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Effects of surface morphological parameters on cleaning efficiency of PV panels
Solar Energy 194 (2019) 840–847
Contents lists available at ScienceDirect
Solar Energy journal homepage: www.elsevier.com/locate/solener
Effects of surface morphological parameters on cleaning efficiency of PV panels Eugene Yu-Ta Chena, Yan Chena, Bing Guob, Hong Lianga,c,
T
⁎
a
Department of Materials Science and Engineering, Texas A&M University, 3003 TAMU, College Station, TX 77843-3003, United States Mechanical Engineering Program, Texas A&M University at Qatar, PO BOX 23874, Doha, Qatar c J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, 3123 TAMU, College Station, TX 77843-3123, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Solar panel Dust particles Surface morphology Cleaning efficiency Surface roughness parameters
Keeping the surface of photovoltaic (PV) panels clean has been a challenge. The fundamental understanding in interactions between a brush, dust particles, and the surface of panels is still lacking. This research investigates the correlation between surface morphological parameters and cleaning efficiency. Experimentally, the cleaning efficiency using brush tips sweeping against five aluminum oxide surfaces was evaluated. We analyzed the cleaning conditions such as the surface roughness and the shape of the surface profiles. Results showed that there are two key factors affecting the removal of dust particles resulting effective cleaning. The first is the roughness of the surface and the second the profile. The dust particle removal involves consideration of the brush and particle size, as well as the panel surface morphology and texture. A physical model illustrating the cleaning mechanism is proposed. The impact of the research contributes to the surface design leading to the ultimate cleaning efficiency of PV panels.
1. Introduction The dust soiling on the surface of solar panels has become an important issue since the rapid development of solar-energy technologies including photovoltaic (PV) panels or concentrating solar power (CSP) (Costa et al., 2016). As renewable energy is widely accepted, more and more large-scale or utility-scale solar power plants have been built and operated (International Energy Agency, 2018; Jäger-Waldau, 2019). It makes the issue of dust soiling urgent especially for those regions with ample solar resource but arid climate. The generation of electricity of PV panels is dependent on the adsorption of sunlight. The negative effect of dust soiling eventually results in the declining performance of PV panels due to the buildup of dust particles on outer layers of those surfaces (Figgis et al., 2016; Hammad et al., 2018; Jamil et al., 2017; Maghami et al., 2016). To prevent further buildup of dust particles, various mitigating strategies have been developed to address the issue of soiling including electrodynamic dust shield (EDS) (Guo et al., 2019, 2017; Kawamoto and Guo, 2018), robotic mechanical cleaning systems (Deb and Brahmbhatt, 2018; Mondal and Bansal, 2015; Parrott et al., 2018), and different types of anti-soiling coatings (Huang et al., 2018; Jamil et al., 2017; de Jesus et al., 2018; Quan and Zhang, 2017; Said et al., 2015; Vüllers et al., 2018). Surface morphology is considered as a key factor in various
⁎
engineering applications. To characterize the surface morphology, various surface morphological parameters are introduced (Bhushan, 2000; Gadelmawla et al., 2002). Average roughness and root-meansquare are common parameters but limited to provide a better and detailed description including shape or feature of a surface (Crawford et al., 2012). More parameters such as skewness or kurtosis are presented for a comprehensive description of a surface. Different engineering surfaces reflect different surface characteristics (Xiao et al., 2007). Identifying important parameters is critical to understand the rationale of specific surface morphology for certain application. The tribological behavior between contact surfaces is influenced by skewness and kurtosis under different regimes (Sedlaček et al., 2012; Wang et al., 2006; Xiao et al., 2007; Yan et al., 2014). For lubrication application, a surface with negative value of skewness is preferred due to better retention of lubricant (Sedlaček et al., 2012). A standard set of surface morphological parameters were proposed to evaluate the bacterial adhesion of medical implants (Crawford et al., 2012). The performance of dental implants is associated with bone response influenced by surface morphological features (Francisco et al., 2018; Hansson and Hansson, 2011; Hotchkiss et al., 2016; Kournetas et al., 2017). In the study, we evaluated the correlation of cleaning efficiency of five different surfaces with various surface morphological parameters
Corresponding author at: Department of Materials Science and Engineering, Texas A&M University, 3003 TAMU, College Station, TX 77843-3003, United States. E-mail address: [email protected] (H. Liang).
https://doi.org/10.1016/j.solener.2019.10.087 Received 8 September 2019; Received in revised form 26 October 2019; Accepted 29 October 2019 0038-092X/ © 2019 Published by Elsevier Ltd on behalf of International Solar Energy Society.
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using the tribometer with brush-on-disk configuration. The five substrates are all aluminum oxide based surfaces including the anodic alumina oxide (AAO), aluminum foil, and sapphire. Aluminum foil is natively oxidized in ambient conditions and sapphire is single crystal of aluminum oxide. The AAO possesses superior surface properties which can be applied to applications such as template synthesis, membranes for molecular separation, sensors or self-cleaning surfaces (Md Jani et al., 2013; Si and Guo, 2015). The highly ordered porous structures can be altered by controlling anodizing conditions including applied voltage, current, pH and electrolyte (Lee and Park, 2014). AAO with those features is considered as an appropriate template to investigate dust particle-surface interactions. The primary aim of this research is to understand a better surface design and criteria for ultimate cleaning efficiency. The relationships of cleaning efficiency and different surface morphological parameters were investigated to identify key surface morphological parameters. This study is not only expected to contribute to the mitigation of dust soiling of solar energy systems but also other industrial applications involving cleaning.
load was set at 0.06 N to minimize the surface damage while a tip was sliding on the surface. The highest sliding speed could reach 0.8 cm/s and the sliding distance was 16 mm. The tip was sliding in a halfway reciprocating motion. After cleaning experiments, the optical images were obtained on a fixed location of each cleaning track using an optical microscope (Dino-Lite). The areal coverage of dust particles on different surfaces was calculated using ImageJ. The cleaning efficiency was calculated using the areal coverage of dust particles before and after cleaning. 2.3. Surface characterization The surface morphology of each surface was measured using an optical interferometer (NewView 600, Zygo) with a 20× objective lens. The lateral resolution of the objective lens is 0.548 µm. The scanning area of each image was 0.35 mm × 0.26 mm. Six locations of each surface were measured to acquire the surface morphology images and parameters including average surface roughness (Sa), root-mean-square (RMS) roughness (Sq), skewness (Ssk), kurtosis (Sku), the average spacing between local peaks (S), and the average spacing between peaks at the mean line (Sm). These surface parameters were calculated using the MetroPro software from Zygo.
