A comparison of soot nanostructure obtained using two high resolution transmission electron microscopy image analysis algorithms

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available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/carbon

A comparison of soot nanostructure obtained using two high resolution transmission electron microscopy image analysis algorithms Kuen Yehliu, Randy L. Vander Wal, Andre´ L. Boehman

*

The EMS Energy Institute, The Pennsylvania State University, 405 Academic Activities Building, University Park, PA 16802, USA

A R T I C L E I N F O

A B S T R A C T

Article history:

The present work addresses the validation process of an in-house developed image

Received 29 August 2010

analysis tool to extract fringe length, tortuosity, and separation from high resolution trans-

Accepted 1 June 2011

mission electron microscopy images of carbonaceous materials. In order to validate the

Available online 6 June 2011

algorithm, we compare fringe properties that are extracted from high resolution transmission electron microscopy (HRTEM) images through (1) the in-house developed tool (new algorithm) and (2) a tool that has been validated and published (previous algorithm). X-ray diffraction and Raman spectroscopy are used to crosscheck the results for fringe length and fringe separation extracted from the HRTEM images. The algorithm of extracting fringe tortuosity is validated by the images of two disordered soot samples, and a heatpretreated, highly-ordered sample. Tortuosity results are compared with the results of fringe separation. These comparisons validate the algorithm for extracting fringe tortuosity and confirm that tortuosity is an indicator of the degree of disorder within the carbon framework. Statistical results for each property extracted from the HRTEM images by the newly developed image analysis tool are presented in the form of a histogram and characteristic values (mean and median). The characteristic values quantitatively distinguish between the different carbon nanostructures of various soot samples.  2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Particulate matter (PM) generated by combustion in diesel engines is known to have negative impacts on human health [1]. As a consequence, US and European emission legislation on PM emissions from diesel vehicles has become increasingly strict. For example, the PM emissions of new passenger cars regulated by Euro V (0.005 g/km, 2009) are one-fifth of Euro IV (0.025 g/km, 2005).1 The currently accepted approach for reducing PM is the use of diesel particulate filters (DPF) with an upstream oxidation catalyst [2]. The upstream oxidation catalysts oxidize the unburned hydrocarbon component of the PM. The remaining carbon particulate component, or

soot, is trapped when the exhaust passes through the DPF. The accumulated soot particles are then removed by oxidation within the DPF. One important factor that governs the soot oxidation rate is soot reactivity reflecting the chemical and physical properties of soot particles [3]. Therefore, soot surface functionality [4] and soot nanostructure have both become significant research subjects [5–8]. The term nanostructure is used here to indicate the dimensions, separation distance and curvature of the graphene segments. Carbon nanostructure is often studied by X-ray diffraction (XRD), Raman spectroscopy, and HRTEM [9]. These three characterization methods have also been used to investigate the nanostructure of soot, because soot

* Corresponding author: Fax: +1 814 865 3248. E-mail address: [email protected] (A.L. Boehman). 1 Dieselnet Diesel Emissions Online. Available from: http://www.dieselnet.com (accessed August 2010). 0008-6223/$ - see front matter  2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.carbon.2011.06.003

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Fig. 1 – The original image with selected region of interest. Cabot R250 after heat-treatment for 30 min at: (a) 1350 C, (b) 1950 C, (c) 2300 C, and (d) 3000 C. The region of interest was automatically selected based on the skeleton images of [23] (Fig. 2 of [23]) except (d). The ROI of (d) was selected such that the scale bar was excluded.

is chiefly composed of carbon [10,11]. XRD has been used to derive crystalline parameters, such as d002, La, Lc [5,12–14]. For carbon materials with a single structural carbon component, such as graphite, the crystalline sizes and lattice constants can be accurately determined through a standard, calibrated procedure [15]. For less ordered carbons such as soot, Raman spectroscopy is used for its sensitivity to both crystal structure and short range order [16–19]. Derived from Raman spectra by curve fitting, the integrated intensity ratios of the defect peaks to the graphitic peak indicate the degree of disorder of the soot nanostructure. Although XRD and Raman spectroscopy provide information of carbonaceous nanostructure, neither of these two techniques reveals the configurations of the graphene layers of soot. To compensate for this deficiency, HRTEM has been used to investigate the nanostructure of soot [9–11,20–23]. Detail explanations of the relation between physical structure and the data conveyed in a TEM image can be found in textbooks [24–26]. Yet before the development of the digital image processing techniques, analyzing HRTEM images quantitatively was difficult. With the advent of digital image processing techniques, Palotas et al. processed HRTEM images with a band-pass filter in the frequency domain, and studied the effects of changing binarization threshold values. In processed images, graphene layers are referred to as carbon

