Single-Atom Cu Anchored on a UiO-66 Surface-Enhanced Raman Scattering Sensor for Trace and Rapid Detection of Volatile Organic Compounds

<p id="p00015">Volatile organic compounds (VOCs) in human exhaled breath can act as biochemical indicators for the objective determination and evaluation of physiological or pathological conditions. In humans, the pathophysiology associated with malignant tumors causes metabolic changes that alter the types and concentrations of VOCs in exhaled breath. However, as exhaled breath has a complex composition and low VOC content, meeting the direct detection requirements of analytical instruments is difficult. Therefore, the direct detection of VOCs in exhaled breath requires the development of a simple, fast, and efficient enrichment technique. In this study, graphitic carbon nitride (g-C₃N₄) and the metal-organic framework UiO-66 were combined to prepare a three-dimensional nanoflower-like g-C₃N₄/UiO-66 solid-phase microextraction (SPME) coating. The g-C₃N₄/UiO-66 composite was characterized using Fourier transform infrared spectroscopy, thermogravimetric analysis, X-ray diffraction, and scanning electron microscopy. UiO-66, which exhibited a regular ortho-octahedral structure with sharp corners, was embedded among g-C₃N₄ nanoflowers, and the surface was encapsulated in g-C₃N₄ to form a hydrophobic barrier. Five biomarkers (benzene, toluene, ethylbenzene, <italic>o</italic>-xylene, and decanal) in the exhaled breath of patients with lung cancer were enriched using the developed SPME fiber and then detected using gas chromatography-mass spectrometry. The optimum extraction time and temperature of the nanoflower-like SPME fiber were 10 min and 60 ℃, respectively, whereas the optimum desorption time and temperature were 3 min and 260 ℃, respectively. The adsorption efficiencies of the g-C₃N₄/UiO-66 composite coating, a single UiO-66 coating, and a 30 μm polydimethylsiloxane commercial coating were compared under optimal conditions. The five VOCs showed good linearity in the investigated concentration ranges, with correlation coefficients of 0.9919-0.9977, limits of detection of 0.16-4.50 μg/L, and limits of quantification of 0.54-9.18 μg/L. The intra- and inter-day precisions for six parallel determinations with the SPME fiber were 3.3%-7.3% and 4.0%-6.6%, respectively. Owing to the presence of nanoflower-like interlayer channels and g-C₃N₄ micropores in g-C₃N₄/UiO-66, the composite coating exhibited a high enrichment efficiency and good repeatability. The spiked recoveries of the five VOCs were in the range of 84.6%-113.2%. Finally, the method was applied to analyze exhaled breath for the detection of lung cancer. The benzene contents in the exhaled breath of both healthy individuals and patients with lung cancer were generally low. However, the toluene contents differed significantly, with patients with lung cancer having much higher levels than healthy individuals. The established method is expected to provide a reference for the noninvasive early diagnosis of lung cancer.

Endoscopic surveillance for Barrett’s esophagus (BE) is invasive but remains the standard modality for early diagnosis of esophageal adenocarcinoma (EAC) and intervention. Human breath contains an array of volatile organic compounds (VOC) that change in disease conditions. VOC detection provides a potential source of biomarkers for non-invasive, real-time identification of EAC. This study aimed to characterize a VOC-profile applicable to the detection of EAC and to provide pilot data to design a future validation trial. Methods Breath samples were collected in our endoscopy unit from BE, EAC, and control patients. Samples were collected in FlexFoil bags (SKC ltd) using previously standardized methods. Hydrogen, methane and other VOCs were quantified by QuinTron BreathTracker® and selected-ion flow tube mass spectrometry (SIFT-MS, Syft®) respectively. 250 reported cancer-related VOCs were selected for analysis. Non-parametric tests were used to identify candidate VOCs, and logistic regression analysis was then applied to determine the best predictors for EAC. Receiver Operating Characteristic (ROC) Curves were developed to determine the sensitivity and specificity of the model. Results 68 individuals were enrolled in the study (Controls, n = 37; BE, n = 21; EAC, n = 10). 8 VOCs were identified with significant concentration differences between the three groups: Trimethylbenzene (3 iso-forms), Dimethyl Sulfide, 4-isopropyl toluene, 1-butanol, trichloroethylene, hydrogen sulfide, methyl mercaptan, p-isopropenyl toluene. Logistic regression analysis of these 10 compounds demonstrated predictive probability of EAC from other groups with ROC curves calculating an area under the curve of 0.85. Conclusion Previous studies have supported the utility of VOCs in exhaled breath as non-invasive real-time tests for the identification of some other cancer types. This pilot study has identified potential VOCs which might identify individuals with EAC. A larger study will be needed to validate and confirm these findings.

