Commercial Gas Sensors Research Articles

(Invited) Multi-Modal Solid-State Gas Sensors-Based Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Exploring the ammonia gas sensing properties of Gd doped CeO2 thin films deposited by spray pyrolysis method

The present study investigates the synthesis and characterization of Gd-doped CeO2 thin films using a simple nebulizer spray pyrolysis method for ammonia sensing application. The films were prepared with varying Gd concentrations from 1 to 5 wt%. The structural, morphological, and optical properties of the samples were analyzed to examine the effect of Gd doping on physical parameters, and the results show that the 4 wt% Gd doped sample exhibits the highest crystallite size, uniform particle size distribution, and a smaller optical band gap. Additionally, the presence of defects and oxygen vacancies in the host lattice is enhanced in this sample. The gas sensing properties of the 4 wt% Gd doped CeO2 exhibits a gas responsivity of 731 %, rise time of 10.4 s, and fall time of 9.2 s indicating that the fabricated thin film sensor was found to be ideal for room temperature gas sensing application. Overall, the study demonstrates that the 4 wt% Gd doped CeO2 thin film is a promising candidate for commercial gas sensor applications.

The Identification of Acetic Acid-Ethanol Mixture Using Gas Sensor Array and Ensemble Regression

Identification of acetic acid-ethanol mixtures using a commercial gas sensor array equipped with ensemble regression has been carried out. The gas sensor analysis was simple, rapid, and fast since it did not require any sample preparation. A quantitative analysis of the acetic acid-ethanol mixture was carried out to determine the sensitivity and selectivity of the sensor in distinguishing the concentration of the acetic acid and ethanol mixture. This study focuses on the coefficient of determination of 80% of the calibration data set and recovery of 20% of the testing data set. The models showed excellent performance,specifically, the Bagging and Random Forest r2 for the ethanol calibration data reached 0.91 and 0.94, respectively. The corresponding ethanol test recoveries were 99.95% and 97.84%, indicating the robustness of the model in accurately predicting ethanol concentration. Acetic acid test recoveries were 100.56% and 101.38% with r2 of 0.89 and 0.93 for Bagging and Random Forest regression, respectively. Hence, the commercial gas sensor array equipped with ensemble regression can be applied to the quantification of the acetic acid – ethanol mixture and demonstrate opportunities for the practical use of this gas sensor array in analyzing real samples, i.e. human breath or environmental monitoring samples.

Odors Detection and Recognition Based on Intelligent E-Nose

Electronic noses have become more common as a result of developments in sensor technology, machine learning (ML), and Artificial Intelligence (AI). At the moment, the majority of e-nose research is conducted in laboratories; access from other locations is not possible with e-nose. Very few real-time smell detection applications, including tiny drones equipped with commercial gas sensors or biosensors made of insect antennas, have been created. The scope of this work is to design an intelligent E-Nose for odors detection. Smell Inspector developer kit has been used to get measurements. The Smell Inspector consists of sensors for temperature and humidity in addition to four smell iX16 chips. The Smell Inspector creates digital fragrance fingerprints using a variety of separate gas detectors. The approach for object recognition and classification using artificial intelligence techniques is presented in this paper. These techniques include Machine Learning (ML), clustering, and regression algorithms. Using the K-Nearest Neighbor (KNN) algorithm, the experimental results' array sensors were able to recognize; clean air, onion, garlic, coffee, spices, lemon, vinegar, gasoline, petrol, diesel, and perfumes with 78% accuracy rate.

Geosmin and 2-Methylisoborneol Detection in Water Using Semiconductor Gas Sensors

Geosmin and 2-methylisoborneol (MIB) are the most common causes of unpleasant odours in drinking water. A method was proposed to detect and recognise these compounds in water and determine their concentrations. The method utilises commercial solid-state gas sensors and data analysis. Sample preparation plays an important role. The aqueous solution is converted into a gas sample using a specially designed dynamic headspace. The responses of the sensors are recorded during exposure to headspace vapours in a dynamic mode. The best limit of detection for geosmin, LOD = 6.20 µg/L, was attained with a TGS2602 sensor. The best limit of detection for MIB, LOD = 0.52 µg/L, was attained with a TGS826 sensor. Geosmin and MIB recognition was 100% successful based on TGS826 and TGS2602 response classifications. Geosmin and MIB concentrations were effectively determined in solutions containing one or both compounds. The respective mathematical models utilised the responses of TGS826 and TGS2602. The smallest concentration prediction error was RMSE = 2.19 µg/L (for geosmin) and RMSE = 0.33 µg/L (for MIB). The study demonstrated the application potential of non-specific gas sensors for the early warning monitoring of geosmin and MIB presence in water. Further studies are needed to develop a system that can be tested in field conditions.

