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The adsorption of oxygen and its reaction with target gases are the basis of the gas detection mechanism by using metal oxides. Here, we present a theoretical analysis of the sensor response, within the ionosorption model, for an n-type polycrystalline semiconductor. Our goal of our work is to reveal the mechanisms of gas sensing from a fundamental point of view. We revisit the existing models in which the sensor response presents a power-law behavior with a reducing gas partial pressure. Then, we show, based on the Wolkenstein theory of chemisorption, that the sensor response depends not only on the reducing gas partial pressure but also on the oxygen partial pressure. We also find that the obtained sensor response does not explicitly depend on the grain size, and if it does, it is exclusively through the rate constants related to the involved reactions.

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Palladium phthalocyanine (PdPc) nanowires (NWs) were developed to achieve the gas sensing of NO2 in the sub-parts-per-million (ppm) range. Non-substituted metal phthalocyanine are well known for their p-type semiconducting behavior, which is responsible for its gas-sensing capabilities. Nanofabrication of the PdPc NWs was performed by physical vapor deposition (PVD) on an interdigitated gold electrode (IDE). The coordination of palladium in the structure was confirmed with UV-Vis spectroscopy. Gas-sensing experiments for NO2 detection were undertaken at different sensed gas concentrations from 4 ppm to 0.5 ppm at room temperature. In this work, the responses at different gas concentrations are reported. In addition, structural studies of the PdPc NWs with scanning electron microscopy (SEM) and electron-dispersive X-ray diffraction (EDS) are shown.

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The goal of the sensor industry is to develop innovative, energy-efficient, and reliable devices to detect molecules relevant to economically important sectors such as clinical diagnoses, environmental monitoring, food safety, and wearables. The current demand for portable, fast, sensitive, and high-throughput platforms to detect a plethora of new analytes is continuously increasing. The 2D transition metal dichalcogenides (2D-TMDs) are excellent candidates to fully meet the stringent demands in the sensor industry; 2D-TMDs properties, such as atomic thickness, large surface area, and tailored electrical conductivity, match those descriptions of active sensor materials. However, the detection capability of 2D-TMDs is limited by their intrinsic tendency to aggregate and settle, which reduces the surface area available for detection, in addition to the weak interactions that pristine 2D-TMDs normally exhibit with analytes. Chemical functionalization has been proposed as a consensus solution to these limitations. Tailored surface modification of 2D-TMDs, either by covalent functionalization, non-covalent functionalization, or a mixture of both, allows for improved specificity of the surface-analyte interaction while reducing van der Waals forces between 2D-TMDs avoiding agglomeration and precipitation. From this perspective, we review the recent advances in improving the detection of biomolecules, heavy metals, and gases using chemically functionalized 2D-TMDs. Covalent and non-covalent functionalized 2D-TMDs are commonly used for the detection of biomolecules and metals, while 2D-TMDs functionalized with metal nanoparticles are used for gas and Raman sensors. Finally, we describe the limitations and further strategies that might pave the way for miniaturized, flexible, smart, and low-cost sensing devices.

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Molybdenum trioxide is an abundant natural, low-cost, and environmentally friendly material that has gained considerable attention from many researchers in a variety of high-impact applications. It is an attractive inorganic oxide that has been widely studied because of its layered structure, which results in intercalation ability through tetrahedral/octahedral holes and extension channels and leads to superior charge transfer. Shape-related properties such as high specific capacities, the presence of exposed active sites on the oxygen-rich structure, and its natural tendency to oxygen vacancy that leads to a high ionic conductivity are also attractive to technological applications. Due to its chemistry with multiple valence states, high thermal and chemical stability, high reduction potential, and electrochemical activity, many studies have focused on the development of molybdenum oxide-based systems in the last few years. Thus, this article aims to briefly review the latest advances in technological applications of MoO3 and MoO3-based materials in gas sensors, lithium-ion batteries, and water pollution treatment using adsorption and photocatalysis techniques, presenting the most relevant and new information on heterostructures, metal doping, and non-stoichiometric MoO3-x.

