A non-invasive, small, and fast device is needed for food freshness monitoring, as current techniques do not meet these criteria. In this study, a resistive sensor composed of a single semiconductor ...nanowire was used at different temperatures, combining the responses and processing them with multivariate statistical analysis techniques. The sensor, very sensitive to ammonia and total volatile basic nitrogen, proved to be able to distinguish samples of fish (marble trout, Salmo trutta marmoratus) and meat (pork, Sus scrofa domesticus), both stored at room temperature and 4 °C in the refrigerator. Once separated, the fish and meat samples were classified by the degree of freshness/degradation with two different classifiers. The sensor classified the samples (trout and pork) correctly in 95.2% of cases. The degree of freshness was correctly assessed in 90.5% of cases. Considering only the errors with repercussions (when a fresh sample was evaluated as degraded, or a degraded sample was evaluated as edible) the accuracy increased to 95.2%. Considering the size (less than a square millimeter) and the speed (less than a minute), this type of sensor could be used to monitor food production and distribution chains.
A chemosensor consisting of one single tin oxide nanowire is used to determine the freshness status of mackerel fish (Scomber scombrus) in a quick and non-invasive way. The tiny chemoresistive sensor ...is first tested with pure ammonia and then used to measure the total volatile basic nitrogen from different samples of fish at different degrees of freshness. The sensor has proved capable of determining the freshness of a sample in few seconds compared to traditional methods such as microbial count and chromatography, which take hours. The sensor response is well correlated with the total viable count (TVC), proving that the total volatile basic nitrogen is a good way to quickly test the bacterial population in the sample. After calibrating the sensor (following the degradation of the fish during almost two days), it has been tested with random double blind samples, proving that it can well discriminate the degree of freshness of the fish preserved at different temperatures.
•One single SnO2 nanowire bridging 2 electrodes is used as resistive sensor.•The nanosensor undergoes a thermal gradient, becoming a virtual array.•7 Different gases are tested, giving each a ...different thermal fingerprint.•The nanosensor shows classification (94.3%) and quantitative estimate (error <25%).
Nowadays the analysis of ambient atmosphere in order to monitor the presence of dangerous gases and volatile compounds is more and more important. For this reason, a network of tiny sensors capable to discriminate the presence of pollutants and distinguish them is crucial. We present here a resistive sensor based on a single tin oxide nanowire (60 nm in diameter and 3.5 μm long) that can detect the presence of different gases and estimate their concentration in the range of 1–50 ppm. The SnO2 nanowire (NW) is grown by chemical vapor deposition and then used to bridge to metal electrodes. Under a temperature gradient, 5 signals can be extracted, forming the thermal fingerprint of each specific gas that can be present in the measuring chamber. Applying machine learning algorithms to these thermal fingerprints, the system can recognize which gas is present in the chamber (with an 94.3% accuracy) and estimate the concentration of the gas (with an average error of 24.5%). The limit of detection has been found to be under 1 part per million for all the gases tested.
Methanol, naturally present in small quantities in the distillation of alcoholic beverages, can lead to serious health problems. When it exceeds a certain concentration, it causes blindness, organ ...failure, and even death if not recognized in time. Analytical techniques such as chromatography are used to detect dangerous concentrations of methanol, which are very accurate but also expensive, cumbersome, and time-consuming. Therefore, a gas sensor that is inexpensive and portable and capable of distinguishing methanol from ethanol would be very useful. Here, we present a resistive gas sensor, based on tin oxide nanowires, that works in a thermal gradient. By combining responses at various temperatures and using machine learning algorithms (PCA, SVM, LDA), the device can distinguish methanol from ethanol in a wide range of concentrations (1–100 ppm) in both dry air and under different humidity conditions (25–75% RH). The proposed sensor, which is small and inexpensive, demonstrates the ability to distinguish methanol from ethanol at different concentrations and could be developed both to detect the adulteration of alcoholic beverages and to quickly recognize methanol poisoning.
•SnO2 nanowires are grown via chemical vapor deposition in a horizontal quartz tube.•Nanowires are decorated with Pt nanoparticles through γ-ray radiolysis.•Response values at different temperatures ...are combined and used as gas fingerprint.•Different machine learning techniques are used to classify 5 different gases.•Support vector regression is used to estimate the gas concentration of each gas.
The detection of volatile compounds is important for a broad variety of applications. Metal oxide gas nanosensors are tiny, inexpensive devices that can be integrated into any application, but they lack selectivity. On the other hand, electronic noses consisting of sensors arrays comprised of different active materials are complex as well as expensive to fabricate and use. This paper presents a novel approach using Pt-decorated tin oxide (SnO2) nanowires at different working temperatures to produce a virtual sensor array exploiting the thermal fingerprints of the different gases. With only one nanostructured material (Pt-SnO2) and 5 temperature values, the system could qualitatively and quantitatively discriminate all the gasestested (all reducing gases). The sensor could detect selectively which gas is present (with an accuracy of 100%) at what concentration (with an overall average error of approximately 14%, down to 3.7% for benzene). The results showed that single metal oxide resistive nanosensors could achieve a good level of real selectivity exploiting the thermal fingerprints from a temperature gradient.
