Graphene-based Gas Sensors Research Articles

Building a cm2 scale CVD graphene-based gas sensor: modelling the kinetic with a three-site adsorption/desorption Langmuir model

In this article, we aim to develop and study a highly sensitive and selective cm2 scale graphene-based gas sensor. We present the technology used to fabricate sensors which integrate monolayer chemical vapour deposition graphene: photolithography and transfer of layers. Characterization techniques (optical microscopy, AFM, micro-Raman spectroscopy, transport electrical measurements) ensure a diagnosis of graphene ribbons and allow good reproducibility of technological processes. We present the results of gas characterizations after a 200 ppm NO2 exposure. We propose a novel approach for the modelling of the sensor response with a three-site adsorption/desorption Langmuir model. This innovative way of modelling the sensor response should provide a better understanding of the sensor’s kinetic and help to overcome the long response time observed with graphene gas sensors.

Enhancement of gas adsorption on transition metal ion-modified graphene using DFT calculations.

Graphene-based nanomaterial was widely used in gas sensors, detection, and separation. However, weak adsorption and low selectivity of the pristine graphene used for gas sensors are major problems. Here, using density functional theory (DFT) calculations, we reported the significant increase of four gas molecules (N2, CO2, C2H2, and C2H4) adsorption on the transition metal ion (Fe3+, Co2+, Ni2+)-modified graphene complex (Fe3+/Co2+/Ni2+-G) comparing to be absorbed on the pristine graphene (G). Moreover, the Co2+-G is suitable for the selective separation of C2H4/C2H2 due to the larger adsorption energy difference (8.5kcal/mol) between them. The addition of transition metal ions also decreased the HOMO-LUMO gap of the systems, which benefits the enhancement of electrical conductivity. This suggests that the transition metal ion-modified graphene can be used to distinguish the different gas molecule's adsorption, facilitating the design of graphene-based gas sensors and selective separation. All the density functional theory (DFT) calculations were performed by B3LYP with the GD3 dispersion method using Gaussian 16 software. The basis set 6-31G(d) was used for C, H, O, and N atoms, and Lanl2DZ was used for transition metal ions (Fe3+, Co2+, Ni2+). The DOS analysis and energy decomposition analysis were performed using the Multiwfn program.

Graphene-based nanocomposites for gas sensors: challenges and opportunities

Abstract Exposure to toxic gases resulting from rapid industrialization poses significant health risks living organisms including human. Consequently, researchers in this modern scientific era have shown keen interest in the selective detection of these toxic gases. The development of fast, economical, selective, and highly sensitive gas sensors has become a crucial pursuit to accurately detect toxic gases and mitigate their adverse effects on the natural environment. Graphene-based nanocomposites have emerged as promising candidates for selectively detecting toxic gases due to their extensive surface area. This review paper provides a comprehensive summary of recent advancements in graphene-based gas sensors. The paper also offers an overview of various synthetic strategies for graphene and its hybrid architectures. Additionally, it delves into the detailed sensing applications of these materials. Challenges and limitations in this field have been critically evaluated and highlighted, along with potential future solutions.

Understanding sensing mechanism of N-doped graphene-based toxic gas sensors using electronic, thermal, mechanical, electrical, and sensing properties:A DFT study

A facile and feasible protocol for the evolution of sensing characteristics of three dissimilar nitrogen-doped graphene (N-G)-based ethanol, methanol, and nitrogen-oxide gas sensors using first-principle calculations. Simulated X-ray diffraction (XRD) investigation demonstrates the sharp peak located at 7.9, 11, 13.8, and 15.4° represents (100), (111), (026), and (012) facets. Electronic properties calculations predict that toxic gas adsorption reduces the charge carrier concentration and induces internal resistance. Thermal properties analyses suggest that the nature of adsorption depends on changes in Gibbs free energy. Charge density and distribution analysis disclose that the adsorption of toxic gases leads to charge transfer between toxic gases and N-G. Mechanical properties analyses predict the mechanical superiority. The N-G's electrical conductivity and resistivity of are 49.95 × 103 Ω/m, and 2 × 10−5 Ω-m respectively. The electrical properties demonstrate that the adsorption of toxic gases would be a reason for the change in electrical conductivity and resistivity. The sensing response of N-G-M in terms of resistivity is 96.04 %. The sensing response of ethanol, NO are 17.12 %, and 5.95 %. Responses of N-G-E, N-G-M, N-G-N″-Z are 0.01, 0.06, 0.14 respectively.

Graphene-based chemiresistive gas sensors.

