Harvesting of environmental mechanical energy as an eco-friendly energy generation method is particularly a promising solution and plays an increasingly important role in driving wearable electronics and sensor networks in the IoTs. Based on the triboelectrification effect and electrostatic induction, the use of TENG invented in 2012 by Zhong Lin Wang has been demonstrated as a cost-effective, clean, and sustainable strategy to convert mechanical energy into electricity with comprehensive advantages of light-weight, small size, a wide choice of materials, and high efficiency even at low frequencies, which shows great potential in promoting the miniaturization trend of self-powered systems. With the gradually increase in triboelectric charge density, electrostatic breakdown, which is generally considered as a negative effect in the conventional TENG, becomes an issue that must to be considered. The theoretical and experimental studies of air breakdown in TENGs are important to promote the development of this field. By taking advantage of the electrostatic discharge, several types of DC-TENG have been demonstrated to power electronics directly without a rectifier or a capacitor. Comparing with the output characteristics of conventional TENG (AC consisted of pulse series), a constant-current output (crest factor ~1) is achieved by coupling triboelectrification

(**Figure 9f**). Unlike when driven by the conventional AC-TENG,

**62**

rate of 500 r min−1

**6. Conclusions**

The authors would like to thank the financial supports from the National Key R& D Project from Minister of Science and Technology (2016YFA0202704) and National Natural Science Foundation of China (Grant Nos. 61774016, 21773009, 51432005,<PHONE_NUMBER>,<PHONE_NUMBER>1).

### **Conflict of interest**

There is no conflict of interest.

### **Author details**

Jie Wang1,2\*, Di Liu1,2, Linglin Zhou1,2 and Zhong Lin Wang1,2,3

1 Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, P. R. China

2 College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, P. R. China

3 School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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10.1016/0304-3886(92)90073-3

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[54] Zhao K, Gu GQ, Zhang YN, Zhang B, Yang F, Zhao L, et al. The self-powered CO2 gas sensor based on gas discharge induced by triboelectric nanogenerator. Nano Energy. 2018;**53**:898-905. DOI: 10.1016/j. nanoen.2018.09.057

[55] Lv SS, Yu B, Huang T, Yu H, Wang HZ, Zhang QH, et al. Gas-enhanced triboelectric nanogenerator based on fully-enclosed structure for energy harvesting and sensing. Nano Energy. 2019;**55**:463-469. DOI: 10.1016/j. nanoen.2018.11.022

[56] Yang Y, Zhang HL, Wang ZL. Direct-current triboelectric generator. Advanced Functional Materials. 2014;**24**(24):3745-3750. DOI: 10.1002/ adfm.201304295

[57] Luo JJ, Xu L, Tang W, Jiang T, Fan FR, Pang YK, et al. Direct-current triboelectric nanogenerator realized by air breakdown induced ionized air channel. Advanced Energy Materials. 2018;**8**(27):1800889. DOI: 10.1002/ aenm.201800889

[58] Zhu XX, Li ZB, Li XS, Su L, Wei XY, Kuang SY, et al. Triboelectrificationenabled thin-film tactile matrix for self-powered high-resolution imaging. Nano Energy. 2018;**50**:497-503. DOI: 10.1016/j.nanoen.2018.05.061

[59] Ryu H, Lee JH, Khan U, Kwak SS, Hinchet R, Kim SW. Sustainable direct current powering a triboelectric nanogenerator via a novel asymmetrical design. Energy & Environmental Science. 2018;**11**(8):2057-2063. DOI: 10.1039/c8ee00188j

[60] Liu D, Yin X, Guo H, Zhou L, Li X, Zhang C, et al. A constant-current triboelectric nanogenerator arising from electrostatic breakdown. Science Advances. 2019;**5**(4):eaav6437. DOI: 10.1126/sciadv.aav6437

Chapter 5

Abstract

TENG device.

optimization strategy

1. Introduction

69

Theoretical Prediction and

Optimization Approach to

He Zhang and Liwei Quan

Triboelectric Nanogenerator

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

Keywords: triboelectric nanogenerator, theoretical analysis, scaling law,

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 contact-separation process of the two insulators to generate electric power. By separating the tribo-pair with contact-induced 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 [7–11]. Besides, TENGs can be made from low-cost materials by easy manufacture processes because of its simple device structures and

#### Chapter 5

## Theoretical Prediction and Optimization Approach to Triboelectric Nanogenerator

He Zhang and Liwei Quan

#### 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 contact-separation process of the two insulators to generate electric power. By separating the tribo-pair with contact-induced 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 [7–11]. Besides, TENGs can be made from low-cost 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].

of a TENG device, the device figure of merits of TENG is proposed and used to

Although various theoretical models have been established for TENG, most of them are based on single-parameter 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 V-Q-x 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 optimiza-

2. The theoretical prediction and optimization strategies for TENG

The four basic modes of TENG are of similar structure with two layers of different materials (dielectric-dielectric or dielectric-electrode), which are usually called tribo-pair. When the tribo-pair 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

As Figure 1 shows, there are two different motion patterns including contactseparation and sliding in these four modes of TENG. For the contact-separation mode and single-electrode mode TENG, the tribo-pair moves vertically and creates

an air gap in between, while for the contact-sliding mode and freestanding

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 V-Q-x 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

tion strategy for TENG are presented.

2.1 The V-Q-x relationship of TENG

order to balance the electric potential drop created.

TENG are provided.

71

determine the maximum output power density of a TENG [22–24].

DOI: http://dx.doi.org/10.5772/intechopen.86992

Theoretical Prediction and Optimization Approach to Triboelectric Nanogenerator

TENG has four basic operational modes with different structures: contactseparation mode, sliding mode, single-electrode 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 self-powered smart sensing like vector sensor, tactile sensor, vibration detection, human physical signal detection, etc. [12–16].

