Grain Boundaries of Self-Assembled Porous Polymer Films for

Subscriber access provided by YORK UNIV

Article

Grain Boundaries of Self-Assembled Porous Polymer Films for Unclonable Anti-Counterfeiting Bai-Heng Wu, Chao Zhang, Ning Zheng, Lian-Wei Wu, Zhi-Kang Xu, and Ling-Shu Wan ACS Appl. Polym. Mater., Just Accepted Manuscript • DOI: 10.1021/acsapm.8b00031 • Publication Date (Web): 12 Dec 2018 Downloaded from http://pubs.acs.org on December 16, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Applied Polymer Materials

Grain Boundaries of Self-Assembled Porous Polymer Films for Unclonable Anti-Counterfeiting Bai-Heng Wu,† Chao Zhang,† Ning Zheng,‡ Lian-Wei Wu,† Zhi-Kang Xu,† and Ling-Shu Wan*,† †MOE

Key Laboratory of Macromolecular Synthesis and Functionalization, and Key Laboratory of Adsorption and Separation Materials & Technologies of Zhejiang Province, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, P. R. China ‡State

Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, P. R. China. *Corresponding

author. Email: [email protected]

Supporting Information

ABSTRACT: Grain boundaries are widely existed in two-dimensional materials and they are often considered as defects harming the physicochemical properties of materials. Here, we report unclonable anti-counterfeiting films based on the grain boundaries of patterned porous structures. The porous films are prepared from hydroxyl-end-functionalized polystyrene (OH-PS), polystyrene-block-poly(N,N-dimethylaminoethly methacrylate) (PS-b-PDMAEMA), and cyclic polystyrene (c-PS) via a typical breath figure process, during which the evaporation of solvents provides enough instability and promises the uniqueness of surface grain boundaries. The anticounterfeiting tags are convenient to be authenticated by portable devices. And the self-assembly nature of the arrays endows the films with an extremely high encoding capacity. Moreover, the present anti-counterfeiting films are configurable, transparent, flexible, and aesthetic appealing to adapt to various applications. This work demonstrates highly secure unclonable anticounterfeiting films and provides new understandings to grain boundaries of two-dimensional materials. 1

ACS Paragon Plus Environment

ACS Applied Polymer Materials 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

KEYWORDS: grain boundaries, breath figure, anti-counterfeiting, physical unclonable function, high encoding capacity

1. INTRODUCTION Fast growing counterfeiting market has become a serious global problem.1-3 Numerous advanced anti-counterfeiting tags have been developed and they are based on technologies such as radio frequency identification,4 colorimetric or fluorometric approaches,5-8 and stimuli-responsive characteristics.9-12 However, all the mentioned tags are, strictly speaking, clonable, because they are usually prepared via a deterministic process and information carried by them is predictable. Developing unclonable anti-counterfeiting tags ensuring absolute security has drawn multidisciplinary attention. Pappu et al. proposed physical unclonable function (PUF) for the first time.13 The intrinsic roughness on non-reflective surfaces can be used as a source of physical uniqueness.14 Randomly distributed nanoparticles,15-16 carbon nanotubes,17 and others1820

can serve as good candidates for PUF anti-counterfeiting tags. These tags are easy to be

fabricated, and they are difficult to replicate even by the manufacturers because of the stochastic manufacturing process. The major limitation for these anti-counterfeiting tags is that the accurate signature can only be read by specialized and expensive instruments. Inspired by human fingerprint, Bae and co-workers reported artificial fingerprints made by stochastically wrinkled polymer particles.21 Similarly, nondeterministic folding of plasmonic gel22 and imperfections in 2D materials23 were introduced to unclonable optical tagging. Generally, unclonable anticounterfeiting tags are prepared by stochastic processes. To improve the encoding capacity, there are two common strategies, i.e., decreasing the size of individual identifier and increasing the encoding density. The former is easy to be realized; but, at the same time, it brings difficulty to 2


Page 2 of 21

Page 3 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the authentication. As for the latter, it is quite difficult because high encoding density may cause the overlapping of identifiers, leading to difficulties in the decoding process. In this way, many stochastic processes must be controlled at high dilution to insure a high resolution. It is well known that a two-dimension plane can be fully covered by a layer of building blocks via self-assembly, which brings a possibility to fulfill a maximum encoding density. However, it is generally insufficient to provide enough instabilities to guarantee the uniqueness. How to utilize a self-assembly process to develop physical unclonable anti-counterfeiting tags is still a great challenge. In this work, we prepared honeycomb-patterned porous films from hydroxylend-functionalized

polystyrene

(OH-PS),

polystyrene-block-poly(N,N-dimethylaminoethly

methacrylate) (PS-b-PDMAEMA), and cyclic polystyrene (c-PS), and demonstrated unclonable anti-counterfeiting tags with extremely high encoding capacity based on nondeterministic traces, the grain boundaries, by the breath figure method. As far as we know, it is the first time for materials with ordered structures originating from self-assembly to be used as PUF anticounterfeiting tags.

