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The growing demand for wearable healthcare devices has led to an urgent need for cost-effective, wireless and portable breath monitoring systems. However, it is essential to explore novel nanomaterials that combine state-of-the-art flexible sensors with high performance and sensing capabilities along with scalability and industrially acceptable processing. In this study, we demonstrate a highly efficient NiS2-based flexible capacitive sensor fabricated via a solution-processible route using a novel single-source precursor [Ni{S2P(OPr)2}2]. The developed sensor could precisely detect the human respiration rate and exhibit rapid responsiveness, exceptional sensitivity and selectivity at ambient temperatures, with an ultra-fast response and recovery. The device effectively differentiates the exhaled breath patterns including slow, fast, oral and nasal breath, as well as post-exercise breath rates. Moreover, the sensor shows outstanding bending stability, repeatability, reliable and robust sensing performance and is capable of contactless sensing. The sensor was further employed with a user-friendly wireless interface to facilitate smartphone-enabled real-time breath monitoring systems. This work opens up numerous avenues for cost-effective, sustainable and versatile sensors with potential applications for Internet of Things-based flexible and wearable electronics.This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

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Owing to the correlation between acetone in human's exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is important for early diagnosis of diabetes diseases. In this article, a noninvasive blood glucose detection device through acetone sensing in EB, based on an α-Fe2O3-multiwalled carbon nanotube (MWCNT) nanocomposite, was successfully developed. Different amounts of α-Fe2O3 were added to the MWCNTs by a simple solution method. The optimized acetone gas sensor showed a response of 5.15 to 10 ppm acetone gas at 200 °C. Also, the fabricated sensor showed very good sensing properties even in an atmosphere with high relative humidity. Since the EB has high humidity, the proposed sensor is a promising device to exactly detect the amount of acetone in EB with high humidity. The sensor was powered by a 3200 mAh battery with the possibility of charging using mains electricity. To increase the reliability and calibration of the sensing device, a practical test was taken to detect acetone EB from 50 volunteers, and a deep learning algorithm (DLA) was used to detect the effect of various factors on the amount of acetone in each person's acetone EB. The proposed device with ±15 errors had almost 85% correct responses. Also, the proposed device had excellent response, short response time, good selectivity, and good repeatability, leading it to be a suitable candidate for noninvasive blood glucose sensing.

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Polymeric bags are a widely applied, simple, and cost-effective method for the storage and offline analysis of gaseous samples. Various materials have been used as sampling bags, all known to contain impurities and differing in their cost, durability, and storage capabilities. Herein, we present a comparative study of several well-known bag materials, Tedlar (PVF), Kynar (PVDF), Teflon (PTFE), and Nalophan (PET), as well as a new material, ethylene vinyl copolymer (EVOH), commonly used for storing food. We investigated the influences of storage conditions, humidity, bag cleaning, and light exposure on volatile organic compound concentration (acetone, acetic acid, isoprene, benzene, limonene, among others) in samples of exhaled human breath stored in bags for up to 48 h. Specifically, we show high losses of short-chain fatty acids (SCFAs) in bags of all materials (for most SCFAs, less than 50% after 8 h of storage). We found that samples in Tedlar, Nalophan, and EVOH bags undergo changes in composition when exposed to UV radiation over a period of 48 h. We report high initial impurity levels in all the bags and their doubling after a period of 48 h. We compare secondary electrospray ionization and proton transfer reaction mass spectrometry in the context of offline analysis after storage in sampling bags. We provide an analytical perspective on the temporal evolution of bag contents by presenting the intensity changes of all significantm/zfeatures. We also present a simple, automated, and cost-effective offline sample introduction system, which enables controlled delivery of collected gaseous samples from polymeric bags into the mass spectrometer. Overall, our findings suggest that sampling bags exhibit high levels of impurities, are sensitive to several environmental factors (e.g. light exposure), and provide low recoveries for some classes of compounds, e.g. SCFAs.