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/m2 and mW/m2 to tens of W/m2 . In recent works, a TENG with power density up to 500 W/m2 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 trial-and-error process experimentally, which is of high cost and time-consuming. 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 [17–21]. In addition, to establish a standard for evaluating different architectures

Figure 1. Four fundamental modes of triboelectric nanogenerator [1].

#### Theoretical Prediction and Optimization Approach to Triboelectric Nanogenerator DOI: http://dx.doi.org/10.5772/intechopen.86992

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 [22–24].

Although various theoretical models have been established for TENG, most of them are based on single-parameter 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 V-Q-x 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 V-Q-x 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 V-Q-x relationship of TENG

The four basic modes of TENG are of similar structure with two layers of different materials (dielectric-dielectric or dielectric-electrode), which are usually called tribo-pair. When the tribo-pair 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 contact-separation mode and single-electrode mode TENG, the tribo-pair moves vertically and creates an air gap in between, while for the contact-sliding mode and freestanding

configurations. These advantages make this new technology a better choice as

Electrical Discharge - From Electrical breakdown in Micro-gaps to Nano-generators

TENG has four basic operational modes with different structures: contactseparation mode, sliding mode, single-electrode 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 self-powered smart sensing like vector sensor, tactile sensor, vibration detection,

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/m2

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 trial-and-error process experimentally, which is of high cost and time-consuming. 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 [17–21]. In addition, to establish a standard for evaluating different architectures

500 W/m2 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.

. In recent works, a TENG with power density up to

power source for devices and microsystems [1, 2].

human physical signal detection, etc. [12–16].

and mW/m2 to tens of W/m2

Figure 1.

70

Four fundamental modes of triboelectric nanogenerator [1].

tribo-layer mode, the tribo-pair 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.

Substituting Eq. (1) into Eq. (2), we will have the equation for Q as

Theoretical Prediction and Optimization Approach to Triboelectric Nanogenerator

With initial condition of Q tð Þ¼ ¼ 0 0, the expression of Q(t) will be

� σ Rε0 e � 1 RSε0 d0tþ Ðt 0 x tð Þdt � � ðt

þ σð Þ d0 þ x tð Þ ε0

separation gap x tð Þ between tribo-pair (see Figure 3a, black solid line).

A sin πt ηT

function to describe the motion process of the tribo-pair as

8 < :

ð Þ 1 � η T < t ≤T when the tribo-pair keeps contact (x ¼ 0).

x tðÞ¼

exp � d0ð Þ t � τ RSε0

ð Þþ d0 þ x tð Þ σx tð Þ

exp � 1

This V-Q-x relationship provides a means for evaluation of the electric output of contact-mode TENG, which may also be used for single-electrode 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 contact-mode TENG. Glass and polydimethylsiloxane (PDMS) are used as the tribo-pair 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

According to the experimental settings in Ref. [16], we introduce a piecewise

Here, η is the separation ratio representing the ratio of the separation time to the entire period. The smaller the separation ratio, the faster the tribo-pair gets into contact and thus the longer the contact time. In each period, the dielectric plates are supposed to start the charging process which occupies the time period from 0 to

Parameter Value Thickness of PDMS plate d1 100 μm Relative permittivity of PDMS εr1 2.7 Thickness of glass d2 1 mm Relative permittivity of glass εr2 7.2 Permittivity of vacuum ε0 8.854 � 10�12 F/m Area of the dielectrics S 25 cm2 Surface charge density σ 5–40 μC/m2

0, 0≤ t≤ð Þ 1 � η T,

� �, ð Þ 1 � η T < t≤ T:

RSε0

ðt τ x zð Þdz

� 1 RSε0

� �dτ

ε0

0 d0e 1 RSε0 d0zþ

d0t þ

� � � �

ðt 0 x tð Þdt (3)

dz

(4)

(5)

(6)

Ð z 0 x tð Þdz � �

dt ¼ � Q Sε0

R dQ

RSε0 d0tþ Ðt 0 x tð Þdt � �

DOI: http://dx.doi.org/10.5772/intechopen.86992

dt ¼ � σd0 ε0

> d0 þ x tð Þ RSε0

ðt 0

With the output voltage as

Q ¼ σS � σSe� 1

V tðÞ¼ R dQ

Table 1.

73

Parameters of contact-mode TENG [16].

þ σd0 ε0

#### 2.1.1 Contact-separation mode TENG

According to the materials used and device structures, the contact-separation mode TENGs fall into two categories: dielectric-to-dielectric contact and conductorto-dielectric 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 E in the tribo-pair and the total charge density ρ is ∇ � E ¼ ρ=ε0. When the charged surfaces move, according to Maxwell's equations, the electric displacement field is defined as D ¼ ε0E þ P; therefore, the current density is JD ¼ ε0 ∂E ∂t þ ∂P ∂t . Thus, we can get the V-Q-x relationship of TENG [18].

The thicknesses of the dielectric plates in tribo-pair are assumed to be d1 and d2 with the relative dielectric constants εr1 and εr2, respectively. According to the Gauss theorem, the electric field strengths of each layer in the dielectric-todielectric mode TENG are E1 ¼ �Q=Sε0εr1 for dielectric plate 1, E2 ¼ �Q=Sε0εr2 for dielectric plate 2, and Eair ¼ �ð Þ Q=S þ σð Þt =ε0 in the air gap, respectively. Here ε0 is the vacuum permittivity, σ is the charge density at the contact surface of the tribo-pair, and S is the contact area of the tribo-pair. The voltage between the two electrodes is V ¼ E1d1 þ E2d2 þ Eairx tð Þ. For the conductor-to-dielectric structure TENG, there will be no dielectric plate 2, i.e., d2 and E2 are zero; thus the voltage becomes V ¼ E1d1 þ Eairx tð Þ. If we define the equivalent thickness of the dielectric plates to be d0 ¼ d1=εr1 þ d2=εr2, the V-Q-x relationship could be expressed as follows:

$$V = -\frac{Q}{\mathcal{S}\varepsilon\_0} (d\_0 + \varkappa(t)) + \frac{\sigma \varkappa(t)}{\varepsilon\_0} \tag{1}$$

Here, x tð Þ is the varying gap distance between the tribo-pair due to external mechanical loading imposed to the TENG device. In special circuit conditions, for short-circuit condition with the output voltage V ¼ 0, the current is Isc ¼ dQ dt ¼ Sσd0 ð Þ d0þx tð Þ 2 dx dt ; for open-circuit condition with transferred charge Q ¼ 0, the voltage is Voc ¼ σx tð Þ ε0 .