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Detecting volatile organic compounds is a fundamental step in water quality analysis. Methylisoborneol (MIB) provides a lousy odor to water, whereas geosmin (GEO) is responsible for its sour taste. A widely-used technique for their detection is gas-phase chromatography. On the other hand, an electronic nose from organic thin-film transistors is a cheaper and faster alternative. Poly(2,5-bis(3-tetradecyl-thiophen-2-yl)thieno[3,2-b]thiophene) (PBTTT-C14) features semiconducting properties suitable for organic electronics. However, in order to expose the active layer in a bottom-gate transistor structure with photolithographically patterned electrodes, a cross-linked dielectric such as poly(4-vinyl phenol) (PVP) is necessary. In this work, the cross-linking was demonstrated using FTIR and Raman spectroscopies, as well as high-k capacitors with a dielectric constant of 5.3. The presence of enhanced crystallinity with terrace formation in the semiconducting film was confirmed with UV-visible spectrophotometry, atomic force microscopy, and X-ray diffraction. Finally, for the first time, a PBTTT-C14 transistor on cross-linked PVP was shown to respond to isoborneol with a sensitivity of up to 6% change in mobility per ppm. Due to its similarity to MIB, a system comprising these sensors must be investigated in the future as a tool for sanitation companies in real-time water quality monitoring.

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In this research, we present a prototype optical system that offers significant advances in detecting hydrochloric acid (HCl) and ammonia (NH3) vapors. The system utilizes a natural pigment sensor based on Curcuma longa that is securely attached to a glass surface support. Through extensive development and testing with HCl (37% aqueous solution) and NH3 (29% aqueous solution) solutions, we have successfully demonstrated the effectiveness of our sensor. To facilitate the detection process, we have developed an injection system that exposes C. longa pigment films to the targeted vapors. The interaction between the vapors and the pigment films triggers a distinct color change, which is then analyzed by the detection system. By capturing the transmission spectra of the pigment film, our system allows a precise comparison of these spectra at different concentrations of the vapors. Our proposed sensor exhibits remarkable sensitivity, allowing the detection of HCl at a concentration of 0.009 ppm using only 100 µL (2.3 mg) of pigment film. In addition, it can detect NH3 at a concentration of 0.03 ppm with a 400 µL (9.2 mg) pigment film. Integrating C. longa as a natural pigment sensor in an optical system opens up new possibilities for detecting hazardous gases. The simplicity and efficiency of our system, combined with its sensitivity, make it an attractive tool in environmental monitoring and industrial safety applications.

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Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin". For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation-extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.

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Metal oxide (MOx) gas sensors have attracted considerable attention from both scientific and practical standpoints. Due to their promising characteristics for detecting toxic gases and volatile organic compounds (VOCs) compared with conventional techniques, these devices are expected to play a key role in home and public security, environmental monitoring, chemical quality control, and medicine in the near future. VOCs (e.g., acetone) are blood-borne and found in exhaled human breath as a result of certain diseases or metabolic disorders. Their measurement is considered a promising tool for noninvasive medical diagnosis, for example in diabetic patients. The conventional method for the detection of acetone vapors as a potential biomarker is based on spectrometry. However, the development of MOx-type sensors has made them increasingly attractive from a medical point of view. The objectives of this review are to assess the state of the art of the main MOx-type sensors in the detection of acetone vapors to propose future perspectives and directions that should be carried out to implement this type of sensor in the field of medicine.

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Many people around the world are concerned about meat safety and quality, which has resulted in the ongoing advancement of packaged food technology. Since the emergence of graphene in 2004, the number of studies on layered two-dimensional materials (2DMs) for applications ranging from food packaging to meat quality monitoring has been expanding quickly. Recently, scientists have been working hard to develop a novel class of 2DMs that keep the good things about graphene but don't have zero bandgaps at room temperature. Much work has been done on layered transition metal dichalcogenides (TMDCs) like different metal sulfides and selenides for meat spoilage gas sensors. This review looks at (i) the main indicators of meat spoilage and (ii) the detection methods that can be used to find out if meat has been spoiled, such as chemiresistive, electrochemical, and optical methods. (iii) the role of 2DMs in meat spoilage detection and (iv) the emergence of advanced methods for selective classification of target analytes in meat/food spoilage detection in recent years. Thus, this review demonstrates the potential scope of 2DMs for developing intelligent sensor systems for food and meat spoilage detection with high viability, simplicity, cost-effectiveness, and other multipurpose tools.