Gas sensors allow the monitoring of the chemical environment of humans, which is often crucial for their wellbeing or even survival. Miniaturization, reversibility, and selectivity are some of the key challenges for serial use of chemical sensors. This tutorial review describes critical aspects when using nanomaterials as sensing substrates for the application in chemiresistive gas sensors. Graphene has been shown to be a promising candidate, as it allows gas sensors to be operated at room temperature, possibly saving large amounts of energy. In this review, an overview is given on the general mechanisms for gas-sensitive semiconducting materials and the implications of doping and functionalization on the sensing parameters of chemiresistive devices. It shows in detail how different challenges, like sensitivity, response time, reversibility and selectivity have been approached by material development and operation modes. In addition, perspectives from the area of data analysis and intelligent algorithms are presented, which can further enhance these sensors' usability in the field.

Characteristic of graphene-based thick film gas sensor for ethanol and acetone vapor detection at room temperature

<p>Ethanol and acetone are volatile organic compound gases widely used in food processing. Health problems such as irritation of the eyes, nose, and throat can affect human health if exposed to these gases. Two graphene gas sensors were fabricated using a screen-printing technique onto a glass substrate to compare their performance to the acetone and ethanol vapors at room temperature. The graphene paste was prepared by mixing 95 wt.% of the binder with 5 wt.% of graphene nanoflakes. A silver paste was used asthe interdigitated electrode of the gas sensor and becamethe first layer of the gas sensor. The silver paste was deposited on the glass substrate using a screen-printing technique and fired at 150°C for 15 minutes. Next, the graphene paste was depositedonto the interdigitated electrode using a screen-printing technique and becamethe second layer of the gas sensor. The graphene was annealed at 200°C for 30 minutes. Both graphene gas sensors responded well to ethanol and acetone vapor with an n-typed gas sensor at room temperature. As a comparison, the graphene gas sensor showed better characteristics in terms of response and recovery characteristicsto ethanol vapor than acetone vapor at room temperature. The response and response time of the graphene-based thick film gas sensor to ethanol were approximately 21.89 and 24.08, respectively.</p>

Electron regulation and gas-sensitivity analysis of hBN-Graphene lateral heterojunctions——First principle study

In this paper, the first-principle calculations of the lateral heterojunction model synthesized by hBN-Graphene were carried out, and it was found that the bandgap of graphene varied with the change in the proportion of hBN, and the bandgap was best regulated with a bandgap of 1.177 eV when the proportion of hBN was 66.67 %. At this time, the adsorption structures of HCN, CO, NH3, and Cl2 were established and energy band calculations were performed on the hBN and Graphene portions of the hBN-Graphene lateral heterojunctions, respectively, and it was found that the adsorption of Cl2 resulted in a significant change in the band gap, which showed a very high electrical sensitivity. To further investigate the adsorption mechanism of Cl2 on the surface of hBN-Graphene lateral heterojunction, the energy band structure, PDOS, charge transfer, adsorption energy, and recovery time of each stabilized adsorption site of Cl2 on the surface of hBN-Graphene lateral heterojunction were calculated. The results show that the adsorption of Cl2 on the surface of hBN-Graphene lateral heterojunction is a stable chemisorption, and the band gap of C-Top1 increases to 1.274 eV, and the band gaps of C-Top3, N-Top1, and N-Top2 decrease to 0.684 eV, 0.376 eV, and 0.398 eV, respectively, and the changes of band gaps are significant and easy to be electrically detection. The recovery time of Cl2 on the surface of hBN-Graphene lateral heterojunction was 7.36 s–2.59 s in visible light and in the temperature interval of 273 K–283 K. The recovery time of Cl2 on the surface of hBN-Graphene lateral heterojunction was 7.36 s–2.59 s in visible light and in the temperature interval of 273 K–283 K. These findings have implications for the research and application of graphene-based Cl2 gas sensors.

CO2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions.

Designing and optimizing graphene-based gas sensors in silico entail constructing appropriate atomistic representations for the physisorption complex of an analyte on an infinite graphene sheet, then selecting accurate yet affordable methods for geometry optimizations and energy computations. In this work, diverse density functionals (DFs), coupled cluster theory, and symmetry-adapted perturbation theory (SAPT) in conjunction with a range of finite and periodic surface models of bare and supported graphene were tested for their ability to reproduce the experimental adsorption energies of CO2 on graphene in a low-coverage regime. Periodic results are accurately reproduced by the interaction energies extrapolated from finite clusters to infinity. This simple yet powerful scheme effectively removes size dependence from the data obtained using finite models, and the latter can be treated at more sophisticated levels of theory relative to periodic systems. While for small models inexpensive DFs such as PBE-D3 afford surprisingly good agreement with the gold standard of quantum chemistry, CCSD(T), interaction energies closest to experiment are obtained by extrapolating the SAPT results and with nonlocal van der Waals functionals in the periodic setting. Finally, none of the methods and models reproduce the experimentally observed CO2 tilted adsorption geometry on the Pt(111) support, calling for either even more elaborate theoretical approaches or a revision of the experiment.