Improvement in ammonia gas sensing properties of La doped MoO3 thin films fabricated by nebulizer spray pyrolysis method

In the present work, different doping concentration (0, 1, 2, 3, 4 and 5 wt%) of Lanthanum (La) doped Molybdenum trioxide (MoO3) thin films were prepared using low cost Nebulizer spray pyrolysis (NSP)technique. The crystalline, morphology, optical and gas sensing properties of thin films were investigated. The presence of monoclinic MoO3 structure was confirmed through the XRD investigation for all the samples with highest crystallite of 61 nm was observed for 2 wt% La doped MoO3 thin film. The surface morphology of the produced thin films exhibits a reticulate nanofibrous mesh shape with increased particle size and porosity on addition of 2% La dopants in MoO3.The UV–Vis results showed the band gap values for the prepared thin films in the range 3.12–3.26 eV and the minimum bandgap was observed for the 2 wt% of La doped MoO3 thin film. Also the PL results confirmed the presence of more oxygen vacancies for the 2 wt% of La doped MoO3 thin film. The NH3 gas sensing study was conducted for the prepared films at room temperature, the 2 wt% of La doped MoO3thin film exhibits a maximum sensor response of 900, faster response times (28 s and 2.9s) compared to other fabricated devices. The results suggest that the 2% La doping in MoO3 could be an optimum doping concentration for the preparation of commercial NH3 gas sensors.

Surface-Catalyzed Zinc Oxide Nanorods and Interconnected Tetrapods as Efficient Methane Gas Sensing Platforms

Nanostructured metal oxide semiconductors have proven to be promising for the gas sensing domain. However, there are challenges associated with the fabrication of high-performance, low-to-room-temperature operation sensors for methane and other gases, including hydrogen sulfide, carbon dioxide, and ammonia. The functional properties of these semiconducting oxides can be improved by altering the morphology, crystal size, shape, and topology. Zinc oxide (ZnO) is an attractive option for gas sensing, but the need for elevated operating temperatures has limited its practical use as a commercial gas sensor. In this work, we prepared ZnO nanorod (ZnO-NR) arrays and interconnected tetrapod ZnO (T-ZnO) network sensing platforms as chemiresistive methane sensors on silicon substrates with platinum interdigitated electrodes and systematically characterized their methane sensing response in addition to their structural and physical properties. We also conducted surface modification by photochemical-catalyzed palladium, Pd, and Pd-Ag alloy nanoparticles and compared the uniformly distributed Pd decoration versus arrayed dots. The sensing performance was assessed in terms of target gas response magnitude (RM) and response percentage (R) recorded by changes in electrical resistance upon exposure to varying methane concentration (100–10,000 ppm) under thermal (operating temperatures = 175, 200, 230 °C) and optical (UV A, 365 nm illumination) excitations alongside response/recovery times, and limit of detection quantification. Thin film sensing platforms based on T-ZnO exhibited the highest response at 200 °C (RM = 2.98; R = 66.4%) compared to ZnO-NR thin films at 230 °C (RM = 1.34; R = 25.5%), attributed to the interconnected network and effective bandgap and barrier height reduction of the T-ZnO. The Pd-Ag-catalyzed and Pd dot-catalyzed T-ZnO films had the fastest response and recovery rates at 200 °C and room temperature under UV excitation, due to the localized Pd nanoparticles dots resulting in nano Schottky barrier formation, as opposed to the films coated with uniformly distributed Pd nanoparticles. The experimental findings present morphological differences, identify various mechanistic aspects, and discern chemical pathways for methane sensing.

