Breath Analysis: An ACS Sensors Special Issue

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Learn More CiteCitationCitation and abstractCitation and referencesMore citation options ShareShare onFacebookXWeChatLinkedInRedditEmailBlueskyJump toExpandCollapse EditorialMarch 28, 2025Breath Analysis: An ACS Sensors Special IssueClick to copy article linkArticle link copied!Hohyung Kang*Hohyung KangDepartment of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science Technology, 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea*Email: [email protected]; [email protected]More by Hohyung Kanghttps://orcid.org/0000-0001-5373-4460Hee-Tae Jung*Hee-Tae JungDepartment of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science Technology, 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea*Email: [email protected]More by Hee-Tae Junghttps://orcid.org/0000-0002-5727-6732Open PDFACS SensorsCite this: ACS Sens. 2025, 10, 3, 1505–1506Click to copy citationCitation copied!https://pubs.acs.org/doi/10.1021/acssensors.5c00717https://doi.org/10.1021/acssensors.5c00717Published March 28, 2025 Publication History Received 3 March 2025Published online 28 March 2025Published in issue 28 March 2025editorialCopyright © Published 2025 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissionsThis publication is licensed for personal use by The American Chemical Society. ACS PublicationsCopyright © Published 2025 by American Chemical SocietySubjectswhat are subjects Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Biomarkers Cancer Computational chemistry Gases Sensors Special IssuePublished as part of ACS Sensors special issue "Breath Sensing".The average adult breathes approximately 15 times per minute, with each breath composed of nitrogen, oxygen, carbon dioxide, water vapor, and trace amounts of volatile organic compounds (VOCs) and other inorganic gases. Every exhalation reflects the activity of numerous metabolic pathways, carrying vital physiological information. There are about 3000 known gases in human breath, and variations in their concentrations can serve as biomarkers for diverse physiological processes and diseases. (1) Breath analysis is an innovative diagnostic method that evaluates the chemical composition of exhaled breath to assess an individual's health. While conventional diagnostic methods such as blood tests, biopsies, endoscopy, colonoscopy, and various imaging techniques (e.g., X-rays, magnetic resonance imaging, and computed tomography) provide valuable information, they often require considerable time, specialized equipment, and professionals for the diagnosis. In contrast, breath analysis is noninvasive, safe, rapid, cost-effective, in real-time, and portable, making it a convenient and attractive method in diagnostics. (2,3)However, breath analysis remains in the clinical validation stage. Unlike conventional gas sensors for industrial safety or environmental monitoring that detect gases at higher concentrations under controlled conditions, gas sensors for breath analysis must operate under far more challenging conditions. Target biomarker gases typically exist in parts-per-billion (ppb) concentrations within exhaled breath at 31–35 °C, 65–89% relative humidity (RH), and in the presence of numerous interfering gases. This makes detection akin to finding a needle in a haystack. Therefore, several challenges must be addressed: (i) sensors must detect biomarker gas at extremely low concentrations; (ii) interindividual variability from diet, health conditions, sampling methods, and metabolic patterns must be considered; (iii) the complexity of the breath matrix necessitates sensors with enhanced selectivity or the use of pretreatment methods during sampling to ensure accurate analysis. Thus, breath sensors must exhibit high sensitivity, selectivity, humidity resilience, precision, accuracy, cost-effectiveness, and energy efficiency to be viable for clinical applications. (4)Currently, the gold standard for breath analysis is gas chromatography–mass spectrometry (GC-MS) due to its high precision and accuracy in detecting minute concentrations of biomarker gases. Nonetheless, various gas sensors, including solid-state, optical, and electrochemical types, have made significant progress in breath analysis through advancements in materials science, signal processing, and hardware engineering. Studies have shown the detection of elevated acetone concentrations for diabetes diagnosis and increased nitric oxide for asthma. (5,6) In addition, numerous