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Gastric cancer remains a leading cause of cancer-related mortality, requiring the urgent development of innovative diagnostic tools for early detection. This study presents an integrated infrared spectroscopic electronic nose system, a novel device that combines infrared (IR) spectroscopy and electronic nose (eNose) concepts for analyzing volatile organic compounds (VOCs) in exhaled breath. This system was calibrated using relevant gas mixtures and then tested during a feasibility study involving 26 gastric cancer patients and 32 healthy controls using chemometric analyses to distinguish between exhaled breath profiles. The obtained results demonstrated that the integration of IR spectroscopy and eNose technologies significantly enhanced the accuracy of VOCs fingerprinting via principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA). Distinct differences between the study groups were revealed with an accuracy of prediction of 0.96 in exhaled breath samples. This combined system offers a high sensitivity and specificity and could potetially facilitate rapid on-site testing rendering the technology an accessible option for early screening particularly in underserved populations.

Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer., (© 2024 IOP Publishing Ltd.)

Stress is becoming increasingly commonplace in modern times, making it important to have accurate and effective detection methods. Currently, detection methods such as self-evaluation and clinical questionnaires are subjective and unsuitable for long-term monitoring. There have been significant studies into biomarkers such as HRV, cortisol, electrocardiography, and blood biomarkers, but the use of multiple electrodes for electrocardiography or blood tests is impractical for real-time stress monitoring. To this end, there is a need for non-invasive sensors to monitor stress in real time. This study looks at the possibility of using breath and skin VOC fingerprinting as stress biomarkers. The Trier social stress test (TSST) was used to induce acute stress and HRV, cortisol, and anxiety levels were measured before, during, and after the test. GC-MS and sensor array were used to collect and measure VOCs. A prediction model found eight different stress-related VOCs with an accuracy of up to 78%, and a molecularly capped gold nanoparticle-based sensor revealed a significant difference in breath VOC fingerprints between the two groups. These stress-related VOCs either changed or returned to baseline after the stress induction, suggesting different metabolic pathways at different times. A correlation analysis revealed an association between VOCs and cortisol levels and a weak correlation with either HRV or anxiety levels, suggesting that VOCs may include complementary information in stress detection. This study shows the potential of VOCs as stress biomarkers, paving the way into developing a real-time, objective, non-invasive stress detection tool for well-being and early detection of stress-related diseases.

Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.

Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist ( e.g ., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats ( n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group ( n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health.

Volatile organic compounds (VOCs) in urine are valuable biomarkers for noninvasive disease diagnosis. Herein, a facile coordination-driven modular assembly strategy is used for developing a library of gas-sensing materials based on porous MXene frameworks (MFs). Taking advantage of modules with diverse composition and tunable structure, our MFs-based library can provide more choices to satisfy gas-sensing demands. Meanwhile, the laser-induced graphene interdigital electrodes array and microchamber are laser-engraved for the assembly of a microchamber-hosted MF (MHMF) e-nose. Our MHMF e-nose possesses high-discriminative pattern recognition for simultaneous sensing and distinguishing of complex VOCs. Furthermore, with the MHMF e-nose being a plug-and-play module, a point-of-care testing (POCT) platform is modularly assembled for wireless and real-time monitoring of urinary volatiles from clinical samples. By virtue of machine learning, our POCT platform achieves noninvasive diagnosis of multiple diseases with a high accuracy of 91.7%, providing a favorable opportunity for early disease diagnosis, disease course monitoring, and relevant research.

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer., (© 2021 The Authors. Advanced Healthcare Materials published by Wiley-VCH GmbH.)

Current methods for embryo selection are limited. This study assessed a novel method for the prediction of embryo developmental potential based on the analysis of volatile organic compounds (VOCs) emitted by embryo samples. The study included mice embryos monitored during the pre-implantation period. Four developmental stages of the embryos were tested, covering the period from 1 to 4 days after fecundation. In each stage, the VOCs released by the embryos were collected and examined by employing two different volatolomic techniques, gas-chromatography coupled to mass-spectrometry (GC-MS) and a nanoarray of chemical gas sensors. The GC-MS study revealed that the VOC patterns emanating from embryo samples had statistically different values at different stages of embryo development. The sensor nanoarray was capable of classifying the developmental stages of the embryos. The proposed volatolomics analysis approach for embryos presents a promising potential for predicting their developmental stage. In combination with conventional morphokinetic parameters, it could be effective as a predictive model for detecting metabolic shifts that affect embryo quality before preimplantation.

In vitro studies can help reveal the biochemical pathways underlying the origin of volatile indicators of numerous diseases. The key objective of this study is to identify the potential biomarkers of gastric cancer. For this purpose, the volatilomic signatures of two human gastric cancer cell lines, AGS (human gastric adenocarcinoma) and SNU-1 (human gastric carcinoma), and one normal gastric mucosa cell line (GES-1) were investigated. More specifically, gas chromatography mass spectrometry has been applied to pinpoint changes in cell metabolism triggered by cancer. In total, ten volatiles were found to be metabolized, and thirty-five were produced by cells under study. The volatiles consumed were mainly six aldehydes and two heterocyclics, whereas the volatiles released embraced twelve ketones, eight alcohols, six hydrocarbons, three esters, three ethers, and three aromatic compounds. The SNU-1 cell line was found to have significantly altered metabolism in comparison to normal GES-1 cells. This was manifested by the decreased production of alcohols and ketones and the upregulated emission of esters. The AGS cells exhibited the increased production of methyl ketones containing an odd number of carbons, namely 2-tridecanone, 2-pentadecanone, and 2-heptadecanone. This study provides evidence that the cancer state modifies the volatilome of human cells.

