<figure><img src="image_33.png" /><figcaption>FIG. 20. NG-generated electric potential interfaced with a biological system. (a) Schematic illustration and comparison of electric potential generated in a biological system (left), a piezoelectric material (up-right), and a triboelectric pair (lower-right). (b) Schedule illustration of charge distribution and transport at the interface between a NG electrode and a biological system.</figcaption></figure>

treatment parameters, further supporting the advantages of ES from NGs.

Above two examples of closed-loop ES enabled by NGs are not exclusive. We believe this strategy could be applied to most, if not all, ES therapeutic treatments with enhanced effectiveness and alleviated side effects. Bone healing is another well-known biological process that can be controlled by endogenous electric fields and externally applied ES.⁴⁷³⁻⁴⁷⁷ In a preliminary *in vitro* study, Tian *et al*. showed that the ES generated by a NG would be able to stimulate bone cell proliferation, demonstrating the possibility that the NG-driven ES could promote osteoblasts attachment, proliferation and differentiation.⁴⁷⁸ Nevertheless, the unique clinical advantages of using NG for bone healing still needs to be further demonstrated by more direct *ex vivo* and *in vivo* experiments and head-to-head comparison.

In addition to cell growth, ES also plays an important role in muscle stimulations. One typical example is artificial heart pacing, a common cardiac treatment for a variety of arrhythmias normally implemented by an implantable pacemaker. Current research on NGs for pacemakers are primarily focused on charging the pacemaker batteries by NGs using the energy from nearby body

motions, such as hearting beating or breathing.⁴²⁷,⁴⁷⁹ This strategy would enable a self-powered operation, i.e., the electrical power solely comes from the body motions. For closed-loop ES, more direct ES is expected that bypasses the energy storage component. For example, Hwang *et al*. used a piezoelectric NG to stimulate the heart of rat through two electrode leads directly from the NG without any electronic circuits [Fig. 21(c)].⁴⁰³ This concept may revolutionize the pacemaker design with a much simpler and smaller configuration compared to current ones. Nevertheless, this demonstration was implemented only by using an external mechanical energy source. This strategy may raise risks to timely deliver ES to heart muscles with desired frequency and intensity. How to adapt a reliable biomechanical source with a sufficiently high energy to close the stimulation loop may not be an easy task to accomplish.

In conclusion, the NG technology stands at a unique position to connect biomedical ES with biomechanical motions. In this short roadmap article, we capitalize the concept of closed-loop ES that depicts a process where the electric pulses used for ES are generated by relevant body motions. It provides a close analog to how the
<figure><img src="image_34.png" /><figcaption>FIG. 21. Examples of closed-loop ES on cells. (a) ES accelerates skin wound recovery from 2 weeks to 3 days on rats. Reprinted with permission from Long et al., ACS Nano 12, 12533 (2018). Copyright 2018 the American Chemical Society. (b) ES accelerates hair follicle (HF) proliferation and hair growth on rats and nude mice. Reprinted with permission from Yao et al., ACS Nano 13, 12345 (2019). Copyright 2019 the American Chemical Society. (c) ES on heart muscle for direct heart pacing. Reprinted with permission from Hwang et al., Adv. Mater. 26, 4880 (2014). Copyright 2014 Wiley-VCH.</figcaption></figure>

body controls its own functions. It may deliver the most direct and
synchronized stimulations to the targets and thus impose efficient
therapeutic effects without any external manipulations. Consider-
ing how our body uses electricity to communicate and to control,
the concept of closed-loop ES would show very broad and promis-
ing application potential. Despite the few successful cases discussed
in this article, research in this direction is still rather immature.
From an engineering perspective, the first critical challenge is how
to generate stable and sufficiently large electric pulses from small
biomechanical motions in vivo. Different body parts have different
motion patterns, most of which are non-linear with small strength
and low displacement. In order to achieve high biomechanical
energy-to-electricity conversion efficiency, flexible NGs and pack-
aged devices need to be designed with specific motion and softness
configurations to perfectly match the corresponding movement pat-
terns without imposing a significant burden to the supporting organ
or muscle. Biologically, many advantages of the NG-enabled closed-
loop ES are hypothetical. Further in-depth cell-level understandings
are particularly needed to quantify the relationship between ES from
NGs and the targeting biological processes. Collaborations between
material and biology scientists would be essential to reveal the
unique correlation of NG-driven ES to specific bio-functions, from
an interdisciplinary angle of biology, dielectrics, and electrochem-
istry. New engineering solutions and biology understandings will

eventually lead the concept of closed-loop ES to a powerful toolset
for both therapeutic and cosmetic treatments with unprecedented
effectiveness.

5. Acknowledgments

This publication was supported by the National Institute of
Biomedical Imaging and Bioengineering of the National Institutes
of Health under Award Nos. R01EB021336 and 1R21EB027857. The
content is solely the responsibility of the authors and does not nec-
essarily represent the official views of the National Institutes of
Health.

C. Energy harvesting powered wireless sensor system
technologies

Zheng Jun Chew, Yang Kuang, Tingwen Ruan,
Meiling Zhu (*m.zhu@exeter.ac.uk)

1. Status

The intended outcome from energy harvesting research is to
power wireless sensor systems, as an alternative or supplement to
batteries. This is because batteries, which have limited capacity and
charge cycles, need to be replaced once they are depleted to allow systems that rely on them as the power source to continue working. The rapidly growing number of wireless sensor systems for applications may soon incur a never-ending battery replacement and disposal maintenance routine that is too much to cope with. Therefore, harvesting ambient energy is a promising method to power wireless sensor systems for long term and sustainable deployment, with very little to no maintenance where the wireless sensor systems can be “fit-and-forget.” Energy harvesting powered wireless sensor system technologies comprise energy harvesters that convert the ambient energy to electricity, power management circuits to convert the harvested electrical energy into a usable form, and energy stores to hold the harvested energy and supply energy for wireless sensors to perform sensing tasks. All these different subsystems of an energy harvesting powered wireless sensor system are inherently multidisciplinary that require research on materials,²²⁸ applied physics,⁴⁸⁰ mechanical designs,¹⁸¹ electronics,⁴⁸² and communication technology.⁴⁸³ While many promising innovations on the individual subsystems of energy harvesting technologies have been achieved,⁴⁸⁴ it is still challenging for the subsystems from different domains to operate in synergy when they are integrated into one system. The challenge mainly lies in the mismatches among the different subsystems of energy harvesting technologies, which can be in terms of their coupling and the powers that are related to generation by the energy harvester and demand by the power management circuit and wireless sensors. These mismatches would not be easily identified or understood if these individual subsystems are being investigated in isolation. Therefore, successful real-world applications of energy harvesting powered wireless sensor systems are very limited so far even though energy harvesting has been extensively studied for almost two decades.⁴⁸⁵,⁴⁸⁶ Many powerful techniques capable to analyze and improve the individual subsystems of energy harvesting technologies are available, which can be used to address the mismatches when they were applied in the perspective of a whole system by considering the interaction with adjacent subsystems. This “whole system approach” will be discussed by looking into the development of a piezoelectric energy harvesting powered wireless sensor system using a series of methodologies from the perspective of the energy harvester design, modeling, power management circuit, and the wireless sensor system.⁴⁸⁷,⁴⁸⁸

## 2. Advances

The development of an energy harvesting powered system always begins with the design of the energy harvester as the energy harvester is responsible for converting the ambient energy into electrical energy to power the rest of the system. Cantilever beams¹⁸⁹,⁴⁹⁰ and cymbal structures⁴⁹¹ are commonly used as piezoelectric energy harvesters. Piezoelectric transducers are one of the most dynamic energy harvesters as their frequency responses and electrical output vary with parameters of the transducers and succeeding circuits.⁴⁹² Modeling using finite element (FE) method is a powerful way to understand, predict, and optimize the transducers.⁴⁸¹ To understand the interaction of an energy harvester with its adjacent subsystem, which is the power management circuit, the model needs to include the circuit. However, the complete circuit is very difficult to be directly modeled and simulated in FE analysis. A way forward is to simplify the circuit as a load of its equivalent impedance.⁴⁹²⁻⁴⁹⁴ Such a simplified system-level model that coupled the energy

harvester with the load now forms the basis for FE analysis of an energy harvester as the load has been proven to affect the harvested power and the resonant frequency, which would not be revealed if only the uncoupled model is used in the modeling. From the FE analysis, the lumped parameters of an energy harvester in its equivalent circuit model can be determined. A system-level coupled electrical model that consists of the energy harvester and the circuit can then be built and analyzed using circuit analysis software to simulate the operation of the circuit that is connected to energy harvester.⁴⁹³

