Metal Oxide Sensors Research Articles

Detection and Identification of Volatile Organic Compounds Based on Temperature-Modulated ZnO Sensors

A novel detection method based on ZnO metal–oxide sensors, capable of detecting various species and concentrations of aimed volatile organic compounds (VOCs), has been proposed in this article. In conjunction with signal processing algorithms, the characteristic signals of the sensors operated under temperature modulating are investigated. When the sensor is operating in the temperature modulation mode and is exposed to different detection gases, it will display different output waveforms. Considering the stability of the sensors in practical applications, general regression neural network (GRNN) is used to distinguish the species and concentration of gas in detail.

Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow ‘breath-prints’ to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity—95.0%, specificity—100.0%, accuracy—97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated March 15, 2017.

Development of instrumentation setup for discrimination of analytes using principal component analysis

An indigenous instrumentation set up was designed and developed for the discrimination of analytes. The instrumentation consists of a sensor array, measurement unit, data acquisition software, and a pattern recognition tool. The conductivity changes of three metal oxide sensors (ZnO (S1), In2O3 (S2) and SnO2 (S3)) with different analytes (hydrogen (A1), formaldehyde (A2) and hydrazine (A3)) were measured using in-house developed instrumentation and is sent to PC to store the sensor data for discrimination of analytes using pattern recognition technique. The data acquisition software collects the data in PC to train the data for discrimination of analytes using pattern recognition tool in real time. The various features of sensor response were derived from the response curve and the Principal Component Analysis (PCA) algorithm was implemented in software to extract the information from sensor response data for discrimination of analytes. The instrument set up is able to distinguish among hydrogen, formaldehyde, and hydrazine in real time.

Simulation and Optimization of Artificial Neural Network Based Air Quality Estimator

E-sensor which are generally based on concept of E-nose are specially made to distinguish odours .In the present research work. E-sensor is developed using artificial intelligence technique to identify the concentration of carbon monoxide in a polluted environment. Data record access using Metal oxide sensor. The available data is broken into the number of segments .The length of data segment and the neurons in hidden layer is varied in number to find the optimized model of artificial neural network model using Mat Lab Coding. The artificial neural network model is optimized by verification in terms of mean squared error and regression. The regression is verified for training ,testing, validation and all. The mean squared error and regression are the artificial neural network model performance parameter.

Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution.

Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID.

Developing a Low-Cost Wearable Personal Exposure Monitor for Studying Respiratory Diseases Using Metal–Oxide Sensors

Global industrialization and urbanization have led to increased levels of air pollution. Those with respiratory diseases, such as asthma, are at the highest risk for adverse health effects and reduced quality of life. Studying the relationship between pollutants and symptoms is usually achieved with data from government air quality monitoring stations, but these fail to report the spatial and temporal resolution required to track a person's true exposure, especially when the majority of their time is spent indoors. We develop and build eight wrist-worn wearable devices, weighing only 64 g, to measure known asthma symptom triggers: ozone, total volatile organic compounds, temperature, humidity, and activity level. The devices use commercial off-the-shelf components, costing under $150 each to build. This report focuses on the design, calibration, and testing of the devices. Emphasis is placed on the calibration of a metal-oxide-semiconductor gas sensor for detecting ozone, which is a difficult task because of the large variations in ambient temperature and humidity found when using a wearable device. Examples of testing the devices in four real environments are also discussed: 11 days inside a well-ventilated laboratory, ten days outdoors during the summer, alternating the devices between indoor and outdoor environments to examine their response to quickly changing environments, and a field test where scripted activities are performed for a full day. The work demonstrates a wearable device for environmental health studies and addresses the challenges of existing sensors for real-world applications.

A Gas Mixture Prediction Model Based on the Dynamic Response of a Metal-Oxide Sensor.

