VOC Detection Research Articles

Electrophoretically deposited graphene oxide/molybdenum disulfide composite on quartz crystal microbalance for ethyl acetate VOC detection

Electrophoretically deposited graphene oxide/molybdenum disulfide composite on quartz crystal microbalance for ethyl acetate VOC detection

Tin oxide nanoparticles anchored on ordered mesoporous carbon for efficient acetone sensing

Rationally designed combinations of semiconductor metal oxides (SMO) and carbon materials can lead to the development of sensing materials with excellent gas performance. Herein, SnO2 nanoparticles (NPs) decorated on ordered mesoporous carbon (CMK-3) nanorods were synthetized by solvothermal and high-temperature calcination methods. Various characterization and gas sensing tests indicated that the resulting one-dimensional (1D) self-assembled SnO2@CMK-3 composite had a large specific surface area (88.02 m2/g) and excellent gas sensing performance. Specifically, at optimal working temperature, the as-prepared SnO2@CMK-3 sensor had a high response (122.1), low limit of detection, good linear fitting (R2 = 0.9892), rapid response/recovery time (5/27 s) towards 50 ppm acetone. Meanwhile, by detecting six gases, including acetone, ammonia, formaldehyde, xylene, triethylamine, and ethanol, it was found that the SnO2@CMK-3 sensor showed good selectivity to acetone. The unique nanostructure, large specific surface area, high oxygen vacancy content, and the formation of heterojunctions accounted for the good performance to acetone. These results highlighted the potential application of the SnO2@CMK-3 composite for the detection of acetone gas and presented a promising approach to prepare SMO@carbon composites for VOCs detection.

VOC Detection with Zinc Oxide Gas Sensors: A Review of Fabrication, Performance, and Emerging Applications

Energy‐efficient, high‐specificity gas sensors provide practical suitability for stability and response factors. The recognition of ignitable gases (methane (CH <sub>4</sub>), propane (C <sub>3</sub>H <sub>8</sub>), and hydrogen (H <sub>2</sub>) and harmful gases (carbon oxide (CO) and hydrogen sulfide (H <sub>2</sub>S)) in an enclosed and out‐of‐door space are essential to safeguard the human lives and infrastructural spaces. One of the crucial conductive‐type metal oxide semiconductor (MOS) gas sensors yielding wide applications is zinc oxide (ZnO). This study highlights the various types of ZnO gas sensors, their fabrication techniques, and specific vital characterizations. The devices based on MOS are utilized to sense various target gases through redox reactions. The variation in oxide surface with target gas interactions is transduced to a change of sensor conductance. This review also provides insight into integrating ZnO gas sensors with technologies such as materials engineering, the Internet of things and big data. Moreover, this review addresses ZnO gas sensors' challenges and future directions.

Remote detection of polluted gases based on thermal infrared spectral imaging in the field

This paper reports the new progress of long wave infrared remote sensing in the field, focusing on the implementation process of window scanning spatiotemporal modulation Fourier spectroscopic imaging technology. Utilizing the self-developed CHIPED-1 device, the spatial modulation interference was achieved through the use of a cone mirror Michelson interferometer. By combining it with a cooled long-wave infrared focal plane detector component and applying data processing steps such as acquisition, recombination, calibration, etc., high-spectral resolution imaging in long-wave infrared region was accomplished. The detection sensitivity index nesr (Noise Equivalent Spectral Radiance) of the self-developed CHIPED-1 long wave infrared hyperspectral imaging principle experimental device reaches 5.6 × 10−8w/(cm−1.sr.cm2) in single pixel，Equivalent to commercial time modulation interferometric hyperspectral imagers; It reflects the progressiveness of the technology, and leaves much room for improvement.By testing the transmittance curve of polypropylene film, the spectral response range of CHIPED-1 infrared hyperspectral imaging principle experimental device reached 11.5 μm.The article also studied the hyperspectral imaging detection method for two-dimensional distributed chemical gas VOCs, taking the detection experiments of field high-rise buildings and ether gas as examples.Under complex backgrounds and low experimental concentrations, the presence of ether vapor cannot be observed from the infrared spectrum slices at the same wave number. However, after differential spectral processing, the spatial distribution of ether vapor can be clearly seen.The hyperspectral method is applied in the field of infrared detection of organic vapor VOCs, which has many advantages over wide-band thermal imaging methods, such as high sensitivity, strong anti-interference ability, and wide recognition range.