2. EXPERIMENTAL SECTION 2.1 Materials Hydroxyl-end-functionalized polystyrene (OH-PS), polystyrene-block-poly(N,Ndimethylaminoethly methacrylate) (PS-b-PDMAEMA) and cyclic polystyrene (c-PS) (Scheme S1 in the Supporting Information) were synthesized according to our previous work.24-26 Polycarbonate substrates were commercially obtained from Taobao. Other reagents were purchased from Sinopharm Chemical Reagent Co. and used as received. 2.2 Preparation of breath figure arrays as anti-counterfeiting tags. A typical breath figure method was used to prepare honeycomb films. An aliquot of polymer solution using carbon 3



disulfide as the solvent (2 mg/mL) was cast onto a piece of polycarbonate substrate placed under a 2 L/min humid airflow (25 C and ~80% RH). The evaporation of solvent causes nucleation and growth of water droplets, and with the help of Marangoni flow, water droplets arrange into patterned structure in minutes. The final honeycomb films can be tailored into various shapes by a laser engraver and transferred as reported in our previous work.27 2.3 Image processing of breath figure arrays and grain boundaries extraction. The images of honeycomb films were captured by microscope (eclipse Ti-U inverted research microscope, Nikon Instruments Inc.) or a cell phone (iphone 6s plus, Apple Inc.) with a portable microscope lens (QiQi Tech Inc.). We adopted customized codes for image processing to enhance the morphology of honeycomb films. In brief, we converted each image into gray scale (‘rgb2gray’ function) and proper thresholds were chosen depending on the histograms (‘graythresh’ function commonly). Then, noise was removed by ‘erode’ and ‘dilate’ functions. After image segmentation (‘regionprops’, ‘delaunay’ and ‘voronoi’ function), Voronoi diagrams were obtained based on delaunay triangulation algorithm. For detail, please check matlab codes for image segmentation in the Supporting Information. The fingerprint image was created using FPGenerator (v1.0.1). 2.4 Primary anti-counterfeiting by machine learning classifiers. The pore density and feature pore density data was divided into two groups named as training group (63 samples) and verification group (10 samples). The machine learning process was conducted by MATLAB classification learner toolbox. Different machine learning models were trained by the training group data and tested by the verification group. 2.5 Individuality analysis of grain boundaries based anti-counterfeiting tags. Crosscorrelation was performed to analyze the uniqueness or individuality of grain boundaries on 4


Page 4 of 21

Page 5 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


honeycomb films. Customized MATLAB codes were performed to carry out this process. We used ‘normxcorr2’ function to calculate cross-correlation values of an arbitrary pair of grain boundaries matrixes. The binary matrixes were obtained via the image process with a size of 3000 by 1854 pixels. To provide sufficient tolerance to pixel error, a 2-D Gaussian distribution was utilized centered at the feature pores within a given pixel range (  = 2 pixels, diameter = 6 empirically)21. 2.6 Code capacity calculation. The code capacity is related to the diameter of pores and the size of anti-counterfeiting tags. As far as we know, the minimum pore size for breath figure is about 150 nm28. In order to fulfill the maximum code capacity, small pores are favorable. But, when it comes to practical applications, the ideal pose size is several micrometers for convenience of authentication by a portable device. In this case, we settle the pore diameter at 4.5 m and the tag square about 200 m. It should be noted that only the inner 80% of entire anti-counterfeiting tag area has been taken into the calculation to promise the fidelity. According to the statistical results, the effective number of pores is 1280 on average with 10.9 % of them is nonhexagonal (140 in 70 70 225 number). The final estimated maximum code capacity is C70 if we assume 1280 ∗ 6 ∗ 6 ≈ 10

that there are only two types of feature pores (pentagons and heptagons) and they show up in pairs. The misorientation angle of grain boundaries pore pair (from 0 to 30) was classified into 6 groups by every 5 for simplicity.