investigations have demonstrated the ability to detect various biomarker gases for disease screening. For example, methane has been linked to colorectal cancer; ammonia to renal failure, oral cavity disease, and Helicobacter pylori infection; ethane to cystic fibrosis, scleroderma, Alzheimer's disease, and atherosclerosis; hydrogen sulfide to halitosis and airway inflammation; carbonyl sulfide to liver disease; toluene to lung cancer; methanol to breast cancer and central nervous system diseases; isoprene to lung cancer; aldehydes to lung cancer, tuberculosis, and Wilson's disease; and hydrogen cyanide to cystic fibrosis. (7−9) Notably, a single gas may serve as a biomarker for multiple diseases, while a given disease might be characterized by several biomarker gases due to its impact on diverse metabolic pathways. Therefore, identifying the origin of each biomarker gas and elucidating its relationship with specific diseases is critical. (10,11)To advance breath analysis beyond clinical validation, five key areas require further development: (1) material engineering, (2) signal processing, (3) hardware engineering, (4) biomarker origin and mechanism studies, and (5) field testing. First, material engineering requires research on methods to ensure superior selectivity and sensitivity under humid conditions. This includes screening material candidates, employing structural and chemical modifications for stronger analyte interactions, and applying protective coatings to minimize unwanted reactions while selectively targeting biomarker gases. Additionally, the rational design of electronic nose systems is needed to improve the accuracy and precision in capturing biomarker fingerprints for disease diagnosis. Second, interpreting complex breath data necessitates robust signal processing methods. Both conventional mathematical models (such as principal component analysis and linear discriminant analysis) and advanced machine learning (ML) models (including convolutional neural networks, random forests, autoencoders, and recurrent neural networks) should be explored to optimize predictive performance and reduce interindividual variability. Moreover, addressing the "black box" nature of ML through explainable AI, feature visualization, and rule extraction is essential to enhance interpretability and secure clinical trust. Third, developing durable, portable, and energy-efficient sensors is critical. Optimized components, such as electrodes and integrated heaters, can improve measurement precision while reducing power consumption. Advances in sensor fabrication techniques and an improved understanding of airflow dynamics are also necessary to minimize variability and ensure reliable measurements. Fourth, comprehensive studies are needed to determine the origins of specific gases, establish correlations with physiological states, and refine concentration ranges of biomarkers for accurate diagnostics. Finally, validation through real breath sample studies and deeper mechanistic investigations is crucial for confirming the clinical applicability of breath analysis. (12,13)To foster progress in this field, ACS Sensors presents a special issue on breath analysis, featuring a total of 36 state-of-the-art research contributions spanning materials engineering, mechanistic studies, biomarker identification, sensor technologies, signal processing, and systematic reviews from world-leading experts. This issue covers a broad spectrum of health monitoring applications, including lung cancer, COVID-19, Parkinson's disease, H. pylori infection, chronic obstructive pulmonary disease, and diabetes, through the detection of various biomarker gases. It also highlights advancements in materials and signal processing that enhance the sensitivity of solid-state, optical, electrochemical, and surface acoustic wave sensors, as well as improvements in predictive accuracy. Furthermore, systematic reviews on topics such as lung cancer breath analysis, wearable breath analysis, catalysts for breath sensors, Raman-based breath analysis, and COVID-19 screening provide valuable guidance for future research. We hope this collection will foster further innovation and inspire new breakthroughs in breath analysis. Given the multidisciplinary nature of this challenging field, ACS Sensors welcomes novel ideas and innovations that address unresolved questions and limitations.Dr. Hohyung KangKorea Advanced Institute of Science Technology, Daejeon, Republic of KoreaHee-Tae Jung, Associate Editor, ACS SensorsKorea