Classes Of Volatile Organic Compounds Research Articles

High-performance virtual sensors array based on a single-chip FBAR for volatile organic compounds (VOCs) detection and classification

The detection and classification of volatile organic compounds (VOCs) are of great significance in atmospheric environment monitoring and have garnered extensive attention in recent decades. This study proposes a virtual sensor array (VSA) for VOC detection and classification fabricating a single-chip high-Q-factor film bulk acoustic resonator (FBAR) functionalized with MIL-101(Cr) thin film, known for its porous crystal structure and high specific surface area. The sensing performance of five different VOCs was evaluated to characterize the VOCs, achieving high detection accuracy calibrated with the Langmuir adsorption model. The FBAR’s frequency response was utilized to determine the sensitivity and limit of detection (LOD), yielding sensitivity and LOD of 85.45 Hz/ppm and 51 ppm, respectively, for isopropanol. The identification and classification capabilities of the proposed VSA for VOCs and their mixtures were assessed using statistical methods and machine learning algorithms, realizing classification accuracies of 100 % for pure VOCs and 94.6 % for mixtures. The developed VSA demonstrates superior performances in VOC detection and classification, with potential applications across various domains.

Volatile organic compounds in typical coal chemical industrial park in China and their environmental and health impacts

The coal chemical industry produces a large amount of volatile organic compounds (VOCs), and the emission characteristics and associated impact on the environment and health of the residents are still unclear. This study determined the VOC concentrations and compositions in the Jinjie Coal Chemical Industry Park which is located in northern China. The average concentrations of total measured VOCs (TVOCs) in the industrial areas in summer and winter were 231.5 and 103.2 μg/m3, which were higher than those in the residential areas (123.7 and 70.3 μg/m3), respectively. Aromatics, Oxygenated volatile organic compounds (OVOCs), and alkanes were the dominant VOC classes in the industrial areas, while halocarbons, OVOCs, and alkenes had higher compositions in the residential areas where were not only affected by industrial emissions and also other anthropogenic sources. OVOCs contributed over 43% of ozone formation potential (OFP), while aromatics contributed over 61% of secondary organic aerosol (SOA) formation in the Park in both seasons. Using the source apportionment method, biogenic emission and anthropogenic source (gasoline production, coking emission, fuel combustion, solvent coating, and vehicle exhaust) were major contributors to VOCs in residential areas. The industrial-related emissions were the main components of anthropogenic source, accounting for 53.5%–58.7% of the overall VOCs. With reliable estimations of the health aspects, exposures to acrolein (HQ: 7.4–126.6) and formaldehyde (ILCR: 5.5 × 10−3-5.7 × 10−2) posed the highest non-carcinogenic and carcinogenic risks, accounting for 94.3%–98.6% and 55.8%–93.8% of the total HQ and ILCR, respectively. The results demonstrated that substantial environmental and health co-benefits to the residents could be achieved by reducing the industrial emissions from gasoline production, coking process, and diesel-fueled vehicles in the Jinjie Coal Chemical Industry Park. Prioritizing the establishment of efficient air pollution measures and tightening industrial emission standards, especially for hazardous VOCs, are recommended according to the findings of the valuable work.

Constructing binuclear sites to modulate the charge distribution of MIL-101 for enhanced toluene adsorption performance: experimental and theoretical studies

Toluene is a class of volatile organic compounds (VOCs) in the air that harm environmental quality and human health. To enhance the adsorption performance of adsorbents for toluene, based on the principle that unsaturated metal sites in MOFs can be replaced, nanoscale M@MIL-101(M represents Mg, Ca, Sr) with binuclear sites was prepared by a one-step solvothermal method. The second metal was successfully introduced into the topology of MIL-101 by partially substituting Cr sites. The results of the dynamic adsorption experiments showed that 20%Sr@MIL-101 exhibited the highest toluene adsorption capacity, which was about 60% higher than that of the undoped MIL-101. Analysis using a monolayer and multilayer model based on statistical physics theory indicated that the Sr/Cr binuclear sites could enhance the interaction strength of the adsorption system, promoting the adsorption of more layers of toluene molecules on the surface of MIL-101. Characterization and simulation together confirmed that constructing Sr/Cr binuclear sites in MIL-101 can cause significant redistribution of charge within the pores, enhancing the van der Waals interactions, π-π stacking interactions, hydrogen bonding, and Lewis acid-base interactions between the pore interior and the adsorbate molecules. Additionally, density functional theory (DFT) revealed that the adsorption sites of toluene in MIL-101 were distributed in a trigonal prism arrangement and were symmetrically aligned with a 180° rotation of the MIL-101 triangular cage.

