Search

Over the last 40 years, the term "electronic nose" (EN) has defined a device equipped with an array of not selective gas sensors capable of providing a response as a function of a stimulus provided by volatile chemical compounds (VOCs). Numerous studies have started from this idea, which have led to significant improvements and advantages, especially useful for providing a device capable of monitoring situations and applications in real-time. Applications that have strongly pushed the evolution of the “electronic nose” technology away from the laboratories and closer to more complex and stimulating real situations (Comini and Sberveglieri, 2010). One of the very initial goals of the EN was to simulate the mammalian nose to obtain a fast response regarding the characteristics of the analyte, high sensitivity for odours and high discrimination between them. In the last few years, a lot of upgrades have been made to the EN technology, thanks to artificial intelligence, machine learning evolutions, stability of the sensing elements, cloud processing, predictive algorithms, etc. Thanks to this strong commitment of all, this technology is reopening great interest in the industrial and consumers application field, managing to arrive directly in the transformation chains. The types of gas sensors used are various and are based on the modification of a physical or chemical parameter caused by the gases themselves. Conductometer sensors are the most common, being able to transduce a chemical signal in an electrical resistance signal. Other types of sensors have been developed and can be part of a functional array: Optical sensor, polymer sensor, electrochemical gas sensor, Quartz microbalances or SAW (Paolesse et al., 2017). In this presentation we will review the different sensor arrays most commonly used and a brief history of their evolution. From the point of view of sensor preparation technology, the one based on MEMS is becoming more and more widespread. A brief mention will also be made of the sensors used in the EN standard (called S3+) made by Nano Sensor Systems S.r.l. spin-off of the University of Brescia. We will conclude by presenting the evolution of sensors in recent years to better understand how the multisensory, multidisciplinary and cloud computing approach has positively influenced the real potential of Electronic Noses.

Biogas is becoming one of the most used and profitable renewable sources. It is obtained through many different processes that frequently involve different green renewable sources. During the different steps of the process, it generates several by-products that could be reused as organic fertilizers. The aim of this work was to study the volatile fraction and determine the volatile fingerprint of 6 different organic samples involved in biogas production by the innovative Small Sensor System (S3) based on Semiconductor Metal Oxide (MOX) gas sensors. Obtained results show the volatile profile of each sample that at the same time support the sensor's device results. S3 result shows a perfect discrimination of the volatile fraction of the different studied matrices based on the different composition of their volatile set. matrix and shows how the sensors are able, in real time, to cluster and discriminate the fingerprint of these renewable sources. In the end, S3 results are very promising to enhance the traceability and the origin of the sources in the biogas industry at each specific stage of production, focusing on the possible release of off-flavors in the environment from different types of organic biomass and the reuse of their by-products supporting circular economy. The aim of this work was to find and identify the VOCs set that characterizes different types of organic renewable sources through the use of the tailor made Small Sensor Systems S3 to distinguish between different odor sources based on their qualitative and quantitative differences in VOCs profile, in order to be able in the future to produce a user friendly fast, economic and with auto learning capabilities to support the aforementioned green industrial processes.

Nowadays, innovation in food technologies is fundamental and consumers are increasingly aware and demanding. To create a final product that is more and more appealing, health and safety guidelines are pushing towards new challenges. It is precisely due to the high quality required by the producers that the aim discussed in this project has been conceived. Until today, the controls on the entire production line have been slowed down by the limitations of the technologies involved, including the high cost of instrumentation for microbiological analysis, the need for qualified personnel to carry them out, the long execution times and the invasiveness of the techniques themselves. This project has, therefore, proposed a user-friendly solution that is minimally invasive, fast and at a lower cost. This system makes use of classical microbiological analysis and, in parallel, use of an innovative electronic-nose small sensor system (S3+), which can be trained to recognize the volatile fingerprint of a specific product and customized for a specific use. The aim of this project was to develop a system that is able to detect the mold contamination on fruit and vegetable jams and marmalades, using a new kind of innovative metal semiconductor gas sensor (MOS) device. The application of this technology has, therefore, made it possible to classify various samples of uncontaminated and contaminated fruit and vegetable preparations. Thanks to the classification implemented by a data-driven algorithm, it has been possible to build an anomaly detector that is able to recognize the occurrence of possible contamination, thus acting as an early alert system in the food chain. All this will occur in less than 1 min once the system is trained, in contrast with classical microbiological or chemical techniques that normally require longer timeframes to obtain a result and involve the use of reagents, increasing the costs.

