Highly Sensitive and Selective VOC Sensor Systems Based on Semiconductor Gas Sensors: How to?

Fit-for-purpose WWTP unmanned aerial systems: A game changer towards an integrated and sustainable management strategy

In the ongoing Anthropocene era, air quality monitoring constitutes a primary axis of European and international policies for all sectors, including Waste Water Treatment Plants (WWTPs). Unmanned Aerial Systems (UASs) with proper sensing equipment provide an edge technology for air quality and odor monitoring. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry has been used in civil engineering, environmental (water) quality assessment and lately for industrial facilities monitoring. This study constitutes a systematic review of the late advances and limitations of germane equipment and implementations. Despite their unassailable flexibility and efficiency, the employment of the aforementioned technologies in WWTP remote monitoring is yet sparse, partial, and concerns only particular aspects. The main finding of the review was the lack of a tailored UAS for WWTP monitoring in the literature. Therefore, to fill in this gap, we propose a fit-for-purpose remote monitoring system consisting of a UAS with a platform that would integrate all the required sensors for air quality (i.e., emissions of H2S, NH3, NOx, SO2, CH4, CO, CO2, VOCs, and PM) and odor monitoring, multispectral and thermal cameras for photogrammetric structural health monitoring (SHM) and wastewater/effluent properties (e.g., color, temperature, etc.) of a WWTP. It constitutes a novel, supreme and integrated approach to improve the sustainable management of WWTPs. Specifically, the developments that a fit-for-purpose WWTP UAS would launch, are fostering the decision-making of managers, administrations, and policymakers, both in operational conditions and in case of failures, accidents or natural disasters. Furthermore, it would significantly reduce the operational expenditure of a WWTP, ensuring personnel and population health standards, and local area sustainability.

Enhance Ethanol Sensing Performance of Fe-Doped Tetragonal SnO2 Films on Glass Substrate with a Proposed Mathematical Model for Diffusion in Porous Media

This research enhances ethanol sensing with Fe-doped tetragonal SnO2 films on glass, improving gas sensor reliability and sensitivity. The primary objective was to improve the sensitivity and operational efficiency of SnO2 sensors through Fe doping. The SnO2 sensors were synthesized using a flexible and adaptable method that allows for precise doping control, with energy-dispersive X-ray spectroscopy (EDX) confirming homogeneous Fe distribution within the SnO2 matrix. A morphological analysis showed a surface structure ideal for gas sensing. The results demonstrated significant improvement in ethanol response (1 to 20 ppm) and lower temperatures compared to undoped SnO2 sensors. The Fe-doped sensors exhibited higher sensitivity, enabling the detection of low ethanol concentrations and showing rapid response and recovery times. These findings suggest that Fe doping enhances the interaction between ethanol molecules and the sensor surface, improving performance. A mathematical model based on diffusion in porous media was employed to further analyze and optimize sensor performance. The model considers the diffusion of ethanol molecules through the porous SnO2 matrix, considering factors such as surface morphology and doping concentration. Additionally, the choice of electrode material plays a crucial role in extending the sensor's lifespan, highlighting the importance of material selection in sensor design.

Volatile organic compounds: A threat to the environment and health hazards to living organisms - A review

Volatile organic compounds (VOCs) are the organic compounds having a minimum vapor pressure of 0.13 kPa at standard temperature and pressure (293 K, 101 kPa). Being used as a solvent for organic and inorganic compounds, they have a wide range of applications. Most of the VOCs are non-biodegradable and very easily become component of the environment and deplete its purity. It also deteriorates the water quality index of the water bodies, impairs the physiology of living beings, enters the food chain by bio-magnification and degrades, decomposes and manipulates the physiology of living organisms. To unveil the adverse impacts of volatile organic compounds (VOCs) and their rapid eruption and interference in the living world, a review has been designed. This review presents an insight into the currently available VOCs, their sources, applications, sampling methods, analytic procedures, imposition on the health of aquatic and terrestrial communities and their contamination of the environment. Elaboration has been done on representation of toxicological effects of VOCs on vertebrates, invertebrates, and birds. Subsequently, the role of environmental agencies in the protection of environment has also been illustrated.

