of brominated CNTs to eight different gaseous media. Chemiresistors based on brominated CNTs were reproducible and showed clear and noticeable response for concentrations under 1 ppm.
Another approach to sensing device production is using field effect transistors (FET). The working principle of these sensors is slightly different than in chemiresistors. FET devices consist of two electrodes: source and drain connected by a conducting material which is the charge carrier. Along with the conductive material there is another electrode separated from rest of the setup with a thin dielectric layer called the gate, to which the external voltage is connected. Gate voltage creates external electric field which has an impact in the resistivity/conductivity of sensor active material . Figure 2 shows the schematics of chemiresistors and FET devices.
The first FET device based on single-walled CNTs was presented by J. Kong et al.  to detect NO2 and NH3. Metal oxide-based gas sensors exposed to NH3 gas in concentration from 200 ppm to 1% usually have response time around 1 min. with relative sensitivity from 0.1 to 100 depending on the metal oxide used in the gas sensing experiment. The NH3 sensor also showed efficiency at temperature higher than 350 °C. For NO2 metal oxide-based gas sensors they also a have quite fast response, which is approximately 1 min. In this case the relative selectivity was from 1 to 300 and, like in NH3 case, they usually operate in temperatures from 250 to 600 °C . They also observed that their device has a rapid change in material conductivity and the response time vary from 0.5 min to 5 min. for NO2 and 10 min. for NH3. The response time is related with the analyte concentration, what they clearly observe in NO2 experiment. However, in comparison to the metal oxide devices, the biggest advantage of the J. Kong et al. sensor is the ability to work and be repeatable at room temperature. T. Someya et al.  prepared devices based on single-walled CNTs and investigated the possibility to detect low concentrations of different alcohol vapors. The device was sensitive for a wide range of alcohol vapors and they highlighted the dependence of the sensor response relative to their vapors partial pressure value . Figure 3 presents the change in drain current during exposure to various alcohol vapors.
Another interesting approach to apply CNTs to FET devices is presented by A. Star et al. methodology . They decorated CNTs with several metals, mostly from D block of the periodic table, and exposed them to H2, NO2, NH3, H2S and CO. They carried the sensing experiment at room temperature and in air with constant level of relative humidity. Figure 4 presents the sensor response for different gases of numerous metals used to decorate the CNT surface. The results were normalized so that 0.0 (blue color) means no response for the tested media and 1.0 (red color) is highest sensor response obtained. Their research highlighted the importance of nanomaterial functionalization to change their properties.
J. P. Novak et al. , similarly to Y. Wang et al. , worked on a device that was able to detect DMMP, but J. P. Novak et al. used FET setup to detect DMMP by applying a positive gate bias to fully desorb DMMP from the CNT surface instead of exposing it to UV radiation or heating inside an oven. Their work also brings a very interesting idea to improve the selectivity between target gas/vapor and contaminants present in air like fuel vapors or humidity. They used special filters (built from glass wool and hydrogen-bonding acidic polycarbosilane called HC) to shield the sensor from the influence of impurities. The ChemFET device was able to detect DMMP in the ppb range with high response and gate voltage assisted recovery has promising perspective in future applications . Also, in comparison to Y. Wang group chemiresistor , J. P. Novak et al. FET sensor was able to detect lower concentrations of DMMP.
2.2. Carbon Nanotubes as Biosensors
There is a growing interest in the biochemistry of the human body as well as a growing need to increase the sensitivity and selectivity of sensor devices used in medicine. In most cases CNTs biosensors are based on CNT functionalized by covalent or non-covalent bond. The setup needed for that kind of measurement is slightly different than for the previously mentioned devices. CNT-based biosensors mostly use electrochemical techniques like amperometry, voltamperometry, chronoamperometry as a base for their research. Recently there are numerous works related with biosensing devices, report of multiple systems for detecting glucose, proteins or neurotransmitters like dopamine or serotonin.
Y. Lin et al.  worked on CNT-based glucose biosensors functionalized with GOx (glucose oxidase) enzyme at the tip of the nanotubes. The sensor was used in amperometric measurement and tested for the detection ability of glucose with detection limits at level of 0.08 mM and signal to noise ratio equal 3. Further work by numerous groups related with glucose detection was based on different CNT functionalization, but most of them had common feature which was partially functionalized CNT with glucose oxidize enzyme. Another very common way to improve selectivity in CNT-based sensors is preparing quasi composite of Naftion and CNTs, for instance in the works of X. Liu et al. , Y. L. Yao et al.  and K. Zhao et al. . The purpose of Naftion addition is to decrease the negative impact of ascorbic acid which is one of the products of glucose metabolism in organism or uric acid which has an increased level and is related with diabetes.
Proteins are biopolymers that present in all organism from viruses to humans. The ability to detect low concentrations of protein can be helpful to find the cause or even cure for diabetes or HIV. One of the first work related with insulin sensor device was presented by J. Wang et al.  in which they prepared ruthenium oxide decorated CNTs as a coating to glassy carbon electrode. They achieve 1 nM detection limit. Over a decade later after, E. Martínez-Periñán with his group  presented an insulin sensor based on nickel (II) hydroxide CNT system with detection limits in the range of µM with confirmed stability (initial loss of the response was 10%) during multiple hours of continuous work .
Dopamine and serotonin are one of the most important neurotransmitters in the human organism. Their lowered level is related with many diseases, for instance with depression or other psychological disorders. The ability of detecting these two neurotransmitters at low concentrations is important from the medical point of view. Neurotransmitter biosensors research lies primordially on modified CNTs and on amperometric measurements method. B. E. K. Swammy et al.  prepared electrodes coated with oxidized CNTs and Naftion to detect simultaneously serotonin and dopamine. Their biosensor limit of detection for both neurotransmitters was lower than 300 nM. Y. Sun et al.  prepared multi-walled CNTs ionic liquid composite to realize similar experiment as above described. They prepared devices with high selectivity, sensitivity for both analytes with limit of detection under 100 nM.