To obtain a close to accurate simulation results, a transducer model with very detailed features that resembles the actual device is essential, but it would lead to an unprecedentedly complex model that is extremely time-consuming to solve. For example, the macrofiber composite (MFC) is a very popular piezoelectric transducer with an interleaving multi-material layered structure for energy harvesting, sensing and actuation applications.⁴⁹⁵ Modeling an MFC with its detailed structure has been proven to be quite complex that encouraged many research efforts to devise homogenized models of MFC as a single piezoelectric material with equivalent properties.⁴⁹⁴ A good solution is to use the representative volume subsystem of a MFC to determine the equivalent properties of a homogeneous MFC model in FE analysis.⁴⁹⁶,⁴⁹⁷ However, the equivalent properties are subjected to variations according to the chosen electrical assumption and boundary conditions. Considering that output power is the key parameter of interest from an energy harvester, an equal power-output method FEM was proposed to evaluate the equivalent properties by applying electrical boundary conditions that ensure equal voltage, electrical charge, and, thus, equal power output between the heterogeneous and homogeneous MFC models when connected to a given load.⁴⁹⁴ Apart from electrical output, FE analysis can also be used to study mechanical and material properties of a designed transducer,⁴⁹² which is vital to understand its reliability that ensure a long-lasting operation in the real-world.⁴⁹² Thus, an initiative has been taken to study and develop a transducer with high reliability, high output power, and broadband operational frequency using a novel deformable force amplification structure with a strongly coupled piezoelectric transducer that is pre-stressed by a mechanical transformer.⁴⁹²

A power management circuit in its simplest form generally comprises a rectifier to convert the AC electrical energy from an energy harvester into DC energy and a DC-DC converter to convert the rectified DC voltage into an appropriate level for charging up the energy storage as well as powering the wireless sensor. The optimum value of the impedance that is connected to an energy harvester for maximum power transfer changes with the vibration frequency applied onto the energy harvester. Therefore, maximum power point tracking (MPPT) is an indispensable feature in a power management circuit. Many design methods of MPPT control circuits are to track the electrical output from an energy harvester and varies the duty cycle of a DC-DC converter as the effective impedance of a DC-DC converter changes with its duty cycle.⁴⁹⁸ Most methods are based on that maximum power transfer occurs when the impedances of both the power management circuit and the energy harvester match. Popular MPPT techniques in energy harvesting applications include hill-climbing⁴⁹⁹ and fractional open-circuit voltage (FOCV).⁵⁰⁰ The hill-climbing method periodically samples the output power obtained from the energy harvester using
different control parameters and gradually tunes the parameters until maximum power is obtained.⁵⁰¹ Thus, the power consumption of this method increases with the duty cycle of the MPPT control circuit. A simple way of keeping the average power low is to carefully reduce the duty cycle of the MPPT control circuit so that its operating frequency is still higher than the excitation frequency on the energy harvester to maintain a relatively high MPPT accuracy.⁴⁹⁹ An algorithm that linearizes the system with control parameters independent of nonlinear functions was also proposed to simplify the control as well as save power.⁵⁰¹

The FOCV is an increasingly popular method based on the findings that the voltage across a load is half of the open-circuit voltage when it matches the intrinsic impedance of the source for maximum power transfer.⁵⁰² General implementation of this method also requires periodical sampling of the open-circuit voltage of the energy harvester as the reference to determine the half open-circuit voltage by temporarily disconnecting the energy harvester from the DC-DC converter. Similar to the hill climbing method, the sampling frequency for the open-circuit voltage is directly related to the power consumption of the MPPT control circuit and accuracy of the MPPT. One method to reduce the power losses is to use a small sensing capacitor for sampling the open-circuit voltage. It allows a short sampling time, which reduces the time that the energy harvester is disconnected from the DC-DC converter for power transfer.⁵⁰³ Another innovative method is derived from the equivalent circuit model of the energy harvester that forms an RC circuit with the rectifier. The voltage waveform of the RC circuit formed by the energy harvester and the rectifier is used to determine the half open-circuit voltage of the energy harvester by using analog filter circuit and differentiator without the use of a microcontroller.⁵⁰⁰ This method occurs in real-time, has a low power consumption of around 3–7 µW, and does not disconnect the energy harvester from the DC-DC converter.⁴⁸²

Given that the instantaneous power from energy harvesters is usually lower than the power requirement of wireless sensor systems to operate, the harvested energy needs to be accumulated in energy storage devices. In energy harvesting applications, supercapacitors are preferred over rechargeable batteries as the energy storage device because they are more rugged against factors such as temperature variations, unregulated voltage, and current charging and have much higher charge-discharge cycles than rechargeable batteries. However, the voltage across a capacitor is dependent on the energy stored in the supercapacitor. This is unlike a battery, which provides a relatively steady voltage, regardless of its state of charge. On the other hand, wireless sensor systems can only start working properly above a specific threshold voltage. Therefore, a supercapacitor has to be charged up first to accumulate sufficient energy and also to reach the voltage level that is required by the wireless sensor system. Early designs failed to start-up or properly operated the wireless sensor system, which is directly connected to the supercapacitor.⁵⁰⁴,⁵⁰⁵ The wireless sensor system, which draws energy, can be simply modeled as a resistor, which dissipates power. In that sense, the wireless sensor system will simply discharges the supercapacitor once they are connected. This prevents energy accumulation and voltage increment across the supercapacitor to the minimum required level for a proper operation of the wireless sensor node. Therefore, an interface that is aware of the energy level in the supercapacitor is required to control the energy flow. Early design of

such an energy-aware interface (EAI) mainly consists of a voltage detector and a switch.⁵⁰⁴,⁵⁰⁵ Once the voltage across the supercapacitor exceeds the threshold voltage of the voltage detector, the switch will be turned on to connect the wireless sensor system to the supercapacitor. However, with only one threshold voltage used, the capacitor voltage may drop quickly below the threshold voltage once the wireless sensor system starts operating. The wireless sensor system will then be turned off again or operating with an unpredictable behavior.⁵⁰⁵

A more practical design of the EAI consists of two different thresholds for connecting and cutting off the capacitor to the wireless sensor system.⁵⁰⁶,⁵⁰⁷ There are important considerations to determine the appropriate threshold values.⁵⁰⁸ The turn-on threshold voltage has to be higher than the turn-off threshold voltage where their absolute limits are equal to the operating voltage range of the wireless sensor system, which provides a sufficiently large operating window. The hardware energy-aware approach is essential to reduce the sleep power consumption of wireless sensor systems and power the systems without the start-up issue in the condition of mismatch between the energy generated by harvesters and demanded by the systems. For the turn-off threshold voltage, there could still be energy wastage with just the hardware implementation. It may be possible that the wireless sensor system is turned off by the EAI when the capacitor voltage reaches the turn-off threshold even though the wireless sensor system has not finished its tasks. The wireless sensor system may also finish its tasks early and keep draining the energy without doing any meaningful tasks until the EAI turns it off. To better use the harvested energy, an energy-aware algorithm was developed to provide a flexible turn-off threshold voltage.⁵⁰⁷ The algorithm was programmed in the microcontroller of the wireless sensor system to determine the usable energy in the capacitor. The wireless sensor system varies its tasks or active time according to the energy availability. The wireless sensor system will be able to switch off earlier when it finishes its tasks or end its task in an orderly and prepared manner so that it is not turned off abruptly by the EAI, before the capacitor voltage reaches the turn-off voltage of the EAI.