Adjusting surface coverage of Pt nanocatalyst decoration for selectivity control in CMOS-integrated SnO2 thin film gas sensors

Smart gas-sensor devices are of crucial importance for emerging consumer electronics and Internet-of-Things (IoT) applications, in particular for indoor and outdoor air quality monitoring (e.g., CO2 levels) or for detecting pollutants harmful for human health. Chemoresistive nanosensors based on metal-oxide semiconductors are among the most promising technologies due to their high sensitivity and suitability for scalable low-cost fabrication of miniaturised devices. However, poor selectivity between different target analytes restrains this technology from broader applicability. This is commonly addressed by chemical functionalisation of the sensor surface via catalytic nanoparticles. Yet, while the latter led to significant advances in gas selectivity, nanocatalyst decoration with precise size and coverage control remains challenging. Here, we present CMOS-integrated gas sensors based on tin oxide (SnO2) films deposited by spray pyrolysis technology, which were functionalised with platinum (Pt) nanocatalysts. We deposited size-selected Pt nanoparticles (narrow size distribution around 3 nm) by magnetron-sputtering inert-gas condensation, a technique which enables straightforward surface coverage control. The resulting impact on SnO2 sensor properties for CO and volatile organic compound (VOC) detection via functionalisation was investigated. We identified an upper threshold for nanoparticle deposition time above which increased surface coverage did not result in further CO or VOC sensitivity enhancement. Most importantly, we demonstrate a method to adjust the selectivity between these target gases by simply adjusting the Pt nanoparticle deposition time. Using a simple computational model for nanocatalyst coverage resulting from random gas-phase deposition, we support our findings and discuss the effects of nanoparticle coalescence as well as inter-particle distances on sensor functionalisation.

Response Surface Modeling of the Steady-State Impedance Responses of Gas Sensor Arrays Comprising Functionalized Carbon Nanotubes to Detect Ozone and Nitrogen Dioxide

Carbon nanotube (CNT) sensors provide a versatile chemical platform for ambient monitoring of ozone (O3) and nitrogen dioxide (NO2), two important airborne pollutants known to cause acute respiratory and cardiovascular health problems. CNTs have shown great potential for use as sensing layers due to their unique properties, including high surface to volume ratio, numerous active sites and crystal facets with high surface reactivity, and high thermal and electrical conductivity. With operational advantages such as compactness, low-power operation, and easy integration with electronics devices, nanotechnology is expected to have a significant impact on portable low-cost environmental sensors. Enhanced sensitivity is feasible by functionalizing the CNTs with polymers, metals, and metal oxides. This paper focuses on the design and performance of a two-element array of O3 and NO2 sensors comprising single-walled CNTs functionalized by covalent modification with organic functional groups. Unlike the conventional chemiresistor in which the change in DC resistance across the sensor terminals is measured, we characterize the sensor array response by measuring both the magnitude and phase of the AC impedance. Multivariate response provides higher degrees of freedom in sensor array data processing. The complex impedance of each sensor is measured at 5 kHz in a controlled gas-flow chamber using gas mixtures with O3 in the 60-120 ppb range and NO2 between 20 and 80 ppb. The measured data reveal response change in the 26-36% range for the O3 sensor and 5-31% for the NO2 sensor. Multivariate optimization is used to fit the laboratory measurements to a response surface mathematical model, from which sensitivity and selectivity are calculated. The ozone sensor exhibits high sensitivity (e.g., 5 to 6 MΩ/ppb for the impedance magnitude) and high selectivity (0.8 to 0.9) for interferent (NO2) levels below 30 ppb. However, the NO2 sensor is not selective.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Calculations of adsorption-dependent refractive indices of metal-organic frameworks for gas sensing applications

Detection of volatile organic compounds (VOCs) is one of the most challenging tasks in modelling breath analyzers because of their low concentrations (parts-per-billion (ppb) to parts-per-million (ppm)) in breath and the high humidity levels in exhaled breaths. The refractive index is one of the crucial optical properties of metal-organic frameworks (MOFs), which is changeable via the variation of gas species and concentrations that can be utilized as gas detectors. Herein, for the first time, we used Lorentz-Lorentz, Maxwell-Ga, and Bruggeman effective medium approximation (EMA) equations to compute the percentage change in the index of refraction (Δn%) of ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr) and HKUST-1 upon exposure to ethanol at various partial pressures. We also determined the enhancement factors of the mentioned MOFs to assess the storage capability of MOFs and the biosensors' selectivity through guest-host interactions, especially, at low guest concentrations.

