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A new quantitative approach for microstructural analysis of coal char using HRTEM images
Fuel 78 (1999) 1203–1212 www.elsevier.com/locate/fuel
A new quantitative approach for microstructural analysis of coal char using HRTEM images A. Sharma*, T. Kyotani, A. Tomita Institute for Chemical Reaction Science, Tohoku University, 2-1-1 Katahira, Sendai 980-8577, Japan Accepted 4 March 1999
Abstract HRTEM is a useful technique to observe the structure of coal char at an atomic level. To analyze the char structure quantitatively, we developed a new filtration technique for HRTEM images and a computer algorithm to obtain information such as the graphene layer size, interlayer spacing, the number of layers per stack, and its distribution from the post-filtered extracted HRTEM images. By using a computerized imaging system and image analysis algorithm, it is possible to analyze many images from different sample spots, which helps to make the analyzed result as general as possible. The aim of this article is to describe the usefulness of the technique to analyze the structure of carbonaceous materials. We applied this technique to study quantitatively the crystallinity of Pocahontas coal chars gasified in CO2 up to various conversion levels at 12008C. TEM observation showed a remarkable structural change during the gasification. q 1999 Elsevier Science Ltd. All rights reserved. Keywords: Char; Structure; Gasification; Transmission electron microscopy
1. Introduction There has been considerable interest in the study of structural change of coal char during combustion or gasification. Over the years, the presence of residual or unburned carbon in fly ash during pulverized coal combustion and high temperature coal gasification has attracted much attention, as it is directly related to the efficiency of combustion as well as to the quality of ash in its post-utilization in the cement industry [1–8]. The reactivity of the residual char generally becomes very low, and this has been attributed to the change in carbon structure and/or to the loss of catalytic activity of mineral matter. The relative importance of these factors depends on the nature of coal sample as well as on the reaction conditions. The structural change of coal chars due to gasification has been studied by using various techniques. Among all, X-ray diffraction (XRD) and microscopy (high resolution transmission electron microscopy (HRTEM), scanning electron microscopy and optical microscopy) are the most powerful techniques. The lattice fringe image detected by HRTEM gives direct information on the structure of char at an atomic level, although we need to keep in mind that “things are seldom what they seem [9]”. Also we should try to avoid dangers * Corresponding author. Tel.: 1 81-22-217-5627; fax: 1 81-22-2175626.
implicit in the use of microscopy such as eclecticism and tendentiousness [10]. So far several qualitative to quantitative analyses through TEM observation have been proposed. Furuta et al. [11] reported the structural change of resin char during CO2 gasification at 9008C. Davis et al. [6] developed a quantitative analytical method which consists of the filtration of raw image using Fourier transform (FT) to eliminate the non-periodical structure and the analysis of the filtered image. From this analysis they obtained average fringe lengths and relative amounts of crystalline structure. Wornat et al. [12] used HRTEM to study the structural transformation of biomass chars during combustion. Palota´s et al. [13] used HRTEM and an image analysis system to study quantitatively the structural changes of soot and carbon black particles during combustion. Their method was also the use of FT of the TEM image for extraction of structural data, followed by reverse transform. They concluded that the changing structure of the carbon black during oxidation influences its rate of oxidation. Shim and Hurt [14] further developed their algorithm to determine the mean crystallite diameter, non-graphitic amorphous index, orientational order parameter, and tortuosity. In these studies, they correlated the crystalline transformation of coal chars with the decrease in global oxidation reactivity. Zielin˜ska-Blajet et al. [15] characterized the structure of activated carbons using XRD and TEM techniques, and observed a qualitative agreement between the results from the two techniques. In
0016-2361/99/$ - see front matter q 1999 Elsevier Science Ltd. All rights reserved. PII: S0016-236 1(99)00046-0
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Fig. 1. Comparison of the conventional and the present filtration technique.
another study, Endo et al. [16,17] used HRTEM combined with an image processor for observation and analysis of pores in activated carbon fibres. Very recently, Yoshizawa et al. [18] used a similar FT technique to analyze the HRTEM pattern and evaluate the layer length and stacking number distribution of KOH-activated carbons prepared at various activation temperatures. The main aim of the present article is to introduce a new filtration and statistical analysis technique to analyze the coal char structure quantitatively from HRTEM images. The results from such statistical analyses are very sensitive to the filtration procedure applied for the processing of the HRTEM images. The filtration technique introduced here is quite different from those reported previously. For statistical analysis, we developed a new algorithm in Fortran 77 to extract quantitative structural information from TEM micrographs. We determined by this method not only the graphene layer size, but also the interlayer spacing, the number of layers per stack (or the stacking number), and
their distributions. To demonstrate the ability of the foregoing technique to analyze the structure of carbonaceous materials quantitatively, we have studied the transformations in the crystallinity of Pocahontas No. 3 coal chars (POC) due to high temperature gasification. Three coal char samples were prepared by gasifying in CO2 at 12008C up to conversion of 42, 75 and 92%. In this study we have also attempted to correlate the char gasification profile with the change in the carbon structure based on quantitative HRTEM image analysis technique.