### 3. Challenges

The very narrow power bandwidth of many piezoelectric energy harvesters, such as cantilever beam structures, is one of the biggest challenges for their applications in the real world as most of the vibration sources exhibit broadband spectrum. Many research efforts have been put into the design of broadband piezoelectric energy harvesting. There were some success with very specific designs that utilize frequency up conversion⁵⁰⁹ and resonance frequency tuning techniques,⁵¹⁰ but they are limited to very niche applications or operating conditions. Usable output power, at least a few millwatts power for applications,⁴⁸⁷ is also a main challenge. There is generally a size constraint on the energy harvesting powered wireless sensor systems due to limited space in the area for deployment. The output power scales according to the size of the energy harvester, which means a reduction in the size lowers the power.⁵¹¹ There are some ongoing research efforts in using novel deformable force amplification structures with strongly coupled transducers as discussed earlier but that has not been sufficiently studied yet.⁴⁹² Studies on power management for piezoelectric energy harvesters using analytical model have resulted in many variants of
techniques, such as SSHI<sup>512,513</sup> and SECE,<sup>514,515</sup> to extract power from the inherent capacitance of piezoelectric energy harvesters that tends to hold some of the energy. However, practical implementation of many of the techniques as a standalone system is still limited. Some of the issues include the requirement for stringent control using digital signal processing from a computer,<sup>514,515</sup> the need for a large transformer,<sup>516,517</sup> and overdamping of the energy harvester,<sup>514</sup> which reduces the vibration displacement and harvested energy.

There is no general energy harvester design method that suits different applications due to factors such as narrow bandwidth of the transducer and different environmental conditions. Application-oriented bespoke designs are necessary on a case-by-case basis to achieve a high performance system by choosing optimal design parameters such as capacitor size and duty cycle based on the energy availability.<sup>518</sup> Therefore, the lack of real-world environmental data will make it almost impossible to design a working system for a given application. Apart from the data, the system design requires different software, tools, and expertise, which is a complicated process. For instance, important design parameters, such as material properties and geometry of energy harvesters, for a reliable long-term operation can be optimized using FE analysis and simple analyses of electrical power from the energy harvester and electronics is possible. However, there is currently no directly interlinked system analytical model. It is still to use FE analysis first and then equivalent circuit model for the analyses. Due to the generally low harvested power, it is crucial for the power management circuit to consume as little power as possible. This is usually achieved by designing circuits that are bespoke to the energy harvester with almost no adaptation capability to different applications.<sup>519</sup> Given the uniqueness of every single design, the lack of transferable design parameters would make the whole system design process even more tedious.

## 4. Future directions

For real-world applications, it would be necessary to understand environmental conditions and requirements to design a system using the whole system approach. For example, a system that uses MFC as its energy harvester to harvest strain energy from structures has been developed. Although the MFC is operating at a very low frequency that is far from its resonance, hundreds of microwatts to a few milliwatts of power can be generated when the strain is exerted on the MFC at a couple of hundred microstrain.<sup>487</sup> The whole system was then designed by applying a number of novel methods and techniques, such as coupled system modeling, MPPT, and EAI, as discussed earlier. The capability to characterize different parts of the complete whole system simultaneously using an online multichannel characterization testbed is also crucial to give a full understanding and appreciation of the synergetic link among them.<sup>482,487</sup> Furthermore, continuous research effort in the creation of novel energy harvesting materials and structures to increase energy density output is crucial for successful applications of energy harvesting powered systems. A significant breakthrough in the energy conversion efficiency of the active materials of energy harvesters is necessary to generate sufficiently high power for useful applications. Also, continuous research effort in the electronic to further reduce the power consumption of the power management circuit and the wireless sensor system is necessary to reduce the gap between the generation and demand. Innovation in the circuit topology that makes the circuit self-adaptive

and self-configurable to a wide range of different energy harvesters and operating conditions but still consumes extremely low power would significantly reduce the time required to design an energy harvesting powered wireless sensor system as such a circuit is versatile for different applications.<sup>520</sup> It is clear that relying solely on the expertise in a single domain may still generate inspiring innovations for a particular subsystem of the energy harvesting technologies. However, they may not be compatible with other subsystems and cannot be implemented as a complete system. A complete whole energy harvesting powered wireless sensor system approach is essential and requires a multidisciplinary and cross-sector joint effort that includes academia and industry. Understanding of the energy harvesting technology capability and challenging bottleneck of the technology among technology solution providers and the end-users will drive the research on its path for real-world applications.

# D. Nanogenerators and sensors for E-textiles

S. P. Beeby (*spb@ecs.soton.ac.uk)

## 1. Abstract

Electronic textiles (e-textiles) concern the integration of electronic and sensing functionality within an otherwise conventional textile resulting in smart, functional fabrics. Achieving invisible integration, reliable operation and powering e-textiles systems are current barriers to the development of the technology and its wider adoption in practical applications. The current status of textile-based triboelectric and piezoelectric nanogenerators and sensors is briefly reviewed. While several examples are presented in the literature demonstrating the feasibility of these technologies, these are far from practical solutions and significant challenges remain in the practical and robust implementation suitable for real world applications.

Electronic textile technology (e-textiles) whereby electronic and sensing functionality is embedded invisibly within an otherwise conventional textile has the potential to revolutionize wearable technologies. While great strides are being made in the integration of discrete electronics and sensors<sup>521,522</sup> within a textile, there is currently no alternative to powering e-textile systems other than by using conventional rigid batteries that are incompatible with the feel and nature of a fabric. Energy harvesting technologies convert ambient energy from, for example, physical movement into electrical energy, and by using suitable nanomaterials, both triboelectric and piezoelectric, electrochemical harvesting and sensing have been demonstrated to be incorporated into the textile itself. This offers the potential to recharge textile integrated batteries<sup>523</sup> and supercapacitors<sup>524</sup> or even directly power the e-textile system itself.

## 2. State-of-the-art

The triboelectric effect can be used for both energy harvesting and sensing and the coupling between the physical movement associated with human motion and the associated displacement of a textile garment is well suited to this transduction mechanism. Furthermore, textile based triboelectric devices can be implemented without compromising the feel of the textile. The triboelectric effect
can be achieved either by functionalizing the surface of a complete textile or the yarn itself, which may be subsequently woven or knitted into a fabric or embroidered onto an existing fabric.

Capturing human motion in the form of a smart glove has been demonstrated by sewing the sensing yarn into patterns on certain parts of the fingers of the glove.⁵²⁵ The all-textile triboelectric sensor (ATTS) smart yarn is formed from a stainless steel fiber surrounded by polyester fibers resulting in a diameter of around 200 µm (see Fig. 22). The sensing yarn is sewn into a knitted nylon fabric with the interaction between the polyester and nylon fibers forming the triboelectric combination with the stainless steel core collecting the electrical signal (Fig. 22). Compressive forces cause contact and separation between the fibers resulting in signals of around 1.8 V and 5 nA and it also functions when the textile is deformed, such as from the finger bending. This represents an elegant approach to sensing a range of gestures and grips. Wash testing did show some deterioration in the output signal and was limited to only ten washes and repeatability of sensing signal across a range of forces was not demonstrated.

Textile pressure sensors based on triboelectric nanogenerators (TENGs) have also been demonstrated using Cu-coated polyacrylonitrile (Cu-PAN) yarns and parylene coated Cu-PAN (parylene-Cu-PAN) yarns suitable for weaving, knitting, or sewing.⁵²⁶ The Cu coating is 300 nm thick, and the parylene coating is 5 µm, resulting in a resistance along the length of the yarn of 0.15 Ω/cm. While the suitability of the yarns for different textile manufacturing approaches is good and yields an interesting insight into the effect of the different structures on the output of the TENGs, the yarn coating processes

are based on polymer-assisted metal deposition and chemical vapor deposition, which are not ideal for low cost mass manufacture of the yarns itself.

Fibers based upon a piezoelectric nanowire composite of Polyvinylidene fluoride (PVDF)/Antimony sulfoiodide (SbSI) have also been embedded in a textile for energy harvesting and sensing applications.⁵²⁷ The piezoelectric fibers were produced using an extrusion process that was capable for producing 2.5 km formed at a velocity of up to 500 m/min, which indicates excellent potential for industrial scale production. The piezoelectric activity of the fibers was demonstrated, but the actual energy harvested or electrical signals obtained in use case scenarios have not been quantified.

The vertically aligned ZnO nanorods have been deposited on to the surface of a nylon fabric using a novel hydrothermal method forming a piezoelectric nanogenerator (PENG).⁵²⁸ The nylon fabric is first coated with a screen printed silver electrode layer after which a ZnO seed layer and hexagonal ZnO nanorods were formed by immersion in a precursor solution at a controlled temperature. The final textile device is achieved by adding a second Ag coated nylon fabric on top forming the second electrode with the ZnO nanorods sandwiched between the fabric/electrode layers (see Fig. 23). The textile structure produces clear electrical signals from palm clapping and finger bending with output voltages and currents of about 4 V, 20 nA, and 0.8 V, 5 nA, respectively. Energy harvesting performance is again not meaningfully quantified.