Oxygen adsorption at metal oxide (MOX) surfaces and the formation of sub-surface depletion zones in thin nanometric sheets of MOX materials are theoretically investigated. It is shown that—under conditions of sufficient oxygen mobility—the bulk thermal generation of oxygen vacancy donors and the adsorption of surface oxygen ions cooperate in a self-organizing manner to form narrow sub-surface depletion zones which optimally fit into the limited spaces of MOX layers with nanometric cross sections. With this self-organization process in place, both the oxygen adsorption at free surfaces and the bulk generation of oxygen vacancy donors continuously increases as the MOX sheet thickness L is reduced, maintaining at the same time overall electro-neutrality and a state of perfect volume depletion of free carriers in bulk. This process comes to an end when MOX sheet thicknesses of L ≈ 1 nm are approached and when 3d-volumes of about 1 nm3 contain only one single double-donor and two surface oxygen ions on average. It is argued that at this limit of miniaturization, different interpretations of MOX gas sensing phenomena might be required than on larger length scales.

The marmalade and jam market is growing worldwide, with the European countries being the main producers in this sector. The market has ancient origins and the production is aimed at conserving the surplus fruits during some period of the year. Nowadays, the automatic production processes are wide-ranging but start with high-quality raw materials and follow an appropriate cooking process to conserve the main features of the final product. On the other hand, cases of overcooking may occur which lead to the production of hydroxy-methyl-furfural and derivatives with consequent browning and poor organoleptic characteristics of the final product. This study aimed to use chemical oxide nanowire gas sensors device S3 coupled with optical techniques and recognizing algorithms to create a multi-actor platform able to control the production process of jams and marmalades with a fast response time, to assist the production process and avoid economical losses in the sector. PCA shows that this innovative technology can recognize changes in the volatile fingerprint, distinguishing when the positive and more natural organoleptic characteristics of the fruit are still present from the appearance of the organoleptic defects due to a faulty production process.

There are different methods to extract and brew coffee, therefore, coffee processing is an important factor and should be studied in detail. Herein, coffee was brewed by means of a new espresso professional coffee machine, using coffee powder or portioned coffee (capsule). Four different kinds of coffees (Biologico, Dolce, Deciso, Guatemala) were investigated with and without capsules and the goal was to classify the volatiloma of each one by Small Sensor System (S3). The response of the semiconductor metal oxide sensors (MOX) of S3 where recorded, for all 288 replicates and after normalization ∆R/R0 was extracted as a feature. PCA analysis was used to compare and differentiate the same kind of coffee sample with and without a capsule. It could be concluded that the coffee capsules affect the quality, changing on the flavor profile of espresso coffee when extracted different methods confirming the use of s3 device as a rapid and user-friendly tool in the food quality control chain.

Metal oxide (MOX) gas sensors and gas sensor arrays are widely used to detect toxic, combustible, and corrosive gases and gas mixtures inside ambient air. Important but poorly researched effects counteracting reliable detection are the phenomena of sensor baseline drift and changes in gas response upon long-term operation of MOX gas sensors. In this paper, it is shown that baseline drift is not limited to materials with poor crystallinity, but that this phenomenon principally also occurs in materials with almost perfect crystalline order. Building on this result, a theoretical framework for the analysis of such phenomena is developed. This analysis indicates that sensor drift is caused by the slow annealing of quenched-in non-equilibrium oxygen-vacancy donors as MOX gas sensors are operated at moderate temperatures for prolonged periods of time. Most interestingly, our analysis predicts that sensor drift in n-type MOX materials can potentially be mitigated or even suppressed by doping with metal impurities with chemical valences higher than those of the core metal constituents of the host crystals.

