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Facile Fabrication of Large-scale Porous and Flexible Three-dimensional Plasmonic Network Yunjeong Lee, Seungki Lee, Chang Min Jin, Jung A Kwon, Taewook Kang, and Inhee Choi ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b11055 • Publication Date (Web): 27 Jul 2018 Downloaded from http://pubs.acs.org on July 29, 2018

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ACS Applied Materials & Interfaces

Facile Fabrication of Large-scale Porous and Flexible Three-dimensional Plasmonic Network Yunjeong Lee1, Seungki Lee1, Chang Min Jin1, Jung A Kwon1, Taewook Kang2* and Inhee Choi1* 1

Department of Life Science, University of Seoul, Seoul 130-743, Republic of Korea

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Department of Chemical and Biomolecular Engineering, Sogang University, Seoul 121-742,

Republic of Korea

KEYWORDS: Plasmonic network; sugar crystal; surface-enhanced Raman scattering (SERS); biomolecules; organic pollutants

ACS Paragon Plus Environment

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ABSTRACT

Assembling metallic nanoparticles and trapping target molecules within the probe volume of the incident light are important in plasmonic detection. Porous solid structures with threedimensionally (3D) integrated metal nanoparticles would be very beneficial in achieving these objectives. Currently, porous inorganic oxides are being prepared under stringent conditions and further subjected to either physical or chemical attachment of metal nanoparticles. In this study, we propose a facile method to fabricate large-scale porous and flexible 3D plasmonic networks. Initially, uncured polydimethylsiloxane (PDMS), in which metal ions are dissolved, diffuses spontaneously into simple sugar crystal template via capillary action. As PDMS is cured, metal ions are automatically reduced to form a dense array of metal nanoparticles. After curing, the sugar template is easily removed by water treatment to obtain porous 3D plasmonic networks. We controlled the far-field scattering and near-field enhancement of the network by changing either the metal ion precursor or its concentration. In order to demonstrate the key advantages of our 3D plasmonic networks, such as simple fabrication, optical signal enhancement, and molecular trapping, we conducted sensitive Raman detection of several important molecules, including adenine, humidifier disinfectants, and volatile organic compounds.


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INTRODUCTION Although plasmonic optical detection has been extensively studied due to its high sensitivity, selectivity, and rapid response time,1-7 the three-dimensional (3D) assembly of metal nanoparticles as well as molecular trapping in the probe volume of an incident light still remain challenging tasks.8,9 3D porous structures in which metal nanoparticles can be assembled and whose pore sizes are large enough to avoid the diffusion of target molecules would be especially useful when achieving those tasks. For this purpose, porous structures such as mesoporous silica,10-13 anodic aluminum oxide (AAO),14-19 zinc oxide (ZnO),20-25 and aerogels26,27 have been extensively used for the 3D assembly of metal nanoparticles. In a typical process, metal nanoparticles are assembled on these porous structures by sequentially conducting multiple processes including chemical treatment30,31 or vaporized metal deposition.15,16,19 For example, amine or thiol group-terminated silanes are used to link metal nanoparticles with the surface of mesoporous silica, whereas metal deposition with additional thermal annealing at elevated temperatures is used to produce metal nanoparticles on the AAO surface. Porous ZnO and aerogels are also utilized to assemble nanoparticles.23-25 However, the preparation of these porous templates generally involves complicated chemical syntheses, and the integration of metal nanoparticles into the porous templates often requires either toxic organic solvents or expensive vacuum instrumentation. In this study, we report a facile and robust method for the fabrication of porous and flexible 3D plasmonic networks. Our proposed method is schematically illustrated in Figure 1a. In a typical procedure, aqueous metal ions are added to an uncured polydimethylsiloxane (PDMS) solution. Meanwhile, a porous template is prepared by stacking sugar crystals. When the sugar template is immersed in the PDMS solution, the solution spontaneously diffuses into


