Breath Sensors for Health Monitoring

Sensing nature's alarm: SnO2/MXene gas sensor unveils methyl jasmonate signatures of plant insect stress

The incorporation of artificial intelligence into agriculture presents challenges, particularly due to hardware limitations, especially in sensors. Currently, pest detection relies heavily on manual scouting by humans. Therefore, the objective of this study is to create a chemoresistive sensor that enables early identification of the characteristic volatile compound, viz., methyl jasmonate, released during pest infestations. Given the lower reactivity of esters, we have fine-tuned a composite consisting of SnO2 nanoparticles and 2D-MXene sheets to enhance adsorption and selective oxidation, resulting in heightened sensitivity. The optimized composite demonstrated a notable response even at concentrations as low as 120 ppb, successfully confirming pest infestations in tomato crops.

Molecular design and architectonics towards film-based fluorescent sensing

The past few decades have witnessed encouraging progress in the development of high-performance film-based fluorescent sensors (FFSs) for detecting explosives, illicit drugs, chemical warfare agents (CWAs), and hazardous volatile organic chemicals (VOCs), among others. Several FFSs have transitioned from laboratory research to real-world applications, demonstrating their practical relevance. At the heart of FFS technology lies the sensing films, which play a crucial role in determining the analytes and the resulting signals. The selection of sensing fluorophores and the fabrication strategies employed in film construction are key factors that influence the fluorescence properties, active-layer structures, and overall sensing behaviors of these films. This review examines the progress and innovations in the research field of FFSs over the past two decades, focusing on advancements in fluorophore design and active-layer structural engineering. It underscores popular sensing fluorophore scaffolds and the dynamics of excited state processes. Additionally, it delves into six distinct categories of film fabrication technologies and strategies, providing insights into their advantages and limitations. This review further addresses important considerations such as photostability and substrate effects. Concluding with an overview of the field's challenges and prospects, it sheds light on the potential for further development in this burgeoning area.

High-Precision and Real-Time Measurement of Water Isotope Ratios Based on a Mid-Infrared Optical Sensor

A compact spectrometer based on a mid-infrared optical sensor has been developed for high-precision and real-time measurement of water isotope ratios. The instrument uses laser absorption spectroscopy and applies the weighted Kalman filtering method to determine water isotope ratios with high precision and fast time response. The precision of the measurements is 0.41‰ for δ18O and 0.29‰ for δ17O with a 1 s time. This is much faster than the standard running average technique, which takes over 90 s to achieve the same level of precision. The successful development of this compact mid-infrared optical sensor opens up new possibilities for its future applications in atmospheric and breath gas research.

Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization

This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin-Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentrations, amounting to 2500 data points per parameter set. After seven trials with four initial parameter sets (ten parameter sets were tested in total), the DBI was successfully reduced from 2.1 to 1.5. The classification accuracy for the test gases based on the support vector machine tends to increase with decreasing the DBI, indicating that the DBI acts as a good OBJ. Additionally, the accuracy itself increased from 85.4% to 93.2% through optimization. The deviation from the tendency that the accuracy increases with decreasing the DBI for some parameter sets was also discussed. Consequently, it was demonstrated that the proposed optimization method based on BO is promising for temperature modulation.

The transition from genomics to phenomics in personalized population health

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

Mixed Potential Type Isoprene Sensor for the Application in Real-Time Monitoring of Biomarker Gases

Monitoring of isoprene in exhaled breath is expected to provide a noninvasive and painless method for dynamic monitoring of physiological and metabolic states during exercise. However, for real-time and portable detection of isoprene, gas sensors have become the best choice for gas detection technology, which are crucial to achieving the goal of anytime, anywhere, human-centered healthcare in the future. Here, we first report a mixed potential type isoprene sensor based on a Gd2Zr2O7 solid electrolyte and a CdSb2O6 sensing electrode, which enables sensitive detection for isoprene with sensitivities of -21.2 mV/ppm and -65.8 mV/decade in the range of 0.05-1 and 1-100 ppm. The sensing behavior of the sensor follows the mixed potential sensing mechanism and was further verified by the electrochemical polarization curves. The significant differentiation between the sensor response to exhaled breath of healthy individuals and simulated breath containing different concentrations of isoprene demonstrates the potential of the sensor for the detection of isoprene in exhaled breath. Simultaneously, monitoring of isoprene during exercise signifies the feasibility of the sensor in dynamic monitoring of physiological indicators, which is not only of great significance for optimizing training and guiding therapeutic exercise intervention in sporting scenarios but also expected to help further reveal the interaction between exercise, muscle, and organ metabolism in medicine.

Porous materials as effective chemiresistive gas sensors

Chemiresistive gas sensors (CGSs) have revolutionized the field of gas sensing by providing a low-power, low-cost, and highly sensitive means of detecting harmful gases. This technology works by measuring changes in the conductivity of materials when they interact with a testing gas. While semiconducting metal oxides and two-dimensional (2D) materials have been used for CGSs, they suffer from poor selectivity to specific analytes in the presence of interfering gases and require high operating temperatures, resulting in high signal-to-noise ratios. However, nanoporous materials have emerged as a promising alternative for CGSs due to their high specific surface area, unsaturated metal actives, and density of three-dimensional inter-connected conductive and pendant functional groups. Porous materials have demonstrated excellent response and recovery times, remarkable selectivity, and the ability to detect gases at extremely low concentrations. Herein, our central emphasis is on all aspects of CGSs, with a primary focus on the use of porous materials. Further, we discuss the basic sensing mechanisms and parameters, different types of popular sensing materials, and the critical explanations of various mechanisms involved throughout the sensing process. We have provided examples of remarkable performance demonstrated by sensors using these materials. In addition to this, we compare the performance of porous materials with traditional metal-oxide semiconductors (MOSs) and 2D materials. Finally, we discussed future aspects, shortcomings, and scope for improvement in sensing performance, including the use of metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and porous organic polymers (POPs), as well as their hybrid counterparts. Overall, CGSs using porous materials have the potential to address a wide range of applications, including monitoring water quality, detecting harmful chemicals, improving surveillance, preventing natural disasters, and improving healthcare.

