Nature-Inspired Metallic Networks for Transparent Electrodes

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Nature-Inspired Metallic Networks for Transparent Electrodes

Jinwei Gao, Zhike Xian, Guofu Zhou, Jun-Ming Liu, and Krzysztof Kempa*

Nature offers structural solutions to various optimization problems. For example, an optimal, low-shedding water transport at various scales is achieved with quasi-fractal structures, shown to be close to optimal. In a series of projects, metallic network analogs of some of these solutions to make high-efficiency transparent conductors are studied. Specifically, trans- parent conductors are developed by directly metalizing leaf venations, spider webs, and other organic fibers. Also, the natural process of self-cracking, similar to that occurring in the mud of dried-out riverbeds, is employed to develop masks for metallic network fabrications. These comprehensive studies and developments contributed to, and in some cases initiate new directions in the field of network transparent conductors. These structures offer performance exceeding those of conventional oxide-based films, while providing a possibility of reduced processing expense. This paper provides a concise, comparative review of this study and other groups’ efforts in recent years. In the context of applications, the performance criteria are defined, and with those as a guideline, practicality of the most promising networks is discussed.

DOI: 10.1002/adfm.201705023

Prof. G. Zhou

Shenzhen Guohua Optoelectronics Tech. Co. Ltd.

Shenzhen 518110, P. R. China Prof. J.-M. Liu

Laboratory of Solid State Microstructures and Innovative Center of Advanced Microstructures

Nanjing University Nanjing 210093, P. R. China Prof. K. Kempa

Department of Physics Boston College

Chestnut Hill, MA 02467, USA

transparent conductors play the crucial role of transparent electrodes, leading to rapidly growing demand. For example, the touch screen transparent conductors market will reach $4.8 billion by 2019, from only about

$956 million in 2012.^[8]

Conventional transparent conductor technology is quite old, with CdO powder as the first transparent conductor made in 1902,^[9] and the first transparent conducting oxide developed by oxidizing the sputtered Cd film in 1907.^[10] In 1951 Corning developed and patented the most successful transparent conducting oxide, indium tin oxide (In₂O₃: Sn, ITO),^[11] which has become the workhorse of the transparent conductor industry, and continues to be such until today.^[12–15] ITO can be viewed as a metal with its plasma frequency in the infrared range, and thus transparent in the visible range.^[16,17] The strength of ITO (as well as other similar transparent conducting oxides) is its straightforward one-step fabrication by sputtering,^[14,18,19] and its chemical stability.^[20,21] The resulting films are also low haze, and dense, which make them ideal for displays, in particular small scale, including touch screen. However, ITO has several limitations:

(1) the increasing price of indium resulting from rising consump- tion and limited supply, (2) the need for vacuum processing, and (3) the brittleness of ITO (it cracks and fractures at relatively low strains of 2–3%), which restricts its application in the increasingly important market of flexible electronic devices.^[5,22–24] These limitations drive the need for transparent conductors, which are not based on ITO, or similar transparent conducting oxides.

Metallic Networks

Prof. J. Gao, Z. Xian, Prof. J.-M. Liu, Prof. K. Kempa

Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials South China Academy of Advanced Optoelectronics South China Normal University

Guangzhou 510006, P. R. China E-mail: [email protected] Prof. G. Zhou

Institute of Electronic Paper Displays

South China Academy of Advanced Optoelectronics South China Normal University

Guangzhou 510006, P. R. China

1. Introduction

Transparent conductors optimize two requirements: high electrical dc conductivity and, simultaneously, high transparency (typically in the visible frequency range). These are contradictory requirements, since high conduction requires high density of charge carriers, which in turn scatter photons, and thus reduce transmission. Transparent conductors are critically important for a range of most successful, recently developed everyday products, such as smart phones, Television monitors, light emitting diode (LED), smart windows, and solar panels.^[1–7] In these applications

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adfm.201705023.

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In response to this need, many new technologies have been proposed and demonstrated, with examples of microstructures resulting from some of these shown in Figure 1. These examples include conducting polymers (Figure 1a),^[25–28] nanotube networks (Figure 1c),^[29–31] graphene films (Figure 1d),[3,4,32–34]

as well as metallic networks (Figure 1b,e–h).[2,6,35–46] While carbon-based structures (nanotube arrays and graphene films) offer some advantages (flexibility, wet chemistry processing, etc.), their electro-optical performance, and manufacturability are still inferior to those of the oxide-based transparent conductors. Metallic networks, made of individual nanowires or continuous metallic lines, are the most promising candidates for the nonoxide transparent conductors. Conductivity of the metallic networks is determined by the continuity of the metallic lines and their cross-sectional area. The metallic network transparency, in turn, depends on the density of the network lines and the ratio of their perpendicular dimensions to the light wavelength. Metallic nanowire networks consist of random distributions of long metallic nanowires.[29,40,47–55]

While these can be inexpensively processed by wet chemistry, they suffer from poor conductivity due to the large wire-to- wire contact resistance and large number of such contacts involved. Metallic networks made of continuous metallic lines have been recently developed to remedy this deficiency, and these include periodic^[42–44] (Figure 1b) and random metallic grids,[2,6,35–41] shown in Figure 1e–h.

In the search for self-assembled network structures on which such metallic grids could be based, our group has been inspired by Nature, which continually develops many diverse bio-microstructures with surprising properties.^[56–78] These include the lotus leaf, with its super-hydrophobic surface,^[59,60]

butterfly photonic crystal wing structures, with their reflective interference colors,^[61–64] gecko feet with controllable adhe- sion,^[65–67] nacre with super hardness,^[68,69] and wood-cellulose

Jinwei Gao is a Professor at the Institute for Advanced Materials (IAM), South China Normal University (SCNU), where he leads the Optoelectronics Mateials Laboratory. He received his PhD in 2010 from South China University and Technology (SCUT) and Massachusetts Institute of Technology (MIT) (Joint).

From 2010 to 2015, he worked as an associated Professor in IAM of SCNU. Since 2016, he has worked as a full Professor in SCNU. His research interests focus on optoelectronic materials and advices and nature inspired materials for light management and energy conversion.

Krzysztof Kempa is a Professor of Physics at the Physics Department, Boston College. He is a Fellow of the American Physical Society and Presidential Professor of the Republic of Poland.

Since 2010 he has been also a Distinguished Visiting Professor at the South China Normal University. His research has been focused on nanoscience, with particular interest in nano-optics and plasmonic effects.

Figure 1. SEM images of the microstructure of the transparent conductors not based on transparent conducting oxides: a) Conducting polymer film.

Reproduced with permission.^[28] b) Periodic metallic grid. Reproduced with permission.^[44] Copyright 2012, American Chemical Society. c) Carbon nanotube film. Reproduced with permission.^[46] Copyright 2006, IOP Publishing Ltd. d) Graphene film.^[3] e) Crack nanonetwork (CNN). Inset: Photograph of the crack network on a PET substrate, showing high network flexibility and transparency.^[38] f) Leaf venation (LV) network. g) Spider silk web (SSW) network. (f) and (g) are reproduced with permission.^[37] Copyright 2014, Nature Publishing Group. h) Nanowire network. Reproduced with permission.^[47] Copyright 2015, American Chemical Society.

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substrates with their hierarchical structures,^[70–72] etc.

Amongst these are also spider silk web (SSW), with excellent elasticity and mechanical strength,^[73,74] and leaf venation (LV) with quasi-fractal structure of microfluidic channels, close to optimal for fluid delivery at high transparency through the structure.^[75–78] We chose those as self-assembled bioscaffolds, which turn into metallic grids of transparent conductors upon metallization (by sputtering or evaporation). Figure 1f shows an scanning electron microscopy (SEM) image of the network based on the LV, characterized by the quasi-fractal structure.

