Discrimination Of Volatile Organic Compounds Research Articles

A Hybrid Wireless Electrodeless QCM-D for the Discrimination of Volatile Organic Compounds

A Hybrid Wireless Electrodeless QCM-D for the Discrimination of Volatile Organic Compounds

Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip

Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform.

Discrimination of volatile organic compounds using a sensor array via a rapid method based on linear discriminant analysis

We investigated a discriminant method for volatile organic compounds (VOCs) in exhaled breath under contaminant air as well as purified air conditions using an array of multiple semiconductive gas sensors and the linear discriminant analysis (LDA) space. We analyzed the sensor signals of resistance changes employing our developed discriminant method in the LDA space. In particular, the sensor resistances at intervals at which a resistance change occurred owing to target gas introduction were used as test data. The linear discriminant (LD) scores from the test data in the two-dimensional (2-D) LDA space gradually shifted in the direction of the correct LD scores of the training data. Therefore, the target gases were discriminated using the angle of locus of the test data in the 2-D LDA space. Our results proved the viability of the proposed method to discriminate target gases under contaminant air conditions.

Characterization and discrimination of volatile organic compounds and lipid profiles of truffles under different treatments by UHPLC-QE Orbitrap/MS/MS and P&T-GC–MS

The lipid profiles of the truffles with different treatments were determined by ultra-high-performance liquid chromatography-Quadrupole-Exactive Orbitrap mass spectrometry (UHPLC-QE Orbitrap/MS/MS) and the volatile organic compounds (VOCs) were identified by purge-and-trap-gas chromatography-mass spectrometry (P&T-GC–MS). A total of 37 lipid molecular species and 28 VOCs were tentatively identified. Lysophophatidylcholine (LPC), triacylglycerol (TG) and sphingomyelin (SM) in heat-drying truffles, phosphatidic acid (PA) in freeze-drying and fresh truffles might be the key lipids that bound VOCs. Furthermore, the correlation between lipids and VOCs were analyzed by 19 differential lipids and 7 VOCs. The findings indicated that TG 18:2/18:2/18:2 and Cardiolipin (CL) 16:0/16:0/18:2/18:2 might be the key lipid molecule species for the formation of 2-methoxyphenol. The study helps to understand the effect of different treatments on the lipid profiles and provides the mechanistic insights to the relationship between the lipids and VOCs of truffles.

A multi-layered graphene based gas sensor platform for discrimination of volatile organic compounds via differential intercalation

Selective and sensitive detection of volatile organic compounds (VOCs) is of critical importance for environmental monitoring, disease diagnosis and industrial applications. Graphene Foam based sensor platform suggests selective determination of VOCs.

A miniaturized all-in-one optical nose based on 3D printing technology and patterned paper substrate for discrimination of volatile organic compounds

Herein, an integrated design of a paper-based colorimetric sensor array, which includes both sensor array and sampling vessel is described. The paper substrate was patterned for providing hydrophobic barriers to deposit sensing elements, while a hydrophilic regions were embedded on the paper for sample solution. In this way, the paper substrate works as a sensing device and sampling vessel, simultaneously. A miniaturized cubic reaction chamber sizing (30 × 30 × 0.02) mm, equipped with a small aperture for sample injection was fabricated using 3D printing technology to hold the paper substrate. The required sample volume was lowered down dramatically (13.0 μL) due to the all-in-one sampling and sensing device. This miniaturized sensor array concept was employed to detect and discriminate volatile organic compounds (VOCs) relating to different solvent families that are known as cancer biomarkers. A colorimetric sensor array was fabricated by spotting 12 indicators belonging to three families of solvatochromic dyes, pH indicators and metallochromic dyes. This simple proposed device was able to discriminate the studied solvents into three chemical families including polar protic, dipolar aprotic and nonpolar with 93% accuracy. In addition, this sensor showed potential for quantitative and semi-quantitative analysis of volatile compounds in their mixtures. In total, the designed device is portable, rapid, and cost-effective and requires low volumes of samples that can be considered a good candidate for various VOC analysis with limited volume.

Hierarchical Graphene‐Dye Bilayers for Multimodal Optoelectronic Sensing and Decoupling of Complex Stimuli

AbstractSignals obtained by chemical or calorimetric sensors are highly coupled and complex, making it very challenging to precisely detect and discriminate between simultaneous stimuli. The development of sensors that provide multiple sensory outputs is a good way to tackle such a long‐lasting challenge. Herein, a new design of multimodal sensors capable of generating both colorimetric and electrical sensory outputs is introduced. This is achieved by coupling two functional nanolayers, graphene (electrically active) and dyes (colorimetric) in which each layer can work either autonomously or in conjugation with the other sensing layer. It is shown that the interfacial interaction of graphene with the wide variety of dyes creates unique interfacial sensing sites for the detection of chemicals. This endows each sensor or sensor array, in the format of (opto)electronic nose, with a “fingerprint” of varying bonding possibilities, enlarging the spectrum of gas–sensor interactions. Furthermore, it is shown that the hierarchical nanobilayer structure allows the separation and discrimination of volatile organic compounds (VOCs) based on their diffusion kinetics. Taken together, the bilayer design qualifies as a superior sensor compared to unimodal devices by offering improved detection limits, wider dynamic ranges, and higher sensitivity and selectivity in the binary discrimination of a wide range of stimuli (temperature, relative humidity, and VOCs).