3. RESULTS AND DISCUSSION The breath figure method is a highly efficient self-assembly process to prepare hexagonally ordered porous films.29-35 As shown in Figure 1a, when the volatile solution is exposed to humid air flow, evaporation of the solvent is accelerated, and nucleation of water droplets happens. 5



Then, the droplets grow up gradually along with further evaporation of the solvent and arrange into hexagonal close-packed blocks with the help of Marangoni convection and capillary force.2931

The growth of water droplets and the shrink of surface area of solution result in the

compression of the hexagonally ordered blocks (Figure 1b and Figure S1). Finally, solidification leaves a polymeric imprint, that is the honeycomb film also known as breath figure arrays. Due to the fast solidification, the neighboring hexagonally ordered blocks do not have enough time to weld perfectly, resulting in fingerprint-like grain boundaries. From the point of view of biomimetics, the formation of grain boundaries is highly similar to the formation of fingerprints (Figure S2), because they are all the results of surface instabilities.36 As a result, the grain boundaries can be considered as the fingerprints of breath figure arrays.

6


Page 6 of 21

Page 7 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1. Formation and feature analysis of grain boundaries. (a) Formation of grain boundaries in the breath figure process. Water droplets arrange into hexagonal close-packed blocks during growth, and the red welding lines become grain boundaries after solidification. (b) Optical images of grain boundaries observed in situ by a microscope (scale bar: 50 m). The left image was captured just before solidification and the right image was the final morphology. (c) Grain boundaries extracted from the image in (b) based on Voronoi diagram operated by MATLAB R2012b (scale bar: 20 m). Pentagonal and heptagonal packed pores are marked by blue and red, respectively. Pores filled by yellow indicate defeats formed by polymer layer encapsulating a single water droplet under the surface. (d) Grain boundary analysis. For given misorientation angles, characteristic grain boundaries are extracted and the distance between two nearest nonhexagonal pore pairs was calculated. We investigated whether these grain boundary patterns can serve as unique encodings. Grain boundary recognition is the basis of this work. We first collected the digital images of breath figure arrays using a microscope. By applying a Delaunay triangulation algorithm (for more details see experimental procedures and Figure S3), non-hexagonal feature pores at grain boundaries can be successfully extracted (Figure 1c). Pentagonal and heptagonal pores always come in pairs at grain boundaries, and there are some defects distributed randomly. Obviously, the feature pores extracted from the breath figure arrays are specific enough to reveal the uniqueness of each sample. The continuity of grain boundaries processed by Voronoi diagram is determined by the misorientation angles () between different grain blocks (Figure 1d). To describe the relationship, an empirical mathematical model associating the distance between two nearest nonhexagonal pore pairs (D) with  is expressed as: 

𝑅

(1)

sin 2 = 2𝐷

7



Page 8 of 21

where R is the average pore distance. According to this equation, the characteristic distance increases with misorientation. When D is less than or equal to 2R, the grain boundary is continuous. The corresponding misorientation angle should be greater than or equal to 28.96, which fits very well to our observation. It should be pointed out that this empirical model is based on a presupposition, i.e., pore density at grain boundaries is smaller than that of hexagonally ordered block, which is verified by results in Figure S4. One advantage of the present method is that the anti-counterfeiting tags based on grain boundaries can be easily manipulated by changing the operation conditions (Figure 2). To value the difference, the pores were counted by the polygons distinguished by Voronoi diagram. Apparently, the density of feature pores (i.e., nonhexagonal packed pores) is proportional to the encoding capacity of anti-counterfeiting tags, which determines the security level directly.3 It is known that polymer architecture is a major influencing factor in breath figure method.34,

37-39

Here, some polymers with different architectures including hydroxyl-end-functionalized polystyrene (OH-PS), polystyrene-block-poly(N,N-dimethylaminoethly methacrylate) (PS-bPDMAEMA), and cyclic polystyrene (c-PS) were chosen to prepare honeycomb films. The films prepared from OH-PS show uniform distributions in both all pores and feature pores (Figure 2a). As for the block copolymer, the pore distribution is relatively narrow, but the distribution of feature pores is wide. However, the two distributions are both wide for c-PS. These results demonstrate that different polymers possess various film-forming abilities, which also influence the formation of grain boundaries. It seems that the characteristics of breath figure array from different polymers can be used for primary anti-counterfeiting (Figure S5). The pore density is determined by pore distance; small distance is beneficial for increasing pore density. Feature pore density is related to the grain boundaries basically. The misorientation angle, 8


Page 9 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


number and area of grain blocks will influence the feature pore density together. Because the security level is proportional to the feature pore density, tags based on grain boundaries can be classified into high (H), medium (M), and low (L) according to feature pore densities.21 Operation conditions also affect the grain boundaries for anti-counterfeiting tags (Figure 2b). Decreasing air flow rate can prolong the droplets self-assembly time and, thus, the grain areas are increased and feature pore density is decreased. Increasing the concentration of polymer solution leads to declined pore distance,24 and, thus, the pore density increases remarkably. In this way, grain boundaries can be further manipulated to vary the encoding capacities.

