Metal Oxide Sensors Research Articles

Several ML Algorithms and Their Feature Vector Design for Gas Discrimination and Concentration Measurement with an Ultrasonically Catalyzed MOX Sensor.

Although gas-borne ultrasound catalysis has been developed as a new method to discriminate gas species and measure the concentration, applications of machine learning methods in gas analyses with a single metal oxide (MOX) gas sensor catalyzed by gas-borne ultrasound are still scarce. In this work, with an ultrasonically catalyzed MOX gas sensor, we explored the effectiveness of K-nearest neighbors (KNN), support vector machine (SVM), and single-hidden-layer BP-ANN (SHBP) in gas discrimination and the application of the SHBP in concentration measurement. The target gases in this work are ethanol, acetone, methanol, hydrogen, and n-butane in clean air, respectively, and the discrimination and concentration regression are implemented by two different ML models. With the properly designed feature vectors, the SHBP method has an acceptable capability of both of species discrimination and concentration regression (success rate of gas discrimination = 99.5%, relative error of concentration regression = 6.406%). The KNN and SVM methods have similar capabilities of gas discrimination as the SHBP. This work also demonstrates a method to design the feature vectors for the ultrasonically catalyzed MOX gas sensor and to choose the feature parameters.

Pt-decorated ZnFe2O4 sensors prepared via a facile impregnation method free of reductants and surfactants for conductometric acetone sensing

Noble metals have been widely reported to improve sensing properties. However, conventional methods of preparing noble metal-decorated metal oxide sensors usually involve the use of multiple reductants and surfactants, as well as complex equipment and procedures, and most importantly, waste noble metal sources. In this study, we develop a novel modified impregnation method, which is a green, energy-saving, fast, convenient technique for preparing Pt-decorated ZnFe2O4 gas sensors. This method consists only of mixing ZnFe2O4 powder into a H2PtCl6 solution and then fabricating sensors by a general procedure, without the use of additional reagents or further treatment. Detailed structural and chemical analyses revealed that over 88% of Pt element was uniformly dispersed on the surface of ZnFe2O4. The average activation energy decreased by 51% after Pt was decorated onto the surface of ZnFe2O4, resulting in a shorter response time. As expected, the thus prepared Pt-decorated ZnFe2O4 sensors are superior to pristine commercial ZnFe2O4 in terms of better sensing properties, a higher response, and a lower optimal temperature. The proposed reductant-free impregnation method was demonstrated to provide noble-metal-decorated metal oxides successfully, easily, quickly, and economically.

A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets

Metal oxide sensor is widely used in many research fields, including E-nose for gas detection due to their tunable sensitivity, space efficiency and low cost. One of the most popular open data sets in electronic nose research contains data on various gases sampled using a MOx sensor in a wind tunnel over 16 months. A recent study published in 2022 by Nik Dennler has reported the discovery of the drift effect of a public dataset due to incorrect experimental design. they reported that the order of gas collection was not randomized and further discovered that a select set of gases were collected over a particular period. This paper expands the previous paper, by analyzing the drift effect with low signal, zero-offset subtracted signal’s mean, and standard deviation value by location and time, and examining it with TSNE, a dimensional reduction method. In addition, the accuracy by time and location was analyzed by applying it to various Deep Learning methods. According to the results, we confirmed that gas information is already classified before the gas leaks in terms of temporal and spatial domain. Therefore, the classification accuracy overestimates the actual accuracy that can be obtained due to the drift effect. Based on the results of this study, it is necessary to thoroughly verify the temporal and spatial validity of the gas dataset when using the publicly available gas dataset to develop gas detection algorithms.

Modification of Metal Oxide Semiconductor Gas Sensors Using Conducting Polymer Materials

Conducting polymers have shown great potential in detecting gases at room temperature. This has caused the rapid development of these materials for gas sensing because their conductivity can be altered when exposed to oxidant or reductant molecules at room temperature. Nevertheless, due to their relatively high attraction to volatile organic and water molecules and low conductivity, they show low sensitivity, selectivity, and thermal instability that impede their practical applications as sensors. Moreover, semiconducting inorganic (metal oxide) sensors show distinct advantages in sensing gases, including high sensitivity, rapid response to low‐concentration analytes, versatile exterior or surface chemistry, and high‐surface area. However, their high‐temperature operation has limited their sensing applications while performing excellently at room temperature. Combining these two sensitive materials brings synergistic effects. The hybrid increases sensitivity, selectivity, long‐term stability, and low‐temperature operation, which has concerned extensive interest in their use as sensors. This review summarizes the use of semiconducting metal oxides, conducting polymers, and the recent advancement in metal oxide‐conducting polymer composites for their gas‐sensing applications. Moreover, in this review, the advantages and limitations of these sensor materials are presented, and why the composites give an excellent synergistic effect by increasing sensing performance have been highlighted.