3. 2D Carbon Nanostructures
A. K. Geim and K. S. Novoselov kickstarted the research and development related with 2D carbon materials by presenting graphene obtained by Scotch-Tape method in 2004 . Briefly, 2D carbon materials are referred as crystalline structures with a honeycomb lattice and thickness of a single to few carbon atoms . In the 2D carbon nanomaterials family it is possible to distinguish single and few layer graphene layers with very good thermal and electrical conductivity, large surface area (larger than the CNT case) . Another very important member of the 2D carbon nanomaterials family is the graphene oxide and its derivatives based on chemical functionalization, with most prominent representative in the form of reduced graphene oxide. Briefly, the graphene oxide pursue a quasi-graphene structure consisting mostly of an aromatic sp2 backbone with the presence of oxygen containing functional groups like carboxyl, carbonyl and hydroxyl groups [78,79]. The presence of functional groups and other defects in the sp2 structure have a direct impact onto the material properties, which leads to a decrease in conductivity and other properties in comparison to graphene [80,81]. To improve the electrical properties of GO-based materials it is usually applied chemical or thermal reduction to the starting material . One of the biggest advantages of using graphene oxide in sensing applications is increased affinity of graphene oxide-based material to analyte substances due to the presence of functional groups and much bigger reactivity of the material, facilitating further functionalization. This can lead to precise tailoring of properties on demand. Herein, we will briefly refer application of 2D carbon nanomaterials as a sensor device.
3.1. 2D Nanomaterials as Chemiresistors and ChemFET
G. Ko et al.  prepared chemiresistors based on multi-layered graphene for NO2 detection working at room temperature but with UV light-supported recovery of the sensor. Their sensor presented fast response and selectivity between NO2 and air. They obtained almost 10% of sensitivity for 100 ppm of NO2. R. Pearce et al.  reported the shielding mechanism of multiple layers of graphene in the case of their chemi-resistive response, because single layer graphene has over 55000 higher response to gas concentration in comparison its multilayer counterpart at 25 °C for one hour exposure to 2.5 ppm of NO2. Figure 5 presents the responses of single (left) and multilayer graphene (right) to different concentrations of NO2.
F. Yavari et al.  also presented a graphene-based gas sensor for NO2 and NH3 detection. Their device operated at room temperature and atmospheric pressure. The sensor was exposed to concentrations from 0.1 to 200 ppm in the case of NO2 and from 0.5 to 1000 ppm in the case of NH3. They also stated that in comparison to other types of materials like conductive polymers  or metal oxide  their device performance is impressive. They achieved around 19% higher sensor response for 200 ppm exposure to NO2 than for polypyrrole sensor exposed to 1000 ppm.
Another interesting approach to graphene-based NO2 gas sensor was proposed by H. Choi et al. . Their sensor is based on a multilayer graphene grown using CVD and transferred to flexible polyimide surface with Au interdigitated electrodes. They investigated the sensor response in “flat” state for NO2 diluted in N2. The gas mixture concentration was from 0.2 ppm to 5 ppm. They also researched the influence of device bending to sensor response. The gas sensor was exposed to 1 ppm NO2 for 3 min. There was no decrease in signal when compared between flat and bend state. All measurements were carried out at room temperature. However, similar to the previously mentioned CNTs- and graphene-based devices, the desorption of gas molecules was very slow and the sensor did not come back to ground state spontaneously. Therefore, they used external heating as a sensor recovery method.
H. J. Yoon et al.  proposed a device based on a few layer (~4 layers) graphene obtained by mechanical cleavage, which was firstly exposed to CO2. They obtained promising results with quite fast responses and recovery time under 10 s. Their device was tested in tree temperature values: 22, 40 and 60 °C. In all three cases they obtained a large increase in relative conductance over 20%. The results suggest that this sensor architecture has a great application potential in environmental monitoring systems.
K. R. Nemade et al.  proposed a device based on few-layer graphene (~6 layers) obtained by electrochemical exfoliation. Their chemiresistor sensor was exposed to CO2 and the commercial mixture of propane and butane (LPG). They also assess the influence of temperature on the performance of the gas sensor for all the tested substances. They carried out the experiment at CO2 concentrations from 30 to 50 ppm in a temperature range from 46 to 206 °C. For all chosen concentrations they estimated that the optimal temperature of operation for CO2 gas sensor was 150 °C. They used the same protocol in the case of LPG and got the highest sensor response at 125 °C. The device showed almost perfect stability for constant exposure to 100 ppm solutions of both analytes for 30 days exposure. Their few-layer graphene-based sensor was also able to operate at room temperature condition, but the sensor response was quite low (under 1%) in both cases.
The first graphene-based FET gas sensor was proposed by F. Schedin et al. . They prepared a FET sensor based on a single layer graphene obtained by micromechanical cleavage of graphite. The experiment was carried out in an evacuated glass container and filled in with analyte (NO2 or NH3 or CO or H2O) diluted to 1 ppm concentration with inert gas (either nitrogen or helium). The device presented rapid response for NO2, NH3, CO and H2O with limits of detection in the order of 1 ppb. However, after removing the detected gas from the chamber, the sensor still presented stable signal of response. They concluded that the adsorption of gas molecules at room temperature on the graphene surface is stable. The F. Schedin team used annealing at 150 °C under vacuum to fully recover the sensing ability of the device.
M. Guatam et al.  prepared a graphene-based FET sensor for detecting NH3. They carried out an experiment in range of temperatures from 27 to 100 °C in dry air and continued vacuum. They observed that with the temperature increase, the sensing response of devices grow linearly. They also reported general problems with the sensor recovery only for the dry air flow experiment. Therefore, they proposed other ways in which the air flow is combined with infra-red irradiation or dry air flow combined with vacuum annealing.
