Tin Oxide Gas Sensors Research Articles

Discrimination of Diabetes Mellitus Patients and Healthy Individuals Based on Volatile Organic Compounds (VOCs): Analysis of Exhaled Breath and Urine Samples by Using E-Nose and VE-Tongue

Studies suggest that breath and urine analysis can be viable non-invasive methods for diabetes management, with the potential for disease diagnosis. In the present work, we employed two sensing strategies. The first strategy involved analyzing volatile organic compounds (VOCs) in biological matrices, such as exhaled breath and urine samples collected from patients with diabetes mellitus (DM) and healthy controls (HC). The second strategy focused on discriminating between two types of DM, related to type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), by using a data fusion method. For this purpose, an electronic nose (e-nose) based on five tin oxide (SnO2) gas sensors was employed to characterize the overall composition of the collected breath samples. Furthermore, a voltametric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. To evaluate the diagnostic performance of the electronic sensing systems, algorithm tools including principal component analysis (PCA), discriminant function analysis (DFA) and receiver operating characteristics (ROC) were utilized. The results showed that the e-nose and VE-tongue could discriminate between breath and urine samples from patients with DM and HC with a success rate of 99.44% and 99.16%, respectively. However, discrimination between T1DM and T2DM samples using these systems alone was not perfect. Therefore, a data fusion method was proposed as a goal to overcome this shortcoming. The fusing of data from the two instruments resulted in an enhanced success rate of classification (i.e., 93.75% for the recognition of T1DM and T2DM).

Detection of H2S gas sensing performance of sol gel prepared metal oxide (In2O3, SnO2, Sn doped In2O3) thin films

Gas monitoring and control of hazardous and polluting gases (CO, H2S, CH4, H2, NH3, and alcohol) are required in industrial and laboratory applications where air quality must be regulated to protect public health, the environment, and security. Transparent conducting metal oxide semiconductors (TCO) have been shown to be extremely sensitive to both reducing and oxidising gases. They are caused by a lack of oxygen, also known as anion deficit. The present emphasis of scientific and technical endeavours is to improve the selectivity, stability, and practicability of response. Wide bandgap semiconducting oxides such as SnO2, In2O3 and Sn doped In2O3 have long been employed in gas sensing. The sensitivity of the gas sensor has increased as the thickness of the metal oxide semiconductor film or particle used to produce it has decreased. In the presence of reducible gases and adsorbed oxygen on the crystal surface, the conductivity is lowered by surface potential, when SnO2 is heated to high temperature, however, conductivity increases noticeably for In2O3 and indium tin oxide films. Indium oxide and tin oxide gas sensors detected H2S gas at working temperatures of 115 °C and 120 °C, respectively. Response and recovery times for indium oxide ranged from 10 s to 26 s and 34 s to 91 s, respectively, and for tin oxide from 3 s to 37 s and 82 s to 40 s. At 100 °C working temperature, ITO sensors with 10% tin concentration had the highest H2S sensitivity.

Evaluating the power-law response for tin dioxide nanostructured sensors in the presence of oxygen and reducing gases

It is well known that the electrical conductance of gas sensors, based on polycrystalline metal-oxide semiconductors, obeys a power-law response with a gas pressure as p -γ . The value of the exponent γ, which determines the sensor sensitivity, is regularly derived resorting to the mass action law applied to proposed reactions at the surface of the semiconductor. Conversely, based on the experimentally observed values of γ for different gases, researchers also determine the possible species present at the surface and the reactions that can occur. In line with this approach, we present experimental results, for oxygen and a typical reducing gas, which confirm that oxygen is adsorbed/desorbed neutral, as predicted by the Wolkenstein theory of chemisorption, and ionosorbs doubly charged. • The power law exponent for the electrical resistance of tin oxide gas sensors is derived resorting to the mass action law. • Possible mechanisms compatible with the power law exponent for oxygen and a typical reducing gas are presented. • We found that the surface chemistry is correctly dictated by the Wolkenstein theory of chemisorption.

