Review of fabrication methods of large-area transparent graphene electrodes for industry

Abstract

Graphene is a two-dimensional material showing excellent properties for utilization in transparent electrodes; it has low sheet resistance, high optical transmission and is flexible. Whereas the most common transparent electrode material, tin-doped indium-oxide (ITO) is brittle, less transparent and expensive, which limit its compatibility in flexible electronics as well as in low-cost devices. Here we review two large-area fabrication methods for graphene based transparent electrodes for industry: liquid exfoliation and low-pressure chemical vapor deposition (CVD). We discuss the basic methodologies behind the technologies with an emphasis on optical and electrical properties of recent results. State-of-the-art methods for liquid exfoliation have as a figure of merit an electrical and optical conductivity ratio of $\mathrm{43.5}$, slightly over the minimum required for industry of $\mathrm{35}$, while CVD reaches as high as $\mathrm{419}$.

Graphical abstract

Keywords

transparent electrodes / graphene / liquid exfoliation / chemical vapor deposition (CVD)

1 Introduction

Machine vision is a promising solution to automatic visual inspection and performs competitively against human vision. Machine vision generally consists of image acquisition, image processing, and decision making. Image processing is important in the detection process. It extracts features that are useful for decision making. Image-based defect detection methods are used in steel bars [1], printed circuit boards [2], fabric [3], solar wafers [4,5], thin-film transistor-liquid crystal displays [6], bottle caps [7], and many other applications.

Buttons, as important clothing accessories, contain various defects, such as cracks, stains, printing deviation, fading, and uneven surfaces. Button defects are irregular and difficult to identify. The fast production of buttons and the various patterns on their surfaces make the automatic detection of defects even more difficult. Therefore, a fast and generally applicable defect detection method is urgently needed.

Several researchers have converted the defect detection problem to a template matching problem by calculating the similarity between an inspection image and a corresponding defect-free template image [8,9]; this approach is called the referential method. Normalized cross correlation is widely used to measure the similarity. In the frequency domain, many techniques, such as Fourier transform [10], wavelet transform [11,12], and Gabor filter [13,14], are also widely used in defect detection.

Although calculating the similarity between an inspection image and a corresponding defect-free template image is a practical and widely used method, the process requires high alignment accuracy. To reduce the false alarms caused by misalignment, Wang et al. [15] applied the gray-level threshold method to extract significant points in an inspection window and calculated the correlation coefficient of only the selected significant pixel points. Bai et al. [8] extracted salient regions through phase-only Fourier transform and detected defects in these regions. Meanwhile, Zontak and Cohen [16] proposed a defect detection method based on anisotropic kernel reconstruction. In their method, the source image is estimated with the weighted sum of neighboring features from the reference image. Their method does not require image alignment and is robust to pattern variations. However, its computation cost is relatively high. Tsai et al. [9] proposed a dissimilarity measure based on the optical-flow technique. The measure is robust to misalignment and random product variation. It is applicable to gray-level images and suitable for detecting defects that are sparse signals with a uniform background.

Static templates and gray-level images are widely used in all these methods. The methods are sensitive to illumination vibration because the templates are static. A referential method using the weighted least-squares model was developed in the current study. The method does not require precise alignment and is robust to illumination variations. Instead of using gray-level images, all three channels of red–green–blue (RGB) images are used to improve the accuracy of the method.

The remainder of this paper is organized as follows. The proposed model and its implementation are presented in Section 2. The experiment results and a comparison of the proposed method with two other methods are provided in Section 3. The tuning of key parameter values is also included in this section. Section 4 presents the conclusions.

2 Weighted least-squares model and implementation details

The referential method relies heavily on alignment accuracy. Even if two compared images are perfectly aligned, local deviations may still exist in the aligned image due to random product variations in manufacturing. As a result, the residual image exhibits high intensity near the edge regions. To address this problem, the proposed method uses the weighted least-squares model to reconstruct a template image for each test sample adaptively (some details are shown in Section 2.2) and obtain a corresponding residual image. To segment defects effectively, the Gaussian mixture model (GMM) is used to model the background distribution according to the defect-free residual images. The entire procedure is summarized as follows (Fig. 1).

Fig.1 Block diagram of the proposed method for defect detection

Full size|PPT slide

1) Preprocess original images to extract the region of interest (ROI) and align the images to achieve a similar orientation;

2) Combine defect-free images to obtain matrix B through the weighted least-squares model;

3) Input test samples and decompose them into defect-free template and residual images adaptively;

4) Learn the GMM parameters with the defect-free residual images;

5) Input a test sample’s residual image and detect the defect region with the GMM parameters.

2.1 Preprocessing

In this step, the objects (buttons) were extracted from original RGB images {I} ($I\in R^{\mathrm{640}\times \mathrm{480}\times \mathrm{3}}$). The buttons were then rotated at certain degrees to ensure that all buttons had the same orientation. Most types of buttons have a common characteristic: circular holes distributed around the center symmetrically. Therefore, we calculated the rotation degrees after locating the holes and button center by using auto-threshold algorithms. Subsequently, we rotated the buttons around their centers to achieve the same orientation.

2.2 Weighted least-squares model

Given the data set$\{y_i,x_{i\mathrm{1}},x_{i\mathrm{2}},\mathrm{...},x_{in}\},i=\mathrm{1},\mathrm{2},\mathrm{...},m$, we assumed that response variable $y_i$ can be represented by a linear regression of n-vector variable$x_i$with residual term${\epsilon }_i$. The common least-squares model is in the form

(1)$y_i={\beta }_{\mathrm{1}}{x}_{i\mathrm{1}}+{\beta }_{\mathrm{2}}{x}_{i\mathrm{2}}+\mathrm{...}+{\beta }_n{x}_{in}+{\epsilon }_i,i=\mathrm{1},\mathrm{2},\mathrm{...},m.$

This equation can be presented in a vectorized form as

(2)$\mathit{y}=\mathit{X} \mathit{\beta }+\mathit{\epsilon },$

where $y=\left[\begin{array}{l}{y}_{\mathrm{1}}\\ ⋮\\ y_m\end{array}\right],\epsilon =\left[\begin{array}{l}{\epsilon }_{\mathrm{1}}\\ ⋮\\ {\epsilon }_m\end{array}\right],\beta =\left[\begin{array}{l}{\beta }_{\mathrm{1}}\\ ⋮\\ {\beta }_n\end{array}\right]$, and $X=\left[\begin{array}{lll}{x}_{\mathrm{11}}& \cdots & x_{\mathrm{1}n}\\ ⋮& \ddots & ⋮\\ x_{m\mathrm{1}}& \cdots & x_{mn}\end{array}\right]$. The error function is defined as

(3)$J(\beta )={\displaystyle \sum _i{\epsilon }_i^{\mathrm{2}}}.$

To minimize $J(\beta )$, its derivatives must be determined with respect to $\beta$. Thereafter, these derivatives need to be set to zero.

(4)${\nabla }_{\beta }J(\beta )=X^{T}X\beta -X^{T}y=\mathrm{0},$

(5)$\beta =\arg {\min }_{\mathit{\beta }}J(\beta )={(X^{T}X)}^{-\mathrm{1}}{X}^{T}y.$

To minimize the ith error term ${\epsilon }_i$ in $\epsilon$, a large weight ${\omega }_i$ should be provided to ${\epsilon }_i$. The weighted least-squares model is described as follows:

(6)$J^{*}(\beta )={\displaystyle \sum _i{\omega }_i{\epsilon }_i^{\mathrm{2}}}={\left(W^{\frac{\mathrm{1}}{\mathrm{2}}}\epsilon \right)}^T\left(W^{\frac{\mathrm{1}}{\mathrm{2}}}\epsilon \right),$

where $W=\left[\begin{array}{lll}{\omega }_{\mathrm{1}}& \cdots & \mathrm{0}\\ ⋮& \ddots & ⋮\\ \mathrm{0}& \cdots & {\omega }_m\end{array}\right]$. The weighted estimated parameters are

(7)$\beta =\arg {\min }_{\mathit{\beta }}{J}^{*}(\beta )={(X^{T}WX)}^{-\mathrm{1}}{X}^{T}Wy=By,$

where$B={(X^{T}WX)}^{-\mathrm{1}}{X}^{T}W$. For each RGB image $I\in R^{M\times N\times \mathrm{3}}$, M and N represent the height and width of an image, respectively, and 3 represents the three channels of an RGB image. $I(i,j,c)$ is the intensity of the pixel at (i, j) of an image in the c channel. Furthermore, I can be vectorized as a 1D column vector$x\in R^{m\times \mathrm{1}},m=\mathrm{3}MN$.

If defect-free samples are selected and vectorized into column vectors, the training data matrix can be described as$X=\left[\begin{array}{llll}{x}^{\mathrm{1}}& x^{\mathrm{2}}& \cdots & x^n\end{array}\right]$, where n is the number of training samples and $x^k$represents the kth sample in the training set. In the training step, the acquisition of matrix B can be implemented offline. In the testing step, inspection image D is vectorized as column vector d. As a result, the best parameter,$\beta =B d$, and the appropriate item can be described as $a=X \beta$. Adaptive template image$A$can be reconstructed from vector a, and residual image is$E=\left|D-A\right|$, as shown in Fig. 2.

Fig.2 Image decomposition process. (a) Inspection image; (b) adaptive template image; and (c) residual image

Full size|PPT slide

In the experiment, the residual images sometimes showed high intensity near the edge, and this high intensity may cause a false alarm when the common least-squares method is used (Fig. 3). In the weighted least-squares model, a large weight is provided to pixels near the edge regions to solve the problem. Figure 3(c) shows that the residual error was suppressed near the edge regions.

Fig.3 Detection results of different methods. (a) is the input image. (b) and (d) are the results of the common least-squares model. (b) is the enhanced residual image, and (d) is the defect segmentation result. (c) and (e) are the results of the weighted least-squares model. (c) is the enhanced residual image, and (e) is the defect segmentation result

Full size|PPT slide

In all the experiments, the weight matrix was in the form

(8)$W=\mathrm{diag}(\overline{\nabla I}),$

where $\overline{\nabla I}$ represents the mean gradient intensity of the training samples. $W$ is a diagonal matrix with the mean gradient intensity lying on the main diagonal.