Since the multiorder quasi-fractal nature of these networks is the most important optimizing factor, a whole family of hierarchical structures has been proposed and demonstrated.^[41]

Another natural scaffold proposed and demonstrated by our group was the SSW.^[37] Figure 1g shows an SEM image of this network. Another group of our nature-inspired transparent conductors consists of crack nanonetworks (CNN).^[36,38,39]

Due to the thermal and mechanical stresses in a film of cer- tain materials, stresses accumulate causing the film to crack, similar to the mud cracking in riverbeds in the dry season.

In some materials the cracks form a network of continuous gaps, and thus such network of cracks can be used as a mask for subsequent metal deposition, e.g., sputtering. The mask is removed after the film processing. Figure 1e shows an SEM image of the typical CNN.

In this review we begin with the CNN networks, also in a hierarchical combination with metallic nanowires, which enable plasmonic super-transparency effects. These networks can be fabricated inexpensively, even with vacuum-free wet chemical processes. Next, we discuss bioinspired networks in- depth, including LV and SSW, as well as some unpublished

networks based on the lotus fiber. We conclude with other nature-inspired ideas such as bubble stacking, coffee ring effect, electrospinning, nanomesh by grain boundary template, nanotrough, and crystal salt and ice template. Finally, we define performance criteria, with which we discuss the practicality of the most promising networks.

2. Networks Based on Cracked Film Masks

As mentioned above, a natural phenomenon which is very useful in this context is the stress-induced self-cracking of brittle materials.^[79,80] This generic self-cracking effect occurs at various scales, ranging from the mud cracking in dried-out river beds, to the microcracking of thin films of various materials. Figure 2a–c shows several examples of such self-cracked films. These can be used as masks for selective metal deposition. In the final step of processing the masks are removed, leaving behind the nanoribbons of metal.^[38] Thus, CNN is a nanoribbon network, with metal thickness (≈100 nm), much smaller than the ribbon width (approximately few µm), and interribbon distance (of the order of 50 µm), as shown in the inset in Figure 2d. This ribbon nature of CNN is important.

First, due to flat-top surfaces of the ribbons, the stray reflec- tions are minimized, resulting in very low haze. Second, due to large spacing between the ribbons, these networks can be heavily plated without significantly reducing light transmission. A few micrometer overcoat of this ribbon by plating (shown in the inset in Figure 2e) dramatically reduces its resistance but leads to only a small reduction of the light transmission.

Figure 2. Images of self-cracking templates and the corresponding crack nanonetworks (CNN). Photograph of self-cracked TiO2 a), egg-white b), and nail polish c). d) SEM image of CNN based on egg-white mask, with sputtered Ag lines. The inset shows SEM image of a fragment of this network, clearly showing the ribbon-nature of the network lines. The arrow points to a small fragment of egg-white mask that has not been removed.

f) Vacuum-less Ag-based CNN, with the inset showing small section of the network.^[36]

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2.1. Crack Nanonetworks Based on Vacuum Metal Deposition The simplest CNN can be obtained by vacuum evaporation or sputtering of metal through the cracked mask.^[38,43,81] The procedure shown schematically in Figure 3 (top route) includes four steps: (1) synthesis and deposition of the cracking layer, (2) self-cracking, (3) metallic film deposition, and (4) cracked mask lift-off. Examples of cracking materials include an oxide powder (e.g., TiO₂),^[38] polymer (for example, CA600),^[38] a nail polish,^[82,83] and egg-white.^[39] One of the most promising materials is egg-white; it is cost effective, environment friendly, and exhibits easily controllable cracking. It has been used in the fabrication of high-quality metallic networks (see Figure 2d),^[39] and recently in a large-scale production pilot line at South China Normal University (SCNU) (see Figure 8a,b).

The main advantage of these sputtered/evaporated networks is the simplicity of fabrication as well as a flexibility of the metal choice (almost any metal can be sputtered). The disadvantage is the relative high cost due to need for vacuum processing.

2.2. Crack Nanonetworks Based on Vacuum Metal Deposition and Electroplating

As discussed above, due to the ribbon structure of the CNN one can employ plating techniques to improve the network conductivity, without significantly sacrificing the transmission. This was demonstrated by our group^[39] and others.^[84,85]

The procedure, shown schematically in Figure 3 (middle route), is as follows.^[39] First, the cracked mask is formed, followed by deposition (by sputtering) of a very thin metallic seed-layer (≈10 nm), much thinner than the ≈100 nm metal deposition used for CNN networks based on the vacuum

metal processing only (as described in Section 2.1). The cracked mask removal represents the third step. The final step is electroplating, which overcoats the thin seed network with a chosen metal. Compared to the process described in Section 2.1, this process is more economic; only a very thin layer of seed metal is deposited via the wasteful sputtering process. Note that the electroplating deposits metal only where it is needed: on the seeded network. Figure 2e shows morphology of this kind of CNN, with the inset showing strongly overcoated ribbons.

2.3. Crack Nanonetworks Based on Vacuum-Less Processing While the CNN networks discussed above outperform ITO and other networks (this will be shown below), their processing expense is comparable to that of ITO, since the vacuum metal deposition is also used. This is the main reason for the relatively slow implementation of transparent metallic network technologies. To reduce that cost, a vacuum-less processing has been proposed and demonstrated by our^[36] and other groups.^[43,55,82]

SEM images of such a vacuum-less CNN process developed recently by our group are shown in Figure 2f and the inset.

This network shows good performance, comparable to that of the CNN relying on the vacuum metal deposition (described in Sections 2.1 and 2.2).

The fabrication procedure for our vacuum-less CNN process, shown schematically in Figure 3 (third route) is described in detail in ref. [36]. The rough outline of this process is as follows: first, a layer of the cyclic transparent optical polymer (CYTOP)^[86,87] is deposited on a polished surface of a c-Si wafer (step 1), followed by deposition of thin egg-white or CA600 films (step 2). In step 3, the egg-white film self-cracks upon drying and in step 4 a low vacuum plasma etching transfers Figure 3. Schematic of the CNN processes.

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the template crack-pattern into the corresponding pattern of etched grooves in CYTOP. The remains of the egg-white film are then removed by washing, and in step 5, electroless plating is used to selectively deposit metal into the grooves.

Finally, in step 6, CYTOP is removed by plasma etching. To transfer the network onto another substrate, the network (still on the c-Si wafer) is coated with UV-curable glue, and then a substrate, flexible polyethylene terephthalate (PET) is pressed onto it. After the glue is crosslinked under UV radiation, the PET substrate is peeled-off, with the network attached to it.

The network remains embedded in the UV glue, which, in addition to protecting the network, also reduces the surface roughness. This process can be scaled up and is compatible with the roll-to-roll processing. Strictly speaking, this is not a vacuum-free process, since plasma etching is used, even though this only requires inexpensive low vacuum. However, in another version of our process, plating-based on an ITO PET substrate, plasma etching is entirely eliminated, making it truly a vacuum-less process (not published)^[36]

3. Transparent Conductors Obtained by Metallization of Biostructures

These networks employ biomaterial scaffolds, which become conductive when metalized.

