Detection Of Volatile Organic Compounds Research Articles

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Early diagnosis of lung cancer, the leading cause of cancer-related death worldwide, is critical for reducing mortality rate. However, current diagnostic methods are invasive, time-consuming, costly, and may not always provide accurate diagnoses. For early diagnosis, recent research has focused on noninvasive approaches, including the detection of volatile organic compounds (VOCs) in human exhaled breath. Changes in the composition and concentration of VOCs in exhaled breath may indicate lung cancer, and this approach offers several advantages over traditional diagnostic methods. Moreover, the combination of a breath gas sensing system and machine learning algorithms provides a more accurate diagnosis. In this study, for the early diagnosis of lung cancer, a breath analysis system was developed using a gas sensor array and deep learning algorithm. The breath analysis system was designed to detect multiple VOCs in exhaled breath using ten semiconductor metal oxide (SMO), one photoionization detector (PID), nine electrochemical (EC) gas sensors. In total, 181 clinical breath samples (from 74 healthy controls and 107 lung cancer patients) were collected and analyzed using a 1D convolutional neural network (CNN) algorithm. The results showed an overall accuracy of 97.8% in classifying healthy controls and lung cancer patients using a complete clinical dataset. Through a comparison of the single-sensor type data and multimodal sensor data and performance analysis of three different deep learning models (multilayer perceptron, recurrent neural network, and CNN), we validated the potential of the breath analyzer with a multimodal sensor system and a 1D CNN as a lung cancer diagnostic device.

Preconcentrating sensor systems toward indoor low-concentration VOC detection by goal-oriented, sequential, inverse design strategy

The techniques capable of accurately and effectively detecting low-concentration volatile organic compounds (VOCs) are promising for environmental contaminant monitoring and non-invasive disease diagnosis. However, low-concentration measurement and selective quantification of multiple components are two major challenges for VOC detection. This work proposes the idea of a sensing system that incorporates preconcentration and multiple sensor arrays to address the two challenges. The design principles and strategies of the sensing system were discussed, as well as the key requirements and challenges of each step of the design process. We further developed a goal-oriented, sequential, inverse design method for an accurate sensing system of indoor VOCs, enabling the realization of high cross-selectivity detection. We also demonstrate the potential and feasibility of the sensing system with a preconcentrator through illustrative examples. The proposed system is expected to be scalable and intermittent, moving it closer to reliable, commercial, and portable near real-time determinations of specific VOC concentrations. It would promote the effectiveness of indoor air quality (IAQ) assessments and interventions.

Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis.

The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors. An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota. The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types. The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.

Association between blood volatile organic aromatic compound concentrations and hearing loss in US adults.

Benzene, ethylbenzene, meta/para-xylene, and ortho-xylene, collectively referred to as benzene, ethylbenzene, and xylene (BEX), constitute the main components of volatile organic aromatic compounds (VOACs) and can have adverse effects on human health. The relationship between exposure to BEX and hearing loss (HL) in the adult U.S. population was aimed to be assessed. Cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) for the years 2003-2004, 2011-2012, and 2015-2016 were analyzed. This dataset included complete demographic characteristics, pure-tone audiometry measurements, and volatile organic compound detection data from the NHANES database. A weighted multivariate logistic regression model was employed to investigate the associations between blood BEX concentrations HL, low-frequency hearing loss (SFHL), and high-frequency hearing loss (HFHL). 2174 participants were included, with weighted prevalence rates of HL, SFHL, and HFHL being 46.81%, 25.23%, and 45.86%, respectively. Exposure to benzene, ethylbenzene, meta/para-xylene, and ortho-xylene, and cumulative BEX concentrations increased the risk of hearing loss (odds ratios [ORs] were 1.36, 1.22, 1.42, 1.23, and 1.31, respectively; all P < 0.05). In the analysis with SFHL as the outcome, ethylbenzene, m-/p-xylene, o-xylene, benzene, and overall BEX increased the risk (OR 1.26, 1.21, 1.28, 1.20, and 1.25, respectively; all P < 0.05). For HFHL, exposure to ethylbenzene, m-/p-xylene, o-xylene, benzene, and overall BEX increased the risk (OR 1.36, 1.22, 1.42, 1.22, and 1.31, respectively; all P < 0.05). Our study indicated that a positive correlation between individual or cumulative exposure to benzene, ethylbenzene, meta/para-xylene, and ortho-xylene and the risk of HL, SFHL, and HFHL. Further research is imperative to acquire a more comprehensive understanding of the mechanisms by which organic compounds, notably BEX, in causing hearing loss and to validate these findings in longitudinal environmental studies.