lattice fringes, or simply fringes. Parameters that characterized fringes, such as length, circularity, and interplanar spacing, were determined from the binarized images [7]. Other methodologies also appeared in the literature. Shim et al. [27] applied filtering, thresholding, and skeletonizing functions to HRTEM images. These authors also used twodimensional and three-dimensional parameters to describe the orientation order of soot. Another quantitative image analysis approach was implemented by Sharma et al. [28]. They developed algorithms to correct artifacts in the processed images. Additionally, the parallelism of adjacent fringes was determined based on the angles of inclination (±10) relative to a reference fringe. The perpendicular distance between two layers can only be obtained if the parallelism criterion is satisfied. Goel et al. [29] calculated the actual arc length divided by the circumference of a imaginary circle to characterize the fullerenic structure in the HRTEM images. Galvez et al. [30] compensated the intensity variations by tophat transformation, and eliminated fringes that are shorter than the size of a single aromatic ring (0.25 nm). In addition, the parallel stacked layers were determined according to relative orientation, fringe tortuosity, and interfringe spacing. As image processing techniques developed further, commercial image processing packages were used in conjunction with macros [7,23,27,31]. Vander Wal et al. [21,23,32] used Optimas

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6.5 (Media Cybernetics, 1999) to implement algorithms to extract fringe length, tortuosity and fringe separation from HRTEM images. For HRTEM, the highly localized analysis usually casts doubt on whether or not the images can be representative of the bulk samples. Therefore, when analyzing HRTEM images of carbonaceous materials, XRD and Raman spectroscopy are often used for comparison and traditional quantifi-

cation [21,23]. The crystalline parameters, such as d002, derived from XRD patterns should be consistent with the mean fringe separation distance derived from HRTEM images [23]. The Raman intensity ratio (IG/ID) has the same trend as the medians of the fringe length histogram obtained from HRTEM images [21]. In addition to the fringe length and fringe separation distance, the fringe tortuosity is another parameter of carbon nanostructure. High tortuosity of carbonaceous

Fig. 2 – The original image with selected ROIs: (a) acetylene 1650 C/1.0 slpm total flow, and (b) ethanol 1650 C/1.0 slpm total flow. The ROI was automatically selected based on skeleton images of [23]. (Figs. 13 and 14 of [23].)

Fig. 3 – Skeleton images of Fig. 1.

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material is an indication of high percentage of odd-numbered carbon rings, such as 5-membered or 7-membered carbon rings, in the aromatic framework [33,34]. Tortuous fringes can severely distort graphitic nanostructure by preventing the stacking of layers, and increase the fringe separation distance [23]. Except by analyzing HRTEM images, there is no other physical technique that can directly measure graphene layer tortuosity. The present work addresses the validation process of an in-house developed image analysis tool to extract fringe length, tortuosity, and separation distance from HRTEM images of carbonaceous materials. The development of a new image analysis tool extends previous work on diesel soot characterization [10,11,35–37]. Starting from literature approaches, the HRTEM image analysis algorithms used here were developed and implemented using MATLAB (The Math Works Inc., Natick, MA). Program specifications are separately reported in detail [38], and briefly summarized as Supplementary material I. In this study, the same HRTEM images of [23] were processed using the new algorithm to obtain the fringe space

distance and fringe length distribution from HRTEM images of carbon black heat-treated at four different temperatures for comparisons. Additionally, this paper presents calculations of the tortuosity using the previous and new algorithms from HRTEM images of carbon generated from ethanol and acetylene, and an HRTEM image of a highly ordered carbon [23]. Therein the present work for the first time makes a comprehensive comparison of two different HRTEM image analysis tools. The new analysis results were also compared against XRD and Raman spectroscopy data, which highlights the possibility to characterize carbon nanostructure using HRTEM image analysis as a standalone technique. In summary, the present work uniquely contains the following features: 1. It fully documents the analysis algorithm (via supplementary information and [38]), so that there are no hidden processing parameters or details. 2. Comparison to a prior algorithm by rigorous quantitative comparison against the data extracted from the same HRTEM images.