Nanosheet-type tin oxide gas sensor array for mental stress monitoring

Mental stress management has become significantly important because excessive and sustained mental stress can damage human health. In recent years, various biomarkers associated with mental stress have been identified. One such biomarker is allyl mercaptan. A nanosheet-type tin oxide exhibited high gas selectivity for allyl mercaptan; thus, in this study, a sensor array comprising nanosheet-type tin oxide gas sensors was fabricated to detecting allyl mercaptan. Supervised learning algorithms were use to build gas classification models based on the principal component analysis of the sensor signal responses from the sensor array. The comprehensive data provided by the classification models can be used to forecast allyl mercaptan with high accuracy.

A femtosecond laser-assembled SnO2 microbridge on interdigitated Au electrodes for gas sensing

We unexpectedly found a femtosecond laser deposition (fs-LD) technique for single-step fabricating Tin-oxide gas sensors coupling with peripheral interdigitated electrodes. Photons hit the precursor for an ultrafast nucleation and crystal growth at a nanometric voxel to grow non-directional SnO2 nanocrystal. We utilized the tunable laser exposure dose for morphological control, and the SnO2 nanospheres were optically assembled into micro-structured bridges interconnecting metal electrodes. Au-based interdigital electrodes (thickness < 20 nm) contacted the laser-deposited SnO2 layer forming a hetero-material interfacial area. More quick passages to absorb and desorb gas were created by the gap during nanomaterials and bumpy surfaces. The nanostructured gas sensor exhibited abundant surface oxygen vacancies, desirable gas and ultraviolet light sensitivity, and the capability of detecting analytes (hydrogen sulfide, ethanol, and methanol) down to 20 ppb within 10 s at a footprint area of < 300 × 300 μm2. Results validated the reliability of the invented fs-LD technique, which promised more micro/nanoengineered, shape/function-programmable SnO2 sensors beyond demonstrations here.

Effects of Schottky barrier height fluctuations on conductivity: Consequences on power-law response in tin oxide gas sensors

Potential barriers formed at the intergrain of polycrystalline metal-oxide semiconductors, such as tin oxide, present fluctuations that are usually ignored in the study of gas sensors responses. Fluctuations in the Schottky-type barriers arise from the punctual character and distribution of the charges at the depletion region. Accordingly, we present a numerical model that allows to determine the distribution of the barrier heights. We then analyze the influence of fluctuations on the electrical intergrain conductivity as a function of oxygen pressure, since the signal detection in polycrystalline metal-oxide semiconductors gas sensors comes from the change of material conductivity after interaction with target gas molecules. To do so, we incorporate the fluctuation distribution found in our numerical model in the calculations of conductance. Based on these results, we could also relate the oxygen pressure with the adsorbate coverage and the fluctuating intergranular barrier. Finally, consequences on the known power-law response for gas sensors are discussed.

Identifying Binary Mixtures of Volatile Organic Compounds With Isomeric Components Using a Single Thermal Shock-Induced Generic SnO2 Gas Sensor

Identifying the components and their relative proportions in the binary mixtures of the structurally similar Volatile Organic Compounds (VOCs) are required for many applications. In this work, the binary vapor mixtures of VOCs with isomeric components at variable combined concentrations ranging from 200 to 2500 ppm are discriminated using a single generic tin oxide gas sensor. Sharp fluctuations in the pellet temperature bring in more valuable analyte nature-related information into the sensor's response patterns, which can be extracted and used for discriminating among complex mixtures. The classic staircase voltage waveform fails to produce the abrupt variations in the pellet temperature facilitated via the thermal shock induction method applied for the virtual array formation out of a single tin oxide chemiresistor. Linear discriminant analysis is applied after preprocessing and feature selection of the sensor response patterns. Two simple classifiers operating based on the Euclidean distance in the feature space are utilized for analyte classification. The recognitions of binary vapor mixtures of 1-propanol, 2-propanol, 1-butanol, and 2-butanol are demonstrated as examples. A total of 546 individual experiments are carried out to confirm the accuracy of the performance; all the binary test mixtures are identified correctly; with 2.3% of error in compositions determination. The experiments are satisfactorily repeated in the form of independent work sessions within a 1-month time interval to represent the repeatability of the response patterns. The thermal shock induction appears as a replacement for the sensor arrays which suffer from the multi-dimensional drift of the array components.

