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Professor Xu Lin from Jilin University and Professor Jong Seung Kim from Korea University led a research team to address the challenges faced by current flexible electronic devices in mimicking skin structures, synchronously encoding multiple stimulus information, and simplifying the design of flexible logic circuits. The research team proposed a method to in-situ grow Cu3(HHTP)2 particles on the surface of hollow spherical Ti3C2Tₓ to simulate the stratum corneum and granule layer of the skin. By mimicking the three-dimensional structure of human skin and its encoding capability, the system achieved independent sensing of NO2 gas and pressure stimuli: the system achieved a response rate of up to 86% in NO2 gas sensing and a rapid response time of 7 seconds, while demonstrating a response/recovery time of 0.9/0.9 seconds in pressure sensing, with a sensitivity of up to 15.5 kPa–¹. This system can be applied to remote monitoring of chronic diseases such as asthma, achieving a recognition accuracy of 97.6% with the assistance of machine learning algorithms, providing an innovative solution for remote medical diagnosis. The first author of this paper is Qingqing Zhou from the School of Electronic Science and Engineering, Jilin University. The related work was published in “Nano-Micro Letters” under the title “A Flexible Smart Healthcare Platform Conjugated with Artificial Epidermis Assembled by Three‑Dimensionally Conductive MOF Network for Gas and Pressure Sensing.”
Related work can be seen in the series of posts by Blue Fatty iTextiles:
Small University of Macau, Shenzhen Advanced Institute 3D bionic fiber hair structure self-adhesive electronic skin: Human-machine interaction – let machines feel human touch.
Advance Science Motion sensing electronic skin: Stick it on as you wish, breathable, and waterproof wearable technology.
Professor Zhang Yihui’s team from Tsinghua University successfully developed a new type of 3D electronic skin (3DAE-Skin) that mimics human skin.
The human skin, as the most extensive organ in the body, can interact and communicate with various external stimuli and receptors, inducing various bioelectric impulses based on the nature of mechanical stimuli. Inspired by the unique three-dimensional (3D) topological interconnected structure of skin, scientists have meticulously designed a series of electronic skins (e-skin) with 3D interlocking hierarchical structures for health monitoring and clinical diagnosis. However, most current electronic skin models exhibit similar or even the same resistance change mechanisms when responding to gas or pressure stimuli, resulting in mixed electrical outputs that severely interfere with signal reception and output. Therefore, there is an urgent need to develop an independent, multifunctional sensor that can provide interference-free response signals and high-precision identification of stimuli through a streamlined and effective process.
By in-situ growing Cu3(HHTP)2 particles on the surface of hollow spherical titanium carbide (Ti3C2Tx), a device was constructed to simulate both the stratum corneum and granule layer of skin simultaneously. By integrating a dual-module sensor into a flexible printed circuit, a flexible wearable alarm system with mobile application terminals was developed. This system can assess risk factors associated with asthma, such as external NO2 gas stimuli, abnormal exhalation behavior, and finger pressure, achieving a recognition accuracy of 97.6% with the assistance of machine learning algorithms. This innovative device provides a feasible pathway for developing intelligent multifunctional medical devices for emerging transformative remote medical diagnostics.
Highlight 1: Integration of Bionic Structure and Multi-Sensing Functions
This study successfully constructed an artificial epidermis device inspired by the three-dimensional interlocking layered structure of skin. By in-situ heterogeneously assembling gas-sensitive Cu3(HHTP)2 on the surface of Ti3C2Tₓ with a hollow spherical structure, it simulated the stratum corneum and granule layer of skin. This structural design enables the device to respond to both NO2 gas and pressure stimuli simultaneously, achieving the integration of multi-sensing functions. For instance, in gas sensing tests, the Ti3C2Tx@Cu3(HHTP)2 sensor exhibited a response of up to 86% to 100ppm of NO2 gas, demonstrating better performance than single materials.