As mentioned earlier, one of the biggest advantages of functionalized graphene-based materials (graphene oxide, reduced graphene oxide, functionalized graphene oxide) in comparison to pristine graphene is the increased affinity to detect molecules due to the presence of sp2 structural defects (e.g., presence of functional groups). That is why these types of 2D materials are more promising in gas sensing applications.
In the case of graphene oxide (GO) there is a small amount of reports on chemiresistors and FET devices due to its more insulating than conductive properties . That fact was noticed and highlighted by S. Prezioso et al.  They prepared a device by drop casting GO material onto platinum interdigitated structure, and carried humidity sensing experiment for GO and annealed GO at 200°C in ultra-high vacuum. The goal of that research was to study the effect of the chemical composition of the material during experiment in higher experimental temperature. They chose two ranges: from 25 to 150 °C and from 25 to 200 °C. The experiments were performed in a dry air atmosphere. They concluded that the chemical composition of the material has a clear impact onto the baseline conductivity and desorption time, which was faster for the annealed GO. They also tested the GO-based sensor for detecting low concentrations of NO2. Their device was able to detect NO2 at concentrations on the ppb level at 150 °C.
A. P. Taylor et al.  proposed an electro-sprayed GO chemiresistive detector with a four point electrodes connection and compared its performance with commercial humidity detectors (Honeywell HIH-4000). They carried out two types of experiments. The first part was based on quick change of relative humidity value to check if the GO-based sensor was able to detect dynamic relative humidity changes. It turns out that the GO-based sensor was able to surpass the response of the commercial one. This suggests that GO-based humidity sensors are able to track changes in relative humidity continuously. The second part of experiments demonstrated that the conductivity change of the material is in linear relation with the percentage value of relative humidity.
Our group  works on the impact of functionalization to investigate the detecting ability of GO in comparison to reduced and functionalized GO with thiol groups. Our goal was to prepare a gas sensing device operating fully at room conditions (e.g., room temperature and atmospheric pressure) without any external recovery promotion mechanism like the ones mentioned before such as heating or UV irradiation. It was also our aim to highlight the impact of the chemical composition of the active material in relationship to the device behavior exposed to chosen analytes, namely NO2 and ethanol. The experiments were carried out by flushing the sensor in 5-minutes-cycles of exposure to NO2 or ethanol diluted in nitrogen to concentrations from 100 to 300 ppm. Prior to the measurement the chamber was flushed for 15 min with inert gas, in this case nitrogen. We observed different behaviors of the device exposed to analytes in the case of GO sensors, reduced GO and partially reduced thiolinated GO. Every sensor type had different responses, recovery time and relative sensor response in comparison to each other. We also studied the reproducibility and stability of the sensor. This was performed by measuring at least four cycles for a certain concentration and repeated the measurement conditions a few days later. The sensors prepared from GO and GO-based materials have stable and reproducible response with marginal changes in the signal shape, response time, recovery time, and amplitude.
There are many more reported works related with reduced GO as a gas sensor. J. D. Fowler et al.  proposed chemiresistors based on hydrazine reduced GO for the detection of NO2, NH3, and 2,4-dinitrotoluene (DNT). They carried out experiments at room temperature with 10 min sensor exposure to analyte followed by 10 min. purging in dry nitrogen. They observed a clear change in sensor resistivity for all three analytes: 13% for NO2, 2.5% for NH3 and 0.03% for DNT, with limit of detection in case of DNT equals to 28 ppb. Their result for NO2 and NH3 was similar to the one proposed by F. Schedin et al.  done with micromechanical cleavage graphene. They also carried out detection of 5 ppm NO2 on micro hotplate and concluded that, as in the previous cases, the analyte desorption from sensor surface is promoted by heating (sensor recovery is faster) but at the expense of loss in sensitivity. G. Lu et al.  prepared thermal reduced GO for NO2 detection. In their case, the GO was prepared by Hummers method and was thermally reduced by annealing in argon atmosphere for 1 h. They conducted the experiments in various concentrations and reported a linear dependence between concentration and relative response percentages. They obtained similar results in the case of sensor response percentages in comparison to F. Schedin et al.  graphene device and J. D. Fowler et al.  hydrazine reduced GO.
G. Lu et al.  prepared back-gated FET sensor for detecting NO2 and NH3 based on GO reduced by hydrazine. Their device showed a superior performance of the FET device when compared to the chemiresistors reported previously in reference . The switching of sensor architecture from chemiresistors to FET device improved sensor response from ~2.5% for the chemiresistors to almost 10% for the FET device, which is 4 times better than the chemiresistor device. In the case of NH3 they also noticed an improvement of performance. A chemiresistor reported by G. Lu et al.  had 0.4 lower response in resistivity during exposure to 1% solution of NH3 in relation to reduced GO-based FET device reported in reference .
V. Dua et al.  incorporated in their research two novel approaches in reduced GO-based gas sensors. In the first, they used ascorbic acid (Vitamin C) for an environmentally friendly reduction of GO for gas sensing applications. The authors reported that their Vitamin C reduced GO layer have a similar conductivity to previously reported hydrazine reduced GO. Due to the toxic and explosive nature of hydrazine, applying a new environmentally friendly reducing agent in gas sensing applications is definitely an advantage. Another unique approach was presented by preparing a flexible device by printing the active material layer onto a polymer surface. Such flexible sensors showed response to NO2, NH3, Cl2, methanol, ethanol, toluene and dichloromethane. Figure 6 presents example response plots and the sensor selectivity diagram. The sensors operated in a special chamber in which they were exposed to an analyte. The recovery process was quite slow (approximately 2 h) under vacuum. External UV irradiation was used to promote desorption and the time needed for the sensor to get back to the ground state was under 5 min.