2. Materials and methods 2.1. Dust and substrates
3. Results and discussion The source of the dust particles was obtained from the solar panels at the solar test field of Qatar Science and Technology Park (QSTP) in Doha, Qatar. To calculate the particle size distribution, dust particles were deposited on a glass slide with a dust blower, and optical images were acquired using a digital optical microscope (MU1000, AmScope; PMG 3, Olympus). The images were analyzed, and the particle size distribution of Doha dust was calculated using a software, ImageJ (NIH). Five different surfaces were prepared for the study including the anodic aluminum oxide (AAO) membranes with the diameters of 20 nm and 200 nm (Whatman Anodisc™ 25, GE Healthcare), aluminum foil (Reynolds), and a α-Al2O3 (sapphire) lens with polished and frosted sides. The selection of aluminum oxide (Al2O3) based materials is for the following reasons. These surfaces are all made of Al2O3, which is one of the major components in glass. Aluminum oxide is also considered chemically stable, so chemical reactions are not required to take into consideration to complicate the study. Additionally, aluminum oxides provide such versatility of creating surface textures including smooth, rough, and ordered nano-scale surfaces. In the following texts, these surfaces were denoted as AAO_200nm, AAO_20nm, Al_foil, Sapphire_frosted, and Sapphire_polished. The prepared surface was fixed on a pre-cleaned glass slide (Thermo Scientific) using scotch tapes. To create a clear boundary between clean and dusted region, the specimen of each surface was partially covered with parafilm to avoid dust contamination during deposition. Before dust deposition, a glass slide was placed in a container (3″ × 5″ × 3″) against the side wall. 0.03 g of dust particles were placed at the center of the container. The dust particles were aerosolized using a dust blower. It took at least one minute for dust particles to settle down on the surfaces. The dust-deposited surface was taken out of the container and the parafilm was removed.
3.1. Surface morphology of substrates In the study, the morphology of five different surfaces and the corresponded line profiles are shown in Fig. 1. The pore structure of AAO membranes is not detected completely due to the lateral resolution of the interferometer. More pores are detected on the surface of AAO_200nm. The profiles of AAO membranes of 20 nm and 200 nm are both considered flat and smooth. The profile of Al foil is relatively smooth with the peak-to-valley around 3 µm. The difference between the surface morphology of Sapphire_polished and Sapphire_frosted is significant. The surface morphology of Sapphire_frosted shows a rougher surface profile with more deep valleys compared to that of Sapphire_polished. Since it was polished, smooth surface profile and shallow valleys are observed. Fig. 2 shows the particle size distribution and the optical image of dust particles deposited on a glass slide. From the particle size distribution, the average diameter of the dust particles is 21.2 µm. Most of the dust particles are primarily smaller than 20 µm. Agglomeration of dust particles can be observed from the optical image. The main composition of the dust particles from Qatar has been characterized in the previous study, which is calcite, dolomite, quartz, and gypsum (Chen et al., 2018; Javed et al., 2017). The surface morphological parameters of five surfaces and the cleaning efficiency of three different diameters of nylon tips are summarized in Table 1. When a nylon spherical tip contacts with a flat surface under an applied load, it will form a circular contact area. The Hertzian contact theory is used to calculate the maximum contact pressure (Pmax ) and the contact radius (a ) between the nylon tips and the substrates (Hertz, 1881; Johnson, 1985). The results can be computed in the following equations
2.2. Cleaning experiments
a=
The detailed setup and procedure of dust sweeping experiments were described in the previous study (Chen et al., 2018). A tribometer with brush-on-disk apparatus was used to evaluate the cleaning efficiency in the study. The main purpose of using tribometer is to measure the sweeping force while cleaning the surface. The nylon spherical tips with three different diameters of 3.2 mm, 4 mm, and 4.76 mm (McMaster-Carr) were used in the study. Nylon is a widely used engineering plastic and common material of brush bristles. The applied
3
Pmax =
3F (1 − ν12)/ E1 + (1 − ν22)/ E2 , 1/ d2 ≈ 0 8 1/ d1 + 1/ d2
3F 2πa2
(1) (2)
where F is the applied load, ν1and ν2 are the Poisson’s ratio of the nylon tips and the substrates, respectively, E1and E2 are the Young’s modulus of nylon tips and the substrates, respectively, and d1 is the diameter of a nylon tip. Since it is the contact between a sphere tip and a flat surface, 1/ d2 approximately equals to zero. To calculate the Pmax of AAO 841
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Fig. 1. Surface morphology images and corresponded line profiles of five different surfaces (a) AAO_200 nm, (b) AAO_20 nm, (c) Al foil, (d) Sapphire_frosted, and (e) Sapphire_polished The scale bar of surface morphology images is 100 µm.
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Fig. 1. (continued)
The area percentages of pores of AAO_20nm and AAO_200nm are 47% and 60%, respectively. Thus, the contact pressure of AAO_20nm and AAO_200nm is 1.8 and 2.5 times larger compared to other surfaces. From Eqs. (1) and (2), a nylon tip with larger diameter forms larger contact area with the substrate, which results in lower contact pressure. On the other hand, a nylon tip with small diameter results in higher contact pressure. However, the change of contact pressure due to the diameter of tips did not result in significant change of cleaning efficiency. The only exception is between the 3.2 mm tip and the surface of Sapphire_frosted, resulting in low cleaning efficiency of 12.76 ± 6.03%. This is likely due to similar contact diameter and the spacing between valleys of the surface, which can be confirmed by surface morphology (Fig. 1(d). The main force to clean the dust particles is the tangential force or sweeping force during sliding, which the tip did not exert additional pressure on Chen et al. (2018). The dust particles that are removed by a tip will move on the surface profile. In previous study, increasing contact pressure does not significantly improve cleaning efficiency on smooth surface such as glass slides if a firm contact is maintained (Chen et al., 2018). The cleaning efficiency vs. contact pressure of all five surfaces with various degree of surface roughness are shown in Fig. S1. The data points are highly scattered without clear relationship. This also indicates the importance of finding the surface morphology leading to better cleaning. In the following discussion, we will discuss the effect of different
400
Count
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0
0
20
40
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160
180
Particle size (um)
Fig. 2. Particle size distribution and optical image of dust particles with a scale bar of 200 µm on a glass slide at the magnification of 200×.
membranes, we need to consider the area percentage of the pores ( A0 ). The Pmax for AAO membranes is modified to Eq. (3).