Structure-Function Relationship of Highly Reactive CuOx Clusters on Co3 O4 for Selective Formaldehyde Sensing at Low Temperatures

Designing reactive surface clusters at the nanoscale on metal-oxide supports enables selective molecular interactions in low-temperature catalysis and chemical sensing. Yet, finding effective material combinations and identifying the reactive site remains challenging and an obstacle for rational catalyst/sensor design. Here, the low-temperature oxidation of formaldehyde with CuOx clusters on Co3 O4 nanoparticles is demonstrated yielding an excellent sensor for this critical air pollutant. When fabricated by flame-aerosol technology, such CuOx clusters are finely dispersed, while some Cu ions are incorporated into the Co3 O4 lattice enhancing thermal stability. Importantly, infrared spectroscopy of adsorbed CO, near edge X-ray absorption fine structure spectroscopy and temperature-programmed reduction in H2 identified Cu+ and Cu2+ species in these clusters as active sites. Remarkably, the Cu+ surface concentration correlated with the apparent activation energy of formaldehyde oxidation (Spearman's coefficient ρ = 0.89) and sensor response (0.96), rendering it a performance descriptor. At optimal composition, such sensors detected even the lowest formaldehyde levels of 3 parts-per-billion (ppb) at 75°C, superior to state-of-the-art sensors. Also, selectivity to other aldehydes, ketones, alcohols, and inorganic compounds, robustness to humidity and stable performance over 4 weeks are achieved, rendering such sensors promising as gas detectors in health monitoring, air and food quality control.

Embedding Pd into SnO2 drastically enhances gas sensing

Combustion aerosol processes can uniquely embed noble metals into semiconducting particles. Here, monocrystalline SnO2 particles embedded with Pd and/or PdOx were made by flame spray pyrolysis (FSP) of appropriate precursors through microexplosions by droplet-to-particle conversion as the crystal size was proportional to the cube root of precursor solution concentration, C. These particles were air-annealed and leached with nitric acid for removal of metallic Pd from their surface. The SnO2 crystal size varied from 11 to 24 nm and was in close agreement with the primary particle size determined by nitrogen adsorption. The embedded fraction of Pd ranged from about 30 to 80% of the nominal Pd-content. This was achieved by judiciously varying the C, Pd content and the ratio of precursor solution to dispersion oxygen flowrates during FSP. The response of sensors made by doctor blading films of such particles to 1 ppm of acetone and CO was evaluated at 350 °C and 50% relative humidity. Embedding Pd/PdOx into SnO2 significantly increased the sensor response: 2-6 times over that of pure or conventionally-made Pd-containing SnO2 sensors at low nominal Pd-contents (0.2 mol%). For higher ones (i.e. 1 mol% Pd), the sensor response was enhanced by up to two orders of magnitude. This is attributed to Pd atoms in the SnO2 lattice near the particle surface and/or Pd/PdOx clusters acting as nanoelectrodes into SnO2 films and altering their transducing properties as shown by high resolution electron microscopy, XPS and baseline resistance measurements of pure and Pd-embedded SnO2 sensing films.

Humidity activated ultra-selective room temperature gas sensor based on W doped MoS2/RGO composites for trace level ammonia detection

The lack of highly efficient, cost effective and stable ammonia gas sensors functionable at room temperature even in extreme humid environments poses significant challenge for the future generation gas sensors. The prime factors that impede the development of such next generation gas sensors are the strong interference of humidity and sluggish selectivity. Herein, we fabricated tungsten doped molybdenum disulphide/reduced graphene oxide composite by an in-situ hydrothermal method to exploit the adsorption, dissolution (solubility), ionization and transmission process of ammonia and thereby to effectuate its trace level detection even in indispensable humid environments. The protype based on 5 at.% Tungsten doped MoS2/RGO (W5) gas sensor exhibited 3.8-fold increment in its response to 50 ppm of ammonia when the relative humidity varied from 20 % to 70 % with ultra-high selectivity at room temperature. The as prepared gas sensor revealed a practical detection limit down to 1 ppm with a substantial response and rapid recovery time. Furthermore, W5 gas sensor exhibited a 42-fold increment in response to 50 ppm of ammonia relative to its pristine (MoS2/RGO) MG composite with a RH of 70 %. The proton hopping mechanism accountable for such an enormous enhancement in ammonia sensing and its potential for breath sensor are briefly annotated.

Real-time breath analysis towards a healthy human breath profile

The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

Self-powered triboelectric nanogenerator sensor for detecting humidity level and monitoring ethanol variation in a simulated exhalation environment

Respiration stands as a vital process reflecting physiological and pathological human health status. Exhaled breath analysis offers a facile, non-invasive, swift, and cost-effective approach for diagnosing and monitoring diseases by detecting concentration changes of specific biomarkers. In this study, we employed Polyethylene oxide/copper (I) oxide composite nanofibers (PCNFs), synthesized via the electrospinning method as the sensing material to measure ethanol levels (1-200 ppm) in an exhaled breath simulator environment. The integrated contact-separation triboelectric nanogenerator was utilized to power the self-powered PCNFs exhaled breath sensor. The PCNFs-based gas sensor demonstrates promising results with values of 0.9 and 3.2 for detecting 5 ppm and 200 ppm ethanol, respectively, in the presence of interfering gas at 90% relative humidity (RH). Notably, the sensor displayed remarkable ethanol selectivity, with ratios of 10:1 to methanol and 25:1 to acetone. Response and recovery times for 200 ppm ethanol at 90 RH% were rapid, at 2.7 s and 5.8 s, respectively. The PCNFs-based exhaled breath sensor demonstrated consistent and stable performance in practical conditions, showcasing its potential for integration into wearable devices. This self-powered breath sensor enabling continuous monitoring of lung cancer symptoms and facilitating compliance checks with legal alcohol consumption limits.

Data-Driven Approach to Modeling Microfabricated Chemical Sensor Manufacturing

We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (μPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.

Application of p and n-Type Silicon Nanowires as Human Respiratory Sensing Device

Accurate and fast breath monitoring is of great importance for various healthcare applications, for example, medical diagnoses, studying sleep apnea, and early detection of physiological disorders. Devices meant for such applications tend to be uncomfortable for the subject (patient) and pricey. Therefore, there is a need for a cost-effective, lightweight, small-dimensional, and non-invasive device whose presence does not interfere with the observed signals. This paper reports on the fabrication of a highly sensitive human respiratory sensor based on silicon nanowires (SiNWs) fabricated by a top-down method of metal-assisted chemical-etching (MACE). Besides other important factors, reducing the final cost of the sensor is of paramount importance. One of the factors that increases the final price of the sensors is using gold (Au) electrodes. Herein, we investigate the sensor's response using aluminum (Al) electrodes as a cost-effective alternative, considering the fact that the electrode's work function is crucial in electronic device design, impacting device electronic properties and electron transport efficiency at the electrode-semiconductor interface. Therefore a comparison is made between SiNWs breath sensors made from both p-type and n-type silicon to investigate the effect of the dopant and electrode type on the SiNWs respiratory sensing functionality. A distinct directional variation was observed in the sample's response with Au and Al electrodes. Finally, performing a qualitative study revealed that the electrical resistance across the SiNWs renders greater sensitivity to breath than to dry air pressure. No definitive research demonstrating the mechanism behind these effects exists, thus prompting our study to investigate the underlying process.