2.3 Defect segmentation

GMM has been widely used in automated visual surveillance. GMM segments moving regions from image sequences. Given that our defect detection problem is rather similar to moving object detection, GMM was used to segment defects. The basic idea was to model each background pixel by a mixture of K Gaussian distributions, where K is a small number from 3 to 5. The probability that a certain pixel with a value of x exists at time t can be written as

(9)$p(x)={\displaystyle \sum _{i=\mathrm{1}}^K{\omega }_t^i•\eta (x;{\mu }_t^i,{\Sigma }_t^i)},$

where K is the number of Gaussian components and ${\omega }_t^i$is the weight of theith Gaussian component. It satisfies${\displaystyle \sum _{i=\mathrm{1}}^K{\omega }_t^i}=\mathrm{1}$.$\eta (x;{\mu }_t^i,{\Sigma }_t^i)$is the normal distribution of the component represented by

(10)$\eta (x;{\mu }_t^i,{\Sigma }_t^i)={\displaystyle \frac{\mathrm{1}}{{(\mathrm{2}\pi )}^{d/\mathrm{2}}|{\Sigma }_t^i{|}^{\mathrm{1}/\mathrm{2}}}}\exp [-{\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}{(x-{\mu }_t^i)}^T{({\Sigma }_t^i)}^{-\mathrm{1}}(x-{\mu }_t^i)],$

where ${\mu }_t^i$ and ${\Sigma }_t^i$ are the mean value and covariance of the ith component, respectively. In particular, ${\Sigma }_t^i$ is defined as ${\Sigma }_t^i={({\sigma }_t^i)}^{\mathrm{2}}I$. To model background distributions, parameters are updated by adding new samples and discarding old ones. The parameters of the first Gaussian component are updated when they match the value of the new sample. The implementation details can be found in Refs. [17–19]. In the test stage, each new pixel value is checked against existing model components according to the order of the fitness value. The match criterion is

(11)$\mathrm{dist}(x, {\mu }_t^i)\le C{\sigma }_t^i,$

where C is the control constant and dist(x, y) represents the Euclidean distance between vectors x and y. If no match is found, this pixel will be identified as a foreground pixel (defect).

3 Experiment results and discussion

The performance of the proposed method was evaluated in terms of true positive recognition (TPR) rate, true negative recognition (TNR) rate, and recognition (R) rate, which are defined as

(12)$\mathrm{TPR}={\displaystyle \frac{N_{\mathrm{TP}}}{N_{\mathrm{TP}}+N_{\mathrm{FN}}}},$

(13)$\mathrm{TNR}={\displaystyle \frac{N_{\mathrm{TN}}}{N_{\mathrm{TN}}+N_{\mathrm{FP}}}},$

(14)$R={\displaystyle \frac{N_{\mathrm{TP}}+N_{\mathrm{TN}}}{N_{\mathrm{TP}}+N_{\mathrm{TN}}+N_{\mathrm{FP}}+N_{\mathrm{FN}}}},$

where$N_{\mathrm{TP}}$ is the number of correctly detected defect-free samples,$N_{\mathrm{FN}}$ is the number of falsely detected defect-free samples,$N_{\mathrm{TN}}$ is the number of correctly detected defective samples, and$N_{\mathrm{FP}}$ is the number of falsely detected defective samples. Positive and negative means defect-free and defective, respectively.

We evaluated the performance of the proposed method on three typical types of buttons by comparing it with two other methods, namely, saliency-based method in Ref. [8] and independent component analysis (ICA)-based method in Ref. [5]. For simplicity, we denoted these three types of buttions as sample1, sample2, and sample3. The details of the test samples are presented in Table 1. The proposed method was implemented in Matlab 2014a on an Intel 2.1 GHz PC with 4 GB of memory. The speed of this method was about 15 fps, which meets the requirement of button quality assurance.

Tab.1 Details of the test samples used in the experiments

sample	number of defect-free buttons	number of defective buttons	ROI size/pixel
sample1	51	104	233×233×3
sample2	60	39	199×199×3
sample3	151	189	167×167×3

The detailed detection rates are shown in Table 2. The saliency-based method showed a relatively high true positive rate but a low true negative rate because several defective samples were missed. The ICA-based method showed a good true negative rate but a low true positive rate because several normal regions were incorrectly identified as defects. In this experiment, the performance of our method was better than that of the two other methods in terms of the three detection rates.

Tab.2 Experimental performance of different methods

sample	saliency-based method		ICA-based method		proposed method
	TPR		TNR		R	TPR	TNR	R	TPR	TNR	R
sample1	0.92	0.72	0.78		0.78	0.91	0.87		0.94	0.94	0.94
sample2	0.98	0.51	0.79		0.77	0.87	0.81		0.98	1	0.99
sample3	0.95	0.71	0.82		0.75	0.86	0.81		0.97	0.99	0.98

3.1 Comparison of detection results

The defect detection results of samples 1–3 are shown in Fig. 4. In the experiment, the parameters were according to the recommendation of ICA-based [5] and saliency-based [8] methods. Figure 4(a) shows the detection results for sample1, in which printed words exist on the surfaces of buttons. The main defect types were printing deviation, stain, crack, and other defects. The defect points are shown in white in the binary images. The first column presents one defect-free and three defective samples. The second and third columns show the detection results of the saliency-based and ICA-based methods, respectively. The last column presents the detection results of the proposed method.

Figure 4(b) shows the detection results for sample2, which contains the fade-type defect. The first column presents one defect-free and two defective samples. The second and third columns show the detection results of the saliency-based and ICA-based methods, respectively. The results of the proposed method are shown in the last column. The proposed method performed better than the two other methods because the two methods ignored color information. This result suggests that color information contributes significantly to the detection of fade-type defects.

Figure 4(c) presents detection results for sample3, whose main defects consist of uneven surfaces and cracking. The first column shows one defect-free and three defective samples. The second column presents the detection results of saliency-based method. This method detected edge-type and dot-type defects effectively, given that these defects showed large salient values in the saliency detection step. Owing to the use of the search window strategy (SW), the method is robust to spatial misalignments. The third column shows detection results of the ICA-based method, which is sensitive to misalignment and has a high false alarm rate. The result of proposed method is shown in the last column. Our method is robust to spatial misalignments and has a high recognition rate.

Fig.4 Comparison of defect detection results on three samples by using different methods. (a)–(c) correspond to samples1–3. The first column contains the original images; the second and third columns show the results of saliency-based and ICA-based methods, respectively; and the last column presents the detection results of the proposed method

Full size|PPT slide

3.2 Tuning of training dataset size

An experiment was conducted to reveal the relationship between recognition rate and training dataset size. Figure 5 shows that the recognition rate increased as the size of the training dataset increased. However, the recognition rate decreased slightly when the number was over 40 with the increasing computation cost. Therefore, the appropriate size of the dataset was 40. Our training dataset was composed of eight buttons. Aside from the original images, another four deviation images were generated by each button by rotating at a small angle around the button center. From the view of linear regression, insufficient data may cause underfitting, and excessive data may cause overfitting. Both scenarios may cause poor performance in the test dataset.

Fig.5 Recognition rate versus training dataset sizes in samples 1–3

Full size|PPT slide

3.3 Effect of weight matrix on recognition rate

We adopted a weight matrix to reduce the false alarms caused by large residual errors near edges. An experiment was conducted to verify the effectiveness of the weight matrix. Three weight matrices were used.$W\propto \mathrm{diag}(\nabla I)$ indicates that the mean gradient intensity was used as weight matrix W in the proposed method. $W\propto \mathrm{diag}(\mathrm{1})$ indicates that W is an identity matrix, and all pixels have the same weight. $W\propto \mathrm{diag}(\exp (-\nabla I))$ indicates that pixels on edges have a smaller weight than non-edge pixels.

Table 3 shows that when $W\propto \mathrm{diag}(\nabla I)$ was used, the recognition rate improved. This result means that using the mean gradient intensity as the weight matrix can improve the recognition result.

Tab.3 Relationship between recognition rate and weight matrix W

	recognition rate
	sample1	sample2	sample3
$W\propto \mathrm{diag}(\nabla I)$	0.94	0.99	0.98
$W\propto \mathrm{diag}(\mathrm{1})$	0.88	0.93	0.95
$W\propto \mathrm{diag}(\exp (-\nabla I))$	0.75	0.78	0.89

3.4 Effect of parameter setting

The key parameters included in the segmentation with GMM consisted of control constant C and number of Gaussian components K. Sensitivity analyses of these parameters were conducted as follows.

3.4.1 Receiver operating characteristic (ROC) analysis of control constant C

Control constant C determines if a pixel belongs to a Gaussian component. A large control constant provides a loose control limit and may ignore several defects, whereas a small control constant provides a tight control limit and may generate false alarms. To verify the detection performance quantitatively, the ROC curve of control constant C was constructed by scanning the control constant from a small number (1.5) to a large number (10), as shown in Fig. 6. In this experiment, the parameters of the number of Gaussian components K were all set to 5.

Fig.6 Receiver operating characteristic curves of the proposed method

Full size|PPT slide

The ROC curve of the proposed method rises swiftly upward when C ranges from 1.5 to 4.5. In Fig. 6, when C is equal to 4, good performance is obtained with both low false positive and high true positive rates.

3.4.2 Analysis of Gaussian components K

Gaussian components K determines the ability to describe the distribution of the background. A large K can describe a highly complex distribution. If K is set to 1, the model degenerates into a single Gaussian model. According to Refs. [17,18], Kshould be set from 3 to 5. To further verify this point, we conducted experiments on samples 1–3 under different K values (from 1 to 5). Control constant C was set to 4. The experiment results are presented in Table 4. The table shows that setting K from 4 to 5 is appropriate, so K was set to 5 in the rest of our experiments.

Tab.4 Recognition rate versus number of Gaussian components K

Gaussian components K	recognition rate
	sample1	sample2	sample3
1	0.61	0.62	0.66
2	0.82	0.88	0.81
3	0.90	0.98	0.98
4	0.94	0.99	0.98
5	0.94	0.99	0.98

3.5 Effect of rotation deviation

In the proposed method, we rotated the buttons to the same orientation as that in the preprocessing step. Therefore, all buttons were coarsely aligned. However, small deviations still existed among the aligned buttons (most of the deviations were within 5° according to the experiment result). To evaluate the robustness of proposed method to this subtle rotation deviation, we generated another seven test sets by rotating the aligned images at angles ranging from 1° to 7° and evaluated the performances in the eight test sets. Figure 7 shows the performance in sample3 using the three methods. The saliency-based method and the proposed method presented a more stable performance than the ICA-based method, and the proposed method achieved a higher detection rate.

Fig.7 Comparison of the recognition rate of the proposed method with those of the saliency-based and ICA-based methods in terms of rotation deviation angles

Full size|PPT slide

3.6 Effect of illumination variation

To visualize defect detection performance, Fig. 8 shows the detection results for the same defective button under different illumination conditions. The first column presents the original inspection images under different illumination intensities ranging from 2500 to 4000 lx. The second column shows the reconstructed adaptive template images corresponding to the first column. The last column presents the binary results, which show that the four defective images can be detected steadily. In the experiment, illumination variation was equal to the effect of adding a light intensity offset to every pixel of the inspection image. The reason for the robustness to illumination variation is that to minimize the error function (Eq. (6)), the proposed method adjusts combination coefficients adaptively. As a result, it generates the most appropriate template image adaptively for each inspection image.

