Parameters of contactmode TENG [16].
Abstract
Triboelectric nanogenerator (TENG) is a new type of electrostatic generator based on the principle of Maxwell displacement current. It could be designed as a device for either smart sensing or energy harvesting via converting mechanical energy into electric power efficiently. To predict its output characteristic, investigate its working mechanism, and enhance its working performance, the theoretical analysis and optimization work in either experimental or theoretical means are of great significance. In this chapter, we plan to introduce the progress of theoretical analysis and optimization approach to TENG with four different modes. Three parts of work will be introduced in the manuscript: (1) the theoretical prediction approach for electric output performance of TENG device, (2) the optimization strategies for TENG device based on figure of merits, and (3) the scaling laws between the normalized electric outputs and multiple physical properties of the TENG device.
Keywords
 triboelectric nanogenerator
 theoretical analysis
 scaling law
 optimization strategy
1. Introduction
Triboelectric nanogenerator (TENG) [1, 2] is a revolutionary mechanical energy harvesting technology based on triboelectrification and induction effects of two materials with opposite electric polarities. This device contains two dissimilar dielectric films facing with each other, and there are electrodes deposited on the top and the bottom surfaces of the two films. The working mechanism of TENG is based on triboelectric effect and principle of displacement current. TENG may utilize the surface electrostatic charges produced in physical contactseparation process of the two insulators to generate electric power. By separating the tribopair with contactinduced triboelectric charges, a potential drop will be generated which forces the electrons to flow between the two electrodes. Compared with other renewable energy technologies including solar cells [3], biofuels [4], tide generator [5], etc., TENGs are less restricted by environmental or climatic conditions. In applications of mechanical energy harvesting, TENGs have much higher power output and energy conversion efficiency than those of electromagnetic [6] and piezoelectric energy harvesters [7, 8, 9, 10, 11]. Besides, TENGs can be made from lowcost materials by easy manufacture processes because of its simple device structures and configurations. These advantages make this new technology a better choice as power source for devices and microsystems [1, 2].
TENG has four basic operational modes with different structures: contactseparation mode, sliding mode, singleelectrode mode, and freestanding mode. As Figure 1 shows, these different modes are designed to meet the needs in different fields of applications. Based on these four modes, various TENG devices have been developed for different applications. Using these four modes and their combinations, a range of TENG devices have been invented for energy harvesting like wind and water, structure vibration and biomechanical motion energy harvesters, and selfpowered smart sensing like vector sensor, tactile sensor, vibration detection, human physical signal detection, etc. [12, 13, 14, 15, 16].
With the progress of materials, structure design, and theories in fundamental mechanisms, the output performance of TENG is improved vigorously. Since its invention, the output power density of TENGs increases from initially a few μW/m^{2} and mW/m^{2} to tens of W/m^{2}. In recent works, a TENG with power density up to 500 W/m^{2} and energy conversion efficiency more than 70% has been achieved, which is adequate to power most microelectronic devices and systems [2]. It is the optimization that makes the performance of the device greatly improved.
To further improve the output performance of the device, optimization design is of great importance and has already attracted attentions. In early works, the development of a new type of TENG devices or optimization of TENG design is typically realized through trialanderror process experimentally, which is of high cost and timeconsuming. Compared with experimental means, theoretical analysis is useful and more powerful in understanding the working mechanism of the device and could offer better optimization strategy for device structural design, material selection, and operation conditions. It is more convenient to realize the optimization of TENG with theoretical method rather than in experimental means. Based on the basic working mechanism of TENGs, the theoretical models for TENG have been established, a series of parameter analysis works have been carried out, and the relationship between the parameters and the output performance is obtained [17, 18, 19, 20, 21]. In addition, to establish a standard for evaluating different architectures of a TENG device, the device figure of merits of TENG is proposed and used to determine the maximum output power density of a TENG [22, 23, 24].
Although various theoretical models have been established for TENG, most of them are based on singleparameter analysis which focuses on investigating the effect of single variable on device performance with others fixed. For instance, in optimization of the thickness of dielectric materials, we may obtain its effect with other parameters fixed. The same procedure is required in repetition for other working conditions with different sets of fixed parameters. Yet the optimized structures and conditions may not be the best as many parameters are correlated.
