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Integrated Smart Micro-Systems Towards Personalized Healthcare
Presents a thorough summary of recent advances in microelectronic systems and their applications for personalized healthcare
Integrated Smart Micro-Systems Towards Personalized Healthcare provides up-to-date coverage of developments in smart microelectronics and their applications in health-related areas such as sports safety, remote diagnosis, and closed-loop health management. Using a comprehensive approach to the rapidly growing field, this one-stop resource examines different methods, designs, materials, and applications of systems such as multi-modal sensing biomedical platforms and non-invasive health monitoring sensors.
The book’s five parts detail the core units of micro-systems, self-charging power units, self-driven monitor patches, self-powered sensing platforms, and integrated health monitoring systems. Succinct chapters address topics including multi-functional material optimization, multi-dimensional electrode preparation, multi-scene application display, and the use of multi-modal signal sensing to monitor physical and chemical indicators during exercise. Throughout the text, the authors offer key insights on device performance improvement, reliable fabrication processing, and compatible integration designs.
- Provides an overview self-powered, wearable micro-systems with emphasis on personalized healthcare
- Covers the working mechanisms and structural design of different energy-harvesting units, energy storage units, and functional units
- Introduces an integrated self-charging power unit consisting of triboelectric nanogenerators with supercapacitor
- Describes a general solution-evaporation method for developing porous CNT-PDMS conductive elastomers
- Examines a fully-integrated self-powered sweat sensing platform built on a wearable freestanding-mode triboelectric nanogenerator
Integrated Smart Micro-Systems Towards Personalized Healthcare is an essential text for researchers, electronic engineers, entrepreneurs, and industry professionals working in material science, electronics, mechanical engineering, bioengineering, and sensor development.
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Seitenzahl: 319
Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title Page
Copyright
Preface
1 Introduction
1.1 Overview of Integrated Smart Micro‐systems
1.2 Three Core Units of Smart Micro‐systems
1.3 The Progress of the Integration of Smart Micro‐systems
1.4 The Progress of Applications of Integrated Smart Micro‐systems
1.5 Scope and Layout of the Book
Abbreviations
References
2 Core Units of Smart Micro‐systems
2.1 Triboelectric Nanogenerators for Energy Harvesting
2.2 Supercapacitors for Energy Storage
2.3 Piezoresistive Sensors for Function Sensing
2.4 Summary
Abbreviations
References
3 Sandwiched Self‐charging Power Unit
3.1 Self‐charging Power Unit
3.2 Enhancement of TENG Based on Surface Optimization
3.3 Flexible Paper Electrode–Based Supercapacitor
3.4 Performance Characterization of SCPU
3.5 Applications of SCPU
3.6 Summary
Abbreviations
References
4 All‐in‐one Self‐driven Monitor Patch
4.1 Self‐driven Monitor Patch
4.2 Fabrication Process of Self‐driven Monitor Patch
4.3 Performance Characterization of Self‐driven Monitor Patch
4.4 Applications of Self‐driven Monitor Patch
4.5 Summary
Abbreviations
References
5 Fully Integrated Self‐powered Sweat‐Sensing Platform
5.1 Structural Design of Self‐powered Sweat‐Sensing Platform
5.2 Freestanding Triboelectric Nanogenerator
5.3 Potentiometric Electrochemical Sensing Unit
5.4 System‐level Integrated Circuit Module
5.5 Applications of Fully Integrated Self‐powered Sweat‐Sensing Platform
5.6 Summary
Abbreviations
References
6 Multimodal Sensing Integrated Health‐Monitoring System
6.1 Multimodal Sensing Platform
6.2 LEG‐based Chemical Sensor for UA and Tyr Detection
6.3 LEG‐based Physical Sensor for Vital Signs Monitoring
6.4 System‐Level Circuity Module
6.5 On‐body Evaluation of Integrated Health‐Monitoring System
6.6 Health‐Monitoring System for Non‐invasive Gout Management
6.7 Summary
Abbreviations
References
7 Progress and Perspectives
7.1 The Progress of the Micro‐systems
7.2 Perspectives of the Micro‐systems
Abbreviations
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Fabrication parameters for the laser‐patterning process.
Table 2.2 Comparison between different CNT–PDMS elastomers.
Chapter 4
Table 4.1 Comparison between different sponge‐like PRSs.
Chapter 5
Table 5.1 Dimension parameters of FTENG.
List of Illustrations
Chapter 1
Figure 1.1 Health‐monitoring approaches in the field of medical electronics....
Figure 1.2 Development progress of portable health‐monitoring methods.
Figure 1.3 Schematic illustration of integrated smart micro‐systems toward h...
Figure 1.4 Core units and representative devices of smart micro‐systems towa...
Figure 1.5 Schematic illustrations of various energy‐harvesting units. (a) E...
Figure 1.6 Four working modes and representative works of triboelectric nano...