Fig.8 Effect of illumination variations. (a) Original inspection images under different illumination intensities (top to bottom correspond to 2500, 3000, 3500, and 4000 lx); (b) adaptive template images corresponding to the first column; and (c) binary results corresponding to the first column

Full size|PPT slide

To further evaluate the robustness of our method to illumination variation, we performed experiments with sample3 under different illuminations (ranging from 2500 to 4500 lx). In this experiment, the samples under 3500 lx were used as training datasets. Table 5 shows that the proposed method presents a good recognition rate in both under-exposed (2500 lx) and over-exposed (4500 lx) situations.

Tab.5 Recognition rates under different illuminations

illumination/lux	2500	3000	3500	4000	4500
recognition rates	0.93	0.96	0.98	0.97	0.93

4 Conclusions

Referential methods have been widely used in surface quality inspection, but these methods rely heavily on alignment accuracy. To address this problem, we proposed a defect detection method based on the weighted least-squares model to suppress the residual error near the image edge. The defects were segmented with GMM.

The proposed method can be applied successfully to different types of buttons. It does not require precise alignment and is robust to both illumination variation and rotation deviation. The method can be potentially applied to various patterned surfaces without high alignment accuracy. To further improve the detection rate, defect detection considering local texture features is worthy of further investigation.

References

[1] Novoselov K S, Geim A K, Morozov S V, Jiang D, Zhang Y, Dubonos S V, Grigorieva I V, Firsov A A. Electric field effect in atomically thin carbon films. Science, 2004, 306(5696): 666–669

[2] Peierls R. Quelques propriétés typiques des corps solides. Annales de l'Institut Henri Poincaré, 1935, 5(3): 177–222

[3] Landau L D. On the theory of phase transitions. Ukrainian Journal of Physical, 1937, 11: 19–32

[4] Mermin N D. Crystalline order in two dimensions. Physical Review, 1968, 176(1): 250–254

[5] Nelson D R, Peliti L. Fluctuations in membranes with crystalline and hexatic order. Journal de Physique (Paris), 1988, 49(1): 139

[6] Banszerus L, Schmitz M, Engels S, Goldsche M, Watanabe K, Taniguchi T, Beschoten B, Stampfer C. Ballistic transport exceeding 28 mm in CVD grown graphene. Nano Letters, 2016, 16(2): 1387–1391

[7] Mayorov A S, Gorbachev R V, Morozov S V, Britnell L, Jalil R, Ponomarenko L A, Blake P, Novoselov K S, Watanabe K, Taniguchi T, Geim A K. Micrometer-scale ballistic transport in encapsulated graphene at room temperature. Nano Letters, 2011, 11(6): 2396–2399

[8] Wang L, Meric I, Huang P Y, Gao Q, Gao Y, Tran H, Taniguchi T, Watanabe K, Campos L M, Muller D A, Guo J, Kim P, Hone J, Shepard K L, Dean C R. One-dimensional electrical contact to a two-dimensional material. Science, 2013, 342(6158): 614–617

[9] Hwang E H, Adam S, Sarma S D. Carrier transport in two-dimensional graphene layers. Physical Review Letters, 2007, 98(18): 186806

[10] Lee G H, Cooper R C, An S J, Lee S, van der Zande A, Petrone N, Hammerberg A G, Lee C, Crawford B, Oliver W, Kysar J W, Hone J. High-strength chemical-vapor-deposited graphene and grain boundaries. Science, 2013, 340(6136): 1073–1076

[11] Xu J, Yuan G, Zhu Q, Wang J, Tang S, Gao L. Enhancing the strength of graphene by a denser grain boundary. ACS Nano, 2018, 12(5): 4529–4535

[12] Lee C, Wei X, Kysar J W, Hone J. Measurement of the elastic properties and intrinsic strength of monolayer graphene. Science, 2008, 321(5887): 385–388

[13] Nair R R, Blake P, Grigorenko A N, Novoselov K S, Booth T J, Stauber T, Peres N M R, Geim A K. Fine structure constant defines visual transparency of graphene. Science, 2008, 320(5881): 1308

[14] Balandin A A. Thermal properties of graphene and nanostructured carbon materials. Nature Materials, 2011, 10(8): 569–581

[15] Zheng Q, Ip W H, Lin X, Yousefi N, Yeung K K, Li Z, Kim J K. Transparent conductive films consisting of ultralarge graphene sheets produced by Langmuir-Blodgett assembly. ACS Nano, 2011, 5(7): 6039–6051

[16] Pham V P, Mishra A, Young Yeom G. The enhancement of Hall mobility and conductivity of CVD graphene through radical doping and vacuum annealing. RSC Advances, 2017, 7(26): 16104–16108

[17] Suzuki S, Yoshimura M. Chemical stability of graphene coated silver substrates for surface-enhanced raman scattering. Scientific Reports, 2017, 7(1): 14851

[18] Pisana S, Lazzeri M, Casiraghi C, Novoselov K S, Geim A K, Ferrari A C, Mauri F. Breakdown of the adiabatic Born-Oppenheimer approximation in graphene. Nature Materials, 2007, 6(3): 198–201

[19] Novoselov K S, Jiang Z, Zhang Y, Morozov S V, Stormer H L, Zeitler U, Maan J C, Boebinger G S, Kim P, Geim A K. Room-temperature quantum Hall effect in graphene. Science, 2007, 315(5817): 1379

[20] Zhang Y, Tan Y W, Stormer H L, Kim P. Experimental observation of the quantum Hall effect and Berry’s phase in graphene. Nature, 2005, 438(7065): 201–204

[21] Bolotin K I, Ghahari F, Shulman M D, Stormer H L, Kim P. Observation of the fractional quantum Hall effect in graphene. Nature, 2009, 462(7270): 196–199

[22] Qiao Z, Yang S A, Feng W, Tse W K, Ding J, Yao Y, Wang J, Niu Q. Quantum anomalous Hall effect in graphene from Rashba and exchange effects. Physical Review B: Condensed Matter and Materials Physics, 2010, 82(16): 161414

[23] Levy D, Castellón E. Transparent Conductive Materials: Materials, Synthesis, Characterization, Applications. New York: John Wiley & Sons, 2018

[24] Hu Y, Diao X, Wang C, Hao W, Wang T. Effects of heat treatment on properties of ITO films prepared by rf magnetron sputtering. Vacuum, 2004, 75(2): 183–188

[25] Alzoubi K, Hamasha M M, Lu S, Sammakia B. Bending fatigue study of sputtered ITO on flexible substrate. Journal of Display Technology, 2011, 7(11): 593–600

[26] Im H G, Jeong S, Jin J, Lee J, Youn D Y, Koo W T, Kang S B, Kim H J, Jang J, Lee D, Kim H K, Kim I D, Lee J Y, Bae B S. Hybrid crystalline-ITO/metal nanowire mesh transparent electrodes and their application for highly flexible perovskite solar cells. NPG Asia Materials, 2016, 8(6): e282

[27] Park S H, Lee S J, Lee J H, Kal J, Hahn J, Kim H K. Large area roll-to-roll sputtering of transparent ITO/Ag/ITO cathodes for flexible inverted organic solar cell modules. Organic Electronics, 2016, 30: 112–121

[28] Novoselov K S, Geim A K, Morozov S V, Jiang D, Zhang Y, Dubonos S V, Grigorieva I V, Firsov A A. Electric field effect in atomically thin carbon films. Science, 2004, 306(5696): 666–669

[29] Suslick K S. Sonochemistry. Science, 1990, 247(4949): 1439–1445

[30] Yi M, Shen Z. A review on mechanical exfoliation for the scalable production of grapheme. Journal of Materials Chemistry A, Materials for Energy and Sustainability, 2015, 3(22): 11700–11715

[31] Chen X, Dobson J F, Raston C L. Vortex fluidic exfoliation of graphite and boron nitride. Chemical Communications (Cambridge), 2012, 48(31): 3703–3705

[32] Paton K R, Varrla E, Backes C, Smith R J, Khan U, O’Neill A, Boland C, Lotya M, Istrate O M, King P, Higgins T, Barwich S, May P, Puczkarski P, Ahmed I, Moebius M, Pettersson H, Long E, Coelho J, O’Brien S E, McGuire E K, Sanchez B M, Duesberg G S, McEvoy N, Pennycook T J, Downing C, Crossley A, Nicolosi V, Coleman J N. Scalable production of large quantities of defect-free few-layer graphene by shear exfoliation in liquids. Nature Materials, 2014, 13(6): 624–630

[33] Lin Z, Karthik P S, Hada M, Nishikawa T, Hayashi Y. Simple technique of exfoliation and dispersion of multilayer graphene from natural graphite by ozone-assisted sonication. Nanomaterials (Basel, Switzerland), 2017, 7(6): 125

[34] Blake P, Brimicombe P D, Nair R R, Booth T J, Jiang D, Schedin F, Ponomarenko L A, Morozov S V, Gleeson H F, Hill E W, Geim A K, Novoselov K S. Graphene-based liquid crystal device. Nano Letters, 2008, 8(6): 1704–1708

[35] Hernandez Y, Nicolosi V, Lotya M, Blighe F M, Sun Z, De S, McGovern I T, Holland B, Byrne M, Gun’Ko Y K, Boland J J, Niraj P, Duesberg G, Krishnamurthy S, Goodhue R, Hutchison J, Scardaci V, Ferrari A C, Coleman J N. High-yield production of graphene by liquid-phase exfoliation of graphite. Nature Nanotechnology, 2008, 3(9): 563–568

[36] Bunch J S, Yaish Y, Brink M, Bolotin K, McEuen P L. Coulomb oscillations and Hall effect in quasi-2D graphite quantum dots. Nano Letters, 2005, 5(2): 287–290

[37] Hernandez Y, Lotya M, Rickard D, Bergin S D, Coleman J N. Measurement of multicomponent solubility parameters for graphene facilitates solvent discovery. Langmuir, 2010, 26(5): 3208–3213

[38] Bergin S D, Nicolosi V, Streich P V, Giordani S, Sun Z, Windle A H, Ryan P, Niraj N P P, Wang Z T T, Carpenter L, Blau W J, Boland J J, Hamilton J P, Coleman J N. Towards solutions of single-walled carbon nanotubes in common solvents. Advanced Materials, 2008, 20(10): 1876–1881

[39] Coleman J N. Liquid-phase exfoliation of nanotubes and graphene. Advanced Functional Materials, 2009, 19(23): 3680–3695

[40] Liang Y T, Hersam M C. Highly concentrated graphene solutions via polymer enhanced solvent exfoliation and iterative solvent exchange. Journal of the American Chemical Society, 2010, 132(50): 17661–17663

[41] Park K H, Kim B H, Song S H, Kwon J, Kong B S, Kang K, Jeon S. Exfoliation of non-oxidized graphene flakes for scalable conductive film. Nano Letters, 2012, 12(6): 2871–2876