Metal-oxide (MOX) gas sensors are widely used for gas concentration estimation and gas identification due to their low cost, high sensitivity, and stability. However, MOX sensors have low selectivity to different gases, which leads to the problem of classification for mixtures and pure gases. In this study, a square wave was applied as the heater waveform to generate a dynamic response on the sensor. The information of the dynamic response, which includes different characteristics for different gases due to temperature changes, enhanced the selectivity of the MOX sensor. Moreover, a polynomial interaction term mixture model with a dynamic response is proposed to predict the concentration of the binary mixtures and pure gases. The proposed method improved the classification accuracy to 100%. Moreover, the relative error of quantification decreased to 1.4% for pure gases and 13.0% for mixtures.

Optimal Bio-Inspired Sniffing for Improved E-Nose Detection

It is common knowledge that dogs have a keen sense of smell. Many people have sought to capitalize on such outstanding olfactory sense by using canines with the special forces, as drug detecting tools, and for medical diagnosis [1]. Small mammals such as mice have been shown to quickly learn how to almost perfectly discriminate between two presented odors [2]. On the other size extreme, elephants are extremely adept with their olfactory system and are being used to detect substances at low concentration such as TNT [3]. A key component to the mammalian sense of smell at all size scales is the dynamic sniff cycle. While there has been work done to discover the airflow patterns inside mammalian nasal cavities [4], little is known of the fluid mechanics of gathering and concentrating odors onto sensors. Elucidating the odor collection response of an oscillatory airflow could be applied to greatly enhance the abilities of electronic noses. Blowing fluid past a sensor can enhance signal more than passive diffusion [5]. While constant speed flow might be the easiest to implement, there are inherent advantages in oscillator flow like a sniff. For instance, noise fluctuations can be used as a feature for better identification [6]. Such methods are commonly referred to as fluctuation enhanced sensing (FES) [7]. There have also been other attempts at using bio-inspired sniffing electronic noses which use each sniff cycle as a unique trial [8]. Our central aim is to enhance the amount of information gleaned from inexpensive metal oxide gas sensors by applying a sniffing-inspired flow pattern across the sensor. When metal oxide sensors are exposed to a reducing gas such as ethanol, the resistance of the tin dioxide layer decreases. Accordingly, higher concentrations of ethanol elicit increased resistance changes. The goal becomes bringing analyte to the gas sensors by manipulating the flow so that the sensor experiences the highest concentration and duration of flow as quickly as possible. We hypothesize that using sniffing type will best pre-concentrate air flows. The sniffing airflow successfully gives the sensor information on a shorter time scale than simply diffusion alone. We find the amplitude of the resistance response during oscillation is synchronized to the sniffing frequency, and increases with ethanol concentration. Thus, oscillating the flow profile around a metal oxide sensor can provide an additional level of temporal information which could enhance sensor performance. There is a limit to increasing frequency however. At higher frequencies, the chemical-laden air flows faster near the walls of the chamber where the sensors are arranged. Without sufficient duration near the sensors, decreased sensor resistance response is experienced. Such trade-off leads to an ideal case where a system sniffs quickly enough to utilize the novel temporal information but slowly enough so the signal does not disappear into noise. The novel temporal information provides information on the order of a few seconds whereas the standard steady state feature takes over a minute to retrieve information about the analyte in the sensor’s environment. A device utilizing this new set of information has the potential to recognize an analyte in 20% of the time it takes a standard device without this new information. [1] T. Jezierski et al.,“Efficacy of drug detection by fully-trained police dogs varies by breed, training level, type of drug and search environment,” Forensic Sci. Int., vol. 237, pp.112–118, 2014.[2] N. Bodyak and B. Slotnick, “Performance of Mice in an Automated Olfactometer: Odor Detection, Discrimination and Odor Memory,” Chem. Senses, vol. 24, no. 6, pp.637–645, 1999.[3] A. K. Miller et al., “African elephants (Loxodonta africana) can detect TNT using olfaction: Implications for biosensor application,” Appl. Anim. Behav. Sci., vol. 171, pp.177–183, 2015.[4] M. E. Staymates et al., “Biomimetic Sniffing Improves the Detection Performance of a 3D Printed Nose of a Dog and a Commercial Trace Vapor Detector,” Sci. Rep., vol. 6, p.36876, 2016.[5] Y. Wang, J. Xing, and S. Qian, “Selectivity Enhancement in Electronic Nose Based on an Optimized DQN,” Sensors , vol.17, no.10, Oct. 2017.[7] P. Sedlák, P. Kuberský, and F. Mívalt, “Effect of various flow rate on current fluctuations of amperometric gas sensors.” [8] J. Smulko and M. Trawka, “Gas selectivity enhancement by sampling-and-hold method in resistive gas sensors,” Sens. Actuators B Chem., vol. 219, pp.17–21, Nov. 2015.[9] A. Ziyatdinov, J. Fonollosa, L. Fernández, A. Gutierrez-Gálvez, S. Marco, and A. Perera, “Bioinspired early detection through gas flow modulation in chemo-sensory systems,” Sens. Actuators B Chem., vol. 206, pp. 38–547, Jan. 2015.