Preparation and characterization of different metal oxide (NiO, WO3, ZnO, SnO2 and Nb2O5) nanostructures for chemical sensing are presented. p-Type (NiO) and n-type (WO3, SnO2, ZnO and Nb2O5) metal oxide nanostructures were grown on alumina substrates using evaporation–condensation, thermal oxidation and hydrothermal techniques. Surface morphologies and crystal structures were investigated through scanning electron microscopy and Raman spectroscopy. Furthermore, different batches of sensors have been prepared, and their sensing performances towards carbon monoxide and nitrogen dioxide have been explored. Moreover, metal oxide nanowires have been integrated into an electronic nose and successfully applied to discriminate between drinking and contaminated water.

A hybrid nanostructure based on reduced graphene oxide and ZnO has been obtained for the detection of volatile organic compounds. The sensing properties of the hybrid structure have been studied for different concentrations of ethanol and acetone. The response of the hybrid material is significantly higher compared to pristine ZnO nanostructures. The obtained results have shown that the nanohybrid is a promising structure for the monitoring of environmental pollutants and for the application of breath tests in assessment of exposure to volatile organic compounds.

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors’ array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%.

Food poisoning is still the first cause of hospitalization worldwide and the most common microbial agent, Campylobacter jejuni, is the most commonly reported gastrointestinal disease in humans in the EU (European Union) as is reported by the European Union One Health 2018 Zoonoses Report styled by the EFSA (European Food Safety Authority) and ECDC (European Center for Disease Prevention and Control). One of the vehicles of transmission of this disease is milk. Nanostructured MOS (Metal Oxide Semiconductor) sensors have extensively demonstrated their ability to reveal the presence and follow the development of microbial species. The main objective of this work was to find a set up for the detection and development follow up of C. jejuni in milk samples. The work was structured in two different studies, the first one was a feasibility survey and the second one was to follow up the development of the bacteria inside milk samples. The obtained results of the first study demonstrate the ability of the sensor array to differentiate the contaminated samples from the control ones. Thanks to the second study, it has been possible to find the limit of microbial safety of the contaminated milk samples.

The surface of SnO2 nanowires was functionalized by chitosan for the development of room-temperature conductometric humidity sensors. SnO2 nanowires were synthesized by the seed-mediated physical-vapor-deposition (PVD) method. Chitosan layers were deposited on top of the SnO2 nanowires by spin coating. Surface morphology, crystal structure, and optical properties of the synthesized hybrid nanostructure were investigated by scanning electron microscope, grazing incidence X-ray diffraction, and UV−Vis absorption measurements. During electrical conductivity measurements, the hybrid nanostructure showed unusual behavior towards various relative humidity (RH) concentrations (25%, 50%, 75%), under UV-light irradiation, and in dark conditions. The highest sensor responses were recorded towards an RH level of 75%, resulting in 1.1 in the dark and 2.5 in a UV-irradiated chamber. A novel conduction mechanism of hybrid nanowires is discussed in detail by comparing the sensing performances of chitosan film, SnO2 nanowires, and chitosan@SnO2 hybrid nanostructures.

The drift of metal oxide semiconductor (MOX) chemical sensors is one of the most important topics in this field. The work aims to test the performance of MOX gas sensors over the aging process. Firstly, sensors were tested with ethanol to understand their behavior and response changes. In parallel, beers with different alcoholic content were analyzed to assess what happened in a real application scenario. With ethanol analysis, it was possible to quantify drift of the baseline of the sensors and changes that could affect their responses over time (from day 1 to day 51). Conversely, the beer dataset has been exploited to evaluate how two different classifiers perform the classification task based on the alcohol content of the samples. A hybrid k-nearest neighbors artificial neural network (k-NN-ANN) approach and “standard” k-NN were used to evaluate to distinguish among the samples when the measures were affected by drift. To achieve this goal, data acquired from day one to day six were used as training to predict data collected up to day 51. Overall, performances of the two methods were similar, even if the best result in terms of accuracy is reached by k-NN-ANN (96.51%).