Figure 2. Configurable encoding capacity of tags based on grain boundaries. Pore density and feature pore density are calculated from SEM images. (a) Security levels (High, Medium, and Low) are adjusted by polymers with different architectures (scale bar: 20 m). (b) Feature pore

9



density is influenced by the operation conditions of breath figure method (scale bar: 20 m). SEM images on the right show representative grain boundaries. The main advantage of the present method for anti-counterfeiting is that breath figure arrays can fully cover a 2D plane and, thus, the number of identifiers is larger than other methods. However, if only the feature pores work as identifiers, the encoding capacity is still limited. Thanks to the grain boundaries, the hexagonally ordered blocks orientate differently, as discussed above, and all pores in breath figure arrays may contribute to anti-counterfeiting. The number of producible grain boundaries on 200 m2 area with 4.5 m pore distance (Level M in Figure 2a) is approximately 10225 (see the experimental section). We can exponentiate this number effectively by decreasing the pore distance. Overall, the grain boundary patterns possess formidable individuality and are practically impossible to duplicate even by the manufacturer. The uniqueness of tags based on grain boundaries is verified by comparing 35 samples. All the tags were imaged two times by a cell phone with a portable lens. Then, these two groups of images were recognized by customized image process codes, and a database composing of two set of 2D matrix was built up. To quantitatively evaluate the similarity, cross-correlation values were calculated between each two of tags in the database (see details in experimental section). The results show that high correlation values only appear along the diagonal line which represents intracorrelation (pairwise correlation between two images captured from the same tag). As for intercorrelation (pairwise correlation between two images captured from different tags), much lower correlation values were acquired. In addition, histogram of the cross-correlation values exhibits a clear separation between intracorrelation and intercorrelation. It can be concluded that each tag possesses a unique grain boundaries pattern and can be used for anticounterfeiting application (Figure 3a, b and Figure S6). 10


Page 10 of 21

Page 11 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3. Uniqueness verification of tags based on grain boundaries. (a) Heat map of crosscorrelation values from 35 samples. Each tag is imaged and second scanned by a cell phone with a portable lens (as shown in (g)). The cross-correlation calculations are executed between the two series of images. Points on the diagonal line indicate intracorrelation values, while other points represent intercorrelation values. (b) Histogram displaying the distribution of crosscorrelation values from the same database. (c,d) Radial distribution function of nonhexagonal and hexagonal pores. (e,f) Average density of nonhexagonal and hexagonal pores. (g) Hash code based on two-dimensional bond-order parameter of feature pores on grain boundaries (scale bar: 20 m). Additionally, for a given tag, the uniqueness can be expressed mathematically. Because each pore in the breath figure arrays possesses unique environment, we performed radial distribution calculation to the pores at the center of images (Figure 3g). The radial distribution function is defined as g(r) = (1/n2)〈(𝑟 + 𝑟)〉〈(𝑟)〉

(2)

11



Page 12 of 21

𝑁(𝑡)

where  = ∑𝑖 = 1(𝑟 ― 𝑟𝑖(𝑡)) is the distribution of pores in the field of view, and 𝑛 is the number density of the pores. The angular brackets denote an average over time and space.40 The radial distribution is divided into two groups according to the number of the nearest pores. The distribution of nonhexagonal center pores and hexagonal center pores is shown in Figure 3c and 3d, respectively. For hexagonal center pores, the radial distribution functions show more regularly spaced peaks, confirming the ordered structures within a single grain. As a contrast, the distributions are not uniform and vary with each other. We also calculated the average pore densities distinguished by nonhexagonal or hexagonal packing (Figure 3e and 3f). The distance between every two pores indicate that the distances from a neighbor pore to a hexagonal center pore follows 5.67, 9.72, 11.58, 14.87, and 17.07 m, which fits the theoretical value of perfect two-dimensional crystal R,