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the sugar template by capillary action under degassing conditions. When liquid PDMS is cured, the metal ions are rapidly converted into metal nanoparticles due to the reducing capability of the incorporated curing agent. After curing, the sugar template is selectively dissolved by water to obtain porous PDMS networks integrated with metal nanoparticles (hereafter we call them ‘plasmonic networks’). The resulting plasmonic networks are quite flexible and thus their morphology is maintained even after bending, twisting, and folding (Figure 1b). In addition, the intrinsic hydrophobic and adhesive properties of PDMS allow liquid solvents or solid powders to be effectively trapped by simply swiping with the plasmonic network (Figure 1c). Our flexible and porous plasmonic networks exhibit the following key advantages over previously reported 3D plasmonic sensing platforms. (1) It is easy to prepare 3D porous templates on a large scale, (2) the 3D assembly of metal nanoparticles in the porous template is spontaneously achieved without a chemical linker or metal deposition, (3) hydrophilic and hydrophobic molecules can also be trapped within the porous network without any surface modification, and (4) the 3D plasmonic networks developed by this method are elastomeric (Figure 1b) and easy-to-handle for further fieldwork applications (Figure 1c). To demonstrate these benefits, our porous plasmonic networks were tested to trap and detect a wide variety of small molecules via surface-enhanced Raman spectroscopy (SERS).


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Figure 1. Schematic illustration of the large-scale fabrication of porous and flexible 3D plasmonic structures. (a) Fabrication procedure of a plasmonic network using a sugar crystal template, metal precursor, and liquid PDMS. The scale bar indicates 2 mm. (b) Flexibility of the plasmonic network. (c) Adsorbing properties of the plasmonic network with respect to liquid solvent (i) and solid powder (ii). For clear demonstration, the solvent was colored with a black dye and a white microplastic powder was used.


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RESULTS AND DISCUSSION Morphological and optical characterization of plasmonic networks. In order to prepare porous sugar templates, sugar crystals34 were densely packed in a disk-shaped mold. Each sugar crystal is rectangular with a short axis length of about (500 ± 92) µm (averaged from 50 different sugar crystals). To stick sugar crystals together, a small amount of water was added as the glue. Liquid PDMS with a metal precursor (i.e., PDMS base with a curing agent and metal precursor) was then allowed to soak into the sugar crystal template via capillary forces under degassing conditions; the template was then placed in an oven for curing. The sugar crystal template could be easily removed using water (Figure 2a(i)-(iii)). According to the shown optical image, the pore size of the structure was approximately 300–600 µm (Figure 2a-(ii)). The porometer data shows that 50% of the flow occurs through pores larger than 360 µm (Figure 2b); this observation also demonstrates the existence of micropores. By adjusting the composition and concentration of the metal precursor, we synthesized Au- and Ag-plasmonic networks (Figure 2c-e) with sponge-like porous morphologies. Due to the presence of metal nanoparticles, plasmonic networks exhibit roughened surfaces, as shown in scanning electron micrographs (Figure S1). The resulting Au-plasmonic networks were purple in color, while the Ag-plasmonic networks were yellowish grey in color. When the concentrations of the metal precursors increased, the colors of the Au- and Ag-plasmonic networks respectively turned deeper purple and yellowish grey. These changes in surface morphology and colors of the plasmonic networks might be attributed to the changes in sizes and shapes as well as increase in the number of nanoparticles, as shown in the transmission electron micrographs (Figure S2). Dark-field scattering images also display distinct scattering colors – reddish for the Au plasmonic structure and yellowish for the Ag plasmonic structure (Figure 2d-f). As HAuCl4


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concentration increases, the scattering spectra of the Au-plasmonic networks red-shift in the range of 600–700 nm (Figure 2g), which are in longer wavelengths than those of the Agplasmonic networks (Figure 2h). The spectral peak of the Ag-plasmonic structure appears at ~600 nm. Especially for the Au-plasmonic network, large shift to longer wavelengths is attributable to the formation of merged Au nanoparticles with increasing the HAuCl4 concentration, as shown in Figure S2. In the scattering spectra before the normalization (Figure S3), the intensity generally tends to increase with the concentration of metal precursor solutions we used for fabrication. UV/Vis spectra also show the concentration-dependent increase of intensities for both cases of the Au- and Ag-plasmonic networks (Figure S4). These suggest the increases in the numbers of nanoparticles for both cases. Collectively, the observed color change in the plasmonic networks is derived from the changes in the numbers and the sizes (or shapes) of the embedded nanoparticles.