A reliable chemiresistive sensor of nickel-doped tin oxide (Ni-SnO2) for sensing carbon dioxide gas and humidity

Herein, we report the chemiresistive gas and humidity sensing properties of pristine and nickel-doped tin oxide (Ni-SnO2) gas sensors prepared by a microwave-assisted wet chemical method. The structural and optical properties are characterised using X-ray diffraction, scanning electron microscopy, scanning transmission electron microscopy, ultraviolet spectroscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The structural elucidation and morphology analyses confirm a particle size of 32–46 nm, tetragonal rutile crystal structure and small cauliflower-type surface appearance. Nickel doping can tune the structure of NPs and morphology. The tested carbon dioxide gas and humidity sensing properties reveal a rapid sensing performance with high-to-moderate sensitivity. Also, the materials favour gas sensing because their sensitivity is enhanced with the increase in nickel concentration. The sensing results suggest that nickel is a vibrant metal additive to increase the gas sensitivity of the sensor. However, nickel doping decreases the electron density and increases the oxygen vacancies. Ultimately, the gas sensor produces highly rapid sensing with a response time of 4 s.

Tin oxide gas sensor on tin oxide microheater for high-temperature methane sensing

Despite the ever-increasing demand for methane detectors in residential and industrial locations, a long life semiconductor-based methane sensor is yet elusive. The problem is rooted in the high decomposition temperature of the methane molecule. Here, we disclose a tin oxide gas sensor heated up to its operating temperature using a ten micron-thick undoped tin oxide microheater which can operate at temperatures as high as 850 °C, sufficient for the spontaneous pyrolysis of methane. Operating at temperatures above 700 °C, the device selectively senses methane in atmospheres contaminated with CO and H2. The response and recovery times are both ~10 s, and the CH4 detection limit is 50 ppm, rendering the developed sensor appropriate for online measurement of methane level in hot exhaust gases. Operating continuously at 700 °C in air, the device exhibits no noticeable aging in a test duration of 30 days.

An Approach to Non Invasive Neural Network based Diagnostics of Asthma using Gas Sensors Array

Objectives: This paper presents a neural network based non-invasive diagnostics methodology of asthma using gas sensors array working as artificial olfactory. Methods/Statistical Analysis: A series of invasive clinical trials are recommended by international bodies to justify the diagnosis of asthma. So during these tests the patient has to undergo a lot of physical trauma. To ease the suffering of the patient, in this paper, a non-invasive method for asthma detection has been proposed. Findings: In this paper, an array consisting of five semiconductor gas sensors has been developed. The sensor array along with the data acquisition system has been developed for the non-invasive detection of the asthma. Five data sets of asthma related toxic gas in different concentrations are obtained by a signal acquirement system having tin oxide gas sensor array. Obtained data are put for training and analysis on Artificial Neural Network (ANN). Proposed neural network has been trained using back propagation algorithm. From the results obtained, it can be seen that the developed model ensures the proper accuracy and consistent results. Application/Improvements: Experimental results show good classification of asthma associated exhaled toxic gas with the ambient air using only few samples and also presents the efficiency of Feed Forward Back Propagation Neural Network on the data driven from different gas sensors.

Highly sensitive and low operating temperature SnO2 gas sensor doped by Cu and Zn two elements

In order to improve the sensitivity and reduce working temperature of tin oxide sensor, two elements of copper, zinc doped tin oxide gas sensor has been successfully synthesized using one-step hydrothermal method. During the preparation process, the common zinc acetate and cupric chloride were adopted as copper, zinc source, respectively. The morphology and structure of the as-prepared sample were further characterized using X-ray diffraction (XRD), energy dispersive spectrometry (EDS), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), high resolution transmission electron microscopy (HR-TEM), selected area electron diffraction (SAED), and ASAP 2460 (used for mesoporous distribution analysis). It’s found that octahedral nanoparticle enclosed by (221) high energy crystal facet had been prepared. The results also demonstrated that the as-prepared tin oxide doped by copper, zinc elements was single crystal structure of tetragonal system and the size of the crystalline grain was about 50nm. During the gas sensing performance experiments, the sample’s gas-sensing properties have been proved to be greatly enhanced in sensitivity and working temperature. The response value to 50ppm ethanol at 110°C of the sample was increased to 210 that was almost 10 times higher than reported values. Besides, the optimal working temperature was decreased to be 110°C from reported 350°C.