Multiwavelength Photoacoustic Breath Analysis Sensor for the Diagnosis of Lung Diseases: COPD and Asthma

Breath analysis is emerging as a universal diagnostic method for clinical applications. The possibility of breath analysis is being explored vigorously using different analytical techniques. We have designed and assembled a multiwavelength UV photoacoustic spectroscopy (PAS) sensor for the said application. To optimize laser wavelength for sample excitation, photoacoustic signals from disease and normal conditions are recorded with different laser excitations (213, 266, 355, and 532 nm) on exhaled breath samples. Principal component analysis (PCA) of the PA signals has shown that 213, 266, and 355 nm laser excitations are suitable for breath analysis, with reliable descriptive statistics obtained for 266 nm laser. The study has, therefore, been extended for breath samples collected from asthma, chronic obstructive pulmonary disease (COPD), and normal subjects, using 266 nm laser excitation. PCA of the PA data shows good classification among asthma, COPD, and normal subjects. Match/No-match study performed with asthma, COPD, and normal calibration set has demonstrated the potential of using this method for diagnostic application. Sensitivity and specificity are observed as 88 and 89%, respectively. The area under the curve of the ROC curve is found to be 0.948, which justifies the diagnostic capability of the device for lung diseases. The same samples were studied using a commercial E-Nose, and the measurement outcome strongly supports the PAS results.

Self-Powered Sensing in Wearable Electronics─A Paradigm Shift Technology

With the advancements in materials science and micro/nanoengineering, the field of wearable electronics has experienced a rapid growth and significantly impacted and transformed various aspects of daily human life. These devices enable individuals to conveniently access health assessments without visiting hospitals and provide continuous, detailed monitoring to create comprehensive health data sets for physicians to analyze and diagnose. Nonetheless, several challenges continue to hinder the practical application of wearable electronics, such as skin compliance, biocompatibility, stability, and power supply. In this review, we address the power supply issue and examine recent innovative self-powered technologies for wearable electronics. Specifically, we explore self-powered sensors and self-powered systems, the two primary strategies employed in this field. The former emphasizes the integration of nanogenerator devices as sensing units, thereby reducing overall system power consumption, while the latter focuses on utilizing nanogenerator devices as power sources to drive the entire sensing system. Finally, we present the future challenges and perspectives for self-powered wearable electronics.

Nano Hotplate Fabrication for Metal Oxide-Based Gas Sensors by Combining Electron Beam and Focused Ion Beam Lithography

Metal oxide semiconductor (MOS) gas sensors are widely used for gas detection. Typically, the hotplate element is the key component in MOS gas sensors which provide a proper and tunable operation temperature. However, the low power efficiency of the standard hotplates greatly limits the portable application of MOS gas sensors. The miniaturization of the hotplate geometry is one of the most effective methods used to reduce its power consumption. In this work, a new method is presented, combining electron beam lithography (EBL) and focused ion beam (FIB) technologies to obtain low power consumption. EBL is used to define the low-resolution section of the electrode, and FIB technology is utilized to pattern the high-resolution part. Different Au++ ion fluences in FIBs are tested in different milling strategies. The resulting devices are characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), and secondary ion mass spectrometry (SIMS). Furthermore, the electrical resistance of the hotplate is measured at different voltages, and the operational temperature is calculated based on the Pt temperature coefficient of resistance value. In addition, the thermal heater and electrical stability is studied at different temperatures for 110 h. Finally, the implementation of the fabricated hotplate in ZnO gas sensors is investigated using ethanol at 250 °C.

Synergism of Edge Effect and Interlayer Engineering of VS2 on CNFs for Rapid and Precise NO2 Detection

Although two-dimensional (2D) transition-metal dichalcogenides (TMDs) exhibit attractive prospects for gas-sensing applications, the rapid and precise sensing of TMDs at low loss remains challenging. Herein, a NO2 sensor based on an expanded VS2 (VS2-E)/carbon nanofibers (CNFs) composite (abbreviated as VS2-E-C) with ultrafast response/recovery at a low-loss state is reported. In particular, the impact of the CNF content on the NO2-sensing performance of VS2-E-C was thoroughly explored. Expanded VS2 nanosheets were grafted onto the surface of hollow CNFs, and the combination boosted the charge transport, exposing abundant active edges of VS2, which enhanced the adsorption of NO2 efficiently. The activity of the VS2 edge is further confirmed by stronger NO2 adsorption with a more negative adsorption energy (-3.42 eV) and greater than the basal VS2 surface (-1.26 eV). Moreover, the exposure of rich edges induced the emergence of the expanded interlayers, which promoted the adsorption/desorption of NO2 and the interaction of gas molecules within VS2-E-C. The synergism of edge effect and interlayer engineering confers the VS2-E-C3 sensor with ultrafast response/recovery speed (9/10 s) at 60 °C, high sensitivity (∼2.50 to 15 ppm NO2), good selectivity/stability, and a low detection limit of 23 ppb. The excellent "4S" functions indicate the promising prospect of the VS2-E-C3 sensor for fast and precise NO2 detection at low-loss condition.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Handheld device quantifies breath acetone for real-life metabolic health monitoring

Non-invasive breath analysis with mobile health devices bears tremendous potential to guide therapeutic treatment and personalize lifestyle changes. Of particular interest is the breath volatile acetone, a biomarker for fat burning, that could help in understanding and treating metabolic diseases. Here, we report a hand-held (6 × 10 × 19.5 cm³), light-weight (490 g), and simple device for rapid acetone detection in breath. It comprises a tailor-made end-tidal breath sampling unit, connected to a sensor and a pump for on-demand breath sampling, all operated using a Raspberry Pi microcontroller connected with a HDMI touchscreen. Accurate acetone detection is enabled by introducing a catalytic filter and a separation column, which remove and separate undesired interferents from acetone upstream of the sensor. This way, acetone is detected selectively even in complex gas mixtures containing highly concentrated interferents. This device accurately tracks breath acetone concentrations in the exhaled breath of five volunteers during a ketogenic diet, being as high as 26.3 ppm. Most importantly, it can differentiate small acetone changes during a baseline visit as well as before and after an exercise stimulus, being as low as 0.5 ppm. It is stable for at least four months (122 days), and features excellent bias and precision of 0.03 and 0.6 ppm at concentrations below 5 ppm, as validated by proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). Hence, this detector is highly promising for simple-in-use, non-invasive, and routine monitoring of acetone to guide therapeutic treatment and track lifestyle changes.

