Metal Oxide Sensors Research Articles

Chemoresistive Nanostructured Sensors for the Analysis of Human Tumor Tissue Exhalations

One of the greatest goals in medicine is early-stage tumor detection. An effective prevention is crucial to allow physicians and surgeons to intervene on tumor affected patients with the available therapies and surgical techniques with a high probability of success. The purpose of this research relies on the identification of cancer presence by detecting the volatile organic biomarkers directly produced by cancer cells. Cancer cells produce different biomarkers with respect to healthy cells, mainly due to the altered metabolism and cellular membrane peroxidation. This difference can be recognized by using an innovative device manufactured in the Sensor Laboratory of the University of Ferrara, hosting a specific sensor array chosen for biological sample investigation. The sensor array is composed by four chemoresistive metal-oxide nanostructured sensors, able to detect gases in low concentration (up to 1 ppm) with excellent stability and repeatability. In this study, a fast-responding, reliable and reproducible sensing device, named SCENT B1[1], showed to be capable of discriminating cancerous cells from healthy ones, proving to be an efficient and low-invasive screening system.The device combines a specific electronic and pneumatic system to a specifically selected sensor array. The sample is inserted inside a sample box through a support composed by Petri dishes, while a stabilized ambient airflow conveys the exhalations to sensors. Sensor responses are then statistically analyzed by principal component analysis (PCA).Measurements have been performed on cancer and healthy tissues, extracted during surgery from human colon and rectum, with the future aim of extending the study to the other tumor types.Sensors chosen are the following: a mixture of tin and titanium oxides with addition of gold (ST25Au), tungsten oxide (W11), a mixture of tin, titanium and niobium oxides (STN) and a solid solution of titanium oxide, tantalum and vanadium (TiTaV). Sensors have been selected after a feasibility study performed on immortalized cells of diverse types, where sensors responses have been correlated with cell type and cell initial plating concentration of the samples; the measures have been performed after 24, 48, 72 hours of incubation.The sensor output signal is a voltage directly proportional to the sensor conductance; it depends on the chemical redox reactions taking place on sensor surface [2,3]. In Figure (a) the response R=ÄG/G for each sensor is reported, where ÄG is the difference between the sensor conductance with and without the metabolites expelled by the cells of a tissue. All four sensors gave larger responses (although with different amplitudes) to the tumor tissue with respect to the healthy one. Smaller and reproducible responses were given by the breeding ground (DMEM) only, confirming that it does not alter the measurement.The results are consistent with the stronger metabolism of tumor cells with respect to the healthy ones, because the former emits larger amounts of volatile biomarkers [4,5]. Other tests proved also that different initial plating concentrations (250k, 500k and 1M) of cell give increasing responses, as shown in Figure (b). From this study what emerges is that the device is capable of distinguishing different cell samples basing on their health status and concentrations. The encouraging results of this study are the basis of a deepen study for the clinical validation of the device as a oncologic screening device.

Design and Development of Instrumentation for Remote Detection of Hydrogen Using Metal Oxide Sensor

Indigenous instrumentation setup was designed, developed and demonstrated for remote detection of hydrogen using metal oxide (MO) sensors. The metal oxide sensor was developed to sense the low concentration of hydrogen. The hardware developed was based on a function generator configuration and uniquely designed to give low excitation voltage across the sensor to improve the life of the sensor and reduces the number of components. The data acquisition software was developed to collect the sensor response in PC remotely. The hardware was calibrated, tested with standard resistance and found to give an accuracy of ±0.5% and high stability and reliability. The sensor response towards hydrogen was measured by the instrument. Different features of sensor response viz., sensitivity, response time and recovery time were studied towards different concentrations of hydrogen at optimized temperature.