Discriminating among different tea leaves using an operating temperature-modulated tin oxide gas sensor

We report distinguishing different types of tea leaves from each other based on their aroma using a thermal shock-induced generic tin oxide gas sensor. The sensor used in this work consists of a microheater and a tin oxide pellet, both connected to outside circuitry with noble metal contacts. The heater is powered with a series of narrow high magnitude voltage impulses of predetermined thermal impacts adjusted to produce step-like temperature rises of different magnitudes on the gas sensitive pellet. The sensor is exposed to aromas collected from various types of tea leaves at different concentrations. Within 4.5 s, nine 500 ms-wide voltage pulses, each as high as 9.3 V in magnitude, are applied to the microheater. Each pulse causes a step-like temperature jump on the pellet temperature. The transient responses recorded for different tea leaves look different even after amplitude normalization. The sensor profiles are recorded, digitized, and compared with the database of previous experiences. A heuristically defined high dimensional feature vector is automatically generated for each analyte. Classifications are graphically achieved in a 3-D feature space after applying principle component analysis for dimension reduction.

MLP Neural Network Based Gas Classification System on Zynq SoC

Systems based on wireless gas sensor networks offer a powerful tool to observe and analyze data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in the case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a multi-layer perceptron (MLP) artificial neural network to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex micro hotplates. The overall system acquires the gas sensor data through radio-frequency identification (RFID), and processes the sensor data with the proposed MLP classifier implemented on a system on chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and the achieved results have shown that an accuracy of 97.4% has been obtained.

Stress in three-dimensionally integrated sensor systems

The ability to incorporate gas sensing devices into always-on wearable technology such as smart phones, tablets, and wrist watches will revolutionize the environmental health and safety industry by providing individuals with a convenient way to detect harmful chemicals in the environment. Although thin metal oxide films have shown their gas-sensing ability, several challenges must still be overcome in order to enable full CMOS integration with a reduced cost of production. A micromachined tin oxide (SnO2) gas sensor on a suspended membrane is presented and its operation, heat flux away from the membrane, and stress generation are analyzed. The device operates based on the adsorption of oxygen at its surface when heated to temperatures between 250∘C and 550∘C, where the presence of oxygen ions results in the formation of a depletion region inside the SnO2 layer. The thermal flux away from the heated membrane is calculated, resulting in a total power loss of 32.5mW. In this calculation, the heat flux through the membrane as well as the air conduction and radiation are accounted for. The stress through the membrane is calculated to be around 500MPa to 550MPa with a maximum displacement of 6.6 μm through its middle. The intrinsic stress through the tin oxide layer is analyzed during film growth using the Volmer-Weber model, resulting in a 200MPa stress and a 1.69J/m2 surface free energy of the deposited material. The use of spray pyrolysis, a CMOS-friendly deposition technique, in order to deposit the SnO2 layer on the membrane resulted in a total thermo-mechanical stress of 380MPa.

The electric field effect on the sensitivity of tin oxide gas sensors on nanostructured substrates at low temperature

A novel low-temperature SnO2 gas sensor was prepared and studied on silicon nanostructures formed by femtosecond laser irradiation. By applying a bias voltage on the silicon substrate to alter the charge distribution on the surface of the SnO2, carbon monoxide (CO), and ammonia (NH3) gas can be distinguished by the same sensor at room temperature. The experimental results are explained with a mechanism that the sensor works at low temperature because of adsorption of gas molecules that trap electrons to the surface of the SnO2.