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Background: The analysis of volatile organic compounds (VOCs) collected in breath samples has the potential to be a rapid, non-invasive test to aid in the clinical diagnosis and tracking of chronic conditions such as Parkinson's disease (PD)., Objective: To assess the feasibility and utility of breath sample analysis done, both at point of collection in clinic and when sent away to be analyzed remotely, to diagnose, stratify and monitor disease course in a moderately large cohort of patients with PD., Methods: Breath samples were collected from 177 people with PD and 37 healthy matched control individuals followed over time. Standard clinical data (MDS-UPDRS & cognitive assessments) from the PD patients were collected at the same time as the breath sample was taken, these measures were then correlated with the breath test analysis of exhaled VOCs., Results: The breath test was able to distinguish patients with PD from healthy control participants and correlated with disease stage. The off-line system (remote analysis) gave good results with overall classification accuracies across a range of clinical measures of between 73.6% to 95.6%. The on-line (in clinic) system showed comparable results but with lower levels of correlation, varying between 33.5% to 82.4%. Chemical analysis identified 29 potential molecules that were different and which may relate to pathogenic pathways in PD., Conclusion: Breath analysis shows potential for PD diagnostics and monitoring. Both off-line and on-line sensor systems were easy to do and provided comparable results which will enable this technique to be easily adopted in clinic if larger studies confirm our findings.

Tuberculosis (TB) is an infectious disease that threatens >10 million people annually. Despite advances in TB diagnostics, patients continue to receive an insufficient diagnosis as TB symptoms are not specific. Many existing biodiagnostic tests are slow, have low clinical performance, and can be unsuitable for resource-limited settings. According to the World Health Organization (WHO), a rapid, sputum-free, and cost-effective triage test for real-time detection of TB is urgently needed. This article reports on a new diagnostic pathway enabling a noninvasive, fast, and highly accurate way of detecting TB. The approach relies on TB-specific volatile organic compounds (VOCs) that are detected and quantified from the skin headspace. A specifically designed nanomaterial-based sensors array translates these findings into a point-of-care diagnosis by discriminating between active pulmonary TB patients and controls with sensitivity above 90%. This fulfills the WHO's triage test requirements and poses the potential to become a TB triage test., (© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Cell-cell cross talk is of great importance in cancer research due to its major role in proliferation, differentiation, migration, and influence on the apoptotic pathway. Different cell-cell communication mechanisms have come mainly from proteomic and genomic approaches. In this paper, a new route is reported for cross talk between cancer cells that occurs, even when they are far away from each other. Single-cell and culture analysis shows that upregulation of cancer cells emits hundreds of volatile organic compounds (VOCs) into their headspace. Part of the VOCs remains without any change, disregarding the biological environment around it. The other part of the VOCs is exchanged between monocultures of the cells as well as between co-cultures of the cells with no physical contact between them, leading to different changes in growth than when left on their own. The chemical nature and composition of these VOCs have been determined and are discussed herein. Cell-to-cell cross talk has the advantage of being suitable for transfer/diffusion over relatively long distances. It would thus be expected to serve as a shuttling pad toward the development of advanced approaches that could enable very early detection of cancer and/or monitoring of metastasis and related cancer therapy., (© 2020 The Authors published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Most of the currently used diagnostics for cancerous diseases have yet to meet the standards of screening, as they are insufficiently accurate and/or invasive and risky. In this review, we describe the rationale, the progress made to date, and the potential of analysing the exhaled volatile organic compounds as a pathway for enabling early diagnosis of cancer and, therefore, for achieving better clinical prognosis and survival rates. The review highlights the major advancements made in this field, from fundamentals, up to translational phases and clinical trials, with a special emphasis on sensing platforms based on nanomaterials. The prospects for breath analysis in early cancerous disease are presented and discussed., Competing Interests: Conflict of interest: R. Einoch Amor has nothing to disclose. Conflict of interest: M.K. Nakhleh has nothing to disclose. Conflict of interest: O. Barash has nothing to disclose. Conflict of interest: H. Haick has nothing to disclose., (Copyright ©ERS 2019.)

This review presents and discusses a new frontier for fast, risk-free and potentially inexpensive diagnostics of respiratory diseases by detecting volatile organic compounds (VOCs) present in exhaled breath. One part of the review is a didactic presentation of the overlaying concept and the chemistry of exhaled breath. The other part discusses diverse sensors that have been developed and used for the detection of respiratory diseases ( e.g. chronic obstructive pulmonary disease, asthma, lung cancer, pulmonary arterial hypertension, tuberculosis, cystic fibrosis, obstructive sleep apnoea syndrome and pneumoconiosis) by analysis of VOCs in exhaled breath. The strengths and pitfalls are discussed and criticised, particularly in the perspective in disseminating information regarding these advances. Ideas regarding the improvement of sensors, sensor arrays, sensing devices and the further planning of workflow are also discussed., Competing Interests: Conflict of interest: D. Hashoul has nothing to disclose. Conflict of interest: H. Haick has nothing to disclose., (Copyright ©ERS 2019.)

Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a cohort of 23 volunteers using proton transfer reaction mass spectrometry. The breathing maneuvers embraced distinct depths of respiration, exhalation manners, size of the mouth opening and different sampling distances. Two inhalation modes (normal, relaxed breathing and deep breathing) and two exhalation manners (via smaller and wider lips opening) forming four sampling scenarios were selected. A sampling distance of approximately 2 cm was found to be a reasonable trade-off between sample dilution and requirement of no physical contact of the subject with the analyzer. All four scenarios exhibited comparable measurement reproducibility spread of around 10%. For normal, relaxed inspiration both dead-space and end-tidal phases of exhalation lasted approximately 1.5 s for both expiration protocols. Deep inhalation prolongs the end-tidal phase to about 3 s in the case of blowing via a small lips opening, and by 50% when the air is exhaled via a wide one. In conclusion, non-contact breath sampling can be considered as a promising alternative to the existing breath sampling methods, being relatively close to natural spontaneous breathing.

Background: Detection of diseases via exhaled breath remains an attractive idea despite persisting gaps in understanding the origin of volatile organic compounds (VOCs) and their relationship with the disease of interest. Data on factors potentially influencing the results of breath analysis remain rather sparse and often controversial. In this study, we aimed to investigate the associations of common VOCs in exhaled breath of average-risk individuals with socio-demographic and lifestyle factors, medical conditions as well as diet., Methods: Alveolar breath samples of 1447 men and women were collected in the morning after fasting and were analyzed using gas-chromatography linked with mass-spectrometry. Study participants were 40-64 years old, cancer-free, with overall good health status. The associations between selected VOCs and various factors determined from the questionnaire data were assessed using two-part-Wilcoxon test and Jonckheere-Terpstra trend test., Results: Fifteen VOCs where each of them was detected in at least 80% of the study population were included in this analysis. Statistically significant associations with various VOCs were demonstrated for gender and consumption of certain foods, such as coffee, leeks and garlic, while smoking was not associated with any of the analyzed compounds., Conclusion: Factors potentially modifying the composition of exhaled breath, such as dietary factors, deserve careful attention in the design and analysis of studies accessing the use of VOCs as diagnostic markers.

Early diagnosis of Parkinson's disease (PD) is important because it affects the choice of therapy and is subject to a relatively high degree of error. In addition, early detection of PD can potentially enable the start of neuroprotective therapy before extensive loss of dopaminergic neurons of the substantia nigra occurs. However, until now, studies for early detection of PD using volatile biomarkers sampled only treated and medicated patients. Therefore, there is a great need to evaluate untreated patients for establishing a real world screening and diagnostic technology. Here we describe for the first time a clinical trial to distinguish between de novo PD and control subjects using an electronic system for detection of volatile molecules in exhaled breath (sensor array). We further determine for the first time the association to other common tests for PD diagnostics as smell, ultrasound, and nonmotor symptoms. The test group consisted of 29 PD patients after initial diagnosis by an experienced neurologist, compared with 19 control subjects of similar age. The sensitivity, specificity, and accuracy values of the sensor array to detect PD from controls were 79%, 84%, and 81% respectively, in comparison with midbrain ultrasonography (93%, 90%, 92%) and smell detection (62%, 89%, 73%). The results confirm previous data showing the potential of sensor arrays to detect PD.

Purpose: Chronic rhinosinusitis (CRS) is one of the most common chronic diseases treated by primary care physicians. It is increasingly recognized that CRS and nasal polyposis (NP) comprise several disease processes with diverse causes. Hence, subgroups of sinusitis need to be differentiated so that patients can be screened appropriately and personalized medical treatment provided., Patients and Methods: To address this need, we use a cross-reactive nanoarray based on either molecularly modified gold nanoparticles or molecularly modified single-walled carbon nanotubes, combined with pattern recognition for analyzing breath samples. Breath samples were collected from three groups of volunteers (total 71) at the Hillel Yaffe Medical Center: CRS, NP, and control., Results: Nanoarray results discriminated between patients with sinusitis and the control group with 87% sensitivity, 83% specificity, and 85% accuracy. The system also discriminated well between the subpopulations: 1) CRS vs control (76% sensitivity, 90% specificity); 2) CRS vs NP (82% sensitivity, 71% specificity); and 3) NP vs control (71% sensitivity, 90% specificity)., Conclusion: This preliminary study shows that a nanoarray-based breath test for screening population for sinusitis-related conditions is feasible., Competing Interests: Disclosure The authors report no conflicts of interest in this work.

The presence of certain volatile organic compounds (VOCs) in the breath of patients with gastric cancer has been reported by a number of research groups; however, the source of these compounds remains controversial. Comparison of VOCs emitted from gastric cancer tissue to those emitted from non-cancerous tissue would help in understanding which of the VOCs are associated with gastric cancer and provide a deeper knowledge on their generation. Gas chromatography with mass spectrometric detection (GC-MS) coupled with head-space needle trap extraction (HS-NTE) as the pre-concentration technique, was used to identify and quantify VOCs released by gastric cancer and non-cancerous tissue samples collected from 41 patients during surgery. Excluding contaminants, a total of 32 VOCs were liberated by the tissue samples. The emission of four of them (carbon disulfide, pyridine, 3-methyl-2-butanone and 2-pentanone) was significantly higher from cancer tissue, whereas three compounds (isoprene, γ-butyrolactone and dimethyl sulfide) were in greater concentration from the non-cancerous tissues (Wilcoxon signed-rank test, p < 0.05). Furthermore, the levels of three VOCs (2-methyl-1-propene, 2-propenenitrile and pyrrole) were correlated with the occurrence of H. pylori; and four compounds (acetonitrile, pyridine, toluene and 3-methylpyridine) were associated with tobacco smoking. Ex vivo analysis of VOCs emitted by human tissue samples provides a unique opportunity to identify chemical patterns associated with a cancerous state and can be considered as a complementary source of information on volatile biomarkers found in breath, blood or urine.

