First Author: Qingqing Zhou
Corresponding Authors: Jong Seung Kim, Lin Xu
Affiliations: Korea University, Jilin University
DOI: 10.1007/s40820-024-01548-5
Background Introduction
Wearable electronic products characterized by intelligence have shown amazing potential in remote healthcare and human-machine interaction (HMI). This immense potential stems from breakthrough advances in sensory and artificial intelligence technologies. One key application of wearable sensors lies in providing efficient, technologically advanced means for unobtrusive, continuous, and real-time monitoring of chronic diseases such as asthma, diabetes, and Parkinson’s disease. Specifically, through on-site analysis of human sweat, pulse, and respiratory behavior, wearable sensors can assist in establishing an electronic database for personal health assessments. However, the daunting task of evaluating early warning signals for asthma attacks and tracking human-machine interaction coordination sets stringent performance standards for existing sensors. These sensing devices also require outstanding flexibility, elasticity, and resilience to accommodate significant deformations. Furthermore, the emergence of multi-responsive flexible electronic devices undoubtedly determines the huge potential of HMI, marking the arrival of a revolutionary era driven by authentic and rich interactive experiences. Therefore, the development and breakthroughs in the field of multifunctional devices characterized by enhanced sensory capabilities become crucial. Such devices capable of processing and extracting complex information become invaluable for portable health monitoring and clinical diagnostics.
As the most expansive organ in the skin system, human skin interacts and transmits various external stimuli to exteroceptors, inducing various bioelectrical pulses based on the nature of mechanical stimuli. Drawing inspiration from the advanced structural properties and signal processing capabilities of the skin, a visionary model can be provided for designing personal bionic devices. Since the skin typically encodes information through highly compact, parallel, and reliable operational modes, it offers a blueprint for simplifying the interaction channel between humans and machines. In fact, the epidermis is the outermost layer of the skin, composed of microspheres with interlocking structures. Learning from the unique three-dimensional (3D) topological interconnected architecture of the skin, scientists have meticulously designed a series of electronic skins (e-skin) with 3D interlocking hierarchical structures. This includes components such as micropyramids, microspheres, and echinoderm-like microcapsules, aimed at enhancing the perception of specific stimuli. On one hand, e-skin possesses an impressive elastic modulus, enabling it to support a wide distribution of compressive deformations. Furthermore, the close contact of spherical tips of e-skin elements provides excellent electromechanical conversion capabilities and strong mechanical elasticity. These unique properties not only focus and amplify local stresses but also enhance sensitivity even within a wide operating window.
Currently, most artificial electronic skins with interconnected structures primarily focus on simultaneously sensing pressure, strain, and temperature variations. Meanwhile, other important gas environmental and physiological parameters, such as hazardous gases and abnormal respiratory patterns, which are important indicators for assessing human health, are often overlooked. For instance, NO2 can act as a trigger and exacerbator of asthma. In fact, the synchronous integration of sensors for gas and pressure can develop more precise and complex methods to screen trace harmful gas molecules and detect bodily tremors indicative of early diseases. Specifically, the unique layout of the 3D interlocking layered structure of e-skin is highly favorable for promoting gas adsorption and diffusion. It is expected to efficiently and rapidly convert gas stimuli into electrical pulses. However, most current e-skin models exhibit similar or even identical resistance conversion mechanisms under gas or pressure stimuli. This similarity often leads to mixed electrical outputs or, regrettably, severe signal interference or crosstalk. Therefore, there is an urgent need to develop an independent multifunctional sensor capable of providing undisturbed response signals and high-precision stimulus recognition through simplified and effective processes.
Highlights of the Article
1. This work constructs an artificial skin device by in-situ growing Cu3(HHTP)2 particles on the surface of hollow spherical Ti3C2Tx, aiming to simultaneously mimic the stratum corneum and granular layer of the skin epidermis.
2. The bionic Ti3C2Tx@Cu3(HHTP)2 exhibits independent NO2 and pressure responses, as well as new functionalities such as acoustic characteristic perception and Morse code encrypted message communication.
3. By integrating a dual-mode sensor into a flexible printed circuit, a wearable alarm system with a mobile application terminal has been independently developed. This system can assess risk factors associated with asthma, such as external NO2 gas stimulation, abnormal exhalation behavior, and finger exertion level, achieving a recognition accuracy of 97.6% with the assistance of machine learning algorithms.
Visual Analysis
Figure 1. Conceptual design of this study. a, b Inspired by biological tactile structures and neuromorphic systems, a flexible smart wearable alarm system with multifunctional bionic sensors is developed for wireless monitoring and distinguishing physiological signals of asthma patients, supplemented by a 1-D CNN-based machine learning algorithm.