[42] Tomašević-Ilić T, Pešić J, Milošević I, Vujin J, Matković A, Spasenović M, Gajić R. Transparent and conductive films from liquid phase exfoliated graphene. Optical and Quantum Electronics, 2016, 48(6): 319

[43] Majee S, Song M, Zhang S L, Zhang Z B. Scalable inkjet printing of shear-exfoliated graphene transparent conductive films. Carbon, 2016, 102: 51–57

[44] Narayan R, Kim S O. Surfactant mediated liquid phase exfoliation of graphene. Nano Convergence, 2015, 2(1): 20

[45] Liu N, Luo F, Wu H, Liu Y, Zhang C, Chen J. One-step ionic-liquid-assisted electrochemical synthesis of ionic-liquid-functionalized graphene sheets directly from graphite. Advanced Functional Materials, 2008, 18(10): 1518–1525

[46] Khan U, O’Neill A, Lotya M, De S, Coleman J N. High-concentration solvent exfoliation of graphene. Small, 2010, 6(7): 864–871

[47] Li J, Yan H, Dang D, Wei W, Meng L. Salt and water co-assisted exfoliation of graphite in organic solvent for efficient and large scale production of high-quality graphene. Journal of Colloid and Interface Science, 2019, 535: 92–99

[48] Zhang M, Parajuli R R, Mastrogiovanni D, Dai B, Lo P, Cheung W, Brukh R, Chiu P L, Zhou T, Liu Z, Garfunkel E, He H. Production of graphene sheets by direct dispersion with aromatic healing agents. Small, 2010, 6(10): 1100–1107

[49] Liu L, Rim K T, Eom D, Heinz T F, Flynn G W. Direct observation of atomic scale graphitic layer growth. Nano Letters, 2008, 8(7): 1872–1878

[50] Tung T T, Yoo J, Alotaibi F K, Nine M J, Karunagaran R, Krebsz M, Nguyen G T, Tran D N H, Feller J F, Losic D. Graphene oxide-assisted liquid phase exfoliation of graphite into graphene for highly conductive film and electromechanical sensors. ACS Applied Materials & Interfaces, 2016, 8(25): 16521–16532

[51] Majee S, Song M, Zhang S L, Zhang Z B. Scalable inkjet printing of shear-exfoliated graphene transparent conductive films. Carbon, 2016, 102: 51–57

[52] Shin D W, Barnes M D, Walsh K, Dimov D, Tian P, Neves A I S, Wright C D, Yu S M, Yoo J B, Russo S, Craciun M F. A new facile route to flexible and semi-transparent electrodes based on water exfoliated graphene and their single-electrode triboelectric nanogenerator. Advanced Materials, 2018, 30(39): 1802953

[53] Fukushima T, Aida T. Ionic liquids for soft functional materials with carbon nanotubes. Chemistry (Weinheim an der Bergstrasse, Germany), 2007, 13(18): 5048–5058

[54] Su C Y, Lu A Y, Xu Y, Chen F R, Khlobystov A N, Li L J. High-quality thin graphene films from fast electrochemical exfoliation. ACS Nano, 2011, 5(3): 2332–2339

[55] Liu J, Notarianni M, Will G, Tiong V T, Wang H, Motta N. Electrochemically exfoliated graphene for electrode films: effect of graphene flake thickness on the sheet resistance and capacitive properties. Langmuir, 2013, 29(43): 13307–13314

[56] Parvez K, Wu Z S, Li R, Liu X, Graf R, Feng X, Müllen K. Exfoliation of graphite into graphene in aqueous solutions of inorganic salts. Journal of the American Chemical Society, 2014, 136(16): 6083–6091

[57] Zhang Y, Xu Y. Simultaneous electrochemical dual-electrode exfoliation of graphite toward scalable production of high-quality graphene. Advanced Functional Materials, 2019, 29(37): 1902171

[58] Roscher S, Hoffmann R, Prescher M, Knittel P, Ambacher O. High voltage electrochemical exfoliation of graphite for high-yield graphene production. RSC Advances, 2019, 9: 29305–29311

[59] Hummers W S Jr, Offeman R E. Preparation of graphitic oxide. Journal of the American Chemical Society, 1958, 80(6): 1339

[60] Hirata M, Gotou T, Horiuchi S, Fujiwara M, Ohba M. Thin-film particles of graphite oxide 1: high-yield synthesis and flexibility of the particles. Carbon, 2004, 42(14): 2929–2937

[61] Shahriary L, Athawale A A. Graphene oxide synthesized by using modified hummers approach. International Journal of Renewable Energy and Environmental Engineering, 2014, 2(1): 58–63

[62] Dreyer D R, Todd A D, Bielawski C W. Harnessing the chemistry of graphene oxide. Chemical Society Reviews, 2014, 43(15): 5288–5301

[63] Mattevi C, Eda G, Agnoli S, Miller S, Mkhoyan K A, Celik O, Mastrogiovanni D, Granozzi G, Garfunkel E, Chhowalla M. Evolution of electrical, chemical, and structural properties of transparent and conducting chemically derived graphene thin films. Advanced Functional Materials, 2009, 19(16): 2577–2583

[64] Wang S J, Geng Y, Zheng Q, Kim J K. Fabrication of highly conducting and transparent graphene films. Carbon, 2010, 48(6): 1815–1823

[65] Geng J, Jung H T. Porphyrin functionalized graphene sheets in aqueous suspensions: from the preparation of graphene sheets to highly conductive graphene films. Journal of Physical Chemistry C, 2010, 114(18): 8227–8234

[66] Pham V H, Cuong T V, Hur S H, Shin E W, Kim J S, Chung J S, Kim E J. Fast and simple fabrication of a large transparent chemically-converted graphene film by spray-coating. Carbon, 2010, 48(7): 1945–1951

[67] Alahbakhshi M, Fallahi A, Mohajerani E, Fathollahi M R, Taromi F A, Shahinpoor M. High-performance Bi-stage process in reduction of graphene oxide for transparent conductive electrodes. Optical Materials, 2017, 64: 366–375

[68] Becerril H A, Mao J, Liu Z, Stoltenberg R M, Bao Z, Chen Y. Evaluation of solution-processed reduced graphene oxide films as transparent conductors. ACS Nano, 2008, 2(3): 463–470

[69] Wang J, Liang M, Fang Y, Qiu T, Zhang J, Zhi L. Rod-coating: towards large-area fabrication of uniform reduced graphene oxide films for flexible touch screens. Advanced Materials, 2012, 24(21): 2874–2878

[70] De S, Coleman J N. Are there fundamental limitations on the sheet resistance and transmittance of thin graphene films? ACS Nano, 2010, 4(5): 2713–2720

[71] Lotya M, Hernandez Y, King P J, Smith R J, Nicolosi V, Karlsson L S, Blighe F M, De S, Wang Z, McGovern I T, Duesberg G S, Coleman J N. Liquid phase production of graphene by exfoliation of graphite in surfactant/water solutions. Journal of the American Chemical Society, 2009, 131(10): 3611–3620

[72] De S, King P J, Lotya M, O’Neill A, Doherty E M, Hernandez Y, Duesberg G S, Coleman J N. Flexible, transparent, conducting films of randomly stacked graphene from surfactant-stabilized, oxide-free graphene dispersions. Small, 2010, 6(3): 458–464

[73] Au C T, Ng C F, Liao M S. Methane dissociation and syngas formation on Ru, Os, Rh, Ir, Pd, Pt, Cu, Ag, and Au: a theoretical study. Journal of Catalysis, 1999, 185(1): 12–22

[74] Nandamuri G, Roumimov S, Solanki R. Chemical vapor deposition of graphene films. Nanotechnology, 2010, 21(14): 145604

[75] An H, Lee W J, Jung J. Graphene synthesis on Fe foil using thermal CVD. Current Applied Physics, 2011, 11(4): S81–S85

[76] Cushing G W, Johánek V, Navin J K, Harrison I. Graphene growth on Pt(111) by ethylene chemical vapor deposition at surface temperatures near 1000 K. Journal of Physical Chemistry C, 2015, 119(9): 4759–4768

[77] Imamura G, Saiki K. Synthesis of nitrogen-doped graphene on Pt(111) by chemical vapor deposition. Journal of Physical Chemistry C, 2011, 115(20): 10000–10005

[78] Zhao L, Rim K T, Zhou H, He R, Heinz T F, Pinczuk A, Flynn G W, Pasupathy A N. Influence of copper crystal surface on the CVD growth of large area monolayer graphene. Solid State Communications, 2011, 151(7): 509–513

[79] Sun Z, Yan Z, Yao J, Beitler E, Zhu Y, Tour J M. Growth of graphene from solid carbon sources. Nature, 2010, 468(7323): 549–552

[80] Virojanadara C, Syväjarvi M, Yakimova R, Johansson L I, Zakharov A A, Balasubramanian T. Homogeneous large-area graphene layer growth on 6 H-SiC(0001). Physical Review B: Condensed Matter and Materials Physics, 2008, 78(24): 245403

[81] Wassei J K, Mecklenburg M, Torres J A, Fowler J D, Regan B C, Kaner R B, Weiller B H. Chemical vapor deposition of graphene on copper from methane, ethane and propane: evidence for bilayer selectivity. Small, 2012, 8(9): 1415–1422

[82] Wan X, Chen K, Liu D, Chen J, Miao Q, Xu J. High-quality large-area graphene from dehydrogenated polycyclic aromatic hydrocarbons. Chemistry of Materials, 2012, 24(20): 3906–3915

[83] Backes C, Abdelkader A M, Alonso C, Andrieux-Ledier A, Arenal R, Azpeitia J, Balakrishnan N, Banszerus L, Barjon J, Bartali R, Bellani S, Berger C, Berger R, Bernal Ortega M M, Bernard C, Beton P H, Beyer A, Bianco A, Bøggild P, Bonaccorso F, Barin G B, Botas C, Bueno R A, Carriazo D, Castellanos-Gomez A, Christian M, Ciesielski A, Ciuk T, Cole M T, Coleman J, Coletti C, Crema L, Cun H, Dasler D, Fazio D D, Díez N, Drieschner S, Duesberg G S, Fasel R, Feng X, Fina A, Forti S, Galiotis C, Garberoglio G, García J M, Garrido J A, Gibertini M, Gölzhäuser A, Gómez J, Greber T, Hauke F, Hemmi A, Hernandez-Rodriguez I, Hirsch A, Hodge S A, Huttel Y, Jepsen P U, Jimenez I, Kaiser U, Kaplas T, Kim H, Kis A, Papagelis K, Kostarelos K, Krajewska A, Lee K, Li C, Lipsanen H, Liscio A, Lohe M R, Loiseau A, Lombardi L, López M F, Martin O, Martín C, Martínez L, Martin-Gago J A, Martínez J I, Marzari N, Mayoral A, Melucci M J, Méndez J, Merino C, Merino P, Meyer A P, Miniussi E, Miseikis V, Mishra N, Morandi V, Munuera C, Muñoz R, Nolan H, Ortolani L, Ott A K, Palacio I, Palermo V, Parthenios J, Pasternak I, Patane A, Prato M, Prevost H, Prudkovskiy V, Pugno N, Rojo T, Rossi A, Ruffieux P, Samorì P, Schué L, Setijadi E, Seyller T, Speranza G, Stampfer C, Stenger I, Strupinski W, Svirko Y, Taioli S, Teo K B K, Testi M, Tomarchio F, Tortello M, Treossi E, Turchanin A, Vazquez E, Villaro E, Whelan P R, Xia Z, Yakimova R, Yang S, Yazdi G R, Yim C, Yoon D, Zhang X, Zhuang X, Colombo L, Ferrari A C, Garcia-Hernandez M. Production and processing of graphene and related materials. 2D Materials, 2020, 7(2): 022001