The Role of Volatile Organic Compounds (VOCs) in Determining Seed Physiological Quality: A Review

Volatile organic compounds (VOCs) emitted by seeds serve as promising biomarkers for assessing seed vigor, viability, and deterioration during storage. This review synthesizes current knowledge on the types and chemical classes of VOCs released by seeds, factors affecting their emission, and methods for their collection and analysis. VOCs indicate seed aging, with increased emissions of alcohols, aldehydes, and ketones associated with deterioration processes like lipid peroxidation. Volatile organic compounds (VOCs) associated with seed deterioration include alcohols like ethanol, which can indicate fermentation, aldehydes such as hexanal, which is linked to lipid oxidation, and ketones like 2-heptanone, which can result from microbial activity and contribute to off-flavors and rancidity. The quantity and composition of VOCs correlate with the extent of seed deterioration, potentially offering a rapid, non-destructive alternative to traditional germination tests for evaluating seed quality. VOCs also mediate interactions between seeds and microorganisms, influencing germination and stress responses. Different research findings regarding volatile organic compounds (VOCs) in seeds indicate their potential as indicators of seed quality, which could lead to improved seed management strategies. By utilizing VOC profiling, farmers can make informed decisions on seed selection and treatment, ultimately enhancing crop yield and resilience in agricultural practices. While VOC analysis shows promise for integration into seed quality testing, challenges remain in standardizing protocols and identifying robust markers across different seed types, species and storage conditions. Advances in VOC research may ultimately lead to novel solutions for improving seed and crop productivity. Future research directions in VOC analysis for seed quality testing should focus on standardizing VOC profiles across diverse seed species, integrating VOC analysis with precision agriculture technologies, exploring environmental influences on VOC emissions, developing non-invasive testing methods, conducting longitudinal studies on seed storage, applying VOCs in breeding programs, and establishing links between VOC emissions and disease resistance.

Detection of volatile primary aliphatic amines: Highly selective and sensitive vapoluminescent sensing of n-butylamine

Detection of volatile amines is of great interest for environmental and industrial monitoring for the protection of human health from exposure to toxic substances. This contribution describes the vapochromic and vapoluminescent properties of a Lewis acidic Zn(salen)-type Schiff-base complex and its application as a chemosensor for detection of n-butylamine (BA) vapors. Annealed films of the complex were initially involved for vapochromic and vapoluminescence screening experiments to evaluate their response towards compounds representative of each of the different chemical classes of volatile organic compounds (VOCs). Then, a cost-effective, disposable paper-based vapoluminescent sensor, characterized by high optical uniformity and repeatability, was developed for the selective and sensitive direct in-situ detection of BA vapors. Under static exposure conditions, the sensor allows a sensitive vapoluminescent detection of BA, with a LOD down to 2.0 ppm, below the permissible exposure limit of 5 ppm established by OSHA for BA, and a linear dynamic range up to 50 ppm. The sensor response is not affected by the exposure temperature, only a different exposure time is required as the temperature varies, while average relative humidity levels lead to almost negligible changes in the sensor response. The sensor is highly selective for BA with respect to ammonia and the different chemical classes of VOCs, including relevant primary aliphatic amines, with the exception of propylamine and sec-butylamine, especially at low concentrations (tens of ppm). Selectivity for BA is also demonstrated by competitive experiments in the presence of a 10-fold ppm amount of all representative VOCs of each class as potential interferents.