Figure 1.7 Working principles and representative works of energy‐storage dev...
Figure 1.8 Flexible solid‐state supercapacitors. (a) Fiber‐shaped supercapac...
Figure 1.9 “Lab‐on‐the‐skin” for real‐time monitoring of various physiologic...
Figure 1.10 Classification and representative works of strain sensors. (a) S...
Figure 1.11 Exploration direction of integrated micro‐systems.
Figure 1.12 Self‐charging power units. (a–c) Wearable textile, and (d–f) str...
Figure 1.13 Self‐driven monitor patches. (a) All‐in‐one structural design....
Figure 1.14 Self‐powered sensing platforms. (a) Subtle skin deformation....
Figure 1.15 Real‐time health monitoring. (a) Multiplexed sweat chemical sign...
Figure 1.16 Multifunctional Human–Machine Interaction. (a) Virtual reality w...
Figure 1.17 Assisted precision therapy. (a) Epidermal drug delivery.
Figure 1.18 Scope of the integrated smart micro‐systems.
Figure 1.19 Layout of the integrated smart micro‐systems.
Chapter 2
Figure 2.1 Working principle of single‐electrode TENG based on conductive fa...
Figure 2.2 Testing method of output performance from single‐electrode TENG. ...
Figure 2.3 Output characterization of the single‐electrode TENG. (a) The out...
Figure 2.4 Energy characterization of single‐electrode TENG. (a) The effecti...
Figure 2.5 Stability of single‐electrode TENG. (a) The resistance of conduct...
Figure 2.6 Energy harvesting and motion sensing of single‐electrode TENG und...
Figure 2.7 Freestanding TENG. (a) Schematic illustration and (b) microscopic...
Figure 2.8 Working principle of FTENG.
Figure 2.9 Output characterization of FTENG. (a) Output voltage during one‐c...
Figure 2.10 Cycling stability of FTENG. (a, b) The output voltage waveforms ...
Figure 2.11 Surface morphology of the PTFE film before and after the long‐te...
Figure 2.12 Schematic illustration of power management module for FTENG.
Figure 2.13 Comparison of output performance with PMM and standard circuit. ...
Figure 2.14 Flexible CNT–cotton fabric electrode. (a) Optical image of flexi...
Figure 2.15 Surface morphology of CNT–cotton fabric electrode. (a, b) SEM im...
Figure 2.16 Electrochemical behavior of the wearable supercapacitor based on...
Figure 2.17 Power output and stability of the wearable supercapacitor. (a) R...
Figure 2.18 Working principle of supercapacitors with different structures. ...
Figure 2.19 Schematic illustration of planar MSC with interdigital electrode...
Figure 2.20 Fabrication process of planar MSC.
Figure 2.21 Surface morphology of the MSC. (a) Optical image of the device a...
Figure 2.22 SEM images of the surface morphology of the MSC. (a–d) SEM image...
Figure 2.23 Electrochemical performance comparison of MSCs with different li...
Figure 2.24 Electrochemical performance of the MSC 200. (a) CV curves at dif...
Figure 2.25 Applications of the MSC in portable energy electronics. (a) Seri...
Figure 2.26 Working principle of the PRS with porous structure.
Figure 2.27 The preparation process of CNT–PDMS sponge. (a) Detailed prepara...
Figure 2.28 Surface morphology of sponge structure. (a–d) SEM images of (a) ...
Figure 2.29 Performance of the CNT–PDMS sponge. (a, b) The relationship of (...
Figure 2.30 Strain–stress curves of sponge structures. (a) Strain–stress cur...
Figure 2.31 PRS based on CNT–PDMS conductive sponge. (a) Schematic illustrat...
Figure 2.32 Resistance response of PRS. (a) Resistance response of different...
Figure 2.33 Sensitivity of PRS. (a) Stress–resistance curve for PRS. (b) The...
Figure 2.34 Fabrication process of porous CNT–PDMS conductive elastomer. (a)...
Figure 2.35 Surface morphology of porous CNT–PDMS conductive elastomer. (a, ...
Figure 2.36 Electrical performance of CNT–PDMS conductive elastomer. (a) The...
Figure 2.37 Resistance response of PRS based on porous CNT–PDMS conductive e...
Figure 2.38 PRS for real‐time monitoring of human health information. (a, b)...
Chapter 3
Figure 3.1 Sandwiched SCPU. (a) Structural design, and (b–e) detailed workin...
Figure 3.2 Contact–separation TENG. (a) Theoretical model, and (b) equivalen...
Figure 3.3 Formation mechanism of wrinkle structure of double‐layer material...
Figure 3.4 Fabrication of TENG based on wrinkle process.
Figure 3.5 Surface morphology of wrinkle PDMS. (a) SEM image and (b) corresp...
Figure 3.6 Simulation of CNT network percolation. (a,b) In the first drop‐dr...
Figure 3.7 Modification on CNTs to enhance the water solubility. (a) Covalen...
