Detection Of Volatile Organic Compounds Research Articles

Template preparation of porous Pt-modified SnO2 microflowers for high-response detection of VOCs

Porous SnO2 microflowers consisting of uniform nanosheets modified by Pt nanoparticles were synthesized using a self-sacrificing template followed by a reduction strategy. The Pt@SnO2, SnO2–Se, and SnO2–S samples were characterized using a series of techniques. The gas-sensing behaviors of the samples toward volatile organic compounds (VOCs) were studied. The Pt@SnO2 sensors exhibited superior gas-sensing performance for formaldehyde gas. The Pt@SnO2 sensors presented a stronger response (100.2), shorter response–recovery times (14 s/23 s), and lower working temperature (200 °C) for 100 ppm formaldehyde gas. The Pt@SnO2 sensors also showed excellent responses toward other VOCs, such as 100 ppm ethanol (82.5), acetone (73.9), and isopropanol (62.1). The gas-sensing mechanism of the Pt@SnO2 sensors was examined. The larger available surface area, favorable hierarchical flower-like nanostructure assembled by nanosheets, larger pore size, and larger proportion of oxygen vacancies and adsorption-state oxygen species, smaller crystallite size, and spillover effect of Pt catalysts contributed to the enhanced gas-sensing behaviors of Pt@SnO2 sensors. Overall, the synthetic strategy for flower-like SnO2 nanostructures can be applied widely.

LiFe[formula omitted]O[formula omitted]-based gas sensor: A machine learning approach for selective VOCs detection

This work presents the development of novel LiFe5O8 micro-flowers through a single-step microwave-assisted hydrothermal approach. LiFe5O8 micro-flowers were used as a sensing element for the detection of highly cross-sensitive volatile organic compounds (VOC). Various characterization methods were also used to validate the material characteristics of the sensor. The measured response/recovery curve revealed that these unique micro flowers can detect even at 5 ppm concentrations of acetone, ethanol, and methanol with response values of 1.17, 1.37, and 1.23 at 100 °C respectively. The sensor’s selectivity issue was addressed by employing simpler Machine Learning (ML) algorithms (decision tree, random forest, K-nearest neighbor, and support vector machine) using the measured response values ranging from 5 ppm to 200 ppm. Further, the classification of the mixture and pure gases was performed using a random mixture of the three VOCs to enhance the selectivity. The article compares various extracted features to determine the best feature set for the deployed ML algorithms. Optimization algorithms such as grid and randomized search were employed to identify the most suitable parameters for achieving the best models. The testing results report an average classification accuracy of 98% and an R2-score of 0.98 for the three gases from the best model.

Nanoporous MoS2 Field-Effect Transistor Based Artificial Olfaction: Achieving Enhanced Volatile Organic Compound Detection Inspired by the Drosophila Olfactory System.

Olfaction, a primal and effective sense, profoundly impacts our emotions and instincts. This sensory system plays a crucial role in detecting volatile organic compounds (VOCs) and realizing the chemical environment. Animals possess superior olfactory systems compared to humans. Thus, taking inspiration from nature, artificial olfaction aims to achieve a similar level of excellence in VOC detection. In this study, we present the development of an artificial olfaction sensor utilizing a nanostructured bio-field-effect transistor (bio-FET) based on transition metal dichalcogenides and the Drosophila odor-binding protein LUSH. To create an effective sensing platform, we prepared a hexagonal nanoporous structure of molybdenum disulfide (MoS2) using block copolymer lithography and selective etching techniques. This structure provides plenty of active sites for the integration of the LUSH protein, enabling enhanced binding with ethanol (EtOH) for detection purposes. The coupling of the biomolecule with EtOH influences the bio-FETs potential, which generates indicative electrical signals. By mimicking the sniffing techniques observed in Drosophila, these bio-FETs exhibit an impressive limit of detection of 10-6% for EtOH, with high selectivity, sensitivity, and detection ability even in realistic environments. This bioelectric sensor demonstrates substantial potential in the field of artificial olfaction, offering advancements in VOC detection.