VOC Mixture Sensing with a MOF Film Sensor Array: Detection and Discrimination of Xylene Isomers and Their Ternary Blends.

Detection and recognition of volatile organic compounds (VOCs) are crucial in many applications. While pure VOCs can be detected by various sensors, the discrimination of VOCs in mixtures, especially of similar molecules, is hindered by cross-sensitivities. Isomer identification in mixtures is even harder. Metal-organic frameworks (MOFs) with their well-defined, nanoporous, and versatile structures have the potential to improve the VOC sensing performance by tailoring the adsorption affinities. Here, we detect and identify ternary xylene isomer mixtures by using an array of six gravimetric, quartz crystal microbalance (QCM)-based sensors coated with selected MOF films with different isomer affinities. We use classical molecular simulations to provide insights into the sensing mechanism. In addition to the attractive interaction between the analytes and the MOF film, the isomer discrimination is caused by the rigid crystalline framework sterically controlling the access of the isomers to different adsorption sites in the MOFs. The sensor array has a very low limit of detection of 1 ppm for each pure isomer and allows the isomer discrimination in mixtures. At 100 ppm, 16 different ternary o-p-m-xylene mixtures were identified with high classification accuracy (96.5%). This work shows the unprecedented performance of MOF-sensor arrays, also referred to as MOF-electronic nose (MOF-e-nose), for sensing VOC mixtures. Based on the study, guidelines for detecting and discriminating complex mixtures of volatile molecules are also provided.

Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature

Machine learning (ML) methodologies were applied to detect and discriminate five indoor volatile organic compounds (VOCs) such as benzene, xylene, toluene, formaldehyde, and ethanol using a sensor array constructed of five In2O3-based semiconducting metal oxide (SMO) gas sensors. The sensor array was evaluated using principal component analysis (PCA) and neural network-based classification in terms of the gas sensor data type/amount, neural network algorithms, sensor combinations, and environmental factors. The PCA analyses indicated the limitations on the discrimination of VOCs under temperature- and/or humidity-interfered gas sensing environments. Gas detection/discrimination could be improved significantly by using three supervised algorithms, i.e., artificial neural networks (ANNs), deep neural networks (DNNs), and 1-dimensional convolutional neural networks (1D CNNs). The neural network algorithm prediction based on the entire gas sensing/purge transient data outperforms deep learning-assisted predictions based on partial gas sensing transients. Compared to 1D CNNs, DNNs are more appropriate in terms of training/validation/test datasets. The effects due to humidity variation are more significant than those due to temperature fluctuation. A 2-sensor mode combination can be exploited to replace the 5-sensor operation in ML-based applications. The indoor pollutants can be successfully discriminated even under the variation of ambient humidity and temperature by ML-based approaches.

Surfactant-like Peptide Self-Assembled into Hybrid Nanostructures for Electronic Nose Applications.

An electronic nose (e-nose) utilizes a multisensor array, which relies on the vector contrast of combinatorial responses, to effectively discriminate between volatile organic compounds (VOCs). In recent years, hierarchical structures made of nonbiological materials have been used to achieve the required sensor diversity. With the advent of self-assembling peptides, the ability to tune nanostructuration, surprisingly, has not been exploited for sensor array diversification. In this work, a designer surfactant-like peptide sequence, CG7-NH2, is used to fabricate morphologically and physicochemically heterogeneous "biohybrid" surfaces on Au-covered chips. These multistructural sensing surfaces, containing immobilized hierarchical nanostructures surrounded by self-assembled monolayers, are used for the detection and discrimination of VOCs. Through a simple and judicious design process, involving changes in pH and water content of peptide solutions, a five-element biohybrid sensor array coupled with a gas-phase surface plasmon resonance imaging system is shown to achieve sufficient discriminatory capabilities for four VOCs. Moreover, the limit of detection of the multiarray system is bench-marked at <1 and 6 ppbv for hexanoic acid and phenol (esophago-gastric biomarkers), respectively. Finally, the humidity effects are characterized, identifying the dissociation rate constant as a robust descriptor for classification, further exemplifying their efficacy as biomaterials in the field of artificial olfaction.