Pmax , AAO =
3F 2πa2 (1 − A0 )
(3)
Table 1 The summary of the results of surface morphological parameters and the cleaning efficiency of different surfaces under three different tip diameters. Samples
Materials (Al2O3)
Diameter of tip (mm)
Contact area (µm2)
Maximum contact pressure (MPa)
Sa (µm)
Sq (µm)
Ssk
Sku
Sm (µm)
S (µm)
Cleaning efficiency (%)
AAO_200nm
200 nm pore size
3.2
1021
87
0.41
0.82
−5.81
61.57
10.51
6.25
72.88 ± 3.49
4 4.76 3.2
1208 1368 1352
75 66.5 65.7
0.41 0.41 0.26
0.82 0.82 0.39
−5.81 −5.81 −7.62
61.57 61.57 229.63
10.51 10.51 26.99
6.25 6.25 11.53
61.61 ± 6.37 62.44 ± 8.94 84.02 ± 4.66
4 4.76 3.2
1600 1813 2642
56.6 50.2 34.1
0.26 0.26 0.39
0.39 0.39 0.49
−7.62 −7.62 0.04
229.63 229.63 5.69
26.99 26.99 14.54
11.53 11.53 4.42
58.96 ± 9.16 76.03 ± 11.22 49.51 ± 13.14
4 4.76 3.2
3117 3421 2551
29.4 26.1 34.7
0.39 0.39 0.96
0.49 0.49 1.37
0.04 0.04 −1.95
5.69 5.69 13.94
14.54 14.54 8.86
4.42 4.42 3.30
44.75 ± 11.91 49.89 ± 7.53 12.76 ± 6.03
4 4.76 3.2
3019 3421 2551
29.9 26.6 34.7
0.96 0.96 0.2
1.37 1.37 0.35
−1.95 −1.95 −3.66
13.94 13.94 52.43
8.86 8.86 19.62
3.30 3.30 8.97
38.65 ± 8.73 40.07 ± 5.32 89.22 ± 1.61
4 4.76
3019 3421
29.9 26.6
0.2 0.2
0.35 0.35
−3.66 −3.66
52.43 52.43
19.62 19.62
8.97 8.97
68.11 ± 12.24 81.48 ± 9.39
AAO_20nm
Al foil
Sapphire frosted
Sapphire polished
20 nm pore size
Al foil with native oxide
Rough single crystal
Smooth single crystal
Note: Sa: Average surface roughness, Sq: RMS roughness, Ssk: Skewness, Sku: Kurtosis, Sm: Mean spacing at the mean line, and S: Mean spacing of adjacent local peaks. 843
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(a)
AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
100
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20
0.0
0.2
0.4
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1.0
AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
100
Cleaning efficiency (%)
Cleaning efficiency (%)
80
(b)
1.2
80
60
40
20
0.0
0.2
Sa (μm)
0.4
0.6
0.8
1.0
1.2
1.4
Srms (μm)
Fig. 3. The Cleaning efficiency of different surfaces vs. (a) Surface roughness (Sa) and (b) Root-mean-square roughness (Sq) under three different tip diameters.
surface morphological parameters on cleaning efficiency including surface roughness, skewness, kurtosis and spacing parameters. The correlation between the surface morphological parameters and cleaning efficiency can provide the insight of designing a surface that optimizes the cleaning efficiency.
3.2. Effects of surface roughness on cleaning efficiency To evaluate the smoothness of a surface, two parameters are commonly used including average surface roughness and RMS roughness. Both parameters are to evaluate the height variation of surface profiles. The definitions of surface roughness and root-mean-square roughness are as follows:
Sa =
Sq =
1 A
∬ |Z (x, y)| dxdy A
1 A
Fig. 4. The line profile comparisons of five different surfaces.
(4)
3.3. Effects of surface profile on cleaning efficiency
∬ Z 2 (x, y) dxdy A
(5)
In addition to average surface roughness and RMS roughness that are evaluated based on height variation, the distribution of height values can also be equally important. The distribution of height can provide a better description about shapes of surface profiles, which complements that arithmetic roughness and root-mean-square roughness fail to provide. The common cited parameters are skewness and kurtosis, and the definitions are given below
where Z (x , y ) represents the surface profiles. Smaller Sa or Sq indicates a smooth surface with smaller height variation. Fig. 3(a) shows the cleaning efficiency and the surface roughness of each surface. It shows that the surface of Sapphire_polished has the smallest Sa followed by AAO_20nm, AAO_200nm, and Al_foil. The Sapphire_frosted is the roughest surface with the highest surface roughness closed to 1 µm. From Fig. 3(a), the cleaning efficiency and the surface roughness exhibits a linear relationship, indicating that dust particles on the rougher surface are difficult to be removed and cleaned. Fig. 3(b) shows a similar linear trend as well between the cleaning efficiency and the rootmean-square roughness. To understand the reason, the line profiles among the different surfaces are shown in Fig. 4. For smooth surfaces like Sapphire_polished or AAO_20nm, the line profiles can be considered as a flat line. When the tip is sliding on a flat surface, the dust particles are easily removed. If the surface becomes rougher with increasing surface roughness, more and more peaks and valleys are observed. While dust particles are removed by the tip, more dust particles are possibly trapped within the valleys. Thus, the cleaning efficiency decreases with the rougher surface. In our previous study (Chen et al., 2018), the glass slide was used as the substrate for dust sweeping experiments. The surface roughness of the glass slide is extremely small about 0.024 µm with up to 95% cleaning efficiency. It also shows that the smaller surface roughness promotes cleaning efficiency.
Ssk =
Sku =
1 ⎡1 Sq3 ⎢ A ⎣
∬ Z 3 (x, y) dxdy⎤⎥
1 ⎡1 Sq4 ⎢ A ⎣
∬ Z 4 (x, y) dxdy⎤⎥
A
A
⎦
⎦
(7)
(8)
where Z (x , y ) represents the surface profiles, and Sq is root-meansquare roughness. Skewness is a measure of the symmetry of height distribution. A positive value of skewness refers to the surface profiles consisting of many peaks. On the other hand, a surface profile with a negative value of skewness mainly consists of valleys. Fig. 5(a) shows the plot between the cleaning efficiency and the skewness of different surfaces. Among five surfaces, four surfaces are negative-skewed surfaces. The skewness of Al_foil is close to zero, which indicates a symmetrical distribution of height. The surfaces with negative values of skewness have higher cleaning efficiencies up to 90%. However, the cleaning efficiency of Al_foil is lower compared to other surfaces with 844
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AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
100
80
(b)
Cleaning efficiency (%)
Cleaning efficiency (%)
(a)
60
40
20
-8
-7
-6
-5
-4
-3
-2
-1
0
1
100
80
60
40
AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
20
0
Ssk
50
100
150
200
250
Sku
Fig. 5. The Cleaning efficiency of different surfaces vs. (a) Skewness (Ssk) and (b) Kurtosis (Sku) under three different diameters of tip.