Figure 3.8 Preparation and mechanical performance of flexible CNT–paper elec...
Figure 3.9 Flexible CNT–paper electrode‐based SC. (a,b) Schematic illustrati...
Figure 3.10 Optical image of the SCPU.
Figure 3.11 Output characterization of the TENG. (a, b) For a single TENG, (...
Figure 3.12 Charging performance comparison. (a,b) The charging curve of a 1...
Figure 3.13 Output performance of parallel TENGs. (a) The output voltage wav...
Figure 3.14 Stability test of parallel TENGs. (a) The output voltage wavefor...
Figure 3.15 Electrochemical performance of the SC. (a) CV curves at differen...
Figure 3.16 Long‐term stability of the SC.
Figure 3.17 Self‐charging performance of SCPU. (a) Circuit diagram of the SC...
Figure 3.18 SCPU for continuously driving calculator.
Figure 3.19 SCPU for smart display of electrochromic device. (a) Working mec...
Chapter 4
Figure 4.1 All‐in‐one self‐driven monitor patch. (a) Structural design, and ...
Figure 4.2 Mechanical analysis of porous conductive elastomer. (a) Ideal hon...
Figure 4.3 Fabrication process of porous CNT–PDMS conductive elastomer based...
Figure 4.4 The variation of CNT ratios on the resistance of conductive elast...
Figure 4.5 Surface morphology of different sizes of sugar particles with cor...
Figure 4.6 Fabrication process of planar MSC based on the porous conductive ...
Figure 4.7 The geometric parameters of the MSC. (a) The parameter definition...
Figure 4.8 Surface characterization of the MSC. (a) Optical image of flexibl...
Figure 4.9 Optical image of all‐in‐one self‐driven monitor patch.
Figure 4.10 Mechanical properties of the PRS. (a) Measurements of the PRS wi...
Figure 4.11 Sensitivity of the PRS. (a) The resistance responses of the PRSs...
Figure 4.12 The correlation between electrical and mechanical properties of ...
Figure 4.13 Electrochemical evaluation of the MSC. (a) CV curves at differen...
Figure 4.14 Energy‐storage performance of the MSC. (a) Ragone plots of the M...
Figure 4.15 Mechanical stability of the MSC. (a,b) Under different bending s...
Figure 4.16 Self‐driven monitor patch for real‐time health monitoring. (a,b)...
Figure 4.17 Resistance response of the self‐driven monitor patch as a 3D tou...
Figure 4.18 Extraction of four specific parameters of the 3D touch. (a,b) Co...
Figure 4.19 Flow chart of human–machine in user identification with data ana...
Figure 4.20 Flow chart of human–machine in security communication with data ...
Figure 4.21 Arrayed self‐driven monitor patch matrix. (a) Schematic diagram ...
Figure 4.22 Resistance responses by different 3D characters with correspondi...
Figure 4.23 Dynamic tactile trajectory of patch matrix. (a) Recorded resista...
Chapter 5
Figure 5.1 Fully integrated self‐powered sweat‐sensing platform.
Figure 5.2 Structure of self‐powered sweat‐sensing platform. (a) FTENG based...
Figure 5.3 Theoretical model of an FTENG.
Figure 5.4 Working principle and simulation analysis of FPCB‐based FTENG. (a...
Figure 5.5 Parameter definition of the FTENG with stator and slider layers....
Figure 5.6 Output performance of the FTENG. (a) Short‐circuit currents of an...
Figure 5.7 Output performance of the FTENG with different panels. (a) Transf...
Figure 5.8 Long‐term charging stability of an FTENG. (a) Long‐term stability...
Figure 5.9 Schematic illustration of the working process of the electrochemi...
Figure 5.10 Basic configuration of potentiometric sensor.
Figure 5.11 Schematic illustration of flexible electrochemical sensor unit. ...
Figure 5.12 Fabrication process of the flexible electrochemical sensor unit....
Figure 5.13 Schematic illustration of microfluidic design. (a) Optical image...
Figure 5.14 Open‐circuit potential responses of electrochemical sensors. (a)...
Figure 5.15 Selectivity study of the electrochemical sensors. (a, b) Selecti...
Figure 5.16 Repeatability of the electrochemical sensor array. (a,b) Repeata...
Figure 5.17 Reproducibility of the electrochemical sensor array. (a, b) Repr...
Figure 5.18 System‐level integration of self‐powered sweat‐sensing system. (...
Figure 5.19 Schematic and layout of the flexible circuit.
Figure 5.20 Charging curves of the self‐powered sweat‐sensing system. (a) Re...
Figure 5.21 Long‐term stability of self‐powered sweat‐sensing platform. (a, ...
Figure 5.22 Validation of the accuracy of self‐powered sweat‐sensing platfor...
Figure 5.23 Stability of the electrochemical sensor array. (a) Response stab...