Facile E-nose based on single antenna and graphene oxide for sensing volatile organic compound gases with ultrahigh selectivity and accuracy

Volatile organic compounds (VOCs) gases exist as important indicators in various processes such as human breath, food spoilage, plant disease, and industrial production, which makes VOC sensing a promising nondestructive detection method. Nanomaterial enabled electronic noses (e-noses) are gaining increasing attention for their exceptional ability to distinguish between multiple VOCs. To achieve this, multiple sensors with distinct nanomaterials are crucial for providing enough diversity for successful discrimination. Apparently if e-nose can be implemented solely on single sensor with single common nanomaterial operating at room temperature, the complexity and power consumption can be greatly decreased. In this study, a significant milestone is achieved as, for the first time, a single antenna sensor coated with commercial graphene oxide, demonstrates comparable performance to array-based e-noses, and even outperforms them in terms of the ultimate ambition of selectivity—isomeric VOC classification. An impressive classification accuracy of 96.7 % is attained for multiple VOC gases, including isomers. Additionally, the concentration of each component in VOC isomer mixtures is accurately determined. Unlike conventional antenna sensors, the sensor maintains stable communication during sensing operations. Finally, the practical feasibility of the antenna e-nose (Ant-nose) is successfully demonstrated with a series of food quality assessments.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses.

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

Integrated Transcriptomic and Metabolomic Analysis Revealed Abscisic Acid-Induced Regulation of Monoterpene Biosynthesis in Grape Berries.

Monoterpenes are a class of volatile organic compounds that play crucial roles in imparting floral and fruity aromas to Muscat-type grapes. However, our understanding of the regulatory mechanisms underpinning monoterpene biosynthesis in grapes, particularly following abscisic acid (ABA) treatment, remains elusive. This study aimed to explore the impact of exogenous ABA on monoterpene biosynthesis in Ruiduhongyu grape berries by employing Headspace Solid-Phase Micro-Extraction Gas Chromatography-Mass Spectrometry (HS-SPME/GC-MS) analysis and transcriptome sequencing. The results suggested significant differences in total soluble solids (TSS), pH, and total acid content. ABA treatment resulted in a remarkable increase in endogenous ABA levels, with concentrations declining from veraison to ripening stages. ABA treatment notably enhanced monoterpene concentrations, particularly at the E_L37 and E_L38 stages, elevating the overall floral aroma of grape berries. According to the variable gene expression patterns across four developmental stages in response to ABA treatment, the E_L37 stage had the largest number of differential expressed genes (DEGs), which was correlated with a considerable change in free monoterpenes. Furthermore, functional annotation indicated that the DEGs were significantly enriched in primary and secondary metabolic pathways, underlining the relationship between ABA, sugar accumulation, and monoterpene biosynthesis. ABA treatment upregulated key genes involved in the methylerythritol phosphate (MEP) pathway, enhancing carbon allocation and subsequently impacting terpene synthesis. This study also identified transcription factors, including MYB and AP2/ERF families, potentially modulating monoterpene and aroma-related genes. Weighted gene co-expression network analysis (WGCNA) linked ABA-induced gene expression to monoterpene accumulation, highlighting specific modules enriched with genes associated with monoterpene biosynthesis; one of these modules (darkgreen) contained genes highly correlated with most monoterpenes, emphasizing the role of ABA in enhancing grape quality during berry maturation. Together, these findings provide valuable insights into the multifaceted effects of exogenous ABA on monoterpene compounds and grape berry flavor development, offering potential applications in viticulture and enology.

Site‐Selective MoS2‐Based Sensor for Detection and Discrimination of Triethylamine from Volatile Amines Using Kinetic Analysis and Machine Learning

AbstractDetection and discrimination of volatile organic compounds (VOCs) is important to provide a more realistic assessment of their potential implication in complex environments and medical diagnostics based on volatile biomarkers. Herein, chemiresistive sensors are fabricated using stacked MoS2 nanoflakes with defects and exposed‐edge sites. The sensor is found to be extremely selective to triethylamine (TEA) over polar, non‐polar VOCs and atmospheric gases. The sensor exhibits a sensitivity of 1.72% ppm−1, fast response/recovery (19 s/39 s) to 100 ppm TEA at room temperature, low limit of detection (64 ppb), device reproducibility, humidity tolerance (RH 90%) and stability tested up to 60 days. The kinetic analysis of sensing curves reveals two discrete adsorption sites corresponding to edge and basal sites of interaction, with a higher rate constant of association and dissociation for TEA. The Density Functional Theory (DFT) studies support higher adsorption energy of TEA on MoS2 surface with respect to other volatile amines. The sensor demonstrates TEA recognition and composition estimation capability in a binary mixture of a similar class of VOCs using Machine Learning driven analysis with 95% accuracy. The ability to discriminate amines in binary mixture of other volatile amines paves the way for the advancement of next‐generation devices in the field of disease diagnosis.