Electrochemical based transduction has also been demonstrated for sensing and energy harvesting from yarns. Biocompatible

<figure><img src="image_35.png" /><figcaption>**FIG. 22.** Schematic illustration of the ATTS fabrication process. (a) Yarn assembly. (b) Sensing yarn sewn into knitted nylon textile.</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>**FIG. 23.** (a) The structure of the fully assembled PENG. (b) PENG cross section. Taken with permission from Zhang *et al.*, Physica E **105**, 212 (2019). Copyright 2019 Elsevier B.V. All rights reserved.</figcaption></figure>
Prussian blue (PB) active coatings on polyester-carbon nanotube (CNT) threads demonstrate a mechanical-electrochemical coupling effect that results in area power densities of up to 3.8 µW/cm² at low frequencies associated with human motion using aqueous or polymer gel electrolytes.⁵²⁹ Forest-drawn carbon multiwalled nanotubes (MWNTs) formed into high-strength twisted yarns have also been shown to convert torsional and tensile mechanical motion into electrical energy.⁵³⁰ Stretching these twisted yarns has generated up to 250 W/kg of peak electrical power at 30 Hz cycling frequency.

Further examples can be found in more comprehensive reviews of textile-based triboelectric and piezoelectric nanogenerators.⁶⁸,¹⁰⁸,⁵³¹

### 3. Future challenges

Textiles are challenging materials to work with given their physical characteristics (flexible, stretchable, breathable, etc.), the nature of the mass manufacturing processed used within the textile industry and the rigors of actual use typified by machine wash cycles. Textiles are incredibly robust and the transduction mechanisms must achieve the same level of robustness without compromising the feel of the textile. While current research is successfully demonstrating the feasibility of different energy harvesting and sensing approaches, insufficient attention is being paid to the issue of robustness and ensuring that potential solutions are practical. Achieving the required level of robustness will influence implementation of the technologies and this must be considered at the outset in order to minimize the overall impact on the feel of the textile.

The flexible and compliant nature of textiles also represents a challenge with respect to the force/strain/displacements that are coupled to the transducer. The compliant nature of a textile makes it comfortable for the wearer, but this reduces the mechanical energy coupled into the energy harvesting/sensing transducer, which in turn reduces the energy that can be harvested. Most wearable applications involve bending or straining the textile or have some sliding between two separate textiles, and this makes triboelectric harvesting an attractive approach. Piezoelectric harvesting relies on the active material being strained and this may require engineering methods to increase the strain coupled to the piezoelectric element. This may involve, for example, designing a garment in such a way that strain is concentrated through the energy harvester. Further materials research to improve the performance of the energy harvesters and achieve improved levels of flexibility also remains an important requirement.

### 4. Concluding remarks

There are considerable opportunities for application in a wide range of application domains, including consumer electronics, human-machine interfaces, robotics, and healthcare. It is unlikely a one-size fits all solution will be identified given the very different use case scenarios, expected activity levels, and sensing requirements. However, it is clear energy harvesting and sensing technologies that can be integrated seamlessly and invisibly within a textile and can survive the rigors of use will revolutionize e-textiles and their use in wearable technology applications.

## E. Triboelectric nanogenerators as highly sensitive sensors for human-machine interface

Xianjie Pu, Hengyu Guo, Chenguo Hu
(*hucg@cqu.edu.cn)

### 1. Abstract

Human-machine interface (HMI) is an indispensable part of our lives. In the past few years, the triboelectric nanogenerator (TENG) becomes not only an energy harvesting technology but also an advanced sensor technology in various areas, including HMIs. As a highly sensitive and self-powered sensor, TENG can detect the mechanical micro-motion of the skin around the corners of eyes, respond to acoustic vibration in a wide broadband, quantify the rotations of a hinge model via mechanical displacements, and position the touch point on a flexible interface, which enable TENG to be used in HMI control nowadays including eye-movement, voice, gesture, and touch. Besides, the TENG can also play a role in the feedback stage of HMIs in visual, auditory, or tactile modes worthy of further investigations. This paper gives a brief review of the current status of TENG-based sensors in HMIs, with focus on several works of our group around the control-feedback loop, together with discussion of the current challenges and future prospects.

### 2. State-of-the-art

Human-machine interface (HMI) has become an indispensable part of our lives. From the original keyboards to the virtual reality today, the goal of HMI is to make life more convenient and fascinating. In recent years, the fast development of nanotechnology provides theory basis and techniques to improve HMIs.⁴¹⁹,⁵³²,⁵³³ Among these technologies, triboelectric nanogenerator (TENG)²⁸—based on the contact electrification and electrostatic induction⁴,⁶¹—was invented for energy harvesting with unique advantages of high output pulifhtedt, light weight, low cost, applicability of design, stability, and robustness.⁵⁰,⁵³⁴⁻⁵³⁶ Since TENGs can generate electricity from almost all mechanical motions, including vibration, rotation, sliding, touching, etc., they can also act as self-powered sensors for these kinds of mechanical motions usually used in HMIs.⁵³⁷⁻⁵⁴⁴ In this review, four general types of TENG-based HMIs are introduced, mainly focusing on our four works aiming at intuitive interfaces through eye movement,⁴¹⁹ voice,⁵⁴⁵ gesture,⁵⁴⁶ and touch,⁵⁴⁷ as illustrated in Fig. 24. From HMIs in certain scenarios to HMIs in daily applications, these four types of work are reviewed successively.

As a communication channel between humans and external devices, HMI is one way to turn a virtual thought into realistic action, which plays a unique role in helping the disabled, such as the patients suffering from amyotrophic lateral sclerosis (ALS).⁵⁴⁸ That is precisely what brain-computer interface (BCI) stands out from HMI for Ref. 549. However, BCIs based on noninvasive bioelectrical signals, including electroencephalogram (EEG)⁵⁵⁰ and similar electromyogram (EMG)⁵⁵¹ and electro-oculogram (EOG),⁵⁵² have inherent disadvantages of low signal-to-noise ratio (SNR), lack of efficient resolution, poor stability, and durability.⁵⁵⁰,⁵⁵¹,⁵⁵³ These drawbacks limit its wide use in daily life. With TENG technology, a non-invasive, sensitive, stable, reusable, comfortable, and aesthetical sensor is developed based on bionic membrane sensors exquisitely designed by the early researchers to replace these bioelectrical-based
<figure><img src="image_37.png" /><figcaption>**FIG. 24.** TENG-based sensors in the control-feedback loop of HMIs. Reproduced with permission from Pu *et al.*, Sci. Adv. 3, e1700694 (2017). Copyright 2017 American Association for the Advancement of Science. Reproduced with permission from Guo *et al.*, Sci. Rob. 3, eaat2516 (2018). Copyright 2018 American Association for the Advancement of Science. Reproduced with permission from Pu *et al.*, Nano Energy 54, 453 (2018). Copyright 2018 Elsevier. Reproduced with permission from Pu *et al.*, Nano Energy 76, 105047 (2020). Copyright 2020 Elsevier.</figcaption></figure>

HMI sensors.<sup>419,554</sup> This sensor is named mechnosensational TENG (msTENG),<sup>419</sup> as shown in Fig. 25(a). It is designed with a multi-film structure based on a single-electrode mode and thus could be flexibly mounted and hidden behind a pair of glasses to form a wearable sensor. An acrylic thin annulus is tightly attached as a spacer between the two tribo-layers to realize vertical contact/separation driven by eye-blink and other mechnosensational movements. The synchronous measurement illustrates that the voltage amplitude from the msTENG is significantly larger (~750 times) than that of EOG. Through threshold sets, the voluntary and involuntary eye-blink can be easily distinguished. Based on this distinct advantage, with a simple signal processing circuit, the msTENG glasses are adopted in the control of household appliances. Furthermore, via a wireless module, a hand-free virtual keyboard typing system is developed to help the ALS patients. This study realizes applying the TENG to the field of mechnosensational HMIs and will greatly help people achieving a convenient and colorful life.