Metal-organic frameworks (MOFs): A review of volatile organic compounds (VOCs) detection.

BackgroundExisting biomarkers including cerebrospinal fluid and positron emission tomography for Alzheimer’s disease (AD) diagnosis are relatively invasive and expensive. Application of exhaled breath collection and volatile organic compound (VOC) detection for AD diagnosis remains unclear.MethodIn this cross‐sectional study, high‐pressure photon ionization time‐of‐flight mass spectrometry (HPPI‐ToF‐MS) was used to detect VOCs from breath in three datasets and patients diagnosed as Parkinson’s disease (PD). Plasma p‐tau181 was quantified using ultra‐sensitive Simoa technology. GC x GC TOF‐MS was used to detect VOCs in plasma. Near‐infrared spectroscopy was carried out to elucidate alterations of brain metabolism.ResultA total of 861 participants were enrolled. Patients with AD were diagnosed according to the 2011 NIA‐AA diagnosis criteria and further confirmed in dataset 1 (n=30) based on the A+T+N+ framework. Datasets 1 and 2 were used to recognize the different VOCs between patients with AD and controls. The diagnostic power of VOCs for AD was confirmed separately in train datasets 1 and 2 and independent test dataset 3. The specific VOCs in AD diagnosis were further compared with patients with PD. Among all participants, no statistical difference was observed in demographic characteristics for dataset 1. Six VOCs, including ethanol (m/z = 46), isopropanol (m/z = 60), chloroethane (m/z = 64), pyrrole/3‐butenenitrile (m/z = 67), 1‐butanol (m/z = 74), and benzene (m/z = 78), were significantly different between the two groups in dataset 1 and were verified in dataset 2 and dataset 3. Ethanol (m/z=46) and pyrrole/3‐butenenitrile (m/z=67) presented areas under the ROC curve (AUCs) values of 0.907 and 0.895, respectively, in datasets 1 and 2 and 0.849 and 0.974, respectively, in dataset 3 (community). The six VOCs were associated with cognitive decline as reflected by neuropsychological tests, five (m/z = 46, 64, 67, 74, and 78) were correlated with plasma p‐tau181 levels, and five (m/z = 46, 60, 67, 74, and 78) showed consistent alteration in plasma. Moreover, five (m/z = 46, 60, 67, 74, and 78) were associated with altered brain connectivity.ConclusionSpecific breath VOCs may be used as simple biomarkers for AD detection.

On-site chemical sensing of compounds associated with security and terrorist attacks is of worldwide interest. Other related bio-monitoring topics include identification of individuals posing a threat from illicit drugs, explosive manufacturing, as well as searching for victims of human trafficking and collapsed buildings. The current status of field analytical technologies is directed towards the detection and identification of vapours and volatile organic compounds (VOCs). Some VOCs are associated with exhaled breath, where research is moving from individual breath testing (volatilome) to cell breath (microbiome) and most recently to crowd breath metabolites (exposome). In this paper, an overview of field-deployable chemical screening technologies (both stand-alone and those with portable characteristics) is given with application to early detection and monitoring of human exposome in security operations. On-site systems employed in exhaled breath analysis, i.e. mass spectrometry (MS), optical spectroscopy and chemical sensors are reviewed. Categories of VOCs of interest include (a) VOCs in human breath associated with exposure to threat compounds, and (b) VOCs characteristic of, and associated with, human body odour (e.g. breath, sweat). The latter are relevant to human trafficking scenarios. New technological approaches in miniaturised detection and screening systems are also presented (e.g. non-scanning digital light processing linear ion trap MS (DLP-LIT-MS), nanoparticles, mid-infrared photo-acoustic spectroscopy and hyphenated technologies). Finally, the outlook for rapid and precise, real-time field detection of threat traces in exhaled breath is revealed and discussed.