According to Ohm's law, when the TENG is connected with a load resistance R to form a circuit, the output voltage in the circuit can be expressed as

$$V = IR = R\frac{dQ}{dt} \tag{2}$$

#### Figure 2.

Basic structure and model of the contact-mode TENG. (a) Dielectric-to-dielectric mode TENG and (b) conductor-to-dielectric mode TENG [18].

Theoretical Prediction and Optimization Approach to Triboelectric Nanogenerator DOI: http://dx.doi.org/10.5772/intechopen.86992

Substituting Eq. (1) into Eq. (2), we will have the equation for Q as

$$R\frac{dQ}{dt} = -\frac{Q}{Se\_0}(d\_0 + \varkappa(t)) + \frac{\sigma \varkappa(t)}{\varepsilon\_0} \tag{3}$$

With initial condition of Q tð Þ¼ ¼ 0 0, the expression of Q(t) will be

$$\mathbf{Q} = \sigma \mathbf{S} - \sigma \mathbf{S} e^{-\frac{1}{\mathbf{E}\varepsilon\_0} \left( d\_0 t + \int\_0^t \mathbf{x}(t) d\mathbf{r} \right)} - \frac{\sigma}{R\varepsilon\_0} e^{-\frac{1}{\mathbf{E}\varepsilon\_0} \left( d\_0 t + \int\_0^t \mathbf{x}(t) d\mathbf{r} \right)} \int\_0^t d\_0 e^{\frac{1}{\mathbf{E}\varepsilon\_0} \left( d\_0 \mathbf{z} + \int\_0^\mathbf{z} \mathbf{x}(t) d\mathbf{z} \right)} \mathbf{d} \mathbf{z}} \tag{4}$$

With the output voltage as

tribo-layer mode, the tribo-pair moves laterally. For these two different kinds of motion process, the theoretical models are quite different, which will be discussed

Electrical Discharge - From Electrical breakdown in Micro-gaps to Nano-generators

According to the materials used and device structures, the contact-separation mode TENGs fall into two categories: dielectric-to-dielectric contact and conductorto-dielectric 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 E in the tribo-pair and the total charge density ρ is ∇ � E ¼ ρ=ε0. When the charged surfaces move, according to Maxwell's equations, the electric displacement field is defined as D ¼ ε0E þ P; therefore, the current density is

. Thus, we can get the V-Q-x relationship of TENG [18]. The thicknesses of the dielectric plates in tribo-pair are assumed to be d1 and d2

dielectric mode TENG are E1 ¼ �Q=Sε0εr1 for dielectric plate 1, E2 ¼ �Q=Sε0εr2 for dielectric plate 2, and Eair ¼ �ð Þ Q=S þ σð Þt =ε0 in the air gap, respectively. Here ε0 is the vacuum permittivity, σ is the charge density at the contact surface of the tribo-pair, and S is the contact area of the tribo-pair. The voltage between the two electrodes is V ¼ E1d1 þ E2d2 þ Eairx tð Þ. For the conductor-to-dielectric structure TENG, there will be no dielectric plate 2, i.e., d2 and E2 are zero; thus the voltage becomes V ¼ E1d1 þ Eairx tð Þ. If we define the equivalent thickness of the dielectric plates to be d0 ¼ d1=εr1 þ d2=εr2, the V-Q-x relationship could be expressed as

ð Þþ d0 þ x tð Þ σx tð Þ

Here, x tð Þ is the varying gap distance between the tribo-pair due to external mechanical loading imposed to the TENG device. In special circuit conditions, for short-circuit condition with the output voltage V ¼ 0, the current is

According to Ohm's law, when the TENG is connected with a load resistance R to

V ¼ IR ¼ R dQ

Basic structure and model of the contact-mode TENG. (a) Dielectric-to-dielectric mode TENG and (b)

ε0

; for open-circuit condition with transferred charge Q ¼ 0, the

dt (2)

(1)

with the relative dielectric constants εr1 and εr2, respectively. According to the Gauss theorem, the electric field strengths of each layer in the dielectric-to-

> V ¼ � Q Sε0

form a circuit, the output voltage in the circuit can be expressed as

in Sections 2.1.1 and 2.1.2, respectively.

2.1.1 Contact-separation mode TENG

JD ¼ ε0

follows:

Isc ¼ dQ

Figure 2.

72

dt ¼ Sσd0 ð Þ d0þx tð Þ 2

voltage is Voc ¼ σx tð Þ

dx dt

ε0 .

conductor-to-dielectric mode TENG [18].

∂E ∂t þ ∂P ∂t

$$\begin{split} V(t) &= R\frac{dQ}{dt} = -\frac{\sigma d\_0}{\varepsilon\_0} + \frac{\sigma(d\_0 + \varkappa(t))}{\varepsilon\_0} \exp\left[ -\frac{1}{R\mathcal{S}\varepsilon\_0} \left( d\_0 t + \int\_0^t \varkappa(t) d\mathbf{t} \right) \right] \\ &+ \frac{\sigma d\_0}{\varepsilon\_0} \frac{d\_0 + \varkappa(t)}{R\mathcal{S}\varepsilon\_0} \int\_0^t \exp\left( -\frac{d\_0(t-\tau)}{R\mathcal{S}\varepsilon\_0} - \frac{1}{R\mathcal{S}\varepsilon\_0} \int\_\tau^t \varkappa(\mathbf{z}) d\mathbf{z} \right) d\tau \end{split} \tag{5}$$

This V-Q-x relationship provides a means for evaluation of the electric output of contact-mode TENG, which may also be used for single-electrode 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 contact-mode TENG. Glass and polydimethylsiloxane (PDMS) are used as the tribo-pair 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 x tð Þ between tribo-pair (see Figure 3a, black solid line).

