Smart Home Sleep Respiratory Monitoring System Based on a Breath-Responsive Covalent Organic Framework

A smart home sleep respiratory monitoring system based on a breath-responsive covalent organic framework (COF) was developed and utilized to monitor the sleep respiratory behavior of real sleep apnea patients in this work. The capacitance of the interdigital electrode chip coated with COFTPDA-TFPy exhibits thousands-level reversible responses to breath humidity gases, with subsecond response time and robustness against environmental humidity. A miniaturized printed circuit board, an open-face-mask-based respiratory sensor, and a smartphone app were constructed for the wearable wireless smart home sleep respiratory monitoring system. Leveraging the sensitive and rapid reversible response of COFs, the COF-based respiratory monitoring system can effectively record normal breath, rapid breath, and breath apnea, enabling over a thousand cycles of hour-level continuous monitoring during daily wear. Next, we took the groundbreaking step of advancing the humidity sensor to the clinical trial stage. In clinical experiments on real sleep apnea patients, the COF-based respiratory monitoring system successfully recorded hour-level sleep respiratory data and differentiated the breathing behavior characteristics and severity of sleep apnea patients and subjects with normal sleep function and primary snoring patients. This work successfully advanced humidity sensors into clinical research for real patients and demonstrated the enormous application potential of COF materials in clinical diagnosis.

Detection of Asthma Inhaler Use via Terahertz Spectroscopy

Inhaled medications are commonplace for administering bronchodilators, anticholinergics, and corticosteroids. While they have a defined legitimate use, they are also used in sporting events as performance-enhancing drugs. These performance enhancers can be acquired via both legal (i.e., at a pharmacy through over-the-counter medications or through a prescription) and illicit (i.e., black market and foreign pharmacies) means, thus making monitoring procurement impossible. While urine tests can detect these pharmacological agents hours after they have been inhaled, there is a significant lag time before they are observed in urine. Direct detection of these inhaled agents is complicated and requires a multiplexed approach due to the sheer number of inhaled pharmacological agents. Therefore, detection of propellants, which carry the drug into the lungs, provides a simpler path forward toward detection of broad pharmacological agents. In this paper, we demonstrate the first use of terahertz spectroscopy (THz) to detect inhaled medications in human subjects. Notably, we were able to detect and quantitate the propellant, HFA-134a, in breath up to 30 min after using an asthma inhaler, enabling the use of a point-of-care device to monitor exhaled breath for the presence of propellants. We also demonstrate via simulations that the same approach can be leveraged to detect and identify next-generation propellants, specifically HFA-152a. As a result, we provide evidence that a single point-of-care THz sensor can detect when individuals have used pressure-mediated dose inhalers (pMDIs) without further modification of the hardware.

Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring

Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO₂ nanopillars covered with a 15 nm metal-organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5-80 μmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.

Flexible, Disposable Cellulose-Paper-Based MoS2/Cu2S Hybrid for Wireless Environmental Monitoring and Multifunctional Sensing of Chemical Stimuli

Multifunctional sensors responding to different chemical stimuli fabricated using functional nanomaterials still remain a challenge because of the usage of the same sensor multiple times for different sensing applications and unreliable front-end processing of the sensing data. This challenge is intensified by the lack of suitable techniques for fabricating disposable sensors, which can be integrated into smartphones with a dedicated application developed for each sensing application. A novel MoS₂/Cu₂S hybrid grown on disposable cellulose paper by the hydrothermal method is reported for its utilization in sensing humidity, temperature, breath, and ethanol adulteration, wherein the data can be wirelessly transmitted to a smartphone with the dedicated application module for each sensing application. The sensor can be utilized for a particular sensing application and then can be disposed, avoiding the need for utilizing the same sensor for different sensing applications, thereby increasing the accuracy of the sensing data. The sensing mechanism of the fabricated sensor is explained for each stimulus in terms of change in the transport properties of the MoS₂/Cu₂S hybrid. The development of such unique hybrid materials for wireless disposable multifunctional sensors is a great step ahead in flexible and wearable electronics having potential applications in medical, security, Internet of things, etc.