Deep learning enabled classification of real-time respiration signals acquired by MoSSe quantum dot-based flexible sensors

Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring

Respiratory pattern is one of the most crucial indicators for accessing human health, but there has been limited success in implementing fast-responsive, affordable, and miniaturized platforms with the capability for smart recognition. Herein, a fully integrated and flexible patch for wireless intelligent respiratory monitoring based on a lamellar porous film functionalized GaN optoelectronic chip with a desirable response to relative humidity (RH) variation is reported. The submillimeter-sized GaN device exhibits a high sensitivity of 13.2 nA/%RH at 2-70%RH and 61.5 nA/%RH at 70-90%RH, and a fast response/recovery time of 12.5 s/6 s. With the integration of a wireless data transmission module and the assistance of machine learning based on 1-D convolutional neural networks, seven breathing patterns are identified with an overall classification accuracy of >96%. This integrated and flexible on-mask sensing platform successfully demonstrates real-time and intelligent respiratory monitoring capability, showing great promise for practical healthcare applications.

In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose-Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition

Flexible strain sensors have significant progress in the fields of human-computer interaction, medical monitoring, and handwriting recognition, but they also face many challenges such as the capture of weak signals, comprehensive acquisition of the information, and accurate recognition. Flexible strain sensors can sense externally applied deformations, accurately measure human motion and physiological signals, and record signal characteristics of handwritten text. Herein, we prepare a sandwich-structured flexible strain sensor based on an MXene/polypyrrole/hydroxyethyl cellulose (MXene/PPy/HEC) conductive material and a PDMS flexible substrate. The sensor features a wide linear strain detection range (0-94%), high sensitivity (gauge factor 357.5), reliable repeatability (>1300 cycles), ultrafast response-recovery time (300 ms), and other excellent sensing properties. The MXene/PPy/HEC sensor can detect human physiological activities, exhibiting excellent performance in measuring external strain changes and real-time motion detection. In addition, the signals of English words, Arabic numerals, and Chinese characters handwritten by volunteers measured by the MXene/PPy/HEC sensor have unique characteristics. Through machine learning technology, different handwritten characters are successfully identified, and the recognition accuracy is higher than 96%. The results show that the MXene/PPy/HEC sensor has a significant impact in the fields of human motion detection, medical and health monitoring, and handwriting recognition.

Toward Continuous Breath Monitoring on a Mobile Phone Using a Frugal Conducting Cloth-Based Smart Mask

A frugal humidity sensor that can detect changes in the humidity of exhaled breath of individuals has been fabricated. The sensor comprises a humidity-sensitive conducting polymer that is in situ formed on a cloth that acts as a substrate. Interdigitated silver electrodes were screen-printed on the modified cloth, and conducting threads connected the electrodes to the measurement circuit. The sensor's response to changing humidity was measured as a voltage drop across the sensor using a microcontroller. The sensor was capable of discerning between fast, normal, and slow breathing based on the response time. A response time of ∼1.3 s was observed for fast breathing. An Android-based mobile application was designed to collect sensor data via Bluetooth for analysis. A time series classification algorithm was implemented to analyze patterns in breathing. The sensor was later stitched onto a face mask, transforming it into a smart mask that can monitor changes in the breathing pattern at work, play, and sleep.

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.

Selenization governs the intrinsic activity of copper-cobalt complexes for enhanced non-radical Fenton-like oxidation toward organic contaminants

Non-radical oxidation pathways in the Fenton-like process have a superior catalytic activity for the selective degradation of organic contaminants under complicated water matrices. Whereas the synthesis of high-performance catalysts and research on reaction mechanisms are unsatisfactory. Herein, it was the first report on copper-cobalt selenide (CuCoSe) that was well-prepared to activate hydrogen peroxide (H₂O₂) for non-radical species generation. The optimized CuCoSe+H₂O₂ system achieved excellent removal of chlortetracycline (CTC) in 10 min at neutral pH along with pleasing reusability and stability. Moreover, it exhibited great anti-interference capacity to inorganic anions and natural organic matters even in actual applications. Multi-surveys verified that singlet oxygen (¹O₂) was the dominant active species in this reaction and electron transfer on the surface-bound of CuCoSe and H₂O₂ likewise played an important role in direct CTC oxidation. Where the synergetic metals of Cu and Co accounted for the active sites, and the introduced Se atoms accelerated the circulation efficiency of Co³⁺/Co²⁺, Cu²⁺/Cu⁺ and Cu²⁺/Co²⁺. Simultaneously, the produced Se/O vacancies further facilitated electron mediation to enhance non-radical behaviors. With the aid of intermediate identification and theoretical calculation, the degradation pathways of CTC were proposed. And the predicted ecotoxicity indicated a decrease in underlying environmental risk.