<b>Introduction:</b> Monitoring of volatile organic compounds (VOCs) in exhaled breath has been used for lung cancer (LC) diagnosis in recent years. However, a standardized VOC analysis method for candidate VOCs as LC biomarkers has not been developed yet. <b>Aims and objectives:</b> To describe a new strategy for LC diagnosis by fingerprinting VOCs in exhaled breath (EB) using selected ion monitoring (SIM) mode and machine learning algorithms. <b>Methods:</b> In this study, 70 patients with LC and 96 controls without LC were included. Their fresh exhaled breath samples were sampled by lab-made polythiophene solid-phase microextraction (SPME) fiber under dynamic conditions for the VOCs detection.&nbsp;After the sampling process, adsorbed VOCs on fiber were analyzed by gas chromatography-mass spectrometry (GC-MS) using SIM mode between 13-94 m/z. Specific patterns of VOC biomarkers in each m/z chromatogram were found by comparing relevant characteristic retention times (RT) and its areas between LC and control. The performance of each VOC as a biomarker in labeling LC and control individuals was performed using machine learning algorithms. <b>Results:</b> Performance of the model for differentiation LC from control with accuracy, AUC and F Score values were 0.818, 0.816 and 0.817, respectively. <b>Conclusion:</b> The selected ion tracking mode, supported by machine learning algorithms, can successfully differentiate LC patients from non-cancer individuals. <b>Acknowledgments:</b> We thank TUBITAK (113Z672) and T.C. Presidency of Strategy and Budget&nbsp;(2019K12-149080) for their grant supports. The patent-pending of this study was made to the Turkish Patent Institute (# 2021/011862)

Indoor volatile organic compounds (VOCs) have been associated with asthma, but there is little epidemiologic work on ambient exposures, and no data on relationships between respiratory health and exhaled breath VOCs, which is a biomarker of VOC exposure. We recruited 26 Hispanic children with mild asthma in a Los Angeles community with high VOC levels near major freeways and trucking routes. Two dropped out, three had invalid peak expiratory flow (PEF) or breath VOC data, leaving 21. Children filled out symptom diaries and performed PEF maneuvers daily, November 1999-January 2000. We aimed to collect breath VOC samples on asthma episode and baseline symptom-free days, but six subjects only gave samples on symptom-free days. We analyzed 106 breath samples by GC-MS. Eight VOCs were quantifiable in >75% of breath samples (benzene, methylene chloride, styrene, tetrachloroethylene, toluene, m,p-xylene, o-xylene, and p-dichlorobenzene). Generalized estimating equation and mixed linear regression models for VOC exposure-response relationships controlled for temperature and respiratory infections. We found marginally positive associations between bothersome or more severe asthma symptoms and same day breath concentrations of benzene [odds ratio (OR) 2.03, 95% confidence interval (CI) 0.80, 5.11] but not other breath VOCs. Ambient petroleum-related VOCs measured on the same person-days as breath VOCs showed notably stronger associations with symptoms, including toluene, m,p-xylene, o-xylene, and benzene (OR 5.93, 95% CI 1.64, 21.4). On breath sample days, symptoms were also associated with 1-h ambient NO(2), OR 8.13 (1.52, 43.4), and SO(2), OR 2.36 (1.16, 4.81). Consistent inverse relationships were found between evening PEF and the same ambient VOCs, NO(2), and SO(2). There were no associations with O(3). Given the high traffic density of the region, stronger associations for ambient than for breath VOCs suggest that ambient VOC measurements were better markers for daily exposure to combustion-related compounds thought to be causally related to acute asthma. Alternatively, the low sample size of symptom responses (15-21 responses per 108 breath samples) may have led to the nonsignificant results for breath VOCs.

