A novel electronic nose based on porous In2O3 microtubes sensor array for the discrimination of VOCs

Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose.

Emerging single-atom catalysts in the detection and purification of contaminated gases

Single atom catalysts (SACs) show exceptional molecular adsorption and electron transfer capabilities owing to their remarkable atomic efficiency and tunable electronic structure, thereby providing promising solutions for diverse important processes including photocatalysis, electrocatalysis, thermal catalysis, etc. Consequently, SACs hold great potential in the detection and degradation of pollutants present in contaminated gases. Over the past few years, SACs have made remarkable achievements in the field of contaminated gas detection and purification. In this review, we first provide a concise introduction to the significance and urgency of gas detection and pollutant purification, followed by a comprehensive overview of the structural feature identification methods for SACs. Subsequently, we systematically summarize the three key properties of SACs for detecting contaminated gases and discuss the research progress made in utilizing SACs to purify polluted gases. Finally, we analyze the enhancement mechanism and advantages of SACs in polluted gas detection and purification, and propose strategies to address challenges and expedite the development of SACs in polluted gas detection and purification.

Temperature-modulated sensing characteristics of ultrafine Au nanoparticle-loaded porous ZnO nanobelts for identification and determination of BTEX

The identification and determination of benzene, toluene, ethylbenzene, and xylene (BTEX) has always been a formidable challenge for chemiresistive metal oxide sensors owing to their structural similarity and low reactivity, as well as the intrinsic cross sensitivity of metal oxides. In this paper, a temperature-modulated sensing strategy is proposed for the identification and determination of BTEX using a high-performance chemiresistive sensor. Ultrafine Au nanoparticle-loaded porous ZnO nanobelts as sensing materials were synthesized through an exchange reaction followed by thermal oxidation, which exhibited high response toward BTEX. Under dynamic modulation of working temperature, the distinguishable characteristic curves were demonstrated for each BTEX compound. By employing the linear discrimination and convolutional neural network analyses, highly effective BTEX identification was achieved among all investigated volatile organic compounds, which is difficult to realize for single chemiresistive sensors at constant working temperatures. Furthermore, quantitative analysis of BTEX concentrations was accomplished by establishing the relationship between concentration and response at specific points on their response curves. This developed strategy is expected to pave a new way for constructing highly sensitive gas sensors for the identification and analysis of hazardous gases, thereby enhancing their applicability in environmental monitoring.

Research on electronic nose for compound malodor recognition combined with artificial neural network and linear discriminant analysis

The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods.

Strategies to improve meat-like properties of meat analogs meeting consumers' expectations

Due to environmental and ethical concerns, meat analogs represent an emerging trend to replace traditional animal meat. However, meat analogs lacking specific sensory properties (flavor, texture, color) would directly affect consumers' acceptance and purchasing behavior. In this review, we discussed the typical sensory characteristics of animal meat products from texture, flavor, color aspects, and sensory perception during oral processing. The related strategies were detailed to improve meat-like sensory properties for meat analogs. However, the upscaling productions of meat analogs still face many challenges (e.g.: sensory stability of plant-based meat, 3D scaffolds in cultured meat, etc.). Producing safe, low cost and sustainable meat analogs would be a hot topic in food science in the next decades. To realize these promising outcomes, reliable robust devices with automatic processing should also be considered. This review aims at providing the latest progress to improve the sensory properties of meat analogs and meet consumers' requirements.

Inkjet-printed paper-based sensor array for highly accurate pH sensing

In this work, a novel paper-based colorimetric sensor array was developed by inkjet printing method with polyethylene glycol (PEG) immobilization system. Eight commercially available pH indicators with sequential pH segments in nearly whole pH range were dissolved in nine mixed inks to fabricate the 3 × 3 sensor array on mixed cellulose ester (MCE) paper. Based on homogeneous deposition of inkjet printing, the eight pH indicators were sufficiently immobilized on MCE paper with the assistance of PEG-400, which guaranteed pH detection of aqueous samples on sensor array without hydrophobic barriers. Besides, the indicating range of each indicator obtained an extension through the addition of PEG 400, which remarkably enriched the distinguishable capability of sensor array and benefited for high resolution of pH detection. As such, the as-fabricated paper-based sensor array exhibited an excellent discrimination ability in pH range of 1.00-13.60 with a high resolution of 0.20 pH unit, not only for standard pH buffer solutions but for real aqueous samples.