Fig. 4 – Skeleton images of Fig. 2.

Table 1 – Summary of image processing parameters having same values for all images.a Parameter

Value a

Gaussian filter size (pixels)

Gaussian filter deviation (1/pixels)

Morphological closing structural element (pixel square)

Morphological opening structural element (pixel square)

Fringe length screening threshold (nm)

Spatial resolution (nm/pixel)

11

0.7

1*1

2*2

0.4

0.074

All images were enhanced by the histogram equalization to improve contrast.

Table 2 – Summary of image processing parameters with different values. Images ID Top-hat transformation structural element (radius of disk in pixels) Binarization threshold value

Cabot Cabot Cabot Cabot Acetylene Ethanol R250, 1350 C R250, 1950 C R250, 2300 C R250, 3000 C 1650 C/1.0 slpm 1650 C/1.0 slpm 5

5

5

5

4

4

0.38

0.27

0.26

0.28

0.21

0.20

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3. By comparison to this particular code, the present code is then benchmarked against independent measurements of specific nanostructural parameters.

4. Comparison to controlled sets of varied nanostructural parameters to assess the dynamic range and demonstrate the capability to track nanostructural changes in a process. Such is often applied to carbon products and materials.

Fig. 5 – Direct comparison of skeleton images of different image processing methods: (a) method in [23], and (b) method in this work. (Source image: Fig. 1b.)

Fig. 6 – Detail comparison of Skeleton image from [23] and this work: (a) comparing skeletons of Fig. 1b that were obtained by different image analysis algorithms; (b) enlarged view of (a) in the middle section; and (c) enlarged view of (b). Definition of color: (green pixels) skeletons that appear in both [23] and this work, (blue pixels) skeletons that appear only in [23], and (red pixels) skeletons that appear only in this work.

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35

35

Median: 0.98 nm

30

30

25

25

% of fringes

% of fringes

Median: 0.90 nm

20

15

20

15

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10

5

5

0

0 0

1.8

3.6

5.4

7.2

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0

16

1.8

3.6

5.4

Fringe length (nm)

7.2

9

11

(a)

14

16

(b)

35

35

Median: 1.58 nm

Median: 1.18 nm 30

30

25

25

% of fringes

% of fringes

13

Fringe length (nm)

20

15

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15

10

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5

5

0

0 0

1.8

3.6

5.4

7.2

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Fringe length (nm)

(c)

0

1.8

3.6

5.4

7.2

9

11

13

14

16

Fringe length (nm)

(d)

Fig. 7 – Histograms of the lattice fringe length extracted by the image analysis tool of this work: (a) heat-treated at 1350 C, (b) heat-treated at 1950 C, (c) heat-treated at 2300 C, and (d) heat-treated at 3000 C.

2.

Experimental

In this comparative study, HRTEM images of carbon black samples (Cabot R250) that were heat-treated at 1350, 1950, 2300, and 3000 C under a helium atmosphere are analyzed for fringe length and fringe separation distances. The raw images are shown in Fig. 1. The images in Fig. 1 are the same as Fig. 1a, d, e, h in [23]. For fringe tortuosity distribution, HRTEM images of soot generated by thermal pyrolysis of ethanol and acetylene at 1650 C with 1.0 slpm total flows are compared with an image of the soot heat-treated at 3000 C

under a helium atmosphere, which shows a high degree of ordering in its nanostructure. The raw images are shown in Fig. 2. The images in Fig. 2 are the same as Figs. 5d and 6d in [23]. The detailed procedures for heat-treating carbon samples [21] and for generating soot samples from thermal pyrolysis of fuels [23] have been covered comprehensively. Thus only brief details are provided here. Carbon black samples were contained in a graphite crucible and heat-treated in a resistance heat furnace (LECO EF-400) under a helium atmosphere [21]. The temperature of the furnace was programmed accordingly. The heat-treated

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3.