Hybrid plasmonic aerogel with tunable hierarchical pores for size-selective multiplexed detection of VOCs with ultrahigh sensitivity

Sensitive and rapid identification of volatile organic compounds (VOCs) at ppm level with complex composition is vital in various fields ranging from respiratory diagnosis to environmental safety. Herein, we demonstrate a SERS gas sensor with size-selective and multiplexed identification capabilities for VOCs by executing the pre-enrichment strategy. In particular, the macro-mesoporous structure of graphene aerogel and micropores of metal-organic frameworks (MOFs) significantly improved the enrichment capacity (1.68 mmol/g for toluene) of various VOCs near the plasmonic hotspots. On the other hand, molecular MOFs-based filters with different pore sizes could be realized by adjusting the ligands to exclude undesired interfering molecules in various detection environments. Combining these merits, graphene/AuNPs@ZIF-8 aerogel gas sensor exhibited outstanding label-free sensitivity (up to 0.1 ppm toluene) and high stability (RSD=14.8%, after 45 days storage at room temperature for 10 cycles) and allowed simultaneous identification of multiple VOCs in a single SERS measurement with high accuracy (error < 7.2%). We visualize that this work will tackle the dilemma between sensitivity and detection efficiency of gas sensors and will inspire the design of next-generation SERS technology for selective and multiplexed detection of VOCs.

Controlled assembly of novel Fe2O3/MgFe2O4 nanotubular array structures for rapid detection of dibutylamine

The development of an efficient, fast, and real-time sensor for detecting dibutylamine (C8H19N) gas remains challenging. In this study, a simple controllable and environmentally friendly solvothermal method was used to construct novel Fe2O3/MgFe2O4 nanotube array structures with heterointerfaces. The array structure exhibits a relatively large specific surface area and high-activity heterointerfaces. These features greatly promote the diffusion and adsorption of gas molecules onto the sensitive layer, effectively increasing the surface-adsorbed oxygen species and active sites of the sensing material. The sensing performance of the Fe2O3/MgFe2O4 array structure for C8H19N gas is greatly improved due to the synergistic effect of these multiple factors. The sensor showed a high response of 92.6 to 100 ppm C8H19N at 170 °C, with a short response time of 3.6 s and an ultra-low detection limit of 10 ppb. The sensitization mechanism of Fe2O3/MgFe2O4 to C8H19N was investigated by combining theoretical calculations and experimental techniques. This study broadens the application of gas sensors for the detection of volatile organic compounds (VOCs).

MOF-derived Co3O4-ZnO heterostructure for 3-methyl-1-butanol detection

Microbial volatile organic compounds (MVOCs) detection with a fast response and high selectivity is barely studied. The challenge is developing a material that matches these factors in different sensing conditions, such as operating temperature and humidity. In this study, we searched for a better way to improve the gas sensing properties of cobalt oxide (Co3O4), synthesizing a MOF-derived (ZIF-67-ZIF-8) p-n heterojunction of Co3O4-ZnO and varying the molar concentrations of these metals. The Co3O4 was synthesized through a mixture of cobalt(II) nitrate hexahydrate and 2-methylimidazole in a simple process at room temperature, forming the ZIF-67, which was then calcinated. The Co3O4-ZnO and ZnO-Co3O4 heterostructures were synthesized by adding zinc(II) nitrate hexahydrate to produce ZIF-8 and create a heterojunction with ZIF-67, followed by calcination. The Co3O4-ZnO sample exhibited higher sensing performance than pure Co3O4 and the heterostructure ZnO-Co3O4. In this case, Co3O4-ZnO exhibited a higher response of 14.6 to 3-methyl-1-butanol (3M1B) with a selectivity ratio of 2.79. These findings could improve food control by monitoring the MVOCs produced by bacteria, such as Pseudomonas spp, in the spoilage process of shrimp. Furthermore, under 65% of relative humidity, this sensor demonstrated a response of 10.4. Therefore, improving the Co3O4 performance as a gas sensor was achievable with a p-n heterojunction of zinc and cobalt, indicating a suitable response under different conditions.