Identification of urinary volatile organic compounds as a potential non-invasive biomarker for esophageal cancer

Early diagnosis of esophageal cancer (EC) is extremely challenging. The study presented herein aimed to assess whether urinary volatile organic compounds (VOCs) may be emerging diagnostic biomarkers for EC. Urine samples were collected from EC patients and healthy controls (HCs). Gas chromatography-ion mobility spectrometry (GC-IMS) was next utilised for volatile organic compound detection and predictive models were constructed using machine learning algorithms. ROC curve analysis indicated that an 8-VOCs based machine learning model could aid the diagnosis of EC, with the Random Forests having a maximum AUC of 0.874 and sensitivities and specificities of 84.2% and 90.6%, respectively. Urine VOC analysis aids in the diagnosis of EC.

Thermal desorption from zeolite layer for VOC detection

Volatile organic compounds (VOCs) are being considered for a wide range of applications, since they provide information on specific processes (e.g., metabolic processes in humans and plants). To date, environmental VOCs detection can be accomplished with low selectivity using both portable photoionization techniques or using gold standard laboratory techniques (e.g., gas and liquid chromatography, mass spectrometry), getting higher performances and limitations in terms of portability, availability of equipment and of consumption of time. Hereafter, a modified photoionization technique based on a thin zeolite layer for the selective adsorption of VOCs was investigated, exploiting the processes of thermal adsorption and desorption. Zeolite through the inherent nanoporous structure possesses specific electrical characteristics, strongly influenced by the adsorption of the molecules within the framework. Experimental analysis of its electrical and adsorption/desorption properties of propionic and succinic acids was performed. Results demonstrated how the use of a thin nanoporous layer allows to increase the selectivity of commercial photoionization detector during VOC detection, evidencing the potentiality of this technique for the development of low-cost and high-performance sensors in various applications.

A turn-on type vapofluorochromic methoxybenzylidene methanone Schiff base applied to wearable VOC sensor and smartphone-based detection

This study presents the synthesis, crystallographic data and photophysical properties of a donor-acceptor (D-A) compound, methoxybenzylidene methanone Schiff base (HMPPM). It shows remarkable vapofluorochromism, embodied as turn-on red fluorescence in response to volatile organic compounds (VOCs), with fast response and high signal contrast. HMPPM is further developed into a cellulose sensor via dip-dying method for VOCs detection. We further use RGB and CIELAB color spaces to quantify the vapofluorochromic fluorescence. The limits of detection (LOD) of the sensor reach ppm level. Furthermore, it was fabricated into a wearable VOC sensor, with the introduction of an endogenous/exogenous reference, which conduces to high accuracy and visual identification. The cellulose sensor can be tailored into diversified patterns, with controllable shape and size, which facilitated the encapsulation into various wearables, including shoes, hat and smart band, etc., succeeding in scenario-based VOCs detection. In addition, we built a smartphone-based sensing platform based on HMPPM, which achieves a ppm-level LOD in VOCs monitoring.

Disposable, strain-insensitive, and room-temperature-operated flexible humidity and VOC sensor with enhanced sensitivity and selectivity through interface control

As people are paying more attention to the health and comfort of the indoor environment, the sensors detecting the relative humidity (RH) and volatile organic compounds (VOCs) have attracted widely research interests. However, how to improve the sensitivity and selectivity remains a challenge, especially for flexible sensors working at room temperature (around 25 ℃). In this work, the controlling of solid-gas interface was found to be able to enhance the sensitivity and the manipulation of solid-solid interface increased the selectivity of the sensor for VOC detection. The flexible sensor based on electrospinning cellulose (EC) show high sensitivity for humidity detection due to its large solid-gas interface, which can be used for real-time monitoring of human breathing and speech recognition, simultaneously, it can also detect a variety of VOCs. By introducing silver nanowires (AgNWs) to establish solid-solid interface between silver and expanded graphite (EG), the selectivity of the sensor for VOCs detection was improved, which shown higher sensitivity for EtOH with faster response/recovery time, as well as long-term cycle stability and bending insensitivity. Furthermore, the sensor also can be decomposed by burning, offering a sustainable disposal method. This versatile, low-cost and environmentally friendly flexible electronic device may be used for wearable air monitoring and environmental controlling.