A Compact 16 Channel Embedded System with High Dynamic Range Readout and Heater Management for Semiconducting Metal Oxide Gas Sensors

The simultaneous operation of multiple different semiconducting metal oxide (MOX) gas sensors is demanding for the readout circuitry. The challenge results from the strongly varying signal intensities of the various sensor types to the target gas. While some sensors change their resistance only slightly, other types can react with a resistive change over a range of several decades. Therefore, a suitable readout circuit has to be able to capture all these resistive variations, requiring it to have a very large dynamic range. This work presents a compact embedded system that provides a full, high range input interface (readout and heater management) for MOX sensor operation. The system is modular and consists of a central mainboard that holds up to eight sensor-modules, each capable of supporting up to two MOX sensors, therefore supporting a total maximum of 16 different sensors. Its wide input range is archived using the resistance-to-time measurement method. The system is solely built with commercial off-the-shelf components and tested over a range spanning from 100 Ω to 5 GΩ (9.7 decades) with an average measurement error of 0.27% and a maximum error of 2.11%. The heater management uses a well-tested power-circuit and supports multiple modes of operation, hence enabling the system to be used in highly automated measurement applications. The experimental part of this work presents the results of an exemplary screening of 16 sensors, which was performed to evaluate the system’s performance.

Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context

In a context of increased environmental awareness, the Internet of Things has allowed individuals or entities to build their own connected devices to share data about the environment. These data are often obtained from widely available low-cost sensors. Some companies are also selling low-cost sensing kits for in-house or outdoor use. The work described in this paper evaluated, in the short term, the performance of a set of low-cost sensors for temperature, relative humidity, atmospheric pressure and carbon dioxide, commonly used in these platforms. The research challenge addressed with this work was assessing how trustable the raw data obtained from these sensors are. The experiments made use of 18 climatic sensors from six different models, and they were evaluated in a controlled climatic chamber that reproduced controlled situations for temperature and humidity. Four CO2 sensors from two different models were analysed through exposure to different gas concentrations in an indoor environment. Our results revealed temperature sensors with a very high positive coefficient of determination (r2 ≥ 0.99), as well as the presence of bias and almost zero random error; the humidity sensors demonstrated a very high positive correlation (r2 ≥ 0.98), significant bias and small-yet-relevant random error; the atmospheric pressure sensors presented good reproducibility, but further studies are required to evaluate their accuracy and precision. For carbon dioxide, the non-dispersive infra-red sensors demonstrated very satisfactory results (r2 ≥ 0.97, with a minimum root mean squared error (RMSE) value of 26 ppm); the metal oxide sensors, despite their moderate results (minimum RMSE equal to 40 ppm and r2 of 0.8–0.96), presented hysteresis, environmental dependence and even positioning interference. The results suggest that most of the evaluated low-cost sensors can provide a good sense of reality at a very good cost–benefit ratio in certain situations.

Acetone sensing and modeling through functionalized Ag-catalysts and CNTs via low cost metal-organic framework-derived ZnO nanostructures

Cost-effective synthesis, portability, quick response and fascinating features of semiconducting metal oxide sensors have drawn much accredited to be utilized in wide variety of potential sensing applications. Herein, flexible acetone sensor was fabricated through spray coating method using different conductive inks of carbon nanotubes (CNTs), zinc oxide (ZnO) and silver (Ag) nanoparticles solutions on cellulose paper. Morphological analysis revealed the fiber like morphology of cellulose over which CNTs, ZnO and Ag nanoparticles are uniformly distributed. The energy dispersive X-ray spectroscopic analysis was performed to observe the elemental composition in the as-prepared samples. The recovery time toward acetone was greater for CNTs/Ag acetone sensor at 95% as compared to CNTs/ZnO/Ag sensor. Additionally, sensing time of the samples towards acetone was measured and the observed response time was greater for CNTs/ZnO/Ag (0.52 s) acetone sensor as compared to CNTs/Ag sensor (0.41 s). Hence, CNTs/ZnO/Ag can be easily employed for sensing applications due to quick response.

Enhancement in the Detection Ability of Metal Oxide Sensors Using Defect-Rich Polycrystalline Nanofiber Devices.