[84] Losurdo M, Giangregorio M M, Capezzuto P, Bruno G. Graphene CVD growth on copper and nickel: role of hydrogen in kinetics and structure. Physical Chemistry Chemical Physics, 2011, 13(46): 20836–20843

[85] Gajewski G, Pao C W. Ab initio calculations of the reaction pathways for methane decomposition over the Cu (111) surface. Journal of Chemical Physics, 2011, 135(6): 064707

[86] Kim H, Mattevi C, Calvo M R, Oberg J C, Artiglia L, Agnoli S, Hirjibehedin C F, Chhowalla M, Saiz E. Activation energy paths for graphene nucleation and growth on Cu. ACS Nano, 2012, 6(4): 3614–3623

[87] Xing S, Wu W, Wang Y, Bao J, Pei S S. Kinetic study of graphene growth: temperature perspective on growth rate and film thickness by chemical vapor deposition. Chemical Physics Letters, 2013, 580: 62–66

[88] Colombo L, Li X, Han B, Magnuson C, Cai W, Zhu Y, Ruoff R S. Growth kinetics and defects of CVD graphene on Cu. ECS Transactions, 2010, 28: 109–114

[89] Hao Y, Bharathi M S, Wang L, Liu Y, Chen H, Nie S, Wang X, Chou H, Tan C, Fallahazad B, Ramanarayan H, Magnuson C W, Tutuc E, Yakobson B I, McCarty K F, Zhang Y W, Kim P, Hone J, Colombo L, Ruoff R S. The role of surface oxygen in the growth of large single-crystal graphene on copper. Science, 2013, 342(6159): 720–723

[90] Shu H, Chen X, Tao X, Ding F. Edge structural stability and kinetics of graphene chemical vapor deposition growth. ACS Nano, 2012, 6(4): 3243–3250

[91] Shibuta Y, Arifin R, Shimamura K, Oguri T, Shimojo F, Yamaguchi S. Low reactivity of methane on copper surface during graphene synthesis via CVD process: Ab initio molecular dynamics simulation. Chemical Physics Letters, 2014, 610–611: 33–38

[92] Liao M S, Au C T, Ng C F. Methane dissociation on Ni, Pd, Pt and Cu metal (111) surfaces—a theoretical comparative study. Chemical Physics Letters, 1997, 272(5–6): 445–452

[93] Guéret C, Daroux M, Billaud F. Methane pyrolysis: thermodynamics. Chemical Engineering Science, 1997, 52(5): 815–827

[94] Viñes F, Lykhach Y, Staudt T, Lorenz M P A, Papp C, Steinrück H P, Libuda J, Neyman K M, Görling A. Methane activation by platinum: critical role of edge and corner sites of metal nanoparticles. Chemistry (Weinheim an der Bergstrasse, Germany), 2010, 16(22): 6530–6539

[95] Loginova E, Bartelt N C, Feibelman P J, McCarty K F. Evidence for graphene growth by C cluster attachment. New Journal of Physics, 2008, 10(9): 093026

[96] Loginova E, Bartelt N C, Feibelman P J, McCarty K F. Factors influencing graphene growth on metal surfaces. New Journal of Physics, 2009, 11(6): 063046

[97] López G A, Mittemeijer E J. The solubility of C in solid Cu. Scripta Materialia, 2004, 51(1): 1–5

[98] Kim K S, Zhao Y, Jang H, Lee S Y, Kim J M, Kim K S, Ahn J H, Kim P, Choi J Y, Hong B H. Large-scale pattern growth of graphene films for stretchable transparent electrodes. Nature, 2009, 457(7230): 706–710

[99] Cai W, Piner R D, Zhu Y, Li X, Tan Z, Floresca H C, Yang C, Lu L, Kim M J, Ruoff R S. Synthesis of isotopically-labeled graphite films by cold-wall chemical vapor deposition and electronic properties of graphene obtained from such films. Nano Research, 2009, 2(11): 851–856

[100] Wu P, Zhang W, Li Z, Yang J. Mechanisms of graphene growth on metal surfaces: theoretical perspectives. Small, 2014, 10(11): 2136–2150

[101] Huang L, Chang Q H, Guo G L, Liu Y, Xie Y Q, Wang T, Ling B, Yang H F. Synthesis of high-quality graphene films on nickel foils by rapid thermal chemical vapor deposition. Carbon, 2012, 50(2): 551–556

[102] Li H B, Page A J, Wang Y, Irle S, Morokuma K. Sub-surface nucleation of graphene precursors near a Ni(111) step-edge. Chemical Communications (Cambridge), 2012, 48(64): 7937–7939

[103] Verma V P, Das S, Lahiri I, Choi W. Large-area graphene on polymer film for flexible and transparent anode in field emission device. Applied Physics Letters, 2010, 96(20): 203108

[104] Kalita G, Matsushima M, Uchida H, Wakita K, Umeno M. Graphene constructed carbon thin films as transparent electrodes for solar cell applications. Journal of Materials Chemistry, 2010, 20(43): 9713–9717

[105] Nagai Y, Sugime H, Noda S. 1.5 Minute-synthesis of continuous graphene films by chemical vapor deposition on Cu foils rolled in three dimensions. Chemical Engineering Science, 2019, 201: 319–324

[106] Bae S, Kim H, Lee Y, Xu X, Park J S, Zheng Y, Balakrishnan J, Lei T, Kim H R, Song Y I, Kim Y J, Kim K S, Ozyilmaz B, Ahn J H, Hong B H, Iijima S. Roll-to-roll production of 30-inch graphene films for transparent electrodes. Nature Nanotechnology, 2010, 5(8): 574–578

[107] Kim Y, Kim S, Lee W H, Kim H.Direct transfer of CVD-grown graphene onto eco-friendly cellulose film for highly sensitive gas sensor. Cellulose, 2020, 27: 1685–1693

[108] Kim M, Shah A, Li C, Mustonen P, Susoma J, Manoocheri F, Riikonen J, Lipsanen H. Direct transfer of wafer-scale graphene films. 2D Materials, 2017, 4(3): 035004

[109] Park I J, Kim T I, Yoon T, Kang S, Cho H, Cho N S, Lee J I, Kim T S, Choi S Y. Flexible and transparent graphene electrode architecture with selective defect decoration for organic light-emitting diodes. Advanced Functional Materials, 2018, 28(10): 1704435

[110] Liang X, Sperling B A, Calizo I, Cheng G, Hacker C A, Zhang Q, Obeng Y, Yan K, Peng H, Li Q, Zhu X, Yuan H, Walker A R, Liu Z, Peng L M, Richter C A. Toward clean and crackless transfer of graphene. ACS Nano, 2011, 5(11): 9144–9153

[111] Li X, Zhu Y, Cai W, Borysiak M, Han B, Chen D, Piner R D, Colombo L, Ruoff R S. Transfer of large-area graphene films for high-performance transparent conductive electrodes. Nano Letters, 2009, 9(12): 4359–4363

[112] Gammelgaard L, Caridad J M, Cagliani A, Mackenzie D M A, Petersen D H, Booth T J, Bøggild P. Graphene transport properties upon exposure to PMMA processing and heat treatments. 2D Materials, 2014, 1(3): 035005

[113] Chan J, Venugopal A, Pirkle A, McDonnell S, Hinojos D, Magnuson C W, Ruoff R S, Colombo L, Wallace R M, Vogel E M. Reducing extrinsic performance-limiting factors in graphene grown by chemical vapor deposition. ACS Nano, 2012, 6(4): 3224–3229

[114] Lin Y C, Lu C C, Yeh C H, Jin C, Suenaga K, Chiu P W. Graphene annealing: how clean can it be? Nano Letters, 2012, 12(1): 414–419

[115] Zhang Z, Du J, Zhang D, Sun H, Yin L, Ma L, Chen J, Ma D, Cheng H M, Ren W. Rosin-enabled ultraclean and damage-free transfer of graphene for large-area flexible organic light-emitting diodes. Nature Communications, 2017, 8(1): 14560

[116] Lin Y C, Jin C, Lee J C, Jen S F, Suenaga K, Chiu P W. Clean transfer of graphene for isolation and suspension. ACS Nano, 2011, 5(3): 2362–2368

[117] Kang M H, Prieto López L O, Chen B, Teo K, Williams J A, Milne W I, Cole M T. Mechanical robustness of graphene on flexible transparent substrates. ACS Applied Materials & Interfaces, 2016, 8(34): 22506–22515

[118] Song J, Kam F Y, Png R Q, Seah W L, Zhuo J M, Lim G K, Ho P K H, Chua L L. A general method for transferring graphene onto soft surfaces. Nature Nanotechnology, 2013, 8(5): 356–362

[119] Yoon J C, Thiyagarajan P, Ahn H J, Jang J H. A case study: effect of defects in CVD-grown graphene on graphene enhanced Raman spectroscopy. RSC Advances, 2015, 5(77): 62772–62777

[120] Qin L, Kattel B, Kafle T R, Alamri M, Gong M, Panth M, Hou Y, Wu J, Chan W. Scalable graphene-on-organometal halide perovskite heterostructure fabricated by dry transfer. Advanced Materials Interfaces, 2019, 6(1): 1801419

[121] Chandrashekar B N, Deng B, Smitha A S, Chen Y, Tan C, Zhang H, Peng H, Liu Z. Roll-to-roll green transfer of CVD graphene onto plastic for a transparent and flexible triboelectric nanogenerator. Advanced Materials, 2015, 27(35): 5210–5216

[122] Marchena M, Wagner F, Arliguie T, Zhu B, Johnson B, Fernández M, Chen T L, Chang T, Lee R, Pruneri V, Mazumder P. Dry transfer of graphene to dielectrics and flexible substrates using polyimide as a transparent and stable intermediate layer. 2D Materials, 2018, 5(3): 035022

[123] Shivayogimath A, Whelan P R, Mackenzie D M A, Luo B, Huang D, Luo D, Wang M, Gammelgaard L, Shi H, Ruoff R S, Bøggild P, Booth T J. Do-it-yourself transfer of large-area graphene using an office laminator and water. Chemistry of Materials, 2019, 31(7): 2328–2336