Figure 5.24 Dynamic response of electrochemical sensor with assembled microf...
Figure 5.25 Mechanical properties of electrochemical sensor array. (a–d) Res...
Figure 5.26 Optical images of self‐powered sweat‐sensing platform. (a) Assem...
Figure 5.27 Self‐powered sweat‐sensing platform for harvesting energy from h...
Figure 5.28 On‐body evaluation for wireless, dynamic perspiration analysis. ...
Chapter 6
Figure 6.1 Schematics of all‐laser‐engraved multimodal sensing platform. (a)...
Figure 6.2 Fabrication of laser‐enabled multimodal sensing platform.
Figure 6.3 Morphologies of LEG with different modes. (a) A CO
2
laser‐cutting...
Figure 6.4 Characterization of the LEG with different modes. (a, b) Characte...
Figure 6.5 Microscopic images of the resolution of the laser engraving. (a) ...
Figure 6.6 Low cost and mass production of the flexible multimodal sensor ar...
Figure 6.7 LEG‐CS with raster mode. (a) Schematic of the raster mode for LEG...
Figure 6.8 The detection of UA and Tyr with the LEG‐CS. (a, b) The LEG‐CS fo...
Figure 6.9 The comparison of electrochemical performance among different ele...
Figure 6.10 Repeatability of the LEG‐CS for Tyr and UA sensing. (a) Batch‐to...
Figure 6.11 Mechanical stability of the LEG‐CS during the bending tests. (a,...
Figure 6.12 The dependence of sensor response on the solution pH levels. (a)...
Figure 6.13 The dependence of sensor response on the levels of lactate. (a) ...
Figure 6.14 LEG‐based temperature sensor with vector mode. (a) Mechanisms of...
Figure 6.15 Repeatability and response capability of the LEG‐based temperatu...
Figure 6.16 LEG‐based strain sensor with vector mode. (a) Mechanisms of stra...
Figure 6.17 Electrical performance of the LEG‐based strain sensor. (a) Strai...
Figure 6.18 Heart‐rate monitoring using the LEG‐based strain sensor. (a) The...
Figure 6.19 The long‐term stability of the LEG‐based strain sensor. (a) The ...
Figure 6.20 The circuity module for signal processing and data transmission....
Figure 6.21 Characterization and calibration of LEG‐CS using the FPCB. (a) D...
Figure 6.22 Characterization and calibration of the temperature and strain s...
Figure 6.23 Validation of the LEG‐CS using HPLC analysis. (a, b) The peak am...
Figure 6.24 Integrated system for real‐time health monitoring of various phy...
Figure 6.25 Real‐time monitoring of UA and Tyr during exercise at different ...
Figure 6.26 Real‐time, continuous in situ measurement of RR, temperature, sw...
Figure 6.27 Dynamic monitoring of sweat in physically trained and untrained ...
Figure 6.28 Purine‐rich diets increase the risks of gout attacks.
Figure 6.29 Non‐invasive gout management based on the integrated health‐moni...
Figure 6.30 The influence of diet on the UA levels in sweat and serum. (a–d)...
Figure 6.31 Comparison of UA levels in sweat and serum among different group...
Figure 6.32 Dynamic investigation of UA levels in serum and sweat. (a) Dynam...
Chapter 7
Figure 7.1 Perspectives of the smart micro‐systems toward healthcare monitor...
Guide
Cover Page
Table of Contents
Title Page
Copyright
Preface
Begin Reading
Index
End User License Agreement
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Integrated Smart Micro‐Systems Towards Personalized Healthcare
Yu SongWei GaoHaixia Zhang
Authors
Dr. Yu Song
Peking University
Institute of Microelectronics
No. 5 Yiheyuan Road
Beijing 100871, China
California Institute of Technology
Engineering and Appilied Science
1200 East California Boulevard
Pasadena, CA 91125, United States
Prof. Wei Gao
California Institute of Technology
Engineering and Applied Science
1200 East California Boulevard
Pasadena, CA 91125, United States
Prof. Haixia Zhang
Peking University
Institute of Microelectronics
No. 5 Yiheyuan Road
Beijing 100871, China
Cover Image: © toodtuphoto/Shutterstock
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© 2022 WILEY‐VCH GmbH, Boschstr. 12, 69469 Weinheim, Germany
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Preface
With the development of industry 4.0, the Internet of Things, and the 5G technology, great changes have taken place in all aspects of our lives. Among them, the exploration of medical healthcare field is pretty noticeable. For the traditional Chinese medical modes, “observation, auscultation, inquiry, and palpation” are four basic methods of diagnosis. Normally, doctors require face‐to‐face communication to diagnose the health condition of the patients assisted by the various medical facilities, which cannot provide real‐time information about our health with low efficiency and high cost. As we know, human body can produce various physiological signals, which are able to reflect the health status through both biophysical and biochemical information. The development of material science, analysis technology, and flexible fabrication allows for the real‐time wearable sensing. Furthermore, the integration of wearable biosensors with energy‐harvesting and energy‐storage units enables the flexible smart micro‐systems, which can perform continuous sensing with stable power supply. The collected signals can be processed and transmitted wirelessly to the user interface and cloud server for remote diagnosis, health monitoring, and many other healthcare‐related applications.