This bionic structure not only enhances the sensor’s adsorption to specific gases and electronic signal conversion capabilities but also improves pressure sensing performance. In pressure sensing, the sensor showed efficient responses in the 0 – 6.1kPa pressure range, with a sensitivity of up to 15.5kPa–¹, and response and recovery times of only 0.9/0.9 seconds, with high reproducibility of over 300 cycles.
Highlight 2: Intelligent Health Monitoring and High Recognition Accuracy
Based on the aforementioned dual-mode sensor, researchers developed a flexible intelligent wearable alarm system. This system integrates components such as an analog-to-digital converter, Wi-Fi wireless module, etc., capable of real-time uploading sensor-collected data to the cloud, and displaying and processing through smartphones or computer terminals.
With the help of machine learning algorithms, the system can comprehensively assess risk factors associated with asthma, such as external NO2 gas stimuli, abnormal exhalation behavior, and finger movement degrees. Experiments show that the system achieves a recognition accuracy of up to 97.6%, providing reliable technical support for early warning and health monitoring of diseases like asthma.
Figure 1. Intelligent Wearable Alarm System Integrated with Artificial Skin Device
Figure 1 vividly illustrates the composition and application of this artificial skin device, which mimics the three-dimensional layout of human skin and is composed of dual-mode sensors interlinked, exhibiting optimized synergistic effects, enhanced signal transmission capabilities, and strong hydrophobic properties, enabling stable responses to gas and pressure stimuli in areas of human respiration and sweating. Integrating this dual-mode sensor into flexible printed circuits allows for wireless real-time assessment of risk factors associated with asthma, achieving a recognition accuracy of 97.6% through a 1-D convolutional neural network (CNNs)-based machine learning algorithm, providing innovative solutions for remote medical diagnosis.
Figure 2. Synthesis Process and Microstructural Characterization of Ti3C2Tx@Cu3(HHTP)2Composite Material
Figure 2 shows the preparation process and microstructural characteristics of Ti3C2Tx@Cu3(HHTP)2composite material. By selective etching and self-assembly techniques, Ti3C2Tx MXene sheets and PMMA@Ti3C2Tx spheres were prepared, and hollow Ti3C2Tx foams were formed through high-temperature calcination; Cu3(HHTP)2 particles were synthesized via a solvothermal method and formed a close composite material with Ti3C2Tx foams through strong electrostatic interactions. Scanning electron microscopy and transmission electron microscopy images revealed the microstructure of the material, and high-resolution transmission electron microscopy images and energy dispersive spectroscopy elemental mapping further confirmed the uniform distribution of various elements in the composite material, which are critical for the material’s performance.
Figure 3. Dynamic Response of Various Sensors to NO2Gas at Different Concentration Ranges at Room Temperature
Figure 3 shows the response performance of Ti3C2Tx@Cu3(HHTP)2sensor to NO2gas. The dynamic response curves of the sensor at different concentration ranges demonstrate its sensitivity to NO2 gas, particularly its performance at low concentrations (1-60ppm) and high concentrations (80-200ppm). The relationship between response values and NO2concentration reveals the sensor’s high sensitivity and rapid response capability. Additionally, the sensor exhibited good selectivity in monitoring various volatile organic compounds, and tests under different relative humidity conditions indicated its strong resistance to humidity interference. Reproducibility and long-term stability tests showed that the sensor maintained stable performance after multiple uses, and response variations under different bending angles also indicated its good bending stability. Finally, the performance of the sensor was compared with other reported NO2gas sensors, further validating its superiority in practical applications.