[124] Kang J, Hwang S, Kim J H, Kim M H, Ryu J, Seo S J, Hong B H, Kim M K, Choi J B. Efficient transfer of large-area graphene films onto rigid substrates by hot pressing. ACS Nano, 2012, 6(6): 5360–5365

[125] Fechine G J M, Martin-Fernandez I, Yiapanis G, Bentini R, Kulkarni E S, Bof de Oliveira R V, Hu X, Yarovsky I, Castro Neto A H, Özyilmaz B. Direct dry transfer of chemical vapor deposition graphene to polymeric substrates. Carbon, 2015, 83: 224–231

[126] Cherian C T, Giustiniano F, Martin-Fernandez I, Andersen H, Balakrishnan J, Özyilmaz B. ‘Bubble-free’ electrochemical delamination of CVD graphene films. Small, 2015, 11(2): 189–194

[127] Wang Y, Zheng Y, Xu X, Dubuisson E, Bao Q, Lu J, Loh K P. Electrochemical delamination of CVD-grown graphene film: toward the recyclable use of copper catalyst. ACS Nano, 2011, 5(12): 9927–9933

[128] Pizzocchero F, Jessen B S, Whelan P R, Kostesha N, Lee S, Buron J D, Petrushina I, Larsen M B, Greenwood P, Cha W J, Teo K, Jepsen P U, Hone J, Bøggild P, Booth T J. Non-destructive electrochemical graphene transfer from reusable thin-film catalysts. Carbon, 2015, 85: 397–405

[129] Zhan Z, Sun J, Liu L, Wang E, Cao Y, Lindvall N, Skoblin G, Yurgens A. Pore-free bubbling delamination of chemical vapor deposited graphene from copper foils. Journal of Materials Chemistry C, Materials for Optical and Electronic Devices, 2015, 3(33): 8634

[130] Sun J, Chen Y, Cai X, Ma B, Chen Z, Priydarshi M K, Chen K, Gao T, Song X, Ji Q, Guo X, Zou D, Zhang Y, Liu Z. Direct low-temperature synthesis of graphene on various glasses by plasma-enhanced chemical vapor deposition for versatile, cost-effective electrodes. Nano Research, 2015, 8(11): 3496–3504

[131] Wei D, Peng L, Li M, Mao H, Niu T, Han C, Chen W, Wee A T S. Low temperature critical growth of high quality nitrogen doped graphene on dielectrics by plasma-enhanced chemical vapor deposition. ACS Nano, 2015, 9(1): 164–171

[132] Zheng S, Zhong G, Wu X, D’Arsiè L, Robertson J. Metal-catalyst-free growth of graphene on insulating substrates by ammonia-assisted microwave plasma-enhanced chemical vapor deposition. RSC Advances, 2017, 7: 33185–33193

[133] Schmidt M E, Xu C, Cooke M, Mizuta H, Chong H M H. Metal-free plasma-enhanced chemical vapor deposition of large area nanocrystalline grapheme. Materials Research Express, 2014, 1(2): 025031

[134] Wei N, Li Q, Cong S, Ci H, Song Y, Yang Q, Lu C, Li C, Zou G, Sun J, Zhang Y, Liu Z. Direct synthesis of flexible graphene glass with macroscopic uniformity enabled by copper-foam-assisted PECVD. Journal of Materials Chemistry A, Materials for Energy and Sustainability, 2019, 7(9): 4813–4822

[135] Chen Z, Liu Y, Zhang W, Guo X, Yin L, Wang Y, Li L, Zhang Y, Wang Z, Zhang T. Growth of graphene/Ag nanowire/graphene sandwich films for transparent touch-sensitive electrodes. Materials Chemistry and Physics, 2019, 221: 78–88

[136] Vishwakarma R, Zhu R, Abuelwafa A A, Mabuchi Y, Adhikari S, Ichimura S, Soga T, Umeno M. Direct synthesis of large-area graphene on insulating substrates at low temperature using microwave plasma CVD. ACS Omega, 2019, 4(6): 11263–11270

[137] Park B J, Choi J S, Eom J H, Ha H, Kim H Y, Lee S, Shin H, Yoon S G. Defect-free graphene synthesized directly at 150°C via chemical vapor deposition with no transfer. ACS Nano, 2018, 12(2): 2008–2016

[138] Tran V D, Pammi S V N, Park B J, Han Y, Jeon C, Yoon S G. Transfer-free graphene electrodes for super-flexible and semi-transparent perovskite solar cells fabricated under ambient air. Nano Energy, 2019, 65: 104018

[139] Kwon K C, Kim B J, Lee J L, Kim S Y. Effect of anions in Au complexes on doping and degradation of graphene. Journal of Materials Chemistry C, Materials for Optical and Electronic Devices, 2013, 1(13): 2463–2469

[140] Jang C W, Kim J M, Kim J H, Shin D H, Kim S, Choi S H. Degradation reduction and stability enhancement of p-type graphene by RhCl₃ doping. Journal of Alloys and Compounds, 2015, 621: 1–6

[141] Bult J B, Crisp R, Perkins C L, Blackburn J L. Role of dopants in long-range charge carrier transport for p-type and n-type graphene transparent conducting thin films. ACS Nano, 2013, 7(8): 7251–7261

[142] Liu H, Liu Y, Zhu D. Chemical doping of graphene. Journal of Materials Chemistry, 2011, 21(10): 3335–3345

[143] Chae M S, Lee T H, Son K R, Kim Y W, Hwang K S, Kim T G. Electrically-doped CVD-graphene transparent electrodes: application in 365 nm light-emitting diodes. Nanoscale Horizons, 2019, 4(3): 610–618

[144] Zhang X, Hsu A, Wang H, Song Y, Kong J, Dresselhaus M S, Palacios T. Impact of chlorine functionalization on high-mobility chemical vapor deposition grown graphene. ACS Nano, 2013, 7(8): 7262–7270

[145] Gomez De Arco L, Zhang Y, Schlenker C W, Ryu K, Thompson M E, Zhou C. Continuous, highly flexible, and transparent graphene films by chemical vapor deposition for organic photovoltaics. ACS Nano, 2010, 4(5): 2865–2873

[146] Reina A, Jia X, Ho J, Nezich D, Son H, Bulovic V, Dresselhaus M S, Kong J. Large area, few-layer graphene films on arbitrary substrates by chemical vapor deposition. Nano Letters, 2009, 9(1): 30–35

[147] Zan R, Altuntepe A. Nitrogen doping of graphene by CVD. Journal of Molecular Structure, 2020, 1199: 127026

[148] Kim K K, Reina A, Shi Y, Park H, Li L J, Lee Y H, Kong J. Enhancing the conductivity of transparent graphene films via doping. Nanotechnology, 2010, 21(28): 285205

[149] Bi H, Huang F, Liang J, Xie X, Jiang M. Transparent conductive graphene films synthesized by ambient pressure chemical vapor deposition used as the front electrode of CdTe solar cells. Advanced Materials, 2011, 23(28): 3202–3206

[150] Guo C, Kong X, Ji H. Hot-roll-pressing mediated transfer of chemical vapor deposition graphene for transparent and flexible touch screen with low sheet-resistance. Journal of Nanoscience and Nanotechnology, 2018, 18(6): 4337–4342

[151] Chang J H, Lin W H, Wang P C, Taur J I, Ku T A, Chen W T, Yan S J, Wu C I. Solution-processed transparent blue organic light-emitting diodes with graphene as the top cathode. Scientific Reports, 2015, 5(1): 9693

[152] Tongay S, Berke K, Lemaitre M, Nasrollahi Z, Tanner D B, Hebard A F, Appleton B R. Stable hole doping of graphene for low electrical resistance and high optical transparency. Nanotechnology, 2011, 22(42): 425701

[153] Xu S C, Man B Y, Jiang S Z, Chen C S, Yang C, Liu M, Gao X G, Sun Z C, Zhang C. Flexible and transparent graphene-based loudspeakers. Applied Physics Letters, 2013, 102(15): 151902

[154] Park H, Rowehl J A, Kim K K, Bulovic V, Kong J. Doped graphene electrodes for organic solar cells. Nanotechnology, 2010, 21(50): 505204

[155] Galagan Y, Mescheloff A, Veenstra S C, Andriessen R, Katz E A. Reversible degradation in ITO-containing organic photovoltaics under concentrated sunlight. Physical Chemistry Chemical Physics, 2015, 17(5): 3891–3897

[156] Chochos C L, Spanos M, Katsouras A, Tatsi E, Drakopoulou S, Gregoriou V G, Avgeropoulos A. Current status, challenges and future outlook of high performance polymer semiconductors for organic photovoltaics modules. Progress in Polymer Science, 2019, 91: 51–79

[157] Zhao J, Li Y, Yang G, Jiang K, Lin H, Ade H, Ma W, Yan H. Efficient organic solar cells processed from hydrocarbon solvents. Nature Energy, 2016, 1(2): 15027

[158] Zhao W, Li S, Zhang S, Liu X, Hou J. Ternary polymer solar cells based on two acceptors and one donor for achieving 12.2% efficiency. Advanced Materials, 2017, 29(2): 1604059

[159] Sun C, Pan F, Bin H, Zhang J, Xue L, Qiu B, Wei Z, Zhang Z G, Li Y. A low cost and high performance polymer donor material for polymer solar cells. Nature Communications, 2018, 9(1): 743

[160] Nogay G, Sahli F, Werner J, Monnard R, Boccard M, Despeisse M, Haug F J, Jeangros Q, Ingenito A, Ballif C. 25.1%-efficient monolithic perovskite/silicon tandem solar cell based on a p-type monocrystalline textured silicon wafer and high-temperature passivating contacts. ACS Energy Letters, 2019, 4(4): 844–845

[161] La Notte L, Bianco G V, Palma A L, Di Carlo A, Bruno G, Reale A. Sprayed organic photovoltaic cells and mini-modules based on chemical vapor deposited graphene as transparent conductive electrode. Carbon, 2018, 129: 878–883

[162] Park H, Chang S, Zhou X, Kong J, Palacios T, Gradečak S. Flexible graphene electrode-based organic photovoltaics with record-high efficiency. Nano Letters, 2014, 14(9): 5148–5154

[163] Liu J, Durstock M, Dai L. Graphene oxide derivatives as hole- and electron-extraction layers for high-performance polymer solar cells. Energy & Environmental Science, 2014, 7(4): 1297–1306

[164] Capasso A, Salamandra L, Faggio G, Dikonimos T, Buonocore F, Morandi V, Ortolani L, Lisi N. Chemical vapor deposited graphene-based derivative as high-performance hole transport material for organic photovoltaics. ACS Applied Materials & Interfaces, 2016, 8(36): 23844–23853