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

To develop a new generation of diagnostics for halitosis, replacing the subjective organoleptic assessment, a series of exhaled breath analyzers has been developed and assessed. All three devices rely on the assessment of exhaled volatile sulfuric compounds (VSCs), which are mainly generated in and emitted from the oral cavity, contributing to the malodor. Portable, on-site and easy to use, these devices have potential for non-invasive diagnosis of halitosis. However, global assessment of exhaled VSCs alone has two main drawbacks: (i) the absence of VSCs does not rule out halitosis; (ii) non-sulfuric volatile compounds that could be biomarkers of systemic diseases, found in up to 15% of halitosis cases, are neglected. In this article, we review and discuss progress to date in the field of oral/exhaled volatile compounds as potential non-invasive diagnostics for halitosis. We will briefly describe the generation of these compounds both from local (oral) and distal (extra-oral) sources. In addition, we debate the different analytical approaches in use and discuss the potential value of bio-inspired artificially intelligent olfaction in diagnosing and classifying oral and systemic diseases by analyzing exhaled breath., (© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. All rights reserved.)

Early diagnosis of Parkinson's disease (PD) is of great importance due its progressive phenotype. Neuroprotective drugs could potentially slow down disease progression if used at early stages. Previously, we have reported an altered content of volatile organic compounds (VOCs) in the breath of rats following a 50% reduction in striatal dopamine (DA) content induced by 6-hydroxydopamine. We now report on the difference in the breath-print and content of VOCs between rats with mild and severe lesions of DA neurons, serotonergic neuronal lesions, and transgenic (Tg) rats carrying the PD-producing A53T mutation of the SNCA (α-synuclein) gene. The Tg rats had an increased content of 3-octen-1-ol and 4-chloro-3-methyl phenol in blood, while in brain tissue, hexanal, hexanol, and 2,3-octanedione were present in controls but absent in Tg rats. Levels of 1-heptyl-2-methyl cyclopropane were increased in brain tissue of Tg rats. The data confirm the potential of breath analysis for detection of human idiosyncratic as well as autosomal dominant PD.

Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids (CSF) or by magnetic resonance imaging (MRI) of the brain or spinal cord or both. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease state. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on noninvasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 participants, 146 MS and 58 healthy control individuals. Analysis was performed by gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensor array. Predictive models were derived from the sensors, using artificial neural networks (ANNs). GC-MS analysis revealed significant differences in VOC abundance between MS patients and controls. Sensor data analysis on training sets was able to discriminate in binary comparisons between MS patients and controls with accuracies up to 90%. Blinded sets showed 95% positive predictive value (PPV) between MS-remission and control, 100% sensitivity with 100% negative predictive value (NPV) between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics.

There is accumulating evidence in support of the significant improvement in survival rates and clinical outcomes when pulmonary arterial hypertension (PAH) is diagnosed at early stages. Nevertheless, it remains a major clinical challenge and the outcomes are dependent on invasive right heart catheterisation.Resulting from pathophysiological processes and detectable in exhaled breath, volatile organic compounds (VOCs) have been proposed as noninvasive biomarkers for PAH. Studies have confirmed significant alterations of the exhaled VOCs among PAH patients when compared to controls and/or patients with other respiratory diseases. This suggests exhaled breath analysis as a potential noninvasive medical application in the field of PAH.In this article, we review and discuss the progress made so far in the field of exhaled volatolomics (the omics of VOCs) as a potential noninvasive diagnostics of PAH. In addition, we propose a model including possible biochemical pathways on the level of the remodelled artery, in which specific VOCs could be detectable in exhaled breath during the early phases of PAH. We debate the different analytical approaches used and recommend a diagram including a "bottom-top" strategy, from basic to translational studies, required for promoting the field., (Copyright ©ERS 2017.)

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development., Competing Interests: The authors declare no competing financial interest.

Discovering the volatile signature of cancer cells is an emerging approach in cancer research, as it may contribute to a fast and simple diagnosis of tumors in vivo and in vitro. One of the main contributors to such a volatile signature is hyperglycolysis, which characterizes the cancerous cell. The metabolic perturbation in cancer cells is known as the Warburg effect; glycolysis is preferred over oxidative phosphorylation (OXPHOS), even in the presence of oxygen. The precise mitochondrial alterations that underlie the increased dependence of cancer cells on aerobic glycolysis for energy generation have remained a mystery. We aimed to profile the volatile signature of the glycolysis activity in lung cancer cells. For that an in vitro model, using lung cancer cell line cultures (A549, H2030, H358, H322), was developed. The volatile signature was measured by proton transfer reaction mass spectrometry under normal conditions and glycolysis inhibition. Glycolysis inhibition and mitochondrial activity were also assessed by mitochondrial respiration capacity measurements. Cells were divided into two groups upon their glycolytic profile (PET positive and PET negative). Glycolysis blockade had a unique characteristic that was shared by all cells. Furthermore, each group had a characteristic volatile signature that enabled us to discriminate between those sub-groups of cells. In conclusion, lung cancer cells may have different subpopulations of cells upon low and high mitochondrial capacity. In both groups, glycolysis blockade induced a unique volatile signature.