Development of a gas chromatography system coupled to a metal-oxide semiconductor (MOS) sensor, with compensation of the temperature effects on the column for the measurement of ethene

Abstract. A possible way to reduce the size and complexity of common gas chromatography (GC) systems is the economization of the column temperature regulation system. To this end, a temperature compensation method was developed and validated on a benchtop GC-PDD (pulsed discharge detector) with ethene. An in-house-developed algorithm correlates the retention index of a test gas to the retention index of a previously selected reference gas. To investigate further methods of cost reduction, commercial gas sensors were tested as cheap, sensitive, and versatile detectors. Therefore, CO2 was chosen as a naturally occurring reference gas, while ethene was chosen as a maturity marker for climacteric fruits and hence as a test gas. A demonstrator, consisting of a simple syringe injection system, a PLOT (porous layer open tubular) column boxed in a polystyrene-foam housing, a commercial MOS (metal-oxide semiconductor) sensor for the test gas, and a CO2-specific IR (infrared) sensor, was used to set up a simple GC system and to apply this method on test measurements. Sorption parameters for ethene and CO2 were determined via a van 't Hoff plot, where the entropy S was −11.982 J mol−1 K−1 ΔSEthene0 and 1.351 J mol−1 K−1 ΔSCarbondioxide0, and the enthalpy H was −20.622 kJ mol−1 ΔHEthene0 and −14.792 kJ mol−1 ΔHCarbondioxide0, respectively. Ethene (100 ppm) measurements revealed a system-specific correction term of 0.652 min. Further measurements of ethene and interfering gases revealed a mean retention time for ethene of 3.093 min; the mean predicted retention time is 3.099 min. The demonstrator was able to identify the test gas, ethene, as a function of the reference gas, CO2, in a first approach, without a column heating system and in a gas mixture by applying a temperature compensation algorithm and a system-specific holdup time correction term.

Personal VOCs Exposure with a Sensor Network Based on Low-Cost Gas Sensor, and Machine Learning Enabled Indoor Localization

Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.

E-Nose System Based on Fourier Series for Gases Identification and Concentration Estimation From Food Spoilage

This work presents an electronic nose (EN)-based gas identification and concentration estimation method for the detection of food spoilage. The response data of sensors were acquired through a commercial gas sensor array and data acquisition circuit board and transformed into pictures with the form of the Fourier series. A convolutional neural network (CNN) model was used to identify the pictures from the conversion of sensor data, thus achieving the purpose of identifying the gases (C2H5OH, NH3, and H2S). In order to solve the problem of sample imbalance and to improve the generalization performance of classification models, the synthetic minority oversampling technique (SMOTE) and dropout technique were employed. Fivefold cross-validation was used to evaluate the performance of the model, of which the gas identification accuracy rate reached 96.67%. Moreover, a gas concentration regression model with the advantages of simplicity and strong interpretability was further proposed to estimate the concentrations of C2H5OH, NH3, and H2S. The mean absolute errors and coefficients of determination for the concentration estimation of C2H5OH, NH3, and H2S are (3.71 ppm, 0.968), (0.50 ppm, 0.968), and (0.13 ppm, 0.99), respectively. Furthermore, we used our model to evaluate the freshness of kiwifruit, pork, and beef, and it showed satisfactory predictive performance. The method proposed in this work realizes high-precision detection of gases from food spoilage and has a good application prospect in the rapid judgment of food freshness on the EN system.

Graphite-Based Multianalyte VOC Gas Detection on Multichannel PCB IDE Sensor

In this article, we present the development of a volatile organic compound (VOC) gas sensor based on graphite on a printed circuit board (PCB) multiplexed interdigitated electrode (IDE) sensor. We report the effects of graphite as a sensing material deposited by the spray method and its interaction response to specific gases. In addition, we demonstrate how we designed and manufactured IDE sensors with an integrated heating circuit and thermistor element, as well as the 55- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula> microchamber, which contributed to the fast detection of gases. All experiments were performed with four IDE sensors to verify the reproducibility and sensitivity of the proposed methodology. Our results highlight that acetone presented the highest detection sensitivity, with an electric current increase of ± <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100~\mu \text{A}$ </tex-math></inline-formula> at a 200-ppm cycle with a response of 0.92 s and full recovery at 2.62 s. Furthermore, the developed sensors were able to detect and distinguish between acetone, methanol, isopropanol, ethanol, and chloroform gases. The sensors exhibited outstanding stability and repeatability when evaluated after six months. The sensors’ operation at 2 V makes it suitable for low-power devices with a high potential to be applied in future commercial room-temperature VOC gas sensor devices.