Revolutionizing Precision Medicine: Exploring Wearable Sensors for Therapeutic Drug Monitoring and Personalized Therapy

Precision medicine, particularly therapeutic drug monitoring (TDM), is essential for optimizing drug dosage and minimizing toxicity. However, current TDM methods have limitations, including the need for skilled operators, patient discomfort, and the inability to monitor dynamic drug level changes. In recent years, wearable sensors have emerged as a promising solution for drug monitoring. These sensors offer real-time and continuous measurement of drug concentrations in biofluids, enabling personalized medicine and reducing the risk of toxicity. This review provides an overview of drugs detectable by wearable sensors and explores biosensing technologies that can enable drug monitoring in the future. It presents a comparative analysis of multiple biosensing technologies and evaluates their strengths and limitations for integration into wearable detection systems. The promising capabilities of wearable sensors for real-time and continuous drug monitoring offer revolutionary advancements in diagnostic tools, supporting personalized medicine and optimal therapeutic effects. Wearable sensors are poised to become essential components of healthcare systems, catering to the diverse needs of patients and reducing healthcare costs.

Dynamic Breath Limonene Sensing at High Selectivity

Liver diseases (e.g., cirrhosis, cancer) cause more than two million deaths per year worldwide. This is partly attributed to late diagnosis and insufficient screening techniques. A promising biomarker for noninvasive and inexpensive liver disease screening is breath limonene that can indicate a deficiency of the cytochrome P450 liver enzymes. Here, we introduce a compact and low-cost detector for dynamic and selective breath limonene sensing. It comprises a chemoresistive sensor based on Si/WO₃ nanoparticles pre-screened by a packed bed Tenax separation column at room temperature. We demonstrate selective limonene detection down to 20 parts per billion over up to three orders of magnitude higher concentrated acetone, ethanol, hydrogen, methanol, and 2-propanol in gas mixtures, as well as robustness to 10-90% relative humidity. Most importantly, this detector recognizes the individual breath limonene dynamics of four healthy volunteers following the ingestion (swallowing or chewing) of a limonene capsule. Limonene release and subsequent metabolization are monitored from breath measurements in real time and in excellent agreement (R² = 0.98) with high-resolution proton transfer reaction mass spectrometry. This study demonstrates the potential of the detector as a simple-to-use and noninvasive device for the routine monitoring of limonene levels in exhaled breath to facilitate early diagnosis of liver dysfunction.

Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection

We adapted an existing, spaceflight-proven, robust "electronic nose" (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using "leave-one-out" training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home.

Flexible Humidity Sensor with High Sensitivity and Durability for Respiratory Monitoring Using Near-Field Electrohydrodynamic Direct-Writing Method

The humidity of breath can serve as an important health indicator, providing crucial clinical information about human physiology. Significant progress had been made in the development of flexible humidity sensors. However, improving its humidity sensing performance (sensitivity and durability) is still facing many challenges. In this work, near-field electrohydrodynamic direct writing (NFEDW) was proposed to fabricate humidity sensors with high sensitivity and durability for respiration monitoring. Due to the applied electric field, dense carbon nanotube/cellulose nanofiber (CNT/CNF) networks formed during the printing process that enhance the sensitivity of the sensor. The prepared sensor showed excellent humidity responses, with a maximum response value of 61.5% (ΔR/R₀) at 95% relative humidity (RH). Additionally, the sensitivity film prepared by the NFEDW method closely fits the poly(ethylene terephthalate) (PET) substrate, endowing the sensor with outstanding bending (with a maximum curvature of 4.7 cm^-1) and folding durability (up to 50 times). The sensitivity of the prepared sensor under different simulated conditions, namely, nose breathing, mouth breathing, coughing, yawning, breath holding, and speaking, was excellent, demonstrating the potential of the sensor for the real-time monitoring of human breath humidity. Thus, the high-performance flexible humidity sensor is suitable for human respiration and health monitoring.

Functionalized Hydrogel-Based Wearable Gas and Humidity Sensors

Breathing is an inherent human activity; however, the composition of the air we inhale and gas exhale remains unknown to us. To address this, wearable vapor sensors can help people monitor air composition in real time to avoid underlying risks, and for the early detection and treatment of diseases for home healthcare. Hydrogels with three-dimensional polymer networks and large amounts of water molecules are naturally flexible and stretchable. Functionalized hydrogels are intrinsically conductive, self-healing, self-adhesive, biocompatible, and room-temperature sensitive. Compared with traditional rigid vapor sensors, hydrogel-based gas and humidity sensors can directly fit human skin or clothing, and are more suitable for real-time monitoring of personal health and safety. In this review, current studies on hydrogel-based vapor sensors are investigated. The required properties and optimization methods of wearable hydrogel-based sensors are introduced. Subsequently, existing reports on the response mechanisms of hydrogel-based gas and humidity sensors are summarized. Related works on hydrogel-based vapor sensors for their application in personal health and safety monitoring are presented. Moreover, the potential of hydrogels in the field of vapor sensing is elucidated. Finally, the current research status, challenges, and future trends of hydrogel gas/humidity sensing are discussed.

On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array

Tuberculosis (TB) is among the more frequent causes of death in many countries. For pulmonary TB, early diagnosis greatly increases the efficiency of therapies. Although highly sensitive tests based on nucleic acid amplification tests (NAATs) and loop-mediated isothermal amplification (TB-LAMP) are available, smear microscopy is still the most widespread diagnostics method in most low-middle-income countries, and the true positive rate of smear microscopy is lower than 65%. Thus, there is a need to increase the performance of low-cost diagnosis. For many years, the use of sensors to analyze the exhaled volatile organic compounds (VOCs) has been proposed as a promising alternative for the diagnosis of several diseases, including tuberculosis. In this paper, the diagnostic properties of an electronic nose (EN) based on sensor technology previously used to identify tuberculosis have been tested on-field in a Cameroon hospital. The EN analyzed the breath of a cohort of subjects including pulmonary TB patients (46), healthy controls (38), and TB suspects (16). Machine learning analysis of the sensor array data allows for the identification of the pulmonary TB group with respect to healthy controls with 88% accuracy, 90.8% sensitivity, 85.7% specificity, and 0.88 AUC. The model trained with TB and healthy controls maintains its performance when it is applied to symptomatic TB suspects with a negative TB-LAMP. These results encourage the investigation of electronic noses as an effective diagnostic method for future inclusion in clinical practice.