Sampling Volatile Organic Compound Emissions from Consumer Products: A Review

Volatile organic compounds (VOCs) are common constituents of many consumer products. Although many VOCs are generally considered harmless at low concentrations, some compound classes represent substances of concern in relation to human (inhalation) exposure and can elicit adverse health effects, especially when concentrations build up, such as in indoor settings. Determining VOC emissions from consumer products, such as toys, utensils or decorative articles, is of utmost importance to enable the assessment of inhalation exposure under real-world scenarios with respect to consumer safety. Due to the diverse sizes and shapes of such products, as well as their differing uses, a one-size-fits-all approach for measuring VOC emissions is not possible, thus, sampling procedures must be chosen carefully to best suit the sample under investigation. This review outlines the different sampling approaches for characterizing VOC emissions from consumer products, including headspace and emission test chamber methods. The advantages and disadvantages of each sampling technique are discussed in relation to their time and cost efficiency, as well as their suitability to realistically assess VOC inhalation exposures.

A Virtual Electronic Nose for the Efficient Classification and Quantification of Volatile Organic Compounds

Although many chemical gas sensors report high sensitivity towards volatile organic compounds (VOCs), finding selective gas sensing technologies that can classify different VOCs is an ongoing and highly important challenge. By exploiting the synergy between virtual electronic noses and machine learning techniques, we demonstrate the possibility of efficiently discriminating, classifying, and quantifying short-chain oxygenated VOCs in the parts-per-billion concentration range. Several experimental results show a reproducible correlation between the predicted and measured values. A 10-fold cross-validated quadratic support vector machine classifier reports a validation accuracy of 91% for the different gases and concentrations studied. Additionally, a 10-fold cross-validated partial least square regression quantifier can predict their concentrations with coefficients of determination, R², up to 0.99. Our methodology and analysis provide an alternative approach to overcoming the issue of gas sensors' selectivity, and have the potential to be applied across various areas of science and engineering where it is important to measure gases with high accuracy.

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.

High Sensitivity Monitoring of VOCs in Air through FTIR Spectroscopy Using a Multipass Gas Cell Setup

Human exposure to Volatile Organic Compounds (VOCs) and their presence in indoor and working environments is recognized as a serious health risk, causing impairments of varying severities. Different detecting systems able to monitor VOCs are available in the market; however, they have significant limitations for both sensitivity and chemical discrimination capability. During the last years we studied systematically the use of Fourier Transform Infrared (FTIR) spectroscopy as an alternative, powerful tool for quantifying VOCs in air. We calibrated the method for a set of compounds (styrene, acetone, ethanol and isopropanol) by using both laboratory and portable infrared spectrometers. The aim was to develop a new, and highly sensitive sensor system for VOCs monitoring. In this paper, we improved the setup performance, testing the feasibility of using a multipass cell with the aim of extending the sensitivity of our system down to the part per million (ppm) level. Considering that multipass cells are now also available for portable instruments, this study opens the road for the design of new high-resolution devices for environmental monitoring.

Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis

More than one million new cases of prostate cancer (PCa) were reported worldwide in 2020, and a significant increase of PCa incidence up to 2040 is estimated. Despite potential treatability in early stages, PCa diagnosis is challenging because of late symptoms' onset and limits of current screening procedures. It has been now accepted that cell transformation leads to release of volatile organic compounds in biologic fluids, including urine. Thus, several studies proposed the possibility to develop new diagnostic tools based on urine analysis. Among these, electronic noses (eNoses) represent one of the most promising devices, because of their potential to provide a non-invasive diagnosis. Here we describe the approach aimed at defining the experimental protocol for eNose application for PCa diagnosis. Our research investigates effects of sample preparation and analysis on eNose responses and repeatability. The dependence of eNose diagnostic performance on urine portion analysed, techniques involved for extracting urine volatiles and conditioning temperature were analysed. 192 subjects (132 PCa patients and 60 controls) were involved. The developed experimental protocol has resulted in accuracy, sensitivity and specificity of 83% (CI_95% 77-89), 82% (CI_95% 73-88) and 87% (CI_95% 75-94), respectively. Our findings define eNoses as valuable diagnostic tool allowing rapid and non-invasive PCa diagnosis.