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For hazardous gas monitoring and non-invasive diagnosis of diabetes using breath analysis, porous foams assembled by Co3O4 nanoparticles were designed as sensing electrode materials to fabricate efficient yttria-stabilized zirconia (YSZ)-based acetone sensors. The sensitivity of the sensors was improved by varying the sintering temperature to regulate the morphology. Compared to other materials sintered at different temperatures, the porous Co3O4 nanofoams sintered at 800 °C exhibited the highest electrochemical catalytic activity during the electrochemical test. The response of the corresponding Co3O4-based sensor to 10 ppm acetone was -77.2 mV and it exhibited fast response and recovery times. Moreover, the fabricated sensor achieved a low detection limit of 0.05 ppm and a high sensitivity of -56 mV/decade in the acetone concentration range of 1-20 ppm. The sensor also exhibited excellent repeatability, acceptable selectivity, good O2/humidity resistance, and long-term stability during continuous measurements for over 30 days. Moreover, the fabricated sensor was used to determine the acetone concentration in the exhaled breaths of patients with diabetic ketosis. The results indicated that it could distinguish between healthy individuals and patients with diabetic ketosis, thereby proving its abilities to diagnose and monitor diabetic ketosis. Based on its excellent sensitivity and exhaled breath measurement results, the developed sensor has broad application prospects.

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Exhaled breath electrochemical sensing is a promising biomedical technology owing to its portability, painlessness, cost-effectiveness, and user-friendliness. Here, we present a novel approach for target analysis in exhaled breath by integrating a comfortable paper-based collector into an N95 face mask, providing a universal solution for analyzing several biomarkers. As a model analyte, we detected SARS-CoV-2 spike protein from the exhaled breath by sampling the target analyte into the collector, followed by its detection out of the N95 face mask using a magnetic bead-based electrochemical immunosensor. This approach was designed to avoid any contact between humans and the chemicals. To simulate human exhaled breath, untreated saliva samples were nebulized on the paper collector, revealing a detection limit of 1 ng/mL and a wide linear range of 3.7-10,000 ng/mL. Additionally, the developed immunosensor exhibited high selectivity toward the SARS-CoV-2 spike protein, compared to other airborne microorganisms, and the SARS-CoV-2 nucleocapsid protein. Accuracy assessments were conducted by analyzing the simulated breath samples spiked with varying concentrations of SARS-CoV-2 spike protein, resulting in satisfactory recovery values (ranging from 97 ± 4 to 118 ± 1%). Finally, the paper-based hybrid immunosensor was successfully applied for the detection of SARS-CoV-2 in real human exhaled breath samples. The position of the collector in the N95 mask was evaluated as well as the ability of this paper-based analytical tool to identify the positive patient.

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Recent respiratory outbreaks have garnered substantial attention, yet most respiratory monitoring remains confined to physical signals. Exhaled breath condensate (EBC) harbors rich molecular information that could unveil diverse insights into an individual's health. Unfortunately, challenges related to sample collection and the lack of on-site analytical tools impede the widespread adoption of EBC analysis. Here, we introduce EBCare, a mask-based device for real-time in situ monitoring of EBC biomarkers. Using a tandem cooling strategy, automated microfluidics, highly selective electrochemical biosensors, and a wireless reading circuit, EBCare enables continuous multimodal monitoring of EBC analytes across real-life indoor and outdoor activities. We validated EBCare's usability in assessing metabolic conditions and respiratory airway inflammation in healthy participants, patients with chronic obstructive pulmonary disease or asthma, and patients after COVID-19 infection.