A flexible humidity sensor constructed by ordered-pore-array of slightly reduced graphene oxide with much enhanced sensing response

Reduced graphene oxide (rGO) flexible film humidity sensor has received increasing attention, but the low sensing response caused by lack of available hydrophilic functional groups is still a limitation. Herein, a slightly reduced graphene oxide (SrGO) ordered-pore-array, fabricated via a monolayer colloid crystal template method, was introduced as a resistive humidity sensor. It was obtained based on adsorption between the GO sheets and the template microspheres, in-situ slight reduction of the GO shells and the removal of template. The reduction way allows the functional groups of GO to be retained as much as possible, and the unique structures (e.g., spherical double surfaces and small through-holes on pore-walls) facilitate the substantial exposure of functional groups, the penetration of water molecules and the utilization of buried functional groups. The available functional groups are thereby efficiently increased, giving the sensor an unprecedented high sensing response, more than 2600 times the maximum response of existing rGO sensors. The sensor also demonstrated excellent practical characteristics, and by detecting a single exhale, it could be employed in quick and quantitative evaluation of human activities and health. This strategy paves a facile and promising route to improve the sensing response and application of graphene-based humidity sensors or gas sensors.

DFT investigation of transition metal-doped graphene for the adsorption of HCl gas

Graphene-based toxic HCl gas sensors and adsorption materials are seldom reported. Utilizing density functional theory (DFT), the adsorption energy, charge density, and density of states, the sensitivity and recovery time of HCl gas adsorbed on graphene and six different transition metal-doped graphenes (TMDGs: TM = Ti, V, Cr, Mn, Fe, Co) were investigated. It indicates that TMDG has larger adsorption energy and chemical adsorption capacity than graphene. The Fe-doped graphene (FeDG) demonstrated better adsorption capacity and higher stability even at high temperature. The Mn- and Co-doped graphenes (MnDG & CoDG), exhibited magnetic changes after the adsorption of HCl molecules. Moreover, MnDG has both fast responsiveness and fast recovery time at a certain temperature. Thus, comparative speaking, among the six TMDGs, MnDG is the most suitable material for the detection of HCl gas, its magnetic property implied a novel application in HCl gas sensor.

ZnO/graphene heterostructure for electrical interaction and application for CO2 gas sensing

Recently, the concentration of CO2, one of the major air pollutants for greenhouse effect, is increasing due to the massive use of fossil fuels. Thus, research about gas sensors for monitoring CO2 gas has performed, and conventional methods have the challenge of requiring complex structures. Thus, research about gas sensors using nanomaterials has been conducted, and graphene-based gas sensors have been actively researched for its extraordinary conductivity. However, there are challenges that the gas absorption site is limited in chemically unstable sites. In this study, ZnO/graphene heterostructure to improve the gas absorption area with high conductivity through ZnO on graphene was presented. Each layer acted as a gas adsorption and a carrier conducting layer respectively, and the sensitivity by the thickness of ZnO and the effect of the annealing temperature were evaluated. This work exhibited a sensitivity of 78% at room temperature, and repeatability and selectivity were also studied.

A multi-layered graphene based gas sensor platform for discrimination of volatile organic compounds via differential intercalation

Selective and sensitive detection of volatile organic compounds (VOCs) is of critical importance for environmental monitoring, disease diagnosis and industrial applications. Graphene Foam based sensor platform suggests selective determination of VOCs.

Mobile Robot Gas Source Localization Using SLAM-GDM with a Graphene-Based Gas Sensor