Description of “Ovino Belmontese”, a new semisoft sheep's milk cheese processed using “Italico” cheese technology

The objective of this study was to create a new semisoft sheep's milk cheese called “Ovino Belmontese” cheese (OBCh) by applying the “Italico” cheese-making technology. The cheese production took place under industrial conditions, with the addition of a commercial starter formulation containing Streptococcus thermophilus. The microbiological, physicochemical, and sensory characteristics of OBCh were assessed and compared to those of a commercially available cow's Italico cheese (CICh). Streptococcus thermophilus dominated the microbial community during the cheese-making process, reaching levels of approximately 9.0 Log CFU/g in both OBCh and CICh. Among physical characteristics, no statistically significant difference (p ≥ 0.05) was registered in terms of lightness, redness, yellowness, and hardness between the two cheeses. OBCh exhibited a twofold higher short-chain fatty acid content compared to CICh. Both cheeses displayed similar classes of volatile organic compounds, although their relative percentages differed. The application of Italico cheese technology to process sheep's milk did not negatively affect sensory attributes. This study highlighted that utilizing a cheese-making technology not commonly used for processing sheep's milk represents a promising strategy to diversify Sicilian dairy productions.

Spatiotemporal variations, sources, and atmospheric transformation potential of volatile organic compounds in an industrial zone based on high-resolution measurements in three plants

Industrial emissions are significant sources of volatile organic compounds (VOCs). This study conducted a field campaign at high temporal and spatial resolution to monitor VOCs within three plants in an industrial park in southern China. VOC concentrations showed significant spatial variability in this industrial zone, with median concentrations of 75.22, 40.53, and 29.41 μg/m3 for the total VOCs in the three plants, respectively, with oxygenated VOCs (OVOCs) or aromatics being the major VOCs. Spatial variability within each plant was also significant but VOC-dependent. Seasonal variations in the VOC levels were governed by their industrial emissions, meteorological conditions, and photochemical losses, and they were different for the four groups of VOCs. The temporal and spatial variations in the VOC compositions suggest similar sources of each class of VOCs during different periods of the year in each plant. The diurnal patterns of VOCs (unimodal or bimodal) clearly differed from those at most industrial/urban locations previously, reflecting a dependence on industrial activities. The secondary transformation potential of VOCs also varied temporally and spatially, and aromatics generally made the predominant contributions in this industrial park. The loss rate of OH radicals and ozone formation potential were highly correlated, but the linear relationship substantially changed in summer and autumn due to the intensive emissions of an OVOC species. The lifetime cancer and non-cancer risks via occupational inhalation of the VOCs in the plants were acceptable but merit attention. Taking the secondary transformation potential and health risks into consideration, styrene, xylene, toluene, trichloroethylene, and benzene were proposed to be the priority VOCs regulated in the plants.

Volatile Organic Compounds from Offspring of Stingless Bee Sacrificed in Hygienic Behavior Test.

This study shows the profile of volatile organic compounds (VOCs) from pupae and larvae of Melipona quadrifasciata anthidioides Lepeletier subjected to three death induction techniques for hygienic behavior (HB) studies: freezing in liquid nitrogen (LN2), freezing in a freezer (FRZ) and piercing of offspring with an entomological pin (PIN). The VOCs from larvae and pupae were obtained through headspace solid-phase microextraction and characterized using gas chromatography coupled to mass spectrometry. In addition, an HB test was performed on the colonies. The main classes of VOCs were hydrocarbons, terpenes and alcohols. Multivariate analysis was applied and showed that there was a separation in the compound profiles between the different treatments. The HB test in the colonies showed that 24 hours after the application of the techniques, the bees removed more dead larvae in LN2 treatment (83.5 %), while after 48 hours more larvae were removed in the LN2 and FRZ treatments (92.3 %). When compared to pupae removal, larvae removal was significantly faster in LN2.