One of the newest approaches to flexible gas sensors was proposed by H. J. Park et al. . They prepared electrospun fabric from nylon-6, coated it with GO and reduced it with a solution of iodine acid and acetic acid. Their device showed slightly better (approximately 0.5 higher) sensor relative response for 1 ppm of NO2 than in the work presented by V. Dua et al. . They also showed the ability of detecting low concentrations of NO2 (1 ppm) in the bent state. However, the device response in the bent state was 2 times lower than in the flat state.
A. Lipatov et al.  also studied the ability of thermally reduced GO to detect different alcohol vapors. They tested the response for methanol, ethanol, isopropanol and water vapor. For all analytes they obtained a clear stable response of the device with small differences in response behavior depending on the analyte type. This opens up new possibilities for further research on improving the selectivity of these analytes. Their sensor device did not use external heating or UV irradiation to promote desorption of analyte molecules from the sensor surface.
W. Yuan et al.  and A. Zöpfl et al.  pushed the research forward regarding the impact of functionalization onto GO- and reduced GO-based devices and their selectivity. W. Yuan team used hydrazine as a reducing agent, ethylene diamine (EDA) as both a partial reducing and chemical composition modification agent. Aryl diazonium salt was used to introduce sulfur to the chemical composition of GO. After these 3 types of functionalization, they compared the impact of the introduced functional groups onto the sensing ability of device exposed to various concentrations of NO2. They compared the response signal obtained for reduced, sulfonated and EDA-functionalized graphene-based materials in presence of 50 ppm of NO2. Sulfonated and EDA-functionalized materials exhibited respectively 16- and 4-times higher responses than reduced GO.
A. Zöpfl team compared reduced GO, octadecylamine (ODA)-functionalized and reduced GO-based nanocomposites decorated with metal oxides (TiO2, MnO2) and metal nanoparticles (platinum and palladium). They tested the behavior of chemiresistors for exposures to different concentrations of NO2, H2, CH4 and compared the results between the chosen functionalization materials (for instance, Pd functionalization increased the relative response of gas sensor for hydrogen from 0.1 to 0.6 in comparison to other devices). Their results confirm that functionalized GO is a promising material to tailor the selectivity of the gas sensor.
3.2. 2D Carbon Nanomaterials as Biosensors
One of the first reports on graphene-based glucose sensor was done by C. Shan et al. . The core architecture of their device was based on a nanocomposite electrode consisting of graphene and polyethyleneamine-functionalized ionic liquid. They conducted cyclic voltamperometry measurement with different scan rates. They obtained electrodes with good linear responses for 2 to 14 mM of glucose concentration. The prepared electrode was stable with minimal changes in response and the current did not go over the 5% error barrier over 1 week from the initial measurement. H. Wu et al.  reported bionanocomposite films consisting of glucose oxidase, platinum, chitosan and thermally expanded GO to partially reduced GO. Their composite had good sensitivity for glucose with theoretical detection limit of 0.6 µM. They attributed the results of their cyclic voltamperometry detection experiment to the large surface area of graphene and good electron transfer between partially reduced GO and platinum.
D. H. Shin et al.  decorated graphene grown in CVD with electrodeposited palladium in the presence of different sulfuric acid concentration in the electrolyte, which led to the formation of particles with different morphologies as shown in Figure 7. They used these structures to prepare a FET glucose sensor.
They obtained a device with good response and detection limit of 1 nM glucose, which was lower than in, for instance, CNT-based devices reported by L. Meng et al. . However, the FET sensor based only on Pd/Graphene structure was not selective in the presence of uric acid and ascorbic acid. For this reason, the Shinc group prepared a device with glucose oxide and a Naftion coating. After these modifications the FET sensor was selective for glucose in presence of uric and ascorbic acid.
S. Lin et al.  prepared a flexible electrochemical glucose detector with active material consisting of laser-scribed graphene (LSG-laser reduced GO), chemically reduced GO and laser-scribed graphene coated with copper nanoparticles. The LSG/Cu electrochemical flexible electrode performance dropped only by 10% when stored in ambient conditions for 30 days. The sensor limit of detection was 0.35 µM which was almost two times smaller than, for instance, the work presented by H. Wu et al. . Their sensor was also selective to glucose in the presence of ascorbic and uric acid without the need of a Naftion coating.
X. Xuan et al.  proposed a wearable flexible glucose detector based on reduced GO decorated with platinum and gold nanoparticles in the form of a wristband. Apart from the standard glucose measurement reported earlier, Xuan’s sensor exhibited good analytic activity with response time of 12 s. They also investigated the ability of glucose detection in sweat. The collected human sweat from healthy and diabetic patients. They also prepared glucose solutions in human sweat. The sensor was able to detect 0.1 mM of glucose in sweat, which is a huge success towards the application of that type of device in current and future health care programs.
G. Xu et al.  prepared a laser-scribed graphene-based electrochemical dopamine sensor coated with poly(3,4-ethylenedioxythiophene) (PEDOT) layer. They used cyclic voltammetry and differential pulse voltammetry with nitrogen purging to remove oxygen from the measurement setup. They carried out measurements in phosphate-buffered saline (PBS) electrolyte solution at two pH values, namely 7.0 and 7.4 in the presence of uric and ascorbic acid. They also tested the influence of a PEDOT layer on dopamine detection. It came out that pure graphene electrodes have low affinity and selectivity towards dopamine in presence of ascorbic and uric acid. However, the PEDOT layer drastically increased the current flow in the presence of dopamine. They achieved simultaneous detection of dopamine in the presence of ascorbic and uric acid with high selectivity, sensitivity of 0.22 ± 0.01 µA/µM and a low detection limit of 0.33 µΜ.