Rapid and non-invasive diagnosis of type 2 diabetes through sniffing urinary acetone by a proton transfer reaction mass spectrometry

Urinary acetone in urine is produced from fat metabolism in human body, which can be accelerated in diabetic patients because of insufficient utilization and storage of glucose. In this study, we tried to develop a novel diagnosis method of type 2 diabetes (T2D) through sniffing urinary acetone by a proton transfer reaction mass spectrometry (PTR-MS). A total of 180 T2D patients and 180 healthy volunteers were recruited from three hospitals for multicenter study. Urine samples were collected in the morning when donators were fasting and stored in glass bottles. Acetone in the headspace of these bottles was qualitatively and quantitatively detected by the PTR-MS in 8 h. Using a threshold of 690.1 ppbv, a diagnostic model was established using urinary acetone with an accuracy of 81.3% (sensitivity: 73.3%, specificity: 89.3%) in hospital Ⅰ. In the verification studies, the accuracies were 92.5% (sensitivity: 88.7%, specificity: 96.2%) in hospital Ⅱ and 83.7% (sensitivity: 76.9%, specificity: 90.4%) in hospital Ⅲ, respectively. The accuracy is comparable to that of clinically used diagnosis methods, fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycosylated hemoglobin A1c (HbA1c) test. The sensitivity for 35 newly diagnosed patients was 85.7%. The newly developed technology is completely non-invasive and much more rapid than clinical FPG, OGTT, and HbA1c tests. It has a promising prospect in clinical use. But the applicability in different human races still need more validations.

Regulating the Electron Depletion Layer of Au/V2O5/Ag Thin Film Sensor for Breath Acetone as Potential Volatile Biomarker

Human exhaled breath has been utilized to identify biomarkers for diseases such as diabetes and cancer. The existence of these illnesses is indicated by a rise in the level of acetone in the breath. The development of sensing devices capable of identifying the onset of lung cancer or diabetes is critical for the successful monitoring and treatment of these diseases. The goal of this research is to prepare a novel breath acetone sensor made of Ag NPs/V₂O₅ thin film/Au NPs by combining DC/RF sputtering and post-annealing as synthesis methods. The produced material was characterized using X-ray diffraction (XRD), UV-Vis, Raman, and atomic force microscopy (AFM). The results revealed that the sensitivity to 50 ppm acetone of the Ag NPs/V₂O₅ thin film/Au NPs sensor was 96%, which is nearly twice and four times greater than the sensitivity of Ag NPs/V₂O₅ and pristine V₂O₅, respectively. This increase in sensitivity can be attributed to the engineering of the depletion layer of V₂O₅ through the double activation of the V₂O₅ thin films with uniform distribution of Au and Ag NPs that have different work function values.

Environmental formaldehyde sensing at room temperature by smartphone-assisted and wearable plasmonic nanohybrids

Formaldehyde is a toxic and carcinogenic indoor air pollutant. Promising for its routine detection are gas sensors based on localized surface plasmon resonance (LSPR). Such sensors trace analytes by converting tiny changes in the local dielectric environment into easily readable, optical signals. Yet, this mechanism is inherently non-selective to volatile organic compounds (like formaldehyde) and yields rarely detection limits below parts-per-million concentrations. Here, we reveal that chemical reaction-mediated LSPR with nanohybrids of Ag/AgO_x core-shell clusters on TiO₂ enables highly selective formaldehyde sensing down to 5 parts-per-billion (ppb). Therein, AgO_x is reduced by the formaldehyde to metallic Ag resulting in strong plasmonic signal changes, as measured by UV/Vis spectroscopy and confirmed by X-ray diffraction. This interaction is highly selective to formaldehyde over other aldehydes, alcohols, ketones, aromatic compounds (as confirmed by high-resolution mass spectrometry), inorganics, and quite robust to relative humidity changes. Since this sensor works at room temperature, such LSPR nanohybrids are directly deposited onto flexible wristbands to quantify formaldehyde between 40-500 ppb at 50% RH, even with a widely available smartphone camera (Pearson correlation coefficient r = 0.998). Such chemoresponsive coatings open new avenues for wearable devices in environmental, food, health and occupational safety applications, as demonstrated by an early field test in the pathology of a local hospital.

MXene-based wireless facemask enabled wearable breath acetone detection for lipid metabolic monitoring

Breath acetone (BrAC) detection presents a promising scheme for noninvasive monitoring of metabolic health due to its close correlation to diets and exercise-regulated lipolysis. Herein, we report a Ti₃C₂T_x MXene-based wireless facemask for on-body BrAC detection and real-time tracking of lipid metabolism, where Ti₃C₂T_x MXene serves as a versatile nanoplatform for not only acetone detection but also breath interference filtration. The incorporation of in situ grown TiO₂ and short peptides with Ti₃C₂T_x MXene further improves the acetone sensitivity and selectivity, while TiO₂-MXene interfaces facilitate light-assisted response calibration. To further realize wearable breath monitoring, a miniaturized flexible detection tag has been integrated with a commercially available facemask, which enables facile BrAC detection and wireless data transmission. Through the hierarchically designed filtration-detection-calibration-transmission system, we realize BrAC detection down to 0.31 ppm (part per million) in breath. On-body breath tests validate the facemask in dynamically monitoring of lipid metabolism, which could guide dieter, athletes, and fitness enthusiasts to arrange diets and exercise activities. The proposed wearable platform opens up new possibility toward the practice of breath analysis as well as daily lipid metabolic management.

A Sensor-Aided System for Physical Perfect Control Applications in the Continuous-Time Domain

The recently introduced continuous-time perfect control algorithm has revealed a great potential in terms of the maximum-speed and maximum-accuracy behaviors. However, the discussed inverse model-originated control strategy is associated with considerable energy consumption, which has exceeded a technological limitation in a number of industrial cases. In order to prevent such an important drawback, several solutions could be considered. Therefore, an innovative perfect control scheme devoted to the multivariable real-life objects is investigated in this paper. Henceforth, the new IMC-related approach, strongly supported by the vital sensor-aided system, can successfully be employed in every real-time engineering task, where the precision of conducted processes plays an important role. Theoretical and practical examples strictly confirm the big implementation potential of the new established method over existing ones. It has been seen that the new perfect control algorithm outperforms the classical control law in the form of LQR (considered in two separate ways), which is clearly manifested by almost all simulation examples. For instance, in the case of the multi-tank system, the performance indices ISE, RT, and MOE for LQR without an integration action have been equal to 2.431, 2.4×102, and 3.655×10-6, respectively, whilst the respective values 1.638, 1.58×102, and 1.514×10-7 have been received for the proposed approach.