Layered Double Hydroxide-Based Gas Sensors for VOC Detection at Room Temperature

Miniaturized low-cost sensors for volatile organic compounds (VOCs) have the potentiality to become a fundamental tool for indoor and outdoor air quality monitoring, to significantly improve everyday life. Layered double hydroxides (LDHs) belong to the class of anionic clays and are largely employed for NO x detection, while few results are reported on VOCs. In this work, a novel LDH coprecipitation method is proposed. For the first time, a study comparing four LDHs (ZnAl-Cl, ZnFe-Cl, ZnAl-NO3, and MgAl-NO3) is carried out to investigate the sensing performances. As explored through several microscopy and spectroscopy analyses, LDHs show a morphology characterized by a large surface area and a three-dimensional hierarchical flowerlike architecture with micro- and nanopores that induce a fast diffusion and highly effective surface interaction of the target gases. The fabricated sensors, operating at room temperature, are able to reversibly and selectively detect acetone, ethanol, ammonia, and chlorine vapors, reaching significant sensing response values up to 6% at 21 °C. The results demonstrate that by changing the LDHs' composition, it is possible to modulate the sensitivity and selectivity of the sensor, helping the discrimination of different analytes, and the consequent integration on a sensor array paves the way for electronic nose development.

Real-time quantitative detection of styrene in atmosphere in presence of other volatile-organic compounds using a portable device

Exposure to styrene is a major safety concern in the fibreglass processing industry. This compound is classified by the International Agency for Research on Cancer as a possible human carcinogen. Several types of analytical equipment exist for detecting volatile organic compounds (VOCs) in the atmosphere; however, most of them operate ex-situ or do not provide easy discrimination between different molecules. This work introduces an improved and portable method based on FTIR spectroscopy to analyse toxic gaseous substances in working sites down to a concentration of less than 4 ppm. Styrene and a combination of VOCs typically associated with it in industrial processes, such as acetone, ethanol, xylene and isopropanol, have been used to calibrate and test the methodology. The results demonstrate that the technique offers the possibility to discriminate between different gaseous compounds in the atmosphere with a high degree of confidence and obtain very accurate quantitative information on their concentration, down to the ppm level, even when different VOCs are present in a mixture.

Effects of Night Ventilation on Indoor Air Quality in Educational Buildings—A Field Study

Night ventilation methods have been used in educational buildings to guarantee indoor air quality at the beginning of occupied periods. A typical method has been to pre-start ventilation 2 h before the space usage. Another selection has been to ventilate a building continuously during the night with a minimum airflow rate that can dilute material emissions. In this study, the pre-started, continuous, and intermittent ventilation methods were compared by assessing indoor air quality in field measurements. The daytime ventilation was operating normally. The test periods lasted for 2 weeks. Indoor air quality was assessed by measuring the total volatile organic compounds and microbial concentrations using the quantitative polymerase chain reaction method. Additionally, the thermal conditions, carbon dioxide, and pressure differences over the building envelope were measured. The results show that the night ventilation strategy had negligible effects on microbial concentrations. In most cases, the indoor air microbial concentrations were only a few percent of those found outdoors. The averaged concentration of total volatile organic compounds was at the same level with all the night ventilation methods at the beginning of the occupied periods in the mornings. The concentrations reached a minimum level after 2-h ventilation. The concentrations of total volatile organic compounds were higher during the day than at night. This reveals that space usage had the largest effect on the total volatile organic compounds. Generally, the results show that continuous night ventilation does not significantly affect the biological and chemical contaminants. Consequently, a 2-h flushing period is long enough to freshen indoor air before occupancy.