Zinc oxide (ZnO, n-type) and copper oxide (CuO, p-type) nanowires have been synthesized and preliminarily investigated as innovative materials for the fabrication of a proof-of-concept thermoelectric device. The Seebeck coefficients, electrical conductivity and thermoelectric power factors (TPF) of both semiconductor materials have been determined independently using a custom experimental set-up, leading to results in agreement with available literature with potential improvement. Combining bundles of ZnO and CuO nanowires in a series of five thermocouples on alumina leads to a macroscopic prototype of a planar thermoelectric generator (TEG) unit. This demonstrates the possibility of further integration of metal oxide nanostructures into efficient thermoelectric devices.

Low-dimensional chemical sensors based on metal oxides have received great attention for the applications in security and medical diagnoses. [...]

Synthesis—particularly by electrochemical anodization-, growth mechanism and chemical sensing properties of pure, doped and mixed titania tubular arrays are reviewed. The first part deals on how anodization parameters affect the size, shape and morphology of titania nanotubes. In the second part fabrication of sensing devices based on titania nanotubes is presented, together with their most notable gas sensing performances. Doping largely improves conductivity and enhances gas sensing performances of TiO2 nanotubes

In this work we report on metal oxide (MOX) based gas sensors, presenting the work done at the SENSOR laboratory of the CNR-IDASC and University of Brescia, Italy since the 80s up to the latest results achieved in recent times. In particular we report the strategies followed at SENSOR during these 30 years to increase the performance of MOX sensors through the development of different preparation techniques, from Rheotaxial Growth Thermal Oxidation (RGTO) to nanowire technology to address sensitivity and stability, and the development of electronic nose systems and pattern recognition techniques to address selectivity. We will show the obtained achievement in the context of selected applications such as safety and security and food quality control.

Surface coating with an organic self-assembled monolayer (SAM) can enhance surface reactions or the absorption of specific gases and hence improve the response of a metal oxide (MOx) sensor toward particular target gases in the environment. In this study the effect of an adsorbed organic layer on the dynamic response of zinc oxide nanowire gas sensors was investigated. The effect of ZnO surface functionalisation by two different organic molecules, tris(hydroxymethyl)aminomethane (THMA) and dodecanethiol (DT), was studied. The response towards ammonia, nitrous oxide and nitrogen dioxide was investigated for three sensor configurations, namely pure ZnO nanowires, organic-coated ZnO nanowires and ZnO nanowires covered with a sparse layer of organic-coated ZnO nanoparticles. Exposure of the nanowire sensors to the oxidising gas NO2 produced a significant and reproducible response. ZnO and THMA-coated ZnO nanowire sensors both readily detected NO2 down to a concentration in the very low ppm range. Notably, the THMA-coated nanowires consistently displayed a small, enhanced response to NO2 compared to uncoated ZnO nanowire sensors. At the lower concentration levels tested, ZnO nanowire sensors that were coated with THMA-capped ZnO nanoparticles were found to exhibit the greatest enhanced response. ΔR/R was two times greater than that for the as-prepared ZnO nanowire sensors. It is proposed that the ΔR/R enhancement in this case originates from the changes induced in the depletion-layer width of the ZnO nanoparticles that bridge ZnO nanowires resulting from THMA ligand binding to the surface of the particle coating. The heightened response and selectivity to the NO2 target are positive results arising from the coating of these ZnO nanowire sensors with organic-SAM-functionalised ZnO nanoparticles.

Water is a common fundamental resource for life. The effects of climate change, pollution and waste make it an increasingly scarce resource. Therefore, it becomes very important to monitor water quality to identify situations of possible harm to humans. In this study, incoming wastewater of the depuration plant of A2A Ciclo Idrico in Brescia has been analyzed with an array of MOX nanowire gas sensors. In addition, GC-MS technique has been used to relate sensors responses with the emitted VOCs of the samples. The ability of the system to recognize potable water from wastewater and chemically contaminated wastewater has been found. PCA and ANN methods have been employed in order to see how samples classes cluster and to assess the capability of the device to classify them properly, respectively.