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Figure 2. Optical properties of the synthesized plasmonic networks. (a) Representative morphologies of the Au-plasmonic network (photograph (i), optical image (ii), and scanning electron micrograph (iii)). The scale bars are 5 mm (black), 300 µm (red), and 2 µm (white), respectively. (b) A plot showing the pore size of the networks. (c, e) Top-view images of the Au(c) and Ag- (e) plasmonic networks synthesized with varying concentrations of the metal precursors (HAuCl4 and AgNO3, respectively).; i) 1 mM, ii) 2 mM, iii) 5 mM, and iv) 10 mM. The scale bars are 1 cm. (d, f) Dark-field scattering images of the Au- (d) and Ag- (f) plasmonic networks synthesized with varying concentrations of the metal precursors (HAuCl4 and AgNO3,


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respectively).; i) 1 mM, ii) 2 mM, iii) 5 mM, and iv) 10 mM. The scale bars are 200 µm. (g, h) Corresponding scattering spectra of the Au-(g) and Ag-(h) plasmonic networks. Swelling properties of plasmonic networks for hydrophobic and hydrophilic solvents. To characterize the molecular trapping ability of the developed plasmonic networks, we investigated their swelling ratios in various hydrophilic and hydrophobic solvents. The ratios are calculated according to the following equation (1): Swelling ratio (%) = (WS-Wd)/Wd ×100

(1)

where Ws indicates the weight of the network after solvent absorption and Wd represents the initial weight of the network.37 The intrinsic swelling property of porous PDMS structures without metal nanoparticles was tested using water and organic solvents (toluene, benzene, and ethanol). Figure 3a shows representative images of the porous PDMS network without metal nanoparticles before and after treatment with toluene. For toluene, the volume of the PDMS network increased dramatically (Figure 3a) due to its high swelling ratio (c.a., 341%), whereas PDMS film did not due to its low swelling ratio (c.a., 17%) (Figure S5). This observation can be attributed to the higher porosity and concomitant larger surface area of PDMS networks than the PDMS film. As shown in Figure 3d, the swelling ratios of the plasmonic networks in hydrophobic solvents (toluene and benzene) are higher than those in hydrophilic solvents (water and ethanol) due to the hydrophobic nature of PDMS.38 For example, a porous PDMS network exposed to water (colored using orange dye) showed almost no change in its volume (Figure 3c). Note that the swelling ratios with water can also be increased by either mechanical vortexing or O2 plasma treatment (Figure 3d). Especially for O2 plasma treatment, preferential adsorption of hydrophilic molecules could be achieved.


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We further examined how the presence of metal nanoparticles in porous PDMS networks affects the swelling of the composite structure. For example, similar to the case of a porous PDMS network, the volume of an Au-plasmonic network increased by 170% when toluene was used as the solvent (Figure 3e). These plasmonic networks also exhibited almost identical swelling properties with PDMS networks when the solvent was either toluene (Figure 3f) or water (Figure 3g-h), irrespective of the presence of metal nanoparticles.

Figure 3. Swelling properties of porous PDMS networks and plasmonic networks in water and organic solvents. (a) Representative images of porous PDMS networks before and after treatment with toluene. (b) Swelling ratios of the PDMS networks in different solvents, including toluene, benzene, ethanol, and water. (c) Representative images of the PDMS networks before and after treatment with water (colored with an orange dye). (d) Water-swelling ratios of the


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PDMS networks measured after O2 plasma treatment or vigorous vortexing. (e) Representative images of an Au-plasmonic network before and after treatment with toluene. (f) Comparison of the toluene-swelling ratios of Au- and Ag-plasmonic networks formed at metal precursor concentrations of 1 mM and 5 mM (the subscript numbers 1 and 5 indicate concentrations). (g) Representative images of the Au-plasmonic network before and after treatment with water (colored with an orange dye). (h) Comparison of the water-swelling ratios of porous PDMS structures and plasmonic networks after vigorous vortexing. Application of plasmonic networks to label-free SERS detection of various small molecules. Having demonstrated the tunable optical properties and solvent swelling properties of our plasmonic networks, we applied them to increase the Raman signals of a couple of Raman dyes. As described in Figure 4a, plasmonic networks provide a beneficial SERS-active sensing layer, in which the embedded metallic nanoparticles and trapped target molecules are placed within the probe volume of an incident laser. Nile Blue A (NBA) and Rhodamine 6G (R6G), two representative Raman-active dyes, were used to test Raman enhancement by the Ag- and Auplasmonic networks, respectively. In order to determine most effective SERS measurement conditions, SERS activity of each Ag- and Au-plasmonic networks by using either 532-nm or 785-nm laser was also examined (Figure S6). Taking into account, for the Ag-plasmonic network, 1 µM NBA and a 532-nm laser were used to measure Raman signals. The NBA signal from the Ag-plasmonic network is 82 times stronger than that obtained from a gold nanospheres solution (Figure 4b). In the case of Au-plasmonic networks, 100 µM R6G and a 785-nm laser were used to measure Raman signals. As in the previous case, when compared to the signal from R6G mixed with gold nanorods solution, the Au-plasmonic network led to considerably enhanced Raman signals (Figure 4c). On the contrary, no obvious SERS signals at the tested