The development of SnO2 and TiO2 polycrystalline nanofiber devices (PNFDs) has been widely researched as a method of protecting humans from household air pollution. PNFDs have three significant advantages. The nanofibers before the annealing process are polymer‐rich materials, which can be used as particulate material (PM) filters. The multiporous nanofibers fabricated by the annealing process have numerous defects that can serve as generation‐recombination centers for electron–hole pairs, enabling the PNFDs to serve as multiple‐wavelength light (from 365 to 940 nm) detectors. Lastly, the numerous surface/interface defects can drastically enhance the toxic gas detection ability. The toxic gas detection range of PNFDs for CO(g) and NO(g) is from 400 to 50 ppm and 400 to 50 ppb, respectively. Quick response times and recovery properties are key parameters for commercial applications. The recovery time of NO(g) detection can be improved from 1 ks to 40 s and the PNFD operating temperature lowered to 50 °C. These results indicate that SnO2 and TiO2 PNFDs have good potential for commercialization and use as toxic gas and photon sensors in daily lives.

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors.

“Electronic nose” technology, including technical and software tools to analyze gas mixtures, is promising regarding the diagnosis of malignant neoplasms. This paper presents the research results of breath samples analysis from 59 people, including patients with a confirmed diagnosis of respiratory tract cancer. The research was carried out using a gas analytical system including a sampling device with 14 metal oxide sensors and a computer for data analysis. After digitization and preprocessing, the data were analyzed by a neural network with perceptron architecture. As a result, the accuracy of determining oncological disease was 81.85%, the sensitivity was 90.73%, and the specificity was 61.39%.

Porous Silicon Gas Sensors: The Role of the Layer Thickness and the Silicon Conductivity.

We studied the influences of the thickness of the porous silicon layer and the conductivity type on the porous silicon sensors response when exposed to ethanol vapor. The response was determined at room temperature (27 C) in darkness using a horizontal aluminum electrode pattern. The results indicated that the intensity of the response can be directly or inversely proportional to the thickness of the porous layer depending on the conductivity type of the semiconductor material. The response of the porous sensors was similar to the metal oxide sensors. The results can be used to appropriately select the conductivity of semiconductor materials and the thickness of the porous layer for the target gas.

A measurement prototype based on gas sensors for detection of pesticide residues in edible mint

In this article, we propose a measurement prototype based on an electronic nose device to discriminate the treated mint from the untreated mint with pesticides and to identify the day of treatment. The process consists of a sample of mint, two types of pesticides (Decis Fluxx and Malyphos 50), an electronic nose composed of a network of metal oxide sensors, a data card acquisition, a personal computer and a data analysis and processing method. The method used is a hybrid method combining the principal component analysis method with the support vector machines method and allowed us to have success rates of 100%, 87.50% and 75% successively for discrimination in the following cases: untreated and treated mint by Decis, untreated and treated mint by Malyphos and untreated and treated mint with both products. For the identification of the treatment day, we obtained success rates of around 87.50% and 100% respectively for the case of the Decis product and the Malyphos product. These results show that our proposal is quick, easy to use and inexpensive for the detection of the treated mint as well as the day of its treatment.

Phosphonic Acid Modified ZnO Nanowire Sensors: Directing Reaction Pathway of Volatile Carbonyl Compounds.

Surface molecular transformations on nanoscale metal oxides are inherently complex, and directing those reaction pathways is still challenging but important for designing their various applications, including molecular sensing, catalysts, and others. Here, a rational strategy to direct a reaction pathway of volatile carbonyl compounds (nonanal: biomarker) on single-crystalline ZnO nanowire surfaces via molecular modification is demonstrated. The introduction of a methylphosphonic acid modification on the ZnO nanowire surface significantly alters the surface reaction pathway of nonanal via suppressing the detrimental aldol condensation reaction. This is directed by intentionally decreasing the probability of two neighboring molecular activations on the nanowire surface. Spectrometric measurements reveal the correlation between the suppression of the aldol condensation surface reaction and the improvement in the sensor performance. This tailored surface reaction pathway effectively reduces the operating temperature from 200 to 100 °C while maintaining the sensitivity. This is because the aldol condensation product ((E)-2-heptyl-2-undecenal) requires a higher temperature to desorb from the surface. Thus, the proposed facile strategy offers an interesting approach not only for the rational design of metal oxide sensors for numerous volatile carbonyl compounds but also for tailoring various surface reaction pathways on complex nanoscale metal oxides.