3.1. Leaf Venation Based Networks

The first network of this kind was obtained by chemically extracting venation of a leaf and, subsequently, metalizing it via sputtering.^[37] The key idea behind employing such a scaffold as an efficient electrode was the similarity between physics of the electrical current flowing along the metalized venation branches, and that of the fluid in the original venation channels. Thus, the proven, quasi-optimal performance of the microfluidic channels of the LV^[37] should translate into similar, near-optimal performance of the metalized LV. Figure 4a shows a photograph of the leaf of the plant Magnolia Alba, used in ref. [37] to extract the LV. The magnified photographic image of the square area marked red in Figure 4a is shown in Figure 4b, with clearly visible LV network. This network has a nonuni- form, quasi-fractal structure, with fractal dimension ≈1.4.^[41]

The extracted LV network, placed against the background of a text, is shown in Figure 4c. Clearly, the veins are visible and, thus, this network cannot be used as a transparent electrode for small displays (e.g., smart phones), but is applicable to high power LED sources and solar cells. In fact, the distance between veins is much smaller than in conventional, commercial c-Si solar cells, which should allow for a better current extraction.

A schematic of this network preparation is shown in Figure 4d.

It involves three steps: removal of mesophyll (the green matter) by etching in alkali solution, followed by sputtering of metal on

Figure 4. Morphology of the Magnolia Alba leaf and the LV network fabrication. a) Photograph of the leaf and b) its magnification in the area marked red in (a). c) Optical image of the leaf venation network placed against a printed text (font: Times 12). d) Schematic of the leaf venation network fabrication. Reproduced with permission.^[37] Copyright 2014, Nature Publishing Group.

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the extracted LV, and finally, transfer of the metalized LV onto any chosen substrate, with good attachment assured with various adhesives.

3.2. Silk Spider Web and Lotus Fiber Based Networks

The second network proposed in ref. [37] was obtained by sputter-metallizing a SSW. Specifically, the web of the spider Agelena labyrinthica was chosen for its 2D character, ideal for planar electrodes. Figure 5a shows a photograph of an SSW from this spider, spun on a holder frame. Clearly visible are bundles of the fibers, and so the SSW network applications are limited to LED light sources and solar cells. Figure 5b shows a schematic of the SSW network fabrication. It consists of three basic steps: collection of SSW onto a sample holder frame, metallization (Ag sputtering), and finally transfer of the SSW network onto a flexible substrate. The SEM image of this Ag- based network is shown in Figure 5c, with the inset showing details of the fiber interconnections. The diameter of the silk fibers of this web is ≈100 nm, with the interfiber spacing of the order of a few micrometers. Fibers of spider webs have exceptional mechanical properties, with high strength and elasticity.^[88,89]

Here, we present also a network from this class that was very recently developed in our group, the microweaved lotus fiber network. The fabrication process begins with the fiber extraction from the lotus (Nelumbo nucifera) stem by pulling. This produces networks with parallel fibers on a holder frame. Then this network is metalized via a sputtering or thermal evaporation

and then simply pressed against an adhesive covered substrate (e.g., polydimethylsiloxane (PDMS)). The microweaved network is obtained after another metalized fiber network is pressed against the same substrate but in the perpendicular direction.

The structure of this network is shown in the SEM image in Figure 5d. The individual fibers have diameter of ≈20 µm, and the interfiber distance is of the order of 500 µm. Figure 5e shows a completed transparent network on PDMS, with clearly visible fibers. This lotus fiber network has a larger-scale microstructure than the SSW network. Thus, possible applications are limited only to the high power LED light sources and solar cells.

4. Performance of the Nature-Inspired Transparent Networks

Performance of the transparent conductors is evaluated in terms of specific applications. For example, nonuniformities much smaller than pixel sizes are needed for small-scale displays (e.g., smart phones). In addition, a very low haze is a must.

While ITO or other uniform oxide materials excel in the quali- ties mentioned above, and this is the main reason for their popularity, only networks with ultrafine structures (SSW, CNN–

nanowire hybrid) can satisfy these criteria. On the other hand, these para meters are not important for LED light sources and solar cells. In those applications, it is the large vertical transport that is critical, and this can be maximized with fractal networks, which otherwise can have large haze, or visible structural features.

Figure 5. Spider silk web (SSW) and microweaved lotus fiber networks. a) Photograph of spider web (Agelena labyrinthica) spun on a metal holder frame. b) Schematic of the SSW network fabrication process. c) SEM image of a multilayer SSW Ag-based network. Inset: higher SEM magnification image of the same network, showing Ag coated silk wires ≈100 nm diameter. (b) and (c) are reproduced with permission.^[37] Copyright 2014, Nature Publishing Group. d) SEM image of a microweaved lotus fiber network. e) Optical image of the microweaved lotus fiber Ag-based network, attached to a flexible polymer, showing good transparency, but also network visibility.

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4.1. In-Plane Electro-Optical Properties

It is customary to characterize films of transparent conductors by their transmittance T, and an in-plane sheet resistance Rs. Figure 6a displays these parameters for samples of the various nature-inspired networks developed by our group, with each point representing a sample. For comparison, an ITO sample is also shown. Transmittance represents the percentage of light flux transmitted across the sample at the vacuum wavelength λ = 550 nm and at vertical light incidence. Sheet resistance is the resistance (at zero frequency) of a square of a thin film of thickness d, measured from side-to-side. Clearly, samples approaching the left upper corner on this plot represent best transparent conductors. The solid lines represent the values of the so-called figure of merit F, a number which numeri- cally characterizes a transparent conductor (larger F implies better in-plane conduction performance). F is simply related to T and R_S via^[2,3,36]

T R Fs

1 188.5 ²

= +

 



−

(1)

and thus it can be determined by fitting Equation (1) to the data points. The plated CNN shows a record-high F = 30 000. Also, the networks based on LV and SSW have large F, in the range of 1000–1700. For comparison, F = 300 represents ITO. How- ever, as discussed above, the in-plane performance is important only for a class of applications.

4.2. Vertical Conduction

Vertical conduction is important for applications in which the transparent conductor film, working as an electrode, is to deliver current to (e.g., LED) or extract current from (e.g., solar cells) the substrate. It turns out that in those cases, quasi-fractal

structures (e.g., LV networks) are superior, as demonstrated in ref. [37]. One way to evaluate vertical conduction is to directly measure the series resistance of a solar cell, which uses a given transparent conductor film as the window electrode. Figure 6b shows three I–V characteristics of a-Si solar cells (under AM1.5 illumination), with window electrodes made of LV, CNN, and ITO.^[37] The corresponding extracted series resistances are shown in the inset, showing that the lowest series resistance is for LV window electrode, followed by CNN and ITO. The cell efficiencies scale the same way, with LV best, CNN second, and ITO last. Two optical images of the solar cells are also shown in this inset. Even though the cell based on the LV network has a clearly visible leaf venation structure, its performance is the best. The superior performance of LV has been identified as due to the quasi-fractal nature of these networks.^[41] The simplest way to understand this is to notice that a fractal structure works by uniformly distributing current to the substrate, through conducting veins self-similarly reducing their dimensions with reduced current demand. This is analogous to the main “highways” in a road network splitting into smaller local

“road” networks, which can handle the lower volume of local traffic. The ways to exploit and maximize this effect will be discussed further below.