Evaluating a Novel Gas Sensor for Ambient Monitoring in Automated Life Science Laboratories.

Air pollution and leakages of hazardous and toxic gases and chemicals are among the dangers that frequently occur at automated chemical and life science laboratories. This type of accident needs to be processed as soon as possible to avoid the harmful side effects that can happen when a human is exposed. Nitrogen oxides and volatile organic compounds are among the most prominent indoor air pollutants, which greatly affect the lifestyles in these places. In this study, a commercial MOX gas sensor, SGP41, was embedded in an IoT environmental sensor node for hazardous gas detection and alarm. The sensor can detect several parameters, including nitrogen oxide index (NOx-Index) and volatile organic compound index (VOC-Index). Several tests were conducted to detect the leakage of nitrogen oxides and volatile organic compounds in different concentrations and volumes, as well as from different leakage distances, to measure the effect of these factors on the response speed and recovery time of the sensors used. These factors were also compared between the different sensors built into the sensor node to give a comprehensive picture of the system used. The system testing results revealed that the SGP41 sensor is capable of implementing the design purposes for the target parameters, can detect a small NO2 gas leakage starting from 0.3% volume, and can detect all the tested VOC solvents ≥ 100 µL.

Novel Design of 0D Nanoparticles-2D Transition-Metal Dichalcogenides Heterostructured Devices for High-Performance Optical and Gas-Sensing Applications

Two-dimensional Transition metal dichalcogenides (TMDCs), have now attracted much attention due to their unique layered structure and physical properties. Up to date, several studies have demonstrated monolayered and few-layered TMDC-based photodetectors with good stability, photo-switching time and broadband detectivity from UV to infrared light region. However, the reported responsivity is not as high as the theoretical expectation, indicating that the light absorption is limited by the atomic thickness of 2D-TMDCs and could still be improved. To overcome the drawback of low absorption in 2D TMDC materials, previous reports have revealed several strategies to enhance the electric field and light-harvesting in these atomically thin TMDC layers by hybridizing plasmonic noble-metal nanoparticles, such as Pt, Au and Ag, to facilitate the light-matter interaction at the surface of semiconductors. In this regard, we aim to combine highly absorptive CuInS2(CIS) nanocrystals with noble metal nanoparticles as the photosensitizer to enhance the intrinsic absorptivity and promote the performance of MoS2-based photodetectors. The interests of noble nanocrystals such as platinum and gold are featured for their distinctive properties of the carrier transportation and the storage when combined with semiconductor materials. The strategy described here acts as a perspective to significantly improve the performance of MoS2-based photodetectors with outstanding detection responsivity with selectable wavelengths by further controlling the size and material of the decorated CIS nanocrystals.In addition to optical sensing, TMDCs have also been developed as a promising candidate for gas-molecule detection. Different from commercial metal oxide gas sensors, TMDCs as sensing materials can be operated at room temperature with good performance, increasing its reliability for future industrial applications. Nevertheless, the relatively low response and long response/recovery time are the main drawbacks of these promising devices. Therefore, we proposed the approach to successfully increase the surface area of TMDCs by a one-step synthesis from WO3 into three-dimensional (3D) WS2 nanowalls through a rapid heating and rapid cooling process. Moreover, the combination of CdS/ZnS or CdSe/ZnS core/shell quantum dots (QDs) with different emission wavelengths and WS2 nanowalls will further improve the performance of WS2-based photodetector devices, including 3.5~4.7 times photocurrent enhancement and shorter response time. The remarkable results of the QD-WS2 hybrid devices to the high non-radiative energy transfer (NRET) efficiency between QDs and our nanostructured material are caused by the spectral overlap between the emission of QDs as the donors and the absorption of WS2 as the acceptors. Additionally, the outstanding NO2 gas-sensing properties of QDs/WS2 devices were demonstrated with a remarkably low detection limit down to 50 ppb with a fast response time of 26.8 s, contributed by tremendous local p-n junctions generated from p-type WS2 nanowalls and n-type CdSe-ZnS QDs in this hybrid system.Our strategies to combine 0D nanoparticles or quantum dots and 2D TMDC materials can significantly enhance the optical sensing and gas molecule sensing properties compared to pristine TMDC-based devices, resulting from the efficient charge or energy transfer between the multi-dimension material system and the creation of local p-n junctions. Moreover, the scalability of these hybrid nanostructures allows our devices to exhibit much more possibilities in advanced multifunctional applications.