Substituent-Controlled Photophysical Responses in Dihydropyridine Derivatives and Their Application in the Detection of Volatile Organic Contaminants.

In the ever-expanding realm of organic fluorophores, structurally simple and synthetically straightforward molecules with unique photophysical properties have received special attention. Among these, 1,4-dihydropyridine (DHP) is an important scaffold that permits fine-tuning of their photophysical properties through substituents on the periphery. Herein, we describe a series of solid-emissive N-substituted 2,6-dimethyl-4-methylene-1,4-dihydropyridine derivatives appended with electron-withdrawing substituents (dicyanomethylene or 2-dicyanomethylene-3-cyano-2,5-dihydrofuran) at the C-4 position and alkyl or alkylaryl groups on the DHP nitrogen. Electronic and steric tuning exerted by these substituents resulted in interesting photophysical properties such as negative solvatochromism, solidstate, and aggregation-induced emission (AIE). Theoretical calculations were carried out to explain the solvatochromic properties. Insight into the AIE properties was obtained through variable-temperature nuclear magnetic resonance and viscosity- and temperature-dependent emission studies. The variations in molecular packing in the crystal lattice with changes in the N-substituents contributed to the tuning of solid state emission properties. Detection of aromatic volatile organic compounds (VOCs) was achieved using the aggregates of the DHP derivatives. Among the VOCs, p-xylene elicited a significant enhancement in emission, allowing its detection at submicromolar levels.

A PVDF-based colorimetric sensor array for noninvasive detection of multiple disease-related volatile organic compounds.

Detection of human-generated volatile organic compounds (VOCs) is a new pathway for assessing health. Herein, a polyvinylidene fluoride (PVDF)-based colorimetric sensor array was designed for detecting disease-related VOCs (DVOCs) within 15min, using a complex of Cu metal-organic framework, graphene aerogel, and dyes as response materials. Fingermaps derived from 28 DVOCs were obtained for further data processing. Pattern recognition was successfully employed in the correct discrimination of 28 DVOCs in low (10μM), medium (100μM), and high (300μM) concentrations. Importantly, the sensor array also presented excellent discrimination ability and application potential when detecting VOCs produced by human cancer and normal cells. In general, VOC acquisition is noninvasive and harmless, and the PVDF-based sensor arrays are simple and visual. Such advantages expand their further application potential.

AI‐Driven Wearable Mask‐Inspired Self‐Healing Sensor Array for Detection and Identification of Volatile Organic Compounds

AbstractVolatile organic compounds (VOCs) sensor arrays have garnered considerable attention due to their potential to provide real‐time information for monitoring pollution levels and personal health associated concerning VOCs in the ambient environment. Here, an AI‐driven wearable mask‐inspired self‐healing sensor array (MISSA), created using a simplified single‐step stacking technique for detecting and identifying VOCs is presented. This wearable MISSA comprises three vertically placed breathable self‐healing gas sensors (BSGS) with linear response behavior, consistent repeatability, and reliable self‐healing abilities. For wearable and portable monitoring, the MISSA is combined with a flexible printed circuit board (FPCB) to produce a mobile‐compatible wireless system. Due to the distinct layers of MISSA, it creates exclusive code bars for four distinct VOCs over three concentration levels. This grants precise gas identification and concentration prognoses with excellent accuracy of 99.77% and 98.3%, respectively. The combination of MISSA with artificial intelligence (AI) suggests its potential as a successful wearable device for long‐term daily VOC monitoring and assessment for personal health monitoring scenarios.