Surface ionization (SI) provides a simple, sensitive, and selective method for the detection of high-proton affinity substances, such as organic decay products, medical and illicit drugs as well as a range of other hazardous materials. Tests on different kinds of SI sensors showed that the sensitivity and selectivity of such devices is not only dependent on the stoichiometry and nanomorphology of the emitter materials, but also on the shape of the electrode configurations that are used to read out the SI signals. Whereas, in parallel-plate capacitor devices, different kinds of emitter materials exhibit a high level of amine-selectivity, MEMS (micro-electro-mechanical-systems) and NEMS (nanowire) versions of SI sensors employing the same kinds of emitter materials provide significantly higher sensitivity, however, at the expense of a reduced chemical selectivity. In this paper, it is argued that such sensitivity-selectivity trade-offs arise from unselective physical ionization phenomena that occur in the high-field regions immediately adjacent to the surfaces of sharply curved MEMS (NEMS) emitter and collector electrodes.

In the present work, a gas sensor device S3 based on an array of eight metal oxides semiconductor gas sensors has been demonstrated and applied to the discrimination of quality and geographical origins of the Italian extra virgin olive oils. Furthermore, the principal component analysis (PCA) and artificial neural networks (ANN) were carried out on the set of data acquired from the sensor array response to the extra virgin olive oil headspace. The preliminary results have shown a good capability of the instrument to distinguish different kind of extra virgin olive oil samples and thus evaluate their quality and origin.

We have obtained and studied the sensing properties of porous titania-based nanostructures. The materials have been prepared using cost-effective techniques. The morphological and structural analyses of the prepared materials have been performed. The sensing properties of the samples have been studied towards carbon monoxide. The obtained results demonstrate that the prepared structures are promising for the potential applications in the area of chemical sensors for the environmental monitoring.

Metal oxide materials have been applied in different fields due to their excellent functional properties. Metal oxides nanostructuration, preparation with the various morphologies, and their coupling with other structures enhance the unique properties of the materials and open new perspectives for their application in the food industry. Chemical gas sensors that are based on semiconducting metal oxide materials can detect the presence of toxins and volatile organic compounds that are produced in food products due to their spoilage and hazardous processes that may take place during the food aging and transportation. Metal oxide nanomaterials can be used in food processing, packaging, and the preservation industry as well. Moreover, the metal oxide-based nanocomposite structures can provide many advantageous features to the final food packaging material, such as antimicrobial activity, enzyme immobilization, oxygen scavenging, mechanical strength, increasing the stability and the shelf life of food, and securing the food against humidity, temperature, and other physiological factors. In this paper, we review the most recent achievements on the synthesis of metal oxide-based nanostructures and their applications in food quality monitoring and active and intelligent packaging.

Metal oxide (MOX) gas sensors sensitively respond to a wide variety of combustible, explosive and poisonous gases. However, due to the lack of a built-in self-test capability, MOX gas sensors have not yet been able to penetrate safety-critical applications. In the present work we report on gas sensing experiments performed on MOX gas sensors embedded in ceramic micro-reaction chambers. With the help of an external micro-pump, such systems can be operated in a periodic manner alternating between flow and no-flow conditions, thus allowing repetitive measurements of the sensor resistances under clean air, R 0 , and under gas exposure, R g a s , to be obtained, even under field conditions. With these pairs of resistance values, eventual drifts in the sensor baseline resistance can be detected and drift-corrected values of the relative resistance response R e s p = ( R 0 − R g a s ) / R 0 can be determined. Residual poisoning-induced changes in the relative resistance response can be detected by reference to humidity measurements taken with room-temperature-operated capacitive humidity sensors which are insensitive to the poisoning processes operative on heated MOX gas sensors.

Atomic force and high resolution scanning tunneling analyses were carried out onnanostructured WO3 films. It turned out that the band gap measured by scanning tunnelingspectroscopy at surface is lower than the band gap reported in the literature. This effect isattributed to the high density of surface states in this material, which allows tunneling intothese states. Such a high density of surface states pins the Fermi level resulting in modestsurface activity at room temperature. Photo activation of WO3 results in unpinning of theFermi level and thereby in higher chemical activity at surface.