Long-term calibration models to estimate ozone concentrations with a metal oxide sensor

Ozone (O3) is a potent oxidant associated with adverse health effects. Low-cost O3 sensors, such as metal oxide (MO) sensors, can complement regulatory O3 measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM2.5 concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O3 although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O3 federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O3 concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O3 concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU’s MO oxidizing species and temperature measurements and DAQ’s solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R2 = 0.491), and the ANN exhibited good performance (R2 = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O3 for five months after the calibration period and the results showed moderate correlations (R2s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O3 levels.

Generic Approach to Boost the Sensitivity of Metal Oxide Sensors by Decoupling the Surface Charge Exchange and Resistance Reading Process

As one of bottlenecking parameters for practical applications of metal oxide semiconductor based gas sensors, sensitivity enhancement has attracted significant attention in the past few decades. In this work, alternative to conventional strategies of designing sensitive surfaces via morphology/defect/heterojunction control (then operating at an optimized isothermal temperature with a maximal response), a facile enhancement approach by decoupling surface charge exchange and resistance reading process (possessing different temperature-dependent behaviors) through pulsed temperature modulation (PTM) is reported. Substantially magnifying electrical responses of a generic metal oxide (e.g.WO3) MEMS sensor toward diverse analyte molecules is demonstrated. Under the optimal PTM conditions, the response toward 10 ppm NO2 can be boosted from (isothermal) 99.7 to 842.7, the response toward 100 ppm acetone is increased from (isothermal) 2.7 to 425, respectively, which are comparable to or even better than most of the state-of-the-art WO3 based sensors. In comparison to conventional (isothermal) operation, PTM allows to sequentially manipulate the physi/chemisorption of analyte molecules, generation of surface reactive oxygen species (ROS) and sensor resistance reading, and thus provides additional opportunity in boosting electrical response of oxide sensors for advanced health and/or environment monitoring in future.

Acetone Sensor Made of Tin Dioxide

Results of investigations of metal oxide chemical sensors for the detection of acetone made of tin dioxide were discussed in this review paper. There are several possibilities and technologies to manufacture of such sensors. Pure (without impurities) SnO2 and other metal oxide have low sensitivity to gases at its rather high pre-heating (operation) temperature. Doping of tin dioxide with some metals or carbon nanotubes is one way of improving the sensitivity of such metal oxide sensors. Another way is the preparation of nanosensors.

Anodic-cathodic formation of pH-sensitive TiO2-MoOx films on titanium

Abstract A new type of solid-state metal-oxide pH sensor based on TiO2-MoOx films on titanium has been fabricated by plasma electrolytic oxidation in the anodic-cathodic mode. The structural features of the films have been investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). According to XPS, the surface layers of the resulting films contain up to 5 at.% Mo in the form of oxides (MoO3, Mo2O5 or Mo4O11) and the near-surface layers additionally contain MoO2. The potentiometric pH response of the TiO2-MoOx electrode reveals near-Nernstian behavior, i.e. a linear working range from pH 2 to 12 with a slope of about 64 ± 2 mV/pH. The mixed films are characterized by very fast response time in alkali and acids solutions shorter than 5 s. The response is not affected by the direction of the pH change and the presence of K+ and Na+ ions. The resulting electrodes have been tested as pH sensors in acid-base titration. The relative error in the determination of NaOH using Ti/TiO2-MoOx electrode does not exceed 2.5%. The possibility of using electrodes based on TiO2-MoOx films to accurately determine the alkalinity of industrial waters is shown. In addition, this sensor offers some analytical characteristics, such as high sensitivity, good reproducibility and simple preparation procedure. Compared to a glass electrode, the Ti/TiO2-MoOx electrode has several advantages, including low cost and high stability.