Besides eye movement, voice communication is another important and effective channel with the outside world. For mature social mobile robotics, a sensitive and energy efficient auditory sensor should be equipped to listen to our instructions and even perceive our vocal intonations.<sup>555</sup> For people with impaired hearing served by the robotics, reconstructing the normal hearing via such kind of auditory sensor is also necessary, as illustrated in Fig. 24. Several research groups have developed this kind of self-powered auditory sensor based on piezoelectric effect, which is different from the traditional ones based on electromagnetic effect, capacitive sensing, and piezo-resistive effect.<sup>377,556–559</sup> However, the piezoelectric auditory sensor<sup>377,556</sup> has a relatively low signal output, and its response range is always higher than frequency ranges of human voice, while a TENG-based auditory sensor can induce a much higher output and can easily realize a selective response range via adjusting structural parameters.<sup>545,560</sup> As shown in Fig. 25(b), this circular type TENG is called triboelectric acoustic sensor (TAS).<sup>545</sup> Similar to the msTENG, the inner film (blue) attached on the acrylic thin annulus will vibrate under air flow. Then, the electrode (yellow) induces

signal. This tiny, thin, and even transparent TAS shows ultra-high sensitivity up to 110 mV/dB. Through the design of the annular or sectorial inner boundary architecture, the TAS achieves the broad-band response from 100 to 5000 Hz, which almost covers the whole frequency range of the human voice.<sup>561</sup> Furthermore, the characteristics of tunable resonant frequency make itself a single channel hearing aid with selected frequency bands, which matches the lost bands of the impaired hearing. This device achieves a complete human-robotic voice communication efficiently with low energy consumption.

In scenarios of manufacturing industry, deep-sea exploration, telesurgery, and disaster salvation, HMIs via gesture control can help bring about an intuitive interface instead of operation based on careful learning and training,<sup>555,562</sup> as illustrated in Fig. 24. Current technologies about gesture control robotic hands include visual tracking, physiological signal collection, and mechanical signal collection, which have the problems of poor wearability, low SNR, complex algorithms, or nonlinearity.<sup>53,563–565</sup> In recent years, epidermal electronics, stretchable polymer semiconductor films, piezoelectric nanodevices, and TENGs provide several possible strategies.<sup>533,566–568</sup> In TENG methods, to realize quantization of fingers' movement, a micro-grated displacement mode is adopted.<sup>546,569</sup> This sensor worn on a finger joint is called joint motion triboelectric quantization sensor (jmTQS),<sup>546</sup> as shown in Fig. 25(c). Verifying by a hinge model,<sup>570</sup> the rotation angle is linear to the tensile displacement at the finger joint. When the slider moves forward/backward under finger's flexion/extension, the relative displacement between slider and fixed interdigitated electrodes induce pulses. Both the slider and the fixed electrodes have the corresponding block part and finer grating part, which induce wide pulses representing the direction and narrow pulses representing the degree, respectively. These two signals constitute the signals shown in Fig. 25(c). For these coupling signals, through calculating the narrow pulses in unit time, the rotation angle and speed can be determined; through judging the positive or negative direction of the coupling signal, the rotation direction can also be determined. Therefore, the jmTQS can directly
<figure><img src="image_38.png" /><figcaption>**FIG. 25.** TENG-based sensors in HMIs of eye-movement, voice, gesture, and touch. (a) The mechnosensational TENG (msTENG) for controlling the equipment via eye-blink. Reproduced with permission from Pu *et al.*, *Sci. Adv.* **3**, e1700694 (2017). Copyright 2017 American Association for the Advancement of Science. (b) The triboelectric auditory sensor (TAS) for interacting with the robotic via voice. Reproduced with permission from Guo *et al.*, *Sci. Rob.* **3**, eaat2516 (2018). Copyright 2018 American Association for the Advancement of Science. (c) The joint motion triboelectric quantization sensor (JimTQS) for constructing a robotic hand synchronous control system. Reproduced with permission from Pu *et al.*, *Nano Energy* **54**, 453 (2018). Copyright 2018 Elsevier. (d) The 3D triboelectric touch pad (3D-TTP) for an effective and efficient touch-screen graphical user interface. Reproduced with permission from Pu *et al.*, *Nano Energy* **76*, 105047 (2020). Copyright 2020 Elsevier.</figcaption></figure>

quantify finger joint's flexion-extension degree/speed and this has been verified on a hinge model test. Based on sensing these intermediate states of finger movement, a robotic hand synchronous control system has been constructed.

In daily life, some traditional interactive modes, such as keyboards and touch pads, still have a strong vitality and practical value.<sup>179,571-573</sup> Based on different physical transduction mechanisms, these keyboards or touch pads are essentially tactile sensors that transduce the physical touches into electrical signals and map them to corresponding positioning units.<sup>572,574,575</sup> Converting physical movement into electrical signal is the original intention of TENG, which addresses the power/energy challenges of devices with powerful sensors nowadays.<sup>555</sup> Therefore, the self-powered TENG technology is exactly a good solution for developing a tactile sensor equipped on these devices.<sup>538,540,574,576-578</sup> In these applications, XY matrix scheme greatly reduces the number of channels and is conducive to high resolution wiring layout.<sup>575,578</sup> In this scheme, crosstalk and target sensing resolution are the problems. To address these problems, a 3D triboelectric touch pad (3D-TTP) with XY Complementary Subdivision Pattern (XYCSP) is developed,<sup>547</sup> as shown in Fig. 25(d). The upper three layers constitute the XY positioning layer, and the lower three layers construct the pressure

sensing layer. The XYCSP is designed to suppress the crosstalk through reducing the overlap between XY electrodes as well as make object positioning robust in object size and contact point. It provides a paradigm of successive subdivision for regulating the object sensing resolution and demonstrates its use in trajectory. Furthermore, with the attached pressure sensing layer, password-based authentication is demonstrated. These two composite functional layers have the potential for tactile sensors for motion monitoring, electronic skin, robotics, and other traditional human-machine interactions.

### 3. Challenges and future prospects

With the rapid development of sensor technology, the increase of various precision sensors poses a challenge to the power source of HMI systems. As self-powered sensors, TENGs transform the various mechanical motions into target signals without power supply, which greatly reduce the energy consumption. However, the back-end data collection devices and signal processing devices still need power supply. To realize a fully self-powered mobile HMI system, it needs to draw support from the TENG's power generating nature and the corresponding power management technique to power the essential back-end devices or at least to meet the wireless transmission of the sensing signal.<sup>419,535,579-582</sup> For signal processing progress,
advanced algorithms based on machine learning and big data are essential for robust HMIs in daily applications.

Taking advantages of various functional materials and advanced fabrication techniques, the TENG sensors can be developed to be more flexible, stretchable, even washable and multifunctional to meet the portable or wearable requirements of HMIs.<sup>583-585</sup> Meanwhile, these improved TENG sensors are also beneficial to collecting environment energy for the back-end equipment.

Besides the illustrated applications in the control stage of HMIs, in the feedback phase of HMI, TENG as a highly sensitive sensor transforming mechanic movements into electric signals can also be used in visual, auditory, and tactile modes. Take the TAS study above, for example, it demonstrates that this resonant frequency-tunable auditory sensor is also a hearing aid for people with impaired hearing in the feedback stage between robotics and them.<sup>545</sup> Furthermore, the 3D-TTP tactile sensor can be another kind of electronic skin of the robotics for perception of various parameters of objects.<sup>547,586,587</sup> Integrated with more other technologies, such as near-field transmission, nanophotonic readout, and electroluminescence, the multiparameter system can be found based on advanced packaging and optimized modularization. With these attempts, we may foresee more potential for TENG-based sensors in the control-feedback loop of HMIs.

## 4. Concluding remarks

In this paper, TENG-based HMIs, including eye-movement, voice, gesture, and touch, are reviewed. Due to their unique characteristics, TENG sensors display advantages of high sensitivity, low cost, light weight, feasible structure design, stability, and robustness and have potential applications in the feedback stage of HMIs. Thus, TENG sensors will surely play a more important role in the control-feedback loop of universal HMIs.

## 5. Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 51902035 and 52073037), the Natural Science Foundation of Chongqing (Grant No. cstc2020cyjc-msxmX0807), and the Fundamental Research Funds for the Central Universities (Grant Nos. 2019CDXZWL001, 2020CDCGJ005, and 2020CDJ-LHSS-001).