According to the experimental settings in Ref. [16], we introduce a piecewise function to describe the motion process of the tribo-pair as

$$\mathbf{x}(t) = \begin{cases} \mathbf{0}, & \mathbf{0} \le t \le (1 - \eta)T, \\\ A \sin\left(\frac{\pi t}{\eta T}\right), & (1 - \eta)T < t \le T. \end{cases} \tag{6}$$

Here, η is the separation ratio representing the ratio of the separation time to the entire period. The smaller the separation ratio, the faster the tribo-pair gets into contact and thus the longer the contact time. In each period, the dielectric plates are supposed to start the charging process which occupies the time period from 0 to ð Þ 1 � η T < t ≤T when the tribo-pair keeps contact (x ¼ 0).

#### Table 1. Parameters of contact-mode TENG [16].

V ¼ σd0 ε0

DOI: http://dx.doi.org/10.5772/intechopen.86992

þ d0 ε0RS

for TENG as

resistance.

Table 2.

75

Area of the dielectrics S (m2

Parameters of sliding-mode TENG [17].

l

l l � x tð Þ ðt

l � x tð Þ exp � d0

Theoretical Prediction and Optimization Approach to Triboelectric Nanogenerator

0 exp

Peff ¼ 1 T

Here,T is the time span of one mechanical loading cycle.

by Eq. (8) agree very well with the experimental result.

ε0RS ðt 0

> d0 ε0RS ðt 0

Based on these output voltage expressions, we can get the average power output

ðtþT t

This V-Q-x relationship provides a method for evaluation of the electric output of

Based on the V-Q-x 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

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 contact-mode [18], sliding-mode [19], single-electrode

) 0.05 � 0.071

Parameter Value Dielectric tribo-pair PTFE-nylon Thickness of nylon plate d1 (μm) 50 Relative permittivity of Nylon εr1 4 Thickness of PTFE plate d2 (μm) 50 Relative permittivity of PTFE εr2 2.1 Permittivity of vacuum ε0 (F/m) 8.854 � 10�12

Surface charge density σ (μCm�2) 200 Maximum separate distance A (m) 0.05 Acceleration a (m=s2) 20 Load resistance R (MΩ) 10–1000

sliding-mode TENG, which may be easily extended as a methodology for sliding freestanding tribo-layer mode TENG. To make sure the accuracy and precision of the proposed V-Q-x relationship of sliding-mode TENG, the experiments from Ref. [17] are carried out for validation. The materials and scale parameters are shown in Table 2. In the experiments, the tribo-pair is fixed on a horizontal tensile loading platform for reciprocating lateral sliding process. The sliding process of the tribo-pair was imposed by the dynamic testing machine with a symmetric accelerationdeceleration mode (Figure 5c). The analytical results of the voltage output obtained

V2

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l l � x tð Þ dt

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l l � xð Þδ

t

0

dδ

dt 0 � 1

R dt (9)


**An Experimental Investigation of the Impact of Washcoat Composition on Gasoline Particulate Filter (GPF) Performance**

**Junjun Wang 1,2, Fuwu Yan 1,\*, Na Fang 2, Dong Yan 2, Guoqing Zhang 1,2, Yu Wang 1 and Wulin Yang 2**

Received: 31 December 2019; Accepted: 27 January 2020; Published: 5 February 2020

**Abstract:** The forthcoming implementation of the China VI emission regulations, which are currently the most stringent around the world targeted at light-duty gasoline engine vehicles, will not only further restrict the emissions of gaseous pollutants, but also put forward, for the first time, the requirements of particulate number (PN) emissions with a limit set at 6 × 1011#/km. To achieve the stringent emission targets, the ceramic wall-flow gasoline particulate filter (GPF) will be effective to achieve the reduction of the particulate number tailpipe emissions in a way similar to the widely applied diesel particulate filter (DPF) in diesel engines. This paper investigated the effect of a coated gasoline particulate filter (GPF) on the PN emission and engine performance. The effects of two factors, including the washcoat powder material bulk density and type of coating, were studied with regard to three primary performances of GPF, including high three-way catalytic performance, low pressure drop, and high PN filtration efficiency, according to the original equipment manufacturer (OEM) requirements. The outcomes show that the use of high bulk density materials resulted in a low washcoat volume and hence a decrease of flow resistance and backpressure, in addition to high PN filtration efficiency. The type of coating had notable influence on the backpressure and PN filtration efficiency. The coating length and coating amount both had notable influence on the backpressure and PN filtration efficiency.

**Keywords:** washcoat; powder material bulk density; coating; backpressure; PN filtration efficiency

#### **1. Introduction**

In order to further improve engine fuel economy and reduce CO2 emissions, turbocharged gasoline direct injection (GDI) engines have prevailed as the power source for light-duty vehicles [1]. However, compared to vehicles equipped with traditional port fuel injection engines, GDI engines are known to have higher engine-out soot and particle number emissions, especially under cold start and high-speed, high-load operating conditions [2–4]. The latest China VI light-duty emission regulations will be implemented in 2020. For China VI, the certification test driving cycle will be changed from the New European Driving Cycle (NEDC) to the Worldwide Harmonized Light Vehicles Test (WLTC), which includes more transient operation points, leading to an increase of the maximum speed and acceleration, as well as a move to larger loads for engine operating regions [5]. Moreover, the pollutants limits will be substantially tighter. The particle number emissions requirement will be introduced in the China VI emission regulations for the first time, with the same limit set of 6 × 1011#/km in the China VI-a and VI-b stages. In addition, the real driving emissions (RDE) certification test will be

implemented in China VI-b stage in 2023, in which the conformity factor (CF) of the particulate number (PN) and NOx will be limited to be 2.1 and the CO2 emissions will be recorded. There is no doubt that the stringent requirements in China VI will be even more challenging for light-duty vehicles equipped with turbocharged GDI engines, especially in terms of meeting the PM/PN limit requirements.