3R, 2R,

7R, and 3R. However, there are no typical 2D crystal

distributions in the average distribution of non-hexagonal pores. Thus, radial distribution curve of each nonhexagonal pore can be utilized as an identifier to describe the uniqueness of a given grain boundaries based anti-counterfeiting tag. To further assess the uniqueness and to simplify the radial distribution function, we performed an image-hashing-based encoding (Figure 3g and Figure S7). Here, sequential pores on grain boundaries were extracted and hashed into a string of decimal encodings within a few seconds by employing a local 2D bond-order parameter defined by equation as follow, which has been widely used to quantify the ordering of 2D crystal nuclei.40

6(𝑟𝑖) = 〈100𝑀 ―1∑𝑗𝑒𝑖6𝑖𝑗〉

(3)

where, 𝑟𝑖 is the center of pore 𝑖, 𝑖𝑗 is the angle subtended between the vector from pore i to its jth nearest neighbor and the arbitrarily chosen axis, and M is the number of nearest neighbors of pore 𝑖. Positions of pores at grain boundary were extracted after the image processing. The 12


Page 13 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


average minimum of the center–center distance 𝑑 between two pores was calculated, and 1.5𝑑 was set as the threshold in determining the nearest neighbors. By calculating the local twodimensional bond-order parameter using this equation, the grain boundary was digitalized into a string of decimal encodings, which has great potential to be used as encryption. It is virtually impossible cracked by brute force techniques. And for a certain part of grain boundary, this calculation presents good reproducibility with an error less than 0.5%. Besides, statistical characteristics of grain boundaries can be used to authenticate the uniqueness of breath figure array (Figure S8).

Figure 4. Demonstration of the authentication by grain boundaries based tags. (a) Anticounterfeiting tags integrated in a student ID. The “fingerprint” of grain boundaries can be easily captured by a cell phone with a portable lens, and revealed by Voromoi algorithm (scale bar: 50 m). (b) Grain boundaries based tags were attached on high value-added commodities such as perfumes and jewelleries, and the corresponding authentication images (scale bar: 50 m). (c) 13



Honeycomb films were manufactured into various shapes to expand its applications (scale bar: 1 cm). (d) A honeycomb film coated on a LED bead serves as an anti-counterfeiting tag for electronic devices and enhances diffuse reflection. Anti-counterfeiting tags based on grain boundaries of breath figure arrays are tailorable to adapt the requirements of various applications. Some authentication scenarios are demonstrated in Figure 4. In a typical authentication procedure, the grain boundaries based tags should be captured, digitalized and stored in the data base before leaving the factory. Then at any links of commodity distribution or by the consumers, the commodity can be easily scanned by a portable device and checked online. For practical use, there should be a balance between encoding capacity and decoding efficiency. In this work, the convenience of authentication (Figure S9) is considered first thanks to the high encoding capacity of the proposed method. The breath figure arrays with relatively large pore distance (~4.5 m) were designed to enable authentication by a cell phone with a portable lens (Figure 4a). The anti-counterfeiting tags have an advantage that they are thin and flexible enough to incorporate with paper work, currency, ID card, etc. In addition, the films are to some extent transparent (~80%) and can cooperate with other anticounterfeiting techniques, such as fluorescent taggants and random distribution of particles or polymer wrinkles, expending the tag from 2D to 3D to further increase the encoding capacity (Figure S10 and S11). Both transmitted and incident illumination can be used to observe the tags based on grain boundaries, which expands applications to various commodities. Besides, the iridescent color induced by the periodic structure of breath figure arrays shows aesthetic appealing and is very suitable to perfumes, rings, and other luxury items (Figure 4b). Furthermore, honeycomb films can be cast onto various substrates27 and be tailored into various shapes (Figure 4c). For example, the anti-counterfeiting tags can be accommodated onto the surfaces of electronic devices such as a LED. The authentication was conducted similarly by a 14


Page 14 of 21

Page 15 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


portable lens. In addition, the tags help with enhancing the diffuse reflection of luminescence and make the shine uniform and gentle (Figure 4d and Figure S10). It is notable that the tags are durable under different environments after post cross-linking or physical protection.41 As we all know that, for close-packed two-dimensional crystals, grain boundaries are quite universal. Even for two dimensional atom crystals, for example, graphene42 and molybdenum disulfide,43 the grain boundaries are also ubiquitous. Therefore, the method developed in this work is enlightening to these materials.