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.

Metal–Organic Framework-Derived Co-Doped ZnO Nanostructures Anchored on N-Doped Carbon as a Room-Temperature Chemiresistive Hydrogen Sensor

The impending need to utilize H2 for multiple applications has surged the need to develop H2 sensors. Development of selective and rapid room-temperature H2 sensors is an uphill task without the doping of precious metals (Pd, Pt, and Au) in semiconductor metal oxide (SMO) sensors. Nanostructure of the SMOs could play a decisive role in gas sensing properties of the material. In this study, metal–organic framework (MOF)-derived Co–ZnO anchored on nitrogen-doped carbon (Co–ZnO–N/C) nanomaterial has been demonstrated as an effective rapid room-temperature H2 sensor. The pyrolysis of monometallic ZIF-8 (Zn) gave rise to amorphous ZnO stabilized on nitrogen-doped carbon (ZnO–N/C) and was found to be innocent for H2 sensing, whereas the pyrolysis of bimetallic ZIF(Co–Zn) resulted in the formation of Co–ZnO–N/C nanostructure with a high dispersion of Co on amorphous ZnO. Co–ZnO–N/C inherited the nanostructure of the parent precursor with rhombododecahedron particles of 200–300 nm and possessed subnanometer pores. Co inclusion into ZnO has converted the innocent amorphous ZnO–N/C into a rapid room-temperature H2 sensor (by measuring the dynamic change in the resistance with respect to time). Co–ZnO–N/C displayed a 3.7% response % with 17–26 s of recovery–response times for 1% concentration of H2 under room-temperature conditions. The unique nanostructure of Co–ZnO–N/C enhanced the signal transduction (via N-doped carbon support) and promoted H2 diffusion (through subnanometer pores). Higher-temperature H2 sensing studies were also conducted at 200 °C, wherein Co–ZnO–N/C displayed an increase in response to 5.7% with 8–16 s of response–recovery times. Co–ZnO–N/C chemiresistor is a rare example that does not contain precious metals yet exhibits room-temperature hydrogen sensing.

Ultra-sensitive triethylamine gas sensor based on ZnO/MoO3 heterostructures with ppb level detection

Selective detection of Triethylamine (TEA) is of great significance for environmental monitoring and daily life. In this work, ZnO/MoO3 heterostructures with ultra-sensitive and rapid response-recovery for TEA gas were prepared by a facile hydrothermal method. The ZnO/MoO3 sensor exhibits remarkable reproducibility and long-term stability. Notably, the ZnO/MoO3 sensor achieves a response of 519 for 100 ppm TEA gas at 180 ºC with a detection limit as low as 10 ppb. The superb sensing performance of the ZnO/MoO3 sensor is mainly attributed to the synergistic effect of ZnO and MoO3, and the enhanced electron transfer rate of the heterostructures. In addition, the density functional theory (DFT) calculations were used to explore the sensing mechanism in detail, further confirming the experimental conclusion. Therefore, ZnO/MoO3 sensor has potential application in practical TEA-detection, thus establishing a feasible strategy for promoting gas-sensing properties of metal oxide sensors.

A Low Cost Electronic Nose System for Classification of Gayo Arabica Coffee Roasting Levels Using Stepwise Linear Discriminant and K-Nearest Neighbor

A low-cost electronic nose (E-Nose) system using metal oxide sensor (MOS) was developed for Gayo arabica coffee roasting level. The developed electronic nose was designed to have a simple, rapid detection, as wells as provides reliable results. The E-Nose system is equipped with MOS sensors, sensor chamber, microcontroller, computer, and data acquisition system. The level of coffee roasting was monitored by read the data from the sensors continuously in real time every second. The sensor signals were recorded in Excel file using data acquisition system and analysed using both stepwise linear discrimination and k-nearest neighbor classifiers. A high percentage (91.67%) of accuracy was obtained using stepwise linear discrimination method. Furthermore, k-nearest neighbor classifier using city block distance demonstrated higher accuracy than stepwise linear discrimination classifier. The results showed that the electronic nose system has a potential for assessing Gayo arabica coffee roasting level. The study confirmed that the proposed electronic nose equipped with at least two MOS sensors was suitable for monitoring the level of coffee roasting level. The result could be used for evaluating other varieties of roasted coffee.