4.3. Elastic Properties

In addition to good electro-optical performance, all nature- inspired networks show also good elastic properties.^[36–39]

Figure 7a shows the sheet resistance of an Ag-based CNN versus repeated elastic bending events. At each event the bending angle was 180 and the bending radius was 1 mm. The inset in this figure shows a zoomed-in fragment of the main curve, showing perfectly repeatable, small (2%) sheet resistance oscillations. The overall resistance changes by less than factor of two, up to 1000 bending cycles. Figure 7b shows the same bending test made on a hybrid network, made by depositing Ag nanowires onto Figure 6. Electro-optical properties of the nature-inspired networks. a) T versus Rs for CNN, LV, SSW, hybrid CNN, lotus network, and ITO.^[36–39,41]

b) Current density versus voltage of solar cells under AM1.5 illumination: red line with circles represents LV network (marked quasi-fractal (QF) in the inset), black line with squares is for CNN (marked C in the inset), and blue line with triangles represents ITO. The inset shows a plot of the corresponding series resistance R_s(Ω). Two optical images of the solar cells are also shown in this inset. Reproduced with permission.^[41] Copyright 2016, Nature Publishing Group.

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Ag-based CNN. The inset shows a zoomed-in fragment of the main curve for this hybrid network compared to ITO. While bending negligibly affects the hybrid network after up to 200 bending events, and only doubles the sheet resistance after 1000 bending cycles, ITO fails completely after a few bending cycles.

Figure 7c shows the SSW network sheet resistance (after tension release) versus strain. The inset shows time evolution of the network resistance subject to a train of eight stretching pulses: 50%

strain (first six pulses) and 100% strain (last two pulses). The elastic properties of this network are superior. For strains smaller than 25%, the resistance of the SSW network does not change at all. For larger strains (less than 100%) the resistance increases, but is fully recoverable at the increased value. The network is also quite insensitive to repeated elastic bending. Figure 7d is an analog of Figure 7a for Ag-based LV. Even though individual bending events lead to a relatively large 25% resistance variation, there is a negligible systematic increase of the resistance up to 1500 bending events.

4.4. Scalability

An important issue with all technologies is scalability.

ITO and other oxide technologies are scalable as long as

sufficiently large sputtering systems are available or such that allow for a roll-to-roll continuous operation. This is possible, but certainly expensive. CNN is the simplest of the nature-inspired networks to massively scale-up. Our group has recently developed a roll-to-roll processing line (see Figure 8a,b), which produces rolls of ≈0.8 m wide, egg- white-based CNN on PET substrate. The large sputtering system allows for an internal roll-to-roll mechanism. The process produces rolls of the cracked egg-white coated PET first, and then these are placed inside the sputtering system, which deposits metal (e.g., Ag) while the substrate moves through the chamber from one driving roll to another of the internal roll-to-roll system. After metal deposition, the processed PET roll is taken out and egg-white is washed away in another roll-to-roll arrangement.

The LV processes is less easy to scale-up. One possible way to accomplish this is to use printing. A schematic of a possible process is shown in Figure 8c. First, an LV network pattern is impressed on the surface of a drum, for example, using the contact photolithography process, shown schematically in Figure 8d. An extracted LV is placed on a photoresist covered drum surface and used as a photomask. Subsequent chemical etching engraves the surface with the LV pattern. Massive replication of this pattern can now be achieved, after the drum Figure 7. Flexibility of nature-inspired networks. a) Ag-based CNN sheet resistance versus bending events (bending radius ≈1 mm). Inset: A zoomed- in fragment of the main curve. b) Same as (a) but for a hybrid CNN-nanowire, Ag-based network. Inset shows the enlarged fragment for both the metal network and ITO. c) SSW network sheet resistance versus strain. The inset shows time evolution of the network resistance subject to a train of eight stretching pulses: 50% strain (first six pulses) and 100% strain (last two pulses). d) Same as in (a) but for Ag-based LV network. (c) and (d) are reproduced with permission.^[37] Copyright 2014, Nature Publishing Group.

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was coated with an Ag, or other metallic ink, and subsequently rolled on a substrate. Sintering of the ink microparticles com- pletes the process. We have tested the spatial resolution of this process by directly coating the LV network with Ag ink and then pressing it on paper. Figure 8e shows the photograph of the resulting print, with all details of the LV network reproduced. Next, we have developed an LV network by using the full process shown in (d). An SEM image of this network is shown in Figure 8f. Again, a good reproduction of all features of the original network was achieved.

Scalability of the SSW networks has been briefly discussed in ref. [37]. One obstacle is the high price of the silk fiber.

A possible practical way could be to base the SSW-like network on inexpensive polymers (e.g., Kevlar nanofiber, nanocellulose, etc.).

5. Nature Inspired Model Networks

As discussed above, the main feature of LV networks, which outperform other networks in vertical conduction, is quasi self-similarity (or fractality). Such networks contain branches, which split into branches with smaller diameters (lower order) at each junction. In a recent work of our group,^[41] it was shown theoretically and confirmed experimentally that strictly fractal networks (impractical anyway) do not optimize the networks (minimize the overall resistance at fixed shading) but are very close to that goal. A strictly optimal quasi-fractal network was identified but shown to be impractical. It was also demonstrated that multiple orders (the number of subsequent branch splits) control the degree of the optimality, not the details of the network geometry (e.g., periodicity, randomness, etc.).

Figure 8. Scalability. a,b) A roll-to-roll processing line for metal-sputtered, egg-white CNN on flexible substrates. c) The concept of a drum printing of LV networks. d) Schematic of the LV network replication with contact photolithography. e) LV image printed on paper directly from an Ag-ink coated LV.

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5.1. Quasi-Fractal (Hierarchical) Model Networks

To test the conclusion that increased number of orders are the most important feature for vertical conduction optimization, a number of networks was developed.^[41] The morphology of these networks is shown in Figure 9.

The first three networks shown in Figures 9 are single-order networks, with a CNN random network (R1) in Figure 9a, a large-scale periodic grid network (P1) in Figure 9b, and an ultrafine periodic grid network (UP1) in Figure 9c. The two- order random network (called R2) is made by depositing silver nanowires onto the R1 network. Its SEM image, shown in Figure 9d, reveals the two-order hierarchy: the CNN network (first-order) and the silver nanowire network (second-order).

A High-resolution transmission electron microscopy (TEM) image in Figure 9e and an electron diffraction pattern in the inset show the high quality of a single nanowire. Figure 9f and the inset show high-resolution SEM images of the R2 network fragment. The nanowires have an average length of ≈50 µm and diameter of ≈50 nm. Figure 9g shows an optical image of the

two-order periodic network (P2), obtained by adding the narrow horizontal and vertical metallic lines (of 200 µm length) to the P1 network. P1 and P2 large-scale networks were made by photolithography. A three-order model network (M3) was made by adding metallic nanowires to the P2 network (Figure 9h).

Finally, Figure 9i shows the LV three-order network (here also called QF).

The in-plane transport network performance is demonstrated in Figure 10a, which shows (as red dots) the sheet resistance R_s (called here R_sq) of three, two-order (R2) hybrid (CNN–nanowires) networks, called here S1, S2, and S3, each with different (increasing correspondingly) nanowire density. SEM images of these networks, together with the bare, single-order CNN network, are shown in Figure 10b. Clearly R_sq decreases strongly with increasing nanowire density, while the transmittance T decreases much slower.

Figure 10c most convincingly illustrates the advantage of increasing hierarchical orders for vertical transport (conduction).

It shows vertical resistance (inverse of conduction) R_vc (called here R_cont) versus network order. As expected, this resistance

Figure 9. Morphology of the hierarchical model networks. Single order networks: a) SEM image of Ag-based CNN, b) optical image of a large-scale periodic grid network, and c) SEM image of ultrafine periodic grid network. Two-order networks: d) SEM image of a hybrid network (CNN with metallic nanowires). e) High-resolution TEM images of a single nanowire used in (d), insets show the electron diffraction pattern, and an atomic resolution zoom-in image in the marked region. f) High-resolution SEM image of the network shown in (d), with inset showing details (scale bar = 100 nm).

g) Optical image of a two-order periodic network. Three-order networks: h) Optical image of a network shown in (g), with metallic nanowires added.