Machine-intelligent inkjet-printed α-Fe2O3/rGO towards NO2 quantification in ambient humidity

Metal oxides (MOx) represent one of the most investigated chemiresistive gas sensing platforms in spite of the challenges in selectivity to analytes and interference from humidity (RH). While selectivity is traditionally improved by cross-referencing sensor arrays, interferences from humidity (RH) in ambient environment, to which the majority of the MOx materials are susceptible, cannot be inherently quantified. For standalone MOx sensors, it is therefore difficult to discriminate responses from analytes and humidity. We develop a framework which employs temperature modulation (TM) algorithms and machine learning (ML) approaches using principal component analysis (PCA) and cluster analysis of transient features, to quantify NO2 concentrations under specific RH conditions. With a single inkjet-printed MOx/reduced graphene oxide (rGO) complementary metal-oxide-semiconductor (CMOS)-integrated sensor, we achieve an overall discrimination accuracy of 97.3%. Our approach may enable the development of predictive systems for humidity sensitive sensors under ambient moisture conditions, towards the realisation of low-power, miniaturised adaptive air quality monitoring.

Continuous methane concentration measurements at the Greenland ice sheet–atmosphere interface using a low-cost, low-power metal oxide sensor system

Abstract. In this paper, the performance of a low-cost and low-power methane (CH4) sensing system prototype based on a metal oxide sensor (MOS) sensitive to CH4 is tested in a natural CH4-emitting environment at the Greenland ice sheet (GrIS). We investigate if the MOS could be used as a supplementary measurement technique for monitoring CH4 emissions from the GrIS with the scope of setting up a CH4 monitoring network along the GrIS. The performance of the MOS is evaluated on the basis of simultaneous measurements using a cavity ring-down spectroscopy (CRDS) reference instrument for CH4 over a field calibration period of approximately 100 h. Results from the field calibration period show that CH4 concentrations measured with the MOS are in very good agreement with the reference CRDS. The absolute concentration difference between the MOS and the CRDS reference values within the measured concentration range of approximately 2–100 ppm CH4 was generally lower than 5 ppm CH4, while the relative concentration deviations between the MOS and the CRDS were generally below 10 %. The calculated root-mean-square error (RMSE) for the entire field calibration period was 1.69 ppm (n=37 140). The results confirm that low-cost and low-power MOSs can be effectively used for atmospheric CH4 measurements under stable water vapor conditions. The primary scientific importance of the study is that it provides a clear example of how the application of low-cost technology can enhance our future understanding on the climatic feedbacks from the cryosphere to the atmosphere.

Single resistive sensor for selective detection of multiple VOCs employing SnO2 hollowspheres and machine learning algorithm: A proof of concept

Selective detection of harmful gasses and volatile organic compounds (VOCs) in the ambient has become a major challenge. Primarily, semiconducting metal-oxide based gas sensors sense various gases simultaneously, hence their selectivity is poor. This paper presents a single chemiresistive metal-oxide gas sensor for identification of multiple VOCs accurately by employing highly sensitive microstructure and machine learning tools. Tin oxide (SnO2) hollowspheres were taken as sensing material that were prepared through optimized hydrothermal route. Different characterizations were carried out to confirm the formation of desired morphology and structural features. The sensor device was fabricated by controlled drop cast technique over gold based interdigitated electrodes. The sensor showed remarkable response towards the target VOCs with high sensitivity and fast recovery time. Incorporation of machine learning algorithm on the obtained sensor data provided accurate identification of all the VOCs (best performance shown by random forest). In addition, the quantitative prediction of gas concentration was performed for each target gas using regression model. In comparison to e-noses (having array of sensors with different sensing material), a single chemiresistive metal-oxide sensor with proper machine learning tool is simple, economic, compact and easy to fabricate.