In this book, we introduce the recent developments of flexible smart micro‐systems toward personalized healthcare in five parts – the core units of the micro‐systems, self‐charging power unit, self‐driven monitor patch, self‐powered sensing platform, and integrated health‐monitoring system and their applications in various areas. The major contents of each part are listed as follows.
Core units of smart micro‐systems, including triboelectric nanogenerators, solid‐state supercapacitors, and piezoresistive sensors, from structural design, fabrication technologies, performance evaluation, to application exploration.
Sandwiched self‐charging power unit, integrated with triboelectric nanogenerator based on single‐step treatment‐induced wrinkle structure, and carbon nanotube–paper based supercapacitor, showing potential applications in driving low‐power electronics and smart display.
All‐in‐one self‐driven monitor patch, including piezoresistive sensor and micro‐supercapacitor, based on porous CNT–PDMS conductive elastomer through general solution‐evaporation process, for health monitoring, human–machine interaction, and dynamic trajectory recognition.
Fully integrated self‐powered sweat‐sensing platform, composed of freestanding triboelectric nanogenerator, microfluidic structure‐assisted electrochemical sensor array and power management circuit module, realizing continuous health monitoring and wireless signal transmission for timely analysis and dynamic feedback.
Multimodal sensing integrated health‐monitoring system based on all‐laser‐engraving process, for wireless in situ measurements of various physiological information, such as temperature, vital signs, and uric acid and tyrosine in sweat at trace level, and showing prospects for non‐invasive gout management.
Therefore, this book adopts the bottom‐up approach and provides a comprehensive introduction of the flexible smart micro‐systems, including the multi‐functional material optimization, multi‐dimensional electrode preparation, multi‐modal signal sensing, and multi‐scene application display. In addition, this book can inspire and expand the flexible smart micro‐systems by the improved device performance, the reliable fabrication processing, and the compatible integration designs. Definitely, this book paves a new way for the healthcare‐related applications, such as sports safety, remote diagnosis, and closed‐loop health management.
I would like to thank Alice Wonderlab's current and former members and partners: Yu Song, Xiaosheng Zhang, Bo Meng, Mengdi Han, Haotian Chen, Zongming Su, Xiaoliang Cheng, Xuexian Chen, Mayue Shi, Hanxiang Wu, Jinxin Zhang, Liming Miao, Hang Guo, Haobin Wang, Ji Wan, Chen Xu, Zehua Xiang, Xiuhan Li, Wei Tang, Zijian Song, and others.
I also want to thank Prof. Wei Gao and his group in California Institute of Technology: You Yu, Yiran Yang, Jihong Min, Xiangjie Bo, Minqiang Wang, Siyi Bi, Changhao Xu, Jiaobing Tu, Rebeca Torrente‐Rodriguez, Daniel Mukasa, Wei Guo, and others.
In addition, I thank our collaborators and coworkers worldwide: Zhonglin Wang, John A Rogers, Juergen Brugger, and others.
Finally, I thank my iCANX team and my family; this book is a memorial gift of COVID‐19 pandemic and for the time when we live online and are locked down at home. No matter what a cold winter we have gone through, the flowers will bloom at spring.
05 May 2021 Alice Zhang
Peking University
1Introduction
This chapter first introduces the development of integrated smart micro‐systems, aiming at health monitoring–related applications. After the introduction of the working mechanism and structural design of different energy‐harvesting units, energy‐storage units, and functional units, the related researches focusing on the integration and applications are further carried out. To solve the issues, including complex processing technology, poor device performance, redundant integrated design, and simple applications, integrated smart micro‐systems toward health monitoring are proposed. Consequently, the motivation, purpose, and innovative contributions are briefly summarized.
1.1 Overview of Integrated Smart Micro‐systems
Since the beginning of the twenty‐first century, with the technological innovation of industrial production and the rapid development of Internet applications, tremendous changes have occurred in people's lifestyles. Smart lifestyle has penetrated into all aspects, such as clothing, food, housing, and transportation. It is possible to know the world well without leaving the house. The development of medical and health field is particularly noticeable.
Advances in flexible electronics, the Internet, and processing technology have provided better assistive technical means for seeking medical treatment and physical examination. The health‐monitoring approach is transiting from the traditional medical model with the help of large equipment in hospital to wearable micro‐systems with real‐time monitoring and remote diagnosis, as shown in Figure 1.1.