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is very similar for periodic and random networks of the same order (with the same T). On the other hand, adding an order significantly reduces R_vc, i.e., improves vertical conduction.

5.2. Plasmonic Effects

Networks utilizing nanowires additionally benefit from the subwavelength scale of nanowire diameters in the visible frequency range. This provides conditions for plasmonic refraction effects.^[40,41] These effects can enhance transparency of such networks above the classic geometric shading limit, which allows the transmittance T_g to be at most T_g = 1 − ν, where ν is the fraction of the surface covered by metal. In ref. [41] it was demonstrated that this classic dependency is well satisfied for a bare CNN R1 network. However, the statistically averaged Tg values for S1, S2, and S3 networks shown in Figure 10b were 85.7%, 83.4%, and 78.7%, respectively, all significantly smaller than the corresponding optically measured T values 90.5%, 88.8%, and 87.5%. This enhancement of transparency above Tg, in structures with strongly subwavelength dimensions

(e.g., nanowires) has been confirmed by simulations, and identified as due to plasmonic refraction, which is also observed in pure nanowire networks.^[40] A simple analysis used in refs. [40,41]

shows that as long as network feature sizes and the film thickness (t_f) are all  λ, the transmission coefficient t is given by^[40]

t 1 2

= −α (2)

where

i2 1 tf/

α= π

(

−ε

) ( )

λ (3)

and ε is the effective dielectric function of the network film.

Equation (2) explains the extraordinary transparency of the nanowire networks.^[40] This can occur near plasmonic reso- nances, which produce ε ≈1, making α ≈ 0 from Equation (3), and thus t ≈ 1. This is plasmonic refraction.

Equation (2) can also be used to derive Equation (1), which, according to Figure 10d and other experiments, is very well satisfied by all nanowire networks, with the slopes directly scaling Figure 10. Electro-optical properties and morphology of the modeled hierarchical networks. a) Measured in-plane sheet resistance Rsq and T of bare CNN, single-order network (R1) (black solid squares) and the corresponding two-order CNN based, R2 networks (red solid circles). The numbers above the data points are the corresponding T values in %. b) SEM images of the CNN-based two-order networks (R2), with increasing nanowire density.

c) Measured vertical resistance Rcont versus hierarchical order, for model structures (each pair of symbols represents the same order). Blue circles represent random and red triangles periodic networks. The three-order network (shown in Figure 9h) is represented by the red circle and the three-order quasi-fractal LV network is represented by the green square. (a)–(c) are reproduced with permission.^[41] Copyright 2016, Nature Publishing Group.

d) Plot of T versus R_s⁻¹ for nanowire networks, with ultralong (red circles) and long (black squares) nanowires.^[40] Upper inset shows SEM image of a network, and the lower inset is a schematic of the nanowires crossing.^[40]

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with F. The derivation requires only assuming that ε is domi- nated by its imaginary part. Then t can be assumed real, and T = ≈ −t² 1 α = −1 i2 1π

(

−ε

) ( )

tf/λ = −1 β

( )

Rs ⁻1 (4) where β = Z0/F, Z₀ is the vacuum impedance, and F = σ(0)/σ(ω) is the figure of merit. It is easy to show that Equation (4) is identical to Equation (1) in the limit of small α.^[40,41]

6. Other Nature-Inspired, Self-Assembly Networks

The networks described above have been developed by the SCNU and Boston College groups. In the remaining part of

this review, we present selected work of other groups.^[45,90–99]

These transparent conductor films are also metallic networks.

6.1. Networks Based on Bubble Stacking

These networks are obtained by employing a natural process of bubble formation in a foaming liquid. Figure 11a shows foaming in a solution of Ag nanowires dispersed in water, caused by adding a surfactant and a thickening agent. Due to surface tension, Ag nanowires self-assemble in a continuous polygonal network pattern when sandwiched between two glass plates, as shown in Figure 11b. These Ag nanowire-based metal meshes show good electro-optic performance R_s = 6.2 Ω sq⁻¹, at T = 84%.

Figure 11. Morphologies of various self-assembly networks. a) Photograph of foaming Ag nanowire water solution. Inset: Bubble edge with dense concentration of nanowires. b) Photograph of the bubble mesh structure, with a magnified fragment shown in the inset. c) Photograph of the bubble mesh film on a glass substrate, showing good transparency and low haze. (a)–(c) are reproduced with the permission.^[94] Copyright 2012, American Chemical Society. d) SEM image of a “coffee-ring” effect network (made of self-assembled Ag nanoparticles). Inset shows magnification of a single ring fragment. Reproduced with permission.^[95] Copyright 2012, American Chemical Society. e) Photograph of a “coffee-ring” effect network, based on CNTs. Insets show subsequent magnifications of the network. Reproduced with permission.^[98] Copyright 2014, Royal Society of Chemistry. f) SEM image of a gold nanotrough network. The inset on the left shows a sketch of the spinning process, and the one on the right shows details of the trough junction. Reproduced with permission.^[45] Copyright 2013, Nature publishing Group. g) SEM image of an Ag-based network formed by the salt crystal self-assembly. Top inset: Magnified SEM image of an Na2CO3 dendrite network. Bottom inset: Corresponding Ag pattern (metalization by spin coating) after the dendrite network removal. Reproduced with permission.^[97] Copyright 2013, American Chemical Society. h) SEM image of a metallic nanonetwork, obtained with the grain boundary template method. Reproduced with permission.^[96] Copyright 2014, Nature Publishing Group.

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Figure 11c demonstrates the high transparency of such a network.^[94] However, due to very large size of the network lines and the interline spacing, this network can be used only for special applications, such as solar cells or high power LED lighting. In a recent paper, a modification of this method has been proposed, which allows for much better control over the evolution of 2D liquid foams, allowing for a micropatterned arrays.^[99]

6.2. Networks Based on the Coffee-Ring Effect

Fast drying droplets of contaminated liquids form character- istic, ring-like deposits of the contamination particles around their original perimeters. This natural process, also called a “coffee-ring” effect, can be used to make transparent conducting networks.^[95,98] Figure 11d shows a photograph of such a random network, consisting of overlapping rings made of Ag nanoparticles (contamination particles).^[95] These networks have electro-optical performance comparable to ITO. Figure 11e show morphology of a “coffee-ring”-effect periodic network, based on carbon nanotubes as the contamination particles.^[98]

Even though these networks suffer from contact problems, similar to those for nanowire networks, they show a reasonable electro-optical performance with T > 90%, at Rs> 100 Ω sq⁻¹, sufficient for large-size touch screen displays.

6.3. Nanotrough Networks Made by Electrospinning

These networks are made by the electrospinning, a method that simulates the natural process of spider web formation.

Electrospinning uses electric force to draw charged threads of a polymer solution into a fiber. The fibers have diameter of ≈10 nm, are junction-free, and ultralong, and thus can be used to form good transparent conducting networks by a method first proposed in ref. [45], and similar to that for SSW networks.

A typical procedure involves the electrospinning of a polymer nanofiber web, metallization of this web by sputtering or evaporation (or a vacuum-less metal deposition), transfer of the metalized network onto a substrate, and finally the polymer template removal by chemical etching. The etching leaves only incomplete shells of the metallic coating, which resemble troughs (thus the nanotrough name). These networks exhibit good electro-optical performance with R_s ≈ 2 Ω sq⁻¹ at T = 90%.