Wearable humidity sensor embroidered on a commercial face mask and its electrical properties

Owing to the rapid development in the field of e-textile-based flexible and portable sensors, the present work demonstrates a fully textile-based stretchable face mask humidity sensor which was created using digital embroidery technique. The design of the sensor was comprised of interdigitated structured electrodes made up of polymer core-based conductive silver-coated threads and hygroscopic threads embedded between them. The fabricated sensor performed well towards moisture detection in accordance with the principle where resistance of the face mask sensor decreased with the increase in the relative humidity along with the changing operational frequency in the range from 1 Hz to 200 kHz. The electrical response (resistance, impedance, capacitance and phase angle) of the novel thread-based sensor towards change in relative humidity was recorded and showed in the present work. The embroidery of polymer-based threads onto the face mask towards humidity sensing offers a novel wearable platform for more extended biomedical applications for detection of various breath biomarkers and thus early diagnosis of diseases.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10853-022-08135-2.

Exhaled breath condensate as bioanalyte: from collection considerations to biomarker sensing

Since the SARS-CoV-2 pandemic, the potential of exhaled breath (EB) to provide valuable information and insight into the health status of a person has been revisited. Mass spectrometry (MS) has gained increasing attention as a powerful analytical tool for clinical diagnostics of exhaled breath aerosols (EBA) and exhaled breath condensates (EBC) due to its high sensitivity and specificity. Although MS will continue to play an important role in biomarker discovery in EB, its use in clinical setting is rather limited. EB analysis is moving toward online sampling with portable, room temperature operable, and inexpensive point-of-care devices capable of real-time measurements. This transition is happening due to the availability of highly performing biosensors and the use of wearable EB collection tools, mostly in the form of face masks. This feature article will outline the last developments in the field, notably the novel ways of EBA and EBC collection and the analytical aspects of the collected samples. The inherit non-invasive character of the sample collection approach might open new doors for efficient ways for a fast, non-invasive, and better diagnosis.

Polymer-Thread-Based Fully Textile Capacitive Sensor Embroidered on a Protective Face Mask for Humidity Detection

The COVID-19 pandemic has created a situation where wearing personal protective masks is a must for every human being and introduced them as a part of everyday life. This work demonstrates a new functionality embedded in single-use face masks through an embroidered humidity sensor. The design of the face mask humidity sensor is comprised of interdigitated electrodes made of polyamide-based conductive threads and common polyester threads which act as a dielectric sensing layer embroidered between them. Therefore, the embroidered sensor acts as a capacitor, the performance of which was studied in increasing humidity conditions in the frequency range from 1 Hz to 100 kHz. The moisture adsorbed by sensitive hygroscopic polyester threads altered their dielectric and permittivity properties which were detected by the change in capacitance values of the face mask sensors at different relative humidity (RH) levels. The calculated limit of detection (LOD) values for the two proposed sensors at different frequencies (1, 10, and 100 kHz) were found in the range from 11.46% RH-27.41% RH and 29.79% RH-38.65% RH. The tested sensors showed good repeatability and stability under different humidity conditions over a period of 80 min. By employing direct embroidery of silver-coated polyamide conductive threads and moisture-sensitive polyester threads onto the face mask, the present work exploits the application of polymer-based textile materials in developing novel stretchable sensing devices toward e-textile applications.

Hollow-Channel Paper Analytical Devices Supported Biofuel Cell-Based Self-Powered Molecularly Imprinted Polymer Sensor for Pesticide Detection

Herein, a paper-based glucose/air biofuel cell (BFC) was constructed and implemented for self-powered pesticide detection. Our developed paper-based chip relies on a hollow-channel to transport fluids rather than capillarity, which reduces analysis times as well as physical absorption. The gold nanoparticles (Au NPs) and carbon nanotubes (CNTs) were adapted to modify the paper fibers to fabricate the flexible conductive paper anode/cathode electrode (Au-PAE/CNT-PCE). Molecularly imprinted polymers (MIPs) using 2,4-dichlorophenoxyacetic acid (2,4-D) as a template were synthesized on Au-PAE for signal control. In the cathode, bilirubin oxidase (BOD) was used for the oxygen reduction reaction. Based on a competitive reaction between 2,4-D and glucose-oxidase-labeled 2,4-D (GOx-2,4-D), the amount of GOx immobilized on the bioanode can be simply tailored, thus a signal-off self-powered sensing platform was achieved for 2,4-D determination. Meanwhile, the coupling of the paper supercapacitor (PS) with the paper-based chip provides a simple route for signal amplification. Combined with a portable digital multi-meter detector, the amplified signal can be sensitively readout. Through rational design of the paper analytical device, the combination of BFC and PS provides a new prototype for constructing a low-cost, simple, portable, and sensitive self-powered biosensor lab-on-paper, which could be easily expanded in the field of clinical analysis and drug delivery.

Recent advances in metal-based nanoporous materials for sensing environmentally-related biomolecules

Highly sensitive, stable, selective, efficient, and short reaction time sensors play a substantial role in daily life/industry and are the need of the day. Due to the rising environmental issues, nanoporous carbon and metal-based materials have attracted significant attention in environmental analysis owing to their intriguing and multifunctional properties and cost-effective and rapid detection of different analytes by sensing applications. Environmental-related issues such as pollution have been a significant threat to the world. Therefore, it is necessary to fabricate highly promising performance-based sensor materials with excellent reliability, selectivity and good sensitivity for monitoring various analytes. In this regard, different methods have been employed to fabricate these sensors comprising metal, metal oxides, metal oxide carbon composites and MOFs leading to the formation of nanoporous metal and carbon composites. These composites have exceptional properties such as large surface area, distinctive porosity, and high conductivity, making them promising candidates for several versatile sensing applications. This review covers recent advances and significant studies in the sensing field of various nanoporous metal and carbon composites. Key challenges and future opportunities in this exciting field are also part of this review.