# F. Powering body-implantable medical devices with triboelectric nanogenerators

Sumanta Kumar Karan, Sang-Woo Kim
(*kimsw1@skku.edu)

## 1. Abstract

Body-implantable medical devices (BIMDs) play a crucial role in early clinical diagnosis and treatment of diseases in the modern world.<sup>588,589</sup> However, they require a power source, such as a battery, and conventional IMDs are powered with primary batteries that demand frequent surgeries for replacement. In order to minimize the problems associated with repeated surgeries, powering various kinds of low power-consuming IMDs with triboelectric energy harvesters is of great concern. In this short review, we explore

recent improvements in methods of powering IMDs with triboelectric nanogenerators (TENGs), which improve patients' quality of life and lower the risks associated with replacement of batteries implanted in the human body. In most of the research, internal biomechanical energy is an important energy source for TENGs used to power IMDs. Alternatively, ultrasound-assisted TENG is emerging as a new technology to power IMDs. We also analyze the challenges that remain in the field and discuss recent developments in TENGs used to power IMDs for in vivo healthcare applications.

## 2. Introduction

a. *Significance of TENGs for powering IMDs.* Body-implantable medical devices (BIMDs) are essential for observing, defining, and recording physical actions in vivo. Unfortunately, repeated surgery is necessary to replace the battery in such devices after it has expired, which may cause physical and mental damage to patients. In addition, although the surgical procedures involved are believed to be safe, any surgery comes with a risk of infection.<sup>590</sup> Modern IMDs (e.g., pacemakers) require internal power sources but have mechanical parts that need to be repaired or replaced after some years.<sup>591</sup> Continuous operation of IMDs with the help of either power transferred from external energy sources in a wireless manner or energy harvested from within the body is required for early clinic diagnosis and treatment of some diseases. One method of charging secondary batteries inside IMDs, wireless electromagnetic power transmission, has been proposed as an innovative powering system, but it has several critical issues that need to be resolved, such as excessive tissue heating, which can affect the patient's metabolism.<sup>592</sup> On the other hand nanogenerators (NGs), have been proposed to harvest the mechanical energy produced by the human body. Triboelectric nanogenerators (TENGs) can generate electricity if there is a physical contact or rubbing.<sup>593</sup> Unlike piezoelectric NGs (PENGs) depend on restricted group of noncentrosymmetric materials [e.g., ZnO, GaN, Pb(Zr,Ti)O<sub>3</sub>, BaTiO<sub>3</sub>, and PVDF],<sup>589,594</sup> TENG has emerged as a promising technology that may bring about self-powered IMDs and healthcare monitoring systems with low power consumption as shown in Fig. 26.

<figure><img src="image_39.png" /><figcaption>FIG. 26. Graphical overview of mechanical energy sources and TENG-based devices, and applications of TENGs in IMDs and self-powered healthcare monitoring systems.</figcaption></figure>
### 3. Energy sources, device types, and TENGs-based IMDs for biomedical applications

TENGs have been explored as a means of converting mechanical energy into electrical energy. TENGs that provide reliable and sustainable energy conversion have gained significant attention, with many options for available materials, compatibility with modern IMDs, flexibility in structural design, and prominent mechanical durability. To power body-implanted devices with TENGs, either biomechanical energy (from various types of muscular movements) or ultrasound energy transferred into the body are excellent mechanical energy sources that can generate electricity. Potential applications of such TENGs include IMDs and self-powered healthcare monitoring systems (Fig. 26). Some important biomedical applications of TENGs include IMDs, cardiac monitoring sensors, laser curing systems for osteogenesis, nerve cell stimulation devices, and real-time monitoring sensors of respiratory rate, heart rate, and blood pressure.⁵⁹¹,⁵⁹² Zheng et al.⁵⁹⁶ explored a self-powered wireless transmission system using a TENG (1.2 × 1.2 cm²) that demonstrated its feasibility for real-time remote cardiac monitoring by transferring an electrical signal obtained from an in vivo heartbeat of rat. The PDMS film (100 µm thick) with pyramid arrays and nanostructured Al foil used as the friction layers. Kapton and gold (Au) film utilized as the supporting substrate and back electrode, respectively. Tang et al.⁵⁹⁷ demonstrated the ability of a self-energized laser treatment method by fixing a TENG (1.5 × 1.0 cm²) to the arm of a living rat. The system significantly accelerated the mouse embryonic osteoblasts proliferation and differentiation. Pyramid array patterned PDMS and indium-tin oxide (ITO) films were acted as the friction materials. A self-powered ultrasensitive TENG (20 × 10 × 0.1 mm³) based pulse sensor is used to monitor heart rate in the in vivo conditions investigated by Ouyang et al.⁵⁹⁸ Nanostructured Kapton and Cu thin films were employed as the two triboelectric layers. Zheng et al.⁵⁹⁶ used a TENG placed in the diaphragm to sense the movement of the lungs. Apart from the above-mentioned works, many recent research has been done concentrating on human-machine interactions (HMI)⁵⁹⁹ and IMDs applications.⁶⁰⁰,⁶⁰¹ The TENG device is capable of recognizing the minute behaviors associated with breathing, which is an essential feature of a medical sensor. Thus, TENGs are regarded as one of the most promising energy solutions for powering BIMDs.

The energy-harvesting performance of TENGs depends upon a given level of biomechanical energy.⁵⁸⁹,⁶⁰² TENGs intended to charge lithium (Li)-ion secondary batteries in IMDs have inherent shortcomings due to a low level of input biomechanical energy. The inherently small amount of mechanical energy available in the body and the limited size of implanted devices present technical challenges for TENGs used to power IMDs. Human body motion from various organs and parts is a rich source of energy and has been utilized to develop self-powered TENG healthcare devices.⁶⁰³⁻⁶⁰⁵ TENG has introduced multiple non-invasive methods to monitor and analyze medical parameters in real-time. Its applications have extended to the biomechanical energy-based application such as heart and respiration monitoring heart, sleep monitoring, sweat sensor respiration, fall detection, Parkinson's disease tremor sensor, and many other circumstances.⁶⁰⁶⁻⁶⁰⁹ Accordingly, there is a need to secure non-invasive and sustainable TENG-based powering technology apart from organ motion-driven TENG systems.

Ultrasound, which has been approved by the US Food and Drug Administration (FDA) for various medical treatments, can trigger a TENG device to generate a large electrical power output into water; this method can achieve an output power of 0.362 W/cm² at an ultrasonic wave frequency of 80 kHz.⁶¹⁰ In addition to typical non-biodegradable TENGs, a fully biodegradable TENG was demonstrated as an implantable power sources with time-transient functionality.⁶¹¹ The device is fabricated using of biodegradable polymers and resorbable metals. The TENG device is degradable and resorbed in an animal body after the work cycle without any long-term effects. The device can generate an open-circuit voltage up to ~40 V and the corresponding short-circuit current up to ~1 µA. The selection of materials used to fabricate the biodegradable TENG is based on the biomaterials point of view, such as biodegradable and biocompatibility. However, the biodegradable TENG was fabricated mostly using biodegradable polymer due the above property as well as it has different tendency to gain/lose electrons when polymer used as the friction part of the TENG.⁶ Furthermore, the polymers used in biodegradable in TENG are low-cost, commercially available, and soluble in suitable solvent systems to promote facile processing methods, including spin coating and solution casting. From the above properties, the best polymers, such poly(L-lactide-co-glycolide) (PLGA), poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) (PHB/V), poly(caprolactone) (PCL), and poly(vinyl alcohol) (PVA), have been used in fabricating biodegradable TENG.⁶¹¹

### 4. Powering IMDs using TENGs

a. TENGs driven by biomechanical energy. Zheng et al.⁵⁹⁶ utilized an implantable TENG to power in vivo biomedical electronics and form a self-powered system [Fig. 27(a)]. The TENG was inserted beneath the left chest membrane of a rat and generated an output voltage and current of ~3.73 V and ~0.14 µA, respectively. They explored a self-powered pacemaker model; the output of a particular stimulation pulse of the developed pacemaker model was almost 25 µA, and the pulse width was about 2 ms, which is analogous to those of commercial pacemakers. Furthermore, Ouyang et al.⁴²⁷ explored an implantable TENG with an output power of ~10 µW/cm² [Fig. 27(b)]. It is noteworthy that RFID tags and wireless sensors can be driven with 1–10 µW/cm². The amount of energy generated solely depends on the function of the heart, which may raise concerns regarding the effectiveness and safety of TENGs in powered devices. Unlike various external movements, the movement of different parts inside the body is so small that a TENG needs to be finely tuned in order to work. Hence, the research community is looking forward to non-invasive and sustainable TENG-based powering systems with high power-generating performance.