In the case of other biomolecules there were reports on graphene-based sensors detecting E. coli bacteria. A. Pandey et al.  prepared a capacity/impedance sensor based on graphene nanoplates and monolayer graphene with a gold electrode interdigitated array. The graphene surface was activated by a PASE linker for further E. coli antibody functionalization. Both devices showed reaction in the presence of E. coli O156L:H7 bacteria. The sensitivity of the developed sensor was 4 pF for graphene nanoplates sensor and 1 pF for monolayer defect-free graphene per unit change in analyte concentrations of 10-100 CFU/mL. However, the monolayer graphene-based device had 10 times better sensitivity in comparison to nanoplates-based device, which contained 100 CFU/mL. Both devices are promising setups for application in the detection of pathogenic E. coli bacteria.
B. Thakur et al.  proposed reduced GO-based FET device covered with gold nanoparticles to anchor E. coli antibodies. The FET device demonstrated rapid response with limits of detection at concentration of 103 CFU/mL. The authors reported that this limit of detection is lower than in cases of some commercial E. coli detectors. The FET device gave a stable and reproducible signal for 3 measurements. They also reported that there is a possibility of recovery of this type of device by using suitable regeneration buffers. R. Singh et al.  recently showed that reduced GO has been integrated in a microfluidic chip to create a reduced GO-based electrochemical immunosensor for the label-free detection of the H1N1 influenza virus. This was done by covalently bonding the NH2 end of monoclonal antibodies specific to virus with carboxyl end reduced GO. The sensor showed a linear detection behavior in the range of 1 to 104 PFU/mL.
4. 3D Carbon Nanostructures
The appearance and rise of two-dimensional carbon nanostructures triggered also the desire to explore the possibility of creating their three-dimensional structures counterparts in order to exploit their thermal and electrical properties for innumerous applications . The realization of three-dimensional structures based on graphene also known by graphene foam was theoretically demonstrated  and successfully produced using reduced graphene oxide as well as grown using CVD [115,116,117,118,119,120]. Such 3D structures change its resistance when perturbed locally, which can serve to sense local changes, for example in temperature and electromagnetic field. These properties rendered studies to investigate pure and functionalized graphene foam as different types of sensors [121,122,123].
4.1. 3D Carbon Nanostructures as Chemical and Electrochemical Sensors
F. Yavari et al.  reported the use of a unfunctionalized and flexible 3D graphene foam, which can be used for sensing NO2 and NH3 with high sensitivity and reversibility. The graphene foam was synthesized using a scaffold of porous nickel as a template for the deposition and growth of graphene with CVD with a posterior removal of the nickel template, remaining only the three dimensional network of graphene . The charge carriers can move rapidly with small resistance, which results in high electrical conductivity. Trace amounts of NO2 and NH3 adsorbed on graphene cause changes in the conductivity of the graphene foam and can be detected. The high porosity of the graphene foam increases the surface area and it facilitates for the homogenous distribution of the investigated gases. It is also important to mention that the whole process is reversible and applied in environmental monitoring and gas detection again terrorism.
The resistance of unmodified 3D graphene structures was also used to detect other organic molecules, among which are chloroform, ether and acetone as demonstrated by H. Hua et al. . The graphene foam was also synthesized using a nickel scaffold as a template while the resistance curves for the various molecules were acquired and an algorithm was applied to discriminate the specific molecules with over 97% accuracy. The results show the promising ability of graphene foam to detect a wide spectrum of molecules combined with algorithms to identify specific compounds or contaminants. A sketch of the graphene foam sensor architecture is shown in Figure 8.
The unique structure of 3D graphene foam for sensors has also been demonstrated for functionalized or hybrid graphene foam architectures . It is known that cobalt oxide (Co3O4) is a promising functional material for electrochemical  and gas sensors [127,128,129]. L. Li et al.  used flower-like Co3O4 nanostructures supported on 3D graphene foam as a platform for sensing ethanol vapor with a concentration as low as 15 ppm at 320 °C. A similar functionalization approach was achieved by Y. Ma et al. . In their work they demonstrated the synthesis of graphene foam functionalized with α-Fe2O3 to build an electrode-like sensor with the ability to detect and quantify the presence of nitrite. The detection of nitrite was validated in various water sources by anodic stripping voltammetry analysis and the electrochemical impedance spectrum.
4.2. 3D Carbon Nanostructures Biosensors
Decorated 3D graphene foam has also been demonstrated to be suitable for biosensors to detect a wide variety of biomolecules [132,133]. The use of 3D graphene foam functionalized with Co3O4 nanowires has been shown as an excellent enzyme-free electrochemical glucose detector. X.-C. Dong et al.  used this 3D graphene/Co3O4 composites to sense the presence of glucose in an extreme low concentration (<25 nM). The use of enzymes to detect glucose using a nitrogen-doped CNT-functionalized 3D graphene foam was demonstrated by P. Fan et al.  as shown in Figure 9. This was achieved by immobilizing glucose oxidase on nitrogen-doped CNT-functionalized 3D graphene foam composite and measuring changes in the conductivity of the system in the presence of glucose in a linear range from 0.05 to 15.55 nM. The detection of glucose using graphene foam decorated with nickel nanoparticles was also demonstrated by L. Wang et al. . The linear range of their electrochemical sensor was from 15.84 µM to 6.48 mM with a detection limit reaching 4.8 µM.
Similarly to Co3O4, manganese oxide (Mn3O4) was also used to decorate 3D graphene foam to detect glucose non-enzymatically as well as hydrogen peroxide (H2O2) as shown by P. Si et al. . The Mn3O4 is found on the graphene walls in form of nanoflakes. The high surface area of the Mn3O4@3D graphene foam has a large surface area, providing abundant active sites for electrocatalytic reactions and electron transport. Cyclic voltammetry was used to study the electrochemical changes in the oxidation/reduction of Mn3O4 in the presence of glucose and H2O2. The selectivity of the biosensor was demonstrated by negligible current responses to the addition of 0.1 mM uric acid, ascorbic acid and acetaminophen, which are common electroactive species in the blood.