Figure 11f shows the morphology of such a network.

6.4. Networks by Self-Assembly of the Salt Crystals

These networks are based on complex patterns of dendrite- shaped crystals of salt or ice, formed during the natural process of water evaporation. Such crystals have been used as a template in transparent conductor fabrication.^[91,97] Figure 11g shows an SEM image of the Ag network obtained by metallization of the Na₂CO₃ dendrite network with spin-coated Ag nanoparticles (silver ink), and subsequent salt template removal with water. A fragment of the dendrite network is shown in the top inset, and the bottom inset is the corresponding Ag pattern after the dendrite network removal. It was also demonstrated that a replica

of the network can be obtained by using PDMS. This replica can subsequently be used with Ag inks to transfer the network pattern onto another chosen substrate. The network parameters can be controlled in a wide range by the parameters of the processing. The in-plane optoelectronic performance of these networks was disappointing, with very large R_s≈ 1000 Ω sq⁻¹ at T = 84%. This was due to many “dead-end” lines in the network.

However, the networks could be useful for vertical transport applications (solar cells and LED), which are not affected by the presence of the “dead-end” lines. In fact, for special growth conditions, the network resembled a fractal structure, which helps optimize vertical transport.

6.5. Networks Based on the Grain Boundary Template

This method of making networks is similar to the cracking template method^[96] and involves bilayer (an In₂O₃ mask layer and an SiO_x as a template) formation, metal deposition, and template lift-off. These ultrafine nanograin-induced metal networks (Figure 11h) show good electro-optical properties (R_s ≈ 7 Ω sq⁻¹ at T = 85.2%) and mechanical properties.

7. Summary, Applications, and Outlook

In this article, we have reviewed nature-inspired transparent conducting networks. We began by justifying the need for metallic networks as a possible replacement for ITO. Next, we provided an in-depth review of the CNN networks, one of the most promising candidates in this class, with excellent in-plane electro-optical parameters, far exceeding those of ITO, and flexibility. These networks can be fabricated inexpensively, in one version even with vacuum-free wet chemical processing that is compatible with a roll-to-roll production fabrication. In combination with metallic nanowires, these CNN networks, with very large optical density, allow for high-resolution touch screen applications. These networks belong to the class of hierarchical model networks, which could also revolutionize vertical transport applications (LED and solar cells) and also enable plasmonic super-transparency. Next, we discussed bioinspired networks in-depth, including LV and SSW, as well as an unpublished work regarding networks based on the lotus fiber.

We also reviewed other nature-inspired ideas, such as bubble stacking, the coffee ring effect, nanotrough via electrospinning, networks by salt crystal self-assembly, and the grain boundary template method. As discussed in the Introduction, transparent conductor development has been driven by numerous, important applications such as touch screens of smart phones, screens of TV monitors, “window” electrodes of LED lights, smart windows, and solar panels. So far, ITO and other oxide materials control the market. These provide good electro-optical performance, high optical uniformity, chemical stability, low haze, and simplicity of fabrication. The breadth of this performance is impressive and hard to match.

The most severe limitation of ITO and other oxides is their brittleness. Thus, applications requiring flexibility can strongly benefit from metallic networks. Those applications are on the rise, and so is the demand for flexible transparent conductors,

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such as metallic networks. The second most significant deficiency of the oxides is their limited electro-optical performance and, in particular, their relatively high resistance. This is a serious limitation for the applications requiring good vertical transport. In particular, high-power LED lighting must have

“window” electrodes with very low resistance, to eliminate the ohmic losses, which can lead to destructive overheating. Simi- larly, low resistance is required for the “window” electrodes of high efficiency solar cells; in this case the ohmic losses reduce efficiency. For these applications, the hybrid model networks (described in Section 5), mimicking the hierarchical (quasi- fractal) structure of the natural LV and benefitting additionally from nanowire-induced plasmonic refraction, are superior and will no doubt be soon used commercially. Even the simplest two-order networks, such as the R2 network (CNN with nanowires), offer a significant advantage. While in those vertical transport applications the extraordinarily high optical uniformity of the oxides (essentially atomic) is not important, it becomes significant for small-scale displays, where the pixel sizes demand a minimal uniformity. Thus, only networks with line-width and line-spacing much smaller, or at least smaller than the pixel size (≈50 µm), and low haze can be used. Clearly, most of the nature-inspired networks discussed above fail this size condi- tion alone, including CNN. However, this can be remedied by combining those with metallic nanowires, which simply bridge the interline spacing with a minimal number of contact points.

Note that low haze is also assured, at least for CNN. The some- what less severe limitation of oxide transparent conductors (ITO included) is their fabrication expense, due to the need for vacuum processing, as well as the shortage of required ele- ments (e.g., indium). Networks offer an advantage in this context, again. Many networks (e.g., nanowire networks) can be processed without the need of vacuum. For example, such a vacuum-less processing is possible for CNN, as demonstrated in Section 2.3. Therefore, the R2 (CNN–nanowires) hybrid also requires no vacuum processing.

In short, nature-inspired metallic networks are expected to become increasingly important in applications currently occu- pied by the oxide materials. The most promising seems to be the two-order R2 hybrid structure of CNN and nanowires, which is the simplest hierarchical (quasi-fractal) model network that is also scalable and inexpensive with the additional benefit of plasmonic refraction. Such a system excels in both in-plane and vertical transport and has sufficient optical density even for small display applications.

Acknowledgements

This work was supported by National Key Research Program of China (2016YFA0201002), Natural Science Foundation of China grants (No. 51571094 and 51431006), and Guangdong province funds (Nos. 2014B090915005, 2016KCXTD009, 2016A010101023, and 2014A030313447) and the Guangdong Innovative Research Team Program (No. 2013C102). All authors thank A. Shvonski for his careful review of the paper and helpful comments.

Conflict of Interest

The authors declare no conflict of interest.

Keywords

fractal structures, leaf venation, metallic networks, self-cracking, spider webs, transparent conducting oxides, transparent conductors

Received: August 31, 2017 Revised: October 8, 2017 Published online:

[1] M. Layani, A. Kamyshny, S. Magdassi, Nanoscale 2014, 6, 5581.

[2] J. Gao, K. Kempa, M. Giersig, E. M. Akinoglu, B. Han, R. Li, Adv. Phys. 2016, 65, 553.

[3] D. S. Hecht, L. Hu, G. Irvin, Adv. Mater. 2011, 23, 1482.

[4] J. Du, S. Pei, L. Ma, H. M. Cheng, Adv. Mater. 2014, 26, 1958.

[5] Y. Song, W. Fang, R. Brenes, J. Kong, Nano Today 2015, 10, 681.

[6] C. F. Guo, Z. Ren, Mater. Today 2015, 18, 143.

[7] D. Langley, G. Giusti, C. Mayousse, C. Celle, D. Bellet, J.-P. Simonato, Nanotechnology 2013, 24, 452001.

[8] ReportsnReports.com, http://www.reportsnreports.com/, August, 2015.

[9] F Streintz, Ann. Phys. 1902, 314, 854.

[10] K. Baedeker, Ann. Phys. 1907, 327, 749.

[11] J. M. Mochel, US Patent 2564,706, 1951.

[12] H. Ohta, M. Orita, H. Hiramatsu, K. Nomura, M. Miyakawa, K. Ueda, M. Hirano, H. Hosono, Adv. Sci. Technol. 2003, 33, 983.