For light-duty gasoline vehicles, in response to the upcoming implementation of the China VI emission regulations, advanced exhaust gas aftertreatment technologies (e.g., GPF) must be applied to reduce the vehicle tailpipe emissions in addition to the adaptation of engine internal parameters, such as air/fuel ratio, fuel injection strategy, and combustion phasing, to further optimize the combustion system in order to reduce the engine-out raw emissions [6–9]. The technology of DPF to solve the problem of diesel particulate emissions has been applied for many years, and its effectiveness on the reduction of particulate emission has been well confirmed [10,11]. It is considered that the use of GPF has the potential to effectively reduce gasoline particulate emissions over a wide range of engine operating conditions [12]. However, employing GPF as a solution to reduce particulate emissions also results in an increase of the pressure drop of the entire exhaust aftertreatment system. Consequently, the drop increases the backpressure of the exhaust manifold, and hence imposes a negative influence on the engine power output, fuel economy, and CO2 emissions [13].

In general, there are two types of particulate filters applied to the exhaust aftertreatment system currently: One is bare GPF without any catalyst coated, and the other is cGPF, which is coated with some amount of catalyst and could even further improve the three-way catalytic performance. Compared to the bare GPF, it is considered that adopting the coated GPF (cGPF) has more advantages, such as reducing the cost of substrates, packaging improvements, and an increase in soot regeneration performance under cold running conditions [14]. It is believed that the coated GPF will become the mainstream in auto original equipment manufacturers (OEMs) in the future. Coated GPF should fulfill design requirements in order to achieve sufficient particulate number filtration efficiency to meet the PN regulation limit. In the meantime, the pressure drop must be minimized to reduce its influence on engine power output and fuel economy. Different kinds of coatings with integrated pore characteristics of the filter wall were confirmed to have an evident effect on the reduction of the pressure drop and to increase PN filtration efficiency [15,16]. Consequently, this study concerned the coated GPF and describes the optimization design methodology. The results are discussed in detail in the subsequent sections.

#### **2. Experimental Section**

In this study, the influence of the pressure drop of the washcoat on the GPF were tested on a cold flow bench (computerized flowbench SF-1020 Probench), as can be seen from the Figure 1 and Table 1. The pressure drop of the test GPF samples was carried out on the SF-1020 Probench with 25 ◦C ambient temperature and an atmosphere pressure of 1 atm. Five air flow rates were studied, ranging from 200 kg/h to 600 kg/h at a step of 100. The GPF design was a cylinder with a diameter of 132.1 mm and a length of 127 mm. The cell structure was 300 cpsi/8 mil and the porosity was approximately 65%. Detailed parameters are listed in the Table 2.

**Table 1.** Measurement conditions of the pressure drop on the gasoline particulate filter (GPF).

**Figure 1.** Measurement equipment SF-1020 Probench.

**Table 2.** Measurement conditions of the pressure drop on the GPF.

In this study, the influence of the particle number filtration and gas pollutant abatement efficiency of the washcoat on the GPF were tested on a light-duty passenger car, equipped with 1.4-liter displacement turbocharged gasoline direct-injection (TGDI) engine, which was employed for the on-engine experiment at test driving cycle Worldwide harmonized Light vehicles Test Cycle (WLTC) on vehicle emission hub of HORIBA test equipment. The tests used China VI emission regulation 92 # gasoline, as can be seen from the Figure 2. The information of the test engine and the washcoat are shown in Table 3.

**Figure 2.** Tests of the particulate number filtration and efficiency.

**Table 3.** Main parameters of the test vehicle.

#### **3. Results and Discussion**

#### *3.1. Washcoat Powder Material Bulk Density*

The effects of washcoat powder material bulk density on gasoline particulate filter performance were first studied. In order to study the factor, the catalyst coating amount was kept constant at 100 g/L. In the meantime, the bulk density of powder material of the washcoat was changed by adjusting the percentage of composition of the powder. Two kinds of compositions were employed in the study, namely aluminum oxide and cerium zirconium composite oxides. Two types of powder with different bulk densities, referred to as GPF-A and GPF-B, were investigated. Detailed information of the powders is listed in the Table 4. As listed in Table 4, GPF-A represents the washcoat, which used low-level bulk density material. The percentage of the aluminum oxide was 0.37 g/mL and the cerium zirconium was 0.27 g/mL. On the other hand, GPF-B represents the high-level bulk density material washcoat, with 0.76 g/mL and 0.55 g/mL for the two materials. For the convenience of comparison, GPF-Ref represents a commercial GPF with same catalyst coating amount.

**Table 4.** Comparison of bulk density of GPF-A and GPF-B (g/mL).

The pressure drop of test GPF samples against the air flow rate is plotted in Figure 3. It can be seen from the Figure 3 that the pressure drop of the GPF-B in the whole test flow range was about 34.9% lower than that of the GPF-A, and 11.1% lower than that of the commercial GPF-Ref. As can be seen from Table 2, the bulk density of the powdery material used in the GPF-B was about twice as high as that of GPF-A. Therefore, the washcoat volume of former coating was smaller than that of the latter under the same coating amount. Consequently, the type of washcoat with a smaller volume avoided extensive build-up in the pores of the substrate, and hence reduced flow resistance. Eventually, it led to a decrease in pressure drop.