Results and discussion

HRTEM images in Fig. 1 show the ROIs of heat treated black carbon (Cabot R250) at four different temperatures (1350, 1950, 2300, and 3000 C). With increasing heat-treatment temperatures, there is an evolution towards polyhedral shells [23]. Skeletons extracted by the new algorithm from the ROIs indicated in Fig. 1 are shown in Fig. 3. From there, the development of graphitic nanostructure is evident as the heat-treatment temperature increases. The heat treatment ultimately leads to hollow, polyhedral shells (Fig. 1d) [23]. The fringe separation and fringe length distributions shown in this paper are based on Fig. 3. The fringe length and fringe separation distance

5

1.8

4

1.6

3

1.4

2

1.2

1

1

0 1000

1500

2000

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3000

Median fringe length (nm)

carbon black were dispersed and sonicated in ethanol, from which a drop of solution was deposited on the TEM grid [23]. The procedures for generating the soot using tube furnace were different for acetylene and ethanol. Acetylene was directly mixed with helium at a fuel concentration of 8.0 · 104 mol/L. The mixture was directed into a 1/4 inch internal diameter alumina tube that extended the length of the furnace. Ethanol was carried by entraining its vapor with helium flowing through a bubbler maintained at room temperature. With no bypass flow, the fuel concentration was 5.0 · 104 mol/L. In both cases the flow through the furnace tube was 1.0 slpm. In addition, the pyrolysis temperature in the furnace is adjustable [39]. The pyrolysis temperature of the soot in Fig. 2 is 1650 C. The soot produced by thermal pyrolysis of fuels was collected at the exit of the tube furnace directly upon a TEM grid [23]. HRTEM images were taken using a Phillips CM200 with Gatan image filter for digital imaging with real-time Fourier transforms. The nominal resolution is 0.14 nm. The previous image analysis tool serves as a standard and was a macro running under Optimas version 6.5. The details have been given previously [21,23]. The results of X-ray diffraction and Raman spectroscopy are used directly as supportive data, so these procedures are not repeated here [21,23]. The details of the new algorithm have been covered separately [38]. The new algorithm is briefly summarized as Supplementary material I. To have a valid comparison of soot nanostructural properties between the two image processing codes, the skeletonized images in [23] with a custom algorithm (morphological close and dilate with disk structural element) were applied to extract the same regions of interest (ROI) that were used in [21,23]. The exception is Fig. 1d, in which the ROI was manually selected to exclude the embedded scale bar. The selected ROI of each image is marked by white lines in Figs. 1 and 2. Other parameters that need to be set for a valid comparison are the minimum acceptable fringe length (0.4 nm) and the allowable range of fringe separation distance (0.32–0.5 nm). Moreover, when using histograms to describe fringe properties, the bin widths (and the number of bins) affect the apparent shapes of the resulted histograms. Therein, the bin widths need to be matched for effectively comparing the previous and new algorithms. In this work, the bin widths for fringe length distribution, fringe separation distance, and fringe tortuosity are 0.3 nm, 0.00625 nm, and 0.02, respectively.

G D

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Temperature (oC) Fig. 8 – Comparing analysis results of [21] and this work.s: Median fringe length of this work (fitted by - – - –), h: median fringe length of [21] (fitted by ––), and : average IG/ID. (fitted by - - - -).

distributions obtained from the new and previous algorithms are overlaid in Supplementary material II. Fig. 2 shows HRTEM images with regions of interest of soot samples derived from acetylene and ethanol. The skeletons extracted from the images in Fig. 2 are shown in Fig. 4. Compared to the skeleton of a highly ordered HRTEM image, such as Fig. 3d, Fig. 4a and b contain fringes that are highly curved. In [23], the skeletonized, binary images for tortuosity distribution were obtained by directly hand-drawing over the HRTEM images in order to reduce the chance to have skewed distribution. In this work, the new algorithm generated skeletons used for obtaining the tortuosity distribution. The tortuosity distributions shown in this paper are based on Fig. 4, and are free from any interference of hand-drawing modification. The tortuosity distribution obtained from the new and old algorithms are overlaid in Supplementary material II. The image processing parameters used to obtain Figs. 3 and 4 are summarized in Tables 1 and 2. Table 1 summarizes the parameters having the same values for all images. The two individually adjusted parameters, as shown in Table 2, were the structural element size (a disk element [38] was used for all cases in this study) for top-hat transformation, and the binarization threshold value. Because the top-hat transformation corrects the uneven illumination of images, the choice of the structural element size depends upon the condition of each image [38]. The binarization threshold values are calculated by Otsu’s method using the histograms of the pixel intensity values [38,40]. Therefore, the choice of threshold values also depends upon the condition of each image. Fig. 5 juxtaposes the skeleton images of Fig. 1b obtained by the previous algorithm in [23] (Fig. 5a) and by the new algorithm in this work (Fig. 5b). In both the previous and new algorithms, the fringe length, fringe separation distance and fringe tortuosity distributions are all derived from the skeleton images. Therefore,