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In this work, the gas-sensing functionality of porous ceramic bodies formed by the slip casting technique was studied using perovskite nanoparticles of an MSnO3 system (M = Ba, Ca, Zn) synthesized by a chemical route. The performance and reliability of the sensitive materials in the presence of different volatile organic compounds (acetone, ethanol, and toluene), and other gases (CO, H2 and NO2) were analysed. The ZnSnO3, BaSnO3, and CaSnO3 sensors showed sensitivities of 40, 16, and 8% ppm-1 towards acetone, ethanol, and toluene vapours, respectively. Good repeatability and selectivity were also observed for these gaseous analytes, as well as excellent stability for a period of 120 days. The shortest response times were recorded for the ZnSnO3 sensors (e.g., 4 s for 80 ppm acetone) with marked responses to low concentrations of acetone (1000 ppb). These results are attributed to the porosity of the sensitive materials, which favours the diffusion of gases, induces surface defects, and provides greater surface area and good sensitivity to acetone, as is seen in the case of ZnSnO3.

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ZnAl2O4 nanoparticles were synthesized employing a colloidal method. The oxide powders were obtained at 300 °C, and their crystalline phase was corroborated by X-ray diffraction. The composition and chemical structure of the ZnAl2O4 was carried out by X-ray and photoelectron spectroscopy (XPS). The optical properties were studied by UV-vis spectroscopy, confirming that the ZnAl2O4 nanoparticles had a direct transition with bandgap energy of 3.2 eV. The oxide's microstructures were microbars of ~18.2 nm in size (on average), as analyzed by scanning (SEM) and transmission (TEM) electron microscopies. Dynamic and stationary gas detection tests were performed in controlled propane atmospheres, obtaining variations concerning the concentration of the test gas and the operating temperature. The optimum temperatures for detecting propane concentrations were 200 and 300 °C. In the static test results, the ZnAl2O4 showed increases in propane response since changes in the material's electrical conductance were recorded (conductance = 1/electrical resistance, Ω). The increases were ~2.8 at 200 °C and ~7.8 at 300 °C. The yield shown by the ZnAl2O4 nanoparticles for detecting propane concentrations was optimal compared to other similar oxides categorized as potential gas sensors.

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Detecting and monitoring air-polluting gases such as carbon monoxide (CO), nitrogen oxides (NOx), and sulfur oxides (SOx) are critical, as these gases are toxic and harm the ecosystem and the human health. Therefore, it is necessary to design high-performance gas sensors for toxic gas detection. In this sense, graphene-based materials are promising for use as toxic gas sensors. In addition to experimental investigations, first-principle methods have enabled graphene-based sensor design to progress by leaps and bounds. This review presents a detailed analysis of graphene-based toxic gas sensors by using first-principle methods. The modifications made to graphene, such as decorated, defective, and doped to improve the detection of NOx, SOx, and CO toxic gases are revised and analyzed. In general, graphene decorated with transition metals, defective graphene, and doped graphene have a higher sensibility toward the toxic gases than pristine graphene. This review shows the relevance of using first-principle studies for the design of novel and efficient toxic gas sensors. The theoretical results obtained to date can greatly help experimental groups to design novel and efficient graphene-based toxic gas sensors.

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Gas sensors are fundamental for continuous online monitoring of volatile organic compounds. Gas sensors based on semiconductor materials have demonstrated to be highly competitive, but are generally made of expensive materials and operate at high temperatures, which are drawbacks of these technologies. Herein is described a novel ethanol sensor for room temperature (25 °C) measurements based on hematite (α­Fe2O3)/silver nanoparticles. The AgNPs were shown to increase the oxide semiconductor charge carrier density, but especially to enhance the ethanol adsorption rate boosting the selectivity and sensitivity, thus allowing quantification of ethanol vapor in 2-35 mg L-1 range with an excellent linear relationship. In addition, the α-Fe2O3/Ag 3.0 wt% nanocomposite is cheap, and easy to make and process, imparting high perspectives for real applications in breath analyzers and/or sensors in food and beverage industries. This work contributes to the advance of gas sensing at ambient temperature as a competitive alternative for quantification of conventional volatile organic compounds.