[13] D. S. Ginley, C. Bright, MRS Bull. 2000, 25, 15.

[14] M. Grundmann, H. Frenzel, A. Lajn, M. Lorenz, F. Schein, H. Von Wenckstern, Phys. Status Solidi A 2010, 207, 1437.

[15] H. Hosono, Thin Solid Films 2007, 515, 6000.

[16] S. H. Brewer, S. Franzen, J. Phys. Chem. B 2002, 106, 12986.

[17] S. H. Brewer, S. Franzen, J. Alloys Compd. 2002, 338, 73.

[18] G. J. Exarhos, X. Zhou, Thin Solid Films 2007, 515, 7025.

[19] Q. Lei, M. Golalikhani, B. A. Davidson, G. Liu, D. G. Schlom, Q. Qiao, Y. Zhu, R. U. Chandrasena, W. Yang, A. X. Gray, E. Arenholz, A. K. Farrar, D. A. Tenne, M. Hu, J. Guo, R. K. Singh, X. Xi, npj Quantum Mater. 2017, 2, 10.

[20] R. G. Gordon, MRS Bull. 2000, 25, 52.

[21] H. Hosono, H. Ohta, M. Orita, K. Ueda, M. Hirano, Vacuum 2002, 66, 419.

[22] F. Guo, X. Zhu, K. Forberich, J. Krantz, T. Stubhan, M. Salinas, M. Halik, S. Spallek, B. Butz, E. Spiecker, T. Ameri, N. Li, P. Kubis, D. M. Guldi, G. J. Matt, C. J. Brabec, Adv. Energy Mater. 2013, 3, 1062.

[23] T. Sekitani, T. Someya, Adv. Mater. 2010, 22, 2228.

[24] X. Yu, T. J. Marks, A. Facchetti, Nat. Mater. 2016, 15, 383.

[25] J. H. Lee, S. Yoshikawa, T. Sagawa, Sol. Energy Mater. Sol. Cells 2014, 127, 111.

[26] S. Ji, W. He, K. Wang, Y. Ran, C. Ye, Small 2014, 10, 4951.

[27] Q. Wang, Y. Shao, Q. Dong, Z. Xiao, Y. Yuan, J. Huang, Energy Environ. Sci. 2014, 7, 2359.

[28] M. Vosgueritchian, D. J. Lipomi, Z. Bao, Adv. Funct. Mater. 2012, 22, 421.

[29] A. Shimoni, S. Azoubel, S. Magdassi, Nanoscale 2014, 6, 11084.

[30] J. Wang, J. Zhang, A. K. Sundramoorthy, P. Chen, M. B. Chan-Park, Nanoscale 2014, 6, 4560.

[31] S. K. Kim, T. Liu, X. Wang, ACS Appl. Mater. Interfaces 2015, 7, 20865.

[32] B. Deng, P. Hsu, G. Chen, B. N. Chandrashekar, L. Liao, Z. Ayitimuda, J. Wu, Y. Guo, L. Lin, Y. Zhou, M. Aisijiang, Q. Xie, Y. Cui, Z. Liu, H. Peng, Nano Lett. 2015, 15, 4206.

[33] N. O. Weiss, H. Zhou, L. Liao, Y. Liu, S. Jiang, Y. Huang, X. Duan, Adv. Mater. 2012, 24, 5782.

[34] T. Qiu, B. Luo, M. Liang, J. Ning, B. Wang, X. Li, L. Zhi, Carbon 2015, 81, 232.

(15)

[35] J. Wang, Z. Yi, P. Peng, L. Lai, X. Ni, IEEE Trans. Nanotechnol. 2017, 16, 687.

[36] a) Z. Xian, B. Han, S. Li, C. Yang, S. Wu, X. Lu, X. Gao, M. Zeng, Q. Wang, P. Bai, M. J. Naughton, G. Zhou, J.-M. Liu, K. Kempa, J. Gao, Adv. Mater. Technol. 2017, 2, 1700061; b) C. Yang, J. M. Merlo, J. Kong, Z. Xian, B. Han, G. Zhou, J. Gao, M. J. Burns, K. Kempa, M. J. Naughton, Phys. Status Solidi A 2017, 1, 1700504.

[37] B. Han, Y. Huang, R. Li, Q. Peng, J. Luo, K. Pei, A. Herczynski, K. Kempa, Z. Ren, J. Gao, Nat. Commun. 2014, 5, 5674.

[38] B. Han, K. Pei, Y. Huang, X. Zhang, Q. Rong, Q. Lin, Y. Guo, T. Sun, C. Guo, D. Carnahan, M. Giersig, Y. Wang, J. Gao, Z. Ren, K. Kempa, Adv. Mater. 2014, 26, 873.

[39] Q. Peng, S. Li, B. Han, Q. Rong, X. Lu, Q. Wang, M. Zeng, G. Zhou, J.-M. Liu, K. Kempa, J. Gao, Adv. Mater. Technol. 2016, 1, 1600095.

[40] R. Li, Q. Peng, B. Han, Y. Ke, X. Wang, X. Lu, X. Wu, J. Kong, Z. Ren, E. M. Akinoglu, Laser Photonics. Rev. 2016, 10, 465.

[41] B. Han, Q. Peng, R. Li, Q. Rong, Y. Ding, E. M. Akinoglu, X. Wu, X. Wang, X. Lu, Q. Wang, G. Zhou, J. M. Liu, Z. Ren, M. Giersig, A. Herczynski, K. Kempa, J. Gao, Nat. Commun. 2016, 7, 12825.

[42] A. J. Stapleton, R. A. Afre, A. V Ellis, N. Kwon, K. Kim, S. Sung, I. Yi, Nanotechnology 2013, 24, 235205.

[43] A. Khan, S. Lee, T. Jang, Z. Xiong, C. Zhang, J. Tang, L. J. Guo, L. Wen-Di, Small 2016, 12, 3021.

[44] J. van de Groep, P. Spinelli, A. Polman, Nano Lett. 2012, 12, 3138.

[45] H. Wu, D. Kong, Z. Ruan, P.-C. Hsu, S. Wang, Z. Yu, T. J. Carney, L. Hu, S. Fan, Y. Cui, Nat. Nanotechnol. 2013, 8, 421.

[46] G. Gruner, J. Mater. Chem. 2006, 16, 3533.

[47] B. Li, S. Ye, I. E. Stewart, S. Alvarez, B. J. Wiley, Nano Lett. 2015, 15, 6722.

[48] J. Lee, D. Kim, J. Lee, S. Sinha-Ray, A. L. Yarin, M. T. Swihart, D. Kim, S. S. Yoon, Adv. Funct. Mater. 2016, 27, 1602548.

[49] Z. Liang, K. R. Graham, ACS Appl. Mater. Interfaces 2015, 7, 21652.

[50] P. Fan, B. Bai, J. Long, D. Jiang, G. Jin, H. Zhang, M. Zhong, Nano Lett. 2015, 15, 5988.

[51] Y. C. G. Kwan, Q. L. Le, C. H. A. Huan, Sol. Energy Mater. Sol. Cells 2016, 144, 102.

[52] A. T. Bellew, A. P. Bell, E. K. McCarthy, J. A. Fairfield, J. J. Boland, Nanoscale 2014, 6, 9632.

[53] T.-H. Duong, N.-H. Tran, H.-C. Kim, Thin Solid Films 2016, 622, 17.

[54] B. Sciacca, J. van de Groep, A. Polman, E. C. Garnett, Adv. Mater.

2015, 28, 905.

[55] S. Ye, A. R. Rathmell, Z. Chen, I. E. Stewart, B. J. Wiley, Adv. Mater.