Advanced Institute of Science Technology, Daejeon, Republic of KoreaAuthor InformationClick to copy section linkSection link copied!Corresponding AuthorsHohyung Kang, Research Associate, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science Technology, 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea, https://orcid.org/0000-0001-5373-4460, Email: [email protected] [email protected]Hee-Tae Jung, Associate Editor, ACS Sensors, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science Technology, 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea, https://orcid.org/0000-0002-5727-6732, Email: [email protected]NotesViews expressed in this editorial are those of the authors and not necessarily the views of the ACS.ReferencesClick to copy section linkSection link copied! This article references 13 other publications. 1Buszewski, B.; Kęsy, M.; Ligor, T.; Amann, A. Human Exhaled Air Analytics: Biomarkers of Diseases. Biomedical Chromatography 2007, 21 (6), 553– 566, DOI: 10.1002/bmc.835 Google Scholar1Human exhaled air analytics: biomarkers of diseasesBuszewski, Boguslaw; Kesy, Martyna; Ligor, Tomasz; Amann, AntonBiomedical Chromatography (2007), 21 (6), 553-566CODEN: BICHE2; ISSN:0269-3879. (John Wiley & Sons Ltd.) A review. Over the last few years, breath anal. for the routine monitoring of metabolic disorders has attracted a considerable amt. of scientific interest, esp. since breath sampling is a non-invasive technique, totally painless and agreeable to patients. The investigation of human breath samples with various anal. methods has shown a correlation between the concn. patterns of volatile org. compds. (VOCs) and the occurrence of certain diseases. It has been demonstrated that modern anal. instruments allow the detn. of many compds. found in human breath both in normal and anomalous concns. The compn. of exhaled breath in patients with, for example, lung cancer, inflammatory lung disease, hepatic or renal dysfunction and diabetes contains valuable information. Furthermore, the detection and quantification of oxidative stress, and its monitoring during surgery based on compn. of exhaled breath, have made considerable progress. This paper gives an overview of the anal. techniques used for sample collection, preconcn. and anal. of human breath compn. The diagnostic potential of different disease-marking substances in human breath for a selection of diseases and the clin. applications of breath anal. are discussed. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXntVSjtrY%253D&md5=bc3f7302ea0107b2861030c86c6d1a3b2Amann, A.; Costello, B. de L.; Miekisch, W.; Schubert, J.; Buszewski, B.; Pleil, J.; Ratcliffe, N.; Risby, T. The Human Volatilome: Volatile Organic Compounds (VOCs) in Exhaled Breath, Skin Emanations, Urine, Feces and Saliva. J. Breath Res. 2014, 8 (3), 034001, DOI: 10.1088/1752-7155/8/3/034001 Google Scholar2The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and salivaAmann, Anton; de Lacy Costello, Ben; Miekisch, Wolfram; Schubert, Jochen; Buszewski, Boguslaw; Pleil, Joachim; Ratcliffe, Norman; Risby, TerenceJournal of Breath Research (2014), 8 (3), 034001CODEN: JBROBW; ISSN:1752-7155. (IOP Publishing Ltd.) A review. Breath anal. is a young field of research with its roots in antiquity. Antoine Lavoisier discovered carbon dioxide in exhaled breath during the period 1777-1783, Wilhelm (Vilem) Petters discovered acetone in breath in 1857 and Johannes Muller reported the first quant. measurements of acetone in 1898. A recent review reported 1765 volatile compds. appearing in exhaled breath, skin emanations, urine, saliva, human breast milk, blood and feces. For a large no. of compds., real-time anal. of exhaled breath or skin emanations has been performed, e.g., during exertion of effort on a stationary bicycle or during sleep. Volatile compds. in exhaled breath, which record historical exposure, are called the exposome. Changes in biogenic volatile org. compd. concns. can be used to mirror metabolic or (patho)physiol. processes in the whole body or blood concns. of drugs (e.g. propofol) in clin. settings, even during artificial ventilation or during surgery. Also compds. released by bacterial strains like Pseudomonas aeruginosa or Streptococcus pneumonia could be very interesting. Me methacrylate (CAS 80-62-6), for example, was obsd. in the headspace of Streptococcus pneumonia in concns. up to 1420 ppb. Fecal volatiles have been implicated in differentiating certain infectious bowel diseases such as Clostridium