b. TENG driven by ultrasound. Ultrasound is an important instrument for diagnosis and treatment in various medical applications approved by FDA. A sound wave can effectively travel through biological tissue and cause oscillation in a specific region. The ultrasound technology has been made using inverse piezoelectric principle and possibility to achieve high energy harvesting performance using advanced piezoelectric materials, designing device, and choosing resonance frequency.⁶¹² The ultrasound based piezoelectric energy harvesting strategy is to use propagating ultrasound waves to carry the available energy, which is essentially a
<figure><img src="image_40.png" /><figcaption>FIG. 27. (a)–(c) Powering IMDs with energy generated from TENGs with different mechanical vibration energy sources. Reproduced with permission from Ouyang et al., Nat. Commun. 10, 1821 (2019). Copyright 2019 Springer Nature. Reproduced with permission from Zheng et al., Adv. Mater. 26, 5851 (2014). Copyright 2014 Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission from Hinchet et al., Science 365, 491 (2019). Copyright 2019 AAAS.</figcaption></figure>

mechanical vibration that propagates in a host medium. The structure of piezoelectric materials (such as PZT, BaTiO₃, and ZnO) structure is deformed by ultrasonic waves, and the charge separation process generates a piezoelectric potential in that will induce a current to drive electronics.⁶¹³ Thus, the electrical signal flowing through the external circuit is consecutively produced as the piezo-potential under dynamic pressure. The microelectromechanical system-based piezoelectric ultrasound energy can be used for IMDs explored by Shi et al.⁶¹⁴,⁶¹⁵ where they used PZT diaphragm array with a resonance frequency of 240–250 kHz capable to generate 3.75 µW/cm² at distance of 1 cm and 1 mW/cm² of ultrasound intensity. In case of TENG, under ultrasound energy, the two different materials with different surface potential can contact together and generate electrical signal via external circuit similar to the conventional triboelectric mechanism, such as contacting/sliding of two different materials layers under ultrasound pressure. Ultrasound pressure can be measured by a hydrophone placed at 1 cm distance from the bulk PZT, and then ultrasound intensity can be

calculated using formula $I = P^2/2Z$, where I is the ultrasound intensity, P is the ultrasound pressure, and Z is the acoustic impedance (for water medium, Z is $1.48 \times 10^6$ kg/m² s).⁶¹⁵ Recently, TENGs have begun to be regarded as a back-up alternative energy technology for powering IMDs due to their powering capability, wide materials selection, etc.⁶¹⁶ Recently, Hinchet et al.⁵⁹² reported transcutaneous ultrasound energy harvesting using a TENG technology for powering IMDs. An ultrasound-based TENG was triggered by application of a 20 kHz ultrasound at 3 W/cm² and reached 9.71 V (output voltage) and 427 µA (output current) (root mean square value). The ultrasound induced µm-scale displacement of a polymer thin membrane to generate electrical energy through contact electrification. For in vivo applications, researchers have experimentally simulated clinical conditions that are closer to those seen in humans in order to predict the TENG's ability to power an IMD. The implanted TENG produced a voltage and current from ultrasound energy transfer of 2.4 V and 156 µA, respectively, under porcine tissue, which could charge a rechargeable Li-ion battery
with a capacity of 0.7 mA h. Hence, the amount of power generated is high enough to recharge secondary batteries in smart IMDs, such as neurostimulators, insulin pumps, cochlear implants, artificial organs, and pacemakers, which only need 1–100 mW of power to run.

## 5. Remarks and future challenges

This review discussed recent advances in implantable TENGs for biomechanical energy harvesting and powering IMDs. The fabrication process of TENG devices is relatively simple and convenient, with low cost. In addition, TENG provides enough output power with high efficiency to power low power-consuming IMDs. Moreover, TENGs can produce continuous electricity using kinetic motion and triboelectric materials (generally polymers) in wearable platforms that can interface with biological systems. However, there are some limitations regarding the powering of IMDs using TENGs. For example, the output power generated is not constant and depends on consistent cyclic movement of the device. A cavity must form between two contact layers. Furthermore, during long-term operation of a TENG, its performance may deteriorate due to unwanted misallocation of the spacers between triboelectric materials.

The research community is expecting substantial improvements in methods of powering IMDs using TENGs in the coming days. The energy-harvesting property of TENGs is an indispensable aspect of IMD application, and a non-invasive solution would be a remarkable advancement. It is difficult for biomechanical energy-driven TENGs to generate enough electrical power to fully charge the batteries in IMDs. In this respect, transcutaneous ultrasound has been proposed in an attempt to enhance the output power of a TENG. This advancement promises the ability to generate higher power levels than traditional methods. Still, miniaturization, higher power output, and a proper power management system for TENGs used to power IMDs are necessary.

In order to develop sustainable implantable TENGs to run IMDs, from a commercial viewpoint, some essential features must be developed and additional efforts are required. (i) Fundamental studies on triboelectrification must be fully supported to increase the power conversion efficiency, flexibility, and sustainability of TENGs for long-term *in vivo* applications. (ii) Researchers must be deeply concerned about the toxicity, biocompatibility, and biodegradability of TENGs, which are essential for the safe implementation of TENGs inside the body. (iii) Strategies for encapsulating TENGs should be developed that prolong the devices' lifespans under implantable environments. (iv) The input ultrasound energy of ultrasound-mediated TENGs needs to be studied as a function of the depth of the device. (v) To improve interfacial adhesion and strain relief for long-lasting couplings, the interface between body tissue and implanted TENGs must be well controlled.

## 6. Acknowledgments

This work was financially Nano Material Technology Development Program (Grant No. 2020M3H4A1A03084600) through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT and the GRRC Program of Gyeonggi Province (GRRC Sungkyunkwan 2017-B05).

## VI. SUMMARY OF CHALLENGES

Qingshen Jing (*qj214@cam.ac.uk), Sohini Kar-Narayan (*sk568@cam.ac.uk)

The field of nanogenerators and piezotronics has witnessed an exponential growth within the last decade, and has ushered in innovative technologies for energy harvesting and sensing in a variety of different settings and applications, as has been highlighted in this roadmap. Progress in this field has been spurred on through advances in materials science and engineering that have led to novel functional materials and devices, as well as the development of new theoretical and computational frameworks that have provided a better understanding of the piezoelectric, piezotronic, photopiezotronic, and triboelectric phenomena that underpin this fast-moving field of research. This roadmap has sought to capture key advances and challenges in the field of nanogenerators and piezotronics, with a view to stimulate further research and innovation in what is undoubtedly a scientifically exciting and technologically relevant area. For example, there still remain open questions about the charge species involved in the operation of triboelectric nanogenerators, or indeed the charge transfer dynamics. Recent studies have indicated that electron transfer is the dominant mechanism for triboelectrification between solid-solid pairs. The same mechanism can be extended to liquid-solid, liquid-gas, and even liquid-liquid pairs, where the interaction is mediated via molecules.⁶¹ While these topics have been researched intensively, a conclusive model is still being developed, and therefore establishing a solid theoretical framework remains an important challenge in the field.

One of the most promising application areas of nanogenerators and piezotronics devices is in wearables and smart textile applications.⁶¹ As discussed in this roadmap, there remain challenges in the integration of such devices into textiles and fibers. For example, in the case of TENGs, the porous structure of fibers reduces charge density, hence energy output, and also absorbed moisture lowers power generation. Therefore, a structural design with complex geometric configuration for multiple TENG working models is required, taking into account decreased performance due to the increase of resistance and decrease of flexibility when fibers are woven together into textiles. Dimensional and output stability related to washability also needs to be improved in order to be compatible with textile-based applications. It is also important to note that while the compliant nature of a textile makes it comfortable for the wearer, it also reduces the mechanical energy coupled into the energy harvesting/sensing transducer, which in turn reduces the energy that can be harvested. This may involve, for example, designing a garment in such a way that strain is concentrated through the energy harvester itself.