**Figure 3.** Influence of washcoat material bulk density on pressure drop.

In order to further check the influence of bulk density on the pressure drop, the microstructure of the GPF washcoat was characterized by scanning electron microscope (SEM). The images of the two types of coating are seen in Figure 4. The white part in the figure represents the washcoat, and the dark grey part represents the substrate. As described above, the washcoat in GPF-A showed extensive piling up, especially on one side of the substrate, which had a great hindering effect on the gas flow. As the material bulk density increased, the washcoat of GPF-B had a lower volume and appeared

to accumulate very little. Hence, the flow resistance of GPF-B was obviously lower than GPF-A, as confirmed in pressure drop test.

**Figure 4.** SEM images of the GPF samples of GPF-A and GPF-B.

The results of fresh state PN efficiency by the two types of washcoat are shown in Figure 5. It can be seen that the efficiency was significantly improved by both washcoats, further confirming the effectiveness of the washcoat on the reduction of particle emission. More importantly, compared with GPF-A, the PN filtration efficiency of GPF-B slightly increased by about 2.9% after raising the bulk density of the coating material. More dispersed distribution of the inner coating in the large pores by application of high bulk density coating material is considered to be the reason for the higher PN filtration efficiency.

**Figure 5.** Influence of the washcoat material bulk density on particulate number (PN) filtration efficiency.

In order to check the influence of the bulk density on the light-off temperature on the emission of gasoline engine, the results of CO/HC/NOx light-off temperature (T50) of engine bench test of GPF-A and GPF-B are further shown in Figure 6. It can be confirmed that the coating material bulk density had little impact on CO/HC/NOx light-off temperature.

**Figure 6.** Influence of coating material bulk density on T50 of CO/HC/NOx.

#### *3.2. Catalysts Coating*

In addition to the material of the washcoat on GPF performance, the impact of catalyst coating on GPF performance was also studied. Two factors, namely the direction and the length of the coating, were investigated via experiment. The catalyst coating amount was kept constant (approx. 100 g/L) for fair comparison. The test samples were prepared in four different types of coating using the S-75% and S-100% to represent single-side coating with 75% and 100% of substrate length for coating length, respectively, whereas T-50% and T-75% represent two-side coating with 50% and 75% of substrate length for coating length, respectively. The sketches of different catalyst coating are shown in Figure 7.

**Figure 7.** Diagram of two different catalyst coating of GPF.

The experiment boundary conditions for the measurements and procedure are same as Table 1. The experimental test was carried out on the cold flow bench with 25 ◦C ambient temperature and an atmosphere pressure of 1 atm. The four catalysts coating tested were S-100%, S-75%, T-50%, and T-75%.

The pressure drop, with four different kinds of catalyst coatings against air flow rate, is plotted in Figure 8. It can be seen that catalyst coating had moderate influence on the pressure drop, including the coating direction and coating length. First, all the coatings increased the pressure drop at each flow range. Moreover, the pressure drop of the S-100% was the highest in the whole flow range in the test, followed by T-75%, T-50%, and S-75%. Different coatings had a great influence on the distribution of the coated washcoat in the interior of GPF. The T-75% coating did not only make the washcoat cover the inlet and outlet wall of substrate more uniformly, but also increased the washcoat density per length, resulting in the highest pressure drop. For the S-100% coating, it was not easy to control the coating process operation because it was prevent the penetration of the washcoat throughout the S-100% length-coating process, and it was also difficult to guarantee the uniformity of the coated washcoat. The T-50% coating did not only greatly reduce the pressure drop, but was also easy to be controlled for coating. In addition, better uniformity and distribution of the coated washcoat can also be achieved by this coating. Although the pressure drop was the lowest for the S-75% coating, a blank of 25% length was left to the substrate, which largely affected the PN filtration efficiency.

**Figure 8.** Influence of catalysts coating on GPF cold flow pressure drop.

Furthermore, series of WLTC emission tests were conducted to determine the impact of several different catalyst coatings on PN filtration efficiency. Figure 9 shows the results of the fresh state PN filtration efficiency of four different kinds of catalyst coating. Corresponding to the results of the pressure drop, the T-75% coating had the highest PN filtration efficiency, reaching more than 95%. The PN filtration efficiency of S-100% and T-50% coatings, respectively, were about 92% and 89%, while the S-75% had the lowest PN filtration efficiency with approximately 85%. It can be concluded that the pressure drop is positively correlated with the PN filtration efficiency after further comprehensive analysis in terms of influence of different coatings on pressure drop and PN filtration efficiency. Based on the principle that the pressure drop should be reduced as much as possible on the premise of the PN filtration efficiency to meet development requirements, the T-50% was determined the optimum catalyst coating.

**Figure 9.** Influence of catalyst coatings on PN filtration efficiency.

#### **4. Conclusions**

In this study, the washcoat for a gasoline particulate filter (GPF) was experimentally studied. The influence of material selection and different kinds of coatings on the pressure drop, PN filtration efficiency, and three-way catalytic performance were examined. The two main conclusions were drawn as follows:

1. The bulk density of powder materials employed in GPF washcoat had a great influence on the backpressure. High bulk density materials resulted in a low washcoat volume and hence the decrease of flow resistance and backpressure. The PN filtration can also be improved using high bulk density powder materials due to the more dispersed distribution of the inner coating.

2. The kind of different coatings, which included coating directions, coating length, and coating amount, had notable influence on the backpressure and PN filtration efficiency. T-50% has been experimentally confirmed as the optimum catalyst coating, which meets both the lower backpressure and PN filtration ratio that fulfilled the development requirements.