Under the traditional medical model, “looking, listening, asking, and feeling the pulse” are necessary means. Doctors communicate with patients face‐to‐face and perform various examinations with the assistance of sophisticated medical equipment. For individuals, this is a hospital‐centered diagnosis method, which takes longer time with low efficiency and cannot fully meet the needs of continuous monitoring of certain diseases. The rapid development of various types of wearable devices and flexible micro‐systems allows for the opportunities to the fields of medical care and health monitoring.
Figure 1.1 Health‐monitoring approaches in the field of medical electronics.
Source: Choi et al. [1]; Lee et al. [2].
1.1.1 The Progress of Portable Smart Micro‐systems
The human body produces a variety of physiological signals in the daily metabolism process [3], including physical signals such as body temperature, blood pressure, biopotential, exercise information, respiratory rate, and heart rate, and chemical signals, such as pH, sodium ions, lactate, uric acid, and glucose in body fluids. These physiological signals are very critical for human health management. For example, it is feasible to monitor obstructive sleep apnea hypopnea syndrome (OSAHS) through changes in heart rate [4], and monitor the cystic fibrosis through changes of chloride ion concentration in sweat [5].
With the help of the miniaturization and intelligence of integrated circuits under Moore's law and the timely sharing of data with the development of the Internet, wearable technology has developed rapidly. A series of smart wearable devices for health monitoring are proposed, such as the new generation of Apple Watch. Authorized by the US Federal Drug Administration (FDA), it can achieve the same accuracy as the clinical electrocardiogram monitor to provide diagnosis and early warning for patients to understand their physical conditions in time.
The entire market of wearable devices is developing rapidly. In 2016, the total number of wearable devices in the global market reached 125 million units. It is estimated that by 2021, the overall number will be close to 900 million units, with an average compound annual growth rate of 23% [6]. According to IDTechEx data, the market share of the wearable health field will grow to more than US$75 billion by 2025 [7], and it will gradually develop into a patient‐centered medical health model.
Figure 1.2 Development progress of portable health‐monitoring methods.
Source: Yu Song.
Figure 1.2 lists the current monitoring methods of various physiological signals. Most of these detection modules use portable and miniaturized equipment, which adopt the working modes of pre‐sampling and in vitro off‐line detection. It is hard to obtain real‐time physiological information and perform long‐term continuous monitoring. In addition, most of these commonly used wearable devices are based on silicon‐based rigid materials with hard modules, which lacks in biocompatibility. Due to the mismatch of Young's modulus between the device and human skin, it is unavailable to realize skin‐interfaced measurement of physiological signals directly. Meanwhile, during the normal movements, these rigid modules will be inevitably misaligned with the soft skin, resulting in the poor accuracy of detection and reduced reliability in health diagnosis. These issues greatly limit the practical applications of wearable devices in real‐time monitoring of human health.
With the rapid development of material science, chemical analysis technology, and flexible fabrication process, the flexible and integrated smart micro‐systems toward health monitoring have attracted huge attention [8–10]. The advantages of lightweight, soft, cheap, and durable properties enable the continuous, sensitive, and accurate monitoring of various physiological information when comfortably attached to the human skin. The further cooperation with wireless signal transmission allows for in situ detection and on‐demand therapy with the assistance of big data analysis. It is feasible to achieve the ultimate goal of personalized medical care and dynamic health management based on the integrated smart micro‐systems [11–13].
1.1.2 Integrated Smart Micro‐systems Toward Healthcare Monitoring
The exploration of flexible bioelectronics technology and the in‐depth research of flexible polymer and multifunctional nanomaterials facilitate the rapid development of integrated smart micro‐systems for health monitoring, and break through the limitations of large medical equipment with poor portability and wearable devices with low sensitivity. Through the materials selection, fabrication optimization, structural design, and seamless integration, the smart micro‐systems can be directly attached to the human skin to achieve accurate monitoring of various physiological signals. The schematic of specific health monitoring and remote diagnosis is shown in Figure 1.3.
On the one hand, various energy‐harvesting and ‐storage devices can efficiently convert human mechanical energy into electrical energy [14–16] and effectively store the energy as a stable power supply [17–19]. On the other hand, through structural design and material optimization, miniaturized multimodal biosensors can continuously acquire physiological signals and provide reliable health information [20–22].
During the measurement of physiological signals, the single transmission and analysis are of vital importance. The sensor data are sent to user interface by the circuit module with Bluetooth low‐energy or Wi‐Fi wireless chip. Through the design of relevant applications, it is available to perform real‐time signal analysis and rapid response. Doctors can acquire the key health‐related parameters remotely to monitor the patients' conditions, such as respiration rate, electrocardiogram (ECG) signal, and temperature. It is feasible to answer the questions online for minor illness, conduct early warning interventions for critically ill patients, and arrange in‐patient medical care in time.
The integrated smart micro‐systems toward health monitoring consist of three core units: an energy‐harvesting unit that converts various types of energy into electrical energy, an energy‐storage unit that effectively stores electrical energy, and the functional units that transform external stimuli into electrical signals. The core units and representative devices of the integrated smart micro‐systems are shown in Figure 1.4.