Figure 4. Piezoelectric Performance and Response Characteristics of Ti3C2Tx@Cu3(HHTP)2Pressure Sensor
Figure 4 mainly shows the piezoelectric performance and response characteristics of the Ti3C2Tx@Cu3(HHTP)2pressure sensor. The current-voltage curve of the sensor indicates that as pressure increases, resistance decreases linearly, demonstrating good ohmic contact characteristics. The response and recovery time test results of the sensor under different pressures show its rapid response capability, and the sensitivity curve reveals its high sensitivity in the low pressure range. It also explains the pressure sensing mechanism of the sensor and demonstrates its potential in capturing human motion signals and sound vibrations through practical applications of the sensor, further proving its wide application prospects in health monitoring and human-machine interaction.
Figure 5. Schematic Diagram of Flexible Intelligent Wearable Alarm System for Monitoring Asthma Signals
Figure 5 illustrates the design of the flexible intelligent wearable alarm system for monitoring asthma signals, including components such as the ESP32 chip, dual-mode sensor, power supply, and Wi-Fi wireless data transmission module, and demonstrates how the flexible dual-mode sensor is integrated into the flexible printed circuit board (FPCB) for detecting different pressures. This system can respond in real-time to NO2 atmosphere, normal and deep breathing, light pressing, and heavy pressure, and employs a machine learning algorithm for signal analysis based on one-dimensional convolutional neural networks (1-D CNNs). The confusion matrix shows the system’s classification accuracy for five body movement patterns (light breathing, deep breathing, light pressing, heavy pressing, and NO2 environment), with an overall recognition accuracy of 97.6%, highlighting the potential use of this system in remote medical diagnosis.
(1) Material Innovation and Performance Breakthrough
This study successfully prepared the Ti3C2Tx@Cu3(HHTP)2composite material, whose unique structural design enables simultaneous detection of NO2 gas and pressure, with non-interfering resistance output signals. In terms of gas sensing performance, the material exhibits a response of up to 86% (at 100ppm of NO2 gas), with a response speed of up to 7 seconds, and boasts long-term stability of 14 days and excellent hydrophobicity. These performances are attributed to its bionic structure and the synergistic effect between Cu3(HHTP)2 and Ti3C2Tₓ, as well as the sensing mechanisms revealed by density functional theory calculations.
In pressure sensing performance, the sensor demonstrates efficient responses in the 0 – 6.1kPa range, with fast response and recovery rates (0.9/0.9s) and strong reproducibility (over 300 cycles). Finite element analysis further confirms its excellent pressure sensing performance. The outstanding signal transduction capability of this material enables strong functionalities in acoustic feature perception, Morse code encrypted information communication, and physiological motion sensing (refer to the results and discussion section for a detailed analysis of material performance).
(2) Intelligent System Applications and Prospects
By integrating the dual-mode sensor with the Wi-Fi transmission module, researchers developed a flexible intelligent alarm platform. This platform can monitor human physiological signals in real-time and accurately identify abnormalities related to asthma using machine learning algorithms, achieving a classification accuracy of up to 97.6%. This system provides realistic application prospects for constructing flexible intelligent electronic wearable alarm systems in the field of remote medical diagnosis.
In the future, this flexible electronic device is expected to further expand its application scope, not only in asthma monitoring but also in early diagnosis and health management of other chronic diseases. Meanwhile, with the continuous development of material science and electronic technology, its performance and functionality will continue to be optimized and expanded, providing more precise and convenient services for people’s healthy lives.
DOI:https://doi.org/10.1007/s40820-024-01548-5
1)Chinese Academy of Sciences University “Nat. Commun.”: A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments. (https://doi.org/10.1038/s41467-021-27672-z)
2)Hong Kong University of Science and Technology “Science Advances”: Biomimetic bimodal haptic perception using triboelectric effect. (https://doi.org/10.1126/sciadv.ado6793)
3)Southern University of Science and Technology “Nano Lett.”: Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring. (https://doi.org/10.1021/acs.nanolett.3c02071)
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Image | Health Textiles Research Group, Pan Chaomei
Text | Health Textiles Research Group, Pan Chaomei
Editor | Blue Fatty
Initial Review | Yu Xi
Re-review | Blue Fatty
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