To address this problem, Zhang proposed a set of formulations for normalized electrical outputs of the device in dimensionless forms [25, 26]. The expressions for these normalized outputs rely on two compound parameters composed of device dimensions (sizes, dielectric layer thickness), electrical properties of the electrode and dielectric materials, loading conditions (loading force, frequency, and motor process), and the circuit conditions (open/short circuit and load resistance). With these expressions, a multiparameter analysis method for theoretical approach for optimization of TENG has been derived. This method makes it possible to realize the optimization of the device by tuning different physical parameters simultaneously, which may reveal the real situation of the TENG that its output performance is influenced simultaneously and coherently by a number of factors.
In this chapter, the theoretical approaches of VQx relationship for different TENGs are presented. Along with these relationships, the output performance of TENG and the optimization method with material and device figure of merits for TENG are discussed. Lastly, the multiparameter analysis method and the optimization strategy for TENG are presented.
2. The theoretical prediction and optimization strategies for TENG
In this section, the theoretical prediction for output performance and optimization strategies for design of TENG are discussed from three different aspects: the theoretical prediction approach for VQx relationship of different modes of TENGs is presented firstly, followed by the principle of material and structure figure of merit for different modes of TENG and corresponding optimization strategies, and, lastly, the scaling laws between the normalized electric output of TENG and multiple physical properties are derived, with which the optimization strategies for TENG are provided.
2.1 The VQx relationship of TENG
The four basic modes of TENG are of similar structure with two layers of different materials (dielectricdielectric or dielectricelectrode), which are usually called tribopair. When the tribopair comes into contact, some charges move from one material to another to equalize their electrochemical potential due to triboelectric effect. When forced to separate, some of the surface charges tend to keep the original state, while the others tend to give electrons away, possibly producing triboelectric charges on the surfaces. The presence of triboelectric charges on dielectric surfaces can be a force for driving electrons in the electrode to flow in order to balance the electric potential drop created.
As Figure 1 shows, there are two different motion patterns including contactseparation and sliding in these four modes of TENG. For the contactseparation mode and singleelectrode mode TENG, the tribopair moves vertically and creates an air gap in between, while for the contactsliding mode and freestanding tribolayer mode, the tribopair moves laterally. For these two different kinds of motion process, the theoretical models are quite different, which will be discussed in Sections 2.1.1 and 2.1.2, respectively.
2.1.1 Contactseparation mode TENG
According to the materials used and device structures, the contactseparation mode TENGs fall into two categories: dielectrictodielectric contact and conductortodielectric contact structures (Figure 2). Based on Gauss’s law, we may consider this kind of TENG as a series of flat plate capacitors; the relationship between electric field
The thicknesses of the dielectric plates in tribopair are assumed to be
Here,
According to Ohm’s law, when the TENG is connected with a load resistance
Substituting Eq. (1) into Eq. (2), we will have the equation for
With initial condition of
With the output voltage as
This VQx relationship provides a means for evaluation of the electric output of contactmode TENG, which may also be used for singleelectrode mode TENG.
To verify the accuracy and precision of the proposed relationship, the experimental results from Ref. [16] are utilized here as an example for contactmode TENG. Glass and polydimethylsiloxane (PDMS) are used as the tribopair in the experiment in planar form. The physical parameters of the device and experiment design are listed in Table 1. The TENG device is driven by a dynamic testing machine with separation gap
Parameter  Value 

Thickness of PDMS plate 
100 μm 
Relative permittivity of PDMS 
2.7 
Thickness of glass 
1 mm 
Relative permittivity of glass 
7.2 
Permittivity of vacuum ε_{0}  8.854 × 10^{−12} F/m 
Area of the dielectrics 
25 cm^{2} 
Surface charge density σ  5–40 μC/m^{2} 
According to the experimental settings in Ref. [16], we introduce a piecewise function to describe the motion process of the tribopair as
Here,
In Figure 3b, the voltage output obtained by Eq. (5) is compared with the experimental results from Ref. [16]. Excellent agreements could be observed between the experimental and analytical results due to different loading frequencies, especially the positive part of the voltage output. For the negative part of the results, the analytical results are slightly larger than the experimental ones, particularly for the low contact frequency case. The results presented above clearly indicate the accuracy of the model and method developed in this work.
2.1.2 Slidingmode TENG
For lateral slidingmode TENG, the basic structure is shown in Figure 4.