**Author Contributions:** Conceptualization, J.W. and F.Y.; Methodology, J.W. and Y.W.; Validation, J.W.; Investigation, N.F and W.Y.; Data Curation, N.F.; Writing—Original Draft Preparation, J.W.; Writing—Review & Editing, N.F., D.Y. and G.Z.; Supervision, F.Y.; Project Administration, F.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded partially by the National Key R&D Program of China (2017YFC0211201).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

SEM Scanning Electron Microscope

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**2. Bioinformatics analysis of RNASeq data**

**RNASeq methods Description Reference**

ribonucleoprotein complexes (miRNPs)

transcripts that undergo degradation

sequencing

114 Next Generation Sequencing - Advances, Applications and Challenges

**Table 1.** Various RNASeq-based methods to study transcriptome

the bioinformatics data analysis steps, tools, and methods.

To identify and sequence the binding sites of cellular RNAbinding proteins (RBPs) and microRNA-containing

It sequences and captures nascent RNA transcripts after immunoprecipitation of RNA Pol II elongation complex

To detect and identify miRNA cleavage sites and uncapped

RNA isoforms are identified after 5' and 3' paired-end

One of the first steps while designing the RNASeq experiment is choosing an appropriate sequencing platform. Several sequencing platforms such as Illumina, Roche, PacBio, and Ion Torrent, which are based on different sequencing chemistry and technology, are available [reviewed in 34, 35]. Current leading platform for RNASeq (and other NGS-based analyses) is the HiSeq series of sequencers from Illumina (https://www.illumina.com/systems.html) because it provides high throughput, deep sequencing, low sequence error, and long enough read data to be useful in multiple applications. Recently, the PacBio RS II (http://www.pacif‐ icbiosciences.com/) is gaining popularity for better transcriptome construction, because of its ability to generate long reads. Once the millions of reads are generated from an RNASeq experiment, the bioinformatics data analysis begins. In the following section, we briefly present

To detect and identify translating mRNAs [174]

Single-cell transcriptomics methods [177]

[172]

[173]

[175]

[176]

PAR-CLIP

Seq(Photoactivatable-Ribonucleoside-Enhanced Cross-Linking and Immunoprecipitation Sequencing)

NETSeq (Native Elongation Transcript Sequencing)

PARESeq (Parallel Analysis of RNA Ends Sequencing) and GMUCT (Genome-wide Mapping of Uncapped

TIFSeq (Transcript Isoform Sequencing) or Paired-End Analysis of Transcription start

CELSeq (Cell Expression by Linear amplification and Sequencing), SMARTSeq (Switching Mechanism At the 5′ end of the RNA Template Sequencing), STRT (Single-cell Tagged Reverse Transcription)

TRAPSeq (Targeted Purification of Polysomal mRNA Sequencing)

Transcripts)

site (PEAT)

Analysis of the RNASeq data is a multistep process that typically includes quality check, data preprocessing, transcriptome assembly (reference-guided and *de novo* transcriptome assem‐ bly), quantification, statistical analysis, and functional annotation (Figure 1). These steps are described in details in the following.

**Figure 1.** Basic RNASeq data analysis workflow. Firstly, raw sequenced data are checked for the quality and, if re‐ quired, low-quality reads and artifacts are removed. In the case of reference-based assembly, the reads are mapped to the reference genome in order to know their location. All the mapped reads are then analyzed for expression profiling. Further, differentially expressed genes and isoforms can be annotated using Gene Ontology (GO) and Pathway enrich‐ ment analyses. In *de novo* assembly approach, after preprocessing of the raw reads, transcriptome can be assembled using different *de novo* transcriptome assemblers. Once transcripts are constructed and abundance estimate is obtained, the complete Open Reading Frames (ORFs) transcripts are predicted. The predicted ORFs can be annotated or ana‐ lyzed for expression profiling and then annotated using remote homology search method, GO, and pathway enrich‐ ment analyses.

#### **2.1. Quality check and data preprocessing**

Next generation sequencers assign a Phred quality score, which is the probability of the base call being inaccurate, to the called bases. Low Phred scores (Q< 30) indicate read data of poor quality. Poor-quality read data can arise from problems in the library preparation or from sequencing itself. Additionally, PCR artifacts, sequence-specific biasness, untrimmed adapter sequences, and other possible contaminants can lead to poor data quality. These factors can affect the downstream analysis and data interpretation, and can give inaccurate results. In order to assess quality of raw sequenced data several tools such as FastQC (http://www.bioin‐ formatics.babraham.ac.uk/projects/fastqc/) and PRINSEQ [36] are available. Once the data are checked for quality, they should be processed to remove reads with low-quality bases, adapter sequences, and other contaminating sequences. Tools such as Cutadapt [37], Trimmomatic [38], TrimGalore (http://www.bioinformatics.babraham.ac.uk/projects/trim\_galore/), FASTX-Toolkit (http://hannonlab.cshl.edu/fastx\_toolkit/), which trim adapter or other contaminants based upon user-provided parameters, can be used for performing these operations. A brief description of some of these quality and data preprocessing tools is provided below:

**FastQC:** FastQC is a simple, easy-to-use tool that evaluates the quality of read data generated from the next generation sequencers. The input file/s for FastQC can be in Fastq, SAM, or BAM format either in the compressed or uncompressed form. FastQC reports basic statistics for the read data such as overrepresented sequences, k-mer content, base quality and content, adapter content, read duplication level, etc. FastQC is available as a stand-alone Java-based program with a graphical user interface and can be run from both Linux (using command line) and Windows systems.

**PRINSEQ:** PRINSEQ reports base quality, GC content, duplicates, adapters, presence of ambiguous sequences represented as "N," poly A tails, etc. Unlike FastQC, PRINSEQ also has the option of trimming and filtering reads. PRINSEQ is available as stand-alone as well as web application (http://prinseq.sourceforge.net/). It accepts uncompressed files in Fasta, Qual, and Fastq formats.