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More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12-24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations.

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Organic thin-film transistors (OTFTs) are miniaturized devices based upon the electronic responses of organic semiconductors. In comparison to their conventional inorganic counterparts, organic semiconductors are cheaper, can undergo reversible doping processes and may have electronic properties chiefly modulated by molecular engineering approaches. More recently, OTFTs have been designed as gas sensor devices, displaying remarkable performance for the detection of important target analytes, such as ammonia, nitrogen dioxide, hydrogen sulfide and volatile organic compounds (VOCs). The present manuscript provides a comprehensive review on the working principle of OTFTs for gas sensing, with concise descriptions of devices' architectures and parameter extraction based upon a constant charge carrier mobility model. Then, it moves on with methods of device fabrication and physicochemical descriptions of the main organic semiconductors recently applied to gas sensors (i.e., since 2015 but emphasizing even more recent results). Finally, it describes the achievements of OTFTs in the detection of important gas pollutants alongside an outlook toward the future of this exciting technology.

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In the search for gas sensing materials, two-dimensional materials offer the possibility of designing sensors capable of tuning the electronic band structure by controlling their thickness, quantity of dopants, alloying between different materials, vertical stacking, and the presence of gases. Through materials engineering it is feasible to study the electrical properties of two-dimensional materials which are directly related to their crystalline structure, first Brillouin zone, and dispersion energy, the latter estimated through the tight-binding model. A review of the electrical properties directly related to the crystalline structure of these materials is made in this article for the two-dimensional materials used in the design of gas sensors. It was found that most 2D sensing materials have a hexagonal crystalline structure, although some materials have monoclinic, orthorhombic and triclinic structures. Through the simulation of the mathematical models of the dispersion energy, two-dimensional and three-dimensional electronic band structures were predicted for graphene, hexagonal boron nitride (h-BN) and silicene, which must be known before designing a gas sensor.

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Nitrogen-based fertilizers have been used in modern agricultural activities resulting in a relevant emission source of nitrogen gases into the atmosphere, mainly nitric oxide (NO), nitrogen dioxide (NO2), and nitrous oxide (N2O). Furthermore, the burning of fossil fuels is the most significant emission source of NOx (i.e., NO + NO2), being the controlling of vehicle exhaust system an essential task. Those compounds can be related to air pollution effects either directly, by emitting a powerful greenhouse gas (i.e., N2O), or indirectly, by formation of nitric acid (HNO3) or ammonium nitrate (NH4NO3) from NO or NO2, responsible for the increase of acid rain and particulate material into the atmosphere. This context requires appropriate sensor technology facilitating in situ and simultaneous monitoring of nitrogen emitted gases, with easiness of operation and compact dimensions. In this communication, we describe an innovative mid-infrared chemical sensor platform for the in situ, real-time, and simultaneous quantification of gaseous NO, NO2, and N2O by combining a compact Fourier transform infrared (FT-IR) spectrometer with the so-called substrate-integrated hollow waveguide (iHWG) as a miniaturized gas cell. The optical platform enabled limits of detection of 10, 1, and 0.5 ppm of NO, NO2, and N2O, respectively. The linear concentration range evaluated in this study is suitable for the application of the sensing platform in vehicle exhaust air samples. Given the high adaptability of the developed infrared sensing device toward preconcentration or ultraviolet conversion modules and also considering the potential for combining tunable interband cascade lasers (ICLs) in lieu of the FT-IR spectrometer, we anticipate the application of the sensing platform for in situ determination of nitrogen gases in a wide range of scenarios.

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In this work, a novel tailored algorithm to enhance the overall sensitivity of gas concentration sensors based on the Direct Absorption Tunable Laser Absorption Spectroscopy (DA-ATLAS) method is presented. By using this algorithm, the sensor sensitivity can be custom-designed to be quasi constant over a much larger dynamic range compared with that obtained by typical methods based on a single statistics feature of the sensor signal output (peak amplitude, area under the curve, mean or RMS). Additionally, it is shown that with our algorithm, an optimal function can be tailored to get a quasi linear relationship between the concentration and some specific statistics features over a wider dynamic range. In order to test the viability of our algorithm, a basic C 2 H 2 sensor based on DA-ATLAS was implemented, and its experimental measurements support the simulated results provided by our algorithm.