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concentrations of NBA (1 µM) and R6G (100 µM) were not measured when using nonporous PDMS films with nanoparticles. For optimization, the SERS signals of porous plasmonic networks prepared at different concentrations of metal precursors and mixing ratios of PDMS base to curing agent (Figure S7a-b) were systematically measured. The intensity at 1647 cm–1 (for NBA) and 1377 cm–1 (for R6G) were compared. The Ag- and Au-plasmonic networks showing the strongest Raman signals, for example, the Au-plasmonic network formed with 2 mM HAuCl4 at a 10:1 PDMS mixing ratio, were then utilized to obtain calibration curves. When we measured R6G SERS signals using the Au-plasmonic network, enhancement factor (EF) was around ~104 and limit of detection (LOD) was calculated to 81.1 nM (Figure S7c–f). SERS signals were quite reproducible and percentage coefficient of variations (CV%) were within only 10% for the plasmonic networks fabricated from the different batch as well as for the different samples and spots (Figure S8). On the basis of the characterization results (Figure 3), we supposed that hydrophilic molecules can be trapped in the pores of the plasmonic networks, while hydrophobic molecules can be intercalated in the PDMS chains.41 Thus, our proposed plasmonic networks would provide 3D SERS-active sensing layers for both hydrophilic and hydrophobic molecules, as illustrated in Figure 4d. The Raman mapping images (Figure 4e-f) of the Au-plasmonic networks sequentially exposed to toluene (hydrophobic) and R6G (hydrophilic) show this feature; both types of molecules can be clearly detected in separate areas of the same network. The developed plasmonic networks were further applied for the detection of small molecules, including biomolecules and toxic environmental molecules. Prior to detection, hydrophilic molecules were dissolved in either water or ethanol. Adenine (100 nM) can be detected using Au-plasmonic networks (Figure 4g, Figure S9a). In the case of 1,2-bis(4-pyridyl)ethylene


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(BPE) and diethyl cyanine iodide (DCI), their SERS signals are detectable at 10 nM and 100 nM concentrations, respectively (Figure 4h-i, Figure S9b-c). Using the Ag-plasmonic network, 5chloro-2-methyl-4-isothiazolin-3-one/2-methyl-4-isothiazolin-3-one (CMIT/MIT), which is a toxic substance used in humidifier disinfectants,42,43 could be successfully detected below the regulation level (15 ppm in the EU Figure 4j, Figure S9d). The detection of volatile organic compounds (VOCs), which are generally hydrophobic,44,45 was also conducted. It is found that VOCs tend to be assemble at the air/water interface and that they are selectively absorbed in the Ag-plasmonic networks (Figure 4k). The SERS signals of benzene, toluene, cumene, and chloroform were very strong and distinguishable from each other (Figure 4l). Even when those VOCs were mixed, a single SERS spectrum of the mixture indicated individual peaks for each VOCs (Figure 4l, Figure S10). Concentrationdependent changes in the SERS spectra (Figure 4m) were recorded for cumene (isopropyl benzene), which is one of the most hazardous VOCs.46,47 A linear calibration curve for cumene, based on the most prominent peak at 1010 cm–1, was obtained in the range of 1%–100% (Figure 4n). Gas-phase detection of VOCs using Ag-plasmonic networks was also carried out by analyzing the time-resolved SERS spectra for the toluene evaporated in a closed chamber (Figure S11).