P4.04C.09 Exhaled VOC Detection in Lung Cancer Screening: A Comprehensive Meta-Analysis

P4.04C.09 Exhaled VOC Detection in Lung Cancer Screening: A Comprehensive Meta-Analysis

Novel carbon quantum dots enhanced carbon nanotubes-graphene hybrid nanocomposite for VOCs detection

In recent years, researchers have shown a significant interest in exploring carbon-based nanomaterials for their potential applications in sensing technologies. This study introduces the development of an innovative composite material through the integration of quantum dots with carbon nanotubes and graphene. This nanohybrid composite serves as a sensing material for detecting ethanol at room temperature. The carbon quantum dots (CQD) used in this study were synthesized through a solvothermal process and integrated with carbon nanotubes (CNT) and graphene nanoplates (GNP) to investigate the synergistic effects on the composite's structure and its sensing properties. The resulting hybrid composite was applied as a sensitive film onto the surface of a quartz crystal microbalance (QCM) sensor. The hybrid nanocomposite-based sensor demonstrated significantly enhanced sensing sensitivity. At an ethanol concentration of 500 ppm, the CQD-enhanced CNT-GNP composite exhibited approximately 10- and 15-fold higher responses compared to CNT- and GNP-coated sensors, respectively. The response/recovery times of the CQD-enhanced CNT-GNP composite sensor were approximately 2/0.5 min, respectively. The sensor maintained reasonable repeatability and good recovery. The findings highlight the remarkable synergistic effects observed in the sensing performance of carbon-based nanohybrids, demonstrating the promising potential of this approach to enhance the sensitivity of such sensors.

The characteristic VOCs of different parts of Artocarpus heterophyllus fruit based on HS-SPME-GC-MS and PTR-TOF-MS

Insight investigation on both edible pulps and inedible parts involving inflorescence axis and shreds of Artocarpus heterophyllus Lam were carried out, a total of 98 VOCs and 201 masses were identified by the combination of HS-SPME-GC-MS and PTR-TOF-MS. Among them, according to the consistency of OAV and results of VIP > 1, p < 0.05, compounds methyl isovalerate (A2), 3-methylbutyl acetate (A5) and octanoic acid, ethyl ester (A21) were recognized as aroma markers to distinguish the pulps, shreds and inflorescence axis. Meanwhile, the inflorescence axis (IC50: 1.82 mg/mL) and shreds (IC50: 16.74 mg/mL) exhibited more excellent antioxidant potency than pulps (IC50: 17.43 mg/mL) in vitro. These findings validated the feasibility of coupling HS-SPME-GC-MS and PTR-TOF-MS for rapid detection of characteristic VOCs of this plant, and offered new prospect of fragrance utilization and waste management of the edible and inedible parts of A. heterophyllus fruit.

Exploring Microwave Reconfigurable Intelligent Surface for Wireless VOC Detection: A Comparative Study of Porous and Solid PDMS Interfaces

AbstractMicrowave gas monitoring with a low‐profile design and enhanced selectivity remains a persistent challenge. This study introduces an innovative approach employing a real‐time wireless reconfigurable intelligent surface (RIS) for comparative investigation of the interaction of two distinct polydimethylsiloxane (PDMS) interfaces, i.e., solid and porous, with acetone gas molecules. The developed PDMS‐coated microwave RIS detects varying concentrations of acetone vapor by wirelessly monitoring variations in the resonant characteristics of the resonating RIS beneath the sensitive interface. This PDMS‐coated microwave RIS is validated through exposure to incremental (15–75) parts per thousand (ppt) acetone concentrations and demonstrated a sensitivity of ≈4.4 MHz/ppt and ≈4.3 MHz/ppt of acetone for solid and porous PDMS, respectively. Integrating PDMS and microwave‐based RIS systems provides a sensitive tool for tracking the interaction of acetone gas and PDMS and demonstrates this system's capability for sensitive gas detection. This study introduces a unique development in the wireless detection of VOCs and presents a compact and passive approach with enhanced sensitivity, making it suitable for monitoring the interaction of polymers and hazardous VOCs. This technology is particularly suited for use in challenging and hard‐to‐reach environments.