WO3 thin films were deposited by pulsed laser deposition (PLD) on glass substrates at 100 mTorr oxygen pressure. Monodispersed palladium nanoparticles (50 nm) were synthesized by hydrogen reduction of PdCl2 drop-casted on the surface of the films. For oxyhydrogen detection, first a saturated coloration by 10%H2/Ar was prepared. Then different oxygen flow with certain O2:H2 ratios were exposed to the colored samples. Depending on this ratio, the optical density of samples was observed to drop into certain new values until vanishes for a ratio about 20. A desirable fast coloration with linear dependency on hydrogen concentration was also observed.

We have developed innovative and efficient strategy for the preparation and improvement of sensing performance of well-ordered titania nanotubes. We have synthesized different materials based on titania nanotubes using cost-effective techniques. The morphological and structural analyses of the prepared materials have been carried out. The sensing properties of the structures have been studied towards NO2. Investigations have shown the functionalization process has crucial effect on sensing properties of titania nanotubes. The obtained results demonstrate that the prepared structures are promising for the potential applications in the area of chemical sensors for the environmental monitoring.

Tungsten and titanium oxide (WO3/Ti) nanowires (NWs) were grown by thermal oxidation of a tungsten and titanium alloy (WTi) metallic layer deposited on 2 × 2 mm2 alumina substrates by DC magnetron sputtering. Thermal oxidation was carried out in a custom evaporator chamber. Several parameters were controlled to achieve the optimal growth conditions. Morphological and structural analysis were performed on samples by a scanning electron microscope (SEM) and RAMAN spectroscopy, respectively. Nanowires, grown directly on the final transducer, were tested towards different gaseous species in a wide range of working temperatures. In this work, results obtained in presence of acetone are proposed.

Metal oxide 1D nanowires are probably the most promising structures to develop cheap stable and selective chemical sensors. The purpose of this contribution is to review almost two-decades of research activity at the Sensor Lab Brescia on their preparation during by vapor solid (n-type In2O3, ZnO), vapor liquid solid (n-type SnO2 and p-type NiO) and thermal evaporation and oxidation (n-type ZnO, WO3 and p-type CuO) methods. For each material we’ve assessed the chemical sensing performance in relation to the preparation conditions and established a rank in the detection of environmental and industrial pollutants: SnO2 nanowires were effective in DMMP detection, ZnO nanowires in NO2, acetone and ethanol detection, WO3 for ammonia and CuO for ozone.

To determine the originality of a typical Italian Parmigiano Reggiano cheese, it is crucial to define and characterize its quality, ripening period, and geographical origin. Different analytical techniques have been applied aimed at studying the organoleptic and characteristic volatile organic compounds (VOCs) profile of this cheese. However, most of the classical methods are time consuming and costly. The aim of this work was to illustrate a new simple, portable, fast, reliable, non-destructive, and economic sensor device S3 based on an array of six metal oxide semiconductor nanowire gas sensors to assess and discriminate the quality ranking of grated Parmigiano Reggiano cheese samples and to identify the VOC biomarkers using a headspace SPME-GC-MS. The device could clearly differentiate cheese samples varying in quality and ripening time when the results were analyzed by multivariate statistical analysis involving principal component analysis (PCA). Similarly, the volatile constituents of Parmigiano Reggiano identified were consistent with the compounds intimated in the literature. The obtained results show the applicability of an S3 device combined with SPME-GC-MS and sensory evaluation for a fast and high-sensitivity analysis of VOCs in Parmigiano Reggiano cheese and for the quality control of this class of cheese.

One-dimensional metal oxide nanostructures such as nanowires, nanorods, nanotubes, and nanobelts gained great attention for applications in sensing devices. ZnO is one of the most studied oxides for sensing applications due to its unique physical and chemical properties. In this paper, we provide a review of the recent research activities focused on the synthesis and sensing properties of pure, doped, and functionalized ZnO quasi-one dimensional nanostructures. We describe the development prospects in the preparation methods and modifications of the surface structure of ZnO, and discuss its sensing mechanism. Next, we analyze the sensing properties of ZnO quasi-one dimensional nanostructures, and summarize perspectives concerning future research on their synthesis and applications in conductometric sensing devices.