Highly Selective Wearable Alcohol Homologue Sensors Derived from Pt-Coated Truncated Octahedron Au

Unhealthy alcohol inhalation is among the top 10 causes of preventable death. However, the present alcohol sensors show poor selectivity among alcohol homologues. Herein, Pt-coated truncated octahedron Au (Pt_m@Au_to) as the electrocatalyst for a highly selective electrochemical sensor toward alcohol homologues has been designed. The alcohol sensor is realized by distinguishing the electro-oxidation behavior of methanol (MeOH), ethanol (EtOH), or isopropanol (2-propanol). Intermediates from alcohols are further oxidized to CO₂ by Pt_m@Au_to, resulting in different oxidation peaks in cyclic voltammograms and successful distinction of alcohols. Pt_m@Au_to is then modified on wearable glove-based sensors for monitoring actual alcohol samples (MeOH fuel, vodka, and 2-propanol hand sanitizer), with good mechanical performance and repeatability. The exploration of the Pt_m@Au_to-based wearable alcohol sensor is expected to be suitable for environmental measurement with high selectivity for alcohol homologues or volatile organic compounds.

Flexible pressure and temperature dual-mode sensor based on buckling carbon nanofibers for respiration pattern recognition

Breathing condition is an essential physiological indicator closely related to human health. Wearable flexible breath sensors for respiration pattern recognition have attracted much attention as they can provide physiological signal details for personal medical diagnosis, health monitoring, etc. However, present smart mask based on flexible breath sensors using single-mode detection can only detect a relatively small number of respiration patterns, especially lacking the ability to accurately distinguish mouth breath from nasal one. Herein, a smart face mask incorporated with a dual-sensing mode breathing sensor that can recognize up to eight human respiration patterns is fabricated. The breathing sensor uses novel three dimensional (3D) buckling carbon nanofiber mats as active materials to realize the function of pressure and temperature sensing simultaneously. The pressure model of the sensors shows a high sensitivity that are able to precisely detect pressure generated by respiratory airflow, while the temperature model can realize non-contact temperature variation caused by breath. Benefit from the capacity of real-time recognition and accurate distinguishing between mouth breath and nasal breath, the face mask is further developed to monitor the development of mouth breathing syndrome. The dual-sensing mode sensor has great potential applications in health monitoring.

Real-Time Monitoring of Breath Biomarkers with A Magnetoelastic Contactless Gas Sensor: A Proof of Concept

In the quest for effective gas sensors for breath analysis, magnetoelastic resonance-based gas sensors (MEGSs) are remarkable candidates. Thanks to their intrinsic contactless operation, they can be used as non-invasive and portable devices. However, traditional monitoring techniques are bound to slow detection, which hinders their application to fast bio-related reactions. Here we present a method for real-time monitoring of the resonance frequency, with a proof of concept for real-time monitoring of gaseous biomarkers based on resonance frequency. This method was validated with a MEGS based on a Metglass 2826 MB microribbon with a polyvinylpyrrolidone (PVP) nanofiber electrospun functionalization. The device provided a low-noise (RMS = 1.7 Hz), fast (<2 min), and highly reproducible response to humidity (Δf = 46−182 Hz for 17−95% RH), ammonia (Δf = 112 Hz for 40 ppm), and acetone (Δf = 44 Hz for 40 ppm). These analytes are highly important in biomedical applications, particularly ammonia and acetone, which are biomarkers related to diseases such as diabetes. Furthermore, the capability of distinguishing between breath and regular air was demonstrated with real breath measurements. The sensor also exhibited strong resistance to benzene, a common gaseous interferent in breath analysis.

EFFECT OF AIR ENDURANCE TRAINING ON ATHLETE’S LOWER LIMB EXPLOSIVE STRENGTH

ABSTRACT Introduction: The explosion force is the neuromuscular system's capacity to overcome resistance with the highest possible contraction speed. It is the result of a kinetic combination between power and speed. The effect of outdoor resistance training is used in several athletics areas to improve the explosive power of the lower limbs. However, there are still few studies focused on basketball athletes. Objective: Verify the effect of outdoor resistance training on the explosive power of the lower limbs of basketball players. Methods: The article uses mathematical statistics and randomized controlled trials by analytical comparison to explore the influence of compound training methods on basketball players’ lower explosive limb power. Results: There was no performance gain in the control group at 30 days. Was an improved ability in the experimental group jumping after high-intensity composite training. Conclusion: Compound training plays a crucial role in improving lower limb explosive power in college basketball players. Evidence Level II; Therapeutic Studies - Investigating the result.

A Dual Sensing Platform for Human Exhaled Breath Enabled by Fe-MIL-101-NH2 Metal-Organic Frameworks and its Derived Co/Ni/Fe Trimetallic Oxides

Limited by the insufficient active sites and the interference from breath humidity, designing reliable gas sensing materials with high activity and moisture resistance remains a challenge to analyze human exhaled breath for the translational application of medical diagnostics. Herein, the dual sensing and cooperative diagnosis is achieved by utilizing metal-organic frameworks (MOFs) and its derivative. The Fe-MIL-101-NH₂ serves as the quartz crystal microbalance humidity sensing layer, which exhibits high selectivity and rapid response time (16 s/15 s) to water vapor. Then, the Co²⁺ and Ni²⁺ cations are further co-doped into Fe-MIL-101-NH₂ host to obtain the derived Co/Ni/Fe trimetallic  oxides (CoNiFe-MOS-n). The chemiresistive CoNiFe-MOS-n sensor displays the high sensitivity (560) and good selectivity to acetone, together with a lower original resistance compared with Fe₂ O₃ and NiFe₂ O₄ . Moreover, as a proof-of-concept application, synergistic integration of Fe-MIL-101-NH₂ and derived CoNiFe-MOS-n is carried out. The Fe-MIL-101-NH₂ is applied as moisture sorbent materials, which realize a sensitivity compensation of CoNiFe-MOS-n sensors for the detection of acetone (biomarker gas of diabetes). The findings provide an insight for effective utilization of MOFs and the derived materials to achieve a trace gas detection in exhaled breath analysis.

Ultrasensitive Chemiresistive Gas Sensor Can Diagnose Asthma and Monitor Its Severity by Analyzing Its Biomarker H2S: An Experimental, Clinical, and Theoretical Study

Asthma is a chronic disease characterized by recurrent attacks of breathlessness and wheezing, which vary in severity and frequency from person to person. H₂S is considered as the biomarker of asthma. Here, an ultrasensitive chemiresistive H₂S gas sensor based on a γ-Bi₂MoO₆-CuO heterostructure with a detection limit of 5 ppb has been fabricated. It can distinguish asthmatic patients from healthy people roughly by analyzing the exhaled breaths of 28 asthmatic patients and 28 healthy people, suggesting that the sensor can be used to assist physicians in the diagnosis of asthma. Pathologically, it is discovered by this sensor that with the relief of asthma, the concentration of H₂S in one's exhaled breath gradually increases. This subtle concentration variation of H₂S can be accurately detected, indicating that this sensor can be used in the asthma severity monitoring too. Physical models have been built by first-principles calculation to reveal the causes of the sensor's ultrasensitivity. The stable adsorption of H₂S on the surface of CuO results in massive charge transferring and the appearance of the defect states, which play the major role in the ultrasensitivity of the sensor. Upon integrating this sensor with circuits, the cheap, smart, and portable H₂S sensing device can be obtained, which can make asthmatic patients' access to this device easy and make the severity monitoring of asthma convenient, especially for children and the aged.