The detection of H2O2 was accomplished using hybrid composites containing 3D graphene foam with high sensitivity. For example, J. Liu et al.  functionalized the graphene surface of the whole 3D structure with a complex composite of horseradish peroxidase, methylene blue and CNTs, rendering excellent performance to detect H2O2 with a low concentration (58 nM) and a fast response. C.-C. Kung et al.  incorporated platinum/ruthenium (PtRu) bimetallic nanoparticles on the porous structure of the graphene foam and reached a detection limit of 0.04 µM for H2O2 also with minimum influence of other electroactive species.
Since copper oxide (CuO) has shown promising results to catalyze biomolecules [140,141], Y. Ma et al.  decorated graphene foam with CuO nano-flowers to construct a sensor with high sensitivity for ascorbic acid (vitamin C). The sensing of ascorbic acid was done by measuring the resistance and changes in the current passing through the decorated graphene foam in the presence of ascorbic acid and a calibrating solution of phosphate buffer solution (PBS). The detection of the important neurotransmitter dopamine was also achieved using 3D graphene foam by X. Dong et al. . Their work showed that hydrophobic and π-π interactions between the dopamine and the graphene walls cause measurable changes in the conductivity of the 3D graphene foam, allowing the detection of dopamine at concentrations as low as 25 nM.
H. Yan Yue et al.  used zinc oxide (ZnO) nanowires to functionalize graphene foam to selectively detect uric acid, ascorbic acid and dopamine by differential pulse voltammetry. They claim that uric acid is of especial attention since it can be used as a biomarker for Parkinson’s disease. Patients with Parkinson’s disease have levels of uric acid 25% lower than in healthy individuals. These molecules coexist in many biological systems, therefore sensors with high sensitivity and selectivity are crucial. Their functionalized 3D graphene foam sensor was able to detect these molecules without any cross interference with a limit of detection of 0.5, 0.5 and 5 µM for uric acid, dopamine and ascorbic acid, respectively.
4.3. Other Sensors Based on 3D Carbon Nanostructures
There is a class of sensors based on 3D carbon nanostructures used to measure strain. These sensors are mostly based on the incorporation of polymers to the porous architecture of the graphene foam to produce a flexible and stretchable sensor. For example, Y. A. Samad et al.  embedded a graphene foam into a polydimethylsiloxane (PDMS) to create a pressure/strain sensor that can measure the human blood pressure and heartbeat.
In order to increase the bending sensitivity of the 3D graphene foam/PDMS composite, R. Xu et al.  introduced a thin layer of polyethyleneterephthalate (PET) in one of the sides of the 3D graphene foam/PDMS composite causing a variance in the electrical resistance of the composite when bended to the side with and without PET due to the different bending properties of the different sizes of the composite. Y. Qin et al.  demonstrated that the infusion of polyimide into a brittle reduced graphene aerogel rendered a superflexible three-dimensional architecture able to sense deformation caused by compression, bending, stretching and torsion with excellent durability as illustrated in Figure 10. Such graphene foam/PDMS composites were also demonstrated to have enormous application possibilities into biomechanical systems and wearable devices. .
Monolithic 3D graphene foam was also used as a precursor for graphene quantum dots, which were used for sensing iron ions (Fe3+) as presented by A. Ananthanarayanan et al. . Such graphene dot architecture could not be synthesized using the 2D graphene form, because the graphene film quickly disintegrates leading to a low yield of graphene quantum dots. The graphene quantum dots were produced by applying a voltage to the 3D graphene foam in a mixture of 1-Butyl-3-methylimidazolium hexafluorophosphate (BMIMPF6) and acetonitrile (10% v/v) as an electrolyte. The resulting solution was centrifuged and the graphene quantum dots were collected. Gel electrophoresis suggests a narrow size distribution of the graphene quantum dots, which emit blue fluorescence under UV light of 365 nm. In the presence of Fe3+ ions there is a significant quenching (68% quenching in 400 µM of Fe3+) whereas other ions such as Mg2+, Fe2+, Zn2+, Co2+, Ni2+, Cd2+ and K+ were not able to show significant quenching of the graphene quantum dots.
5. Summary and Conclusions
In summary, we presented investigations that highlight the great versatility of 1D, 2D and 3D carbon nanostructures as an exceptional high sensitivity and specificity platform for sensing. Carbon nanostructures pursue a large surface area providing huge functionalization potential, which enables the design of sensors with high specificity depending on the type of functionalization. The high sensitivity is related with the unique electrical properties of the various nanostructures. Unlike other materials purely based on metals or metal oxides, carbon nanostructures have a higher compatibility with polymers, which allows the formation of composites that not only present a better sensitivity, but also opens up the field of flexible electronics and allow the design of a wide range of wearables that can constantly monitor the surrounding conditions and health of individuals.
Although great progress has been achieved in the synthesis of various carbon nanostructures, large scale production remains a challenge, especially concerning the fabrication of material without defects and with reproducible properties. For example, the synthesis of graphene leads to the formation of grain boundaries and intrinsic point defects that influence the electrical properties of the material. Moreover the transfer processes of carbon nanostructures from the synthesis substrates to the actual device is complex and in many cases lead to the formation of defects that limit the reproducibility and scalability of sensors based on these materials. For example, the lack of precise defect control in graphene oxide-based sensor may lead to small differences between material batches, which can have huge impact in the sensing mechanism. In other words, there is still a lack in the synthesis control of the nanostructures, separating the design of carbon-based sensors from the commercial application of these devices.
Another general problem of sensors is related with the cross sensitivity. Nowadays it is still a challenge to create a device with a specificity capable of distinguishing between similar chemicals. Progress with carbon nanostructures has been achieved, as shown before . In this sense, we envision that the innumerous possibilities of functionalization is the most promising approach to reach specificity and this tailoring of properties is achievable with carbon-based nanostructures. Moreover, we should not ignore the increasing development and refinement of softwares for data analysis as well as the emerging field of artificial intelligent, which may provide new insights in data analysis that will enable more detailed processing of the detected signal, minimizing the cross-sensitivity problem.