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In this work, we will analyze the response of a Metal Oxide Gas Sensor (MOGS) array to a flow controlled stimulus generated in a pressure controlled canister produced by a homemade olfactometer to build an E-nose. The built E-nose is capable of chocolate identification between the 26 analyzed chocolate bar samples and four features recognition (chocolate type, extra ingredient, sweetener and expiration date status). The data analysis tools used were Principal Components Analysis (PCA) and Artificial Neural Networks (ANNs). The chocolate identification E-nose average classification rate was of 81.3% with 0.99 accuracy (Acc), 0.86 precision (Prc), 0.84 sensitivity (Sen) and 0.99 specificity (Spe) for test. The chocolate feature recognition E-nose gives a classification rate of 85.36% with 0.96 Acc, 0.86 Prc, 0.85 Sen and 0.96 Spe. In addition, a preliminary sample aging analysis was made. The results prove the pressure controlled generated stimulus is reliable for this type of studies.

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Se desarrolló una nariz electrónica que permite la detección de alcoholes de manera sencilla y económica en comparación con las narices electrónicas tradicionales. Estábasada en cuatro sensores de gas de SnO2 (dos comerciales y dos fabricados en el laboratorio), un sistema neumático irregular, un hardware y software para adquisición de datos y un software de reconocimiento de patrones. Se evaluó el comportamiento de la nariz y las condiciones de trabajo con muestras de vapor de alcoholes (metanol, etanol, n-butanol y 1-2 octanol) y se determinó que los alcoholes se pueden detectar con el arreglo de sensores preparado y pueden diferenciarse entre sí haciendo uso del análisis estadístico de componentes principales (PCA). El orden de detección encontrado para los alcoholes lineales fue el siguiente: metanol > etanol > n-butanol > 1-octanol. Se encontró también que haciendo uso del análisis de componentes principales (PCA) y realizando una normalización de los datos en el software de reconocimiento de patrones, la varianza total de las muestras también aumenta del 76% al 85%. Esto demuestra que una nariz simple y económica puede clasificar bien las muestras evaluadas.

An electronic nose for the detection of alcohols, easy to use and inexpensive as compared to traditional electronic noses, was developed. This nose is based on four SnO2 gas sensors (two commercial and two homemade), an irregular pneumatic system, hardware and a software for data acquisition and a software for pattern recognition. The nose behavior and the working conditions with vapor samples of alcohols (methanol, ethanol, n-butanol and 1-octanol) were evaluated. Alcohols could be detected with the array of prepared sensors and could be also differentiated from each other by using principal component analysis (PCA). The detection order for linear alcohols followed the order: methanol > ethanol > n-butanol > 1-octanol. It was also found that by using PCA and performing a standardization of data in software pattern recognition so, the total variance of such information increases from 76% up to 85%. This result confirms that a simple and inexpensive nose can rank well the tested samples.

Foi feito um nariz eletrônico que permitiu a detecção de álcoois de jeito simplis e económico em comparacao com os narices electrónicos tradicionais. Estábaseado em quatro sensores de gás SnO2 (dois comerciais e dois feitos no laboratório), um sistema pneumático irregular, hardware e software para aquisição de dados e software de reconhecimento de padrões. Foi avaliado o comportamento do nariz e as condições de trabalho com amostras de vapor de álcoois (metanol, etanol, n-butanol e 1-octanol) determinou-se que os álcoois podem ser detectados com o arranjo de sensores preparado e pode diferenciar-se entre sim usando análise de componentes principais (PCA). A ordem de detecção para os álcoois lineares encontrados foi os seguinte: metanol> etanol> n-butanol> 1-octanol. Verificou-se que, usando análise de componentes principais (PCA) e a execução de uma normalização dos dados no software de reconhecimento de padrões, a variância das amostras também aumenta de 76% para 85%. Esto demostra que um nariz simples e barato pode classificar bem as amostras avaliadas.