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations.

Flexible and wearable electronic sensors are useful for the early diagnosis and monitoring of an individual's health state. Sampling of volatile organic compounds (VOCs) derived from human breath/skin or monitoring abrupt changes in heart-beat/breath rate should allow noninvasive monitoring of disease states at an early stage. Nevertheless, for many reported wearable sensing devices, interaction with the human body leads incidentally to unavoidable scratches and/or mechanical cuts and bring about malfunction of these devices. We now offer proof-of-concept of nanoparticle-based flexible sensor arrays with fascinating self-healing abilities. By integrating a self-healable polymer substrate with 5 kinds of functionalized gold nanoparticle films, a sensor array gives a fast self-healing (<3 h) and attractive healing efficiency in both the substrate and sensing films. The proposed platform was used in sensing pressure variation and 11 kinds of VOCs. The sensor array had satisfactory sensitivity, a low detection limit, and promising discrimination features in monitoring both of VOCs and pressure variation, even after full healing. These results presage a new type of smart sensing device, with a desirable performance in the possible detection and/or clinical application for a number of different purposes.

Volatile organic compound (VOC) testing in breath has potential in gastric cancer (GC) detection. Our objective was to assess the reproducibility of VOCs in GC, and the effects of conditions modifying gut microbiome on the test results. Ten patients with GC were sampled for VOC over three consecutive days; 17 patients were sampled before and after H. pylori eradication therapy combined with a yeast probiotic; 61 patients were sampled before and after bowel cleansing (interventions affecting the microbiome). The samples were analyzed by: (1) gas chromatography linked to mass spectrometry (GC-MS), applying the non-parametric Wilcoxon test (level of significance p  <  0.05); (2) by cross-reactive nanoarrays combined with pattern recognition. Discriminant function analysis (DFA) was used to build the classification models; and leave-one-out cross-validation analysis was used to classify the findings. Exhaled VOCs profiles were stable for GC patients over a three day period. Alpha pinene (p  =  0.028) and ethyl acetate (p  =  0.030) increased after the antibiotic containing eradication regimen; acetone (p  =  0.0001) increased following bowel cleansing prior to colonoscopy. We further hypothesize that S. boulardii given with the standard eradication regimen to re-establish the gut microbiome was the source for long-term ethyl acetate production. Differences between the initial and the follow-up sample were also revealed in the DFA analysis of the sensor data. VOC measurement results are well-reproducible in GC patients indicating a useful basis for potential disease diagnostics. However, interventions with a potential effect on the gut microbiome may have an effect upon the VOC results, and therefore should be considered for diagnostic accuracy.

Objectives: Timely detection of gastric cancer (GC) and the related precancerous lesions could provide a tool for decreasing both cancer mortality and incidence., Design: 968 breath samples were collected from 484 patients (including 99 with GC) for two different analyses. The first sample was analysed by gas chromatography linked to mass spectrometry (GCMS) while applying t test with multiple corrections (p value<0.017); the second by cross-reactive nanoarrays combined with pattern recognition. For the latter, 70% of the samples were randomly selected and used in the training set while the remaining 30% constituted the validation set. The operative link on gastric intestinal metaplasia (OLGIM) assessment staging system was used to stratify the presence/absence and risk level of precancerous lesions. Patients with OLGIM stages III-IV were considered to be at high risk., Results: According to the GCMS results, patients with cancer as well as those at high risk had distinctive breath-print compositions. Eight significant volatile organic compounds (p value<0.017) were detected in exhaled breath in the different comparisons. The nanoarray analysis made it possible to discriminate between the patients with GC and the control group (OLGIM 0-IV) with 73% sensitivity, 98% specificity and 92% accuracy. The classification sensitivity, specificity, and accuracy between the subgroups was as follows: GC versus OLGIM 0-II-97%, 84% and 87%; GC versus OLGIM III-IV-93%, 80% and 90%; but OLGIM I-II versus OLGIM III-IV and dysplasia combined-83%, 60% and 61%, respectively., Conclusions: Nanoarray analysis could provide the missing non-invasive screening tool for GC and related precancerous lesions as well as for surveillance of the latter., Trial Registration Number: Clinical Trials.gov number, NCT01420588 (3/11/2013)., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Mapping molecular sub-types in breast cancer (BC) tumours is a rapidly evolving area due to growing interest in, for example, targeted therapy and screening high-risk populations for early diagnosis. We report a new concept for profiling BC molecular sub-types based on volatile organic compounds (VOCs). For this purpose, breath samples were collected from 276 female volunteers, including healthy, benign conditions, ductal carcinoma in situ (DCIS) and malignant lesions. Breath samples were analysed by gas chromatography mass spectrometry (GC-MS) and artificially intelligent nanoarray technology. Applying the non-parametric Wilcoxon/Kruskal-Wallis test, GC-MS analysis found 23 compounds that were significantly different (p < 0.05) in breath samples of BC patients with different molecular sub-types. Discriminant function analysis (DFA) of the nanoarray identified unique volatolomic signatures between cancer and non-cancer cases (83% accuracy in blind testing), and for the different molecular sub-types with accuracies ranging from 82 to 87%, sensitivities of 81 to 88% and specificities of 76 to 96% in leave-one-out cross-validation. These results demonstrate the presence of detectable breath VOC patterns for accurately profiling molecular sub-types in BC, either through specific compound identification by GC-MS or by volatolomic signatures obtained through statistical analysis of the artificially intelligent nanoarray responses.