For the lateral slidingmode TENG, the contact area is
For conductortodielectric TENGs when the edge effect can be neglected, the relationship is similar by turning
Based on these output voltage expressions, we can get the average power output for TENG as
Here,
This VQx relationship provides a method for evaluation of the electric output of slidingmode TENG, which may be easily extended as a methodology for sliding freestanding tribolayer mode TENG. To make sure the accuracy and precision of the proposed VQx relationship of slidingmode TENG, the experiments from Ref. [17] are carried out for validation. The materials and scale parameters are shown in Table 2.
Parameter  Value 

Dielectric tribopair  PTFEnylon 
Thickness of nylon plate d1 (

50 
Relative permittivity of Nylon εr1  4 
Thickness of PTFE plate d2 (

50 
Relative permittivity of PTFE εr2  2.1 
Permittivity of vacuum ε0 (F/m)  8.854 × 10^{−12} 
Area of the dielectrics S (m^{2})  0.05 × 0.071 
Surface charge density σ (

200 
Maximum separate distance A (m)  0.05 
Acceleration a (

20 
Load resistance R (MΩ)  10–1000 
In the experiments, the tribopair is fixed on a horizontal tensile loading platform for reciprocating lateral sliding process. The sliding process of the tribopair was imposed by the dynamic testing machine with a symmetric accelerationdeceleration mode (Figure 5c). The analytical results of the voltage output obtained by Eq. (8) agree very well with the experimental result.
Based on the VQx relationships presented from Eqs. (5)–(8), the output performance of TENG is predictable when device structure, material parameters, and motion process are clear. With these equations, the influence of each parameter is clear, while all others are certain. For example, we can change the one parameter such as load resistance while all the others kept unchanged. As a result, we can get the output characteristic of the target TENG device and find out the optimized load resistance.
With this method, parametric analyses are carried out to characterize the output performance of TENGs with different working conditions. Niu et al. studied the output characteristics of contactmode [18], slidingmode [19], singleelectrode mode [20], and freestandingmode [21] TENG under different load resistances, products of velocities, contact area sizes, effective dielectric thicknesses, and gap distances. These works obtained the effect of a series of independent parameters on the output characteristics including load resistance, maximum gap or slid distance, moving speed, device capacitance, and device structure parameters. These works, by numerical calculation of the realtime output characteristics, presented the suitable value range of these preceding parameters with common TENG device design and provided excellent guidance for structural design and optimization strategies for TENG devices.
2.2 Optimization approach based on figure of merit
For various applications, four basic modes of TENGs have been developed. Each mode has its own structure and various triboelectric material choices for the tribopair. That makes it difficult to characterize and compare the output performance of TENG. A universal standard has to be introduced to quantify the performance of the TENGs, regardless of its operation mode. To solve this problem, the figure of merit of TENG is proposed [22]. It gives a quantitative evaluation of TENG’s performance from both structure’s and the materials’ points of view [22, 23, 24]. The application of figure of merit leads to a more efficient design and optimization approach of various TENG structures in practical applications. It may help to establish a series of standards for developing TENGs toward practical applications and industrialization.
The figure of merits of TENG includes performance figure of merit related to the structure and material figure of merit related to surface charge density. The theoretical derivation and simulation of these two figures of merit will be discussed in Sections 2.2.1 and 2.2.2, respectively.
2.2.1 Figure of merit for quantifying the output performance of TENG
Based on the VQx relationship, a series of optimization strategies for independent parameters have been proposed. But their optimization target is the maximum output voltage or power, and the physical properties of the tribopair are not taken into account. To take into consideration the influence of other parameters of the device, the performance figure of merit (FOM_{P}) is developed as a new standard. It stands for the maximum power density of TENG, which represents a quantitative standard to reflect the output capability of TENGs with different configurations [22]. The expression of FOM_{P} is
Here,
The output energy per cycle
The VQ curve is usually obtained from the working process of TENG by the VQx relation. In general, the working process of any type of TENG contains four steps as a cycle (Figure 6a). As Figure 6b shows, the VQ curve becomes steady after a few initial cycles. The output energy per cycle derived from these steady periodic cycles is named as “cycles for energy output” (CEO).
For each CEO, the difference between the maximum and the minimum transferred charges in its steady state is defined as the total cycling charge
The CMEO is related to load resistance of the output circuit: the higher the resistance, the higher the output energy per cycle. Therefore, the maximized output energy could be obtained when an infinitely large resistor as
Here
The average power output
Here
2.2.2 Figure of merit for quantifying material characteristic
From Section 2.1, we notice that the transfer charge
The FOM_{m} is only determined by
The accurate value of
2.2.3 Application of figure of merits
The figure of merits provides a series of quantitative standards to evaluate the working performance of TENG. Their application enables more efficient design and optimization of various TENGs in practical applications. The optimization works based on figure of merits are likely to establish the principles for TENG design and develop TENGs toward practical applications and industrialization.