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Figure 4. Application of plasmonic networks to the label-free SERS detection of various small molecules and VOCs via molecular trapping. (a) Description of the assembled metallic nanoparticles and trapped target molecules within the probe volume of an incident laser for SERS detection. (b) NBA Raman signal obtained using the Ag-plasmonic network and 532-nm laser. (c) R6G Raman signal using the Au plasmonic network and a 785-nm laser. The red


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asterisks indicate peaks from the target molecules and black asterisks indicate background peaks from PDMS in Figs. 4b and c. (d) Schematic illustration of the efficiently trapped hydrophobic and hydrophilic small molecules in plasmonic networks. (e, f) Raman intensity maps of R6G (e) and toluene (f) adsorbed (or trapped) in the Au-plasmonic network. The scale bars indicate 200 µm. (g–j) Concentration-dependent SERS spectra of adenine (g), BPE (h), and DCI (i) measured using the Au-plasmonic network and CMIT/MIT (j) measured using the Ag-plasmonic network. (k) Photographs showing the selective sorption of VOCs into the Ag plasmonic network. For contrast, water was mixed with a blue dye and organic compounds were mixed with black ink. (l) Label-free identification of individual VOCs (benzene, toluene, cumene, and chloroform) and their mixture. (m) Concentration-dependent SERS spectra of cumene. Cumene was diluted with 99% ethanol. (n) Calibration curve of cumene at 1010 cm–1. CONCLUSION In summary, we demonstrated that porous and flexible plasmonic 3D networks can be easily prepared on a large scale using our proposed method, which is based on (1) the use of a simple sugar template, (2) spontaneous capillary rise of liquid PDMS into the template, (3) reduction of metal precursors into metal nanoparticles in the PDMS network, and (4) facile removal of the sugar template with water. Due to porosity and 3D-integrated metal nanoparticles, it is possible to embed metal nanoparticles as well as target molecules into the probe volume of an incident light. The optical properties of the porous 3D plasmonic networks are controlled by changing either the metal ion precursor or its concentration. The Ag- and Au-plasmonic networks allow for the detection of various biologically and environmentally important molecules, such as adenine, DCI, BPE, and CMIT/MIT, by SERS. In addition, the developed plasmonic networks were successfully used to selectively and sensitively detect the Raman


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signals of hazardous VOCs, such as benzene, toluene, cumene, and chloroform. Our proposed plasmonic networks will provide useful 3D platforms for the sensitive label-free detection of various molecules in water or organic solvents as well as in the gas phase. MATERIALS AND METHODS Materials: A PDMS elastomer kit (Sylgard 184) was purchased from Dow Corning Corporation (Michigan, USA). Gold (III) chloride trihydrate (HAuCl4·3H2O, 48.5%–50.25%), silver nitrate (AgNO3), crystalized sugar (sucrose, 99.9%), R6G, NBA perchlorate, adenine, BPE, DCI, toluene (C6H5CH3, 99.8%), benzene (C6H6, 99.9%), and CMIT/MIT were purchased from Sigma-Aldrich, Missouri, USA. Cumene (C9H12, 95%) was purchased from Acros Organics, Geel, Belgium. Chloroform (CHCl3, 99%) was purchased from Junsei Chemicals, Tokyo, Japan. Ethanol (C2H5OH, 95%), sulfuric acid (H2SO4, 95%), and hydrogen peroxide (H2O2, 34.5%) were purchased from Samchun Chemical Co., Seoul, Korea. Fabrication of plasmonic networks: To fabricate plasmonic networks, we used granulated sugar crystal templates and mixed PDMS and metal precursor solutions. Granulated sugar crystals were molded with a few drops of water into a disk shape and dried in an oven to prepare network templates. An aqueous solution of metal ions was mixed with uncured PDMS (mixture of a PDMS base and curing agent) to prepare the base material for the plasmonic network. The sugar template was dipped into the mixed solution and degassed for 15 min; the solution was allowed to soak into the sugar template via capillary action. After degassing, curing was allowed to continue at 65 °C for 1 h to solidify the PDMS network. Once fully cured, solid PDMS was sonicated in water for 30 min to dissolve the sugar template.