Stimuli-Responsive Properties of a Zinc(II) Salen-Type Schiff-Base Complex and Vapochromic Detection of Volatile Organic Compounds.

This contribution reports, through a combined thermogravimetric analysis, differential scanning calorimetry, UV-vis, powder X-ray diffraction, and Rietveld refinement analysis, on the stimuli-responsive chromic properties of a substituted Zn(salmal) Schiff-base Lewis acidic complex with unique and distinct thermo- and vapochromic characteristics. The solid complex obtained in air or by evaporation of the solvent from their THF solutions shows a marked thermochromism associated with a phase transition, unusually triggered by the reversible desorption/adsorption of one lattice water molecule. In contrast, the anhydrous solid, achieved from THF solutions of the complex by evaporation of the solvent under anhydrous conditions, behaves very differently as it does not show any absorption of water or thermochromism and exhibits varied vapochromic properties. Detection of volatile organic compounds having Lewis basicity is demonstrated by using the anhydrous solid or the related cast films on glass or paper substrates. In both cases, a marked vapochromism is observed upon exposure to vapors of various volatile species and involves well-defined optical absorptions and naked-eye color changes, also allowing the discrimination of primary aliphatic amines. Vapochromic behavior with the formation of stable, stoichiometric adducts is also demonstrated for both the solid obtained in air and the anhydrous solid or for the related cast films after exposure to vapors of pyridine.

Volatile organic components as Covid-19 biomarkers: a systematic review

In March 2020, the WHO declared the novel Coronavirus pandemic with 118,000 cases, 4.2 thousand deaths, and 114 affected countries. Amidst the absence of effective treatments and vaccines, scientists focused on diagnosing and preventing transmission. Pathogenic agent infections induce cellular behavior changes, crucial for developing diagnostic techniques. A systematic review, following PRISMA guidelines, investigated COVID-19 and volatile organic compound (VOC) biomarkers. From 35 selected articles, two main VOC detection approaches emerged: trained dogs and chemical-analytical tools with artificial intelligence. VOC detection proved comparable to the gold standard PCR test for COVID-19 diagnosis. While not a replacement, VOC detection complements PCR testing, offering a unique health approach. Overall, the review highlights the efficacy of VOC detection, especially in areas with limited PCR access. However, these techniques should supplement other preventive measures like mask-wearing, social distancing, and hand hygiene.

Electrostatically formed nanowire (EFN) transistor—An ultrasensitive VOC and gas sensor

The perpetual need for high-performance volatile organic compound (VOC) sensors remains prevalent across diverse sectors including environmental health monitoring, industrial operations, and medical diagnostics. Within this context, the electrostatically formed nanowire (EFN) sensor, a silicon-on-insulator-based multiple-gate field-effect transistor, is an ultrasensitive and selective VOC and gas sensing platform. Unlike conventional silicon nanowires (also known for their superior sensitivity to chemical species), in EFN, the nanowire is defined electrostatically post-fabrication through appropriate biasing of the surrounding gates. The fabrication of the EFN leverages established CMOS compatible silicon processing technologies, facilitating the production of inexpensive, scalable, and robust sensors. By precisely controlling gate biases, a conductive channel with a tunable diameter is formed, allowing for the formation of nanowire with diameter below 20 nm. The adjustable size and shape of the nanowire offer tunable sensing parameters, including sensitivity, limit of detection, and dynamic range. The multiple parameters also yield a unique fingerprint for each VOC, thus enabling selective detection of VOCs. By simply altering the biasing configuration, a single EFN sensor can achieve high sensitivity and a broad dynamic range, which is limited in the case of physically defined silicon NW sensors. This review provides a comprehensive overview encompassing the EFN sensor's design, fabrication considerations, process flow, electrical characterization methods, sensing performances to VOCs, and gases at room temperature. Moreover, the scope of advanced sensor designs with array of EFN sensors and integrated heaters is also discussed. Finally, some future perspectives of this technology are presented.