Discriminating gas molecules at room temperature by UV light modulation (ULM) of nonselective metal oxide sensors

Though temperature modulation has demonstrated its capability in addressing selectivity issue of metal oxide semiconductor (MOS) gas sensors, integration of (micro)heater not only drastically adds power consumption and device manufacture complexity, but also hinders the use of flexible polymer substrate/platform. Exploring an approach enabling outstanding selectivity at room temperature (RT) is highly required, but it remains a big challenge. Herein, UV light modulation (ULM) with a rectangular UV wave has been proposed to regulate the transient response signals of sensor arrays, composed of ZnO/(Sr0.6Bi0.305)2Bi2O7 (ZnO/SBO), ZnO and In2O3. Upon pulsed UV excitation, chemisorption (or redox reaction) of injected target vapors (on MOS surface) could be activated at RT. Arising from the specific gas/MOS binding configurations for different gases and MOSs, quite different recovery kinetics under UV illumination (associated with deactivation of chemisorbed target molecules) were found, which endow nonselective MOS sensors with superior selectivity. Multilayer perceptron (MLP) indicates a 99% recognition accuracy ratio of eleven kinds of tested gases, including four kinds of benzene molecules. Our work demonstrates that transient response signals during ULM contain rich features of adsorbed gas molecules, which offers an opportunity for the facile design of advanced smart gas molecule recognition chips on both rigid and flexible platforms.

Nitrocellulose Propellants Standard Stability Tests Enhancement Employing Semiconductor Sensors

AbstractNowadays, solid propulsion is the workhorse of a wide range of applications, from gun ammunition to missiles; and Nitrocellulose, NC, is still widely used in propellant compositions. However, its long‐time storage represents a serious risk due to NC natural autocatalytic degradation process, that may lead to severe accidents. Some National Surveillance Programs continue using the so‐called Stability Tests to monitor Nitrocellulose condition and storage risks; nevertheless, most users consider these tests technologically outdated and difficult to achieve a precise interpretation. This manuscript deals with this NC stability challenge, developing an innovative technology but maintaining Stability Tests phenomenological behaviour, in order to quantify naturally emitted nitrogen oxides, NOx, from Nitrocellulose vapours, as the main indicator for propellants degradation degree. However, accurate NOx quantification is usually strongly interfered by other naturally emitted vapours from gunpowder components, both in Stability Tests and in the technological proposal. Consequently, this document not only describes the technology and main development milestone, but individual components are also studied to reach a proper essay validation, employing Metal Oxide sensors, MOx. Additionally, signal deconvolution is presented to sharpen the NOx determination from interferences with two real samples (NC gunpowder), which are natural and artificially aged, determining performance of sensors and comparing conclusions with those from classical tests. Finally, presented results have shown insignificant interference of other chemicals present in the gunpowder over NOx quantification. According to these conclusions, this technology can be considered a technological improvement on NC Stability Tests.

E-nose, E-tongue Combined with GC-IMS to Analyze the Influence of Key Additives during Processing on the Flavor of Infant Formula.

In order to analyze the influence of key additives during processing on the flavor of infant formula, the headspace-gas chromatography-ion mobility spectrometry, electronic tongue, and electronic nose techniques were used to evaluate flavor during the processing of stage 1 infant formula milk powder (0-6 months), including the analysis of seven critical additives. A total of 41 volatile compounds were identified, involving 12 aldehydes, 11 ketones, 9 esters, 4 olefins, 2 alcohols, 2 furans, and 1 acid. The electronic nose metal oxide sensor W5S had the highest response, followed by W1S and W2S, illustrating that these three sensors had great effects on distinguishing samples. The response results of the electronic tongue showed that the three sensory attributes of bitter, salty, and umami, as well as the richness of aftertaste, were more prominent, which contributed significantly to evaluating the taste profile and distinguishing among samples. Raw milk is an essential control point in the flavor formation process of stage 1 infant formula milk powder. Demineralized whey powder is the primary source of potential off-flavor components in hydrolyzed milk protein infant formula. This study revealed the quality characteristics and flavor differences of key additives in the production process of stage 1 infant formula milk powder, which could provide theoretical guidance for the quality control and sensory improvement of the industrialized production of infant formula.

Metal-Oxide FET Biosensor for Point-of-Care Testing: Overview and Perspective.