and Sapphire_frosted, it indicates that surface profiles become symmetrical consisting of more peaks and valleys where the tip is sliding on. During the sliding, the dust particles are likely to get trapped due to the symmetrical waviness of the surface. This indicates that a surface with symmetrical distribution is not optimized for dust particles cleaning.
similar surface roughness but negative skewness. This shows that a surface with a symmetrical distribution of height does not improve the cleaning efficiency. The other common parameter is kurtosis, which is a measure of the sharpness of height distribution. If the kurtosis is larger than three, the surface profiles tend to have sharper peaks, which is also called leptokurtic. Fig. 5(b) shows the effect of different values of kurtosis on cleaning efficiency. From the curve, the cleaning efficiency reaches the plateau when the kurtosis is above the critical value. It suggests that a sharper surface profile is in favor for higher cleaning efficiency. This is also likely due to the reduction of contact area between dust particles and a surface with sharper peaks, which promotes cleaning efficiency. To understand the contact scenario between the tips and surfaces while cleaning dust particles, the bearing ratio curves can provide more insight. Bearing ratio curves or Abbott-Firestone curves are obtained from the cumulative density function of the histogram of surface height. It provides the ratio between the real contact area (bearing area) and total evaluated length or area at any given height values. In Fig. 6, the bearing ratio curves of Sapphire_polished, AAO_20nm, and AAO_200nm all show similarly flat profiles, which indicates the surfaces is smooth and negatively skewed. If the skewness increases, the profile of the bearing ratio curves gradually inclines and rougher surface profile like Sapphire_frosted inclines the most. While the tip is sliding on a negatively skewed surface, it shows a more firm and complete contact. This can promote the cleaning efficiency while the tip keeps sliding. On the contrary, if the profile of bearing ratio curves inclines more like Al_foil
3.4. Effects of the spatial parameters on cleaning efficiency In addition to amplitude parameters, we also investigate the correlations between the cleaning efficiency and the spatial parameters including the average spacing between the local peaks (S) and the average spacing at the mean line (Sm). Compared with amplitude parameters, these spatial parameters are to describe the horizontal variation of roughness. A smooth surface with low surface roughness value such as AAO_20nm or Sapphire_polished usually has larger spacing between local peaks. On the contrary, a rough surface refers to the smaller average spacing. In Fig. 7(a), while the value of Sm increases, the cleaning efficiency continues to increase and reaches the plateau passing the critical average spacing. The similar trend can also be observed as well in Fig. 7(b) regarding the average spacing at the mean line. The critical average spacing may be relevant to the interplay between the dust particles size and the surface morphology. If a surface has a larger average spacing (smooth surface), either the large or small dust particles can be easily cleaned, but on the rough surface, only larger particles can. Thus, only larger dust particles are relatively easy to be cleaned on smooth and rough surface. 3.5. A physical model to illustrate mechanisms of particle removal In this study, we investigated the relationships between the cleaning efficiency and various surface morphological parameters. Results show that the cleaning efficiency is dependent on the surface roughness and the shape of a surface profile. While a tip sweeps the particles, a physical model representing different interplays between the tip and surface profiles are shown in Fig. 8. There are three scenarios. First, when the tip is sliding on a smooth surface, the tip tends to have firm and complete contact with the surface. Thus, most of the dust particles can be removed during the cleaning. Second, if the surface roughness continues to increase, it indicates that the height variation increases, which forms more gaps between the tip and the substrates due to asperities. During the sliding of tips, the dust particles can be easily inserted into the gaps between the peaks or valleys. Thus, the cleaning efficiency may decrease due to the increasing surface roughness. Third, if the surface is sufficiently rough, the dust particles may be trapped
Fig. 6. The bearing ratio curves of different surfaces showing the contact scenario between the tip and surfaces. 845
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(a)
(b)
80
Cleaning efficiency (%)
Cleaning efficiency (%)
100
60
40
AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
20
0
2
4
6
8
10
100
80
60
40
20
0
12
AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
6
8
10 12 14 16 18 20 22 24 26 28 30
S (μm)
Sm (μm)
Fig. 7. The Cleaning efficiency of different surfaces vs (a) the average spacing between local peaks and (b) the average spacing at the mean line.
Rough Surface
Tip
proposed to explain cleaning processes. Since various anti-reflective coating (ARC) and anti-soiling coating (ASC) are applied in the field to mitigate dust soiling, the design and stability of surface profiles of coatings is critical and should be considered by manufactures in order to reach optimal cleaning maintenance of PV panels. A consistent smooth surface in any weather condition can ensure optimal cleaning efficiency. If surface roughness changes, the cleaning mechanisms will change accordingly. New knowledge generated in this research can not only be applied to maintenance of PV panels but also other industrial cleaning applications such as food processing, medical devices, and structural materials design and fabrication.
Particles Trapped
Partially Trapped and Removed
Smooth Surface
Particles Removed
Declaration of Competing Interest
Fig. 8. The physical model of the cleaning mechanisms of dust particles on different types of the surfaces.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
between the peaks or valleys during cleaning as well as settle between those areas during deposition. This suggests that the dust particles are hardly cleaned leading to minimum cleaning efficiency with those rough surface profiles. This research revealed critical features of material surfaces influencing cleaning efficiency. In terms of application, our findings could potentially be used for future establishment of cleaning standard. The International Electrotechnical Commission (IEC) has been publishing a series of standards for solar energy systems including non-concentrating and concentrator modules. There are some standards about coatings and dust such as EN 1096-2 and IEC 60068-2-68. There are currently developing standards about PV soiling and dusting, such as IEC 627887-3 about accelerated PV abrasion test. Our work is complementary of previous effort.
Acknowledgements Part of this research was sponsored by the Qatar National Research Fund (award number NPRP7-987-2-372), Qatar Foundation. The statements made herein are solely the responsibility of the authors. Authors wish to acknowledge the support by Texas A&M Strategic Research Seed Grant Program. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.solener.2019.10.087.