An electronic nose based on carbon nanotube -titanium dioxide hybrid nanostructures for detection and discrimination of volatile organic compounds

Detection and classification of Volatile Organic Compounds (VOCs) that pose risks to human health even at very low concentrations, is a common requirement in the industry. Herein, we apply a modification to the aligned Carbon Nanotubes (CNTs) grown at low temperature (250 °C) using Plasma Enhanced Chemical Vapor Deposition (PECVD). CNTs are used to identify VOCs in an improved sensitivity condition to gas at room temperature. We improve the sensitivity and stability of carbon nanotube sensors via growing Titanium Dioxide Nanowires (TiO2-NW) on the surface of CNTs through the hydrothermal method. A threefold increase in sensitivity and a 30-second decrease in response time were observed as a result of the CNT-TiO2 sensor compared to the pristine CNT sensor. In order to obtain new data, we deposit four different electrodes on the TiO2 surface as virtual arrays of Electronic nose (E-nose). Temperature modulation and Discrete Wavelet Transform (DWT) of virtual arrays, extract new features of the E-nose data. We select 14 different features from E-nose data and apply Principle Component Analysis (PCA) method for data reduction. We categorize the extracted features by using Support Vector Machine (SVM) algorithms and data covariance properties. This paper achieves 97.5% accuracy in classifying four different VOC gases.

Development of a Battery-Operated Handheld QCM Measurement Electronics

There continues to be major interest in detection and discrimination of volatile organic compounds (VOCs) because these compounds are pervasive in common environments, such as buildings, factories, and in households, among others. These VOCs can be detrimental to both human health and organisms in the environment. The quartz crystal microbalance (QCM) has become a popular analytical instrument to detect various VOCs [1]. However, most QCM studies are performed in a laboratory setting with cumbersome benchtop instruments. To utilize the QCM technique in a manner that permits researchers to be outside of a laboratory environment, such as VOCs detection on-site and point-of-care testing (POCT), it is important to develop a suitably portable QCM measurement system that maintains acceptable system performance, such as frequency resolution and measurement time, which requires low-power input [2, 3]. To fulfill this demand, we propose a QCM measurement electronic system that uses a phase-locked loop (PLL) circuit as a frequency-to-voltage converter and measures an output voltage level to calculate frequency shifts of the resonator. Our proposed system does not rely on the quality of a clock frequency, which is important in typical frequency counters to improve measurement performance. Instead, a common microcontroller is used to calculate frequency shift. In this work, a portable QCM system based on this strategy was developed, and its performance successfully characterized by measuring changes in system output voltage corresponding to input frequency changes. Temperature dependence of frequency changes was also investigated from 15 to 55 °C. With a resonance frequency of 5 MHz, the system showed a frequency resolution of 0.22 Hz, which is significantly less than a common frequency counter. For validation, we employed Ionic Liquids and a Group of Uniform Materials based on Organic Salts (GUMBOS) as chemosensitive coating materials for VOCs detection [4]. Measurement results of the frequency shift caused by VOCs from the developed system were comparable to those obtained from experiments with a commercial system (QSense Analyzer, Biolin Scientific), which demonstrates the feasibility of applying this electronic strategy for development of a portable QCM measurement system.

Mechanistic Insight of Metalloporphyrin-based Fluorescence Sensor Reacting with Volatile Organic Compounds

The theoretical design of a metalloporphyrin-based fluorescence sensor was developed for the discrimination of volatile organic compounds (VOCs). Molecular models of cobalt tetraphenylporphine (CoTPP) with small VOCs were investigated by the density functional theory (DFT) method at the Becke, 3-parameter, Lee–Yang–Parr (B3LYP) and LAN2DZ level. The relative energies of CoTPP and its complexes were calculated at three spin states (low, intermediate, and high) to obtain their optimized geometry structures. Singlets were found to be the most stable states for CoTPP and its complexes, except for CoTPP-oxygen and CoTPP-propanol at triplets. The binding performance was represented by the binding energy (BE), which showed the following order based on the most stable states: propane (L3) < oxygen (O2) < propanol (L2) < hydrogen sulfide (H2S) < propionaldehyde (L6) < nitrogen (N2) < butanone (L5) < ethyl acetate (L4) < trimethylamine (L1). We suggest that the CoTPP-based fluorescence sensor is sensitive to trimethylamine and ethyl acetate, and avoids the interference from propane and oxygen.

Quartz Crystal Microbalance Based Sensor Arrays for Detection and Discrimination of VOCs Using Phosphonium Ionic Liquid Composites.