2014, 26, 6670.

[56] J. B. Park, Biomaterials: An Introduction, Springer-Verlag,New York 1979.

[57] A. J. Ruys, Biomimetic Biomaterials: Structure and Applications, Elsevier, Woodhead Publishing, Cambridge, UK 2013.

[58] H. S. Nalwa, Handbook of Nanostructured Biomaterials and Their Applications in Nanobiotechnology: Biomaterials, American Scientific Publishers, CA 2005.

[59] L. Feng, S. Li, Y. Li, H. Li, L. Zhang, J. Zhai, Y. Song, B. Liu, L. Jiang, D. Zhu, Adv. Mater. 2002, 14, 1857.

[60] X.-M. Li, D. Reinhoudt, M. Crego-Calama, Chem. Soc. Rev. 2007, 36, 1350.

[61] Y. Tan, J. Gu, L. Xu, X. Zang, D. Liu, W. Zhang, Q. Liu, S. Zhu, H. Su, C. Feng, Adv. Funct. Mater. 2012, 22, 1578.

[62] W. Peng, S. Zhu, W. Wang, W. Zhang, J. Gu, X. Hu, D. Zhang, Z. Chen, Adv. Funct. Mater. 2012, 22, 2072.

[63] H. Kim, J. Ge, J. Kim, S. Choi, H. Lee, H. Lee, W. Park, Y. Yin, S. Kwon, Nat. Photonics 2009, 3, 534.

[64] H. Tong, Y. Hong, Y. Dong, Y. Ren, M. Häussler, J. W. Y. Lam, K. S. Wong, B. Z. Tang, J. Phys. Chem. B 2007, 111, 2000.

[65] T. Du, S. Ma, X. Pei, S. Wang, F. Zhou, Small 2017, 13, 1602020.

[66] A. K. Geim, S. V Dubonos, I. V Grigorieva, K. S. Novoselov, A. A. Zhukov, S. Y. Shapoval, Nat. Mater. 2003, 2, 461.

[67] K. Autumn, Y. A. Liang, S. T. Hsieh, W. Zesch, Nature 2000, 405, 681.

[68] L.-B. Mao, H.-L. Gao, H.-B. Yao, L. Liu, H. Cölfen, G. Liu, S.-M. Chen, S.-K. Li, Y.-X. Yan, Y.-Y. Liu, Science 2016, 354, 107.

[69] S. Xia, Z. Wang, H. Chen, W. Fu, J. Wang, Z. Li, L. Jiang, ACS Nano 2015, 9, 2167.

[70] H. Zhu, W. Luo, P. N. Ciesielski, Z. Q. Fang, J. Y. Zhu, G. Henriksson, M. E. Himmel, L. B. Hu,. Chem. Rev. 2016, 116, 9305.

[71] Z. Q. Fang, H. L. Zhu, C. Preston, X. G. Han, Y. Y. Li, S. Lee, X. S. Chai, G. Chen, L. B. Hu, J. Mater. Chem. C 2013, 1, 6191.

[72] C. J. Chen, Y. Zhang, Y. J. Li, J. Q. Dai, J. W. Song, Y. G. Yao, Y. H. Gong, I. Kierzewski, J. Xie, L. B. Hu, Energy Environ. Sci. 2017, 10, 53.

[73] S. J. Blamires, T. A. Blackledge, I. M. Tso, Annu. Rev. Entomol. 2017, 62, 443.

[74] R. V. Lewis, Chem. Rev. 2006, 106, 3762.

[75] F. Wen, C. Li, Acc. Chem. Res. 2013, 46, 2355.

[76] H. Zhou, R. Yan, D. Zhang, T. Fan, Chem. Eur. J. 2016, 22, 9870.

[77] H. J. M. Hou, I. Suleyman, S. I. Allakhverdiev, M. N. Mohammad, Front. Plant Sci. 2014, 5, 232.

[78] K. S. Joya, Y. F. Joya, K. Ocakoglu, R. van de Krol, Angew. Chem., Int.

Ed. 2013, 52, 10426.

[79] D. B. Marshall, B. N. Cox, A. G. Evans, Acta Metall. 1985, 33, 2013.

[80] A. Lucantonio, G. Noselli, X. Trepat, A. Desimone, M. Arroyo, Phys.

Rev. Lett. 2015, 115, 188105.

[81] C. Hunger, K. D. M. Rao, R. Gupta, C. R. Singh, G. U. Kulkarni, M. Thelakkat, Energy Technol. 2015, 64, 638.

[82] N. Gupta, K. D. M. Rao, R. Gupta, F. C. Krebs, G. U. Kulkarni, ACS Appl. Mater. Interfaces 2017, 9, 8634.

[83] R. Gupta, K. D. M. Rao, G. U. Kulkarni, RSC Adv. 2014, 4, 31108.

[84] J. Jang, H. G. Im, J. Jin, J. Lee, J. Y. Lee, B. S. Bae, ACS Appl. Mater.

Interfaces 2016, 8, 27035.

[85] S. Kiruthika, R. Gupta, K. D. M. Rao, S. Chakraborty, N. Padmavathy, G. U. Kulkarni, J. Mater. Chem. C 2014, 2, 2089.

[86] T. Yamada, K. Fukuhara, K. Matsuoka, H. Minemawari, J. Tsutsumi, N. Fukuda, K. Aoshima, S. Arai, Y. Makita, H. Kubo, T. Enomoto, T. Togashi, M. Kurihara, T. Hasegawa, Nat. Commun. 2016, 7, 11402.

[87] W. L. Kalb, S. Haas, C. Krellner, T. Mathis, B. Batlogg, Phys. Rev. B 2010, 81, 1.

[88] J. M. Gosline, P. A. Guerette, C. S. Ortlepp, K. N. Savage, J. Exp.

Biol. 1999, 202, 3295.

[89] F. K. Ko, J. Jovicic, Biomacromolecules 2004, 5, 780.

[90] B. Roy, S. Karmakar, S. Tarafdar, Mater. Lett. 2017, 207, 86.

[91] S. Wu, L. Li, H. Xue, K. Liu, Q. Fan, G. Bai, J. Wang, ACS Nano 2017, 11, 9898.

[92] Y. T. Kwon, J. W. Moon, Y. M. Choi, S. Kim, S. H. Ryu, Y. H. Choa, J. Phys. Chem. C 2017, 121, 5740.

[93] Y. Li, W. Zhang, J. Hu, Y. Wang, X. Feng, W. Du, M. Guo, B. Liu, Adv. Funct. Mater. 2017, 27, 1606045.

[94] T. Tokuno, M. Nogi, J. Jiu, T. Sugahara, K. Suganuma, Langmuir 2012, 28, 9298.

[95] M. Layani, M. Gruchko, O. Milo, I. Balberg, D. Azulay, S. Magdassi, ACS Nano 2009, 3, 3537.

[96] C. F. Guo, T. Sun, Q. Liu, Z. Suo, Z. Ren, Nat. Commun. 2014, 5, 3121.

[97] D.-E. Lee, S. Go, G. Hwang, B. D. Chin, D. H. Lee, Langmuir 2013, 29, 12259.

[98] A. Shimoni, S. Azoubel, S. Magdassi, Nanoscale 2014, 6, 11084.

[99] Z. Huang, M. Su, Q. Yang, Z. Li, S. Chen, Y. Li, X. Zhou, F. Li, Y. Song, Nat. Commun. 2017, 8, 14110.

Nature-Inspired Metallic Networks for Transparent Electrodes