[165] Mackenzie D M A, Buron J D, Whelan P R, Jessen B S, Silajdźić A, Pesquera A, Centeno A, Zurutuza A, Bøggild P, Petersen D H. Fabrication of CVD graphene-based devices via laser ablation for wafer-scale characterization. 2D Materials, 2015, 2(4): 045003

[166] La Notte L, Villari E, Palma A L, Sacchetti A, Michela Giangregorio M, Bruno G, Di Carlo A, Bianco G V, Reale A. Laser-patterned functionalized CVD-graphene as highly transparent conductive electrodes for polymer solar cells. Nanoscale, 2017, 9(1): 62–69

[167] Gomez De Arco L, Zhang Y, Schlenker C W, Ryu K, Thompson M E, Zhou C. Continuous, highly flexible, and transparent graphene films by chemical vapor deposition for organic photovoltaics. ACS Nano, 2010, 4(5): 2865–2873

[168] Park H, Howden R M, Barr M C, Bulović V, Gleason K, Kong J. Organic solar cells with graphene electrodes and vapor printed poly(3,4-ethylenedioxythiophene) as the hole transporting layers. ACS Nano, 2012, 6(7): 6370–6377

[169] Lee B H, Lee J H, Kahng Y H, Kim N, Kim Y J, Lee J, Lee T, Lee K. Graphene-conducting polymer hybrid transparent electrodes for efficient organic optoelectronic devices. Advanced Functional Materials, 2014, 24(13): 1847–1856

[170] La Notte L, Cataldi P, Ceseracciu L, Bayer I S, Athanassiou A, Marras S, Villari E, Brunetti F, Reale A. Fully-sprayed flexible polymer solar cells with a cellulose-graphene electrode. Materials Today Energy, 2018, 7: 105–112

[171] Rezaei B, Afshar-Taromi F, Ahmadi Z, Amiri Rigi S, Yousefi N. Enhancement of power conversion efficiency of bulk heterojunction polymer solar cells using core/shell, Au/graphene plasmonic nanostructure. Materials Chemistry and Physics, 2019, 228: 325–335

[172] Mahakul P C, Sa K, Das B, Subramaniam B V R S, Saha S, Moharana B, Raiguru J, Dash S, Mukherjee J, Mahanandia P. Preparation and characterization of PEDOT:PSS/reduced graphene oxide–carbon nanotubes hybrid composites for transparent electrode applications. Journal of Materials Science, 2017, 52(10): 5696–5707

[173] Ricciardulli A G, Yang S, Wetzelaer G J A H, Feng X, Blom P W M. Hybrid silver nanowire and graphene-based solution-processed transparent electrode for organic optoelectronics. Advanced Functional Materials, 2018, 28(14): 1706010

[174] Wang M, Yu H, Ma X, Yao Y, Wang L, Liu L, Cao K, Liu S, Dong C, Zhao B, Song C, Chen S, Huang W. Copper oxide-modified graphene anode and its application in organic photovoltaic cells. Optics Express, 2018, 26(18): A769–A776

[175] Nan H, Han J, Luo Q, Yin X, Zhou Y, Yao Z, Zhao X, Li X, Lin H. Economically synthesized NiCo₂S₄/reduced graphene oxide composite as efficient counter electrode in dye-sensitized solar cell. Applied Surface Science, 2018, 437: 227–232

[176] Sankar Ganesh R, Silambarasan K, Durgadevi E, Navaneethan M, Ponnusamy S, Kong C Y, Muthamizhchelvan C, Shimura Y, Hayakawa Y. Metal sulfide nanosheet–nitrogen-doped graphene hybrids as low-cost counter electrodes for dye-sensitized solar cells. Applied Surface Science, 2019, 480: 177–185

[177] Silambarasan K, Archana J, Athithya S, Harish S, Sankar Ganesh R, Navaneethan M, Ponnusamy S, Muthamizhchelvan C, Hara K, Hayakawa Y. Hierarchical NiO@NiS@graphene nanocomposite as a sustainable counter electrode for Pt free dye-sensitized solar cell. Applied Surface Science, 2020, 501: 144010

[178] Murugadoss V, Panneerselvam P, Yan C, Guo Z, Angaiah S. A simple one-step hydrothermal synthesis of cobalt nickel selenide/graphene nanohybrid as an advanced platinum free counter electrode for dye sensitized solar cell. Electrochimica Acta, 2019, 312: 157–167

[179] Rehman S, Noman M, Khan A D, Saboor A, Ahmad M S, Khan H U. Synthesis of polyvinyl acetate /graphene nanocomposite and its application as an electrolyte in dye sensitized solar cells. Optik (Stuttgart), 2020, 202: 163591

[180] Chong S W, Lai C W, Juan J C, Leo B F. An investigation on surface modified TiO₂ incorporated with graphene oxide for dye-sensitized solar cell. Solar Energy, 2019, 191: 663–671

[181] Wei L, Wang P, Yang Y, Zhan Z, Dong Y, Song W, Fan R. Enhanced performance of the dye-sensitized solar cells by the introduction of graphene oxide into the TiO₂ photoanode. Inorganic Chemistry Frontiers, 2018, 5(1): 54–62

[182] Sasikumar R, Chen T W, Chen S M, Rwei S P, Ramaraj S K. Developing the photovoltaic performance of dye-sensitized solar cells (DSSCs) using a SnO₂-doped graphene oxide hybrid nanocomposite as a photo-anode. Optical Materials, 2018, 79: 345–352

[183] Sadikin S N, Rahman M Y A, Umar A A, Aziz T H T. Improvement of dye-sensitized solar cell performance by utilizing graphene-coated TiO₂ films photoanode. Superlattices and Microstructures, 2019, 128: 92–98

[184] NREL. Best research-cell efficiencies (National Renewable Energy Laboratory: Golden, Colorado), 2019

[185] Bag M, Renna L A, Adhikari R Y, Karak S, Liu F, Lahti P M, Russell T P, Tuominen M T, Venkataraman D. Kinetics of ion transport in perovskite active layers and its implications for active layer stability. Journal of the American Chemical Society, 2015, 137(40): 13130–13137

[186] Bastos J P, Paetzold U W, Gehlhaar R, Qiu W, Cheyns D, Surana S, Spampinato V, Aernouts T, Poortmans J. Light-induced degradation of perovskite solar cells: the influence of 4-tert-butyl pyridine and gold. Advanced Energy Materials, 2018, 8(23): 1800554

[187] Raga S R, Jung M C, Lee M V, Leyden M R, Kato Y, Qi Y. Influence of air annealing on high efficiency planar structure perovskite solar cells. Chemistry of Materials, 2015, 27(5): 1597–1603

[188] Christians J A, Miranda Herrera P A, Kamat P V. Transformation of the excited state and photovoltaic efficiency of CH₃NH₃PbI₃ perovskite upon controlled exposure to humidified air. Journal of the American Chemical Society, 2015, 137(4): 1530–1538

[189] Domanski K, Correa-Baena J P, Mine N, Nazeeruddin M K, Abate A, Saliba M, Tress W, Hagfeldt A, Grätzel M. Not all that glitters is gold: metal-migration-induced degradation in perovskite solar cells. ACS Nano, 2016, 10(6): 6306–6314

[190] Guerrero A, You J, Aranda C, Kang Y S, Garcia-Belmonte G, Zhou H, Bisquert J, Yang Y. Interfacial degradation of planar lead halide perovskite solar cells. ACS Nano, 2016, 10(1): 218–224

[191] Wang R, Mujahid M, Duan Y, Wang Z, Xue J, Yang Y. A review of perovskites solar cell stability. Advanced Functional Materials, 2019, 29(47): 1808843

[192] Jeong G, Koo D, Seo J, Jung S, Choi Y, Lee J, Park H. Suppressed interdiffusion and degradation in flexible and transparent metal electrode-based perovskite solar cells with a graphene interlayer. Nano Letters, 2020, 20(5): 3718–3727

[193] Tavakoli M M, Tavakoli R, Yadav P, Kong J. A graphene/ZnO electron transfer layer together with perovskite passivation enables highly efficient and stable perovskite solar cells. Journal of Materials Chemistry A, Materials for Energy and Sustainability, 2019, 7(2): 679–686

[194] Kim J M, Jang C W, Kim J H, Kim S, Choi S H. Use of AuCl₃-doped graphene as a protecting layer for enhancing the stabilities of inverted perovskite solar cells. Applied Surface Science, 2018, 455: 1131–1136

[195] Jokar E, Huang Z Y, Narra S, Wang C Y, Kattoor V, Chung C C, Diau E W G. Anomalous charge-extraction behavior for graphene-oxide (GO) and reduced graphene-oxide (rGO) films as efficient p-contact layers for high-performance perovskite solar cells. Advanced Energy Materials, 2018, 8(3): 1701640

[196] Cogal S, Calio L, Celik Cogal G, Salado M, Kazim S, Oksuz L, Ahmad S, Uygun Oksuz A. RF plasma-enhanced graphene–polymer composites as hole transport materials for perovskite solar cells. Polymer Bulletin, 2018, 75(10): 4531–4545

[197] Nouri E, Mohammadi M R, Lianos P. Improving the stability of inverted perovskite solar cells under ambient conditions with graphene-based inorganic charge transporting layers. Carbon, 2018, 126: 208–214

[198] Zhao X, Tao L, Li H, Huang W, Sun P, Liu J, Liu S, Sun Q, Cui Z, Sun L, Shen Y, Yang Y, Wang M. Efficient planar perovskite solar cells with improved fill factor via interface engineering with graphene. Nano Letters, 2018, 18(4): 2442–2449

[199] O’Keeffe P, Catone D, Paladini A, Toschi F, Turchini S, Avaldi L, Martelli F, Agresti A, Pescetelli S, Del Rio Castillo A E, Bonaccorso F, Di Carlo A. Graphene-induced improvements of perovskite solar cell stability: effects on hot-carriers. Nano Letters, 2019, 19(2): 684–691

[200] Yoon J, Sung H, Lee G, Cho W, Ahn N, Jung H S, Choi M. Superflexible, high-efficiency perovskite solar cells utilizing graphene electrodes: towards future foldable power sources. Energy & Environmental Science, 2017, 10(1): 337–345

[201] Heo J H, Shin D H, Song D H, Kim D H, Lee S J, Im S H. Super-flexible bis(trifluoromethanesulfonyl)-amide doped graphene transparent conductive electrodes for photo-stable perovskite solar cells. Journal of Materials Chemistry A, Materials for Energy and Sustainability, 2018, 6(18): 8251–8258

[202] Zhang C, Wang S, Zhang H, Feng Y, Tian W, Yan Y, Bian J, Wang Y, Jin S, Zakeeruddin S M, Grätzel M, Shi Y. Efficient stable graphene-based perovskite solar cells with high flexibility in device assembling via modular architecture design. Energy & Environmental Science, 2019, 12(12): 3585–3594