4. Conclusions
References
In this research, the correlations between the cleaning efficiency and the surface morphological parameters are investigated. An established methodology using a brush-on-disk configuration was used to evaluate the cleaning efficiency of five different surfaces of aluminum oxide. The results showed that surface morphology has profound influence on the cleaning efficiency in three aspects. First, the cleaning efficiency exhibits a linear trend with arithmetic surface roughness and root-mean-square toughness. Second, in the relevance of a cleaning tip and the surface profile, the cleaning efficiency tends to be higher on the surface of negative skewness. Third, there is a relative special ratio between a brush tip, particle size, and surface profile that dominates the removal mechanisms during cleaning. In the end, a physical model is
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847
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Effects of surface morphological parameters on cleaning efficiency of PV panels Eugene Yu-Ta Chena, Yan Chena, Bing Guob, Hong Lianga,c,
T
⁎
a
Department of Materials Science and Engineering, Texas A&M University, 3003 TAMU, College Station, TX 77843-3003, United States Mechanical Engineering Program, Texas A&M University at Qatar, PO BOX 23874, Doha, Qatar c J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, 3123 TAMU, College Station, TX 77843-3123, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Solar panel Dust particles Surface morphology Cleaning efficiency Surface roughness parameters
Keeping the surface of photovoltaic (PV) panels clean has been a challenge. The fundamental understanding in interactions between a brush, dust particles, and the surface of panels is still lacking. This research investigates the correlation between surface morphological parameters and cleaning efficiency. Experimentally, the cleaning efficiency using brush tips sweeping against five aluminum oxide surfaces was evaluated. We analyzed the cleaning conditions such as the surface roughness and the shape of the surface profiles. Results showed that there are two key factors affecting the removal of dust particles resulting effective cleaning. The first is the roughness of the surface and the second the profile. The dust particle removal involves consideration of the brush and particle size, as well as the panel surface morphology and texture. A physical model illustrating the cleaning mechanism is proposed. The impact of the research contributes to the surface design leading to the ultimate cleaning efficiency of PV panels.
1. Introduction The dust soiling on the surface of solar panels has become an important issue since the rapid development of solar-energy technologies including photovoltaic (PV) panels or concentrating solar power (CSP) (Costa et al., 2016). As renewable energy is widely accepted, more and more large-scale or utility-scale solar power plants have been built and operated (International Energy Agency, 2018; Jäger-Waldau, 2019). It makes the issue of dust soiling urgent especially for those regions with ample solar resource but arid climate. The generation of electricity of PV panels is dependent on the adsorption of sunlight. The negative effect of dust soiling eventually results in the declining performance of PV panels due to the buildup of dust particles on outer layers of those surfaces (Figgis et al., 2016; Hammad et al., 2018; Jamil et al., 2017; Maghami et al., 2016). To prevent further buildup of dust particles, various mitigating strategies have been developed to address the issue of soiling including electrodynamic dust shield (EDS) (Guo et al., 2019, 2017; Kawamoto and Guo, 2018), robotic mechanical cleaning systems (Deb and Brahmbhatt, 2018; Mondal and Bansal, 2015; Parrott et al., 2018), and different types of anti-soiling coatings (Huang et al., 2018; Jamil et al., 2017; de Jesus et al., 2018; Quan and Zhang, 2017; Said et al., 2015; Vüllers et al., 2018). Surface morphology is considered as a key factor in various
⁎
engineering applications. To characterize the surface morphology, various surface morphological parameters are introduced (Bhushan, 2000; Gadelmawla et al., 2002). Average roughness and root-meansquare are common parameters but limited to provide a better and detailed description including shape or feature of a surface (Crawford et al., 2012). More parameters such as skewness or kurtosis are presented for a comprehensive description of a surface. Different engineering surfaces reflect different surface characteristics (Xiao et al., 2007). Identifying important parameters is critical to understand the rationale of specific surface morphology for certain application. The tribological behavior between contact surfaces is influenced by skewness and kurtosis under different regimes (Sedlaček et al., 2012; Wang et al., 2006; Xiao et al., 2007; Yan et al., 2014). For lubrication application, a surface with negative value of skewness is preferred due to better retention of lubricant (Sedlaček et al., 2012). A standard set of surface morphological parameters were proposed to evaluate the bacterial adhesion of medical implants (Crawford et al., 2012). The performance of dental implants is associated with bone response influenced by surface morphological features (Francisco et al., 2018; Hansson and Hansson, 2011; Hotchkiss et al., 2016; Kournetas et al., 2017). In the study, we evaluated the correlation of cleaning efficiency of five different surfaces with various surface morphological parameters
Corresponding author at: Department of Materials Science and Engineering, Texas A&M University, 3003 TAMU, College Station, TX 77843-3003, United States. E-mail address: [email protected] (H. Liang).
https://doi.org/10.1016/j.solener.2019.10.087 Received 8 September 2019; Received in revised form 26 October 2019; Accepted 29 October 2019 0038-092X/ © 2019 Published by Elsevier Ltd on behalf of International Solar Energy Society.
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using the tribometer with brush-on-disk configuration. The five substrates are all aluminum oxide based surfaces including the anodic alumina oxide (AAO), aluminum foil, and sapphire. Aluminum foil is natively oxidized in ambient conditions and sapphire is single crystal of aluminum oxide. The AAO possesses superior surface properties which can be applied to applications such as template synthesis, membranes for molecular separation, sensors or self-cleaning surfaces (Md Jani et al., 2013; Si and Guo, 2015). The highly ordered porous structures can be altered by controlling anodizing conditions including applied voltage, current, pH and electrolyte (Lee and Park, 2014). AAO with those features is considered as an appropriate template to investigate dust particle-surface interactions. The primary aim of this research is to understand a better surface design and criteria for ultimate cleaning efficiency. The relationships of cleaning efficiency and different surface morphological parameters were investigated to identify key surface morphological parameters. This study is not only expected to contribute to the mitigation of dust soiling of solar energy systems but also other industrial applications involving cleaning.
load was set at 0.06 N to minimize the surface damage while a tip was sliding on the surface. The highest sliding speed could reach 0.8 cm/s and the sliding distance was 16 mm. The tip was sliding in a halfway reciprocating motion. After cleaning experiments, the optical images were obtained on a fixed location of each cleaning track using an optical microscope (Dino-Lite). The areal coverage of dust particles on different surfaces was calculated using ImageJ. The cleaning efficiency was calculated using the areal coverage of dust particles before and after cleaning. 2.3. Surface characterization The surface morphology of each surface was measured using an optical interferometer (NewView 600, Zygo) with a 20× objective lens. The lateral resolution of the objective lens is 0.548 µm. The scanning area of each image was 0.35 mm × 0.26 mm. Six locations of each surface were measured to acquire the surface morphology images and parameters including average surface roughness (Sa), root-mean-square (RMS) roughness (Sq), skewness (Ssk), kurtosis (Sku), the average spacing between local peaks (S), and the average spacing between peaks at the mean line (Sm). These surface parameters were calculated using the MetroPro software from Zygo.