Peptide-Functionalized Carbon Nanotube Chemiresistors: The Effect of Nanotube Density on Gas Sensing.

Biorecognition element (BRE)-based carbon nanotube (CNT) chemiresistors have tremendous potential to serve as highly sensitive, selective, and power-efficient volatile organic compound (VOC) sensors. While many research groups have studied BRE-functionalized CNTs in material science and device development, little attention has been paid to optimizing CNT density to improve chemiresistor performance. To probe the effect of CNT density on VOC detection, we present the chemiresistor-based sensing results from two peptide-based CNT devices counting more than 60 different individual measurements. We find that a lower CNT density shows a significantly higher noise level and device-to-device variation while exhibiting mildly better sensitivity. Further investigation with SEM images suggests that moderately high CNT density with a stable connection of the nanotube network is desirable to achieve the best signal-to-noise ratio. Our results show an essential design guideline for tuning the nanotube density to provide sensitive and stable chemiresistors.

Detection of heavy metals and VOCs in streambed sediment indicates anthropogenic impact on intermittent streams of the U.S. Virgin Islands

Global surges in industrialization and human development have resulted in environmental contamination. Streambed sediment contamination threatens ecological and human health due to groundwater leaching and downstream contaminant mobilization. This is especially true in the wider Caribbean region, where streambed sediment contamination is understudied. In the current study, we assessed human impact on intermittent streams by measuring heavy metals and volatile organic compounds (VOCs) in streambed sediment on St. Croix in the United States Virgin Islands (USVI), where intermittent streams receive limited conservation and research attention. In contrast to our hypothesis that streambed sediment pollutant concentrations would be higher in developed, compared to undeveloped areas, contaminant concentrations did not vary significantly according to land cover. Elevated lead, mercury, and zinc concentrations were correlated with commercial building density, suggesting an unnatural origin of these elements in streambed sediment. At some sites, levels of arsenic, cadmium, chromium, nickel, lead, thallium, or zinc exceeded regulatory limits. The most prevalent VOCs at both developed and undeveloped sites were benzene and toluene. Sub-groups of heavy metals identified by principal component analysis indicated potential pollution sources, including fuel combustion (chromium, nickel, arsenic, selenium), vehicle exhaust, oil refining, and gasoline leaks (2-butanone and xylenes), and plastics (acetone and styrene). Our results suggest USVI intermittent streams require further research attention and intervention strategies for pollution reduction.

Specific Recognition and Adsorption of Volatile Organic Compounds by Using MIL-125-Based Porous Fluorescence Probe Material.

The severity of the volatile organic compounds (VOCs) issue calls for effective detection and management of VOC materials. Metal-organic frameworks (MOFs) are organic-inorganic hybrid crystals with promising prospects in luminescent sensing for VOC detection and identification. However, MOFs have limitations, including weak response signals and poor sensitivity towards VOCs, limiting their application to specific types of VOC gases. To address the issue of limited recognition and single luminosity for specific VOCs, we have introduced fluorescent guest molecules into MOFs as reference emission centers to enhance sensitivity. This composite material combines the gas adsorption ability of MOFs to effectively adsorb VOCs. We utilized (MIL-125/NH2-MIL-125) as the parent material for adsorbing fluorescent molecules and selected suitable solid fluorescent probes (FGFL-B1) through fluorescence enhancement using thioflavin T and MIL-125. FGFL-B1 exhibited a heightened fluorescence response to various VOCs through charge transfer between fluorescent guest molecules and ligands. The fluorescence enhancement effect of FGFL-B1 on tetrahydrofuran (THF) was particularly pronounced, accompanied by a color change from yellow to yellowish green in the presence of CCl4. FGFL-B1 demonstrated excellent adsorption properties for THF and CCl4, with saturated adsorption capacities of 655.4 mg g-1 and 811.2 mg g-1, respectively. Furthermore, FGFL-B1 displayed strong luminescence stability and reusability, making it an excellent sensing candidate. This study addresses the limitations of MOFs in VOC detection, opening avenues for industrial and environmental applications.