2. Experimental 2.1. Coal char samples Argonne Premium Coal Sample, Pocahontas no. 3, was selected for the investigation. It is a low volatile bituminous coal, and the elemental analysis was Cmaf: 91.1%, Hmaf:
A. Sharma et al. / Fuel 78 (1999) 1203–1212
4.4%, Smf: 0.7% and Omaf: 2.5% (by difference). The ash content on a dry basis was 4.8%, and the ash analysis was SiO2: 32.0%, Al2O3: 20.1%, Fe2O3: 15.8%, CaO: 12.8% and SO3: 12.4%. The coal with a particle size less than 100 mesh, was devolatilized at 8008C for 5 min in a fluidized bed reactor with He gas as the fluidizing agent. The char obtained was then heated in an infrared image furnace from ambient temperature to 12008C at 1008C/min in a high purity He gas. A reference char sample was prepared by keeping the char for 5 min at 12008C in He and then cooling it down to ambient temperature. This char sample was called POC 0. To prepare gasified char samples with different conversions, char samples were kept for 5 min at 12008C in He and then switched over to 1% CO2 –He mixture for 120, 300 and 480 min to achieve 42, 75, and 92% (daf) conversions on char weight basis. The residual char samples were referred to as POC 42, POC 75 and POC 92, respectively. 2.2. Characterization of char The char samples were then subjected to TEM observation and XRD analysis. For TEM observation, they were ground in ethanol and sprayed over a copper microgrid. The char samples on the microgrid were observed under a 200 keV transmission electron microscope (JEOL, JEM2010) equipped with a computerized imaging system for high resolution pictures. For disordered structures or small crystallite sizes, spherical aberration influences on TEM lattice images must be considered. This has been discussed in terms of the linear transfer theory [19]. The phase transfer function was calculated for l 0.00295 nm (electron beam wavelength) and CS 0.5 mm (spherical aberration coefficient) which are the conditions for the present observations. The transfer function is used to obtain the defocus position at which smoothness in contrast was guaranteed for 0.3 nm and a higher basal spacing. To get a general view of the char structure, 10–15 photographs were taken for each sample from different spots. 2.3. Image analysis technique The image analysis technique consists of: (1) filtration of TEM micrographs for noise reduction without losing an appreciable information, (2) identification and reconnection of the fringe layers, which were disconnected during the filtration step to obtain the extracted image for statistical analysis, and (3) the statistical analysis to evaluate the quantifiable structural parameters. Most of the previous studies have essentially used the same filtration and processing technique. We developed a new filtration technique and an image analysis computer algorithm to obtain the structural parameters as described in the following sections. 2.4. New image filtration and processing technique The first step in an image analysis technique is digitization of the TEM micrographs. However, we bypassed this
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step as we used a computerized imaging system by which we stored the image as a digitized image as shown in Fig. 1(a). This direct digitization of the image eliminates the noise and errors involved during the development and the scanning of the picture. The digitized image is then subjected to a filtration procedure. We have developed a new filtration procedure to extract the final image from the TEM image. All filtration procedures use essentially the same methodology of Fourier transform (FT) of the raw image, noise reduction followed by inverse FT, of which the most important step is the noise reduction. The conventional method uses a square filter to take out the frequencies in the frequency domain image which fall in the square filter range for example 0.30–0.50 nm 21 range in the FT domain. The new filter which we have used is a step filter which has only the lower limit which in our case is 0.1 nm 21 in the FT domain. The advantage of a step filter over a square filter is as follows: While the latter extracts structures that occur periodically with a separation distance characteristic of turbostatic carbons, the former extracts not only periodically occurring stacks, but also non-periodically occurring single layers. As a result, a lot of information concerning single layers is lost along with the noise when a square filter is used. The filtered image is then inverse fourier transformed and shows a network of fringes connected by Y and/or T shaped links. The conventional method uses the variable threshold value criterion to separate the fringes which is a subjective criterion. In this process not only small fringe layers are lost, but also the layer size of large fringe layers is reduced. This can be better understood from the fact that different extracted images can be obtained from the same filtered TEM image by changing the threshold value. Thus the statistical analysis of all these extracted images will give different structural parameters though they all come from the same TEM image. We developed a new unique method which not only retains the small layers, but also keeps the size of large fringe layers intact while separating the fringes. This is done, first by separating the fringe layers followed by reconnection of disconnected layers. The separation of the fringe layers is based on the fact that the layers and the Y and/or T shaped links have different brightness and hence can be mathematically separated. Once the fringe layers are separated, we reconnected the disconnected layers using the geometrical parameters of these layers. A comparative description of the preceding two methods has been given in the following section with the help of an example. The digitized TEM image as shown in Fig. 1(a) was first fourier transformed to get the power spectrum as shown in Fig. 1(b). The conventional method uses a square filter as shown in Fig. 1(c) while the step filter used in our method is shown in Fig. 1(d). The step filter can be imagined as a circular disc of radius 0.1 nm 21 placed at the center of the power spectrum shown in Fig. 1(b) while a square filter will be an annular disc of an outer radius 0.5 nm 21 and an inner radius 0.3 nm 21 from the center of the power spectrum. The
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Fig. 2. Flow diagram of the image algorithm for image analysis.