difficile, Campylobacter, Salmonella and Cholera. They have also been used to differentiate other non-infectious conditions such as irritable bowel syndrome and inflammatory bowel disease. In addn., alterations in urine volatiles have been used to detect urinary tract infections, bladder, prostate and other cancers. Peroxidn. of lipids and other biomols. by reactive oxygen species produce volatile compds. like ethane and 1-pentane. Noninvasive detection and therapeutic monitoring of oxidative stress would be highly desirable in autoimmunol., neurol., inflammatory diseases and cancer, but also during surgery and in intensive care units. The study of cell cultures opens up new possibilities for elucidation of the biochem. background of volatile compds. In future studies, combined studies of a particular compd. with regard to human matrixes such as breath, urine, saliva and cell culture studies will lead to novel scientific progress in the field. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhvV2jt77E&md5=65901e0c2c4e746efdca763d468ecb063Jung, H.-T. The Present and Future of Gas Sensors. ACS Sens 2022, 7 (4), 912– 913, DOI: 10.1021/acssensors.2c00688 Google Scholar3The Present and Future of Gas SensorsJung, Hee-TaeACS Sensors (2022), 7 (4), 912-913CODEN: ASCEFJ; ISSN:2379-3694. (American Chemical Society) A review. The present and future of gas sensors. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhtVShtbjM&md5=85183d155224b0a59c5fe6f95efc34f74Liu, K.; Lin, M.; Zhao, Z.; Zhang, K.; Yang, S. Rational Design and Application of Breath Sensors for Healthcare Monitoring. ACS Sens 2025, 10 (1), 15– 32, DOI: 10.1021/acssensors.4c02313 Google ScholarThere is no corresponding record for this reference.5Wang, C.; Sahay, P. Breath Analysis Using Laser Spectroscopic Techniques: Breath Biomarkers, Spectral Fingerprints, and Detection Limits. Sensors 2009, 9 (10), 8230– 8262, DOI: 10.3390/s91008230 Google Scholar5Breath analysis using laser spectroscopic techniques: breath biomarkers, spectral fingerprints, and detection limitsWang, Chuji; Sahay, PeeyushSensors (2009), 9 (10), 8230-8262CODEN: SENSC9; ISSN:1424-8220. (Molecular Diversity Preservation International) A review. Breath anal., a promising new field of medicine and medical instrumentation, potentially offers noninvasive, real-time, and point-of-care (POC) disease diagnostics and metabolic status monitoring. Numerous breath biomarkers have been detected and quantified so far by using the GC-MS technique. Recent advances in laser spectroscopic techniques and laser sources have driven breath anal. to new heights, moving from lab. research to com. reality. Laser spectroscopic detection techniques not only have high-sensitivity and high-selectivity, as equivalently offered by the MS-based techniques, but also have the advantageous features of near real-time response, low instrument costs, and POC function. Of the approx. 35 established breath biomarkers, such as acetone, ammonia, carbon dioxide, ethane, methane, and nitric oxide, 14 species in exhaled human breath have been analyzed by high-sensitivity laser spectroscopic techniques, namely, tunable diode laser absorption spectroscopy (TDLAS), cavity ring down spectroscopy (CRDS), integrated cavity output spectroscopy (ICOS), cavity enhanced absorption spectroscopy (CEAS), cavity leak-out spectroscopy (CALOS), photoacoustic spectroscopy (PAS), quartz-enhanced photoacoustic spectroscopy (QEPAS), and optical frequency comb cavity-enhanced absorption spectroscopy (OFC-CEAS). Spectral fingerprints of the measured biomarkers span from the UV to the mid-IR spectral regions and the detection limits achieved by the laser techniques range from ppm to ppb levels. Sensors using the laser spectroscopic techniques for a few breath biomarkers, e.g., carbon dioxide, nitric oxide, etc. are com. available. This review presents an update on the latest developments in laser-based breath anal. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtlakurnI&md5=b14f7899eec1357f8906a62ab88423156Owen, O. E.; Trapp, V. E.; Skutches, C. L.; Mozzoli, M. A.; Hoeldtke, R. D.; Boden, G.; Reichard, G. A., Jr. Acetone Metabolism During Diabetic Ketoacidosis. Diabetes 1982, 31 (3), 242– 248, DOI: 10.2337/diab.31.3.242 Google ScholarThere is no corresponding record for this reference.7Selvaraj, R.; Vasa, N. J.; Nagendra, S. M. S.; Mizaikoff, B. Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics. Molecules 2020, 25 (9), 2227, DOI: 10.3390/molecules25092227 Google ScholarThere is no corresponding record for this reference.8Kang, H.; Joo, H.; Choi, J.; Kim, Y.