The coordination and integration of three units enable closed‐loop smart micro‐systems, which continuously acquire various physiological signals from human body, perform health status alarm with wireless data transmission and signal processing, and provide diagnosis and personalized health management with improved efficiency. The current advances and challenges of these units and applications will be discussed in detail in the following sections.
Figure 1.3 Schematic illustration of integrated smart micro‐systems toward health monitoring.
Source: Yu Song.
Figure 1.4 Core units and representative devices of smart micro‐systems toward health monitoring.
Source: Bandodkar et al. [14]. Copyright 2017, Royal Society of Chemistry. Lee et al. [15]. Copyright 2014, John Wiley & Sons. Ouyang et al. [16]. Copyright 2019, Elsevier. Liu et al. [17]. Copyright 2016, John Wiley & Sons. Zhai et al. [18]. Copyright 2019, John Wiley & Sons. Yu et al. [19]. Copyright 2017, John Wiley & Sons. Gao et al. [20]. Copyright 2017, John Wiley & Sons. Reeder et al. [21]. Copyright 2017, Springer Nature. Kang et al. [22]. Copyright 2014, Springer Nature.
1.2 Three Core Units of Smart Micro‐systems
The smart micro‐system includes three core units, energy‐harvesting unit, energy‐storage unit, and functional sensing unit. Common energy harvesting units include triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs), and electromagnetic generators. Energy‐storage units mainly include supercapacitors, lithium‐ion batteries, and fuel cells. Functional sensing units include strain sensors, temperature sensors, and electrochemical sensors. The detailed discussion is as follows.
1.2.1 Triboelectric Nanogenerator (Energy‐Harvesting Unit)
Energy‐harvesting devices are categorized and introduced by input energy sources, such as mechanical energy, solar energy, and thermal energy. Piezoelectric, triboelectric, and electromagnetic generators belong to the mechanical energy–harvesting group; solar cells belong to the solar energy–harvesting group; thermoelectric and pyroelectric generators belong to the thermal energy–harvesting group. The detailed working mechanism is shown in Figure 1.5.
Figure 1.5 Schematic illustrations of various energy‐harvesting units. (a) Electromagnetic induction, (b) piezoelectric effect, (c) triboelectric effect, (d) photovoltaic effect, (e) thermoelectric effect, and (f) pyroelectric effect.
Source: Yu Song.
The electromagnetic energy harvester is based on the principle of electromagnetic induction [23]. When the coil and the magnet move relative to each other, the coil cuts the magnetic line to generate electromotive force and induce current in the external circuit. High‐performance electromagnetic energy harvester can convert the mechanical energy into electrical energy with considerable power output, while the intrinsic large size requires specific electromagnetic materials with poor flexibility.
Piezoelectric energy harvesters are based on the piezoelectric effect of piezoelectric materials [24]. The piezoelectric effect is the conversion of mechanical energy into electric energy by breaking central symmetry of crystal structure, which generate internal potential and induce charges flow through external circuits. However, the piezoelectric energy harvester is limited by the material selectivity and specific working mode.
The other mechanical energy–based nanogenerator is the TENG, which is based on triboelectrification and electrostatic induction [25], proposed by Prof. Zhong‐Lin Wang from Georgia Institute of Technology. Surface triboelectric charge polarities that form on materials are determined by the triboelectric series, and these charges induce electric flow through external circuits with specific potential differences. When two different materials come into contact, the interface of the materials transfers a charge, which results in the generation of charges with opposite polarities on the surfaces. The triboelectric potential VT can be derived as
where ρT is the triboelectric charge density, ɛ0 is the vacuum permittivity, and d is the gap distance between two triboelectric materials. The triboelectric current IT can be derived as
where CT is the capacitance of the triboelectric nanogenerator. Therefore, both the structural design and material selection are crucial for improving the capability of the triboelectric nanogenerator.
A solar cell, or photovoltaic cell, is one of the promising green energy harvesters converting infinite solar energy into practical electricity by photovoltaic effect [26]. Work function of the p‐, n‐type semiconductor with electrodes is a critical factor in deciding charge transport ability and generation of voltage. The thermoelectric nanogenerator is based on the Seebeck effect [27]: the induction of electrons and holes diffusion by temperature gradient through p‐ and n‐type semiconductors. Thus, the Seebeck coefficient determines the power performance of the output voltage of the thermoelectric nanogenerator. The other thermal energy–based nanogenerator is the pyroelectric effect–based generator, which is based on spontaneous polarization change by continuous temperature change [28]. Because every pyroelectric material has piezoelectric property, temperature change induces a pyroelectric effect, as well as a piezoelectric effect by thermal expansion of the pyroelectric material. Obviously, these energy harvesters are strongly affected by the external environment, with limited applications in flexible micro‐systems.