Differentiating among Gas Mixtures Using a Single Tin Oxide Gas Sensor

Despite all their positive features oxide-based resistive gas sensors are nonselective and respond similarly for different gas and gas mixtures. The authors have recently demonstrated that the response patterns generated by a generic tin oxide gas sensor induced by thermal shocks contain considerable amounts of information regarding the nature of the present gas. Here, the results of using a similar technique on different two-component gas mixtures are reported. The gas mixtures are (1-butanol)x (2-butanol)1-x, (1-propanol)x (2-butanol)1-x, (1-butanol)x (1-propanol)1-x, and (1-butanol)0.33 (2-butanol)0.33, (1-propanol)0.33, each at various total concentrations. The diagnostic features of the response patterns were extracted, by applying wavelet transform, and used for their discrimination in a three dimensional feature space. The positions of the clusters related to different gases are consistent with their composition and facilitate estimating the individual concentrations of the components.

Drift-Like Terms Minimization in the Responses of a Generic Tin Oxide Gas Sensor

The responses of a tin oxide gas sensor vary with humidity level in the surrounding atmosphere. Such response variations can cause error in the estimation of the concentration level of the target gas and need compensation measures. Different methods have been used to compensate the effect of humidity, which usually require utilization of other parallel environmental sensors and costly data fusion methodology. Particularly, such a drift alters the response patterns obtained from a real or virtual sensor array and hinders gas recognition. Here, we report response patterns recorded from a virtual array made by operating temperature modulation at different ambient humidity levels for three different target gases. Humidity level varied from 30-70% and response patterns were recorded for methanol, ethanol and 1-propanol at a wide concentration range. It is shown that by utilizing the thermal shock-induction method for the temperature modulation of the sensor, the drift levels are low, and with a single set of training data collected at RH=50%, responses obtained in the whole humidity range can be discriminated from each other. The clusters volumes in the feature space grow with the span of the ambient humidity variations, but they remain separate allowing gas recognition.

Recognition of complex odors with a single generic tin oxide gas sensor

We report, for the first time, discriminating among complex odors using a single generic tin oxide gas sensor. The sensor heater is biased with high magnitude voltage impulses of predetermined thermal impacts adjusted to produce step-like pallet temperature rises of different magnitudes. The sensor pallet is exposed to aromas collected from various herbs and spices at different concentrations. The test period is 4s only, during which the sensor pallet undergoes four step-like temperature jumps. The discriminative features extracted from the normalized response patterns recorded for six different herbs and spices, each at various concentrations in air, afford their clear segregation in the feature space and recognition. The capacity of the device to discriminate among complex odors and single-component small molecule gases in a unified feature space is demonstrated. The odor discrimination power of the system is almost aging drift-proof; response patterns recorded for a specified odor at independent experiments carried out during the nominal lifetime (six months) of the sensor join the correct cluster in the feature space.

Gas Diagnosis by the Response Modeling of a Temperature Modulated Tin-Oxide Gas Sensor Using Neural Network Models

Gas Diagnosis by the Response Modeling of a Temperature Modulated Tin-Oxide Gas Sensor Using Neural Network Models

Obtaining Highly Selective Responses from a Bulk Tin Oxide Gas Sensor

Generic gas sensors are commonly used for the detection of different airborne contaminants due to their high sensitivity, long life and low cost, but they generally suffer from the variety of drifts and the lack of selectivity. Different techniques have been developed for selectivity enhancement in metal oxide gas sensors, among which operating temperature modulation is well known. It has been observed that sharp pallet temperature changes provide more analyte-related information. Due to the high thermal capacitance of the device, applying step voltage pulses to a bulk tin oxide gas sensor fails to provide step pallet temperature variations. On the other hand, the low thermal capacity of the custom made microheater gas sensors renders them vulnerable to all kinds of thermal noise and agitations. A novel technique is reported for temperature modulation, which facilitates sharp temperature rises of the gas sensitive pallets in generic gas sensors [. In this technique, a sharp heating voltage spike, considerably surpassing the nominal heating voltage, is applied prior to each heating voltage step. The thermal impact of these spikes is adjusted by controlling v2dt for obtaining the closest variations to the ideal temperature profile. Here, the advantages and effectiveness of the technique are demonstrated by differentiating among iso-butanol, tert-butanol, 1-butanol and 2-butanol contaminations in a wide concentration range in air using only a single generic tin oxide gas sensor.