The detection of many diseases is missed because of delayed diagnoses or the low efficacy of some treatments. This emphasizes the urgent need for inexpensive and minimally invasive technologies that would allow efficient early detection, stratifying the population for personalized therapy, and improving the efficacy of rapid bed-side assessment of treatment. An emerging approach that has a high potential to fulfill these needs is based on so-called "volatolomics", namely, chemical processes involving profiles of highly volatile organic compounds (VOCs) emitted from body fluids, including breath, skin, urine and blood. This article presents a didactic review of some of the main advances related to the use of nanomaterial-based solid-state and flexible sensors, and related artificially intelligent sensing arrays for the detection and monitoring of disease with volatolomics. The article attempts to review the technological gaps and confounding factors related to VOC testing. Different ways to choose nanomaterial-based sensors are discussed, while considering the profiles of targeted volatile markers and possible limitations of applying the sensing approach. Perspectives for taking volatolomics to a new level in the field of diagnostics are highlighted., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We report on an ultrasensitive, molecularly modified silicon nanowire field effect transistor that brings together the lock-and-key and cross-reactive sensing worlds for the diagnosis of (gastric) cancer from exhaled volatolome. The sensor is able to selectively detect volatile organic compounds (VOCs) that are linked with gastric cancer conditions in exhaled breath and to discriminate them from environmental VOCs that exist in exhaled breath samples but do not relate to the gastric cancer per se. Using breath samples collected from actual patients with gastric cancer and from volunteers who do not have cancer, blind analysis validated the ability of the reported sensor to discriminate between gastric cancer and control conditions with >85% accuracy, irrespective of important confounding factors such as tobacco consumption and gender. The reported sensing approach paves the way to use the power of silicon nanowires for simple, inexpensive, portable, and noninvasive diagnosis of cancer and other disease conditions.

Parkinson's disease (PD) is characterized by dopaminergic (DA) neuron depletion. Early detection of PD may help in selecting the appropriate treatment. Biomarkers of PD have been suggested, however none of these is currently in clinical use. The aim of this study was to identify volatile organic compounds (VOCs) as early biomarkers of PD. Our hypothesis was that during PD progression, specific VOCs are generated that are linked to the biochemical pathways characterizing PD. These VOCs can be detected by GC-MS combined with solid-phase microextraction (SPME) technique. Three groups of rats were studied: DA-lesioned rats injected with 6-hydroxydopamine (HDA; 250μg/rat n=11); control rats injected with saline (n=9), and control rats injected with DSP-4 (n=8), a specific noradrenergic neuron toxin. Blood and striatal tissue homogenate were analyzed. In the blood, 1-octen-3-ol and 2-ethylhexanol were found at significantly higher concentrations in HDA versus sham rats. In the striatal homogenate 1-octen-3-ol and other four compounds were found at significantly lower concentrations in HDA versus sham rats. 1-Octen-3-ol is a cytotoxic compound. These results may lead to the development of an early diagnostic test for PD based on profiling of VOCs in body fluids., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Background: Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRAS(V12) mutation, knockdown of TP53 or both with parental HBEC cells., Methods: VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC-MS) or sensor array with discriminant factor analysis (DFA)., Results: In TD-GC-MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80-100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%., Conclusions: Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC-MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers.

Background: Squamous cell carcinoma of the head and neck (HNSCC) are wide-spread cancers that often lead to disfigurement and loss of important functions such as speech and ingestion. To date, HNSCC has no adequate method for early detection and screening., Methods: Exhaled breath samples were collected from 87 volunteers; 62 well-defined breath samples from 22 HNSCC patients (larynx and pharynx), 21 patients with benign tumours (larynx and pharynx) and 19 healthy controls were analysed in a dual approach: (i) chemical analysis using gas chromatography/mass spectrometry (GC-MS) and (ii) breath-print analysis using an array of nanomaterial-based sensors, combined with a statistical algorithm., Results: Gas chromatography/mass spectrometry identified ethanol, 2-propenenitrile and undecane as potential markers for HNSCC and/or benign tumours of the head and neck. The sensor-array-based breath-prints could clearly distinguish HNSCC both from benign tumours and from healthy states. Within the HNSCC group, patients could be classified according to tumour site and stage., Conclusions: We have demonstrated the feasibility of a breath test for a specific, clinically interesting application: distinguishing HNSCC from tumour-free or benign tumour states, as well as for staging and locating HNSCC. The sensor array used here could form the basis for the development of an urgently needed non-invasive, cost-effective, fast and reliable point-of-care diagnostic/screening tool for HNSCC.

Aim: To demonstrate the feasibility of nanomaterial-based sensors for identifying patterns of exhaled volatile organic compound of end-stage renal disease (ESRD) and study the impact of hemodialysis (HD) on these patterns., Patients & Methods: Exhaled breath samples were collected from a group of 37 volunteers (26 ESRD HD patients; 11 healthy controls); a third of the samples were randomly blinded for determining the sensitivity/specificity of the method. Discriminant function analysis was used to build a model for discriminating ESRD patients and healthy controls (classification accuracy for blind samples: 80%), based on the signals of the nanomaterial sensors., Results & Conclusion: The breath pattern of the ESRD patients approached the healthy pattern during the HD treatment, without reaching it completely. Gas chromatography/mass spectrometry identified four volatile organic compounds as potential ESRD biomarkers. Although this pilot study has yielded encouraging results, additional large-scale clinical studies are required to develop a fast, noninvasive breath test for monitoring HD adequacy in real time.