2. Materials and methods 2.1. Dust and substrates
3. Results and discussion The source of the dust particles was obtained from the solar panels at the solar test field of Qatar Science and Technology Park (QSTP) in Doha, Qatar. To calculate the particle size distribution, dust particles were deposited on a glass slide with a dust blower, and optical images were acquired using a digital optical microscope (MU1000, AmScope; PMG 3, Olympus). The images were analyzed, and the particle size distribution of Doha dust was calculated using a software, ImageJ (NIH). Five different surfaces were prepared for the study including the anodic aluminum oxide (AAO) membranes with the diameters of 20 nm and 200 nm (Whatman Anodisc™ 25, GE Healthcare), aluminum foil (Reynolds), and a α-Al2O3 (sapphire) lens with polished and frosted sides. The selection of aluminum oxide (Al2O3) based materials is for the following reasons. These surfaces are all made of Al2O3, which is one of the major components in glass. Aluminum oxide is also considered chemically stable, so chemical reactions are not required to take into consideration to complicate the study. Additionally, aluminum oxides provide such versatility of creating surface textures including smooth, rough, and ordered nano-scale surfaces. In the following texts, these surfaces were denoted as AAO_200nm, AAO_20nm, Al_foil, Sapphire_frosted, and Sapphire_polished. The prepared surface was fixed on a pre-cleaned glass slide (Thermo Scientific) using scotch tapes. To create a clear boundary between clean and dusted region, the specimen of each surface was partially covered with parafilm to avoid dust contamination during deposition. Before dust deposition, a glass slide was placed in a container (3″ × 5″ × 3″) against the side wall. 0.03 g of dust particles were placed at the center of the container. The dust particles were aerosolized using a dust blower. It took at least one minute for dust particles to settle down on the surfaces. The dust-deposited surface was taken out of the container and the parafilm was removed.
3.1. Surface morphology of substrates In the study, the morphology of five different surfaces and the corresponded line profiles are shown in Fig. 1. The pore structure of AAO membranes is not detected completely due to the lateral resolution of the interferometer. More pores are detected on the surface of AAO_200nm. The profiles of AAO membranes of 20 nm and 200 nm are both considered flat and smooth. The profile of Al foil is relatively smooth with the peak-to-valley around 3 µm. The difference between the surface morphology of Sapphire_polished and Sapphire_frosted is significant. The surface morphology of Sapphire_frosted shows a rougher surface profile with more deep valleys compared to that of Sapphire_polished. Since it was polished, smooth surface profile and shallow valleys are observed. Fig. 2 shows the particle size distribution and the optical image of dust particles deposited on a glass slide. From the particle size distribution, the average diameter of the dust particles is 21.2 µm. Most of the dust particles are primarily smaller than 20 µm. Agglomeration of dust particles can be observed from the optical image. The main composition of the dust particles from Qatar has been characterized in the previous study, which is calcite, dolomite, quartz, and gypsum (Chen et al., 2018; Javed et al., 2017). The surface morphological parameters of five surfaces and the cleaning efficiency of three different diameters of nylon tips are summarized in Table 1. When a nylon spherical tip contacts with a flat surface under an applied load, it will form a circular contact area. The Hertzian contact theory is used to calculate the maximum contact pressure (Pmax ) and the contact radius (a ) between the nylon tips and the substrates (Hertz, 1881; Johnson, 1985). The results can be computed in the following equations
2.2. Cleaning experiments
a=
The detailed setup and procedure of dust sweeping experiments were described in the previous study (Chen et al., 2018). A tribometer with brush-on-disk apparatus was used to evaluate the cleaning efficiency in the study. The main purpose of using tribometer is to measure the sweeping force while cleaning the surface. The nylon spherical tips with three different diameters of 3.2 mm, 4 mm, and 4.76 mm (McMaster-Carr) were used in the study. Nylon is a widely used engineering plastic and common material of brush bristles. The applied
3
Pmax =
3F (1 − ν12)/ E1 + (1 − ν22)/ E2 , 1/ d2 ≈ 0 8 1/ d1 + 1/ d2
3F 2πa2
(1) (2)
where F is the applied load, ν1and ν2 are the Poisson’s ratio of the nylon tips and the substrates, respectively, E1and E2 are the Young’s modulus of nylon tips and the substrates, respectively, and d1 is the diameter of a nylon tip. Since it is the contact between a sphere tip and a flat surface, 1/ d2 approximately equals to zero. To calculate the Pmax of AAO 841
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Fig. 1. Surface morphology images and corresponded line profiles of five different surfaces (a) AAO_200 nm, (b) AAO_20 nm, (c) Al foil, (d) Sapphire_frosted, and (e) Sapphire_polished The scale bar of surface morphology images is 100 µm.
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Fig. 1. (continued)
The area percentages of pores of AAO_20nm and AAO_200nm are 47% and 60%, respectively. Thus, the contact pressure of AAO_20nm and AAO_200nm is 1.8 and 2.5 times larger compared to other surfaces. From Eqs. (1) and (2), a nylon tip with larger diameter forms larger contact area with the substrate, which results in lower contact pressure. On the other hand, a nylon tip with small diameter results in higher contact pressure. However, the change of contact pressure due to the diameter of tips did not result in significant change of cleaning efficiency. The only exception is between the 3.2 mm tip and the surface of Sapphire_frosted, resulting in low cleaning efficiency of 12.76 ± 6.03%. This is likely due to similar contact diameter and the spacing between valleys of the surface, which can be confirmed by surface morphology (Fig. 1(d). The main force to clean the dust particles is the tangential force or sweeping force during sliding, which the tip did not exert additional pressure on Chen et al. (2018). The dust particles that are removed by a tip will move on the surface profile. In previous study, increasing contact pressure does not significantly improve cleaning efficiency on smooth surface such as glass slides if a firm contact is maintained (Chen et al., 2018). The cleaning efficiency vs. contact pressure of all five surfaces with various degree of surface roughness are shown in Fig. S1. The data points are highly scattered without clear relationship. This also indicates the importance of finding the surface morphology leading to better cleaning. In the following discussion, we will discuss the effect of different
400
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0
0
20
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Fig. 2. Particle size distribution and optical image of dust particles with a scale bar of 200 µm on a glass slide at the magnification of 200×.
membranes, we need to consider the area percentage of the pores ( A0 ). The Pmax for AAO membranes is modified to Eq. (3).