Highly sensitive and selective Sb2WO6 microspheres in detecting VOC biomarkers in cooked rice: Experimental and density functional theory study.

The palatability of cooked rice is susceptible to the flavor and effective detection of volatile organic compounds (VOCs) can avoid deterioration and improve the taste quality. Herein, hierarchical antimony tungstate (Sb2WO6) microspheres are synthesized through a solvothermal process and the effect of solvothermal temperature on the room temperature gas-sensing properties of gas sensors is investigated. Outstanding sensitivity towards VOC biomarkers (nonanal, 1-octanol, geranyl acetone and 2-pentylfuran) in cooked rice is achieved and the sensors exhibit remarkable stability and reproducibility, which are contributed to the formation of the hierarchical microsphere structure, larger specific surface area, narrower band gap and increased oxygen vacancy content. The kinetic parameters combined with principal component analysis (PCA) effectively distinguish the four VOCs while the enhanced sensing mechanism was substantiated through density functional theory (DFT) calculation. This work provides a strategy for fabricating high performance Sb2WO6 gas sensors which can be practically applied to food industry.

Breath Biopsy Reveals Systemic Immunothrombosis and Its Resolution Through Bioorthogonal Dendritic Nanoprobes.

Immunothrombosis, an inflammation-dependent activation of the coagulation cascade, leads to microthrombi formations in small vessels. It is a dreaded complication of COVID-19 and a major cause of respiratory failure. Due to their size and disseminated nature, microthrombi are currently undetectable. Here, we introduce non-invasive detection of a volatile reporter in the exhaled air for assessment of systemic immunothrombosis. A dendritic nanoprobe, containing high loading of a thrombin-sensitive substrate, was selectively cleaved by thrombin, resulting in release of a synthetic bioorthogonal volatile organic compound (VOC). The VOC was quantitated in the exhaled air biopsies via gas chromatography mass spectrometry (GC-MS), allowing near realtime assessment of systemic immunothrombosis. The VOC detection can be further improved with more rapid and sensitive MS-based technologies. The amount of the VOC in the exhaled air decreased with resolution of the microvascular inflammation and intravascular fibrin depositions. Through conjugation of the thrombin-sensitive peptide with a rhodol derivative, a novel thrombin-sensitive fluorescent nanoprobe was developed for intravital visualization of thrombin activity in actively growing thrombi. These results establish unprecedented detection of thrombin activity in vivo, addressing an unmet medical need. Our novel approach facilitates diagnosis of immunothrombosis in diseases such as diabetic complications, disseminated intravascular coagulation, and COVID-19. This article is protected by copyright. All rights reserved.

Single atom Rh-sensitized SnO2 via atomic layer deposition for efficient formaldehyde detection

Single atom catalysts (SACs) have demonstrated great potential in detection of toxic volatile organic compounds (VOCs) because of their high catalytic efficiency and unique electronic and chemical properties. In this study, we report an extremely sensitive and selective formaldehyde gas sensor based on single atom Rh-sensitized SnO2 nanoparticles prepared by atomic layer deposition (ALD). Gas sensing investigations demonstrate that the SnO2/Rh sensor has a response of 36.3 to 20 ppm formaldehyde, showing a nearly 23-fold enhancement over pure SnO2, as well as high sensitivity, fast response-recovery time (4–29 s) and excellent selectivity. DFT calculations reveal that the atomic Rh dramatically increases the adsorption and charge transfer between formaldehyde molecules and SnO2. By using the developed sensor, a portable formaldehyde detection device has been designed, which indicates a great prospect of our strategy in practical sensors.