Analysis of volatile organic compounds (VOCs) is a promising approach for non-invasive, fast and potentially inexpensive diagnostics. Here, we present a new methodology for profiling the body chemistry by using the volatile fraction of molecules in various body fluids. Using mass spectrometry and cross-reactive nanomaterial-based sensors array, we demonstrate that simultaneous VOC detection from breath and skin would provide complementary, non-correlated information of the body's volatile metabolites profile. Eventually with further wide population validation studies, such a methodology could provide more accurate monitoring of pathological changes compared to the information provided by a single body fluid. The qualitative and quantitative methods presented here offers a variety of options for novel mapping of the metabolic properties of complex organisms, including humans.

A new non-invasive and potentially inexpensive frontier in the diagnosis of cancer relies on the detection of volatile organic compounds (VOCs) in exhaled breath samples. Breath can be sampled and analyzed in real-time, leading to fascinating and cost-effective clinical diagnostic procedures. Nevertheless, breath analysis is a very young field of research and faces challenges, mainly because the biochemical mechanisms behind the cancer-related VOCs are largely unknown. In this review, we present a list of 115 validated cancer-related VOCs published in the literature during the past decade, and classify them with respect to their "fat-to-blood" and "blood-to-air" partition coefficients. These partition coefficients provide an estimation of the relative concentrations of VOCs in alveolar breath, in blood and in the fat compartments of the human body. Additionally, we try to clarify controversial issues concerning possible experimental malpractice in the field, and propose ways to translate the basic science results as well as the mechanistic understanding to tools (sensors) that could serve as point-of-care diagnostics of cancer. We end this review with a conclusion and a future perspective.

The current review provides an assessment of the exhalation kinetics of volatile organic compounds (VOCs) that have been linked with cancer. Towards this end, we evaluate various physicochemical properties, such as 'breath:air' and 'blood:fat' partition coefficients, of 112 VOCs that have been suggested over the past decade as potential markers of cancer. With these data, we show that the cancer VOC concentrations in the blood and in the fat span over 12 and 8 orders of magnitude, respectively, in order to provide a specific counterpart concentration in the exhaled breath (e.g., 1 ppb). This finding suggests that these 112 different compounds have different storage compartments in the body and that their exhalation kinetics depends on one or a combination of the following factors: (i) the VOC concentrations in different parts of the body; (ii) the VOC synthesis and metabolism rates; (iii) the partition coefficients between tissue(s), blood and air; and (iv) the VOCs' diffusion constants. Based on this analysis, we discuss how this knowledge allows modeling and simulating the behavior of a specific VOC under different sampling protocols (with and without exertion of effort). We end this review by a brief discussion on the potential role of these scenarios in screening and therapeutic monitoring of cancer.

Breath-gas analysis has demonstrated that concentration profiles of volatile organic compounds (VOCs) could be used for detecting a variety of diseases, among them gastric cancer (GC) and peptic ulcer disease (PUD). Here, we explore how geographical variation affects the disease-specific changes in the chemical composition of breath samples, as compared to control states (less severe gastric conditions). Alveolar exhaled breath samples from 260 patients were collected at two remotely different geographic locations (China and Latvia), following similar breath-collection protocols. Each cohort included 130 patients that were matched in terms of diagnosis (37 GC/32 PUD/61 controls), average age, gender ratio and smoking habits. Helicobacter Pylori infection, which is a major cause for GC and PUD, was found in part of the patients, as well as in part of the controls, at both locations. The breath samples were analyzed by gas chromatography/mass spectrometry, using the same equipment and protocol-of-experiment. We observed similar characteristic differences in the chemical composition of the breath samples between the study groups at the two locations, even though the exact composition of the breath samples differed. Both in China and Latvia, the GC patients and controls could be distinguished by differences in the average levels of 6-methyl-5-hepten-2-one; PUD patients were distinguished from controls by the levels of aromatic compounds and alcohols; GC and PUD patients could not be distinguished at either site. This pilot study indicates the limitations of chemical breath-gas analysis alone for identifying gastric diseases based on the concentration profiles of separate VOCs in international patient cohorts. We assume that these limitations would apply to other diseases as well. The presented data could potentially be useful for developing an alternative, universally applicable diagnostic method that relies on the detection of changes in the collective patterns of the disease-specific classes of exhaled VOCs.

Brucellosis is of great public health and economic importance worldwide. Detection of brucellosis currently relies on serologic testing of an antibody response to Brucella infection, which suffers from cross-sensitivities to other antibody responses. Here we present a new method for identifying Brucella exposure that is based on profiling volatile organic compounds (VOCs) in exhaled breath. Breath samples from Brucella-seropositive bison and controls were chemically analyzed and demonstrated statistically significant differences in the concentration profiles of five VOCs. A point-of-care device incorporating an array of nanomaterial-based sensors could identify VOC patterns indicative of Brucella exposure with excellent discriminative power, using a statistical algorithm. We show that the patterns were not affected by the animals' environment and that the discriminative power of the approach was stable over time. The Brucella-indicative VOCs and collective patterns that were identified in this pilot study could lead to the development of a novel diagnostic screening test for quickly detecting infected animals chute-side, pen-side, or even remotely in populations of free-ranging ungulates. The promising preliminary results presented encourage subsequent larger scale trials in order to further evaluate the proposed method.