Low-Cost Air Quality Sensing towards Smart Homes

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

Metal Oxide Nanolayer-Decorated Epitaxial Graphene: A Gas Sensor Study

In this manuscript, we explore the sensor properties of epitaxially grown graphene on silicon carbide decorated with nanolayers of CuO, Fe₃O₄, V₂O₅, or ZrO₂. The sensor devices were investigated in regard to their response towards NH₃ as a typical reducing gas and CO, C₆H₆, CH₂O, and NO₂ as gases of interest for air quality monitoring. Moreover, the impact of operating temperature, relative humidity, and additional UV irradiation as changes in the sensing environment have been explored towards their impact on sensing properties. Finally, a cross-laboratory study is presented, supporting stable sensor responses, and the final data is merged into a simplified sensor array. This study shows that sensors can be tailored not only by using different materials but also by applying different working conditions, according to the requirements of certain applications. Lastly, a combination of several different sensors into a sensor array leads to a well-performing sensor system that, with further development, could be suitable for several applications where there is no solution on the market today.

Influence of Mg Doping Levels on the Sensing Properties of SnO2 Films

This work presents the effect of magnesium (Mg) doping on the sensing properties of tin dioxide (SnO2) thin films. Mg-doped SnO2 films were prepared via a spray pyrolysis method using three doping concentrations (0.8 at.%, 1.2 at.%, and 1.6 at.%) and the sensing responses were obtained at a comparatively low operating temperature (160 °C) compared to other gas sensitive materials in the literature. The morphological, structural and chemical composition analysis of the doped films show local lattice disorders and a proportional decrease in the average crystallite size as the Mg-doping level increases. These results also indicate an excess of Mg (in the samples prepared with 1.6 at.% of magnesium) which causes the formation of a secondary magnesium oxide phase. The films are tested towards three volatile organic compounds (VOCs), including ethanol, acetone, and toluene. The gas sensing tests show an enhancement of the sensing properties to these vapors as the Mg-doping level rises. This improvement is particularly observed for ethanol and, thus, the gas sensing analysis is focused on this analyte. Results to 80 ppm of ethanol, for instance, show that the response of the 1.6 at.% Mg-doped SnO2 film is four times higher and 90 s faster than that of the 0.8 at.% Mg-doped SnO2 film. This enhancement is attributed to the Mg-incorporation into the SnO2 cell and to the formation of MgO within the film. These two factors maximize the electrical resistance change in the gas adsorption stage, and thus, raise ethanol sensitivity.

Evaluation of the Indoor Air Quality in Governmental Oversight Supermarkets (Co-Ops) in Kuwait

Examining the indoor air environment of public venues, especially populated supermarkets such as Co-Ops in Kuwait, is crucial to ensure that these venues are safe from indoor environmental deficits such as sick building syndrome (SBS). The aim of this study was to characterize the quality of the indoor air environment of the Co-Ops supermarkets in Kuwait based on investigation of CO2, CO, NO2, H2S, TVOCs, and NMHC. On-site measurements were conducted to evaluate these parameters in three locations at the selected Co-Ops, and the perceived air quality (PAQ) was determined to quantify the air’s pollutants as perceived by humans. Moreover, the indoor air quality index (AQI) was constructed for the selected locations, and the ANOVA test was used to analyze the association between the observed concentrations among these environmental parameters. At least in one spot at each Co-Op, the tested environmental parameters exceeded the threshold limit set by the environmental agencies. The PAQ for Co-Op1, 2, and 3 are 1.25, 1.00, and 0.75 respectively. CO2 was significantly found in an association with CO, H2S, and TVOCs, and its indoor-outdoor concentrations were significantly correlated with R2 values ranges from 0.40 to 0.86 depending on the tested location.

Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources

An array of low-cost sensors was assembled and tested in a chamber environment wherein several pollutant mixtures were generated. The four classes of sources that were simulated were mobile emissions, biomass burning, natural gas emissions, and gasoline vapors. A two-step regression and classification method was developed and applied to the sensor data from this array. We first applied regression models to estimate the concentrations of several compounds and then classification models trained to use those estimates to identify the presence of each of those sources. The regression models that were used included forms of multiple linear regression, random forests, Gaussian process regression, and neural networks. The regression models with human-interpretable outputs were investigated to understand the utility of each sensor signal. The classification models that were trained included logistic regression, random forests, support vector machines, and neural networks. The best combination of models was determined by maximizing the F1 score on ten-fold cross-validation data. The highest F1 score, as calculated on testing data, was 0.72 and was produced by the combination of a multiple linear regression model utilizing the full array of sensors and a random forest classification model.