Towards Gas Sensors for Aquatic Chemical Sensing Inspired By the Star-Nosed Mole

Aquatic chemical sensors, as any other surface submerged in water, are subject to biofouling, by bacteria and algae that quickly form biofilms on the submerged surface [1]. In the case of sensors, accumulated biofilms skew the chemical measurements until the sensors are cleaned and recalibrated. A number of mechanical and chemical solutions have been proposed to scrape off or kill the biofilms, but they can be sensor-specific, costly, or have adverse effects on the environment [2]. The process of biofouling happens over the span of days, making long-term chemical measurements labor-intensive and oftentimes unfeasible. The star-nosed mole, with its bizarre nose geometry, presents a way to circumvent the issue of biofouling. These semi-aquatic moles exhale and inhale bubbles rapidly underwater. When the bubbles contact a target of interest, molecules are collected on the bubble surface and brought back into the nose. In this fashion, its olfactory receptors never contact the water and the mole can use its terrestrial nose to track prey at the bottom of swamps [3]. The appendages that make up the star on their nose stabilize the bubbles, allowing them to consistently blow larger bubbles without the bubble pinching off and floating away [4]. We develop an underwater electronic nose, inspired by the star-nosed mole’s underwater sniffing. A motor pushes and pulls on a syringe to form and contract a 0.5 mL bubble underwater at 1 Hz. A 3D printed star, inspired by the mole’s nose geometry, stabilizes the bubble and prevents pinch-off [4]. The 6 fins of the star, separated by 30-degree angles, are slanted downward at a 35-degree angle to optimally stabilize the largest volume bubble. The air from the bubble is pushed and pulled past an array of metal oxide sensors. As a proof of concept, this underwater e-nose is used in an on-going experiment to detect the presence or absence of sparkler fragments at the bottom of a water sample. In the event of the bubble pinching off, inhaled water shorts out the sensor array. At the same time, if the volume of dead space between the star and the sensor array is greater than the volume of the bubble, tests show that the e-nose will be incapable of detecting changes in the underwater environment. Thus, stabilizing the bubble during the sniff is key in designing this underwater e-nose. This prototype e-nose marks the expansion of machine olfaction to underwater applications. Figure 1. Underwater bubble sniffing can allow gas sensors to be used underwater. A) The star-nosed mole sniffs bubbles to pick up scents underwater. Photo courtesy of K. Catania. B) Mimicking this behavior could allow gas sensors to detect chemicals without directly exposing the sensor to its environment. C) A motor and syringe sniff underwater across a metal oxide sensor to form an underwater e-nose. [1] G. Xu, W. Shen, and X. Wang, “Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey,” Sensors, vol. 14, no. 9, pp. 16932–16954, Sep. 2014. [2] L. Delauney, C. Compère, and M. Lehaitre, “Biofouling protection for marine environmental sensors,” Ocean Sci, vol. 6, no. 2, pp. 503–511, May 2010. [3] K. C. Catania, “Olfaction: Underwater ‘sniffing’ by semi-aquatic mammals,” Nature, vol. 444, no. 7122, p. 1024, Dec. 2006. [4] A. B. Lee and D. L. Hu, “Bubble stabilization by the star-nosed mole,” Phys. Rev. Fluids, vol. 3, no. 12, 2018. Figure 1