We report on a new concept for profiling genetic mutations of (lung) cancer cells, based on the detection of patterns of volatile organic compounds (VOCs) emitted from cell membranes, using an array of nanomaterial-based sensors. In this in-vitro pilot study we have derived a volatile fingerprint assay for representative genetic mutations in cancer cells that are known to be associated with targeted cancer therapy. Five VOCs were associated with the studied oncogenes, using complementary chemical analysis, and were discussed in terms of possible metabolic pathways. The reported approach could lead to the development of novel methods for guiding treatments, so that patients could benefit from safer, more timely and effective interventions that improve survival and quality of life while avoiding unnecessary invasive procedures. Studying clinical samples (tissue/blood/breath) will be required as next step in order to determine whether this cell-line study can be translated into a clinically useful tool., From the Clinical Editor: In this novel study, a new concept for profiling genetic mutations of (lung) cancer cells is described, based on the detection of patterns of volatile organic compounds emitted from cell membranes, using an array of nano-gold based sensors., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Molecularly modified silicon nanowire field effect transistors (SiNW FETs) are starting to appear as promising devices for sensing various volatile organic compounds (VOCs). Understanding the connection between the molecular layer structure attached to the SiNWs and VOCs is essential for the design of high performance sensors. Here, we explore the chain length influence of molecular layers on the sensing performance to polar and nonpolar VOCs. SiNW FETs were functionalized with molecular layers that have similar end (methyl) group and amide bridge bond, but differ in their alkyl chain lengths. The resulting devices were then exposed to polar and nonpolar VOCs in various concentrations. Our results showed that the sensing response to changing the threshold voltage (ΔVth) and changing the relative hole mobility (Δμh/μh-a) have a proportional relationship to the VOC concentration. On exposure to a specific VOC concentration, ΔVth response increased with the chain length of the molecular modification. In contrast, Δμh/μh-a did not exhibit any obvious reliance on the chain length of the molecular layer. Analysis of the responses with an electrostatic-based model suggests that the sensor response in ΔVth is dependent on the VOC concentration, VOC vapor pressure, VOC-molecular layer binding energy, and VOC adsorption-induced dipole moment changes of molecular layer.

The importance of developing new diagnostic and detection technologies for the growing number of clinical challenges is rising each year. Here, we present a concise, yet didactic review on a new diagnostics frontier based on the detection of disease-related volatile organic compounds (VOCs) by means of nanomaterial-based sensors. Nanomaterials are ideal for such sensor arrays because they are easily fabricated, chemically versatile and can be integrated into currently available sensing platforms. Following a general introduction, we provide a brief description of the VOC-related diseases concept. Then, we focus on detection of VOC-related diseases by selective and crossreactive sensing approaches, through chemical, optical and mechanical transducers incorporating the most important classes of nanomaterials. Selected examples of the integration of nanomaterials into selective sensors and crossreactive sensor arrays are given. We conclude with a brief discussion on the integration possibilities of different types of nanomaterials into sensor arrays, and the expected outcomes and limitations.

We show that polycyclic aromatic hydrocarbon (PAH) based field effect transistor (FET) arrays can serve as excellent chemical sensors for the detection of volatile organic compounds (VOCs) under confounding humidity conditions. Using these sensors, w/o complementary pattern recognition methods, we study the ability of PAH-FET(s) to: (i) discriminate between aromatic and non-aromatic VOCs; (ii) distinguish polar and non-polar non-aromatic compounds; and to (iii) identify specific VOCs within the subgroups (i.e., aromatic compounds, polar non-aromatic compounds, non-polar non-aromatic compounds). We further study the effect of water vapor on the sensor array's discriminative ability and derive patterns that are stable when exposed to different constant values of background humidity. Patterns based on different independent electronic features from an array of PAH-FETs may bring us one step closer to creating a unique fingerprint for individual VOCs in real-world applications in atmospheres with varying levels of humidity.

Molecular layers attached to a silicon nanowire field effect transistor (SiNW FET) can serve as antennas for signal transduction of volatile organic compounds (VOCs). Nevertheless, the mutual relationship between the molecular layers and VOCs is still a puzzle. In the present paper, we explore the effect of the molecular layer's end (functional) groups on the sensing properties of VOCs. Toward this end, SiNW FETs were modified with tailor-made molecular layers that have the same backbone but differ in their end groups. Changes in the threshold voltage (ΔVth) and changes in the mobility (Δμh) were then recorded upon exposure to various VOCs. Model-based analysis indicates that the interaction between molecular layers and VOCs can be classified to three main scenarios: (a) dipole-dipole interaction between the molecular layer and the polar VOCs; (b) induced dipole-dipole interaction between the molecular layers and the nonpolar VOCs; and (c) molecular layer tilt as a result of VOCs diffusion. Based on these scenarios, it is likely that the electron-donating/withdrawing properties of the functional groups control the dipole moment orientation of the adsorbed VOCs and, as a result, determine the direction (or sign) of the ΔVth. Additionally, it is likely the diffusion of VOCs into the molecular layer, determined by the type of functional groups, is the main reason for the Δμh responses. The reported findings are expected to provide an efficient way to design chemical sensors that are based on SiNW FETs to nonpolar VOCs, which do not exchange carriers with the molecular layers.