[203] Sung H, Ahn N, Jang M S, Lee J K, Yoon H, Park N G, Choi M. Transparent conductive oxide-free graphene-based perovskite solar cells with over 17% efficiency. Advanced Energy Materials, 2016, 6(3): 1501873

[204] Fu W, Jiang L, van Geest E P, Lima L M C, Schneider G F. Sensing at the surface of graphene field-effect transistors. Advanced Materials, 2017, 29(6): 1603610

[205] Afsahi S, Lerner M B, Goldstein J M, Lee J, Tang X, Bagarozzi D A Jr, Pan D, Locascio L, Walker A, Barron F, Goldsmith B R. Novel graphene-based biosensor for early detection of Zika virus infection. Biosensors & Bioelectronics, 2018, 100: 85–88

[206] Chen S, Sun Y, Xia Y, Lv K, Man B, Yang C. Donor effect dominated molybdenum disulfide/graphene nanostructure-based field-effect transistor for ultrasensitive DNA detection. Biosensors & Bioelectronics, 2020, 156: 112128

[207] Hwang M T, Heiranian M, Kim Y, You S, Leem J, Taqieddin A, Faramarzi V, Jing Y, Park I, van der Zande A M, Nam S, Aluru N R, Bashir R. Ultrasensitive detection of nucleic acids using deformed graphene channel field effect biosensors. Nature Communications, 2020, 11(1): 1543

[208] Kim S, Xing L, Islam A E, Hsiao M S, Ngo Y, Pavlyuk O M, Martineau R L, Hampton C M, Crasto C, Slocik J, Kadakia M P, Hagen J A, Kelley-Loughnane N, Naik R R, Drummy L F. In operando observation of neuropeptide capture and release on graphene field-effect transistor biosensors with picomolar sensitivity. ACS Applied Materials & Interfaces, 2019, 11(15): 13927–13934

[209] Seo G, Lee G, Kim M J, Baek S H, Choi M, Ku K B, Lee C S, Jun S, Park D, Kim H G, Kim S J, Lee J O, Kim B T, Park E C, Kim S I. Rapid detection of COVID-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano, 2020, 14(4): 5135–5142

[210] Loan P T K, Wu D, Ye C, Li X, Tra V T, Wei Q, Fu L, Yu A, Li L J, Lin C T. Hall effect biosensors with ultraclean graphene film for improved sensitivity of label-free DNA detection. Biosensors & Bioelectronics, 2018, 99: 85–91

[211] Zhan H, Cervenka J, Prawer S, Garrett D J. Molecular detection by liquid gated Hall effect measurements of graphene. Nanoscale, 2018, 10(3): 930–935

[212] Li N, Tang T, Li J, Luo L, Li C, Shen J, Yao J. Highly sensitive biosensor with graphene-MoS₂ heterostructure based on photonic spin Hall effect. Journal of Magnetism and Magnetic Materials, 2019, 484: 445–450

[213] Zhou X, Sheng L, Ling X. Photonic spin Hall effect enabled refractive index sensor using weak measurements. Scientific Reports, 2018, 8(1): 1221

[214] Zhao Z, Yang H, Zhao W, Deng S, Zhang K, Deng R, He Q, Gao H, Li J. Graphene-nucleic acid biointerface-engineered biosensors with tunable dynamic range. Journal of Materials Chemistry B, Materials for Biology and Medicine, 2020, 8(16): 3623–3630

[215] Xie K X, Cao S H, Wang Z C, Weng Y H, Huo S X, Zhai Y Y, Chen M, Pan X H, Li Y Q. Graphene oxide-assisted surface plasmon coupled emission for amplified fluorescence immunoassay. Sensors and Actuators B, Chemical, 2017, 253: 804–808

[216] Sun L, Zhang Y, Wang Y, Yang Y, Zhang C, Weng X, Zhu S, Yuan X. Real-time subcellular imaging based on graphene biosensors. Nanoscale, 2018, 10(4): 1759–1765

[217] Xu Y, Zhuang R, Zhang Z, Yi R, Guo X, Qi Z. Single-layer graphene-based surface plasmon resonance biosensors for immunization study. In: Proceedings of the 8th Applied Optics and Photonics China (AOPC 2019), Optical Spectroscopy Imaging, 2019, 11337

[218] Rahman M S, Anower M S, Hasan M R, Hossain M B, Haque M I. Design and numerical analysis of highly sensitive Au-MoS₂-graphene based hybrid surface plasmon resonance biosensor. Optics Communications, 2017, 396: 36–43

[219] Gopalan K K, Paulillo B, Mackenzie D M A, Rodrigo D, Bareza N, Whelan P R, Shivayogimath A, Pruneri V. Scalable and tunable periodic graphene nanohole arrays for mid-infrared plasmonics. Nano Letters, 2018, 18(9): 5913–5918

[220] Siegel P H. Terahertz technology in biology and medicine. IEEE Transactions on Microwave Theory and Techniques, 2004, 52(10): 2438–2447

[221] Jepsen P U, Cooke D G, Koch M. Terahertz spectroscopy and imaging–modern techniques and applications. Laser & Photonics Reviews, 2011, 5(1): 124–166

[222] Sengupta K. Integrated circuits for terahertz communication beyond 100 GHz: are we there yet? In: Proceedings of IEEE International Conference on Communications, Workshop ICC Workshop, 2019

[223] Ajito K, Ueno Y. THz chemical imaging for biological applications. IEEE Transactions on Terahertz Science and Technology, 2011, 1(1): 293–300

[224] Auton G, But D B, Zhang J, Hill E, Coquillat D, Consejo C, Nouvel P, Knap W, Varani L, Teppe F, Torres J, Song A. Terahertz detection and imaging using graphene ballistic rectifiers. Nano Letters, 2017, 17(11): 7015–7020

[225] Yang X, Vorobiev A, Generalov A, Andersson M A, Stake J. A flexible graphene terahertz detector. Applied Physics Letters, 2017, 111(2): 021102

[226] Yang X X, Sun J D, Qin H, Lv L, Su L N, Yan B, Li X X, Zhang Z P, Fang J Y. Room-temperature terahertz detection based on CVD graphene transistor. Chinese Physics B, 2015, 24(4): 047206

[227] Valmorra F, Scalari G, Maissen C, Fu W, Schönenberger C, Choi J W, Park H G, Beck M, Faist J. Low-bias active control of terahertz waves by coupling large-area CVD graphene to a terahertz metamaterial. Nano Letters, 2013, 13(7): 3193–3198

[228] Generalov A A, Andersson M A, Yang X, Vorobiev A, Stake J A. 400-GHz graphene FET detector. IEEE Transactions on Terahertz Science and Technology, 2017, 7(5): 614–616

[229] Kakenov N, Ergoktas M S, Balci O, Kocabas C. Graphene based terahertz phase modulators. 2D Materials, 2018, 5(3): 035018

[230] Shin J W, Cho H, Lee J, Moon J, Han J H, Kim K, Cho S, Lee J I, Kwon B H, Cho D H, Lee K M, Suemitsu M, Cho N S. Overcoming the efficiency limit of organic light-emitting diodes using ultra-thin and transparent graphene electrodes. Optics Express, 2018, 26(2): 617–626

[231] Shin J W, Han J H, Cho H, Moon J, Kwon B H, Cho S, Yoon T, Kim T S, Suemitsu M, Lee J I, Cho N S. Display process compatible accurate graphene patterning for OLED applications. 2D Materials, 2017, 5(1): 014003

[232] Lee J, Han T H, Park M H, Jung D Y, Seo J, Seo H K, Cho H, Kim E, Chung J, Choi S Y, Kim T S, Lee T W, Yoo S. Synergetic electrode architecture for efficient graphene-based flexible organic light-emitting diodes. Nature Communications, 2016, 7(1): 11791

[233] Kwon O E, Shin J W, Oh H, Kang C, Cho H, Kwon B H, Byun C W, Yang J H, Lee K M, Han J H, Sung Cho N, Hyuk Yoon J, Jin Chae S, Sung Park J, Lee H, Hwang C S, Moon J, Lee J I. A prototype active-matrix OLED using graphene anode for flexible display application. Journal of Information Display, 2020, 21(1): 49–56

[234] Zhang Z, Du J, Zhang D, Sun H, Yin L, Ma L, Chen J, Ma D, Cheng H M, Ren W. Rosin-enabled ultraclean and damage-free transfer of graphene for large-area flexible organic light-emitting diodes. Nature Communications, 2017, 8(1): 14560

[235] Torres Alonso E, Karkera G, Jones G F, Craciun M F, Russo S. Homogeneously bright, flexible, and foldable lighting devices with functionalized graphene electrodes. ACS Applied Materials & Interfaces, 2016, 8(26): 16541–16545

[236] Wang Z G, Chen Y F, Li P J, Hao X, Liu J B, Huang R, Li Y R. Flexible graphene-based electroluminescent devices. ACS Nano, 2011, 5(9): 7149–7154

[237] Shin H, Sharma B K, Lee S W, Lee J B, Choi M, Hu L, Park C, Choi J H, Kim T W, Ahn J H. Stretchable electroluminescent display enabled by graphene-based hybrid electrode. ACS Applied Materials & Interfaces, 2019, 11(15): 14222–14228

[238] Chandran A, Joshi T, Sharma I, Subhedar K M, Mehta D S, Biradar A M. Monolayer graphene electrodes as alignment layer for ferroelectric liquid crystal devices. Journal of Molecular Liquids, 2019, 279: 294–298

[239] Hu T, Wang H, Shao Y, Zhang X, Liu G, Li M, Chen H, Lee Y. 66-3: a high reliability PEDOT:PSS/graphene transparent electrode for liquid crystal displays. SID Symposium Digest of Technical Papers, 2017, 48(1): 972–975

[240] Petrov S, Marinova V, Lin S H, Chang C M, Lin Y H, Hsu K Y. Large scale liquid crystal device with graphene-based electrodes. Optical Data Processing and Storage, 2017, 3(1): 114–118

[241] Mustapha N, Fekkai Z, Ibnaouf K H. Improved performance of organic light-emitting diodes based on oligomer thin films with graphene. Journal of Electronic Materials, 2020, 49(3): 2203–2210

[242] Fu Y, Sun J, Du Z, Guo W, Yan C, Xiong F, Wang L, Dong Y, Xu C, Deng J, Guo T, Yan Q F. Monolithic integrated device of GaN micro-LED with graphene transparent electrode and graphene active-matrix driving transistor. Materials (Basel), 2019, 12(3): 428

Acknowledgements

The work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement GrapheneCore2 number 785219, and GrapheneCore3 number 881603. We acknowledge the financial support from Academy of Finland (projects 298297 and 320167-PREIN Flagship).ƒ

Funding note

Open access funding provided by Aalto University.

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.‚

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.‚

To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

RIGHTS & PERMISSIONS

2020 The Author(s) 2020. This article is published with open access at link.springer.com and journal.hep.com.cn