Therefore, in the application of the wearable field, three types of energy harvesters that scavenge mechanical energy are mainly considered, electromagnetic energy harvesters, piezoelectric energy harvesters, and triboelectric energy harvesters. Among them, the TENG has great advantages in material selectivity and structural flexibility. It does not require specific permanent magnetic materials or piezoelectric materials, and various commonly used flexible polymers demonstrate good triboelectric properties.
At the same time, TENG has four basic working modes: contact–separation mode, lateral‐sliding mode, single‐electrode mode, and freestanding mode. For specific applications, it is feasible to develop diverse structural designs to enhance the capabilities of energy harvesting. Figure 1.6 introduces the four working modes of TENGs with corresponding charge distribution and representative works.
In 2016, Prof. Youfan Hu's group of Peking University adopted traditional weaving technology to realize a machine‐washable TENG (Figure 1.6b) [29]. Taking advantages of fiber structure, the yarn experiences a contact–separation process with a maximum short‐circuit current of 15.50 mA m−2 during the exercise. The output performance remains stable after several standardized machine‐washing cycles.
In addition to harvesting energy in vertical direction, Prof. Jong Jin Park's group at Chonnam University in South Korea proposed a TENG with a continuous rotating structure in 2017 (Figure 1.6c) for energy harvesting during lateral sliding process [30]. The fiber with rough surface achieves high output performance of 21.6 V and 0.6 μA, showing the advantages of flexibility and high adaptability.
Figure 1.6 Four working modes and representative works of triboelectric nanogenerators. (a) Four working modes of triboelectric nanogenerators and corresponding charge distributions. (b) Contact–separation mode fabric TENG.
Source: Reproduced with permission from Zhao et al. [29]. Copyright 2016, John Wiley & Sons. (c) Lateral‐sliding mode fiber TENG.
Source: Reproduced with permission from Park et al. [30]. Copyright 2017, Elsevier. (d) Single‐electrode mode stretchable TENG.
Source: Reproduced with permission from Jiang et al. [31]. Copyright 2021, Elsevier. (e) Freestanding layer mode checker‐like TENG.
Source: Reproduced with permission from Guo et al. [32]. Copyright 2015, John Wiley & Sons.
To further simplify the structure of the TENG, Prof. Zhong‐Lin Wang's group used hydrogel materials to prepare a single‐electrode TENG in 2021 (Figure 1.6d), which shows chemical robustness and high stretchability [31]. The output performance of the TENG is nearly unaffected even under harsh environment such as overly acidic, alkaline, or saline conditions. The hydrogel is a great candidate as an excellent durable electrode material.
Meanwhile, a freestanding layer can also simplify the structure and improve the overall integration. In 2015, Prof. Chenguo Hu's group reported a checker‐like interdigital electrode‐based TENG (Figure 1.6e) [32]. It is available to harvest in‐planar omni‐directional mechanical energy, with the maximum output power density of 1.9 W m−2 and open‐circuit voltage of 210 V, respectively.
1.2.2 Solid‐State Supercapacitors (Energy‐Storage Unit)
For wearable energy‐storage units [33], considering practical application scenarios, liquid electrolyte remains problems, such as difficulty in encapsulation and easy leakage to harm human health. Solid‐gel electrolyte is adopted, with the flexible electrodes. Common energy‐storage units mainly include ion batteries and supercapacitors [34]. The working mechanism and representative devices of different energy‐storage units are shown in Figure 1.7.
For the rechargeable ion battery [35], a thorough redox reaction occurs between the electrode materials and the electrolyte ions (Figure 1.7a). The ions of the electrolyte penetrate into the electrode materials and carry out repeated intercalation/deintercalation reactions. It demonstrates a high specific capacity and energy density, with a relatively low‐power density instead.
Different from the working mechanism of ion batteries, no redox reaction occurs for the electrical double‐layer supercapacitors during the process (Figure 1.7b) [36]. Through the physical process of charge adsorption/desorption on the electrode surface, an interface layer is formed between the electrode and electrolyte, which shows great capacity with ideal power density and cycling stability.
In addition, during the charging process of the pseudo‐capacitor [37], the surface redox reaction occurs between the electrode materials and electrolyte ions (Figure 1.7c). Such chemical reaction is highly reversible and the device shows high capacitance. However, the electrode experiences an irreversible decay after repeated redox–oxidation reaction with poor chemical stability.
Intercalation pseudo‐capacitor is newly proposed model in recent years [38], where a redox reaction under a certain depth occurs at the contact interface between the electrode materials and electrolyte ions during the operation (Figure 1.7d). Although this device shows higher power density and capacitance, it requires strict crystal structure and purity of the electrode materials. The complex synthesis process and preparation conditions further limit the practical applications.
Thus, compared with various devices that store charges through chemical reactions, electrical double‐layer supercapacitors have compelling advantages as follows.