-J.; Lee, Y.; Cho, S.-Y.; Jung, H.-T. Top-Down Approaches for 10 Nm-Scale Nanochannel: Toward Exceptional H2S Detection. ACS Nano 2022, 16 (10), 17210– 17219, DOI: 10.1021/acsnano.2c07785 Google Scholar8Top-Down Approaches for 10 nm-Scale Nanochannel: Toward Exceptional H2S DetectionKang, Hohyung; Joo, Heeeun; Choi, Junghoon; Kim, Yong-Jae; Lee, Yullim; Cho, Soo-Yeon; Jung, Hee-TaeACS Nano (2022), 16 (10), 17210-17219CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society) Metal oxide semiconductors (MOS) have proven to be most powerful sensing materials to detect hydrogen sulfide (H2S), achieving part per billion (ppb) level sensitivity and selectivity. However, there has not been a way of extending this approach to the top-down H2S sensor fabrication process, completely limiting their com.-level productions. In this study, we developed a top-down lithog. process of a 10 nm-scale SnO2 nanochannel for H2S sensor prodn. Due to high-resoln. (15 nm thickness) and high aspect ratio (>20) structures, the nanochannel exhibited highly sensitive H2S detection performances (Ra/Rg = 116.62, τres = 31 s at 0.5 ppm) with selectivity (RH2S/Racetone = 23 against 5 ppm acetone). In addn., we demonstrated that the nanochannel could be efficiently sensitized with the p-n heterojunction without any postmodification or an addnl. process during one-step lithog. As an example, we demonstrated that the H2S sensor performance can be drastically enhanced with the NiO nanoheterojunction (Ra/Rg = 166.2, τres = 21 s at 0.5 ppm), showing the highest range of sensitivity demonstrated to date for state-of-the-art H2S sensors. These results in total constitute a high-throughput fabrication platform to commercialize the H2S sensor that can accelerate the development time and interface in real-life situations. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisF2kurnM&md5=5187fd1ee24d2b394e8d5917b1d2cb309Vm, A.; Yerevan State University YSU, 1 Alex Manoukian Metal Oxide Gas Biomarkers of Diseases for Medical and Health Applications. BJSTR 2020, 29 (2), 22328– 22336, DOI: 10.26717/BJSTR.2020.29.004780 Google ScholarThere is no corresponding record for this reference.10Chaudhary, V.; Taha, B. A.; Lucky; Rustagi, S.; Khosla, A.; Papakonstantinou, P.; Bhalla, N. Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers. ACS Sens 2024, 9 (9), 4469– 4494, DOI: 10.1021/acssensors.4c01524 Google ScholarThere is no corresponding record for this reference.11Güntner, A. T.; Abegg, S.; Königstein, K.; Gerber, P. A.; Schmidt-Trucksäss, A.; Pratsinis, S. E. Breath Sensors for Health Monitoring. ACS Sens 2019, 4 (2), 268– 280, DOI: 10.1021/acssensors.8b00937 Google ScholarThere is no corresponding record for this reference.12Li, Y.; Wei, X.; Zhou, Y.; Wang, J.; You, R. Research Progress of Electronic Nose Technology in Exhaled Breath Disease Analysis. Microsyst Nanoeng 2023, 9 (1), 1– 22, DOI: 10.1038/s41378-023-00594-0 Google ScholarThere is no corresponding record for this reference.13Cho, S.-Y.; Lee, Y.; Lee, S.; Kang, H.; Kim, J.; Choi, J.; Ryu, J.; Joo, H.; Jung, H.-T.; Kim, J. Finding Hidden Signals in Chemical Sensors Using Deep Learning. Anal. Chem. 2020, 92 (9), 6529– 6537, DOI: 10.1021/acs.analchem.0c00137 Google Scholar13Finding Hidden Signals in Chemical Sensors Using Deep LearningCho, Soo-Yeon; Lee, Youhan; Lee, Sangwon; Kang, Hohyung; Kim, Jaehoon; Choi, Junghoon; Ryu, Jin; Joo, Heeeun; Jung, Hee-Tae; Kim, JihanAnalytical Chemistry (Washington, DC, United States) (2020), 92 (9), 6529-6537CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society) Achieving high signal-to-noise ratio in chem. and biol. sensors enables accurate detection of target analytes. Unfortunately, below the limit of detection (LOD), it becomes difficult to detect the presence of small amts. of analytes and ext. useful information via any of the conventional methods. In this work, we examine the possibility of extg. "hidden signals" using deep neural network to enhance gas sensing below the LOD region. As a test case system, we conduct expts. for H2 sensing in six different metallic channels (Au, Cu, Mo, Ni, Pt, Pd) and demonstrate that deep neural network can enhance the sensing capabilities for H2 concn. below the LOD. We demonstrate that this technique could be universally used for different types of sensors and target analytes. Our approach can ext. new information from the hidden signals, which can be crucial for next-generation chem. sensing applications and anal. chem. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXntVKlsLw%253D&md5=ec5b1b29b97539145b865441e1eaac13Cited By Click to copy section linkSection link copied!This article has not yet been cited by other publications.Download PDFFiguresReferences Get e-AlertsGet e-AlertsACS SensorsCite this: ACS Sens. 2025, 10, 3, 1505–1506Click to copy citationCitation copied!https://doi.org/10.1021/acssensors.5c00717Published March 28, 2025 Publication History Received 3 March 2025Published online 28 March 2025Published in issue 28 March 2025Copyright © Published 2025 by American Chemical Society. This publication is available under these Terms of Use. 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Find more information on the Altmetric Attention Score and how the score is calculated.Recommended Articles FiguresReferencesThis publication has no figures.References This article references 13 other publications. 1Buszewski, B.; Kęsy, M.; Ligor, T.; Amann, A. Human Exhaled Air Analytics: Biomarkers of Diseases. Biomedical Chromatography 2007, 21 (6), 553– 566, DOI: 10.1002/bmc.835 1Human exhaled air analytics: biomarkers of diseasesBuszewski, Boguslaw; Kesy, Martyna; Ligor, Tomasz; Amann, AntonBiomedical Chromatography (2007), 21 (6), 553-566CODEN: BICHE2; ISSN:0269-3879. (John Wiley & Sons Ltd.) A review. Over the last few years, breath anal. for the routine monitoring of metabolic disorders has attracted a considerable amt. of scientific interest, esp. since breath sampling is a non-invasive technique, totally painless and agreeable to patients. The investigation of human breath samples with various anal. methods has shown a correlation between the concn. patterns of volatile org. compds. (VOCs) and the occurrence of certain diseases. It has been demonstrated that modern anal. instruments allow the detn. of many compds. found in human breath both in normal and anomalous concns. The compn. of exhaled breath in patients with, for example, lung cancer, inflammatory lung disease, hepatic or renal dysfunction and diabetes contains valuable information. Furthermore, the detection and quantification of oxidative stress, and its monitoring during surgery based on compn. of exhaled breath, have made considerable progress. This paper gives an overview of the anal. techniques used for sample collection, preconcn. and anal. of human breath compn. The diagnostic potential of different disease-marking substances in human breath for a selection of diseases and the clin. applications of breath anal. are discussed. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXntVSjtrY%253D&md5=bc3f7302ea0107b2861030c86c6d1a3b2Amann, A.; Costello, B. de L.; Miekisch, W.; Schubert, J.; Buszewski, B.; Pleil, J.; Ratcliffe, N.; Risby, T. The Human Volatilome: Volatile Organic Compounds (VOCs) in Exhaled Breath, Skin Emanations, Urine, Feces and Saliva. J. Breath Res. 2014, 8 (3), 034001, DOI: 10.1088/1752-7155/8/3/034001 2The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and salivaAmann, Anton; de Lacy Costello, Ben; Miekisch, Wolfram; Schubert, Jochen; Buszewski, Boguslaw; Pleil, Joachim; Ratcliffe, Norman; Risby, TerenceJournal of Breath Research (2014), 8 (3), 034001CODEN: JBROBW; ISSN:1752-7155. (IOP Publishing Ltd.) A review. Breath anal. is a young field of research with its roots in antiquity. Antoine Lavoisier discovered carbon dioxide in exhaled breath during the period 1777-1783, Wilhelm (Vilem) Petters discovered acetone in breath in 1857 and Johannes Muller reported the first quant. measurements of acetone in 1898. A recent review reported 1765 volatile compds. appearing in exhaled breath, skin emanations, urine, saliva, human breast milk, blood and feces. For a large no. of compds., real-time anal. of exhaled breath or skin emanations has been performed, e.g., during exertion of effort on a stationary bicycle or during sleep. Volatile compds. in exhaled breath, which record historical exposure, are called the exposome. Changes in biogenic volatile org. compd. concns. can be used to mirror metabolic or (patho)physiol. processes in the whole body or blood concns. of drugs (e.g. propofol) in clin. settings, even during artificial ventilation or during surgery. Also compds. released by bacterial strains like Pseudomonas aeruginosa or Streptococcus pneumonia could be very interesting. Me methacrylate (CAS 80-62-6), for example, was obsd. in the headspace of Streptococcus pneumonia in concns. up to 1420 ppb. Fecal volatiles have been implicated in differentiating certain infectious bowel