Through FOM_{P}, we introduced a universal standard to quantify the power output performance of the TENG regardless of its operation mode and materials. With this standard, we are able to evaluate the performance of the TENGs in different structures/modes and achieve the optimization approach for structure design and working parameter setting. Using Eq. (11), we may obtain the FOM_{P} for different TENGs with same tribopair and contact area through analytical and simulation method. Taking the maximum FOM_{P} as an optimization index, we will achieve the optimized structure/mode for a TENG. On the other hand, for a known TENG, we can find out its maximum FOM_{P} and thus determine the best suitable working condition like load resistance,
Using FOM_{m}, we have introduced a quantitative standard to evaluate the maximum surface charge density for a triboelectric material. With FOM_{m} of different materials, we are able to determine their triboelectric charge polarity. And with different FOM_{m} of tribopairs, we can find the optimal choice for a TENG. With this standard, we found some useful regulations: (1) the optimized choice of materials in a tribopair should be in opposite polarities, and (2) the larger the polarity differential between the materials, the bigger the FOM_{m} of the tribopair [22].
2.3 Optimization strategy for TENG based on multiparameter analysis
For TENGs, their electric output performance is simultaneously and coherently influenced by a group of factors including the dimensions of the electrodes and insulators, electrical properties of the materials and the loading processing, etc. These parameters are interlinked with each other; therefore changing one parameter with the others fixed may break the optimized condition of the device and require new adjustment in other parameters and further optimization. It is necessary to simulate the output performance of a TENG via theoretical models based on multiparameter analysis rather than specific cases with only one variable considered. In addition, TENGs could be used as sensor or energy harvester in either macro or microscale, within which the physical properties of materials and device are quite different. Hence, the dimensionless analysis method is more feasible for parameter analysis of TENG.
Here, to realize the optimization of the device, we developed a series of normalized expressions for output voltage and output power in dimensionless forms, which provide a group of scaling laws between the normalized electric output and two independent compound variables. These scaling laws can facilitate the analysis of the effects in different aspects of the device simultaneously and provide accordance for optimal design of TENG by considering the effects of all factors simultaneously. The optimal electric output could be obtained through the proposed formulations with all parameters of the TENG considered as variables [25, 26].
2.3.1 Contactseparation mode TENG
For the optimization of contactseparation mode TENG, a set of expressions for normalized electric voltage and power in dimensionless forms are proposed in this section. In these dimensionless expressions, the effects of all the parameters involved have been investigated comprehensively and simultaneously. The normalized expressions for output voltage and power may facilitate the optimization based on the scaling laws by tuning different physical properties simultaneously rather than those only focusing on one physical property either in dimensional or dimensionless forms [25].
For the output voltage in a general circuit with the load resistance of
where
While for the application of energy harvest, the output power should be a key variable for characterizing the performance of the generator. According to the definition of output power
Here, we can see that the dimensionless output voltage and power depend only on two combined parameters, i.e.,
If we want to examine the effect of
To investigate the effect of oscillation amplitude and period, another set of dimensionless expressions is derived by multiplying Eqs. (16) and (17) with the factor
The relations in Eqs. (16)–(20) also reveal that the effective output voltage (or power) is proportional to the square of surface charge density. These dimensionless parameters and corresponding scaling laws also show a straightforward optimization method for the magnitude of oscillation at the same electrical load resistance, generator area, or oscillation period.
To verify the dimensionless expressions, we compare the peak dimensionless output voltage based on Eqs. (16) and (19) with the experimental results in corresponding dimensionless forms. Two groups of experiments with various thicknesses
As can be seen from Figure 7, the theoretical predictions agree very well with the experimental measurements despite that the latter are achieved with different device structures, mechanical loadings, and circuit conditions. This demonstrates that the established scaling law reveals the underlying general correlation between the physical properties of the device and its output performance, which may provide robust guidelines for optimization strategies, no matter what way we use to make it, theoretical or experimental. Take the varying parameters
However, the correlations among the parameters and their simultaneous effect on the output performance are hard to be found in traditional optimization techniques based on singleparameter investigations, in neither experimental nor theoretical means. As thus, we can conclude that the scaling law proposed in this paper can be not only used to predict the output performance of a TENG comprehensively and systematically with all parameters being considered simultaneously but also treated as a general and rational optimization criterion for the device toward its best performance. Based on the scaling laws from Eqs. 16 to 20, the output performance of the generator can be optimized by tuning combined parameters or individual physical quantities. The scaling law for the output voltage and power in a form of either in Eqs. (16)–(18) or in Eqs. (19)–(20) provide a universal optimizing strategy to enhance the output voltage for sensing application or maximum output power for energy harvesting application [25].