Pmax , AAO =
3F 2πa2 (1 − A0 )
(3)
Table 1 The summary of the results of surface morphological parameters and the cleaning efficiency of different surfaces under three different tip diameters. Samples
Materials (Al2O3)
Diameter of tip (mm)
Contact area (µm2)
Maximum contact pressure (MPa)
Sa (µm)
Sq (µm)
Ssk
Sku
Sm (µm)
S (µm)
Cleaning efficiency (%)
AAO_200nm
200 nm pore size
3.2
1021
87
0.41
0.82
−5.81
61.57
10.51
6.25
72.88 ± 3.49
4 4.76 3.2
1208 1368 1352
75 66.5 65.7
0.41 0.41 0.26
0.82 0.82 0.39
−5.81 −5.81 −7.62
61.57 61.57 229.63
10.51 10.51 26.99
6.25 6.25 11.53
61.61 ± 6.37 62.44 ± 8.94 84.02 ± 4.66
4 4.76 3.2
1600 1813 2642
56.6 50.2 34.1
0.26 0.26 0.39
0.39 0.39 0.49
−7.62 −7.62 0.04
229.63 229.63 5.69
26.99 26.99 14.54
11.53 11.53 4.42
58.96 ± 9.16 76.03 ± 11.22 49.51 ± 13.14
4 4.76 3.2
3117 3421 2551
29.4 26.1 34.7
0.39 0.39 0.96
0.49 0.49 1.37
0.04 0.04 −1.95
5.69 5.69 13.94
14.54 14.54 8.86
4.42 4.42 3.30
44.75 ± 11.91 49.89 ± 7.53 12.76 ± 6.03
4 4.76 3.2
3019 3421 2551
29.9 26.6 34.7
0.96 0.96 0.2
1.37 1.37 0.35
−1.95 −1.95 −3.66
13.94 13.94 52.43
8.86 8.86 19.62
3.30 3.30 8.97
38.65 ± 8.73 40.07 ± 5.32 89.22 ± 1.61
4 4.76
3019 3421
29.9 26.6
0.2 0.2
0.35 0.35
−3.66 −3.66
52.43 52.43
19.62 19.62
8.97 8.97
68.11 ± 12.24 81.48 ± 9.39
AAO_20nm
Al foil
Sapphire frosted
Sapphire polished
20 nm pore size
Al foil with native oxide
Rough single crystal
Smooth single crystal
Note: Sa: Average surface roughness, Sq: RMS roughness, Ssk: Skewness, Sku: Kurtosis, Sm: Mean spacing at the mean line, and S: Mean spacing of adjacent local peaks. 843
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(a)
AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
100
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20
0.0
0.2
0.4
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AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
100
Cleaning efficiency (%)
Cleaning efficiency (%)
80
(b)
1.2
80
60
40
20
0.0
0.2
Sa (μm)
0.4
0.6
0.8
1.0
1.2
1.4
Srms (μm)
Fig. 3. The Cleaning efficiency of different surfaces vs. (a) Surface roughness (Sa) and (b) Root-mean-square roughness (Sq) under three different tip diameters.
surface morphological parameters on cleaning efficiency including surface roughness, skewness, kurtosis and spacing parameters. The correlation between the surface morphological parameters and cleaning efficiency can provide the insight of designing a surface that optimizes the cleaning efficiency.
3.2. Effects of surface roughness on cleaning efficiency To evaluate the smoothness of a surface, two parameters are commonly used including average surface roughness and RMS roughness. Both parameters are to evaluate the height variation of surface profiles. The definitions of surface roughness and root-mean-square roughness are as follows:
Sa =
Sq =
1 A
∬ |Z (x, y)| dxdy A
1 A
Fig. 4. The line profile comparisons of five different surfaces.
(4)
3.3. Effects of surface profile on cleaning efficiency
∬ Z 2 (x, y) dxdy A
(5)
In addition to average surface roughness and RMS roughness that are evaluated based on height variation, the distribution of height values can also be equally important. The distribution of height can provide a better description about shapes of surface profiles, which complements that arithmetic roughness and root-mean-square roughness fail to provide. The common cited parameters are skewness and kurtosis, and the definitions are given below
where Z (x , y ) represents the surface profiles. Smaller Sa or Sq indicates a smooth surface with smaller height variation. Fig. 3(a) shows the cleaning efficiency and the surface roughness of each surface. It shows that the surface of Sapphire_polished has the smallest Sa followed by AAO_20nm, AAO_200nm, and Al_foil. The Sapphire_frosted is the roughest surface with the highest surface roughness closed to 1 µm. From Fig. 3(a), the cleaning efficiency and the surface roughness exhibits a linear relationship, indicating that dust particles on the rougher surface are difficult to be removed and cleaned. Fig. 3(b) shows a similar linear trend as well between the cleaning efficiency and the rootmean-square roughness. To understand the reason, the line profiles among the different surfaces are shown in Fig. 4. For smooth surfaces like Sapphire_polished or AAO_20nm, the line profiles can be considered as a flat line. When the tip is sliding on a flat surface, the dust particles are easily removed. If the surface becomes rougher with increasing surface roughness, more and more peaks and valleys are observed. While dust particles are removed by the tip, more dust particles are possibly trapped within the valleys. Thus, the cleaning efficiency decreases with the rougher surface. In our previous study (Chen et al., 2018), the glass slide was used as the substrate for dust sweeping experiments. The surface roughness of the glass slide is extremely small about 0.024 µm with up to 95% cleaning efficiency. It also shows that the smaller surface roughness promotes cleaning efficiency.
Ssk =
Sku =
1 ⎡1 Sq3 ⎢ A ⎣
∬ Z 3 (x, y) dxdy⎤⎥
1 ⎡1 Sq4 ⎢ A ⎣
∬ Z 4 (x, y) dxdy⎤⎥
A
A
⎦
⎦
(7)
(8)
where Z (x , y ) represents the surface profiles, and Sq is root-meansquare roughness. Skewness is a measure of the symmetry of height distribution. A positive value of skewness refers to the surface profiles consisting of many peaks. On the other hand, a surface profile with a negative value of skewness mainly consists of valleys. Fig. 5(a) shows the plot between the cleaning efficiency and the skewness of different surfaces. Among five surfaces, four surfaces are negative-skewed surfaces. The skewness of Al_foil is close to zero, which indicates a symmetrical distribution of height. The surfaces with negative values of skewness have higher cleaning efficiencies up to 90%. However, the cleaning efficiency of Al_foil is lower compared to other surfaces with 844
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AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
100
80
(b)
Cleaning efficiency (%)
Cleaning efficiency (%)
(a)
60
40
20
-8
-7
-6
-5
-4
-3
-2
-1
0
1
100
80
60
40
AAO 200 nm AAO 20nm Al foil Sapphire_frosted Sapphire_polished
20
0
Ssk
50
100
150
200
250
Sku
Fig. 5. The Cleaning efficiency of different surfaces vs. (a) Skewness (Ssk) and (b) Kurtosis (Sku) under three different diameters of tip.