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Mean: 0.386 nm

Mean: 0.436 nm

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% of fringes

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(a)

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Mean: 0.383 nm

Mean: 0.378 nm

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% of fringes

0.45

(b)

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0 0.25

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Separation distance (nm)

Separation distance (nm)

(c)

(d)

0.45

0.47

Fig. 9 – Histograms of the fringe separation distance analyzed from corresponding images shown in Fig. 1: (a) heat-treated at 1350 C, (b) heat-treated at 1950 C, (c) heat-treated at 2300 C, and (d) heat-treated at 3000 C.

using Fig. 5a and b, we propose methods to quantify the difference between skeleton images generated from different algorithms. The comparison between the two skeleton images can be quantified by the ratio of the number of skeleton pixels, calculated as follows: %skeleton pixel ¼

number of skeleton pixels total number of pixels

ð1Þ

The numbers of the skeleton pixels in the ROIs of Fig. 5a and b are 2327 and 2401 pixels, respectively. The total pixel number of the ROI in Fig. 5a and b are both 16,384. By Eq. (1), the

percentage of pixels that comprise the skeletons obtained by the previous algorithm is 14.2% (2327/16,384 · 100%), while the percentage is 14.7% (2401/16,384 · 100%) for skeletons obtained by the new algorithm. The percentage of skeleton pixels in the ROIs of Fig. 5a and b illustrate a high degree of similarity for the two algorithms. Although Eq. (1) does give a general idea of skeletal image similarity, it does not provide a pixel-by-pixel comparison.We observe that the skeleton image generated by the new algorithm (Fig. 5b) yields fewer fringes than the previous one (Fig. 5a), especially at the top and bottom of the region of interest. To quantify this differ-

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0.460

0.352

0.440

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0.420

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0.344

0.360

0.342

0.340 1000

1500

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3000

Fringe separation derived from XRD (nm)

Fringe separation derived from HRTEM images (nm)

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Temperature ( oC) Fig. 10 – Comparing analysis results of [23] and this work. m: fringe separation of this work. d: fringe separation of [23], and h: fringe separation derived from XRD.

ence, we define the percentage of the pixel number difference in terms of the reference skeleton images: %pixel number differences ¼

jskeleton pixel number differencej skeleton pixel number of reference method

ð2Þ

Using the number of skeleton pixels of Fig. 5a (8259 pixels) and Fig. 5b (6841 pixels) in Eq. (2), we obtain 17.2% (|8259  6841|/ 8259 · 100%) difference in terms of skeleton pixel number. However, the percentage in pixel number difference is much smaller when comparing the ROI shown in Fig. 5a and b, which is 3.2% (|2327  2401|/2327 · 100%).To further compare the skeletons in Fig. 5a and b, we calculate the pixel difference, Iskel_diff, as follows: Iskel

diff

¼ Iskel

ref

 Iskel

new

ð3Þ

where Iskel_ref is the skeleton obtained by the tool in [23], Iskel_new is the skeleton obtained by the new algorithm. In addition, we calculate the matched pixels, Iskel_match, as follows: Iskel

match

¼ Iskel

ref

^ Iskel

new

ð4Þ

where ^ denotes the element-wise logical conjunction of two binary images. In skeletal images (Iskel_ref and Iskel_new), the numerical value of a skeleton pixel is 1, while the value of a background pixel is 0. Therefore, the element in Iskel_diff can be 1, 1 or 0, while the element in Iskel_match can be just either 1 or 0. When the element in Iskel_diff is 1, it means the skeleton pixel only appears in the skeleton images in [23]. On the contrary, when the element in Iskel_diff is 1, it means the skeleton pixel only appears in the skeleton images obtained by the new algorithm. Furthermore, when the element in Iskel_match is 1, it means the skeleton pixel appears in both the skeleton images in [23] and in this work. The degree of similarity between the two methods can be visualized by assigning different colors according to the values of elements in Iskel_diff and Iskel_match. As demonstrated by Fig. 6, green pixels denote the skeleton pixels that appear in both methods. Blue pixels de-