Ecological functions of fungal sesquiterpenes in the food preference and fitness of soil Collembola.

Volatile organic compounds (VOCs) emitted by fungi play a key role in locating and selecting hosts for fungivorous arthropods. However, the ecological functions of many common VOC classes, such as sesquiterpenes, remain unknown. Mutants of Trichoderma virens, defective in the emission of most sesquiterpenes owing to the deletion of the terpene cyclase vir4, were used to evaluate the role of this compound class in the food preference and fitness of the soil Collembola Folsomia candida. Choice experiments with and without direct contact with fungal mycelium revealed that Collembola were preferentially attracted to Δvir4 mutants impaired in sesquiterpene synthesis compared to wild-type T. virens. Grazing by F. candida on the sesquiterpene deficient T. virens strain had no effect on Collembola survival, reproduction and growth compared to wild-type T. virens. The results suggest that sesquiterpenes play an important role in fungal defence as repellents, but not as deterrents or toxins, against fungivorous Collembola. Our research contributes to the understanding of ecological interactions between fungi and fungivorous arthropods, providing insights into the specific ecological functions of sesquiterpenes. The study has implications for chemical ecology and the dynamics of multitrophic interactions in soil ecosystems.

Using wavelet transform and hybrid CNN – LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection

Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14–21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4–5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed. A controlled experiment was conducted on four maize varieties, and the data obtained were used to develop and validate a hybrid CNN-LSTM model for VOC classification and an LSTM model for ultrasound anomaly detection. The hybrid CNN-LSTM model, enhanced with wavelet data preprocessing, achieved an F1 score of 0.96 and an Area under the ROC Curve (AUC) of 1.00. In contrast, the LSTM model exhibited an impressive 99.98% accuracy in identifying anomalies in ultrasound emissions. Our findings underscore the potential of IoT sensors in early disease detection, paving the way for innovative disease prevention strategies in agriculture. Future work will focus on optimizing the models for IoT device deployment, incorporating chatbot technology, and more sensor data will be incorporated for improved accuracy and evaluation of the models in a field environment.

Comparison of Cu+, Ag+, and Au+ Ions as Ionization Agents of Volatile Organic Compounds at Subatmospheric Pressure.

Ionization of volatile organic compounds (VOCs) by coinage metal ions (Cu+, Ag+, and Au+) generated by laser desorption and ionization (LDI) of a metal nanolayer in subatmospheric conditions is explored. The study was performed in a commercial subatmospheric dual MALDI/ESI ion source. Five compounds representing different VOC classes were chosen for a detailed study of the metal ionization mechanism: ethanol, acetone, acetic acid, xylene, and cyclohexane. In the gas phase, ion molecular complexes of all three metal ions were formed, typically with two ligand molecules. The successful detection of the metal complexes with VOCs strongly depended on the applied voltages across the ion source, minimizing the in-source fragmentation. The employed orbital trap with ultrahigh resolving power and sub-parts-per-million mass accuracy allowed unambiguous identification of the formed complexes based on their molecular formulas. The detection limits of the studied compounds in the gas were in the range 0.1-1.4 nmol/L. Compared to Cu+ and Ag+ ions, Au+ ions exhibited the highest reactivity, often complicating spectra by side products of reactions. On the other hand, they also allowed detecting saturated hydrocarbons, which did not produce any signals with Ag+ and Cu+.

Exploring stingless bee honey from selected regions of Peninsular Malaysia through gas chromatography-mass spectrometry-based untargeted metabolomics.

Volatile organic compounds in honey are known for their considerable impact on the organoleptic properties of honey, such as aroma, flavor, taste, and texture. The type and composition of volatile organic compounds are influenced by entomological, geographical, and botanical origins; thus, these compounds have the potential to be chemical markers. Sixty-two volatile compounds were identified using gas chromatography-mass spectrometry from 30 Heterotrigona itama(H. itama) honey samples from 3 different geographical origins. Hydrocarbons and benzene derivatives were the dominant classes of volatile organic compounds in the samples. Both clustering and discriminant analyses demonstrated a clear separation between samples from distant origins (Kedah and Perak), and the volcano plot supported it. The reliability and predictability of the partial least squares-discriminant analysis model from the discriminant analysis were validated using cross-validation (R2 : 0.93; Q2 : 0.83; accuracy: 0.97) and the permutation test (p<0.001), and the output depicted that the model is legitimate. In combination with the variable importance of projection (VIP>1.0) and the Kruskal-Wallis test (p<0.01), 19 volatile organic compounds (encompassed aldehydes, benzene derivatives, esters, hydrocarbons, and terpenoids) were sorted and named potent chemical markers in classifying honey samples from three geographical origins. In brief, this study illustrated that volatile organic compounds of stingless honey originated from the same bee species, but different geographical origins could be applied as chemical markers.