(Invited) Cross-Reactive Graphene and Metal Oxide Sensors for Gas Discrimination

Discriminating similar molecules remains a very challenging problem for semiconductor gas sensors. Here, we report a method to achieve precise gas discrimination of similar chemical vapors (mesitylene, o-xylene, and toluene) by using cross-reactive arrays consisting of metal oxide semiconductor and graphene sensors. It is difficult to identify these three chemicals as they have very similar responses to these sensors. Through a cross-reactive Principal Component Analysis (PCA) of the sensor response features, however, the discrimination accuracy improved from about 70% with a single gas sensor to almost 100% with the cross-reactive sensor array. Such a precise discrimination and the low-cost planar process make this approach a very attractive candidate for smart gas sensing and for future Internet of Things (IoT) applications.

Real-Time Sensor Response Characteristics of 3 Commercial Metal Oxide Sensors for Detection of BTEX and Chlorinated Aliphatic Hydrocarbon Organic Vapors

The objective of this study was to examine the sensor response characteristics of three commercial Internet of Things (IoT) compatible metal oxide (MOx) sensors in preparation for the development of field-scale sensor networks for the real-time monitoring of volatile organic compounds (VOCs) in indoor environments located in proximity to brownfield sites. Currently, there is limited examination of such sensor responses to relevant mixtures of target VOCs, such as the common petrochemicals benzene, toluene, ethylbenzene, and xylene (BTEX), as well as chlorinated aliphatic hydrocarbon (CAH) contaminants such as tetrachloroethylene (PCE) and trichloroethylene (TCE) which are frequently associated with deterioration of indoor air quality. To address this, a study of three commercial metal oxide (MOx) sensors (SGP30, BME680, and CCS811) was undertaken to examine the sensor response characteristics of individual components as well as mixtures of each of the target BTEX and CAH chemicals over relevant indoor air concentrations within the operating range of the MOx sensors (0–6000 ppb). Our investigation revealed similar response patterns to those previously reported for the thick film MOx sensor to most individual target VOCs, however, response trends for mixtures were more difficult to discern. In general, the MOx sensors we examined demonstrated similar magnitude responses to the CAHs as BTEX compounds indicating reliable detection of CAHs.

An Innovative Modular eNose System Based on a Unique Combination of Analog and Digital Metal Oxide Sensors.

An innovative concept for an electronic nose (eNose) system based on a unique combination of analog and digital sensors for online monitoring is presented. The developed system consists of small sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors in a compact, modular, low sample volume, temperature-controlled sensing chamber. The sensing chamber comprises three compartments, each of which may contain several analog and/or digital MOX sensors. Additional sensors within the digital compartment allow for pressure, humidity, and temperature measurements. The presented prototype eNose system comprises one compartment with 8 analog MOX sensors and two compartments with 10 digital sensors each and was explicitly tested here for the discrimination between midrange (3-18 ppm) concentrations of ethanol and acetone at dry and moderately humid conditions.