note the skeleton pixels that appear only in [23]. Red pixels denote the skeleton pixels that appear only in this work. Fig. 6b and c shows the areas that are locally magnified. The results in Fig. 6b and c demonstrate that the two difference image processing methods produced similar skeleton images, as green pixels compose of the majority of the skeletons. Building on Eqs. (3) and (4), and the idea of Fig. 6, more statistical measures can be defined to study the similarity of two skeleton images. Perfectly matching image processing parameters to obtain highly similar skeletal images was not attempted for two reasons. (1) As shown in Tables 1 and 2, there are more than ten parameters that affect the skeleton images. Some parameters, such as the top-hat transformation structural element and morphological modification structural element, are governed by both shape (disk, square, diamond, etc.) and size. For others, e.g., the result depends on image processing order. Therefore, perfectly matching skeleton images is a complicated and time-consuming process. (2) Using the new algorithm to perfectly create identical skeleton images in [23] can be impractical without the information of the algorithm from each processing step (low-pass filter, binarization, non-uniform illumination correction, fringe morphology modification, etc.) that was used by the tool in [23], which was developed based on commercial software (Optimas 6.5). Finally, our goal was to compare algorithms rather than match processing steps. Taking a more macroscopic view to verify our image analysis code, we used the fringe parameters, including length, separation distance, and tortuosity extracted from images in Figs. 1 and 2. From the parameter distributions, we compare the representative values (median for fringe length, mean for fringe separation distance and fringe tortuosity) with the values published in [21,23]. Fig. 7 shows the fringe length distributions extracted from images in Fig. 1. Comparing the histograms of the four heat-treated carbons in Fig. 7, Fig. 7a shows a narrow fringe distribution concentrated in the region less than 0.9 nm. As the heat-treated temperature increases, the fringe length distribution shifts towards longer fringe length. Notably the length distributions do not show a symmetric shape. Therefore, instead of arithmetic mean, the median of the fringe length was used to characterize the fringe length distribution, since it is a parameter that is more sensitive to the distribution tail. Fig. 8 compares the median fringe length of this work to Fig. 6 in [21], which presented the median lattice fringe lengths and the ratio of G peak to D peak (IG/ID) obtained from the Raman spectra as the standard. Qualitatively, in Fig. 8, the trend of the median length obtained by the new algorithm agrees with the median length obtained by the image analysis algorithm in [21], and the IG/ID ratio obtained by Raman spectroscopy. The numerical discrepancy in the median fringe length may result from the different image processing algorithms, processing parameters, and processing orders. Fig. 9 shows the fringe separation distance distribution of the four samples. Qualitatively each separation distance distribution has a roughly symmetric shape. As the heattreatment temperature increases (from (a) to (d)), the distribution of separation distance narrows and the mean decreases. As in [23], we used the arithmetic mean to

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Mean: 1.11

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Tortuosity

Separation distance (nm)

(a)

(b)

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Mean: 0.441 nm

Mean: 1.23 40

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% of fringes

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Tortuosity

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(d) (Only 13 fringe pairs were counted. Most fringes appear as single layer, and a large portion of fringe separation spaces were not within the physical range 0.32 nm–0.5nm)

Fig. 11 – Comparing tortuosity histogram of a highly structured carbon black (R250 3000 C) (Fig. 1d, acetylene 1250 C 1.0 slpm (Fig. 2a), and ethanol 1250 C 1.0 slpm (Fig. 2b) soot samples. Subplots: (a) tortuosity distribution of carbon black heat-treated at 3000 C, (b) separation distance distribution of carbon black heat-treated at 3000 C, (c) tortuosity distribution of acetylene-derived soot, (d) separation distance distribution of acetylene-derived soot, (e) tortuosity distribution of ethanolderived soot, and (f) separation distance distribution of ethanol-derived soot.

characterize the fringe separation histograms of each of the images in Fig. 9. Fig. 10 shows the mean fringe separation distances extracted by this work with the mean fringe separation dis-

tances and the layer plane separation determined through X-ray diffraction from [23]. Only a portion of the data presented in Fig. 4 of [23] was selected corresponding to the four re-analyzed images. Fig. 10 shows that the trend of mean

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Mean: 0.435 nm

Mean: 1.23

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% of fringes

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Tortuosity

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(e)