Highly Sensitive and Selective Detection of Dimethyl Methyl Phosphonate with Copolymer‐Based QCM Sensors

AbstractIn this work, the volatile organic compounds (VOCs) sensing properties of a quartz crystal microbalance (QCM) transducer coated with six different poly(3‐methylthiophene) (P3MT) copolymerized with polypyrrole (PPy) are investigated. The sensor preparation involves the electrochemical deposition of P3MT, PPy, and P3MT‐co‐PPy on Au‐coated QCM transducers by electrochemical deposition techniques with a three‐electrode cell. The structural properties of the copolymer films are characterized using scanning electron microscopy, and their oxidation/reduction behavior is investigated through cyclic voltammetry. The copolymer‐based QCM sensors exhibit high sensitivity and selectivity to dimethyl methyl phosphonate and benzonitrile, even at low concentrations (&lt;1 ppm) at room temperature. Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich adsorption isotherms are studied to understand the VOCs sensing mechanism machine learning classification algorithms including quadratic discriminant (QD), neural nets, K‐nearest neighbors, linear discriminant, and support vector machines are applied to classify the sensor responses for the 12 different analytes. With the help of machine learning algorithms, tested analytes are successfully classified into their groups. The highest accuracy of 97.34% is achieved using the QD method. The developed sensor, combined with machine learning algorithms, shows promising potential for accurate and reliable detection and classification of VOCs.

Secondary Brown Carbon Formation From Photooxidation of Furans From Biomass Burning

AbstractFurans are a major class of volatile organic compounds emitted from biomass burning. Their high reactivity with atmospheric oxidants leads to the formation of secondary organic aerosol (SOA), including secondary brown carbon (BrC) that can affect global climate via interactions with solar radiation. Here, we investigate the optical properties and chemical composition of SOA generated via photooxidation of furfural, 2‐methylfuran, and 3‐methylfuran under dry (RH &lt; 5%) and humid (RH ∼ 50%) conditions in the presence of nitrogen oxides (NOx) and ammonium sulfate seed aerosol. Dry furfural oxidation has the greatest BrC formation, including reduced nitrogen‐containing organic compounds (NOCs) in SOA, which are dominated by amines and amides formed from reactions between carbonyls and ammonia/ammonium. Based on the products detected, we propose novel formation pathways of NOCs in furfural photooxidation, which can contribute to BrC via accretion reactions during the photochemical aging of biomass burning plumes.

Identification of Different Classes of VOCs Based on Optical Emission Spectra Using a Dielectric Barrier Helium Plasma Coupled with a Mini Spectrometer.

In this study, a micro helium dielectric barrier discharge (μHDBD) plasma device fabricated using 3D printing and molding techniques was coupled with a mini spectrometer to detect and identify different classes of volatile organic compounds (VOCs) using optical emission spectrometry (OES). We tested 11 VOCs belonging to three different classes (straight-chain alkanes, aromatics, and polar organic compounds). Our results clearly demonstrate that the optical emission spectra of different classes of VOCs show clear differences, and therefore, can be used for identification. Additionally, the emission spectra of VOCs with a similar structure (such as n-pentane, n-hexane, n-heptane, n-octane, and n-nonane) have similar optical emission spectrum shape. Acetone and ethanol also have similar emission wavelengths, but they show different line intensities for the same concentrations. We also found that the side-chain group of aromatics will also affect the emission spectra even though they have a similar structure (all have a benzene ring). Moreover, our μHDBD-OES system can also identify multiple compounds in VOC mixtures. Our work also demonstrates the possibility of identifying different classes of VOCs by the OES shape.

Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves

Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves