Three-Dimensional Organization of Surface-Bound Silicone

Article pubs.acs.org/JPCC

Three-Dimensional Organization of Surface-Bound Silicone Nanofilaments Revealed by Focused Ion Beam Nanotomography Georg R. Meseck,† Andres Kac̈ h,‡ and Stefan Seeger*,† †

Department of Chemistry, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland Center for Microscopy and Image Analysis, Winterthurerstr. 190, CH-8057 Zurich, Switzerland

‡

S Supporting Information *

ABSTRACT: One-dimensional (1D) nanostructures have been identified as key technology for future devices and integrated into surface-bound materials. The roughness of surface-bound 1D silicone nanofilaments (SNFs) has been used extensively to create surfaces with extreme wetting properties and as carrier material. Electron microscopy has shown that this material is made of individual filaments with diameters spanning tens of nanometers and a length of several micrometers which arrange into a highly entangled quasi-porous network. However, a comprehensive analysis of the three-dimensional (3D) superstructure has remained elusive so far. In this study, focused ion beam nanotomography (FIB-nt) is used to quantify the otherwise hardly accessible structural parameters roughness (12.68) and volume fraction (2.80). The volume fraction is anisotropic, and two major species of SNFs are quantified to contribute equally to the overall surface area. Spatial statistics reveals a self-avoiding growth pattern of SNFs over the substrate, and a 3D model of the data is rendered. The presented analysis therefore significantly advances the understanding of SNF surface coatings with regard to their structure at the nano- and microscale. Finally, the described procedure may serve as a useful tool to analyze other surface-bound 1D nanostructures of similar complex arrangement.

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environmental and chemical stability.48,49 Though carpets of SNFs have been successfully exploited as a high surface area material in protein retention and as catalyst support,50,51 their surface area could not be quantified so far due to their thin film nature and the complexity of the structure. Standard electron microscopy, which is typically used to elucidate the morphology of ordered 1D nanomaterials, cannot be used to obtain the surface area of materials without geometric order because it offers only 2D projections. At the same time shape, dimensions, and 2D distribution of 1D building blocks can be studied well with scanning electron microscopy (SEM), and micrographs of cross sections can be used to visualize the structure along the axis of growth.25−40,44 Transmission electron microscopy (TEM), on the other hand, is capable to show structural details down to atomic resolution, but sample preparation destroys the 3D superstructure. 3D electron microscopic data can be obtained by focused ion beam nanotomography (FIB-nt) which has become a powerful technique for the characterization of structurally complex specimens in materials science.52−56,56 In brief, a focused ion beam is used to continuously mill a block face of the sample. SEM images are taken from the resulting cross section at defined milling depths to yield a stack of serial micrographs. The imaged volume may have dimensions of several micrometers in length, width, and depth and is subsequently

INTRODUCTION The properties of materials depend on both their chemistry and their morphology. The latteri.e., the influence of structure becomes especially apparent when the material dimensions approach the nanoscale. Here, the high surface to volume ratio together with quantum size effects leads to reactive, magnetic, optical, and electronic behavior that often differs substantially from that of the respective bulk materials.1,2 Within the class of nanomaterials, one-dimensional (1D) structures such as gold and oxidic nanorods,3−11 carbon nanotubes,12−15 and nanowires in general16−20 have been identified as key components for the fabrication of novel nanodevices.21,22 Their assembly into three-dimensional (3D) superstructures of higher order such as nanoarrays, nanoforests, and networks has led to the development of biosensors,23−27 surfaces with superhydrophobic and self-cleaning properties,28,29 and electrode materials for supercapacitors30−34 and dye-sensitized solar cells.35−40 Morphology and surface area of the 3D superstructure are critical parameters in all these applications because they determine the interface with reactants and analytes but are often difficult to quantify. A decade ago surface-bound polysiloxane structures have been added to the family of 1D nanomaterials, and their properties and applications have been reviewed in detail.41−43 Most applications for these so-called silicone nanofilaments (SNFs) have focused on the extreme wetting behavior that can be inferred by this coating.41,44−47 Salient benefits of SNFs are their production via convenient vapor-phase or solvent-based routes at room temperature accompanied by their excellent © 2014 American Chemical Society

Received: July 10, 2014 Revised: September 23, 2014 Published: September 29, 2014 24967

dx.doi.org/10.1021/jp506867n | J. Phys. Chem. C 2014, 118, 24967−24975

The Journal of Physical Chemistry C

Article

bound on the glass substrate. Data in the region between 0 and 0.9 μm from the substrate are therefore not presented in the results for feature counting and volume fraction. To facilitate the discussion of image processing two terms are used in the following: Feature will be used for an object that has been automatically extracted from a binarized 2D cross section. Filament refers to the actual material or an image of it in the original grayscale data. Chemical Vapor Deposition of Silicone Nanofilaments. Microscope glass slides (eight pieces) were cleaned by ultrasonication in a 10% (v/v) aqueous solution of Deconex for 15 min at 50 °C. Subsequently, the substrates were rinsed with copious amounts of deionized water and blown dry in a stream of nitrogen. The cleaned and activated glass slides were mounted upright in a desiccator (V = 6.5 L). The desiccator was sealed, and the relative humidity adjusted to 44% by flushing with an appropriate mixture of dry and humidified nitrogen for 1 h. The reaction was started by injecting ethyltrichlorosilane (500 μL) through a septum onto a receptacle that was placed in the middle of the desiccator and proceeded for 4 h at room temperature. Samples were rinsed with deionized water to remove residues of HCl which forms during the hydrolyzation of the silane. The strongly waterrepellent behavior of the samples during this step serves furthermore as quality control prior to further characterization. FIB-SEM Nanotomography. The work flow comprised several steps that are summarized in Scheme 1 as follows: (1) SNFs were sputter coated with 40 nm of platinum to enhance the contrast. (2) A small plastic vessel (BEEM capsule) was then filled to the rim with the mixture of the epoxy embedding kit which was prepared according to the manufacturer’s description (14.5 g of medium, 10.5 g of nadic methyl anhydride, 5.0 g of dodecenylsuccinic anhydride, and 0.54 g of N-benzyldimethylamine). The SNF sample was placed on top of the BEEM capsule in order to make contact with the liquid resin which was allowed to penetrate into the material at room temperature overnight. (3) The resin was cured at 60 °C for 48 h to obtain a solid epoxy block which was (4) cleaved off the glass substrate by short immersion in liquid nitrogen. (5) The epoxy block was then removed from the BEEM capsule and cut to a length of ca. 3 mm. After mounting on an aluminum SEM sample holder by applying conductive carbon cement at the bottom and the sides, a layer of 20 nm platinum was deposited on the specimen. (6) Milling and image acquisition was performed continuously in an AURIGA 40 CROSSBEAM system (Zeiss, Oberkochen, Germany) using the FIBICS Nanopatterning engine (Fibics Inc., Ottawa, Canada). The gallium-ion beam for milling was set to 30 kV, 600 pA current, and a z-spacing of 20 nm. SEM images were acquired at an acceleration voltage of 1.5 kV using an in-lens energy selective backscattered electron dectector (ESB) with a grid voltage of 1.3 kV and 22 μs dwell time. The pixel size was set to 20 nm and tilt-corrected to obtain isotropic voxels. (7) The obtained stack of serial images was cropped to a size of (1496 × 926) pixels and resliced along the direction of milling (z-axis) to visualize the drift along the y-axis. Images were loaded into MATLAB (The Mathworks Inc., Natick, MA), and a function was fitted through the platinum layer that was observable as a bright line. Shifting the images by this function corrected the drift along the y-axis. Along the other axes no drift correction was necessary. (8) After cropping of the platinum layer the image stack of (1495 × 777 × 904) pixels was further processed in the freely available FiJi distribution of

processed and analyzed to obtain 3D information. FIB-nt has been used extensively to determine shape, particle size distribution, and phase composition in cement.52,53,57−59 Other examples include studies of the macroporosity of a carbon monolith,60 the pore path properties in clay,61 and the structural properties of fuel cell materials.62−65 In contrary, only few studies report the application of FIB-nt to surface-bound 1D structures, and the investigated materials were highly aligned.66−68 In this work, we report the first complete 3D characterization of a surface-bound carpet of SNFs. This material comprises filaments from a few tens of nanometers in diameter to tens of micrometers in length. The superstructure is very complex as the filaments are highly entangled, forming a quasi-network, and is thus difficult to analyze using standard methods. The described protocol using FIB-nt allowed to calculate parameters such as surface area and volume fraction that are necessary for the complete understanding of the material’s properties.

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EXPERIMENTAL SECTION Materials. Ethyltrichlorosilane (99%) was purchased from Acros Organics (Fisher Scientific, Reinach, Switzerland) and stored and handled under a dry nitrogen atmosphere. Standard microscope glass slides were obtained from Menzel-Gläser (Braunschweig, Germany). The alkaline cleaning detergent Deconex 11 Universal was purchased from Borer Chemie (Zuchwil, Switzerland). For preparation of FIB-SEM samples the epoxy embedding kit from Sigma-Aldrich (Buchs, Switzerland) was used. Materials for electron microscopy such as aluminum stubs, BEEM capsules (inner diameter d = 7 mm), and conductive carbon cement were purchased from Plano GmbH (Wetzlar, Germany). Terminology and General Remarks. The axes of the volume were assigned as follows (cf. Scheme 1): The plane of imaging is denoted as xy with the origin of the y-axis defined at the lower limit, i.e. the root, of the SNF carpet. The axis of milling is denoted as z. Upon qualitative examination of the volume it was found that some large filaments had remained Scheme 1. Work Flow of Sample Preparation and Image Processing

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ImageJ.69 The data set was binarized by subtracting the background at a brightness level of 90. (9) The particle analysis module in FiJi was then used to extract features within each cross section of the binarized image stack along the three spatial axes. The main parameters obtained were number, area, perimeter, and coordinates of the features while noise was filtered by excluding objects smaller than two square pixels. The complete data of the feature extraction was transferred to the ORIGIN software package (OriginLab, Northampton, MA) for calculations. The surface area of the material was calculated by multiplication of the total sum of perimeters with the slice thickness of 20 nm and normalized to the geometric surface area to report the roughness according to Wenzel.70 The average volume fraction was calculated as the mean of the area fraction over all slices. Spatial analysis was done on the coordinates of features from a xz-slice using the spatstat package of the open source distribution R (www.r-project.org). Nearest-neighbor distances were calculated, and the Lest function was used with a simulation of 10 000 random point patterns for the envelopes over a range from 0 to 20 μm to evaluate spatial randomness. Further tests were done with the Gest and Fest function using standard settings. (10) A 3D model of the data was rendered in Imaris based on an isosurface (Bitplane AG, Zurich, Switzerland). For the creation of this isosurface, the data were smoothed with a Gaussian filter (width = 20 nm) and the background subtracted by thresholding at the same value (here 90) as in FiJi. Surface area and volume of the material were obtained from the included statistical analysis tool and normalized to the geometric surface area and the imaged volume respectively for comparison with the results from FiJi.

Figure 1. Impact of thresholding. (a) XY cross section of the subvolume. The magnified area (white rectangle) was thresholded with a cutoff at increasing brightness levels. Features decrease in size which eventually leads to the separation of touching objects. (b) Because of the shrinking features, the calculated values of volume fraction and roughness decrease.

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RESULTS AND DISCUSSION Image Processing. After alignment and cropping, a total volume (x, y, z) of (29.90 × 15.54 × 18.08) μm was available from the FIB-nt experiment and used for analysis. This stack of serial SEM images has been animated as a video which is available in the Supporting Information. A representative subvolume corresponding to (8.06 × 15.54 × 8.00) μm was selected from the image stack before evaluation of the full volume to reduce the file size and hence the processing time. The representative FIB-nt cross section (xyplane) of this subvolume in Figure 1a qualitatively indicates the presence of two major species of different size to which the filaments can be assigned. This phenomenon has been also observedhowever less pronouncedfor filaments created from methyltrichlorosilane as precursor.43 Small filaments are abundant in high number to a height of about 2 μm and can be found to about 5 μm from the substrate. Because of the entangled nature of the SNF carpet, there is no exact border where the presence of small filaments ends. However, starting from ca. 5 μm larger filaments exclusively built-up the carpet until the upper limit of the material is reached at a height of 15.5 μm. It is obvious that the large SNFs grow less densely than the small filaments. While these findings can also be obtained from routinely applied scanning electron microscopy of cross sections, the volume obtained from FIB-nt allowed a quantification of the complex 3D structure for the first time. For this purpose automatic image processing was used because it allows to analyze data sets of a size and complexity that cannot be handled manually. At the same time automatization and the several steps that are involved in image processing lead to sources of errors that have to be

considered. The first and most important step toward the extraction of features is binarization of the image stack, i.e., the conversion of the grayscale data with 256 brightness levels to pure black and white images that can be interpreted automatically by the computer. Because of the high impact of this step for the final results, this procedure shall be discussed shortly.71 For binarization the background of the images has to be subtracted which is achieved via thresholding. Figure 1a shows a detail of images thresholded at increasing brightness levels from 84 to 98. Features erode at the border when the threshold value increases which can also lead to the separation of touching filaments. As a result, thresholding affects both number and size of the features that will be extracted from the binarized data. Feature extraction was then done in batch mode on individual slices of the binarized data. For all extracted features the 2D values circumference and area were compiled and subsequently used to calculate their respective 3D analogues surface area and volume.72 These values were normalized for better comparison and reported as (i) the Wenzel roughness,70 which is the ratio of actual to geometric surface area, and (ii) the volume fraction, which denominates the fraction of space that is occupied by the material. Figure 1b emphasizes how these values are affected by binarization of the data. Both roughness and volume fraction strongly decrease because the extracted features shrink with increasing threshold. Filtering of the grayscale images can be useful to reduce noise and thus facilitate finding the appropriate threshold value.72 However, no advantage was found in this case (Figure S1). The best value for thresholding (here 90) was therefore determined 24969



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given in Table 1. The individual values vary only marginally from the mean roughness of 12.68 and the mean volume

by comparing the level of detail in the binarized images with the original grayscale data. Analysis of Structural Parameters. The primary aim of the study was to investigate the SNF material in terms of roughness and volume fraction. First, the binarized image stack was used to analyze the volume fraction of the material along all spatial directions (Figure 2). Note that the material has been physically milled along the z-axis, whereas x- and y-axis have been reconstructed from the 3D data.

Table 1. Extraction of Roughness and Volume Fraction Yields Consistent Results along All Spatial Directions axis of analysis

volume fraction/%

roughnessa

x y z average Imarisb

2.80 2.82 2.79 2.80 3.10

12.22 13.23 12.60 12.68 13.57

a

Ratio of measured surface area to the geometric surface (i.e., the underlying substrate). bValues were obtained from the isosurface modeled in the Imaris software package.

fraction of 2.80 underlining that the procedure for feature extraction is not sensitive to the direction along which the image stack is sliced. The exceptionally low volume fraction that was found demonstrates the high porosity of the SNF carpet and suggests the use of the material as carrier for catalytically active compounds. In the next step the density of features on the substrate was investigated. For this study a region spanning 4 μm along the yaxis in the center of the volume was selected. In this part of the material almost exclusively larger filaments exist, and they are mainly cut perpendicular to their axis which minimizes wrong recognition of features. The number of features was normalized to the area of the underlying substrate and plotted against the distance from the substrate as can be seen in Figure 3a. The decrease of observed features at far distance from the substrate is due to the formation of bundles of SNFs, so they are no longer recognized as individual objects. Remarkable is the region at a distance of 7−8 μm from the substrate where a value of 0.5 features/μm2 remains constant. This value may be translated to a figurative example on the macroscale. The standard microscope glass slide that was used as a substrate for the SNF carpet is accordingly covered with ca. 1 billion of large filaments. Close to the substrate the denser thicket of small SNFs is dominating the structure, and consequently up to 18 features per μm2 are found (Figure S3). As a result of this high density, the small filaments contribute strongly to the total surface area though they exist only close to the substrate as visualized in Figure 3b. It can be seen that 50% of the total surface area are already reached at a distance of 2 μm from the substrate, which is followed by a much lower increase toward the top of the SNF carpet. By extracting small and large features separately, the contribution of small and large filaments was estimated to 47 and 53%, respectively, which is in line with the plot of the cumulative surface area. Motivated by these findings, we wanted to further elucidate the interplay of the two species of SNFs within the material. Therefore, the extracted features were compiled according to their size (i.e., the area the feature covers) and their distance from the substrate into a 2D histogram which is shown in Figure 4a. The histogram shows that small features (cross section 0.06 μm2) are found close to the upper limit of the carpet where SNFs form bundles that bend into the xz-plane. The features within the last micrometer toward the top of the data set do not appear in the histogram because they were too few to be considered in the statistics. Spatial Analysis of Growth Pattern. The characterization of the 3D structure of the carpet as presented above is important with respect to applications as high surface area support and a deeper understanding of the material’s wetting behavior. In addition to these findings, the data set allowed to gain insight into the growth pattern of the filaments on the substrate which is likely related to the mechanism by which the material forms. Unfortunately, the smaller filaments are too entangled and dense to allow for a reliable determination of the positions of individual features, so the study had to focus on the large filaments. Therefore, one slice at a distance of 1.54 μm from the surface was selected from which most of the small filaments were automatically removed during feature extraction (Figure S4). Because it was not possible to remove all small and noncircular features without risking to lose larger features, a manual cleanup was added to prepare the pattern for analysis. In Figure 5a the nearest-neighbor distances of the remaining large features are compiled into a histogram together with the respective Poisson distribution. The latter distribution would be

Figure 5. (a) Histogram of the nearest-neighbor distance with the mean (0.78 μm; black dashed line) indicated. Superimposed in green is the expected pattern for complete spatial randomness (CSR; mean 0.62 μm). (b) Spatial analysis employing the L-function clearly shows a lack of short distances between the features.

expected for a pattern that exhibits complete spatial randomness (CSR), i.e., in which the points are randomly distributed over the surface. In such a CSR pattern mean and variance are identical and correspond to 0.62 μm for the given pattern of 350 points within a frame of (29.90 × 15.54) μm. 24971



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A comparison of observed and CSR distribution shows that the mean of the observed distribution is shifted by 26% to a higher value (0.78 μm), and the variance is almost 6 times higher than in the random distribution. Though it was clear that there is substantial deviation from a CSR pattern, no final conclusion was possible based on this simple analysis, so a point pattern analysis was performed.73 The so-called Lfunction analyzes all distances r between the points in a pattern and not only the nearest-neighbor distances. In addition, this function is very easy to interpret because it shows a straight line for a CSR pattern. Every deviation from this shape points directly to the type of observed pattern, and the validity of the analysis is given by the simulation of confidence intervals. The analysis of the data using the L-function shows the observation lying significantly below the confidence interval for short distances from 0 to 1 μm (Figure 5b). Remarkably, the function is zero for values below 240 nm. The latter finding might be related to the hard-core behavior of the filaments since they cannot physically lie on top of each other. However, the radius of an average large filament (ca. 80−100 nm) is smaller than the 240 nm which were observed. Further statistical tests with the so-called G- and the F-function confirmed this lack of short distances in the observed pattern. These results are discussed together with Figure S5. From the combined analysis it can be concluded that there are substantially less features lying close to each other than it would be expected for a random point pattern. It can be therefore assumed that the growth pattern of the large SNF species shows a self-avoiding behavior. It has to be noted that small filaments were not considered in the analysis and can be found in close vicinity or even adjacent to large filaments. However, it seems unlikely that small filaments are able to push apart the larger filaments which could explain the self-avoiding pattern observed. Given that small and large filaments are made of the same material, it is more likely that large filaments will not bend so much close to the substrate because their higher diameter results in more stiffness. Another reason for self-avoidance might be a competition for precursor molecules during the growth of the filaments. Larger filaments require naturally more resources (silane, water) for formation than small filaments. Therefore, diffusion might limit the surface density of large filaments but only to a negligible extent the density of small filaments. 3D Visualization of the Image Stack. Apart from the mere statistical analysis of the data it was possible to render a 3D model of the material using the commercial software package Imaris (Figure 6). The screenshot of the model demonstrates that all structural details of the raw data are well reproduced. Such a model allows interactive investigation of the imaged volume from all perspectives and to create cross sections through the volume at arbitrary angles. At the same time it opens an additional path to calculate structural parameters. The values for roughness and volume fraction that were obtained from the model are given in Table 1 and are close to the values obtained via image processing of the binarized data in FiJi. The positive deviation of 11% for the volume fraction and 7% for the roughness are likely the result of the additional smoothening by a Gaussian filter that had to be applied for the creation of the model. Another source of deviation might be a difference in the algorithm which the software uses to calculate the parameters. Going beyond mere descriptive parameters, such a 3D model provides the basis to compute transport properties within the quasi-network. This has been shown for porous materials such

Figure 6. Rendered 3D model faithfully reproduces the intricate structure of the SNF carpet.

as a nanoporous carbon layer,62 a silica monolith,74 and a solid oxide fuel cell75 and will be the subject of future work.

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CONCLUSION Following the Wenzel theory for wetting, the inherent roughness of SNF carpets has been demonstrated indirectly in many applications related to the extreme wetting behavior of the coating.43 Though this high surface area has been exploited deliberately more recently, this important structural parameter has not been quantified yet.51 Reasons for this lack of characterization are related to the special structure of the material. First, the carpet of SNFs that we describe in this work is made of individual filaments with diameters between a few tens of nanometers and lengths of tens of micrometers. The high entanglement of the filaments makes it impossible to quantify the quasi-network structure with TEM or SEM because these methods provide only 2D projections. Second, the thin-film nature of the coating prohibits bulk methods such as BET or mercury intrusion porosimetry to obtain surface area and volume fraction. These limitations have been overcome using 3D image data obtained from FIB-nt and are presented in this work. Binarization of the images allowed automatic feature extraction which was the basis for the analysis of the 3D structure. By determining the roughness, it was found that the coating multiplies the surface area of the underlying substrate by a 24972



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factor of ca. 13. At the same time the volume fraction of the carpet is very low (ca. 3%). Such a high porosity is in the range of aerogelsmaterials that are well-known for their high porosity and emerging applications.76 However, being a coating sets the SNF material apart. High roughness and porosity are due to two major species of filaments that build the carpet. Small filaments are present only to about 5 μm from the substrate, but they contribute equally to the total surface area of the coating as the larger filaments which prevail throughout the entire material. The spatial analysis revealed that the larger filaments are distributed over the surface in a self-avoiding pattern. A possible explanation for this behavior is a competition for precursor molecules during the growth of the 1D nanostructures. Finally, a 3D model of the data was rendered which allows to intuitively study the material. Future work will use this model to compute transport properties within the material. It is worth to note that the thickness and structure of the SNF carpet can be adjusted depending on the reaction conditions during chemical vapor deposition.43 Therefore, the presented method will allow to correlate reaction conditions with roughness and porosity which may thus be targeted toward a specific application. In addition to the knowledge that was gained on SNF carpets, we believe that the described FIB-nt protocol can be applied to materials of similar complexity. The procedure may thus serve as a useful tool for others working on surface-bound 1D materials such as nanoforests, arrays, and quasi-networks.77,78 In this respect it should be emphasized that all the image processing was carried out using freely available software and calculations were done by standard spreadsheet analysis.

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ASSOCIATED CONTENT

S Supporting Information *

Video of serial sectioning; additional data on image filter influence, analysis of volume fraction, feature counting, and spatial analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (S.S.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful to Therese Bruggmann and Dr. José Maria Mateos Melero (Center for Microscopy and Image Analysis, University of Zurich) for help with the embedding procedure and technical support. We thank Prof. Reinhard Furrer (Institute of Mathematics, University of Zurich) for his kind support with the spatial statistics and Dr. Hendrik Hähl for help with Matlab. Financial support by the Swiss National Science Foundation (Grant 20-146421) is gratefully acknowledged.

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Three-Dimensional Organization of Surface-Bound Silicone

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