(f) (25 fringe pairs were counted) Fig. 11 (continued)

fringe separation distance obtained in this work agrees with the trend of the mean fringe separation distance and XRD layer plane separation in [23]. As observed from Fig. 10, the mean fringe separation distance does not decrease significantly when the heat-treatment temperature is above 1950 C. Fig. 11 show the tortuosity and separation distance distribution of R250 carbon black heat-treated at 3000 C (Fig. 1d), the acetylene-derived soot obtained at 1650 C and a 1.0 slpm flow rate (Fig. 2a), and the ethanol-derived soot obtained at 1650 C and a 1.0 slpm flow rate (Fig. 2b). The heat-treated carbon black shows narrower spread of tortuosity (Fig. 11a) and interlayer spacing (Fig. 11b) than the acetylene-derived and ethanol-derived soot. Meanwhile, the heat-treated carbon black shows smaller mean tortuosity and interlayer spacing than the acetylene- and ethanolderived soot. These observations indicate that carbon black heat-treated at 3000 C has a higher degree of order. It should be noticed that for acetylene-derived and ethanol-derived soot, it is unlikely to obtain a bell-shaped fringe separation distribution, because the samples are highly disordered. Much fewer parallel fringe pairs can be counted as shown in Fig. 4a and b. This observation is consistent with the results published in [23]. Using the mean values as representative values, we observed that mean separation distance increases as the mean tortuosity increases. This observation provides evidence for the argument that tortuosity prevents the development of stacked layers and increases the separation between adjacent fringes [23]. Therefore, in these cases, tortuosity can be an indicator of the degree of disorder within the carbonaceous materials. The image analysis algorithm has been validated by comparing the fringe length, fringe tortuosity and fringe separation distance distributions of [21,23]. The repeatability and consistency of the algorithm has been reported separately

[41]. A thorough investigation is needed to eliminate the subjective choice of the image processing parameters, such as the choice of region of interest and the size of the Gaussian low-pass filter. Tilting angle of TEM grids is also recommended to verify the two-dimensional curvature projected from a three-dimensional structure. In addition, the sensitivity of image processing parameters on the fringe properties distributions and characteristic values (median for fringe length and mean for fringe tortuosity) can be tested over a range of values. As a suggestion for alternative method of validation, the results of the HRTEM image analysis algorithms can be confirmed by modeling soot structure [42–44]. In particular, the lattice fringe parameter distributions obtained in this study can be compared with the TEM-style projections of the simulated soot molecules, as stated in [44]. If the soot formation process is appropriately modeled, the simulated TEM-style projections and the experimental results obtained from HRTEM images would be consistent [42,44].

4.

Conclusions

We have validated the new algorithm regarding the analysis of fringe length, fringe separation distance, and fringe tortuosity. We used the HRTEM images that were analyzed and discussed in [21,23]. The fringe length and fringe separation distance have been validated by carbon blacks that were heat-treated at different temperatures, which have different levels of ordered structure. Both the fringe length and fringe separation results are compared to the results obtained by the image analysis tool used in [21,23]. Meanwhile, fringe length distributions are cross-checked with the IG/ID peaks obtained through Raman spectroscopy, and the fringe separation distributions are compared with the d-spacing derived through XRD data. All presented data have shown reasonable

CARBON

4 9 ( 20 1 1 ) 4 2 5 6–42 6 8

agreement. For the tortuosity, however, there is no other analytical technique that we can compare with. We used two images of highly disordered soot samples (ethanol- and acetylene-derived soot) and the image of a highly ordered sample (carbon black heat-treated at 3000 C) to validate our code through comparing tortuosity and fringe separation distribution. As predicted from the viewpoint of carbon framework, the high tortuosity results in an increase in the fringe separation. Thus, the algorithm for extracting tortuosity has also been validated.

Disclaimer Trade names, software names or manufactures’ names are used for identification only. The usage does not mean any official endorsement, either expressed or implied by the EMS Energy Institute, The Pennsylvania State University.

Acknowledgements The authors wish to thank the National Science Foundation for their financial support for this work under Grant # CTS0553339. The authors thank General Electric Global Research Center and General Electric Transportation, in particular, David Walker, Omowoleola Akinyemi, Roy Primus, David Watson, Raj Rajiyah, and David Komoroske for their support of this work. A portion of this work was a part of ‘‘Clean and Efficient Diesel Locomotive’’ program sponsored by the U.S. Department of Energy under Contract No. 08NT002788. The authors also thank NCMR C c/o NASA-Glenn for permitting us to use the HRTEM images of soot for comparing the two image analysis methods.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.carbon.2011.06.003.

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