Feature extractions from transfer characteristics of hybrid GO FETs for selective detection of volatile organic compounds

The current work concerns a new strategy for selective classifications of multiple volatile organic compounds (VOCs) by using an array of field effect transistor (FET) type sensors and extraction of device-specific features from transfer characteristics of the FET-sensors to use in classification algorithm(s). A few layered graphene oxide (GO) was used as the base channel materials which was then functionalized very precisely with different metal oxides (WO3, TiO2) and metals (Au, Pd) nanoforms to construct an array of five FET structure sensors on SiO2/Si platform. The deviation in Id-Vgs characteristics in different VOCs were accounted with a distinctive features. The features were organized based on their importance score and used to construct different feature matrices. A minimum four sensors and six features were required to successful classification of seven VOCs by linear discriminant analysis (LDA). The classification accuracy was tested with supportive vector machine (SVM) classifier and it was 92.86%.

Towards detection of cancer biomarkers in human exhaled air by transfer-learning-powered analysis of odor-evoked calcium activity in rat olfactory bulb

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

Temperature Modulation of MOS Sensors for Enhanced Detection of Volatile Organic Compounds

Disease diagnosis through biological fluids, particularly exhaled breath analysis, has gained increasing importance. Volatile organic compounds (VOCs) present in exhaled breath offer diagnostic potential as they reflect altered and disease-specific metabolic pathways. While gas chromatography–mass spectrometry (GC–MS) has been traditionally used for VOCs detection, electronic noses have emerged as a promising alternative for disease screening. Metal oxide semiconductor (MOS) sensors play an essential role in these devices due to their simplicity and cost-effectiveness. However, their limited specificity and sensitivity pose challenges for accurate diagnosis at lower VOCs concentrations, typical of exhaled breath. To address specificity and sensitivity issues, temperature modulation (TM) has been proposed in this paper, introducing a custom-developed electronic nose based on multiple and heterogeneous gas sensors located within an analysis chamber. Four different TM patterns (i.e., square, sine, triangular, and a combination of square and triangular) were applied to the gas sensors to test their response to three different analytes at three distinct concentrations. Data were analyzed by extracting meaningful features from the sensor raw data, and dimensionality reduction using principal component analysis (PCA) was performed. The results demonstrated distinct clusters for each experimental condition, indicating successful discrimination of analytes and concentrations. In addition, an analysis of which set of sensors and modulation pattern yielded the best results was performed. In particular, the most promising TM pattern proved to be the square and triangular combination, with optimal discrimination accuracy between both concentrations and analytes. One specific sensor, namely, TGS2600 from Figaro USA, Inc., provided the best performance. While preliminary results highlighted the potential of TM to improve the sensitivity of gas sensors in electronic nose devices, paving the way for further advancements in the field of exhaled breath analysis.

Plasma Polymerized Organosilicon Thin Films for Volatile Organic Compound (VOC) Detection

Plasma polymerized (PP) thin films deposited in a soft or intermediate plasma discharge from hexamethyldisiloxane (HMDSO) were developed as sensors for the detection of volatile organic compound (VOC) vapors. Energy dispersive X-ray spectroscopy (EDX) and X-ray reflectometry (XRR) were performed to determine the organosilicon films’ elemental composition and density. Spectroscopic ellipsometry measurements were carried out to determine the refractive index of the films. Quartz crystal microbalance (QCM) and ellipsometry coupled to vapor sorption were used to investigate the sorption mechanism of several VOC vapors into the films as a function of the plasma deposition conditions. The density and the refractive index of the PP-HMDSO films increased with the plasma energy due to a different chemical composition and different proportion of free volumes in the material network. The PP-HMDSO films showed different affinities towards the VOC vapors depending on the plasma discharge energy. The films elaborated in the lowest plasma energy revealed a good sensitivity towards the VOCs, especially toluene (one of the BTEX vapors), compared to the other films deposited under higher plasma energy. In addition, the selectivity between toluene and other non-BTEX VOCs such as heptane and ethanol decreased to become zero while increasing the plasma energy.