Metal-oxide semiconducting materials are promising for building high-performance field-effect transistor (FET) based biochemical sensors. The existence of well-established top-down scalable manufacturing processes enables the reliable production of cost-effective yet high-performance sensors, two key considerations toward the translation of such devices in real-life applications. Metal-oxide semiconductor FET biochemical sensors are especially well-suited to the development of Point-of-Care testing (PoCT) devices, as illustrated by the rapidly growing body of reports in the field. Yet, metal-oxide semiconductor FET sensors remain confined to date, mainly in academia. Toward accelerating the real-life translation of this exciting technology, we review the current literature and discuss the critical features underpinning the successful development of metal-oxide semiconductor FET-based PoCT devices that meet the stringent performance, manufacturing, and regulatory requirements of PoCT.

Development and Field Validation of Low-Cost Metal Oxide Nanosensors for Tropospheric Ozone Monitoring in Rural Areas

This work describes the technical features and the performance of two different types of metal-oxide semiconductor sensors, based on ZnO:Ga thin films and SnO2-G nanofibrous layers, for tropospheric ozone monitoring in ambient air. These nanostructures were tested and compared with commercial metal-oxide semiconductor sensors under controlled laboratory conditions and in a field campaign during summer 2021 in Monfragüe National Park (western Spain). The paper also details the design of the electronic device developed for this purpose. A machine learning algorithm based on Support Vector Regression (SVR) allowed the conversion of the resistive values into ozone concentration, which was evaluated afterward. The results showed that the manufactured sensors performed similarly to the commercial sensors in terms of R2 (0.94 and 0.95) and RMSE (5.21 and 4.83 μg∙m−3). Moreover, a novel uncertainty calculation based on European guides for air quality sensor testing was conducted, in which the manufactured sensors outperformed the commercial ones.

Vanadium Dioxide-Based Miniaturized Thermal Sensors: Humidity Effects on Phase Change and Sensitivity

With rapid advancements in technology in the electronics sector, demand for the miniaturization of devices while retaining their functionality is on the rise. Metal oxide-based thermal sensors are desired, owing to their enhanced sensing capabilities and low cost of operation. Highly sensitive metal oxide sensors can enable stable, accurate, and miniaturized thermal sensors tailored to different operational ranges. However, the influence of humidity and how it affects the sensitivity of the material by interacting with the material surface has not been extensively studied. In this work, we report a vanadium dioxide (VO2)-based thin film thermal sensor studied under the influence of varying humidity conditions. The effect of different humidity levels on the overall thermal sensing behavior and the insulator-to-metal transition (IMT) phenomenon was investigated. Further, density functional theory (DFT) studies were conducted to understand the thermal sensing mechanism under changing humidity conditions. The developed sensor exhibited a good response over a broad temperature range of −100 to 100 °C, with a TCR of −0.00243%, high sensitivity, and cyclic repeatability. Wireless measurement capabilities were also demonstrated. Such sensors could potentially be used in environmental sensing applications.

High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level

Developing a better understanding of gas-metal oxide interactions as well as improving the output performance with the aging of the gas sensing array represents a critical challenge. In this work, we have designed a surface-functionalized metal-oxide sensors array containing six units for detecting NO2, SO2, H2S, and CO hazardous gases. The responses of the sensor array to four target gases are enhanced by various catalytic effects on the surface, and they show distinctive responses to each gas. For improved selectivity, a pattern recognition algorithm is implemented under an edge computing-based environment to relieve the overload of a monitoring server. Moreover, a self-calibration method for long-term stability is designed to indirectly calibrate gas responses with aging. With its edge-computing device prototype, which includes a sensor array and its readout integrated circuit, four target gases are distinguished by an artificial neural network algorithm with 97% accuracy. The present work describes the effective platforms for emerging next-generation gas sensors with improved communication and monitoring systems.

TiO2 Gas Sensors Combining Experimental and DFT Calculations: A Review.

Gas sensors play an irreplaceable role in industry and life. Different types of gas sensors, including metal-oxide sensors, are developed for different scenarios. Titanium dioxide is widely used in dyes, photocatalysis, and other fields by virtue of its nontoxic and nonhazardous properties, and excellent performance. Additionally, researchers are continuously exploring applications in other fields, such as gas sensors and batteries. The preparation methods include deposition, magnetron sputtering, and electrostatic spinning. As researchers continue to study sensors with the help of modern computers, microcosm simulations have been implemented, opening up new possibilities for research. The combination of simulation and calculation will help us to better grasp the reaction mechanisms, improve the design of gas sensor materials, and better respond to different gas environments. In this paper, the experimental and computational aspects of are reviewed, and the future research directions are described.