Chemiresistive gas sensors based on layered double hydroxides (LDHs) structures: A review

A wide range of materials and nanomaterials have been considered gas sensors due to the growing needs of developed and developing societies. It has been more than six decades since Taguchi introduced the first commercial gas sensor, yet the development of sensors has grown exponentially and has not slowed down. Researchers always look for new materials with more efficient morphologies. It can probably be said that one of the newest materials discussed is LDH nanosheets, which has been discussed for less than two decades. Thus, in this review, after defining the general structure of LDH and giving a general description of nanomaterials, we have discussed the relevant terms and various methods of synthesis. The film preparation of nanomaterial sensor is then discussed in detail as a scalable approach in line with the various methods of nanomaterial synthesis (especially with a focus on LDH nanosheets). The following is discussed the unique properties of LDH nanosheets and their effect on the detection of various contaminants. Since LDH nanomaterials fall into both n- and p-type categories, the pollutant (gas) measurement approach has been investigated in both categories. This creates a specific comparative approach for the reader. Concisely, this study describes the use of LDH nanosheets in the field of gas sensors and may provide the idea for more extensive and practical research on LDHs.

Clinical studies of detecting COVID-19 from exhaled breath with electronic nose.

The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.

High performance of the rare earth (Er, Gd & Pr) doped MoO3 thin films for advanced applications towards ammonia gas sensing

In this report, an economical spray pyrolysis technique was used to develop pure and 2% of Er, Gd, Pr doped MoO3 thin films. Different characterization techniques were utilized to study the properties of the pure and 2% of Er, Gd, Pr doped MoO3 thin films. The structural XRD analysis revealed monoclinic crystal structure of the MoO3 thin films with high purity and highest crystal size of 59 nm for the Pr doped MoO3 thin film. Furthermore, the lattice constant values increase with the substitution of Er, Gd, Pr dopants in MoO3 thin films. The morphological studies revealed reticulated nano-fibrous mesh morphology for all the samples and tiny rod-shaped protrusions over the surface of Pr doped MoO3 thin film. The optical studies demonstrated lower transparency, small bandgap and important defects. The gas sensing studies established the good response of the Pr doped MoO3 thin film towards the NH3 gas of 1.34 × 104% (250 ppm) with high stability and fast response (54 s) with a recovery time of 12 s. This opens door to our synthesized Pr doped MoO3 thin film materials to be used for the construction of commercial ammonia gas sensors.

A Smart Wristband Integrated with an IoT-Based Alarming System for Real-Time Sweat Alcohol Monitoring.

Breathalyzer is a common approach to measuring blood alcohol concentration (BAC) levels of individuals suspected of drunk driving. Nevertheless, this device is relatively high-cost, inconvenient for people with limited breathing capacity, and risky for COVID-19 exposure. Here, we designed and developed a smart wristband integrating a real-time noninvasive sweat alcohol metal oxide (MOX) gas sensor with a Drunk Mate, an Internet of Thing (IoT)-based alarming system. A MOX sensor acquired transdermal alcohol concentration (TAC) which was converted to BAC and sent via the IoT network to the Blynk application platform on a smartphone, triggering alarming messages on LINE Notify. A user would receive an immediate alarming message when his BAC level reached an illegal alcohol concentration limit (BAC 50 mg%; TAC 0.70 mg/mL). The sensor readings showed a high linear correlation with TAC (R2 = 0.9815; limit of detection = 0.045 mg/mL) in the range of 0.10–1.05 mg/mL alcohol concentration in artificial sweat, achieving an accuracy of 94.66%. The sensor readings of ethanol in water were not statistically significantly different (p > 0.05) from the measurements in artificial sweat and other sweat-related solutions, suggesting that the device responded specifically to ethanol and was not affected by other electrolytes in the artificial sweat. Moreover, the device could continuously monitor TAC levels simulated in real-time in an artificial sweat testing system. With the integration of an IoT-based alarming system, the smart wristband developed from a commercial gas sensor presented here offers a promising low-cost MOX gas sensor monitoring technology for noninvasive and real-time sweat alcohol measurement and monitoring.