The investigations reviewed here suggest that carbon-based sensors are already a reality in the lab and a continuous investment in this field is essential to not only develop more accurate sensors but also incorporate these into wearable devices and in the routine of industrial and medical processes. Table 1 highlights and summarizes all the carbon nanostructures included in this review as well as their functionalization, molecule detected and the corresponding detection limit of the many different sensor architectures described in details within the main text.
R.G.M. contributed in writing and structuring, P.S.W. contributed in writing and structuring, A.B. contributed in writing and structuring, J. S. contributed in writing and corrections, T.G. contributed to writing and corrections, Z.L. contributed in writing and corrections and M.H.R. contributed to writing and corrections.
This work is supported by the National Science Foundation China (NSFC, Project 51672181), the National Science Center for the financial support within the frame of the Sonata Program (Grant agreement 2014/13/D/ST5/02853) and the Opus program (Grant agreement 2015/19/B/ST5/03399). MHR thanks the Sino-German Research Institute for support (project: GZ 1400). SVR acknowledges NSF (ECCS-1509786).
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1. Illustration of the main carbon nanostructures described in this review. (A) 1D carbon nanostructures represented by carbon nanotubes. (B) 2D carbon nanostructures represented by graphene. (C) 3D carbon nanostructure represented by graphene foam, which is an intricate three-dimensional network of graphene.
Figure 2. (A) Schematic chemiresistors and (B) ChemFET with carbon nanotubes as active material. Vg is the gate bias potential and Vsd is the source-drain potential.
Figure 3. Drain current Vsd = −100 mV are shown as a function of time for application of saturated vapor of various kinds of alcohols: methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, tertiary-butanol, 1-pentanol, and 1-octanol. Reproduced with permission from ref. .
Figure 4. Correlation coefficients relating the conductance of devices decorated via metal evaporation with the gas profile of the tested gases, from 0 (no response) to 1 (maximal response). The catalytic metals were evaporated on carbon nanotube devices and tested for H2, CO, CH4, and H2S gases as highlighted in the Periodic Table. Reproduced with permission from ref. .
Figure 5. Left panel present response of single layer graphene exposed different concentrations of NO2. Right panel present response of multilayer graphene exposed to different concentrations of NO2 tested in different temperature. Reproduced with permission from ref. .
Figure 6. Example vapors sensing characterization of RGO/Inject-Printed chemiresistors. (A) Response vs time plot of sensor response exposed to NO2. (B) Response vs time plot of sensor response exposed to Cl2. (C) Summary of sensor response for different measured media. Reproduced with permission from ref. .
Figure 7. SEM images of electrodeposited palladium structure with various sulfuric acid concentrations (A) 1M, (B) 0.1M, (C) 0.01M. Reproduced with permission from ref. .
Figure 8. The fabrication process of 3D graphene foam and the electrical resistance time domain detection system (CCS: constant current source; IA: instrument amplifier; MCU: micro control unit). Reprinted with permission from ref. .
Figure 9. (A) Schematic illustration for the preparation of nitrogen-doped [email protected] graphene foam. (B) Current-time curve of glucose oxidase on nitrogen-doped [email protected] graphene foam for different concentrations of glucose in stirred 0.1 M O2-saturated PBS (pH 7.0) at −0.45 V. (C) Calibration curve of the glucose oxidase on nitrogen-doped [email protected] graphene foam for glucose. Reproduced with permission from ref. .
Figure 10. Images showing the recovery process of a compressed reduced graphene foam/polyimide composite and the high levels of bend and torsion deformations. Reproduced with permission from ref. .
Table 1. Carbon nanostructures used as sensors, their functionalization and corresponding analyte discussed within this review.
|Carbon Nanostructure||Functionalization||Analyte (Detection Limit)||References|
|1D carbon nanostructure|
|Multi-walled CNTs||Au nanoparticles||NO2 (0.1 ppm), CO (2 ppm), C6H6 (-)|||
|Multi-walled CNTs||COOH||O2 (0.3%)|||
|Single-walled CNTs||FeOOH||O3 (4.1 ppb)|||
|Single-walled CNTs||CuCl||CO (20 ppm)|||
|Multi-walled CNTs||SnO2||ethanol (30 ppm), methanol (30 ppm), H2S (9 ppm)|||
|Multi-walled CNTs||maleic acid, acetylene||NH3 (10 ppm)|||
|Multi-walled CNTs||Pt nanoparticles||H2 (4%)|||
|Single-walled CNTs||Pd doping/sputtering||H2 (0.5%)|||