diseases such as Clostridium difficile, Campylobacter, Salmonella and Cholera. They have also been used to differentiate other non-infectious conditions such as irritable bowel syndrome and inflammatory bowel disease. In addn., alterations in urine volatiles have been used to detect urinary tract infections, bladder, prostate and other cancers. Peroxidn. of lipids and other biomols. by reactive oxygen species produce volatile compds. like ethane and 1-pentane. Noninvasive detection and therapeutic monitoring of oxidative stress would be highly desirable in autoimmunol., neurol., inflammatory diseases and cancer, but also during surgery and in intensive care units. The study of cell cultures opens up new possibilities for elucidation of the biochem. background of volatile compds. In future studies, combined studies of a particular compd. with regard to human matrixes such as breath, urine, saliva and cell culture studies will lead to novel scientific progress in the field. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhvV2jt77E&md5=65901e0c2c4e746efdca763d468ecb063Jung, H.-T. The Present and Future of Gas Sensors. ACS Sens 2022, 7 (4), 912– 913, DOI: 10.1021/acssensors.2c00688 3The Present and Future of Gas SensorsJung, Hee-TaeACS Sensors (2022), 7 (4), 912-913CODEN: ASCEFJ; ISSN:2379-3694. (American Chemical Society) A review. The present and future of gas sensors. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhtVShtbjM&md5=85183d155224b0a59c5fe6f95efc34f74Liu, K.; Lin, M.; Zhao, Z.; Zhang, K.; Yang, S. Rational Design and Application of Breath Sensors for Healthcare Monitoring. ACS Sens 2025, 10 (1), 15– 32, DOI: 10.1021/acssensors.4c02313 There is no corresponding record for this reference.5Wang, C.; Sahay, P. Breath Analysis Using Laser Spectroscopic Techniques: Breath Biomarkers, Spectral Fingerprints, and Detection Limits. Sensors 2009, 9 (10), 8230– 8262, DOI: 10.3390/s91008230 5Breath analysis using laser spectroscopic techniques: breath biomarkers, spectral fingerprints, and detection limitsWang, Chuji; Sahay, PeeyushSensors (2009), 9 (10), 8230-8262CODEN: SENSC9; ISSN:1424-8220. (Molecular Diversity Preservation International) A review. Breath anal., a promising new field of medicine and medical instrumentation, potentially offers noninvasive, real-time, and point-of-care (POC) disease diagnostics and metabolic status monitoring. Numerous breath biomarkers have been detected and quantified so far by using the GC-MS technique. Recent advances in laser spectroscopic techniques and laser sources have driven breath anal. to new heights, moving from lab. research to com. reality. Laser spectroscopic detection techniques not only have high-sensitivity and high-selectivity, as equivalently offered by the MS-based techniques, but also have the advantageous features of near real-time response, low instrument costs, and POC function. Of the approx. 35 established breath biomarkers, such as acetone, ammonia, carbon dioxide, ethane, methane, and nitric oxide, 14 species in exhaled human breath have been analyzed by high-sensitivity laser spectroscopic techniques, namely, tunable diode laser absorption spectroscopy (TDLAS), cavity ring down spectroscopy (CRDS), integrated cavity output spectroscopy (ICOS), cavity enhanced absorption spectroscopy (CEAS), cavity leak-out spectroscopy (CALOS), photoacoustic spectroscopy (PAS), quartz-enhanced photoacoustic spectroscopy (QEPAS), and optical frequency comb cavity-enhanced absorption spectroscopy (OFC-CEAS). Spectral fingerprints of the measured biomarkers span from the UV to the mid-IR spectral regions and the detection limits achieved by the laser techniques range from ppm to ppb levels. Sensors using the laser spectroscopic techniques for a few breath biomarkers, e.g., carbon dioxide, nitric oxide, etc. are com. available. This review presents an update on the latest developments in laser-based breath anal. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtlakurnI&md5=b14f7899eec1357f8906a62ab88423156Owen, O. E.; Trapp, V. E.; Skutches, C. L.; Mozzoli, M. A.; Hoeldtke, R. D.; Boden, G.; Reichard, G. A., Jr. Acetone Metabolism During Diabetic Ketoacidosis. Diabetes 1982, 31 (3), 242– 248, DOI: 10.2337/diab.31.3.242 There is no corresponding record for this reference.7Selvaraj, R.; Vasa, N. J.; Nagendra, S. M. S.; Mizaikoff, B. Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics. Molecules 2020, 25 (9), 2227, DOI: 10.3390/molecules25092227 There is no corresponding record for this reference.8Kang, H.; Joo, H.; Choi, J.; Kim, Y.