In Figure 8, the maximum peak voltage is obviously increasing monotonically with the relative oscillation amplitude
Besides the universal optimization with multiple parameters, we may also use individual parameter analysis by tuning its quantity to enhance the output performance. For instance, from Figure 8b it is found that given all other parameters, there exists an optimal load resistance to achieve a maximum output power. While from Figure 8c with given load resistance and oscillation amplitude, it can be found that larger generator area and higher oscillation frequency may enhance the output power until saturated.
As mentioned before, the scaling laws in Eqs. (16)–(19) cannot reflect a clear dependence of the output performance on the oscillation amplitude
2.3.2 Slidingmode TENG
For the optimization of slidingmode TENG, Zhang [26] established a series of dimensionless expressions of output performance of slidingmode TENG according to Eq. (7) as
Here,
In Eqs. (21) and (22), there are two dimensionless compound parameters
To make the normalized output performance better understood from the physical point of view, they can also be described with the dimensionless capacitance
Similar to Eqs. (16)–(18) of contactmode TENG, in this circumstance, the influence of
These equations provide the optimal strategy for TENG design with the two compound parameters related to the TENG device. We may use these equations to study the physical interpretation of dimensionless expressions through the dimensionless capacitance and the dimensionless time constant.
To verify the dimensionless expressions, we compare the theoretical results with the experimental measurements using the peak output voltage in dimensionless forms [17]. The corresponding parameters used in experiment and simulation are listed in Table 2.
As Figure 9 shows, compared with the experimental results, the theoretical curves based on scaling laws exhibit good consistent tendency with the experimental data points.
According to the referring experiments [17], we define the two different forms of sliding process to simulate different mechanical loading conditions, including parabolic (Case I) and triangular (Case II) loading pattern. Based on the scaling laws for the correlation between the normalized output performance and the dimensionless compound parameters, some general optimization strategies will be acquired to improve the electric output with Eqs. (23)–(27).
From Figure 10, the optimal combination of
From Figure 10, it is also obvious that increasing
In Figure 10(c–d), for each
In Figure 10(g–h) for the relationship between the dimensionless output power and compound parameters, the value of
According to Figures 8 and 10, we can conclude that the scaling laws can provide a more comprehensive and rational optimization strategy for both contactseparation mode and slidingmode TENG based on multiparameter analysis. The results can help enhance the output performance of the device as either a smart sensor or an energy harvester and may render a guideline for designing TENG devices.
Acknowledgments
This work was supported by the National Key R&D Program of China under grant No. 2018YFB1600200 and National Natural Science Foundation of China under grant Nos. 11472244, 11621062, and 11772295 and the Fundamental Research Funds for the Central Universities under grant No. 2019QNA4040.
References
 1.
Wang ZL. Triboelectric nanogenerators as new energy technology and selfpowered sensors—Principles, problems and perspectives. Faraday Discussions. 2014; 176 :447458  2.
Wang ZL. On Maxwell’s displacement current for energy and sensors: The origin of nanogenerators. Materials Today. 2017; 20 :7482  3.
Oregan B, Gratzel M. A lowcost, highefficiency solar cell based on dyesensitized colloidal TiO_{2} films. Nature. 1991; 353 :737740  4.
Brennan L, Renew PO. Biofuels from microalgae—A review of technologies for production, processing, and extractions of biofuels and coproducts. Sustainable Energy Reviews. 2010; 14 :557577  5.
Mago PJ, Blunden LS, Bahaj AS. Performance analysis of different working fluids for use in organic Rankine cycles. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy. 2007; 221 :255264  6.
Li Z, Zuo L, Luhrs G, Lin L, Qin Y. Electromagnetic energyharvesting shock absorbers: Design, modeling, and road tests. IEEE Transactions on Vehicular Technology. 2013; 62 (3):10651074  7.