Characterization of Noble and Transition Metal Doped-ZnO Sensing of VOC Analytes Using Broadband Dielectric Spectroscopy

Metal-oxide (MO) semiconductor-based gas sensors such as SnO2 and ZnO, have traditionally been the common materials of choice to be used for gas detection based on the mechanism of chemosensitivity. They are some of the most economic and efficient gas sensing materials, and they show a relatively quick response to several volatile organic compounds (VOCs). Doping of the MO nano surface lattices with noble and transition metals has been shown to further enhance their gas-sensing performance by improving the catalytic properties of the MO, ameliorating their response kinetics and selectivity toward a particular gas. However, the chemosensitivity method requires electrical contacts to the MO sensor material connecting the electrical circuits to detect electrical changes of the MO in response to volatile analytes. This approach introduces inherent errors into the measurements due to the need to make physical electrical contacts with the sensing MO semiconductor surface. In this talk, I will discuss the use of a microwaves-based detection technique known as Broadband dielectric spectroscopy (BDS) to rapidly interrogate a dynamic range of material characteristics such as polarizability, phase, electrical conductivity, etc. This technique eliminates the introduction of parasitic measurement errors which come about due to electrical contacts. Specifically, we show that the microwave signal propagation characteristics (i.e., S-parameters) induces chemical changes which allow us to characterize the detection of VOCs using ZnO doped with a selection of noble and transition metals as the sensing material, at temperatures under 100 °C.

DEVELOPMENT OF DATA ACQUISITION SYSTEM FOR MOBILE ULTRAVIOLET (UV) MONITORING DEVICE

In this age of modernization, smart or self-sufficient frameworks in all domains of work are becoming increasingly important as innovation advances. This involves improving data collecting for UV monitoring system using metal oxide sensors, an underdeveloped market niche. The Data Acquisition (DAQ) system available at the moment is antiquated and frustrating. This project aims to establish a framework for acquiring information about UV monitoring devices using metal oxide sensors. Metal oxide-based sensor uses titanium dioxide nanorod arrays (TNAs). Self-powered UV photosensor creates electricity when exposed to UV light. UV photosensors are photoelectrochemical (PEC). Monitoring the sensor's real-time output voltage in relation to UV intensity evaluates the data acquisition system's performance. A Raspberry Pi is used to evaluate the data security architecture for the Cytron-UNO development board. The sensor's data gathering system detected the current produced and stamped the date and time on it. This paper discusses trait formation and experimental data.

Enhanced Ambient Sensing Environment-A New Method for Calibrating Low-Cost Gas Sensors.

Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The Enhanced Ambient Sensing Environment (EASE) method described in this article, is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration. It involves calibrating sensors inside a duct, drawing in ambient air with similar properties to the site where the sensors will operate, but with the added feature of being able to artificially increases or decrease pollutant levels, thus condensing the calibration period required. Calibration of both metal-oxide (MOx) and electrochemical (EC) gas sensors for the measurement of NO2 and O3 (0–120 ppb) were conducted in EASE, laboratory and field environments, and validated in field environments. The EC sensors performed marginally better than MOx sensors for NO2 measurement and sensor performance was similar for O3 measurement, but the EC sensor nodes had less node inter-node variability and were more robust. For both gasses and sensor types the EASE calibration outperformed the laboratory calibration, and performed similarly to or better than the field calibration, whilst requiring a fraction of the time.

Photon confinement in a silicon cavity of an image sensor by plasmonic diffraction for near-infrared absorption enhancement.

Silicon-based image sensors are attractive for applications in the near-infrared (NIR) range owing to their low-cost and high availability. However, novel approaches are required to enhance their light absorption, hindered by the silicon band gap. In this study, we proposed a light trapping strategy in a silicon absorption layer by plasmonic diffraction and reflection within a pixel to improve the sensitivity at a specific NIR wavelength for complementary metal-oxide semiconductor image sensors. The plasmonic grating diffracted light under the quasi-resonant condition of the surface plasmon polaritons. We simulated the silicon absorption efficiency for plasmonic diffraction combined with metal-filled trenches and a pre-metal dielectric (PMD) layer. Backward propagation light in silicon by a total internal reflection at the bottom decoupled with plasmonic grating. A single SiO2 protrusion was added at the silicon bottom to prevent decoupling by scattering the light in the silicon and trapping it within the pixel. In addition, the light transmitted to the PMD layer is reflected by the wiring layer used as a mirror. The photon confinement in silicon by these constructions improved the absorption by approximately 8.2 times at an NIR wavelength of 940 nm with 3-µm-thick. It is useful for NIR imaging system with active laser illumination.