Gas Sensors Based on Nano/Microstructured Organic Field-Effect Transistors

Benefiting from the advantages of organic field-effect transistors (OFETs), including synthetic versatility of organic molecular design and environmental sensitivity, gas sensors based on OFETs have drawn much attention in recent years. Potential applications focus on the detection of specific gas species such as explosive, toxic gases, or volatile organic compounds (VOCs) that play vital roles in environmental monitoring, industrial manufacturing, smart health care, food security, and national defense. To achieve high sensitivity, selectivity, and ambient stability with rapid response and recovery speed, the regulation and adjustment of the nano/microstructure of the organic semiconductor (OSC) layer has proven to be an effective strategy. Here, the progress of OFET gas sensors with nano/microstructure is selectively presented. Devices based on OSC films one dimensional (1D) single crystal nanowires, nanorods, and nanofibers are introduced. Then, devices based on two dimensional (2D) and ultrathin OSC films, fabricated by methods such as thermal evaporation, dip-coating, spin-coating, and solution-shearing methods are presented, followed by an introduction of porous OFET sensors. Additionally, the applications of nanostructured receptors in OFET sensors are given. Finally, an outlook in view of the current research state is presented and eight further challenges for gas sensors based on OFETs are suggested.

Graphene Decorated with Iron Oxide Nanoparticles for Highly Sensitive Interaction with Volatile Organic Compounds

Gases, such as nitrogen dioxide, formaldehyde and benzene, are toxic even at very low concentrations. However, so far there are no low-cost sensors available with sufficiently low detection limits and desired response times, which are able to detect them in the ranges relevant for air quality control. In this work, we address both, detection of small gas amounts and fast response times, using epitaxially grown graphene decorated with iron oxide nanoparticles. This hybrid surface is used as a sensing layer to detect formaldehyde and benzene at concentrations of relevance (low parts per billion). The performance enhancement was additionally validated using density functional theory calculations to see the effect of decoration on binding energies between the gas molecules and the sensor surface. Moreover, the time constants can be drastically reduced using a derivative sensor signal readout, allowing the sensor to work at detection limits and sampling rates desired for air quality monitoring applications.

FEM Analysis of Sezawa Mode SAW Sensor for VOC Based on CMOS Compatible AlN/SiO₂/Si Multilayer Structure

A Finite Element Method (FEM) simulation study is conducted, aiming to scrutinize the sensitivity of Sezawa wave mode in a multilayer AlN/SiO₂/Si Surface Acoustic Wave (SAW) sensor to low concentrations of Volatile Organic Compounds (VOCs), that is, trichloromethane, trichloroethylene, carbon tetrachloride and tetrachloroethene. A Complimentary Metal-Oxide Semiconductor (CMOS) compatible AlN/SiO₂/Si based multilayer SAW resonator structure is taken into account for this purpose. In this study, first, the influence of AlN and SiO₂ layers’ thicknesses over phase velocities and electromechanical coupling coefficients (k²) of two SAW modes (i.e., Rayleigh and Sezawa) is analyzed and the optimal thicknesses of AlN and SiO₂ layers are opted for best propagation characteristics. Next, the study is further extended to analyze the mass loading effect on resonance frequencies of SAW modes by coating a thin Polyisobutylene (PIB) polymer film over the AlN surface. Finally, the sensitivity of the two SAW modes is examined for VOCs. This study concluded that the sensitivity of Sezawa wave mode for 1 ppm of selected volatile organic gases is twice that of the Rayleigh wave mode.

New Digital Metal-Oxide (MOx) Sensor Platform

The application of metal oxide gas sensors in Internet of Things (IoT) devices and mobile platforms like wearables and mobile phones offers new opportunities for sensing applications. Metal-oxide (MOx) sensors are promising candidates for such applications, thanks to the scientific progresses achieved in recent years. For the widespread application of MOx sensors, viable commercial offerings are required. In this publication, the authors show that with the new Sensirion Gas Platform (SGP) a milestone in the commercial application of MOx technology has been reached. The architecture of the new platform and its performance in selected applications are presented.