Zhang H, Shen MZ, Zhang YY, Chen YS, Lu CF. Identification of static loading conditions using piezoelectric sensor arrays. ASME Journal of Applied Mechanics. 2018; 85 :011008011005  8.
Chen YS, Zhang H, Zhang ZC, Lu CF. Theoretical assessment on piezoelectric energy harvesting in smart selfpowered asphalt pavements. Journal of Vibration Engineering & Technologies. 2018; 6 (1):110  9.
Chen YS, Zhang H, Zhang YY, Li CH, Yang Q, Zheng HY, et al. Mechanical energy harvesting from road pavements under vehicular load using embedded piezoelectric elements. Journal of Applied MechanicsTransactions of the ASME. 2016; 83 (8)  10.
Lu CF, Zhang YY, Zhang H, Zhang ZC, Shen MZ, Chen YS. Generalized optimization method for energy conversion and storage efficiency of nanoscale flexible piezoelectric energy harvesters. Energy Conversion and Management. 2018; 182 :3440  11.
Zhang H, Ye GR, Zhang ZC. Acoustic radiation of a cylindrical piezoelectric power transformer. ASME Journal of Applied Mechanics. 2013; 80 (6) 061019(16)  12.
Yang Y, Zhang H, Lin ZH, Zhou YS, Jing Q, Su Y, et al. Human skin based triboelectric nanogenerators for harvesting biomechanical energy and as selfpowered active tactile sensor system. ACS Nano. 2013; 7 (10):92139222  13.
Ya Y, Hulin Z, Qingshen J, Yusheng Z, Xiaonan W, Zhonglin W. ACS Nano. 2013; 7 (8):73427351  14.
Changbao H, Chi Z, Xiaohui L, Limin Z, Tao Z, Weiguo H, et al. Selfpowered velocity and trajectory tracking sensor array made of planar triboelectric nanogenerator pixels. Nano Energy. 2014; 9 :325333  15.
Zhang H, Zhang JW, Hu ZW, Quan LW, Shi L, Chen JK, et al. Waistwearable wireless respiration sensor based on triboelectric effect. Nano Energy. 2016; 59 :7583  16.
Chen JK, Guo HW, Ding P, Pan RZ, Wang WB, Xuan WP, et al. Nano Energy. 2016; 30 :235241  17.
Niu S, Liu Y, Wang S, Lin L, Zhou YS, Hu Y, et al. Theory of slidingmode triboelectric nanogenerators. Advanced Materials. 2013; 25 (43):61846193  18.
Niu SM, Wang SH, Lin L, Liu Y, Zhou SY, Hu YF, et al. Theoretical study of contactmode triboelectric nanogenerators as an effective power source. Energy & Environmental Science. 2013; 6 :3576  19.
Simiao N, Sihong W, Ying L, Shengyu Z, Long L, Youfan H, et al. A theoretical study of grating structured triboelectric nanogenerators. Energy & Environmental Science. 2014; 7 :2339  20.
Simiao N, Ying L, Sihong W, Long L, Yusheng Z, Youfan H, et al. Theoretical investigation and structural optimization of singleelectrode triboelectric nanogenerators. Advanced Functional Materials. 2014; 24 :33323340  21.
Simiao N, Yin L, Xiangyu C, Sihong W, Yusheng Z, Long L, et al. Theory of freestanding triboelectriclayerbased nanogenerators. Nano Energy. 2015; 12 :760774  22.
Yunlong Z, Simiao N, Jie W, Zhen W, Wei T, Zhonglin W. Standards and figureofmerits for quantifying the performance of triboelectric nanogenerators. Nature Communications. 2015; 6 :8376  23.
Jiajia S, Tao J, Wei T, Xiangyu C, Liang X, Zhonglin W. Structural figureofmerits of triboelectric nanogenerators at powering loads. Nano Energy. 2018; 51 :688697  24.
Peng J, Kang SD, Snyder GJ. Optimization principles and the figure of merit for triboelectric generators. Science Advances. 2017; 3 :eaap 857615  25.
Zhang H, Quan LW, Chen JK, Xu CK, Zhang CH, Dong SR, et al. A general optimization approach for contactseparation triboelectric nanogenerator. Nano Energy. 2019; 56 :700707  26.
Zhang H, Zhang CH, Zhang JW, Quan LW, Huang HY, Jiang JQ, et al. A theoretical approach for optimizing slidingmode triboelectric nanogenerator based on multiparameter analysis. Nano Energy. 2019. DOI: 10.1016/j.nanoen.2019.04.057