Sensors’ array of aspiration ion mobility spectrometer as a tool for bacteria discrimination

The possibility of achieving bacterial discrimination using a miniaturized aspiration ion mobility spectrometer model ChemPro-100i (Environics Oy) has been tested by interrogating the headspace air samples above in vitro bacterial cultures of three species – Escherichia coli, Bacillus subtilis and Staphylococcus aureus, respectively. The ChemPro-100i highly integrated seven sensor array, composed of one a-IMS cell, three MOS (metal oxide sensors), one FET (field effect transistor) sensor and two SC (semiconductor) sensors, provided enough analytical information to discriminate between the three bacterial species. Statistical data processing using either principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA) was accomplished. We concluded that although the data from the aspiration-type ion mobility sensor, with its 16 ion detectors, is absolutely sufficient to discriminate between various bacteria using their volatile compounds’ chemical profile, the other six sensors deliver additional, valuable information.

Pancreatic ductal adenocarcinoma and chronic pancreatitis may be diagnosed by exhaled-breath profiles: a multicenter pilot study.

BackgroundThe diagnosis of pancreatic adenocarcinoma and chronic pancreatitis often rely on expensive and invasive diagnostic approaches, which are not always discriminative since patients with chronic pancreatitis and pancreatic adenocarcinoma may present with similar symptoms. Volatile organic compounds (VOCs) in expired breath, could be used as a non-invasive diagnostic biological marker for detection of pancreatic pathology. Detection and discrimination of pancreatic pathology with an electronic nose has not yet been reported.PurposeThe objective of this pilot study was to determine the diagnostic potential of an electronic nose to identify pancreatic adenocarcinoma and chronic pancreatitis by analyzing volatile organic compoundg (VOC) profiles in exhaled air.Patients and methodsIn a multicenter study, the exhaled air of 56 chronic pancreatitis patients, 29 pancreatic adenocarcinoma patients, and 74 disease controls were analyzed using an electronic nose based on 3 metal oxide sensors (MOS). The measurements were evaluated utilizing an artificial neural network.ResultsVOC profiles of chronic pancreatitis patients could be discriminated from disease controls with an accuracy of 0.87 (AUC 0.95, sensitivity 80%, specificity 92%). Also, VOC profiles of patients with pancreatic adenocarcinoma differed from disease controls with an accuracy of 0.83 (AUC 0.87, sensitivity 83%, specificity 82%). Discrimination between chronic pancreatitis and pancreatic adenocarcinoma showed an accuracy of 0.75 (AUC 0.83, sensitivity 83%, specificity 71%).ConclusionAn electronic nose may be a valuable diagnostic tool in diagnosis of pancreatic adenocarcinoma and chronic pancreatitis. The current study shows the potential of an electronic nose for discriminating between chronic pancreatitis, pancreatic adenocarcinoma and healthy controls. The results from this proof-of-concept study warrant external validation in larger cohorts.

A non-enzymatic glucose sensor enabled by bioelectronic pH control

Continuous glucose monitoring from sweat and tears can improve the quality of life of diabetic patients and provide data for more accurate diagnosis and treatment. Current continuous glucose sensors use enzymes with a one-to-two week lifespan, which forces periodic replacement. Metal oxide sensors are an alternative to enzymatic sensors with a longer lifetime. However, metal oxide sensors do not operate in sweat and tears because they function at high pH (pH > 10), and sweat and tears are neutral (pH = 7). Here, we introduce a non-enzymatic metal oxide glucose sensor that functions in neutral fluids by electronically inducing a reversible and localized pH change. We demonstrate glucose monitoring at physiologically relevant levels in neutral fluids mimicking sweat, and wireless communication with a personal computer via an integrated circuit board.

Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure

The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a scala of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.

A Metal Oxide Gas Sensors Array for Lung Cancer Diagnosis Through Exhaled Breath Analysis.