Herein, we examine two sensing schemes for detection and discrimination of chlorinated volatile organic compounds (VOCs). In this work, phosphonium ionic liquids (ILs) were synthesized and vapor sensing properties examined and compared to phosphonium IL-polymer composites. Pure IL sensors were used to develop a QCM-based multisensory array (MSA), while IL-polymer composites were used to develop an MSA and virtual sensor arrays (VSAs). It was found that by employing the composite MSA, five chlorinated VOCs were accurately discriminated at 95.56%, which was an increase in accuracy as compared to pure ILs MSA (84.45%). Data acquired with two out of three VSAs allowed discrimination of chlorinated VOCs with 100% accuracy. These studies have provided greater insight into the benefits of incorporating polymers in coating materials for enhanced discrimination accuracies of QCM-based sensor arrays. To the best of our knowledge, this is the first report of a QCM-based VSA for discrimination of closely related chlorinated VOCs.

A Langasite Crystal Microbalance Coated with Graphene Oxide-Platinum Nanocomposite as a Volatile Organic Compound Sensor: Detection and Discrimination Characteristics.

We propose a novel langasite crystal microbalance (LCM) sensor with a graphene-based sensing medium to detect and discriminate volatile organic compounds (VOCs) at room temperature. A thin film of graphene oxide embedded with Pt nanostructures (GO-Pt nanocomposite) was deposited on the electrode surface of the LCM, a thickness-shear acoustic wave resonator. Ethyl acetate, acetic acid, and ethanol were chosen as typical VOCs for this study. Sensitivity and selectivity of coated LCM were investigated for different concentrations of the VOCs by analysing the resonant properties of the sensor. When exposed to VOCs, a negative shift in series resonance frequency was observed due to the mass loading of VOC molecules. Simultaneously, changes in equivalent resistance and parallel resonance frequency of the sensor were also observed due to the interaction of VOCs with charge carriers on the GO-Pt nanocomposite film surface. This dual measurement of both series and parallel resonance frequencies allowed for detection and discrimination of VOCs. Moreover, the high thermal stability of langasite makes the proposed sensor suitable even for harsh environmental conditions.

Molecular-level electrochemical doping for fine discrimination of volatile organic compounds in organic chemiresistors

A blend of π-CPs and a solid-state ionic liquid provides an enlarged interfacial area at the molecular scale, thereby enabling two-terminal organic chemiresistors (TOCs) with fine discriminatory abilities for sub-ppm-level VOCs at room temperature.

Detection and discrimination of volatile organic compounds by noble metal nanoparticle functionalized MoS2 coated biodegradable paper sensors

In the current study, noble metal nanoparticle functionalized MoS2 coated biodegradable low-cost paper sensors were fabricated for the selective detection of low concentrations of volatile organic compounds (VOCs).

Hydrophobin-functionalized film bulk acoustic wave resonators for sensitive and polarity-sensitive sensing of volatile organic compounds

Film bulk acoustic wave resonators have demonstrated great potential in the detection of volatile organic compounds owing to their high sensitivity, miniature size, low power consumption, capacity for integration, and other beneficial characteristics. However, it is necessary to functionalize the surfaces of these resonators to enhance the adsorption and discrimination of volatile organic compounds. Here, we report a convenient and reliable method for functionalizing the surfaces of film bulk acoustic wave resonators with hydrophobins via self-assembly to enable highly sensitive and polarity sensitive detection of volatile organic compounds. Experiments conducted using various concentrations of five volatile organic compounds possessing different polarities demonstrated that the hydrophobin coating enhanced the responsivity of the proposed sensor. The obtained results were in good agreement with the Brunauer–Emmett–Teller model of multilayer physisorption, which suggests that the hydrophobin coating enhanced the sensitivity by improving the monolayer adsorption capacity. Our work demonstrates that the combination of multifunctional biosurfactants and microelectromechanical devices can permit high-performance gas sensing.

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.

Detection and Discrimination of Volatile Organic Compounds using a Single Film Bulk Acoustic Wave Resonator with Temperature Modulation as a Multiparameter Virtual Sensor Array.

This paper describes the detection and discrimination of volatile organic compounds (VOCs) using an e-nose system based on a multiparameter virtual sensor array (VSA), which consists of a single-chip temperature-compensated film bulk acoustic wave resonator (TC-FBAR) coated with 20-bilayer self-assembled poly(sodium 4-styrenesulfonate)/poly(diallyldimethylammonium chloride) thin films. The high-frequency and microscale FBAR multiparameter VSA was realized by temperature modulation, which can greatly reduce the cost and complexity compared to those of a traditional e-nose system and can allow it to operate at different temperatures. The discrimination effect depends on the synergy of temperature modulation and the sensing material. For proof-of-concept validation purposes, the TC-FBAR was exposed to six different VOC vapors at six different gas partial pressures by real-time VOC static detection and dynamic detection. The resulting frequency shifts and impedance responses were measured at different temperatures and evaluated using principal component analysis and linear discriminant analysis, which revealed that all analytes can be distinguished and classified with more than 97% accuracy. To the best of our knowledge, this report is the first on an FBAR multiparameter VSA based on temperature modulation, and the proposed novel VSA shows great potential as a compact and promising e-nose system integrated in commercial electronic products.