Conclusions We have developed unclonable anti-counterfeiting tags based on the grain boundaries of breath figure polymer arrays. The highest utilization of 2D space can be easily realized with a maximum encoding capacity of ~10225 when pore diameter is 4.5 m and tag size is 200 m2. This bottom-up method is efficient, robust, low cost and configurable to adapt to various security requirements. As demonstrated, the anti-counterfeiting tags are highly secure and easy to authenticate using a portable device. The grain boundaries based tags are tailorable to comply with the demands of commercial applications, and can be conveniently incorporated with other security strategies to extend the application fields.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: **

15



Chemical structures; grain boundaries formation by breath figure; diagram of fingerprint formation; pore density analysis; primary anti-counterfeiting by machine learning; verification model; feature analysis based on statistics; 3D heat map of normalized crosscorrelation values; additional demonstration for practical applications; image processing code and images as well as demonstrations for integrated anti-counterfeiting strategies. AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] ORCID Ling-Shu Wan: 0000-0003-2570-5202 Notes The authors declare no competing financial interest.

ACKNOWLEDGMENTS Financial support from the National Natural Science Foundation of China (51522305 and 51873192) and the Fundamental Research Funds for the Central Universities (2015XZZX004-26) is gratefully acknowledged.

References 1.

Han, S.; Bae, H. J.; Kim, J.; Shin, S.; Choi, S. E.; Lee, S. H.; Kwon, S.; Park, W.

Lithographically Encoded Polymer Microtaggant Using High-Capacity and Error-Correctable QR Code for Anti-Counterfeiting of Drugs. Adv. Mater. 2012, 24, 5924-5929. 2.

Yoon, B.; Lee, J.; Park, I. S.; Jeon, S.; Lee, J.; Kim, J.-M. Recent Functional Material

Based Approaches to Prevent and Detect Counterfeiting. J. Mater. Chem. C 2013, 1, 2388-2433. 16


Page 16 of 21

Page 17 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


3.

Arppe, R.; Sørensen, T. J. Physical Unclonable Functions Generated through Chemical

Methods for Anti-Counterfeiting. Nat. Rev. Chem. 2017, 1, 0031. 4.

Choi, S. H.; Yang, B.; Cheung, H. H.; Yang, Y. X. RFID Tag Data Processing in

Manufacturing for Track-and-Trace Anti-Counterfeiting. Comput. Ind. 2015, 68, 148-161. 5.

Liu, S.-S.; Wang, C.-F.; Li, C.-X.; Wang, J.; Mao, L.-H.; Chen, S. Hair-Derived Carbon

Dots toward Versatile Multidimensional Fluorescent Materials. J. Mater. Chem. C 2014, 2, 6477-6483. 6.

Hou, X.; Ke, C.; Bruns, C. J.; McGonigal, P. R.; Pettman, R. B.; Stoddart, J. F. Tunable

Solid-State Fluorescent Materials for Supramolecular Encryption. Nat. Commun. 2015, 6, 6884. 7.

Jiang, K.; Zhang, L.; Lu, J.; Xu, C.; Cai, C.; Lin, H. Triple-Mode Emission of Carbon

Dots: Applications for Advanced Anti-Counterfeiting. Angew. Chem. Int. Ed. 2016, 55, 72317235. 8.

Hou, J.; Li, M.; Song, Y. Patterned Colloidal Photonic Crystals. Angew. Chem. Int. Ed.

2018, 57, 2544-2553. 9.

Lee, K. G.; Choi, B. G.; Kim, B. I.; Shyu, T.; Oh, M. S.; Im, S. G.; Chang, S. J.; Lee, T.

J.; Kotov, N. A.; Lee, S. J. Scalable Nanopillar Arrays with Layer-by-Layer Patterned Overt and Covert Images. Adv. Mater. 2014, 26, 6119-6124. 10.

Tsang, M.-K.; Bai, G.; Hao, J. Stimuli Responsive Upconversion Luminescence

Nanomaterials and Films for Various Applications. Chem. Soc. Rev. 2015, 44, 1585-1607. 11.

Duan, X.; Kamin, S.; Liu, N. Dynamic Plasmonic Colour Display. Nat. Commun. 2017,

8, 14606. 12.

Zhong, K.; Li, J.; Liu, L.; Van Cleuvenbergen, S.; Song, K.; Clays, K. Instantaneous,

Simple, and Reversible Revealing of Invisible Patterns Encrypted in Robust Hollow Sphere Colloidal Photonic Crystals. Adv. Mater. 2018, 30, e1707246. 13.

Pappu, R.; Recht, R.; Taylor, J.; Gershenfeld, N. Physical One-Way Functions. Science

2002, 297, 2026-2030. 14.

Buchanan, J. D. R.; Cowburn, R. P.; Jausovec, A. V.; Petit, D.; Seem, P.; Xiong, G.;

Atkinson, D.; Fenton, K.; Allwood, D. A.; Bryan, M. T. 'Fingerprinting' Documents and Packaging. Nature 2005, 436, 475-475.