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INTRODUCTION: Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors. MATERIALS AND METHODS: Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers. RESULTS: A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals. CONCLUSIONS: The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

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BACKGROUND: Theoretically, a rapid urease test (RUT) using a swab of the gastric wall (Swab-RUT) for Helicobacter pylori (H. pylori) is safe. However, the validity and utility of Swab-RUT remain unclear. Therefore, we assessed the validity and utility of Swab-RUT compared to RUT using mucosal forceps of the gastric wall (Forceps-RUT) and 13C-urea breath test (UBT). METHODS: This study was a multicenter prospective observational study. When the examinees were suspected of H. pylori infection during esophagogastroduodenoscopy, we performed Swab-RUT and Forceps-RUT continuously. When the examinees were not suspected of H. pylori infection, we performed Swab-RUT alone. We validated the status of H. pylori infection using UBT. RESULTS: Ninety-four examinees were enrolled from four institutions between May 2016 and December 2020 (median age [range], 56.5 [26-88] years). In this study, the sensitivity, specificity, and accuracy of Swab-RUT to UBT were 0.933 (95% confidence interval: 0.779-0.992), 0.922 (0.827-0.974), and 0.926 (0.853-0.970), respectively. The Kappa coefficient of Swab-RUT to UBT was 0.833, and that of Swab-RUT to forceps-RUT was 0.936. No complications were observed in this study. CONCLUSIONS: Swab-RUT is a valid examination for the status of H. pylori infection compared to the conventional Forceps-RUT.

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A novel Fe2Mo3O8/MoO2@MoS2 nanocomposite is synthesized for extremely sensitive detection of NH3 in the breath of kidney disease patients at room temperature. Compared to MoS2, α-Fe2O3/MoS2, and MoO2@MoS2, it shows the optimal gas-sensing performance by optimizing the formation of Fe2Mo3O8 at 900 °C. The annealed Fe2Mo3O8/MoO2@MoS2 nanocomposite (Fe2Mo3O8/MoO2@MoS2-900 °C) sensor demonstrates a remarkably high selectivity of NH3 with a response of 875% to 30 ppm NH3 and an ultralow detection limit of 3.7 ppb. This sensor demonstrates excellent linearity, repeatability, and long-term stability. Furthermore, it effectively differentiates between patients at varying stages of kidney disease through quantitative NH3 measurements. The sensing mechanism is elucidated through the analysis of alterations in X-ray photoelectron spectroscopy (XPS) signals, which is supported by density functional theory (DFT) calculations illustrating the NH3 adsorption and oxidation pathways and their effects on charge transfer, resulting in the conductivity change as the sensing signal. The excellent performance is mainly attributed to the heterojunction among MoS2, MoO2, and Fe2Mo3O8 and the exceptional adsorption and catalytic activity of Fe2Mo3O8/MoO2@MoS2-900 °C for NH3. This research presents a promising new material optimized for detecting NH3 in exhaled breath and a new strategy for the early diagnosis and management of kidney disease.

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Ammonia (NH3) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH3 is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical absorption spectroscopy (UV-DOAS) and spectral reconstruction fitting neural network (SRFNN) for detecting NH3 in EB. UV-DOAS is introduced to eliminate interference from slow change absorption in the EB spectrum while spectral reconstruction fitting is proposed for the first time to map the original spectra onto the sine function spectra by the principle of least absolute deviations. The sine function spectra are then fitted by the least-squares method to eliminate noise signals and the interference of exhaled nitric oxide. Finally, the neural network is built to enable the detection of NH3 in EB at parts per billion (ppb) level. The laboratory results show that the detection range is 9.50-12425.82 ppb, the mean absolute percentage error (MAPE) is 0.83%, and the detection accuracy is 0.42%. Experimental results prove that the sensor can detect breath NH3 and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH3 with high accuracy and stability in the medical field.

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Breath analysis enables rapid, noninvasive diagnosis of human health by identifying and quantifying exhaled biomarker. Here, we demonstrated an exhaled breath sensing method using the near-infrared laser spectroscopy, and sub parts-per-million (ppm) level ammonia detection inside the exhaled gas was achieved employing a distributed feedback laser centered at 1512 nm and Kalman filtering algorithm. Integration of the ammonia sensor was realized for exhaled breath analysis of kidney patients, and a dual operation mechanism with static and dynamic modes was proposed to make this method applicable for real-time and comprehensive pre-diagnosis of kidney disease.

