Gaseous Volatile Organic Compounds Research Articles

Detection of Volatile Organic Compounds through Spectroscopic Signatures in Nanoporous Fabry-Pérot Optical Microcavities.

Increasingly complex modern gas-monitoring scenarios necessitate advanced sensing capabilities to detect and identify a diverse range of gases under varying conditions. There is a rising demand for individual sensors with multiple responses capable of recognizing gases, identifying components in mixtures, and providing stable responses. Inspired by gas sensors employing multivariable response principles, we develop a nanoporous anodic alumina high-order microcavity (NAA-HOμCV) gas sensor with multiple optical outputs for discriminative gas detection. The NAA-HOμCV architecture, formed by a Fabry-Pérot microcavity with distributed Bragg reflector (DBR) mirrors and an extended-length microcavity layer supporting multiple resonant modes, serves as an effective solid-state fingerprint platform for distinguishing volatile organic compound (VOC) gases. Our research reveals that the coupling strength of light into resonant modes and their evolution depend on the thickness of the DBR mirrors and the dimension of the microcavity layer, which allows us to optimize the discriminative sensing capability of the NAA-HOμCV sensor through structural engineering of the microcavity and photonic crystal mirrors. Gas-sensing experiments conducted on the NAA-HOμCV sensor demonstrate real-time discrimination between physiosorbed VOC gases (isopropanol, ethanol, or acetone) in reversible gas sensing. It also achieves superior ppb-level sensing in irreversible gas sensing of model silane molecules. Our study presents promising avenues for designing compact, cost-effective, and highly efficient gas sensors with tailored properties for discriminative gas detection.

Bedroom Concentrations and Emissions of Volatile Organic Compounds during Sleep.

Because humans spend about one-third of their time asleep in their bedrooms and are themselves emission sources of volatile organic compounds (VOCs), it is important to specifically characterize the composition of the bedroom air that they experience during sleep. This work uses real-time indoor and outdoor measurements of volatile organic compounds (VOCs) to examine concentration enhancements in bedroom air during sleep and to calculate VOC emission rates associated with sleeping occupants. Gaseous VOCs were measured with proton-transfer reaction time-of-flight mass spectrometry during a multiweek residential monitoring campaign under normal occupancy conditions. Results indicate high emissions of nearly 100 VOCs and other species in the bedroom during sleeping periods as compared to the levels in other rooms of the same residence. Air change rates for the bedroom and, correspondingly, emission rates of sleeping-associated VOCs were determined for two bounding conditions: (1) air exchange between the bedroom and outdoors only and (2) air exchange between the bedroom and other indoor spaces only (as represented by measurements in the kitchen). VOCs from skin oil oxidation and personal care products were present, revealing that many emission pathways can be important occupant-associated emission factors affecting bedroom air composition in addition to direct emissions from building materials and furnishings.

Nickle ion surface engineered MOF derived α-Fe2O3 octahedrons for high efficiency toluene detection with high moisture resistance and long-term stability

Metal-organic framework derivatives have become very promising candidates for gas sensors due to their advantages of large specific surface area, many surface active sites, and tunable properties. Meanwhile, Ni ion doping is an effective technique to enhance the moisture resistance of sensing materials. Inspired by this, in this work, we have successfully synthesized porous octahedral structures of Ni-Fe2O3 derived from metal-organic frameworks by in situ doping, in which the Ni contents were 0.4, 0.5 and 0.6 mol%, respectively. Notably, Ni doping changes the conductive type of Fe2O3 from n-type to p-type with a significant decrease in resistance. At the same time, the sensing performance of Fe2O3 octahedrons for volatile organic compound gases, especially toluene, is greatly improved. The 0.5 mol% Ni doped Fe2O3 sensor showed the highest response to toluene at 225°C (100 ppm, 47), which is five times higher than the pristine Fe2O3 (response 9.5). In addition, the sensor demonstrated fast response and recovery time (20 s/40 s), low detection limit (200 ppb), high moisture tolerance (98% RH, 47), and long-term stability of over 60 days. The main reason for the exceptional performance of Ni doped Fe2O3 sensors is its special porous octahedral structure, and more importantly, the change of carrier type of the sensing material after Ni doping and the substantial increase of oxygen vacancies, and the catalytic effect of Ni ions.