Mobile olfaction is one of the applications of mobile robots. Metal oxide sensors (MOX) are mobile robots’ most popular gas sensors. However, the sensor has drawbacks, such as high-power consumption, high operating temperature, and long recovery time. This research compares a reduced graphene oxide (RGO) sensor with the traditionally used MOX in a mobile robot. The method uses a map created from simultaneous localization and mapping (SLAM) combined with gas distribution mapping (GDM) to draw the gas distribution in the map and locate the gas source. RGO and MOX are tested in the lab for their response to 100 and 300 ppm ethanol. Both sensors’ response and recovery times show that RGO resulted in 56% and 54% faster response times, with 33% and 57% shorter recovery times than MOX. In the experiment, one gas source, 95% ethanol solution, is placed in the lab, and the mobile robot runs through the map in 7 min and 12 min after the source is set, with five repetitions. The results show the average distance error of the predicted source from the actual location was 19.52 cm and 30.28 cm using MOX and 25.24 cm and 30.60 cm using the RGO gas sensor for the 7th and 12th min trials, respectively. The errors show that the predicted gas source location based on MOX is 1.0% (12th min), much closer to the actual site than that predicted with RGO. However, RGO also shows a larger gas sensing area than MOX by 0.35–8.33% based on the binary image of the SLAM-GDM map, which indicates that RGO is much more sensitive than MOX in the trial run. Regarding power consumption, RGO consumes an average of 294.605 mW, 56.33% less than MOX, with an average consumption of 674.565 mW. The experiment shows that RGO can perform as well as MOX in mobile olfaction applications but with lower power consumption and operating temperature.

Sensing selectivity of N-doped and laser-pattered graphene-based sensors

The gas selectivity is a gargantuan barrier for gas sensors. In this letter, we proposed a novel sensing material to distinguish NO from CO. The artificial defects of graphene are introduced by laser patterning, and then N groups are doped in the patterned graphene. The patterned and N-doped graphene shows excellent NO gas selectivity. The sensing selectivity ratio of NO to CO is up to 254 %. The as-produced graphene-based gas sensor has an ultra-high gauge factor of 9.4 × 104 with response time of 230 s. This work provides a facile approach and new perspective to improve the selectivity of gas sensor.

Optimization of multilayer graphene-based gas sensors by ultraviolet photoactivation

Nitrogen dioxide (NO2) is a potential hazard to human health at low concentrations, below one part per million (ppm). NO2 can be monitored using gas sensors based on multi-layered graphene operating at ambient temperature. However, reliable detection of concentrations on the order of parts per million and lower is hindered by partial recovery and lack of reproducibility of the sensors after exposure. We show how to overcome these longstanding problems using ultraviolet (UV) light. When exposed to NO2, the sensor response is enhanced by 290 % − 550 % under a 275 nm wavelength light emitting diode irradiation. Furthermore, the sensor's initial state is completely restored after exposure to the target gas. UV irradiation at 68 W/m2 reduces the NO2 detection limit to 30 parts per billion (ppb) at room temperature. We investigated sensor performance optimization for UV irradiation with different power densities and target gases, such as carbon oxide and ammonia. Improved sensitivity, recovery, and reproducibility of UV-assisted graphene-based gas sensors make them suitable for widespread environmental applications.

A Critical Review of the Use of Graphene-Based Gas Sensors

The employment of graphene for multifunctional uses has been a cornerstone in sensing technology. Due to its excellent electrochemical properties, graphene has been used in its pure and composite forms to detect target molecules over a wide range of surfaces. The adsorption process on the graphene-based sensors has been studied in terms of the change in resistance and capacitance values for various industrial and environmental applications. This paper highlights the performance of graphene-based sensors for detecting different kinds of domestic and industrial gases. These graphene-based gas sensors have achieved enhanced output in terms of sensitivity and working range due to specific experimental parameters, such as elevated temperature, presence of particular gas-specific layers and integration with specific nanomaterials that assist with the adsorption of gases. The presented research work has been classified based on the physical nature of graphene used in conjugation with other processed materials. The detection of five different types of gases, including carbon dioxide (CO2), ammonia (NH3), hydrogen sulphide (H2S), nitrogen dioxide (NO2) and ethanol (C2H5OH) has been shown in the paper. The challenges of the current graphene-based gas sensors and their possible remedies have also been showcased in the paper.

Adsorption performance of CNCl, NH3 and GB on modified graphene and the selectivity in O2 and N2 environment

Two-dimensional materials have shown desirable application prospects in the field of gas sensing. Through the modification of graphene gas-sensitive materials, more efficient and sensitive gas sensors can be further developed. The use of metal atoms or oxygen-containing groups to modify graphene can improve its detection performance for gases. Modified-graphene could be selected as an excellent gas sensor for poisonous gas detection. In this work, adsorption systems of CNCl, NH3 and GB on metal doped graphene (Au, Ag, Cu and Fe) and graphene oxide were established by the using of density functional theory (DFT). The adsorption energy, charge transfer, DOS, PDOS and energy band of these adsorption systems were investigated. The gas adsorption strength and gas selectivity factor were introduced to evaluate the selectivity of each substrate to these harmful gases in O2 and N2 environments. The calculation shown strong adsorption between the metal-doped graphene and the adsorbed gas, with Fe-doped graphene having better adsorption performance. By contrast, between graphene oxide and the adsorbed gas, the effect was slight. Moreover, the energy band change of metal- doped graphene was more obvious than that of graphene oxide. In addition, under O2 and N2 environment, Au-doped graphene may have good selectivity for CNCl and NH3 while Ag-doped graphene may have good selectivity for GB. This result will provide reference for the development of graphene based gas sensors for CNCl, NH3 and GB.