In recent times, an increasing number of applications in different fields need gas sensors that are miniaturized but also capable of distinguishing different gases and volatiles. Thermal electronic ...noses are new devices that meet this need, but their performance is still under study. In this work, we compare the performance of two thermal electronic noses based on SnO2 and ZnO nanowires. Using five different target gases (acetone, ammonia, ethanol, hydrogen and nitrogen dioxide), we investigated the ability of the systems to distinguish individual gases and estimate their concentration. SnO2 nanowires proved to be more suitable for this purpose with a detection limit of 32 parts per billion, an always correct classification (100%) and a mean absolute error of 7 parts per million.
The response of a single tin oxide nanowire was collected at different temperatures to create a virtual array of sensors working as a nano-electronic nose. The single nanowire, acting as a ...chemiresistor, was first tested with pure ammonia and then used to determine the freshness status of trout fish (Oncorhynchus mykiss) in a rapid and non-invasive way. The gas sensor reacts to total volatile basic nitrogen, detecting the freshness status of the fish samples in less than 30 s. The sensor response at different temperatures correlates well with the total viable count (TVC), demonstrating that it is a good (albeit indirect) way of measuring the bacterial population in the sample. The nano-electronic nose is not only able to classify the samples according to their degree of freshness but also to quantitatively estimate the concentration of microorganisms present. The system was tested with samples stored at different temperatures and classified them perfectly (100%), estimating their log(TVC) with an error lower than 5%.
Metal oxides are ideal for the fabrication of gas sensors: they are sensitive to many gases while allowing the device to be simple, tiny, and inexpensive. Nonetheless, their lack of selectivity ...remains a limitation. In order to achieve good selectivity in applications with many possible interfering gases, the sensors are inserted into an
that combines the signals from nonselective sensors and analyzes them with multivariate statistical algorithms in order to obtain selectivity. This review analyzes the scientific articles published in the last decade regarding electronic noses based on metal oxide nanowires. After a general introduction, Section 2 discusses the issues related to poor intrinsic selectivity. Section 3 briefly reviews the main algorithms that have hitherto been used and the results they can provide. Section 4 classifies the recent literature into fundamental research, agrifood, health, security. In Section 5, the literature is analyzed regarding the metal oxides, the surface decoration nanoparticles, the features that differentiate the sensors in a given array, the application for which the device was developed, the algorithm used, and the type of information obtained. Section 6 concludes by discussing the present state and points out the requirements for their use in real-world applications.
In this study, we used the concept of overnight hydrolysis of polyvinyl amine to grow cobalt oxide (Co
3
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) nanostructures with enhanced catalytic properties. The controlled synthesis of Co
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...nanostructures was carried out with the hydrothermal method using hydrolyzed products. Results showed that the hydrocarbon chain and amide groups produced during the growth process have a great impact on both the morphology and catalytic properties of Co
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nanostructures. In fact, the hydrolyzed products supported the growth of nanostructures with a well-defined almost one-dimensional (1-D) morphology of Co
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nanowires with a high surface/volume ratio. The as-prepared nanowires were loaded with a high amount of glucose oxidase in order to make them sensitive to glucose and observe a potentiometric response in its presence. The performance of the fabricated biosensor was evaluated in terms of different analytical parameters such as linear range, stability, reproducibility, repeatability, life time, selectivity, and response time. Thus, the obtained Co
3
O
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-based glucose biosensor exhibited a linear response over the concentration range from 0.0005 to 6 mM, with a limit of detection of 0.0001 mM. The estimated Nernstian slope for the glucose biosensor was 42 mV/dec, with stability that exceeds four weeks. Using electrochemical impedance spectroscopy, the Co
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nanowires showed a low charge transfer resistance of 2.2 × 10
3
Ohms. Practically, the biosensor was used successfully to measure the glucose concentration in real blood samples. The results obtained confirm that the proposed glucose biosensor can be used as an alternative tool for monitoring glucose levels. The synthesis procedure described herein has a high potential to produce nanostructured materials on a large scale with well-defined morphology and improved catalytic properties for possible applications in batteries, supercapacitors, and water splitting.
Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a ...nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors.
•Four nanowire-sensors at different distance from a heater form an array on one chip.•Two different chips are decorated with Ag and Pt nanoparticles and then combined.•System is tested with 5 gases: 100% correct classification, 6.9% mean conc. error.•Concentrations 20 times higher than training range: 96% classification, 31.7% error.•Concentrations 20 times lower than training range: 93% classification, 38.6% error.
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