Nanomaterial SnS2-Based Sensor for VOC Gas Detection at Room Temperature

Real-time indoor hazardous gases monitoring has gained interest in ensuring human health. An effort to design gas sensing devices, which are compact in size, flexible, and low power consumption, providing high-performance sensing plays a crucial role in precise sensing of the indoor environment. Here, we have fabricated 2D SnS2 flakes for gas sensor. The 2D ultra-thin SnS2, including a few layers, shows fascinating sensing toward Volatile organic compounds (VOCs) under room temperature with high response and fast reaction speed. The short-term stability versus time of the SnS2 sensor is investigated. Due to the great adsorption of gaseous molecules, the SnS2-gas sensor operates at room temperature without external heating sources. The Schottky junction-based sensor is one of the key factors contributing to the higher performance of the sensor. Furthermore, its VOC-sensing mechanism is explained obviously through the energy band diagram. The SnS2-layer is considered a promising candidate in indoor VOCs monitoring and respiratory biomarker analysis in the future.

Moisture reaction mechanism of Au-modified SnO2 ultrathin nanosheets with exclusive detection of formaldehyde

Exploring the reaction mechanism at the molecular level in detecting different target gases and providing the optimal operating temperature can improve selective detection performance and extend the lifetime of chemiresistors in practical sensing applications. While the humidity effect interaction mechanism between gas and sensor hinders its practical application. Therefore, exploring the reaction mechanism of humidity to detect different target gases at the molecular level can promote the development of exclusive detection performance and service life of sensors. To further disclose this, a one-step chemical method is applied to synthesize the defect-rich SnO2 with an ultrathin nanosheet structure. The experimental results confirmed that the inhibitory effect of humidity on the detection of formaldehyde by Au-SnO2 is significantly lower than that of other interfering volatile organic compounds (VOCs). The response value of Au-SnO2 detect 10 ppm of formaldehyde at 55 °C is 78, which is larger than that of methanol, ethanol, and acetone. This study sheds light on the mechanistic insight into the correlation between operating temperatures and humidity on the excellent performance of the Au-SnO2 chemiresistive sensor. We consider our work significant in developing highly efficient and stable electrochemical sensors to detect VOC gases at room temperature.

Anti-competitive adsorption of gaseous benzene on hydrophilic microporous carbon in humid conditions

The adsorption capture of ambient volatile organic compounds (VOCs) is of practical importance for air quality management. Herein, unique anti-competitive adsorption behavior of benzene on a hydrophilic activated carbon (Procarb-900 (P900)) is evidenced in the presence of competing components (e.g., formaldehyde (FA) and/or moisture). Contrary to general expectations, the adsorption capacity of 10 Pa benzene (QB) onto P900 (30 mg) at the 99 % breakthrough level improves from 144.8 to 187 mg g−1 as the relative humidity (RH) increases from 0 to 25 %. Such pattern is maintained at 183.9 mg g−1 even at the relatively high RH of 50 %. Furthermore, QB exhibits a remarkable increase of 56.1 % (to 226.0 mg g−1) in the binary phase (100 ppm benzene plus 50 ppm FA) relative to its single phase (144.8 mg g−1). The kinetic studies confirm the occurrence of anti-competitive adsorption of benzene under humid conditions with the unusual decrease in rate constants at the elevated RHs (i.e., 25 and 50 %). The thermodynamic studies suggest the exothermic nature of benzene adsorption onto P900. The hydrophilicity of P900's outer surface promotes the preferential adsorption of polar FA and water vapor over non-polar benzene, which deforms the activated carbon texture and lowers the pore size distribution (PSD). The narrow PSD enhances benzene uptake in the complex systems due to the confinement effect. Overall, this study offers insights into the unique anti-competitive adsorption of non-polar VOCs (e.g., benzene) on hydrophilic microporous adsorbents in the presence of potential interferences such as polar water vapor and FA. These findings offer a guideline for the practical implementation of adsorption techniques for gaseous VOCs in humid conditions.