Optimized Detection of Volatile Organic Compounds Utilizing Durable and Selective Arrays of Tailored UiO-66-X SURMOF Sensors.

Metal-organic frameworks (MOFs), with their well-defined and highly flexible nanoporous architectures, provide a material platform ideal for fabricating sensors. We demonstrate that the efficacy and specificity of detecting and differentiating volatile organic compounds (VOCs) can be significantly enhanced using a range of slightly varied MOFs. These variations are obtained via postsynthetic modification (PSM) of a primary framework. We alter the original MOF's guest adsorption affinities by incorporating functional groups into the MOF linkers, which yields subtle changes in responses. These responses are subsequently evaluated by using machine learning (ML) techniques. Under severe conditions, such as high humidity and acidic environments, sensor stability and lifespan are of utmost importance. The UiO-66-X MOFs demonstrate the necessary durability in acidic, neutral, and basic environments with pH values ranging from 2 to 11, thus surpassing most other similar materials. The UiO-66-NH2 thin films were deposited on quartz-crystal microbalance (QCM) sensors in a high-temperature QCM liquid cell using a layer-by-layer pump method. Three different, highly stable surface-anchored MOFs (SURMOFs) of UiO-66-X obtained via the PSM approach (X: NH2, Cl, and N3) were employed to fabricate arrays suitable for electronic nose applications. These fabricated sensors were tested for their capability to distinguish between eight VOCs. Data from the sensor array were processed using three distinct ML techniques: linear discriminant (LDA), nearest neighbor (k-NN), and neural network analysis methods. The discrimination accuracies achieved were nearly 100% at high concentrations and over 95% at lower concentrations (50-100 ppm).

Preference of Pentalonia nigronervosa for infected banana plants tends to reverse after Banana bunchy top virus acquisition

Pentalonia nigronervosa Coquerel (Hemiptera: Aphididae) is the vector of the Banana Bunchy Top Virus (BBTV), the most serious viral disease of banana (Musa spp.) in the world. Before acquiring the virus, the vector is more attracted to infected banana plants in response to the increased emissions of volatile organic compounds (VOCs). Here, we test the hypothesis that BBTV acquisition directly modifies the preference of P. nigronervosa for infected banana plants, and that the change in preference results from the alteration of the organs linked to the VOC detection or to the behaviour of the vector. We found that the preference of P. nigronervosa for infected banana plants reverses after virus acquisition in dessert banana, while it remains similar between healthy and infected banana plants before and after the acquisition of BBTV. At the same time, aphids reared on infected bananas had smaller forewing areas and hind tibia length than aphids reared on healthy bananas, although the number of secondary rhinaria on the antennae was lower on dessert banana-reared aphids than plantain-reared aphids, this was not affected by the infection status of the aphid. These results support the "vector manipulation hypothesis—VMH" of pathogens to promote their spread. They have implications for the BBTV management.

Hydrophobic MOF/PDMS-Based QCM Sensors for VOCs Identification and Quantitative Detection in High-Humidity Environments.

Metal-organic frameworks (MOFs) have great potential in quartz crystal microbalance (QCM) platforms for volatile organic compound (VOCs) detection and recognition due to their unique properties. However, the MOFs' hydrophilicity degrades performance in high-humidity environments, limiting reliable VOC sensing in complex environments. Herein, we propose a novel VOC virtual sensor array (VSA) using a single QCM sensor with an adsorption layer composed of MIL-101(Cr) MOF and polydimethylsiloxane (PDMS), realizing stable sensing and accurate identification for different VOCs under various relative humidity (RH) conditions. The hydrophobic PDMS layer improves the moisture resistance of the sensor to 4 and 14 times in terms of shifts in resonant frequency and scattering parameters, respectively. In addition, performance is maintained over 2 days of water treatment, demonstrating superior water resistance. The highest sensitivity of 2.68 mdB ppm-1 is achieved for isopropanol detection, with the lowest limit of detection of 20.06 ppm for acetone. Combining resonant signals and lumped parameters, the proposed VSA technique effectively discriminates four VOCs (ethanol, 2-propanol, acetone, and acetonitrile) with a high accuracy of 95.3% under both 60% and 90% RH backgrounds. The studies provide a promising solution for reliable low-concentration VOC detection using QCM sensors in high-humidity environments such as underground spaces.