**Trimmomatic:** Trimmomatic is a Java-based program for the preprocessing of NGS read data (http://www.usadellab.org/cms/?page=trimmomatic). It can trim contaminant sequences, adapters, and filter reads based upon the quality. It supports compressed files in Fastq format and generates output in Fastq format. Because of its multithreading option, its data processing speed is higher than other tools available to perform the same function. Unlike some of the other tools, Trimmomatic can analyze both single-end as well as paired-end read data.

**Cutadapt:** Cutadapt is a python-based tool for read preprocessing and can be run as a command line application (https://cutadapt.readthedocs.org/en/stable). It accepts compressed files in Fasta, Qual, and Fastq formats, and supports both paired-end and single-end files. It trims low-quality bases, multiple adapter sequences from either 3', 5', or from both ends. In addition, Cutadapt can remove fixed number of bases from either ends of the sequences and supports demultiplexing, i.e., reads can be written to different output files depending upon the adapter sequence found in the reads. The demultiplexing feature is particularly useful since pooling multiple samples in a single run is an increasingly common practice as a result of increased sequencer throughput.

**TrimGalore:** TrimGalore is a wrapper tool written around FastQC and Cutadapt for quality check and adapter trimming for regular as well as MspI-digested RRBS-type (Reduced Representation Bisufite-Seq) libraries. It accepts compressed Fastq files and supports pairedend and single-end data.

**FASTX-Toolkit:** FASTX-Toolkit is a collection of tools that accepts read data in Fasta and Fastq file formats and trim the data based on base quality and adapter sequence contamination. Additionally, the toolkit has tools that can perform file format conversion, split sequences based upon barcodes, and generate reverse complement of sequences.

sequencing itself. Additionally, PCR artifacts, sequence-specific biasness, untrimmed adapter sequences, and other possible contaminants can lead to poor data quality. These factors can affect the downstream analysis and data interpretation, and can give inaccurate results. In order to assess quality of raw sequenced data several tools such as FastQC (http://www.bioin‐ formatics.babraham.ac.uk/projects/fastqc/) and PRINSEQ [36] are available. Once the data are checked for quality, they should be processed to remove reads with low-quality bases, adapter sequences, and other contaminating sequences. Tools such as Cutadapt [37], Trimmomatic [38], TrimGalore (http://www.bioinformatics.babraham.ac.uk/projects/trim\_galore/), FASTX-Toolkit (http://hannonlab.cshl.edu/fastx\_toolkit/), which trim adapter or other contaminants based upon user-provided parameters, can be used for performing these operations. A brief

116 Next Generation Sequencing - Advances, Applications and Challenges

description of some of these quality and data preprocessing tools is provided below:

Windows systems.

Fastq formats.

increased sequencer throughput.

end and single-end data.

**FastQC:** FastQC is a simple, easy-to-use tool that evaluates the quality of read data generated from the next generation sequencers. The input file/s for FastQC can be in Fastq, SAM, or BAM format either in the compressed or uncompressed form. FastQC reports basic statistics for the read data such as overrepresented sequences, k-mer content, base quality and content, adapter content, read duplication level, etc. FastQC is available as a stand-alone Java-based program with a graphical user interface and can be run from both Linux (using command line) and

**PRINSEQ:** PRINSEQ reports base quality, GC content, duplicates, adapters, presence of ambiguous sequences represented as "N," poly A tails, etc. Unlike FastQC, PRINSEQ also has the option of trimming and filtering reads. PRINSEQ is available as stand-alone as well as web application (http://prinseq.sourceforge.net/). It accepts uncompressed files in Fasta, Qual, and

**Trimmomatic:** Trimmomatic is a Java-based program for the preprocessing of NGS read data (http://www.usadellab.org/cms/?page=trimmomatic). It can trim contaminant sequences, adapters, and filter reads based upon the quality. It supports compressed files in Fastq format and generates output in Fastq format. Because of its multithreading option, its data processing speed is higher than other tools available to perform the same function. Unlike some of the other tools, Trimmomatic can analyze both single-end as well as paired-end read data.

**Cutadapt:** Cutadapt is a python-based tool for read preprocessing and can be run as a command line application (https://cutadapt.readthedocs.org/en/stable). It accepts compressed files in Fasta, Qual, and Fastq formats, and supports both paired-end and single-end files. It trims low-quality bases, multiple adapter sequences from either 3', 5', or from both ends. In addition, Cutadapt can remove fixed number of bases from either ends of the sequences and supports demultiplexing, i.e., reads can be written to different output files depending upon the adapter sequence found in the reads. The demultiplexing feature is particularly useful since pooling multiple samples in a single run is an increasingly common practice as a result of

**TrimGalore:** TrimGalore is a wrapper tool written around FastQC and Cutadapt for quality check and adapter trimming for regular as well as MspI-digested RRBS-type (Reduced Representation Bisufite-Seq) libraries. It accepts compressed Fastq files and supports pairedOnce the read data are filtered and trimmed to remove low-quality bases, adapter sequence, and contaminants, they are ready for transcriptome assembly and profiling analysis. There are two different approaches for constructing full-length transcripts: reference-based assembly (when a reference genome is available) and *de novo* assembly (when the reference genome is not available), a computationally intensive and complex process (Table 2). Reference-based or genome-guided assembly refers to mapping sequenced reads to the reference genome followed by assembling the transcripts. In contrast, in *de novo* transcriptome assembly, transcripts are constructed directly from the overlapping sequenced reads. For the transcrip‐ tome assembly of organisms without reference genome, only *de novo* transcriptome assembly approach is available for transcriptome construction. However, for organisms with known reference genome, both reference-based and *de novo* transcriptome assembly can be employed for transcriptome construction. In fact, in this case, *de novo* transcriptome assembly will be more effective in filling in the gaps (observed due to variation in reference genome sequence and poor-quality annotation) and hence would complement the reference-based transcriptome assembly. More details on these two transcriptome assembly approaches are discussed in the following sections.

**Table 2.** Difference between reference-based and *de novo* assembly approaches