-J.; Lee, Y.; Cho, S.-Y.; Jung, H.-T. Top-Down Approaches for 10 Nm-Scale Nanochannel: Toward Exceptional H2S Detection. ACS Nano 2022, 16 (10), 17210– 17219, DOI: 10.1021/acsnano.2c07785 8Top-Down Approaches for 10 nm-Scale Nanochannel: Toward Exceptional H2S DetectionKang, Hohyung; Joo, Heeeun; Choi, Junghoon; Kim, Yong-Jae; Lee, Yullim; Cho, Soo-Yeon; Jung, Hee-TaeACS Nano (2022), 16 (10), 17210-17219CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society) Metal oxide semiconductors (MOS) have proven to be most powerful sensing materials to detect hydrogen sulfide (H2S), achieving part per billion (ppb) level sensitivity and selectivity. However, there has not been a way of extending this approach to the top-down H2S sensor fabrication process, completely limiting their com.-level productions. In this study, we developed a top-down lithog. process of a 10 nm-scale SnO2 nanochannel for H2S sensor prodn. Due to high-resoln. (15 nm thickness) and high aspect ratio (>20) structures, the nanochannel exhibited highly sensitive H2S detection performances (Ra/Rg = 116.62, τres = 31 s at 0.5 ppm) with selectivity (RH2S/Racetone = 23 against 5 ppm acetone). In addn., we demonstrated that the nanochannel could be efficiently sensitized with the p-n heterojunction without any postmodification or an addnl. process during one-step lithog. As an example, we demonstrated that the H2S sensor performance can be drastically enhanced with the NiO nanoheterojunction (Ra/Rg = 166.2, τres = 21 s at 0.5 ppm), showing the highest range of sensitivity demonstrated to date for state-of-the-art H2S sensors. These results in total constitute a high-throughput fabrication platform to commercialize the H2S sensor that can accelerate the development time and interface in real-life situations. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisF2kurnM&md5=5187fd1ee24d2b394e8d5917b1d2cb309Vm, A.; Yerevan State University YSU, 1 Alex Manoukian Metal Oxide Gas Biomarkers of Diseases for Medical and Health Applications. BJSTR 2020, 29 (2), 22328– 22336, DOI: 10.26717/BJSTR.2020.29.004780 There is no corresponding record for this reference.10Chaudhary, V.; Taha, B. A.; Lucky; Rustagi, S.; Khosla, A.; Papakonstantinou, P.; Bhalla, N. Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers. ACS Sens 2024, 9 (9), 4469– 4494, DOI: 10.1021/acssensors.4c01524 There is no corresponding record for this reference.11Güntner, A. T.; Abegg, S.; Königstein, K.; Gerber, P. A.; Schmidt-Trucksäss, A.; Pratsinis, S. E. Breath Sensors for Health Monitoring. ACS Sens 2019, 4 (2), 268– 280, DOI: 10.1021/acssensors.8b00937 There is no corresponding record for this reference.12Li, Y.; Wei, X.; Zhou, Y.; Wang, J.; You, R. Research Progress of Electronic Nose Technology in Exhaled Breath Disease Analysis. Microsyst Nanoeng 2023, 9 (1), 1– 22, DOI: 10.1038/s41378-023-00594-0 There is no corresponding record for this reference.13Cho, S.-Y.; Lee, Y.; Lee, S.; Kang, H.; Kim, J.; Choi, J.; Ryu, J.; Joo, H.; Jung, H.-T.; Kim, J. Finding Hidden Signals in Chemical Sensors Using Deep Learning. Anal. Chem. 2020, 92 (9), 6529– 6537, DOI: 10.1021/acs.analchem.0c00137 13Finding Hidden Signals in Chemical Sensors Using Deep LearningCho, Soo-Yeon; Lee, Youhan; Lee, Sangwon; Kang, Hohyung; Kim, Jaehoon; Choi, Junghoon; Ryu, Jin; Joo, Heeeun; Jung, Hee-Tae; Kim, JihanAnalytical Chemistry (Washington, DC, United States) (2020), 92 (9), 6529-6537CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society) Achieving high signal-to-noise ratio in chem. and biol. sensors enables accurate detection of target analytes. Unfortunately, below the limit of detection (LOD), it becomes difficult to detect the presence of small amts. of analytes and ext. useful information via any of the conventional methods. In this work, we examine the possibility of extg. "hidden signals" using deep neural network to enhance gas sensing below the LOD region. As a test case system, we conduct expts. for H2 sensing in six different metallic channels (Au, Cu, Mo, Ni, Pt, Pd) and demonstrate that deep neural network can enhance the sensing capabilities for H2 concn. below the LOD. We demonstrate that this technique could be universally used for different types of sensors and target analytes. Our approach can ext. new information from the hidden signals, which can be crucial for next-generation chem. sensing applications and anal. chem. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXntVKlsLw%253D&md5=ec5b1b29b97539145b865441e1eaac13