However, increased porosity may actually be desirable as in the case of piezoelectric ceramics. There are still challenges related to the flexibility of these materials and in the ability to maintain compositional homogeneity and stability of properties particularly for applications in large-scale energy harvesting. There is plenty of scope in this area to further develop materials and systems that suit broadband mechanical energy sources through optimization of materials and devices, including through the development of new ferroelectret materials.
Another class of materials that has generated significant interest in the community is functional 2D materials, such as those exhibiting piezoelectric properties. However, the difficulty in exploring the known 2D piezoelectric materials for practical applications is in achieving consistent piezoelectric response using current fabrication processes. The symmetry in most 2D piezoelectrics results in thickness-dependent piezoelectric properties that impose formidable challenges for the process control. Also, unintentional environmental doping of 2D materials affects the carrier concentration and, hence, the performance. There is therefore a need to develop a sound understanding of the mechanism governing piezoelectricity at the atomic scale, and from a more practical point of view, there is a need to improve and up-scale the fabrication and device integration methods.

In terms of architected material for energy harvesting, innovative metamaterial design schemes that increase the degree of input wave energy amplification are expected to be well-suited to a wide range of frequencies. While significant progress has been made in this area, there are still challenges associated with size reduction required to match the wavelength of acoustic and elastic waves present in ambient mechanical vibrations. Machine learning-based design algorithms could prove to be highly beneficial specific target applications. Also, improvement of impedance matching at the interface between the metamaterials-amplified wave energy and energy harvesting devices needs to be considered in more detail.

An interesting development in the field of nanogenerators has been the emergence of organo-halide perovskites in multimodal piezoelectric generators. In terms of challenges, the intrinsic structural instability of halide perovskites, such as phase instability, and their extrinsic instability (thermal/optical/ambient) need to be considered and addressed. At the same time, novel material architectures need to be explored based on the wide tunability of the perovskite. Decoupling the effect among multiple energetic stimuli to gain fundamental understanding of the mechanisms at play is required, as well as demonstrations of applicability of these materials in sensors and energy generators.

There has been continued interest in the use of polymers for nanogenerators and piezotronics in the field.618 In this regard, smart polymer materials (SPM) have generated significant interest; however, the improvement in certain properties of SPM device tends to be accompanied with a trade-off in other properties. For example, current SPMs used in TENGs are not necessarily situated at extreme positions in the triboelectric series, or they may need advanced fabrication approaches for micro- or nanopatterning.619 When SPM-based TENGs are used as sensors, the output can be easily affected by various factors that reduce the reliability. At the same time, polymer-based PENGs are limited by their low piezoelectric coefficients and thermostability compared to piezoelectric ceramics. This points toward the development of robust polymer–ceramic composites with reproducible and reliable piezoelectric performance. In any case, the conflict between narrow power bandwidth of many piezoelectric energy harvesters and real-world energy sources with broadband spectrum is one of the biggest challenges in the field. Output power is often limited due to the size constraint on the system. There is no general energy harvester design method that suits different applications due to factors such as narrow bandwidth of the transducer and different environmental conditions. Thus, the lack

of real-world environmental data makes it challenging to design a working system for a given application.

In terms of applications of piezoelectric acoustic sensors, the resonant frequencies of the acoustic sensors need to be properly designed for the spectrum range, especially when the dimension of the sensor is downscaled. Going forward, the sensitivity of resonant acoustic sensors could be enhanced for modularization, which requires robust characteristics under noisy conditions, for instance. The combination of an acoustic sensor with optimized machine learning algorithms could pave the way for accurate voice recognition and speech processing. Similarly, nanogenerators could pave the way for closed-loop electrostimulation in biomedical applications. One of the challenges is how to generate stable and sufficiently large electric pulses from small biomechanical motions in vivo. Flexible nanogenerators and packaged devices need to be designed with specific motion and softness configurations, without burdening the organ or muscle to which they are to be attached. Importantly, further in-depth cellular level understanding is required to quantify the relationship between electrostimulation from nanogenerators and the targeted biological processes.

When it comes to biological and biomedical applications, TENGs have proven to be highly promising candidates, both as energy harvesters and as sensors. For example, they can be deployed as highly sensitive sensors for human–machine interface. Although the sensor can actively generate electrical signals without being powered, the associated back-end device still needs an additional power supply. At the same time, more flexibility, stretchability, washability and multi-functionality is desired to meet the portable or wearable requirements of human machine interfaces, thus requiring further development of functional materials and advanced fabrication techniques. Even greater challenges exist in the use of TENGs to power body-implantable medical devices, for example, the output power generated is not constant and depends on consistent cyclic movement of the device. As it is extremely difficult for biomechanical energy-driven TENGs to generate enough electrical power for implantable devices, transcutaneous ultrasound can be deployed to improve TENG output power. More generally, there is a lot of scope for fundamental studies for “body-energy” conversion efficiency, flexibility and sustainability for in vivo applications. Issues related to toxicity, biocompatibility, and biodegradability of TENGs for safe implantation still need to be addressed.

In summary, there is great potential in the field of nanogenerators and piezotronics across many application areas, but there also remain open questions in our understanding of the fundamentals of this field, and challenges in the practical implementation. This roadmap serves to highlight key developments and directions in the field, in order to stimulate further research and innovation that may lead to successful commercial technologies across energy, healthcare, communications, wearables, robotics, and other industries.

## AUTHOR DECLARATIONS

### Conflict of Interest

The authors have no conflicts to disclose.
## Author Contributions

**Philippe Basset:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Stephen Paul Beeby:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Chris Bowen:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Zheng Jun Chew:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Ahmad Delbani:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **R. D. Ishara G. Dharmasena:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Bhaskar Dudem:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Feng Ru Fan:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Dimitri Galayko:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Hengyu Guo:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Jianhua Hao:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Yuchen Hou:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Young Hoon Jung:** Conceptualization (equal); Writing – original draft, Writing – review & editing. **Sumanta Kumar Karan:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Sohini Kar-Narayan:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Miso Kim:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Sang-Woo Kim:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Yang Kuang:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Keon Jae Lee:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Jialu Li:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Zhaoling Li:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Yin Long:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Xianjie Pu:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Tingwen Ruan:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **S. Ravi P. Silva:**

**Qingshen Jing:** Conceptualization (equal); Project administration (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Young Hoon Jung:** Conceptualization (equal); Writing – original draft, Writing – review & editing. **Sumanta Kumar Karan:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Sohini Kar-Narayan:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Miso Kim:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Sang-Woo Kim:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Yang Kuang:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Keon Jae Lee:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Jialu Li:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Zhaoling Li:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Yin Long:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Xianjie Pu:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Tingwen Ruan:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **S. Ravi P. Silva:**

Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Hee Seung Wang:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Kai Wang:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Xudong Wang:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Zhong Lin Wang:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Wenzhuo Wu:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal). **Wei Xu:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Hemin Zhang:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Yan Zhang:** Conceptualization (equal); Writing – original draft (equal); Writing – review & editing (equal). **Meiling Zhu:** Conceptualization (equal); Funding acquisition (lead); Supervision (lead); Writing – original draft (equal); Writing – review & editing (equal).

## DATA AVAILABILITY

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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## AFFILIATIONS

¹Univ. Gustave Eiffel, CNRS, ESYCOM, F-77454 Marne-la-Vallée, France

²School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom

³Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, United Kingdom

⁴College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

⁵Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom

⁶Department of Electrical and Electronic Engineering, Advanced Technology Institute, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom

⁷College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China

⁸Sorbonne Université, LIP6, Paris, France

⁹Department of Applied Physics, Chongqing University, Chongqing 400044, People's Republic of China

¹⁰Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China

¹¹Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, USA

¹²Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, United Kingdom

¹³State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, China

¹⁴James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom

¹⁵Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

¹⁶School of Advanced Materials Science and Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea

¹⁷SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea

¹⁸Key Laboratory of Textile Science and Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai 201620, China

¹⁹Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA

²⁰School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

²¹School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907, USA

²²Flex Laboratory, Purdue University, West Lafayette, Indiana 47907, USA

²³Birck Nanotechnology Center, Purdue University, West Lafayette, Indiana 47907, USA

²⁴ESAT-MNS, University of Leuven, Leuven 3001, Belgium

³) Authors to whom correspondence should be addressed: qj214@cam.ac.uk and sk568@cam.ac.uk