Effect of the Microstructure of ZnO Thin Films Prepared by PLD on Their Performance as Toxic Gas Sensors

In 2008, the modified European Restriction of Hazardous Substances (RoHS) directive prohibited the use of hazardous substances such as lead, cadmium, and mercury. As such, an urgent need for lead-free components emerged in Europe. In this frame, we have decided to study the microstructure influence of zinc oxide thin films on the detection of hydrogen sulfide (H2S). Zinc oxide thin films were deposited by PLD on silicon substrates under different conditions to modify the microstructure. In order to compare our demonstrators to current commercial semiconductor gas sensors, measurements under H2S were also performed with sensors from Figaro and Winsen corporations. Gas sensors were therefore implemented by using commercial cases in view to test them with Simtronics gas detector DG477. The good sensitivity values measured at T = 400 °C under 100 ppm H2S, and response times as low as 30 s, definitely confirm that ZnO thin films could be developed for commercial sensors.

Wireless portable bioelectronic nose device for multiplex monitoring toward food freshness/spoilage

Monitoring food freshness/spoilage is important to ensure food quality and safety. Current methods of food quality monitoring are mostly time-consuming and labor intensive processes that require massive analytical equipment. In this study, we developed a portable bioelectronic nose (BE-nose) integrated with trace amine-associated receptor (TAAR) nanodiscs (NDs), allowing food quality monitoring via the detection of food spoilage indicators, including the biogenic amines cadaverine (CV) and putrescine (PT). The olfactory receptors TAAR13c and TAAR13d, which have specific affinities for CV and PT, were produced and successfully reconstituted in ND structures. TAAR13 NDs BE-nose-based side-gated field-effect transistor (SG-FET) system was constructed by utilizing a graphene micropattern (GM) into which two types of olfactory NDs (TAAR13c ND and TAAR13d ND) were introduced, and this system showed ultrahigh sensitivity for a limit of detection (LOD) of 1 fM for CV and PT. Moreover, the binding affinities between the TAAR13 NDs and the indicators were confirmed by a tryptophan fluorescence quenching assay and biosimulations, in which the specific binding site was confirmed. Gas-phase indicators were detected by the TAAR13 NDs BE-nose platform, and the LODs for CV and PT were confirmed to be 26.48 and 7.29 ppb, respectively. In addition, TAAR13 NDs BE-nose was fabricated with commercial gas sensors as a portable platform for the measurement of NH3 and H2S, multiplexed monitoring was achieved with similar performance, and the change ratio of the indicators was observed in a real sample. The integration of commercial gas sensors on a BE-nose enhanced the accuracy and reliability for the quality monitoring of real food samples. These results indicate that the portable TAAR13 NDs BE-nose can be used to monitor CV and PT over a wide range of concentrations, therefore, the electronic nose platform can be utilized for monitoring the freshness/spoilage step in various foods.

Rapid and Non-Destructive Analysis of Corky Off-Flavors in Natural Cork Stoppers by a Wireless and Portable Electronic Nose

This article discusses the use of a handheld electronic nose to obtain information on the presence of some aromatic defects in natural cork stoppers, such as haloanisoles, alkylmethoxypyrazines, and ketones. Typical concentrations of these compounds (from 5 to 120 ng in the cork samples) have been measured. Two electronic nose prototypes have been developed as an instrumentation system comprise of eight commercial gas sensors to perform two sets of experiments. In the first experiment, a quantitative approach was used whist in the second experiment a qualitative one was used. Machine learning algorithms such as k-nearest neighbors and artificial neural networks have been used in order to test the performance of the system to detect cork defects. The use of this system tries to improve the current aromatic defect detection process in the cork stopper industry, which is done by gas chromatography or human test panels. We found this electronic nose to have near 100 % accuracy in the detection of these defects.