Dynamically crosslinked chiral optics sensing for ultra-sensitive VOCs detection

Chiroptical sensing with real-time colorimetrical detection has been emerged as quantifiable properties, enantioselective responsiveness, and optical manipulation in environmental monitoring, food safety and other trace identification fields. However, the sensitivity of chiroptical sensing materials remains an immense challenge. Here, we report a dynamically crosslinking strategy to facilitate highly sensitive chiroptical sensing material. Chiral nematic cellulose nanocrystals (CNC) were co-assembled with amino acid by a two-step esterification, of which a precisely tunable helical pitch, a unique spiral conformation with hierarchical and numerous active sites in sensing performance could be trigged by dynamic covalent bond on amines. Such a CNC/amino acid chiral optics features an ultra-trace amount of 0.08 mg/m3 and a high sensitivity of 60 nm/(mg/m3) for formaldehyde gas at a molecule level detection, which is due to the three synergistic adsorption enhancement of dynamic covalent bonded interaction, hydrogen bonded interaction and van der Waals interaction. Meanwhile, an enhancement hierarchical adsorption of CNC/amino acid chiral materials can be readily representative to the precise helical pitch and colorimetrical switch for sensitive visualization reorganization.

Boosting the sensitivity of single photon ionization time-of-flight mass spectrometry using a segmented focus quadrupole-ion guide

Single photon ionization time-of-flight mass spectrometry (SPI-TOF-MS) is a powerful analytical technique for real-time detection of trace VOCs. However, efficient ion transmission within the ionization chamber has always been a challenging issue in SPI-TOF-MS. In this study, a novel ion guide termed the Segmented Focus Quadrupole Ion Guide (SFQ-IG) was introduced for SPI-TOF-MS. The SFQ-IG device consists of 12 printed circuit boards (PCB), each containing four quarter-ring electrodes with inner diameters progressively decreasing from 26 to 4 mm. The simulation results demonstrated that SFQ-IG exhibited superior ion transmission efficiency than both ion funnel (IF) field and direct current-only (DC-only) field. By integrating into a SPI-TOF-MS, this ion guide was optimized in terms of the ionization source pressure, direct current gradient, and radio frequency amplitude. Further comparative experiments demonstrated that the SPI-TOF-MS with the SFQ-IG exhibited higher sensitivity than both the IF field (1.3–7.4 times) and DC-only field (3.5–8.8 times) for the test VOCs. The improvements in limit of detection (LOD) with the SFQ-IG ranged from 1.6 to 5.3 times compared to the DC-only field for the test VOCs. Fabricated using PCB technology, the SFQ-IG is characterized by its cost-effectiveness, compact size, and high transmission efficiency, facilitating its integration into other mass spectrometers.

A battery-free, wireless, flexible bandlike e-nose based on MEMS gas sensors for precisely volatile organic compounds detection

Real-time detection of VOCs in the car cabin is crucial for protecting driver’s health. MOS gas sensors, with the advantages of low cost, simplicity and fast response, are widely used for gas monitoring. However, they typically exhibit broad-spectrum responsiveness to multiple gases. Despite numerous improvement strategies proposed from the perspective of MOS material modification, achieving ideal gas selectivity remains challenging. An e-nose based on gas sensor array is a promising approach for gas identification. However, the hardware circuit system and battery supply module in traditional e-nose result in large size, complex structure, and inconvenience in use. In this work, we synthesized MOS materials with different morphologies to form a microarray containing six MEMS gas sensors, and developed a novel belt-shaped wireless and battery-free flexible e-nose, where a mobile phone serves as its wireless power supply and data communication system. This design provides a flexible e-nose with excellent wearability and safety, allowing users to monitor surrounding gas conditions at any time conveniently. Subsequently, we design pattern recognition models using algorithms such as MLP, RF, XGBoost, and LGBM, in combination with ensemble learning strategies, achieving high accuracy in VOCs classification and low-error concentration prediction. This flexible e-nose holds significant application value for gas detection in car cabins and indoor environments.

Positional normalization-based mixed-image data augmentation and ensemble self-distillation algorithm

The prior approach to data augmentation using positional normalization necessitated manual determination of weight parameters for blending two images. However, theoretically, these parameters cannot cater to all samples. We propose a novel data augmentation approach called pnMix (Positional normalization-based data mixing) based on data blending and positional normalization to address these issues. Moreover, we have improved the teacher model of an existing network structure based on self-distillation technology using an ensembling approach called ESKD. Our method enhances performance across multiple datasets and tasks. We outperform previous methods in complex real-world datasets such as imagenet-a and imagenet-o, which we believe are highly meaningful for practical applications. Specifically, we obtain good results on the CIFAR100 and Imagenet image classification datasets, the Pascal VOC and COCO object detection datasets, and the COCO and Cityscapes image segmentation datasets. Furthermore, we analyze the pnMix method's advantages compared to other data augmentation methods through visualization techniques, demonstrating its effectiveness and interpretability. Our source code and pre-trained models are available at https://github.com/sydney72380/pnMix.