Synthesized trimanganese tetraoxide functionalized zinc oxide hierarchical structures with abundant oxygen vacancies for ppb-level volatile organic compounds detection

Hierarchical heterostructures have been proven to be the great material structures for the fabrication of gas sensors, due to they high surface area, well-aligned porosity and synergy effect. Here, ZnO hierarchical structures with various amount of Mn dopant were synthesized through a simple one-step solution route followed by annealing treatment. The characterization results demonstrated that Mn dopant existed in the forms of Mn3O4 nanoparticles and lattice Mn2+ ions. The as-synthesized Mn3O4/ZnO hierarchical structures with abundant oxygen vacancies (MnxZnO with x values of 0, 0.25, 0.5, 1 and 3) were used as the sensing materials toward volatile organic compounds (VOCs) detection, illustrating that the introduction of Mn dopant can significantly improve the gas sensing performance. Especially, under optimal working temperature, Mn0.5ZnO hierarchical structures-based gas sensor exhibits the highest responses, fast response/recovery speed (2/9 s), a low detection limit (150 ppb), good selectivity and long-term stability toward acetone. In addition, the enhanced gas sensing mechanisms were explained by large surface area, oxygen vacancies and p-n heterojunction, boosting the surface reactions, which was further verified through DFT calculations. This work also confirmed that Mn element could be a promising dopant in metal oxides for high-performance VOCs gas sensors.

Near-infrared dual-band sensing of trace volatile organic compound gases via metal-organic framework patch array integrated on metal film

Near-infrared dual-band sensing of trace volatile organic compound gases via metal-organic framework patch array integrated on metal film

Gas-sensing properties of p-type of nitrogen-doped ZnO nanorods prepared by deep cryogenic treatment

This study developed p-type nitrogen-doped zinc oxide nanorods (NZO NRs) using a chemical bath deposition and deep cryogenic treatment. These NRs were employed to create the device structure of metal-oxide-metal (MSM) and p-n junction for a volatile organic compound gas sensor. Deep cryogenic treatment is the process of immersing ZnO NRs in liquid nitrogen to achieve NZO NRs. SEM results show that the morphology of NZO changes from hexagonal to nib shapes. XRD and TEM results demonstrate that the peak of (002) shifts, and lattice dislocation was observed in NZO specimen. XPS and EDS analysis proved that the N atoms were successfully doped into ZnO. Optical analysis showed the lower band gap and a broader absorption region of NZO NRs. The doping mechanism of NZO is deduced as polar molecules distributed on the high-surface area of ZnO NRs, with iron playing a catalytic role. The MSM structure of the p-type NZO gas sensor, exhibiting high response, lifetime and selectivity, was verified at room temperature. The gas sensor with a p-n junction structure exhibits a faster response time to ethanol.

Data-driven exploration of terbium-doped tungsten oxide for Ultra-Precise detection of 3H-2B: Implications for gas sensor applications

Gas sensing materials based on semiconducting metal oxides (SMOs) have gained attention in various fields such as air quality control, environmental monitoring, healthcare, and defense security. To overcome challenges in conventional research, a predictive approach using data-driven models and a comprehensive dataset of 150,000 articles is proposed. This approach evaluates the optimal combination of terbium-doped tungsten oxide as a sensing material. The adverse effects of doping elements on the sensing performance of tungsten oxide are identified, and strategies for optimization are provided. A nanocarbon sphere composite doped with terbium-doped oxide is synthesized and exhibits exceptional sensitivity and selectivity towards volatile organic compounds (VOC) gases, particularly 3-hydroxy-2-butanone (3H-2B). The gas sensor's performance is enhanced by minimizing the loss function of the probability distribution. Functionalized tungsten oxide demonstrates outstanding sensitivity and selectivity towards 3H-2B gas, even in high-interference environments, with a low detection limit of 20 parts per billion (ppb). A multimodal construction approach is used to predict, evaluate, and analyze the performance of sensing materials. This approach enables real-time transmission of sensor test data to cloud platforms, providing an efficient research methodology for the development and design of next-generation functional materials.

Utilizing Data Mining for the Synthesis of Functionalized Tungsten Oxide with Enhanced Oxygen Vacancies for Highly Sensitive Detection of Triethylamine.

The optimal combination of metal ions and ligands for sensing materials was estimated by using a data-driven model developed in this research. This model utilized advanced computational algorithms and a data set of 100,000 literature pieces. The semiconductor metal oxide (SMO) that is most suitable for detecting triethylamine (TEA) with the highest probability was identified by using the Word2vec model, which employed the maximum likelihood method. The loss function of the probability distribution was minimized in this process. Based on the analysis, a novel hierarchical nanostructured tungsten-based coordination with 2,5-dihydroxyterephthalic acid (W-DHTA) was synthesized. This synthesis involved a postsynthetic hydrothermal treatment (psHT) and the self-assembly of tungsten oxide nanorods. The tungsten oxide nanorods had a significant number of oxygen vacancies. Various techniques were used to characterize the synthesized material, and its sensing performance toward volatile organic compound (VOC) gases was evaluated. The results showed that the functionalized tungsten oxide exhibited an exceptionally high sensitivity and selectivity toward TEA gas. Even in a highly disturbed environment, the detection limit for TEA gas was as low as 40 parts per billion (ppb). Furthermore, our findings suggest that the control of oxygen vacancies in sensing materials plays a crucial role in enhancing the sensitivity and selectivity of gas sensors. This approach was supported by the utilization of density functional theory (DFT) computation and machine learning algorithms to assess and analyze the performance of sensor devices, providing a highly efficient and universally applicable research methodology for the development and design of next-generation functional materials.

Materials Enabling Methane and Toluene Gas Treatment.

This paper summarizes the latest research results on materials for the treatment of methane, an important greenhouse gas, and toluene, a volatile organic compound gas, as well as the utilization of these resources over the past two years. These materials include adsorption materials, catalytic oxidation materials, hydrogen-reforming catalytic materials and non-oxidative coupling catalytic materials for methane, and adsorption materials, catalytic oxidation materials, chemical cycle reforming catalytic materials, and degradation catalytic materials for toluene. This paper provides a comprehensive review of these research results from a general point of view and provides an outlook on the treatment of these two gases and materials for resource utilization.

Facile preparation of Au-loaded mesoporous In2O3 nanoparticles with improved ethanol sensing performance.

The in situ monitoring of toxic volatile organic compound gases using metal oxide-based gas sensors is still challenging. Herein, mesoporous In2O3 nanoparticles, assembled using smaller nanoparticles, were synthesized via a facile solvothermal method and used to load Au nanoparticles to prepare mesoporous Au/In2O3 for ethanol detection. The obtained In2O3 and Au/In2O3 were meticulously analysed by XRD, SEM, BET, TEM and XPS techniques. It was revealed that Au nanoparticles were uniformly distributed on mesoporous In2O3 nanoparticles. Notably, the obtained mesoporous 1% Au/In2O3 is highly sensitive to ethanol gas at an optimal working temperature of 180 °C, showing a response of 55 to 50 ppm of ethanol, which is considerably higher compared to that of In2O3 nanoparticles. The significantly enhanced sensitivity results from the electronic and chemical sensitization effects of Au nanoparticles. Moreover, the mesoporous Au/In2O3 nanoparticles also showed eminent selectivity, short response/recovery time, low detection limit, good linear relationship, superb repeatability, and wonderful long-term stability, suggesting that Au/In2O3 nanoparticles have great potential application for in situ monitoring of ethanol gas.

Thermocatalytic oxidation of a binary mixture of formaldehyde and toluene at ambient levels by a titanium dioxide supported platinum catalyst

The thermocatalytic oxidative potential of various supported noble metal catalysts (SNMCs) is well-known for hazardous volatile organic compounds (VOCs), e.g., formaldehyde (FA) and toluene. However, little is known about SNMC performance against ambient VOC pollution with low concentration (subppm levels) relative to industrial effuluents with high concentrations (several hundred ppm). Here, the thermocatalytic oxidation performance of a titanium dioxide (TiO2)-supported platinum catalyst (Pt/TiO2) has been evaluated for a low-concentration binary mixture of FA and toluene at low temperatures and in the dark. A sample of TiO2 containing 1 wt% Pt with thermal reduction pre-treatment under hydrogen achieved 100 % conversion of FA (500 ppb) and toluene (100 ppb) at 130 °C and a gas hourly velocity of 59,701 h−1. Its catalytic activity was lowered by either a decrease in catalyst mass or an increase in VOC concentration, relative humidity, or flow rate. In situ diffuse reflectance infrared Fourier transform spectroscopy, density functional theory simulations, and molecular oxygen (O2) temperature–programmed desorption experiments were used to identify possible VOC oxidation pathways, reaction mechanisms, and associated surface phenomena. The present work is expected to offer insights into the utility of metal oxide-supported Pt catalysts for the low-temperature oxidative removal of gaseous VOCs in the dark, primarily for indoor air quality management.

Selective discrimination of VOCs gases at ppb-level using MOS gas sensor in temperature-pulsed operation mode with modified Hill equation

Due to the complicated gas-sensing process on the surface of metal oxide semiconductors (MOS), it is hard to accurately characterize the transient changes of MOS gas sensor. This paper introduces a modified Hill equation to quantitatively analyze the instantaneous resistance of MOS gas sensors operating in temperature-pulsed operation mode, to extract the gas-sensing dynamic features and key parameters for the selective identification of various volatile organic compounds (VOCs) gases (such as ethanol, formaldehyde, toluene and acetone) by machine learning algorithms. The sensors were tested with the pulse-mode at different temperatures for multiple gases. The characteristic features of the sensing process during the pulse-on period were investigated. Key parameters were extracted via the modified Hill equation, which have shown obvious dependence on the gas type and concentration. Excellent selectivity for the VOC gases was attained at low concentrations of 500 ppb thanks to the equation's further integration with the principal component analysis (PCA) approach. The incorporation of this modified Hill equation in temperature-pulsed operation mode provides the vital features for the possible applications of MOS gas sensors with the emerging machine learning algorithm.

Odorous organic gas emission characteristics from cooked food wastes during aerobic decomposition

Emission of volatile organic compounds (VOCs) from food waste is a key trouble for surrounding residents. Herein, the VOC emission patterns from headspace (gaseous) and produced leachate (aqueous) during decomposition of cooked milk cabbage, tilapia fish, and their mixture (Mix) were investigated in self-made reactors. The average amount of gaseous VOCs from fish (2888.7 mg/kg dry matter (DM)) was higher than Mix (2152.9 mg/kg DM) and cabbage (374.2 mg/kg DM). The dominant VOCs were volatile organic sulfur compounds (VOSCs) and oxygenated VOCs, accounting for 65.8 and 32.5 %, 71.8 and 25.2 %, 39.0 and 36.7 % of total VOC yields in fish, Mix, and cabbage, respectively. From leachate, average leaching fluxes of VOCs followed the order of: Mix (2487.9 μg/(L kg) DM) > fish (2374.7 μg/(L kg) DM) > cabbage (368.7 μg/(L kg) DM). VOSCs were also the major VOCs from leachate, accounting for 87.9, 98.6, and 98.5 % of total VOCs in leachate, respectively. Lactobacillus and Weissella were the dominant genera in cabbage, while Bacteroides were dominant in fish and Mix. Physicochemical factors were significantly related to bacterial composition shifts over time. Furthermore, Lysinibacillus and Vibro were positively correlated with gaseous dimethyl disulfide, while Lactobacillus, Weissella, Savagea, Ignatzschineria, Acinetobacter, and Pseudomonas showed strong relationship with dominant gaseous and aqueous VOCs. These findings provide significant references for understanding distribution and controlling of VOCs released during cooked food waste fermentation.