Characterization of metal oxide gas sensors via optical techniques

Metal oxide (MOX) sensors are increasingly gaining attention in analytical applications. Their fundamental operation principle is based on conversion reactions of selected molecular species at their semiconducting surface. However, the exact turnover of analyte gas in relation to the concentration has not been investigated in detail to date. In the present study, two optical sensing techniques—luminescence quenching for molecular oxygen and infrared spectroscopy for carbon dioxide and methane—have been coupled for characterizing the behavior of an example semiconducting MOX methane gas sensor integrated into a recently developed low-volume gas cell. Thereby, oxygen consumption during MOX operation as well as the generation of carbon dioxide from the methane conversion reaction could be quantitatively monitored. The latter was analyzed via a direct mid-infrared gas sensor system based on substrate-integrated hollow waveguide (iHWG) technology combined with a portable Fourier transform infrared spectrometer, which has been able to not only detect the amount of generated carbon dioxide but also the consumption of methane during MOX operation. Hence, a method based entirely on direct optical detection schemes was developed for characterizing the actual signal generating processes—here for the detection of methane—via MOX sensing devices via near real-time online analysis.Graphical

Nanowire-Based Sensor Array for Detection of Cross-Sensitive Gases Using PCA and Machine Learning Algorithms

In this work, a gas sensor array has been designed and developed comprising of Pt, Cu and Ag decorated TiO2 and ZnO functionalized GaN nanowires using industry standard top-down fabrication approach. The receptor metal/metal-oxide combinations within the array have been determined from our prior molecular simulation results using first principle calculations based on density functional theory (DFT). The gas sensing data was collected for both singular and mixture of NO2, ethanol, SO2 and H2 in presence of H2O and O2 gases under UV light at room temperature. Each gas produced a unique response pattern across the sensors within the array by which precise identification of cross-sensitive gases is possible. After pre-processing of raw data, unsupervised principal component analysis (PCA) technique was applied on the array response. It is found that, each analyte gas forms a separate cluster in the score plot for all the target gases and their mixtures, indicating a clear discrimination among them. Then, four supervised machine learning algorithms such as- Decision Tree, Support Vector Machine (SVM), Naive Bayes (kernel) and k-Nearest Neighbor (k-NN) were trained and optimized using their significant parameters with our array dataset for the classification of gas type. Results indicate that the optimized SVM and NB classifier models exhibited 100% classification accuracy on test dataset. Practical applicability of the considered algorithms has been discussed as well. Moreover, this array device works at room-temperature using very low power and low-cost UV light-emitting diode (LED) as compared to high power consuming commercially available metal-oxide sensors.

Can Electric Nose Breath Analysis Identify Abdominal Wall Hernia Recurrence and Aortic Aneurysms? A Proof-of-Concept Study.

Introduction. This pilot study evaluates if an electronic nose (eNose) can distinguish patients at risk for recurrent hernia formation and aortic aneurysm patients from healthy controls based on volatile organic compound analysis in exhaled air. Both hernia recurrence and aortic aneurysm are linked to impaired collagen metabolism. If patients at risk for hernia recurrence and aortic aneurysms can be identified in a reliable, low-cost, noninvasive manner, it would greatly enhance preventive options such as prophylactic mesh placement after abdominal surgery. Methods. From February to July 2017, a 3-armed proof-of-concept study was conducted at 3 hospitals including 3 groups of patients (recurrent ventral hernia, aortic aneurysm, and healthy controls). Patients were measured once at the outpatient clinic using an eNose with 3 metal-oxide sensors. A total of 64 patients (hernia, n = 29; aneurysm, n = 35) and 37 controls were included. Data were analyzed by an automated neural network, a type of self-learning software to distinguish patients from controls. Results. Receiver operating curves showed that the automated neural network was able to differentiate between recurrent hernia patients and controls (area under the curve 0.74, sensitivity 0.79, and specificity 0.65) as well as between aortic aneurysm patients and healthy controls (area under the curve 0.84, sensitivity 0.83, and specificity of 0.81). Conclusion. This pilot study shows that the eNose can distinguish patients at risk for recurrent hernia and aortic aneurysm formation from healthy controls.