17



15.

Zheng, Y.; Jiang, C.; Ng, S. H.; Lu, Y.; Han, F.; Bach, U.; Gooding, J. J. Unclonable

Plasmonic Security Labels Achieved by Shadow-Mask-Lithography-Assisted Self-Assembly. Adv. Mater. 2016, 28, 2330-2336. 16.

Smith, A. F.; Patton, P.; Skrabalak, S. E. Plasmonic Nanoparticles as a Physically

Unclonable Function for Responsive Anti-Counterfeit Nanofingerprints. Adv. Funct. Mater. 2016, 26, 1315-1321. 17.

Hu, Z.; Comeras, J. M.; Park, H.; Tang, J.; Afzali, A.; Tulevski, G. S.; Hannon, J. B.;

Liehr, M.; Han, S. J. Physically Unclonable Cryptographic Primitives using Self-Assembled Carbon Nanotubes. Nat. Nanotechnol. 2016, 11, 559-565. 18.

Carro-Temboury, M. R.; Arppe, R.; Vosch, T.; Sorensen, T. J. An Optical Authentication

SystemBased on Imaging of Excitation-Selected Lanthanide Luminescence. Sci. Adv. 2018, 4, e1701384. 19.

Wigger, B.; Meissner, T.; Foerste, A.; Jetter, V.; Zimmermann, A. Using Unique Surface

Patterns of Injection Moulded Plastic Components as an Image Based Physical Unclonable Function for Secure Component Identification. Sci. Rep. 2018, 8, 4738. 20.

Takahashi, T.; Kudo, Y.; Ishiyama, R. Mass-Produced Parts Traceability System Based

on Automated Scanning of “Fingerprint of Things”. In 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA); Nagoya University, Nagoya, 2017; pp 202206. 21.

Bae, H. J.; Bae, S.; Park, C.; Han, S.; Kim, J.; Kim, L. N.; Kim, K.; Song, S. H.; Park,

W.; Kwon, S. Biomimetic Microfingerprints for Anti-Counterfeiting Strategies. Adv. Mater. 2015, 27, 2083-2089. 22.

Tian, L.; Liu, K.-K.; Fei, M.; Tadepalli, S.; Cao, S.; Geldmeier, J. A.; Tsukruk, V. V.;

Singamaneni, S. Plasmonic Nanogels for Unclonable Optical Tagging. ACS Appl. Mater. Interfaces 2016, 8, 4031-4041. 23.

Cao, Y. M.; Robson, A. J.; Alharbi, A.; Roberts, J.; Woodhead, C. S.; Noori, Y. J.;

Bernardo-Gavito, R.; Shahrjerdi, D.; Roedig, U.; Fal'ko, V. I.; Young, R. J. Optical Identification Using Imperfections in 2D Materials. 2D Mater. 2017, 4, 045021. 24.

Wu, B. H.; Zhong, Q. Z.; Xu, Z. K.; Wan, L. S. Effects of Molecular Weight Distribution

on the Self-Assembly of End-Functionalized Polystyrenes. Polym. Chem. 2017, 8, 4290-4298.

18


Page 18 of 21

Page 19 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


25.

Wu, B. H.; Zhu, L. W.; Ou, Y.; Tang, W.; Wan, L. S.; Xu, Z. K. Systematic Investigation

on the Formation of Honeycomb-Patterned Porous Films from Amphiphilic Block Copolymers. J. Phys. Chem. C 2015, 119, 1971-1979. 26.

Wu, B. H.; Wu, L. W.; Gao, K.; Chen, S. H.; Xu, Z. K.; Wan, L. S. Self-Assembly of

Patterned Porous Films from Cyclic Polystyrenes via the Breath Figure Method. J. Phys. Chem. C 2018, 122, 3926-3933. 27.

Wu, L. W.; Wan, L. S.; Ou, Y.; Zhu, L. W.; Xu, Z. K. Fabrication of Transferable

Perforated Isoporous Membranes on Versatile Solid Substrates via the Breath Figure Method. Adv. Mater. Interfaces 2015, 2, 1500285. 28.

Stenzel-Rosenbaum, M. H.; Davis, T. P.; Fane, A. G.; Chen, V. Porous Polymer Films

and Honeycomb Structures Made by the Self-Organization of Well-Defined Macromolecular Structures Created by Living Radical Polymerization Techniques. Angew. Chem. Int. Ed. 2001, 40, 3428-3432. 29.