Determination of uptake rates of volatile organic compounds by 24-hour passive sampling and their application in enclosed ship cabin environments

Effective and reliable uptake rates are crucial for applying passive samplers in monitoring gaseous volatile organic compounds (VOCs). However, there is currently a lack of data regarding the 24-h passive uptake rates of VOCs for workplace and environmental monitoring. This study aims to address this gap by utilizing a Tenax TA-based sampling tube to investigate the 24-h uptake rates of 25 kinds of VOCs in enclosed ship cabin environments. The results reveal that the uptake rate of VOCs ranges from 0.32 to 0.44 mL/min. A relationship between diffusive uptake rates and the boiling point of the compounds is proposed. The variation in uptake rate under different sampling durations was examined. The difference in uptake rate between 24 h and 8 h was smaller than that between 24 h and 6 days, with an average difference of 20%. In addition, the indoor uptake rate obtained in this study was compared with the outdoor uptake rates in the literature, and results showed that the outdoor uptake rate was generally higher than the indoor uptake rate, but the difference decreased when the sampling duration was longer. Field measurement was conducted by utilizing the measured uptake rates to characterize VOC pollution in both living and engine cabins in actual ships. This study contributes to facilitating the evaluation of environmental daily average concentrations in enclosed ship cabins, which will be helpful for assessing the effectiveness of air pollution control systems or epidemiological studies in ships.

Investigation on fabrication and VOC sensing properties of PDMS fiber based optical sensor

In this paper, a method for preparing polydimethylsiloxane (PDMS)-based fiber structure was reported and its performance for volatile organic compound (VOC) gas sensing was demonstrated. The PDMS fiber prepared by the physical stretching method was coupled at the end faces of two multimode fibers by adhesion, forming an inter-mode interferometer. The PDMS was not easily dislodged after curing, which contributes to the strong robustness of the whole structure. When the PDMS optical fiber is exposed to VOC gas environment, PDMS will adsorb VOC gas molecules, resulting in changes in its refractive index and volume, which shifts the wavelength of inter-mode interference. Experiments show that the sensor has good sensitivity to VOC gases such as acetone, ethanol, and methanol. In the concentration range of 0–400 ppm, the sensitivity reaches 14.84 pm/ppm, 13 pm/ppm, 4.39 pm/ppm, 7.37 pm/ppm, and 5.99 pm/ppm for acetone, ethanol, methanol, toluene, and ether respectively. The proposed sensor has excellent characteristics such as simple preparation, high sensitivity, good tensile properties, and humidity resistance, which indicates that the sensor has some potential for VOC gas detection.

Volatile Organic Compound Gas Sensing Applications of n-Type SnO2 and p-Type CuO Based on Thin Films

Volatile Organic Compound Gas Sensing Applications of n-Type SnO2 and p-Type CuO Based on Thin Films

Machine-learning-assisted sensor array for detecting COVID-19 through simulated exhaled air

Reverse transcription polymerase chain reaction (RT-PCR), the primary test for COVID-19, requires complicated sample collection and several hours to obtain results. Breath test for exhaled volatile organic compounds (VOCs) has gained substantial research attention as a simple non-invasive and fast screening method. However, a unique VOC fingerprint as a potential prognostic biomarker is still unavailable. Accordingly, this study prepared simulated VOC gases to test the classification performance of a sensing system. The simulated VOC gases were selected according to the actual composition of the exhaled breath of patients with acute respiratory diseases, including COVID-19. Two sets of metal oxide sensor arrays, comprising eight commercial sensors and eight Advanced Institute of Science and Technology (AIST) laboratory-made sensors, were prepared to test their sensing ability for the simulated gases. The principal component analysis (PCA) results revealed that the AIST sensors had better sensing ability than the commercial sensors. Moreover, the recursive feature elimination cross-validation (RFECV) of random forest (RF) further confirmed the superiority of the AIST sensors over the commercial sensors. An artificial neural network (ANN) with excellent prediction performance for gas concentration was developed. This study provides a promising method for rapidly screening respiratory diseases.