Lung cancer high mortality rate is mainly related to late-stage tumor diagnosis. Survival rates and treatments could be greatly improved with an effective early diagnosis. Volatile organic compounds (VOCs) in exhaled breath have been known for long to be linked to the presence of a disease. Exhaled breath analysis for early diagnosis of lung cancer represents a non-invasive, low-cost and user-friendly approach. In this paper we present the design and development of an electronic nose based on a metal oxide sensors array for the early diagnosis of lung cancer. Breath samples collected from healthy controls (n=10) and lung cancer subjects (n=6) were analyzed by the electronic nose, and classification was performed using an artificial neural network (ANN). A sensitivity of 85.7%, specificity of 100%, and accuracy of 93.8% were reached with leave one out cross validation (LOOCV). The presented device demonstrates that a simple, cost-effective, and non-invasive approach based on exhaled breath analysis has the potential to be of great help in decreasing lung cancer mortality.

Self-Repairing classification algorithms for chemical sensor array

Chemical sensors are usually affected by drift, have low fabrication reproducibility and can experience failure or breaking events over the long term. Albeit improvements in fabrication processes are often slow and inadequate for completely surmounting these issues, data analysis can be used as of now to improve the available device performances. The present paper illustrates an algorithm, called Self-Repairing (SR), developed for repairing classification models after the occurrences of failures in sensor arrays. The procedure considers replacing broken sensors with replicas and eventually Self-Repairing algorithm trains these blank elements. Unlike the habitual alternatives reported in literature, SR performs this operation without the need of a whole new recalibration, references gas measurements or transfer dataset and, at the same time, without interrupting the on-going procedure of gas identification. Furthermore, Self-Repairing algorithm can utilize most of the standard classifiers as core algorithm; in this paper SR has been applied to k-NN, PLS-DA and LDA as examples. Models have been tested in a synthetic and real scenario considering sensor arrays affected by drift and eventually by failures. Real experiment has been performed with a set of metal oxide sensors over an 18-months period. Finally, the algorithm has been compared with standard version of chosen classifiers (k-NN, LDA and PLS-DA) showing superior performances of Self-Repairing and increasing the tolerance versus consecutive failures.

Room temperature resistive gas sensor based on ZIF-8/MWCNT/AgNPs nanocomposite for VOCs detection

Metal organic frameworks (MOFs) are a group of hybrid organic–inorganic materials with high surface area and porous structure, which recently have been considered in gas sensing studies. This research is a unique report on application of ZIF-8, a well-known MOF, as sensing material in a resistive gas sensor for detection of some volatile organic compounds (VOCs) at room temperature. ZIF-8 was synthesized and composited with MWCNTs and Ag nanoparticles to obtain a semiconductor sensing material for detection of some VOCs including methanol, ethanol, acetone, acetonitrile and n-hexane at room temperature. In order to investigate the sensor performance, the main sensor parameters such as sensitivity, reusability, repeatability, response/recovery time and stability were evaluated. Because of high surface area of ZIF-8 (1360 m2/g) and its nanoporous structure, sensor exhibited good sensitivity to the analytes. Furthermore, the most sensitivity of sensor was observed for ethanol and methanol due to small size of these molecules. In addition, sensor responses were fast and all measurements showed repeatability. Contrary to metal oxide sensors that are used under high temperature, this sensor works at room temperature. To the best of our knowledge, this is the first report on using this nanocomposite of ZIF-8 as sensing material in fabrication of resistive gas sensor for detection of VOCs.

Effective design and fabrication of low-power-consumption self-heated SnO2 nanowire sensors for reducing gases

Developing metal oxide gas sensors for internet-of-things (IoT) and portable applications require low-power consumption because of the limited battery in devices. This requirement is challenging because metal oxide sensors generally need high working temperatures, especially for reducing gases. Herein, we present an effective design and fabrication method of a SnO2 nanowire (NW) sensor for reducing gases by using the Joule heating effect at NW nanojunctions without needing an external or integrated heater. The sensor’s low-power consumption at around 4 mW was controlled by the size and nanojunction density of the device. The sensor has a simple design and is easy to fabricate. A proof-of-concept of a portable gas sensor module can be realised for monitoring highly toxic reducing gases, such as H2S, NH3 and C2H5OH, by using the developed self-heated NWs.