Cuprous Oxide Based Chemiresistive Electronic Nose for Discrimination of Volatile Organic Compounds

This paper reports a facile functionalization method on a metal-oxide semiconductor and a cuprous oxide (Cu₂O) based chemiresistive electronic nose for the detection of volatile organic compounds (VOCs). A library of functionalized Cu₂O nanospheres was developed through silanization using chemically diverse organosilanes. An electronic nose was fabricated with unmodified Cu₂O nanospheres and five types of functionalized Cu₂O nanospheres as the sensing elements. The electronic nose showed stable and rapid resistance responses to 25-200 ppm model VOCs, with the operating temperature of 180 °C. Single VOCs and ternary VOC mixtures could be discriminated by the electronic nose, and six types of tea leaves were also proved to be distinguishable as an illustration of the application of the electronic nose. We expected that the silanization could provide a simple approach for material diversification and the electronic nose would have further application in identification and discrimination of complex gas samples.

Combined optical and electronic paper-nose for detection of volatile gases

In this work, a paper-based optoelectronic sensor (paper-nose) is presented for sensing volatile gases in air. The proposed optoelectronic sensor is a combination of both colorimetric (optical) and chemiresistive (electronic) sensor arrays in order to improve the selectivity of the paper-nose in the complex air background. The optical sensors are based on chemoresponsive dyes, namely Reichardt's dye (2,6-diphenyl-4-(2,4,6-triphenyl-1-pyridinio)phenolate), bromocresol purple, methyl red, bromothymol blue, brilliant yellow and manganese tetraphenylporphyrin (Mn-TPP). The chemiresistive sensors are based on nanomaterials, such as carbon nanotubes (CNT), PEDOT:PSS, graphite, and an ionic liquid, 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMI TFSI). Sensor is fabricated through direct handwriting of sensing materials using a pen on paper without the need of expensive cleanroom facilities. The optoelectronic sensor is tested in ambient air with different volatile gases such as methanol, ammonia, toluene, acetone and ethanol and their mixtures of varying concentrations. The detected electrical and optical responses together form a unique signature for each volatile gas and its mixture. Support-vector machine (SVM) is applied for target classification and detection. From the SVM result, it is found that better discriminative power is achieved by combining optical and electrical responses.

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.

Intraparticle FRET of Mn(ii)-doped carbon dots and its application in discrimination of volatile organic compounds

To achieve an energy transfer system in emissive nanoparticles, a conventional strategy is to graft an exterior fluorophore onto the surface of the host. In this paper, we report for the first time an intraparticle Förster resonance energy transfer (IPFRET) system formed intrinsically in Mn(ii)-doped carbon dots (MCDs). In virtue of the small particle size of MCDs and the modified band structure, intraparticle energy transfer from a fluorophore-like donor component to a metal-related acceptor component takes place. The IPFRET of MCDs was found to be sensitive to the chemical environment (e.g., polarity) via the effects of external influences on the metal-to-ligand charge transfer (MLCT). Surface enhanced Raman spectroscopy was employed to verify the MLCT-related metal-coordination conformation, and proved capable of collecting bonding information of metal-doped species of carbon dots. Benefitting from the sensitivity of the IPFRET signal, MCDs exhibited high potential in sensing applications.

Detection of Volatile Organic Compounds Using Microfabricated Resonator Array Functionalized with Supramolecular Monolayers

This paper describes the detection of volatile organic compounds (VOCs) using an e-nose type integrated microfabricated sensor array, in which each resonator is coated with different supramolecular monolayers: p-tert-butyl calix[8]arene (Calix[8]arene), 2,3,7,8,12,13,17,18-octaethyl-21H,23H-porphine (Porphyrin), β-cyclodextrin (β-CD), and cucurbit[8]uril (CB[8]). Supramolecular monolayers fabricated by Langmuir-Blodgett techniques work as specific sensing interface for different VOCs recognition which increase the sensor selectivity. Microfabricated ultrahigh working frequency film bulk acoustic resonator (FBAR) transducers (4.4 GHz) enable their high sensitivity toward monolayer gas sensing which facilitate the analyses of VOCs adsorption isotherms and kinetics. Two affinity constants (K1, K2) are obtained for each VOC, which indicate the gas molecule adsorption happen inside and outside of the supramolecular cavities. Additional kinetic information on adsorption and desorption rate constants (ka, kd) are obtained as well from exponential fitting results. The five parameters, one from the conventional frequency shift signals of mass transducers and the other four from the indirect analyses of monolayer adsorption behaviors, thus enrich the sensing matrix (Δf, K1, K2, ka, kd) which can be used as multiparameter fingerprint patterns for highly selective detection and discrimination of VOCs.

Coupling dye-integrated polymeric membranes with smartphone detection to classify bacteria

We report the use of a colorimetric plastic-based device to discriminate four pathogenic bacteria: Klebsiella pneumoniae, Proteus vulgaris, Proteus mirabilis and Escherichia coli.

Three-dimensional ultrathin In2O3 nanosheets with morphology-enhanced activity for amine sensing

We prepared a three dimensional In₂O₃ material built by ultrathin nanosheets with an enhanced amine sensing property.