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Preservation of the breath sample integrity during storage and transport is one of the biggest challenges in off-line exhaled breath gas analysis. In this context, adsorbent tubes are frequently used as storage containers for use with analytical methods employing gas chromatography with mass spectrometric detection. The key objective of this short communication is to provide data on the recovery of selected breath volatiles from Tenax®TA adsorbent tubes that were stored at -80 °C for up to 90 d. For this purpose, an Owlstone Medical's ReCIVA®Breath Sampler was used for exhaled breath collection. The following fifteen compounds, selected to cover a range of chemical properties, were monitored for their stability: isoprene, n-heptane, n-nonane, toluene, p-cymene, allyl methyl sulfide, 1-(methylthio)-propane, 1-(methylthio)-1-propene,α-pinene, DL-limonene,ß-pinene,γ-terpinene, 2-pentanone, acetoin and 2,3 butanedione. All compounds, but one (acetoin), were found to be stable during the first 4 weeks of storage (recovery within ± 2 × RSD). Furthermore, n-nonane was stable during the whole of the investigated period.

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Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In this work, biosensors with remarkable gas-sensing characteristics were developed using a self-assembly method consisting of peptides and MXene. Based on these biosensors, a mimetic biosensor array (MBA) was fabricated and integrated into a real-time testing platform (RTP). In addition, machine learning (ML) algorithms were introduced to improve the RTP's detection and identification capabilities of exhaled gas signals. The synthesized biosensor, with the ability to specifically bind to targeted gas molecules, demonstrated higher performance than the pristine MXene, with a response up to 150% greater. Besides, the MBA successfully detected 15 odor molecules affiliated with five categories of alcohols, ketones, aldehydes, esters, and acids by pattern recognition algorithms. Furthermore, with the ML assistance, the RTP detected the breath odor samples from volunteers of four categories, including healthy populations, patients of lung cancer, upper digestive tract cancer, and lower digestive tract cancer, with accuracies of 100%, 94.1%, 90%, and 95.2%, respectively. In summary, we have developed a cost-effective and precise model for non-invasive tumor diagnosis. Furthermore, this prototype also offers a versatile solution for diagnosing other diseases like nephropathy, diabetes, etc.

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Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

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The Peppermint Initiative, established within the International Association of Breath Research, introduced the peppermint protocol, a breath analysis benchmarking effort designed to address the lack of inter-comparability of outcomes across different breath sampling techniques and analytical platforms. Benchmarking with gas chromatography-ion mobility spectrometry (GC-IMS) using peppermint has been previously reported however, coupling micro-thermal desorption (µTD) to GC-IMS has not yet, been benchmarked for breath analysis. To benchmarkµTD-GC-IMS for breath analysis using the peppermint protocol. Ten healthy participants (4 males and 6 females, aged 20-73 years), were enrolled to give six breath samples into Nalophan bags via a modified peppermint protocol. Breath sampling after peppermint ingestion occurred over 6 h att= 60, 120, 200, 280, and 360 min. The breath samples (120 cm3) were pre-concentrated in theµTD before being transferred into the GC-IMS for detection. Data was processed using VOCal, including background subtractions, peak volume measurements, and room air assessment. During peppermint washout, eucalyptol showed the highest change in concentration levels, followed byα-pinene andß-pinene. The reproducibility of the technique for breath analysis was demonstrated by constructing logarithmic washout curves, with the average linearity coefficient ofR2= 0.99. The time to baseline (benchmark) value for the eucalyptol washout was 1111 min (95% CI: 529-1693 min), obtained by extrapolating the average logarithmic washout curve. The study demonstrated thatµTD-GC-IMS is reproducible and suitable technique for breath analysis, with benchmark values for eucalyptol comparable to the gold standard GC-MS.