Visual Recognition of Volatile Organic Compounds by Photonic Nose Integrated with Multiple Metal-Organic Frameworks.

The photonic nose inspired by the olfactory system is an integrated detection platform constructed by multiple sensing units as channels. However, in the detection of volatile organic compounds (VOCs), the sensing results that cannot be directly readable and the poor ability to distinguish analytes with similar chemical properties are the main challenges faced by this sensor. Here, 8 metal-organic frameworks (MOF)-based photonic crystals are used as the basic sensing units to construct a photonic nose detection platform. The microscopic adsorption of VOCs by MOFs enables the photonic crystals (PCs) to produce macroscopic structural color output, and further makes the photonic nose have specific color fingerprints for different VOCs, the response time of all PCs to VOCs can be within 1s. Through the color fingerprint, the visual identification of VOCs produced by 5 common solvent vapors is realized, and 9 VOCs with similar chemical properties are further distinguished. In addition, the application potential of the photonic nose in the actual environment is verified by identifying different contents of benzene in the paint. It is envisaged that the MOF-based photonic nose has great reference value for the development of intelligent and multi-component synergistic functional gas sensors.

Self-cleaning SERS sensor based on flexible Ni3S2/MoS2@Ag@PDMS composites for label-free multiplex volatile organic compounds detection

Self-cleaning SERS sensor based on flexible Ni3S2/MoS2@Ag@PDMS composites for label-free multiplex volatile organic compounds detection

Review of Predator Emitted Volatile Organic Compounds and Their Potential for Predator Detection in New Zealand Forests

This review covers the volatile organic compounds (VOCs) emitted by the bodies and secretions of introduced mammalian predators in New Zealand forests, with a specific focus on mice, rats, ferrets, stoats, and possums. The primary aim is to compile information on these VOCs and assess the potential of exploiting these VOCs as unique biomarkers for predator detection in the forest. While a universal VOC has yet to be identified, the review discusses various VOCs associated with predators and their potential as unique biomarkers for detecting the presence of predators, including 2-heptanone, 4-heptanone, 2-octanone, and benzaldehyde. Furthermore, a brief overview of different VOC detection techniques is provided, connecting the selection of biosensing techniques with the detection of volatile biomarkers. Further research and advancement in the biosensing field hold substantial potential to enhance biomarker detection technologies and elevate predator management strategies within New Zealand forests.

Design and experiment of high-field asymmetric waveform ion mobility spectrometry chip based on an integrated temperature control printed circuit board structure.

During the detection of volatile organic compounds (VOC) using high-field asymmetric waveform ion mobility spectrometry (FAIMS), the ambient temperature significantly impacts the accuracy of planar FAIMS. To mitigate the influence of ambient temperature on detection accuracy and enhance resolution, a FAIMS system based on the inner impedance characteristics of a printed circuit board (PCB) was designed for temperature control. This study, conducted under standard atmospheric pressure, aimed to assess the signal stability of a planar FAIMS instrument with and without temperature control, and the effect of temperature change on the detection ability of acetone, ethanol, and their mixture was studied using PCB self-heating. Experimental results demonstrated that the base noise in FAIMS with temperature control was 0.2pA, whereas that in FAIMS without temperature control was 1.8pA. Notably, with increasing temperature, the detection ability of FAIMS changes accordingly. The optimal relative detection ability of acetone was observed when the electrode plate was heated to 45°C under an electric field of 15 kV/cm. This study provides a novel approach to improve the resolving power of FAIMS systems and their signal-to-noise ratio. The utilization of a PCB-based temperature control proved effective in stabilizing FAIMS signal characteristics and optimizing detection capabilities, particularly for VOCs such as acetone. These findings have significant implications for improving the accuracy and resolving power of FAIMS systems in VOC detection applications.