Figure 2. a Ti3C2Tx foam, Cu3(HHTP)2 particles, and Ti3C2Tx@Cu3(HHTP)2 composite material; b SEM and c TEM images of PMMA@Ti3C2Tx spheres; SEM and TEM images of hollow Ti3C2Tx foam; f SEM and g TEM images of Cu3(HHTP)2 particles; and h SEM, i TEM, j HRTEM images and k EDX mapping images of Ti3C2Tx@Cu3(HHTP)2 composite material.
Figure 3. a Ti3C2Tx foam, XRD patterns of Cu3(HHTP)2 particles, b FTIR and c TGA curves of Ti3C2Tx@Cu3(HHTP)2 composite material; d N2 adsorption-desorption isotherms of Ti3C2Tx foam (S1), Cu3(HHTP)2 particles (S2); and e BET surface area of Ti3C2Tx@Cu3(HHTP)2 composite material (S3); as well as f Ti 2p, g C 1s, h O 1s, i Cu 2p orbitals, Cu3(HHTP)2 particles, and Ti3C2Tx@Cu3(HHTP)2 composite material.
Figure 4. Dynamic response of various sensors to low (a, 1-60 ppm) and high (b, 80-200 ppm) concentration ranges of nitrogen dioxide (NO2) gas at room temperature, including sensors of Ti3C2Tx, Cu3(HHTP)2, and Ti3C2Tx@Cu3(HHTP)2; c Detection of 1-200 ppm NO2 gas with Cu3(HHTP)2 and Ti3C2Tx@Cu3(HHTP)2 sensors; d Response time and selectivity of three sensors to 100 ppm of various volatile organic compounds, S1-S3 represent Ti3C2Tx, Cu3(HHTP)2, and Ti3C2Tx@Cu3(HHTP)2 sensors; f Dynamic response test of Ti3C2Tx@Cu3(HHTP)2 sensor detecting 100 ppm NO2 gas at various RH from 11% to 85%; g Representative repeatable sensing test and h long-term stability test of Ti3C2Tx@Cu3(HHTP)2 sensor detecting 100 ppm NO2 gas; i The relationship between response of known state-of-the-art NO2-based chemical resistors and NO2 concentration and response time.
Figure 5. a Cu3(HHTP)2 Cu adsorption sites of Ti3C2Tx@Cu3(HHTP)2 composite material; (b, c) PDOS and total curves of c, O, Cu, Ti atoms of Ti3C2Tx@Cu3(HHTP)2 and Ti3C2Tx@Cu3(HHTP)2/NO2 systems; d Charge density distribution and e differential charge density distribution maps of Ti3C2Tx@Cu3(HHTP)2/NO2 systems; f Calculated distances between Cu sites and atoms in NO2; g Schematic diagram of NO2 sensing mechanism of Ti3C2Tx@Cu3(HHTP)2 sensor.
Figure 6. Piezo-resistive performance of Ti3C2Tx@Cu3(HHTP)2 pressure sensor. a I-V curves under different pressures; b Resistance response to different pressure variables from 0.6 to 6.1 kPa; c Response/recovery time of Ti3C2Tx@Cu3(HHTP)2 pressure sensor at a pressure of 0.6 kPa; d Linear curve of Ti3C2Tx@Cu3(HHTP)2 pressure sensor within a pressure range of 0.6 to 6.1 kPa; e Cross-sectional view of flexible strain distribution simulation of Ti3C2Tx@Cu3(HHTP)2 pressure sensor; f Schematic diagram of pressure sensing mechanism; g Repeatable response curve of Ti3C2Tx@Cu3(HHTP)2 sensor with 300 pressing/releasing cycles. h Dynamic pressure response of the sensor at various relative humidity (30% to 90%); i Water contact angle test of Ti3C2Tx@Cu3(HHTP)2 pressure sensor (inset, schematic diagram of sensor structure); j Encoding the acronym “NANO” through short and long-time strong pressing; k Light and heavy pressing of “MXENE” lettering on Ti3C2Tx@Cu3(HHTP)2 sensor; l Real-time response chart monitored by a digital multimeter playing the classic “Canon” audio.
Figure 7. Flexible wearable alarm system for monitoring asthma bodily movement signals; Schematic diagram of the flexible smart wearable alarm system, including ESP32 chip, dual-mode sensor, power supply, and WIFI wireless data transmission; b Optical image showing the integration of flexible dual-mode sensor into flexible printed circuit for detecting different pressures; c Real-time response of the wearable alarm system to i NO2 atmosphere, (ii, iii) normal and deep breathing, and (iv, v) light and heavy pressure; d Schematic diagram of the basic structure of 1-D CNN. e Confusion matrix of five bodily movement patterns: S1, S2, S3, S4, and S5 represent light breathing (normal mode), deep breathing (wheezing mode), light and heavy pressing (limb weakness mode), and NO2 atmosphere exacerbating asthma.
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