Bai, H.; Du, C.; Zhang, A. J.; Li, L. Breath Figure Arrays: Unconventional Fabrications,

Functionalizations, and Applications. Angew. Chem. Int. Ed. 2013, 52, 12240-12255. 30.

Wan, L. S.; Zhu, L. W.; Ou, Y.; Xu, Z. K. Multiple Interfaces in Self-Assembled Breath

Figures. Chem. Commun. 2014, 50, 4024-4039. 31.

Munoz-Bonilla,

A.;

Fernandez-Garcia,

M.;

Rodriguez-Hernandez,

J.

Towards

Hierarchically Ordered Functional Porous Polymeric Surfaces Prepared by the Breath Figures Approach. Prog. Polym. Sci. 2014, 39, 510-554. 32.

Heng, L. P.; Li, J.; Li, M. C.; Tian, D. L.; Fan, L. Z.; Jiang, L.; Tang, B. Z. Ordered

Honeycomb Structure Surface Generated by Breath Figures for Liquid Reprography. Adv. Funct. Mater. 2014, 24, 7241-7248. 33.

Ou, Y.; Lv, C. J.; Yu, W.; Mao, Z. W.; Wan, L. S.; Xu, Z. K. Fabrication of Perforated

Isoporous Membranes via a Transfer-Free Strategy: Enabling High-Resolution Separation of Cells. ACS Appl. Mater. Interfaces 2014, 6, 22400-22407. 34.

Zhang, A. J.; Bai, H.; Li, L. Breath Figure: A Nature-Inspired Preparation Method for

Ordered Porous Films. Chem. Rev. 2015, 115, 9801-9868. 35.

Wang, Y. Z.; Zhou, W. X.; Kang, Q.; Chen, J.; Li, Y.; Feng, X. M.; Wang, D.; Ma, Y.

W.; Huang, W. Patterning Islandlike MnO2 Arrays by Breath-Figure Templates for Flexible Transparent Supercapacitors. ACS Appl. Mater. Interfaces 2018, 10, 27001-27008. 19



36.

Kucken, M.; Newell, A. C. Fingerprint Formation. J. Theor. Biol. 2005, 235, 71-83.

37.

Stenzel, M. H. Formation of Regular Honeycomb-Patterned Porous Film by Self-

Organization. Aust. J. Chem. 2002, 55, 239-243. 38.

Kong, X. L.; Wang, X. F.; Luo, T. C.; Yao, Y.; Li, L.; Lin, S. L. Photomanipulated

Architecture and Patterning of Azopolymer Array. ACS Appl. Mater. Interfaces 2017, 9, 1934519353. 39.

Zhang, S. L.; Xu, T. Y.; Chai, S. C.; Zhang, L. Y.; Wu, L. X.; Li, H. L. Supramolecular

Star Polymer Films with Tunable Honeycomb Structures Templated by Breath Figures. Polymer 2017, 117, 306-314. 40.

Sun, X.; Li, Y.; Zhang, T. H.; Ma, Y.-q.; Zhang, Z. Fabrication of Large Two-

Dimensional Colloidal Crystals via Self-Assembly in an Attractive Force Gradient. Langmuir 2013, 29, 7216-7220. 41.

Du, C.; Zhang, A. J.; Bai, H.; Li, L. Robust Microsieves with Excellent Solvent

Resistance: Cross-Linkage of Perforated Polymer Films with Honeycomb Structure. ACS Macro Lett. 2013, 2, 27-30. 42.

Huang, P. Y.; Ruiz-Vargas, C. S.; van der Zande, A. M.; Whitney, W. S.; Levendorf, M.

P.; Kevek, J. W.; Garg, S.; Alden, J. S.; Hustedt, C. J.; Zhu, Y.; Park, J.; McEuen, P. L.; Muller, D. A. Grains and Grain Boundaries in Single-Layer Graphene Atomic Patchwork Quilts. Nature 2011, 469, 389-392. 43.

Najmaei, S.; Liu, Z.; Zhou, W.; Zou, X.; Shi, G.; Lei, S.; Yakobson, B. I.; Idrobo, J. C.;

Ajayan, P. M.; Lou, J. Vapour Phase Growth and Grain Boundary Structure of Molybdenum Disulphide Atomic Layers. Nat. Mater. 2013, 12, 754-759.

20


Page 20 of 21

Page 21 of 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


For Table of Contents Only

21


Grain Boundaries of Self-Assembled Porous Polymer Films for

Recommend Documents