The emergence of MXene, a group of two-dimensional materials, has shown its potential to be used in diverse fields especially in energy storage since 2011. Being inspired by the intriguing properties of MXene used for energy storage, such as the high electrical conductivity and the ability to accommodate ions in the interlayer space with controllable manner, the possibility to accommodate the volatile organic compounds (VOCs) in the interlayer space and to release the entrapped VOCs with controllable approaches have drawn our attention for the potential application on the controlled release of odors. Additionally, due to the abundant termination groups on the surface of MXene, it could also be the promising candidate for the detection of VOCs. The surface functional groups on MXene could not only serve as the active sites for the interaction between MXene and VOC molecules but also provide the potential anchoring points for the surface functionalization, endowing diverse surface properties by the additional termination groups. In this thesis, Ti3C2 MXene is selected to be the main material for the studies of VOC controlled release and detection due to its superior electrical conductivity, versatile surface functional groups and better thermal stability among the MXene materials. With the polar functional group on the pristine Ti3C2 MXene surface, Ti3C2 MXene is first applied to perform the controlled release of polar aroma VOC. The surface of the Ti3C2 MXene is also functionalized with non-polar groups to improve the adsorption of the non-polar VOCs. With the improved adsorption of non-polar VOCs by surface functionalized Ti3C2 MXene, the modified MXene is further applied for the detection of the non-polar VOC. The controlled release of the aroma VOC is monitored by the ex-situ and in-situ X-ray diffraction, which show the significant change of the interlayer distance during the adsorption and desorption of the VOCs. The adsorption mechanism is also proved by the X-ray photoelectron microscopy. The modified Ti3C2 MXene also shows the improve sensing specificity towards the non-polar VOC. These findings offer the insight of Ti3C2 MXene as a potential material used for the controlled release of aroma VOCs and the detection of VOCs.

Analytical evaluation of polymeric CNTs/CuO nanocomposite electrode for the room temperature detection of volatile organic compounds (VOCs)

Since esophageal carcinoma progresses asymptomatically, for many patients the disease is already advanced at the time of diagnosis. The main methods that are currently used to diagnose esophageal carcinoma are upper gastrointestinal radiographic contrast examinations and upper gastrointestinal endoscopy, but early discovery of this disease remains difficult. There is a need to develop a diagnostic method using biomarkers that is non-invasive while both highly sensitive and specific. Exhaled breath was collected from 17 patients with esophageal squamous cell carcinoma (ESCC), as well as 9 control subjects without history of any cancer. For each fasting subject, 1L of exhaled breath was collected in a gas sampling bag. Volatile organic compounds (VOCs) were then extracted from each sample using Solid phase micro-extraction (SPME) fibers and analyzed by gas chromatography-mass spectrometry (GC-MS). Levels of acetonitrile, acetic acid, acetone, and 2-butanone in exhaled breath were significantly higher in the patient group than in the control group (p = 0.0037, 0.0024, 0.0024 and 0.0037, respectively). ROC curves were drawn for these 4 VOCs, and the results for the area-under-the-curve (AUC) indicated that ESCC patients can be identified with a high probability of 0.93. We found distinctive VOCs in exhaled breath of ESCC patients. These VOCs have a potential as new clinical biomarkers for ESCC. The measurement of VOCs in exhaled breath may be a useful, non-invasive method for diagnosis of ESCC.

A breath of fresh air - the potential for COVID-19 breath diagnostics.