Artificial nose of scalable plasmonic array gas sensor for Multi-Dimensional SERS recognition of volatile organic compounds

Gas sensing is one kind of promising analytical technique for high-flux screening and evaluation. Volatile organic compounds (VOCs) analysis by surface-enhanced Raman spectroscopy (SERS) is arising but usually focuses on a single species of VOCs due to the limitations of complexity, stability, and accuracy in practical applications. Canine animals have a sensitive sense of smell due to their large number of olfactory cells. Inspired by this, here we innovatively integrate scalable plasmonic arrays and multi-dimensional chemometrics for simultaneous SERS detection of multiple food-borne VOCs. Both direct and indirect SERS strategies are integrated by coating the array surface with MOFs or different monolayers to provide multi-dimensional recognition and readouts. Four species of VOCs including bacterial metabolites, hydrogen sulfide, aldehyde, and biogenic amine were selectively captured and produced unique SERS spectral profiles. The multi-dimensional outputs greatly increase the dimensionality of VOC fingerprints, thereby significantly improving the sensitivity, reliability, and accuracy for freshness discrimination and forecasting in real food smell evaluation. By virtue of multi-dimensional SERS recognition and machine learning, the array gas sensor shows higher accuracy than traditional SERS analysis and is a promising platform for in situ, real-time, and high-flux gas sensing and inspection.

Ab initio investigation of functionalization of titanium carbide Ti3C2 MXenes to tune the selective detection of lung cancer biomarkers

Selected volatile organic compounds (VOCs), such as benzene (C6H6), cyclohexane (C6H12), isoprene (C5H8), cyclopropanone (C3H4O), propanol (C3H8O), and butyraldehyde butanal (C4H8O), in exhaled human breath can act as indicators or biomarkers of lung cancer diseases. Detection of such VOCs with low density would pave the way for an early diagnosis of the disease and thus early treatment and cure. In the present investigation, the density-functional theory (DFT) is applied to study the detection of the mentioned VOCs on Ti3C2TX MXenes, saturated with the functional groups Tx = O, F, S, and OH. For selectivity, comparative sensing of other interfering air molecules from exhaled breath, such as O2, N2, CO2, and H2O is further undertaken. Three functionalization (Tx = O, F, and S) are found promising for the selective detection of the studied VOCs, in particular Ti3C2O2 MXenes has shown distinct sensor response toward the C5H8, C6H6, C6H12, and C3H4O. The relatively strong physisorption (Eads≅-0.45to-0.65eV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{ads}\\cong -0.45 to-0.65 {\ ext{eV}}$$\\end{document}), triggered between VOC and MXene due to an enhancement of van der Waals interaction, is found responsible to affect the near Fermi level states, which in turn controls the conductivity and consequently the sensor response. Meanwhile, such intermediate-strength interactions remain moderate to yield small desorption recovery time (of order τ≅μs-ms)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au \\cong \\mu {\ ext{s}}-{\ ext{ms}})$$\\end{document} using visible light at room temperature. Thus, Ti3C2O2 MXenes are found promising candidate material for reusable biosensor for the early diagnosis of lung cancer diseases through the VOC detection in exhaled breath.

A Metal-Free Triazacoronene-Based Bimodal VOC Sensor.

Developing suitable sensors for selective and sensitive detection of volatile organic compounds (VOCs) is crucial for monitoring indoor and outdoor air quality. VOCs are very harmful to our health upon inhalation or contact. Bimodal sensor materials with more than one transduction capability (optical and electrical) offer the ability to extract complementary information from the individual analyte, thus improving detection accuracy and performance. The privilege of manipulating the optoelectronic properties of the polycyclic aromatic hydrocarbon-based semiconducting materials offers rapid signal transduction in multimodal sensing applications. A thiophene-functionalized triazacoronene (TTAC) donor-acceptor-donor (D-A-D) type sensor is reported here for VOC sensing. The single-crystal X-ray structure analysis of the TTAC revealed that a distinctive supramolecular polymer architecture was formed because of cooperative π-π and intermolecular D-A interactions and exhibited rapid signal transduction upon exposure to specific VOCs. The TTAC-embedded green luminescent paper-based test strip exhibited an on-off fluorescence response upon nitrobenzene vapor exposure for 120 s. The selective and rapid response is due to the fast photoinduced electron transfer, as is evident from the time-resolved excited-state dynamics and density functional theory studies. The thick-film-based prototype chemiresistive sensor detects harmful VOCs in a custom-made gas sensing system including benzene, toluene, and nitrobenzene. The TTAC sensor rapidly responds (200 s) at relatively low temperatures (180 οC) compared to other reported metal-oxide-based sensors.