Copper oxide/reduced graphene oxide/graphene composite structure for the chemiresistive detection of acetaldehyde at room temperature

In this work, the acetaldehyde (CH3CHO) sensing performance of Copper(II) oxide nanoparticles/reduced Graphite oxide/Graphene composite structure (CuO NPs/rGO/G) was systematically studied. Firstly, CuO NPs/rGO composite nanostructures were prepared by hydrothermal synthesis, Then, CuO NPs/rGO was uniformly deposited on graphene film to prepare CuO NPs/rGO/G composite structure. CH3CHO sensing tests have shown that the CuO NPs/rGO/G composite structure exhibits a fast and highly sensitive response to CH3CHO at room temperature. At the test concentration of 100 ppm CH3CHO, the response of CuO NPs/rGO/G composite structure is 39.94 times that of graphene, 11.37 times that of CuO NPs/rGO, and 5.90 times that of CuO NPs, up to 82.67 %. The response time is only 22 seconds, the recovery time is 159 seconds, and its theoretical limit of detection is 95.10 ppb. In addition, the CuO NPs/rGO/G composite structure has excellent selectivity, repeatability, and long-term stability for the sensing of CH3CHO. The CuO NPs/rGO/G composite structure studied in this paper solves the problems of poor response, slow response, slow desorption and even inability to desorption of graphene in CH3CHO sensing, and has great application potential in respiratory VOCs detection, environmental harmful gas detection, etc.

Application of Sensing Devices in the Detection of Oral, Pulmonary, and Gastrointestinal Diseases

Biomedical sensing technology is developing at a tremendous pace and is expected to become an effective clinical tool for the diagnosis and monitoring of human health. The development of sensing devices has successfully transformed the specific sensor prototype designed in the laboratory into a commercially feasible clinical disease detection device. Recently, sensing devices have been accelerated and extended to various fields beyond disease detection, including the measurement of gastrointestinal physiological parameters such as pH, VOC detection, small-molecule gas sensing, and noninvasive screening of oral and lung diseases such as oral cancer, gastric cancer, and other major diseases. In this review, the applications of sensors and electronic nose devices in the diagnosis and monitoring of oral, pulmonary, and gastrointestinal diseases are reviewed, as well as the design and application of sensor materials in disease markers and in situ detection. This article also introduces the practical application of sensing devices in human disease detection, critically analyzes their detection mechanisms and clinical utility, and discusses their future development in medicine. We believe that this review will help readers, especially practitioners in the medical field, provide ideas for the development of sensing devices.

Sawtooth wave temperature modulation measurement method for recognizing five kinds of VOCs based on ZnO gas sensor

The dynamic measurement of temperature modulation can improve selectivity of semiconductor gas sensor. However, most of dynamic measurements only have one or two characteristic peaks, which makes great difficulties to subsequent gas identification. To solve this problem, this paper increased the characteristic peaks of dynamic response of ZnO sensor by adjusting the period of sawtooth wave, and expanded the difference of dynamic responses of different gases, so as to realize the efficient detection and identification of different VOCs. The experiments find that when the period of sawtooth wave is 90 s, the dynamic responses of the sensor to the five gases of ethanol, toluene, 2-butanone, acetone and ethyl ether are better, with many characteristic peaks. Combined with support vector machine, the recognition rate reaches 100 %, and it is difficult to misjudge the type and concentration of gas, which improves the selectivity.