Toward a Stochastic Drift Simulation Model for Graphene-Based Gas Sensors

Monitoring air quality in cities as well as indoors has become an important topic in recent years. Being easily and densely deployable and rather low-cost, chemiresistive gas sensors indicate a feasible technology for this problem, especially if they are assembled as e-noses tracking multiple gases in one device. Nonetheless, the long-term stability of such sensors poses a severe problem for their measurement accuracy, which substantiates the need for drift compensation procedures as well as robust algorithms in the gas prediction process. In order to test such drift compensation methods and to generate synthetic data for algorithm training, a simulation model would be highly useful. In this paper, we present our investigations towards a simulation framework aiming at generating typical sensor responses of a graphene-based gas sensor with an emphasis on its drift behavior. Different drift models are studied, implemented and compared to real measurement data from a lab setup. The evaluation shows promising results when compared to experiments from a real chemiresistive sensor to which the model was parametrized. By using a second setup with fundamentally different heating characteristics we observed that recalibration is a necessary step to generalize to different sensing modes and to ensure the overall quality of the simulation data. Overall, these investigations provide a proper basis towards tackling the challenges in drift simulation.

Oxygen Plasma-Assisted Defect Engineering of Graphene Nanocomposites with Ultrasmall Co3O4 Nanocrystals for Monitoring Toxic Nitrogen Dioxide at Room Temperature.

Functional adjustment of graphene with metal oxide can in fact progress the affectability of graphene-based gas sensors. However, it could be a huge challenge to upgrade the detecting execution of nitrogen dioxide (NO2) sensors at room temperature. The ultrasmall size of nanocrystals (NCs) and copious defects are two key variables for moving forward gas detecting execution. Herein, we provide an effective strategy that the hydrothermal reaction is combined with room-temperature oxygen plasma treatment to prepare Co3O4 NCs and reduced graphene oxide (RGO) nanohybrids (Co3O4-RGO). Among all of Co3O4-RGO nanohybrids, Co3O4-RGO-60 W exhibits the most superior NO2 sensing properties and achieves the low-concentration detection of NO2. The sensitivity of Co3O4-RGO-60 W to 20 ppm NO2 at room temperature is the highest (72.36%). The excellent sensing properties can mainly depend on the change in the microstructure of Co3O4-RGO. Compared with Co3O4-RGO, Co3O4-RGO-60 W with oxygen plasma treatment shows more favorable properties for NO2 adsorption, including the smaller size of Co3O4 NCs, larger specific surface area, pore size, and more oxygen vacancies (OVs). Especially, OVs make the surface of NCs have a unique chemical state, which can increase active sites and improve the adsorption property of NO2. Besides, the agreeable impact of the p-p heterojunction (Co3O4 and RGO) and the doping of N molecule contribute to the improved NO2 detecting properties. It is demonstrated that the Co3O4-RGO-60 W sensor is expected to monitor NO2 at room temperature sensitively.

Adsorption and Sensing Properties of Formaldehyde on Chemically Modified Graphene Surfaces

Chemically modifying graphene (such as chemical doping) is a commonly used method to improve its formaldehyde sensing properties, but the microscopic mechanisms of heteroatoms in the adsorption and sensing process are still unclear. In this paper, the adsorption and sensing properties of formaldehyde on graphene surfaces modified by X doping (X = B, N, O, P, S, Mg and Al) were systematically investigated by first-principles calculations. The adsorption geometries, adsorption energies, charge transfers, and electronic structures were obtained and analyzed. The adsorption strengths of HCHO molecule on the Mg- and Al-doped graphene surfaces were stronger than those of non-metal (B, N, O, P and S)-doped cases. These results showed that the Mg- or Al-doped graphene was better for HCHO detecting than the non-metal-doped graphene systems. The sensing properties were simulated by theNEGF method for the two-probe nano-sensors constructed from Al- and Mg-doped graphene. The maximum sensing responses of nano-sensors based on Al- and Mg-doped graphene were obtained to be 107% and 60%, respectively. The present study supplies a theoretical basis for designing superior graphene-based HCHO gas sensors.