Detection of volatile organic compounds (VOCs) has attracted considerable attention for indoor air quality and human breath analysis. Here, resistive-type gas sensors with porous films made of Au-loaded TiO2 nanotubes were developed for the detection of large-sized VOCs. Well-dispersed Au nanoparticles (10 to 20 nm) were photo-chemically deposited on TiO2 nanotubes (80 nm in diameter, 700 nm in length) prepared by a hydrothermal method. The device using the Au-loaded TiO2 nanotubes showed improved sensor responses to ethanol and toluene (50 ppm) in air at 500°C. This suggested that Au nanoparticles deposited on the surface of TiO2 nanotubules without aggregation. We also demonstrated that the device could be used for the detection of large-sized VOC molecule, i.e., 2,6-diisopropylphenol (propofol), an intravenously administered hypnotic drug for induction and maintenance of anesthesia. The Au-loaded TiO2 nanotubes would offer a way to continuously monitor a change in the concentration of VOCs in exhaled air for medical diagnostics.

Carbon-based nanomaterials for the detection of volatile organic compounds: A review

Lung inflammation is associated with a response to pollution or irritants and is reported to release volatile organic compounds (VOCs) in breath, that can be monitored non-invasively. An approach to studying acute lung inflammation is exposure to environmental emissions. This study used short-term exposure to two burning candle types as fine particle (PM2.5) emissions. This was used to discern potential differences in acute airway inflammation among mildly asthmatic individuals. The aim was to explore VOCs linked to lung inflammation by exposure to candle emissions. Seventeen non-smoking asthmatics participated in a randomized controlled double-blind crossover study including two exposure sessions: i) air with candle A emissions (mean PM2.5 54.1 µg/m3), and ii) air with candle B emissions (mean PM2.5 98.4 µg/m3). Emissions were generated and fed into an exposure chamber, where participants were exposed at rest for five hours. Exhaled breath VOCs were assessed before exposure start (0h), immediately after exposure (5h) and the next morning (24h). Ten statistically significant exhaled VOCs showed differences between pre-exposure and immediate post-exposure. Samples collected after 24h resembled baseline samples. For exposure to candle B, there was a distinct differentiation between 0h, 5h and 24h samples, which was not found for candle A. An emission-specific, exhaled breath, multi-VOC profile can detect individuals exposed to inflammation triggers, i.e., candles, suggesting that VOCs could detect acute airway inflammation. This is indicated by a systematic difference in VOCs after 5h in candle B.&#xD;&#xD;Keywords: Candle emissions, Particles, VOCs, Acute respiratory effects, Breath biomarkers.

Lung cancer is a prevalent and life-threatening disease worldwide. The primary diagnostic approach for lung cancer is the utilization of low-dose spiral CT scans. However, repeated scans can expose patients to harmful radiation. Consequently, there is growing interest in exploring alternative methods such as the analysis of exhaled volatile organic compounds (VOCs) for lung cancer detection. In this study, we employed a gas chromatography-mass spectrometry analyzer to identify and quantify a total of 108 VOCs of lung cancer patients. Our objective is to investigate the correlation between VOCs in exhaled breath and lung cancer. Through the application of orthogonal partial least squares-discriminant analysis and correlation analysis, we identified several VOCs, including acetone, ethanol, isopropanol, and ethyl acetate, which exhibited a strong association with lung cancer. Unlike the use of a single marker, our study employed a multi-parameter regression method, resulting in superior accuracy. A diagnostic model based on the neural network algorithm was established, demonstrating an accuracy of 93.5% after screening, surpassing the accuracy before screening at 81%. Furthermore, we optimize the model by incorporating the gender factor, leading to an accuracy exceeding 96%. Numerous studies have demonstrated that the analysis of VOCs in exhaled breath holds significant potential for effectively distinguishing lung cancer patients from healthy individuals. These findings emphasize the potential of respiratory analysis as a novel diagnostic tool for early detection of lung cancer.