Development of dual-photoionization ion trap mass spectrometry and its application for direct analysis of VOCs in fruit aroma

Photoionization-ion trap mass spectrometry (PI-ITMS) is one of the major directions of mass spectrometer miniaturization because of its great potential for rapid on-site VOCs detection in many cases. Traditionally, PI has always been investigated separately and is restrained by ion transmission structure, so a new structure needs to be designed and investigated for simplifying and improving the ion transmission efficiency. Interestingly, our preliminary experiments found that the signal intensity and mass range can be effectively improved by combing atmospheric pressure photoionization (APPI) and low-pressure photoionization (LPPI). Therefore, in this paper, a new dual photoionization - ion trap mass spectrometry (DPI-ITMS) was developed, explored and used to directly analyze complex VOCs. Compared with traditional single PI configuration, it presents two obvious merits: (1) simplified ion transmission structure, eliminating the need to use deflection electrode to repel ions and avoiding breakdown risk. (2) some missing/weak low m/z ion mass spectral peaks in APPI and some high m/z ion mass spectral peaks in LPPI were improved in DPI detection mode. In addition, by combining multivariate statistical analysis, we preliminary achieved in differentiating fruit types and maturity level. In summary, we concluded that the developed DPI-ITMS has moderate detection sensitivity (limited by the homemade ITMS, 0.1–1 ppmv with RSD of 6.36 %), and the DPI-ITMS configuration can be referenced by future PI-MS, and this study also provides a high-throughput, simple, noninvasive and no chemical contamination solution for analyzing main VOCs in fruit aroma.

Exploring the adsorption behavior of O-containing VOCs in human breath on a B2N monolayer using DFT simulations.

We conducted a DFT study of the B2N monolayer pairing with the O-containing volatile organic compounds (O-containing VOCs) in exhaled breath, viz. acetone, ethanol, methanol, and formaldehyde. The most stable configuration of O-containing VOCs on the B2N sheet is also considered and compared with the adsorbed H2O on the desired monolayer. The adsorption energy when both water and O-containing VOC molecules are present shows that the O-containing VOC molecules can be effectively adsorbed on the surface of B2N while maintaining stability in the presence of water molecules. The adsorption energy values for the most stable acetone/B2N, ethanol/B2N, methanol/B2N, formaldehyde/B2N, and H2O/B2N complexes are -0.50, -0.61, -0.56, -0.87, and -0.41 eV, respectively. The computed recovery time at 300 K for the desired complexes without radiation ranges from 2.6 × 10-4 to 440 seconds. Using non-equilibrium Green's function, the electrical current is calculated separately as a function of applied bias voltage of 0-2 volts for each O-containing VOC. The percentage increase in the band gap of the desired B2N sheet is 5, 19, 25, and 35% upon interaction with methanol, formaldehyde, acetone, and ethanol, respectively. These findings highlight the notable sensing capabilities of the desired B2N sheet when compared to other sensors such as the BC6N sheet, pristine MoSe2 monolayer, and phosphorene. Moreover, these findings may have implications for the potential use of B2N nanosheets for the detection of O-containing VOCs in human breath, enabling early disease diagnosis.

Detection and analysis of VOCs in Chili pepper based on HS-SPME-GC×GC-TOFMS and HS-SPME-GC-MS techniques

Detection and analysis of VOCs in Chili pepper based on HS-SPME-GC×GC-TOFMS and HS-SPME-GC-MS techniques

Pristine and aurum-decorated tungsten ditellurides as sensing materials for VOCs detection in exhaled human breath: DFT analysis.

In this research, we employed density functional theory (DFT) to evaluate the sensing capabilities of transition metal-decorated two-dimensional WTe2 TMDs nanosheets toward VOCs such as (acetone, ethanol, methanol, toluene, and formaldehyde) that are exhaled in human breath and can serve as potential biomarkers for detecting specific physiological disorders and also gases interfering in exhaled breath (CO2 and H2O) detection. Au can be physically decorated onto the surface of WTe2. We analyzed the density of states (DOS), adsorption energy, charge transfer, and sensing behavior. The pristine WTe2 monolayer, exhibiting a semiconductor characteristic with a band gap of 0.63 eV, transitions to a metallic state upon Au-decoration, due to its actively stable nature and promising negative adsorption energy value, it triggers the emergence of novel states within the DOS. Computed adsorption energies of VOCs range from -0.08 to -0.57 eV, with greater interaction distances confirming the physisorption behavior of these VOCs biomarkers on Au-WTe2. Ethanol displays greater sensitivity compared to other considered VOCs. Au-WTe2 exhibits promising potential as a viable option for detecting VOCs in breath analysis applications at room temperature, owing to its excellent adsorption capabilities and sensitivity. Overall, our results highlight aurum-decorated tungsten ditelluride's potential as an efficient nano-sensor for detecting VOCs associated with early-stage lung cancer diagnoses.

Multimodal Fusion Induced Attention Network for Industrial VOCs Detection

Multimodal Fusion Induced Attention Network for Industrial VOCs Detection