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Analyzing exhaled volatile organic compounds (VOCs) with an electronic nose (e-nose) is emerging in medical diagnostics as a non-invasive, quick, and sensitive method for disease detection and monitoring. This study investigates if activities like spirometry or physical exercise affect exhaled VOCs measurements in asthmatics and healthy individuals, a crucial step for e-nose technology's validation for clinical use. The study analyzed exhaled VOCs using an e-nose in 27 healthy individuals and 27 patients with stable asthma, before and after performing spirometry and climbing five flights of stairs. Breath samples were collected using a validated technique and analyzed with a Cyranose 320 e-nose. In healthy controls, the exhaled VOCs spectrum remained unchanged after both lung function test and exercise. In asthmatics, principal component analysis and subsequent discriminant analysis revealed significant differences post-spirometry (vs. baseline 66.7% cross validated accuracy [CVA],p< 0.05) and exercise (vs. baseline 70.4% CVA,p< 0.05). E-nose measurements in healthy individuals are consistent, unaffected by spirometry or physical exercise. However, in asthma patients, significant changes in exhaled VOCs were detected post-activities, indicating airway responses likely due to constriction or inflammation, underscoring the e-nose's potential for respiratory condition diagnosis and monitoring.

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The maximum oxygen uptake (VO2max) is a critical factor for endurance performance in soccer. Novel wearable technology may allow frequent assessment of V̇O2max during non-fatiguing warm-up runs of soccer players with minimal interference to soccer practice. The aim of this study was to assess the validity of VO2max provided by a consumer grade smartwatch (Garmin Forerunner 245, Garmin, Olathe, USA, Software:13.00) and the YoYo Intermittent Recovery Run 2 (YYIR2) by comparing it with respiratory gas analysis. 24 trained male youth soccer players performed different tests to assess VO2max: i) a treadmill test employing respiratory gas analysis, ii) YYIR2 and iii) during a non-fatiguing warm-up run of 10 min wearing a smartwatch as recommended by the device-manufacturer on 3 different days within 2 weeks. As the device-manufacturer indicates that validity of smartwatch-derived VO2max may differ with an increase in runs, 16 players performed a second run with the smartwatch to test this claim. The main evidence revealed that the smartwatch showed an ICC of 0.37 [95% CI: -0.25; 0.71] a mean absolute percentage error (MAPE) of 5.58% after one run, as well as an ICC of 0.54 [95% CI: -0.3; 8.4] and a MAPE of 1.06% after the second run with the smartwatch. The YYIR2 showed an ICC of 0.17 [95% CI: -5.7; 0.6]; and MAPE of 4.2%. When using the smartwatch for VO2max assessment in a non-fatiguing run as a warm-up, as suggested by the device manufacturer before soccer practice, the MAPE diminishes after two runs. Therefore, for more accurate VO2max assessment with the smartwatch, we recommend to perform at least two runs to reduce the MAPE and enhance the validity of the findings.

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The development of a portable, low-cost sensor capable of accurately detecting H2S gas in exhaled human breath at room temperature is highly anticipated in the fields of human health assessment and food spoilage evaluation. However, achieving outstanding gas sensing performance and applicability for flexible room-temperature operation with parts per billion H2S gas sensors still poses technical challenges. To address this issue, this study involves the in situ growth of MoS2 nanosheets on the surface of In2O3 fibers to construct a p-n heterojunction. The In2O3@MoS2-2 sensor exhibits a high response of 460.61 to 50 ppm of H2S gas at room temperature, which is 19.5 times higher than that of the pure In2O3 sensor and 322.1 times higher than that of pure MoS2. The In2O3@MoS2-2 also demonstrates a minimum detection limit of 3 ppb and maintains a stable response to H2S gas even after being bent 50 times at a 60° angle. These exceptional gas sensing properties are attributed to the increase in oxygen vacancies and chemisorbed oxygen on In2O3@MoS2-2 nanofibers as well as the formation of the p-n heterojunction, which modulates the heterojunction barrier. Furthermore, in this study, we successfully applied the In2O3@MoS2-2 sensor for oral disease and detection of food spoilage conditions, thereby providing new design insights for the development of portable exhaled gas sensors and gas sensors for evaluating food spoilage conditions at room temperature.

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