A Family of 2D-MXenes: Synthesis, Properties, and Gas Sensing Applications

Gas sensors, capable of detecting and monitoring trace amounts of gas molecules or volatile organic compounds (VOCs), are in great demand for numerous applications including diagnosing diseases through breath analysis, environmental and personal safety, food and agriculture, and other fields. The continuous emergence of new materials is one of the driving forces for the development of gas sensors. Recently, 2D materials have been gaining huge attention for gas sensing applications, owing to their superior electrical, optical, and mechanical characteristics. Especially for 2D MXenes, high specific area and their rich surface functionalities with tunable electronic structure make them compelling for sensing applications. This Review discusses the latest advancements in the 2D MXenes for gas sensing applications. It starts by briefly explaining the family of MXenes, their synthesis methods, and delamination procedures. Subsequently, it outlines the properties of MXenes. Then it describes the theoretical and experimental aspects of the MXenes-based gas sensors. Discussion is also extended to the relation between sensing performance and the structure, electronic properties, and surface chemistry. Moreover, it highlights the promising potential of these materials in the current gas sensing applications and finally it concludes with the limitations, challenges, and future prospects of 2D MXenes in gas sensing applications.

A user preference analysis of commercial breath ketone sensors to inform the development of portable breath ketone sensors for diabetes management in young people

Background

Portable breath ketone sensors may help people with Type 1 Diabetes Mellitus (T1DM) avoid episodes of diabetic ketoacidosis; however, the design features preferred by users have not been studied. We aimed to elucidate breath sensor design preferences of young people with T1DM (age 12 to 16) and their parents to inform the development of a breath ketone sensor prototype that would best suit their diabetes management needs.

Research designs and methods

To elicit foundational experiences from which design preference ideas could be generated, two commercially available breath ketone sensors, designed for ketogenic diet monitoring, were explored over one week by ten young people with T1DM. Participants interacted with the breath ketone sensing devices, and undertook blood ketone testing, at least twice daily for five days to simulate use within a real life and ambulatory care setting. Semi-structured interviews were conducted post-testing with the ten young participants and their caregivers (n = 10) to elicit preferences related to breath sensor design and use, and to inform the co-design of a breath ketone sensor prototype for use in T1DM self-management. We triangulated our data collection with key informant interviews with two diabetes educators working in pediatric care about their perspectives related to young people using breath ketone sensors.

Results

Participants acknowledged the non-invasiveness of breath sensors as compared to blood testing. Affordability, reliability and accuracy were identified as prerequisites for breath ketone sensors used for diabetes management. Design features valued by young people included portability, ease of use, sustainability, readability and suitability for use in public. The time required to use breath sensors was similar to that for blood testing. The requirement to maintain a 10-second breath exhalation posed a challenge for users. Diabetes educators highlighted the ease of use of breath devices especially for young people who tended to under-test using blood ketone strips.

Conclusions

Breath ketone sensors for diabetes management have potential that may facilitate ketone testing in young people. Our study affirms features for young people that drive usability of breath sensors among this population, and provides a model of user preference assessment.

Lightweight Self-Detection and Self-Calibration Strategy for MEMS Gas Sensor Arrays

With the development of Internet of Things (IoT) and edge computing technology, gas sensor arrays based on Micro-Electro-Mechanical System (MEMS) fabrication technique have broad application prospects in intelligent integrated systems, portable devices, and other fields. In such complex scenarios, the normal operation of a gas sensing system depends heavily on the accuracy of the sensor output. Therefore, a lightweight Self-Detection and Self-Calibration strategy for MEMS gas sensor arrays is proposed in this paper to monitor the working status of sensor arrays and correct the abnormal data in real time. Evaluations on real-world datasets indicate that the strategy has high performance of fault detection, isolation, and data recovery. Furthermore, our method has low computation complexity and low storage resource occupation. The board-level verification on CC1350 shows that the average calculation time and running power consumption of the algorithm are 0.28 ms and 9.884 mW. The proposed strategy can be deployed on most resource-limited IoT devices to improve the reliability of gas sensing systems.

Portable Respiration Monitoring System with an Embroidered Capacitive Facemask Sensor

Respiration monitoring is a very important indicator of health status. It can be used as a marker in the recognition of a variety of diseases, such as sleep apnea, asthma or cardiac arrest. The purpose of the present study is to overcome limitations of the current state of the art in the field of respiration monitoring systems. Our goal was the development of a lightweight handheld device with portable operation and low power consumption. The proposed approach includes a textile capacitive sensor with interdigitated electrodes embroidered into the facemask, integrated with readout electronics. Readout electronics is based on the direct interface of the capacitive sensor and a microcontroller through just one analog and one digital pin. The microcontroller board and sensor are powered by a smartphone or PC through a USB cable. The developed mobile application for the Android™ operating system offers reliable data acquisition and acts as a bridge for data transfer to the remote server. The embroidered sensor was initially tested in a humidity-controlled chamber connected to a commercial impedance analyzer. Finally, in situ testing with 10 volunteering subjects confirmed stable operation with reliable respiration monitoring.

A nanoforest-based humidity sensor for respiration monitoring

Traditional humidity sensors for respiration monitoring applications have faced technical challenges, including low sensitivity, long recovery times, high parasitic capacitance and uncalibrated temperature drift. To overcome these problems, we present a triple-layer humidity sensor that comprises a nanoforest-based sensing capacitor, a thermistor, a microheater and a reference capacitor. When compared with traditional polyimide-based humidity sensors, this novel device has a sensitivity that is improved significantly by 8 times within a relative humidity range of 40–90%. Additionally, the integration of the microheater into the sensor can help to reduce its recovery time to 5 s. The use of the reference capacitor helps to eliminate parasitic capacitance, and the thermistor helps the sensor obtain a higher accuracy. These unique design aspects cause the sensor to have an excellent humidity sensing performance in respiration monitoring applications. Furthermore, through the adoption of machine learning algorithms, the sensor can distinguish different respiration states with an accuracy of 94%. Therefore, this humidity sensor design is expected to be used widely in both consumer electronics and intelligent medical instrument applications.