|Single-walled CNTs||-||ethanol, methanol, 1-propanol, 2-propanol, 1-butanol, tertiary-butanol, 1-pentanol, 1-octanol (2 mmHg for all analytes)|||
|Multi-walled CNTs||ethyl cellulose||benzene (2.5 ppm)|||
|Single-walled CNTs||poly(vinylpyrrolidone)||isopropyl (100 ppm)|||
|Single-walled CNTs||LaFeO3||methanol (1 ppm)|||
|Multi-walled CNTs||-||dopamine (500 nM), glutamate (10 µM)|||
|Multi-walled CNTs||Pt-Ni alloy||uric acid (0.03 µM)|||
|Single-walled CNTs||-||O2 (1 × 10−10 torr)|||
|Single-walled CNTs||3-aminopropyltrimethysilane||dimethyl methylphosphonate (5 ppm)|||
|Multi-walled CNTs||-||NO2 (10 ppb)||[39,40]|
|Single-walled CNTs||Br||ethanol (608 ppb), HCl (769 ppb), NH3 (1645 ppb), sulfuric acid (286 ppb)|||
|Double- and Multi-walled CNTs||-||NO2 (0.1 ppm)|||
|Multi-walled CNTs||thiol||formaldehyde (10 ppm)|||
|Multi-walled CNTs||amine||formaldehyde (20 ppb)|||
|Single-walled CNTs||Au, Pt, Pd, Rh||H2 (0.4%), CH4 (0.5%), CO (2%), H2S (50 ppm)|||
|Single-walled CNTs||-||dimethyl methylphosphonate (1 ppb)|||
|Multi-walled CNTs||glucose oxidase||glucose (0.08 mM)|||
|Single-walled carbon nanohorns||glucose oxidase, nafion||glucose (6 µM)|||
|Multi-walled CNTs||horseradish peroxidase, glucose oxidase||glucose (0.5 µM)|||
|Multi-walled CNTs||Pt nanoparticles||glucose (1 × 10−5 mol/L)|||
|Single-walled CNTs||Nafion||dopamine (250 nM), serotonin (130 nM)|||
|Multi-walled CNTs||1-butyl-3-methylimidazolium hexafluorophosphate||dopamine (60 nM), serotonin (8 nM)|||
|Multi-walled CNTs||RuOx||insulin (1 nM)|||
|Multi-walled CNTs||Ni(OH)2-Nafion||insulin (85 nM)|||
|Single-walled CNTs||Pd nanoparticles||glucose (0.2 µM)|||
|Single-layered graphene||SnO2||CO (30 ppm)|||
|SiO2-Graphite||CuO||glucose (0.02 mmol/L)|||
|Single-layered graphene||-||NO2 (100 ppm; 2.5 ppm; 100 ppm), NH3 (500 ppb), CO2 (10 ppm)||[82,83,84,87,89,90]|
|Multi-layered graphene||-||CO2 (3 ppm), liquid petroleum gas (4 ppm)|||
|Graphene oxide||COOH, OH||NO2 (20 ppb), NH3 (500 ppm), 2,4-dinitrotoluene (28 ppb)||[91,92,94,95]|
|Graphene oxide||ascorbic acid, thiol||ethanol (100 ppm), NO2 (100 ppm)|||
|Reduced graphene oxide||-||NH3 (1%; 100 ppm), Cl2 (100 ppm), NO2 (100 ppm; 1 ppm), methanol (500 ppm), ethanol (500 ppm), isopropanol (500 ppm)||[96,97,98,99,100]|
|Reduced graphene oxide||sulfophenyl, ethylenediamine||NO2 (3.6 ppm)|||
|Reduced graphene oxide||Octadecylamine, Pd- and Pt-doping, MnO2 and TiO2 nanoparticle||NO2 (25 ppm), CH4 (1000 ppm), H2 (500 ppm)|||
|Multi-layered graphene||Poly(vinylpyrrolidone), glucose oxidase||glucose (2 mM)|||
|Multi-layered graphene||glucose oxidase, Pt, chitosan||glucose (0.6 µM)|||
|Multi-layered graphene||Pd nanoflower, Nafion, glucose oxidase||glucose (1 nM)|||
|Graphene oxide||Cu nanoparticles||glucose (0.35 µM)|||
|Reduced graphene oxide||Au-Pt alloy, chitosan-glucose oxidase||glucose (5 µM)|||
|Graphene oxide||poly(3,4-ethylenedioxythiophene)||dopamine (0.33 µM)|||
|Multi-layered graphene||E. coli O157:H7 specific antibodies||E. coli bacteria (10 to 100 cells/mL)|||
|Reduced graphene oxide||Al2O3, Au nanoparticles||E. coli bacteria (100 to 100,000 cells/mL)|||
|Reduced graphene oxide||H1N1 specific monoclonal antibodies||H1N1 influenza virus (1 to 104 virus/mL)|||
|Carbon paste||NiO nanoparticles, 1-butyl-3-methylimidazolium tetrafluoroborate||ascorbic acid (0.04 µM)|||
|Graphene foam||-||NH3 (20 ppm), dopamine (25 nM) and selectivity measurement for chloroform, acetone, ether||[124,125,143]|
|Graphene foam||Co3O4||ethanol (50 ppm; 15 ppm)||[127,130]|
|Graphene foam||α-Fe2O3||NO2 (0.12 µM)|||
|Graphene foam||prussian blue nanoparticles, CuNi nanoparticles, Co nanoparticles||H2O2 (0.1 µM), glucose (2.3 µM), aminoacids (0.02mM)|||
|Graphene foam||Au||carcinoembryonic antigen (0.024 pg/mL)|||
|Graphene foam||Co3O4 nanowires||glucose (25 nM)|||
|Graphene foam||N-doped carbon nanotubes, glucose oxidase||glucose (5 µM)|||
|Graphene foam||Ni nanoparticles||glucose (4.8 µM)|||
|Graphene foam||Mn3O4||glucose (10 µM), H2O2 (1 µM)|||
|Graphene foam||polydopamine, horseradish peroxidase, methylene blue||H2O2 (58 nM)|||
|Graphene foam||PtRu nanoparticles||H2O2 (0.04 µM)|||
|Graphene foam||CuO nanoparticles||glucose (1 µM)|||
|Graphene foam||CuO nanoflower||ascorbic acid (0.43 µM)|||
|Graphene foam||polydimethylsiloxane||human blood pressure (60 kPa−1)|||
|Graphene foam||ZnO nanowires||uric acid (0.5 µM), dopamine (0.5 µM), ascorbic acid (5 µM)|||
|Graphene foam||polydimethylsiloxane||strain (gauge factor 98.66)||[145,147]|
|Graphene foam||Polydimethylsiloxane, polyimide||strain (low pressure: 0.18 kPa−1; large pressure: 0.023 kPa−1)|||
|Graphene foam-graphene quantum dots||-||Fe3+ (7.22 µM)|||
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