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Transmission Electron Micrograph (TEM): To characterize the metal nanoparticle embedded in the plasmonic network, TEM experiment was performed using the LIBRA 120 (Carl Zeiss, Germany) instrument at an acceleration voltage of 120 kV. To prepare TEM samples, we dissolved the nanoparticles formed in the networks by using toluene before PDMS completely being cured and the suspensions were dropped onto a 300-mesh copper grid with incubation for 1 h at room temperature and rinsed out by toluene. Scanning Electron Micrograph (SEM): The surface morphologies of the bottom in the plasmonic network were characterized by field-emission scanning electron microscopy (SUPRA 55VP, Carl Zeiss, Germany) at an accelerating voltage of 2 kV. Optical characterization of plasmonic networks via dark-field nanospectroscopy: To observe the optical characteristics of the developed plasmonic networks, they were placed on the glass slides cleaned in a piranha solution (sulfuric acid:hydrogen peroxide = 7:3 v/v) for 120 min. A darkfield optical microscope (Olympus BX43, Tokyo, Japan) equipped with a hyperspectral imaging spectrophotometer (CytoViva Hyperspectral Imaging System, Auburn, AL, USA) was used to visualize the morphology of the plasmonic networks in detail. The scattering spectra of the networks were mapped with a 10× optical lens. Each spectrum was collected at an exposure time of 0.5 s and a total of 50 spectra obtained from the network were averaged. Characterization of solvent swelling properties of plasmonic networks: To measure the swelling ratios of the networks, the weights of both PDMS and plasmonic networks were measured before and after exposure to various solvents. The networks were then treated with organic solvents (i.e., toluene, benzene, and ethanol) and DI water. After incubation for 1 min, the weights of the networks were once again measured and used to calculate the swelling ratios. For water, O2


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plasma-treated networks and vigorously vortexed networks were also tested. Plasma treatment was carried out at 100 W at an oxygen flow rate of 2 sccm for 10 min. SERS detection of small molecules using plasmonic networks: For SERS measurements, a microRaman system combining a commercial Raman spectrometer SR-303i (Andor Technology), an Olympus BX51 microscope with a 20× IR (NA = 0.45) objective lens, a 532-nm laser module PSU-III-FDA (Changchun New Industries Optoelectronics Technology Co., Ltd.), and a 785-nm laser module I0785SR0100B1 (Innovative Photonic Solution Inc.), was used. The analytes were prepared with different solvents (water and ethanol) and could be easily trapped in the porous structures. The hydrophilic solutions were trapped in the fabricated networks by physical forces, such as pipetting or vortexing, before SERS measurement. For R6G, adenine, DCI, and BPE, a 785-nm laser was used. In the case of NBA and VOCs, a 532-nm laser was used. Calibration curves were obtained from the average spectra collected from 10 different positions of the plasmonic networks at each concentration. To dilute BPE, 10% ethanol was used. Four types of VOCs, including benzene, toluene, cumene, and chloroform, were also tested. To visualize the efficient swelling performance of the plasmonic networks, benzene was mixed with black ink and dispersed onto a 100 µM blue-colored NBA aqueous solution for color contrast. To dilute cumene, 99.9% ethanol was used. SERS intensity mapping: To demonstrate the versatile applicability of the plasmonic network for molecular detection, both hydrophobic (toluene) and hydrophilic molecules (R6G in water) were sequentially treated to the network and an identical area was mapped with Raman intensities at representative peaks of toluene (1013 cm–1) and R6G (1527 cm–1). During the mapping procedure, the plasmonic network was placed in the prepared PDMS well on the Si wafer and covered with a cover slip to avoid the leakage of toluene. A computer-controlled XY translation


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stage was used to acquire mapping images. The scan area, 1 mm × 1 mm, was divided into acquisition grids, each of 20 µm size. All the spectra were obtained at an exposure time of 0.1 s with a 785-nm laser operating at 200 mW. ASSOCIATED CONTENT Supporting Information The supporting Information is available free of charge on the ACS publication website. Figures S1-S11 showing plasmonic network’s morphological characterization by SEM and TEM images, scattering spectra and UV-vis spectra of plasmonic network by concentration, enhanced swelling property compared with plasmonic film, optimization of Ag and Au plasmonic networks for SERS measurements, reproducibility test with the optimized plasmonic networks, calibration curves for the small molecules detected by SERS using the plasmonic networks, a SERS spectrum for mixed VOCs, and time-resolved SERS spectra of gas-phase toluene monitored by plasmonic network were included. AUTHOR INFORMATION Corresponding Authors * Correspondence: [email protected] * Correspondence: [email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ACKNOWLEDGMENT


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This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF2017R1A2B4003267) for Y.L., S.L., C.M., J.A., and I.C. This research was also supported by the Mid-Career Researcher Support Program (NRF-2016R1A2B3014157) through the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning for T.K.


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Facile Fabrication of Large-scale Porous and ... - ACS Publications

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