and Sapphire_frosted, it indicates that surface profiles become symmetrical consisting of more peaks and valleys where the tip is sliding on. During the sliding, the dust particles are likely to get trapped due to the symmetrical waviness of the surface. This indicates that a surface with symmetrical distribution is not optimized for dust particles cleaning.
similar surface roughness but negative skewness. This shows that a surface with a symmetrical distribution of height does not improve the cleaning efficiency. The other common parameter is kurtosis, which is a measure of the sharpness of height distribution. If the kurtosis is larger than three, the surface profiles tend to have sharper peaks, which is also called leptokurtic. Fig. 5(b) shows the effect of different values of kurtosis on cleaning efficiency. From the curve, the cleaning efficiency reaches the plateau when the kurtosis is above the critical value. It suggests that a sharper surface profile is in favor for higher cleaning efficiency. This is also likely due to the reduction of contact area between dust particles and a surface with sharper peaks, which promotes cleaning efficiency. To understand the contact scenario between the tips and surfaces while cleaning dust particles, the bearing ratio curves can provide more insight. Bearing ratio curves or Abbott-Firestone curves are obtained from the cumulative density function of the histogram of surface height. It provides the ratio between the real contact area (bearing area) and total evaluated length or area at any given height values. In Fig. 6, the bearing ratio curves of Sapphire_polished, AAO_20nm, and AAO_200nm all show similarly flat profiles, which indicates the surfaces is smooth and negatively skewed. If the skewness increases, the profile of the bearing ratio curves gradually inclines and rougher surface profile like Sapphire_frosted inclines the most. While the tip is sliding on a negatively skewed surface, it shows a more firm and complete contact. This can promote the cleaning efficiency while the tip keeps sliding. On the contrary, if the profile of bearing ratio curves inclines more like Al_foil
3.4. Effects of the spatial parameters on cleaning efficiency In addition to amplitude parameters, we also investigate the correlations between the cleaning efficiency and the spatial parameters including the average spacing between the local peaks (S) and the average spacing at the mean line (Sm). Compared with amplitude parameters, these spatial parameters are to describe the horizontal variation of roughness. A smooth surface with low surface roughness value such as AAO_20nm or Sapphire_polished usually has larger spacing between local peaks. On the contrary, a rough surface refers to the smaller average spacing. In Fig. 7(a), while the value of Sm increases, the cleaning efficiency continues to increase and reaches the plateau passing the critical average spacing. The similar trend can also be observed as well in Fig. 7(b) regarding the average spacing at the mean line. The critical average spacing may be relevant to the interplay between the dust particles size and the surface morphology. If a surface has a larger average spacing (smooth surface), either the large or small dust particles can be easily cleaned, but on the rough surface, only larger particles can. Thus, only larger dust particles are relatively easy to be cleaned on smooth and rough surface. 3.5. A physical model to illustrate mechanisms of particle removal In this study, we investigated the relationships between the cleaning efficiency and various surface morphological parameters. Results show that the cleaning efficiency is dependent on the surface roughness and the shape of a surface profile. While a tip sweeps the particles, a physical model representing different interplays between the tip and surface profiles are shown in Fig. 8. There are three scenarios. First, when the tip is sliding on a smooth surface, the tip tends to have firm and complete contact with the surface. Thus, most of the dust particles can be removed during the cleaning. Second, if the surface roughness continues to increase, it indicates that the height variation increases, which forms more gaps between the tip and the substrates due to asperities. During the sliding of tips, the dust particles can be easily inserted into the gaps between the peaks or valleys. Thus, the cleaning efficiency may decrease due to the increasing surface roughness. Third, if the surface is sufficiently rough, the dust particles may be trapped
Fig. 6. The bearing ratio curves of different surfaces showing the contact scenario between the tip and surfaces. 845
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(a)
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Fig. 7. The Cleaning efficiency of different surfaces vs (a) the average spacing between local peaks and (b) the average spacing at the mean line.
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proposed to explain cleaning processes. Since various anti-reflective coating (ARC) and anti-soiling coating (ASC) are applied in the field to mitigate dust soiling, the design and stability of surface profiles of coatings is critical and should be considered by manufactures in order to reach optimal cleaning maintenance of PV panels. A consistent smooth surface in any weather condition can ensure optimal cleaning efficiency. If surface roughness changes, the cleaning mechanisms will change accordingly. New knowledge generated in this research can not only be applied to maintenance of PV panels but also other industrial cleaning applications such as food processing, medical devices, and structural materials design and fabrication.
Particles Trapped
Partially Trapped and Removed
Smooth Surface
Particles Removed
Declaration of Competing Interest
Fig. 8. The physical model of the cleaning mechanisms of dust particles on different types of the surfaces.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
between the peaks or valleys during cleaning as well as settle between those areas during deposition. This suggests that the dust particles are hardly cleaned leading to minimum cleaning efficiency with those rough surface profiles. This research revealed critical features of material surfaces influencing cleaning efficiency. In terms of application, our findings could potentially be used for future establishment of cleaning standard. The International Electrotechnical Commission (IEC) has been publishing a series of standards for solar energy systems including non-concentrating and concentrator modules. There are some standards about coatings and dust such as EN 1096-2 and IEC 60068-2-68. There are currently developing standards about PV soiling and dusting, such as IEC 627887-3 about accelerated PV abrasion test. Our work is complementary of previous effort.
Acknowledgements Part of this research was sponsored by the Qatar National Research Fund (award number NPRP7-987-2-372), Qatar Foundation. The statements made herein are solely the responsibility of the authors. Authors wish to acknowledge the support by Texas A&M Strategic Research Seed Grant Program. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.solener.2019.10.087.
4. Conclusions
References
In this research, the correlations between the cleaning efficiency and the surface morphological parameters are investigated. An established methodology using a brush-on-disk configuration was used to evaluate the cleaning efficiency of five different surfaces of aluminum oxide. The results showed that surface morphology has profound influence on the cleaning efficiency in three aspects. First, the cleaning efficiency exhibits a linear trend with arithmetic surface roughness and root-mean-square toughness. Second, in the relevance of a cleaning tip and the surface profile, the cleaning efficiency tends to be higher on the surface of negative skewness. Third, there is a relative special ratio between a brush tip, particle size, and surface profile that dominates the removal mechanisms during cleaning. In the end, a physical model is
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