difference in the information retained from these two filters is that using a step filter all the information except that covered by a circular disc is retained while using a square filter only the information contained in the annular disc is retained as shown by shaded portions in Fig. 1(c) and (d). One can well imagine the difference in the magnitude of the information thus obtained by these two filters. The inverse FT filtered images from the conventional and our methods are converted to two color images as shown in Fig. 1(e) and (f), respectively. The images are very different from each other in terms of fringe layer content though both show fringes connected by Y and T shaped links. To obtain the extracted image, the conventional method changes the threshold value of the image (Fig. 1(e)) to such a value that the fringes are separated, followed by skeletonization. In contrast, our method only skeletonizes the image (Fig. 1(f)) without altering the threshold value as shown in Fig. 1(g). As a result, this skeletonized image shows a network of
layers connected to each other by Y or T shaped links, which are now mathematically separated in the next step. First the threshold value of the skeletonized image is increased to such a value that fringes disappear and only nodes remain. This image containing only nodes (Fig. 1(h)) is then subtracted from the original skeletonized image (Fig. 1(g)) to get the image with separated fringes free from the Y and T shaped links. The extracted images from the two techniques are shown in Fig. 1(i) and (j). The pixel information of the image shown in Fig. 1(j) is then stored and subjected to the reconnection algorithm. The algorithm reconnects the fringe layers using several geometrical parameters of these layers and the fact that the nodes are typically of 2–4 pixel length. The input file contains the information of the image in binary mode. The algorithm first searches for a pixel with a value 1 (dark) and then finds whether or not there is a pixel adjacent to it with a value 1. When there is break in this search i.e. when it encounters a pixel with a value 0 (white), it looks for the next 2–4 pixels in the direction of its search. If it finds a pixel with a value 1 within the next 2–4 pixels, it replaces the in-between pixels having a value 0 with 1 and thus connects the two layers. The final image is regenerated using the NIH Image from the output file which contains the pixel information of the image. The final image is shown in Fig. 1(k). The two images, Fig. 1(i) and (k) give very different information about the fringes. The extracted images such as those in Fig. 1(k) were then subjected to analysis to identify and characterize the fringes. The filtration and characterization processes were done on a Power Macintosh model 7300/180 computer using the public domain NIH Image program (developed at the US National Institutes of Health and available in the Internet at http://rsb.info.nih. gov/nih-image) while the reconnection algorithm, written in Fortran 77, was compiled and executed on a Power Challenge XL (Silicon Graphics, Inc.). The characteristics of the fringes obtained using the NIH Image, were stored in the computer and then processed using the image analysis algorithm developed by us which gives a graphene layer size, interlayer spacing and the number of layers per stack and their distribution. 2.5. New image analysis algorithm The extracted image shows fringes as black lines. Some of these fringes show curvilinearity. The NIH Image software is able to characterize these layers by first identifying a layer and then fitting an ellipse to the layer. The midpoint coordinates of the major axis of the fitted ellipse are assigned as the X-, Y-coordinates and the angle of inclination of the major axis to the X-axis is assigned as the angle of inclination of the layer. By counting the number of pixels, the actual length of the layer can be obtained. These total number of layers (NL), angle of inclination (u ), X-, Y-coordinates (x,y), major and minor axes (rx, ry) of the fitted ellipse and the layer length, L, become the input data for the image analysis algorithm for statistical analysis. The
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3. Overlap view parameter: This parameter identifies the layers that can form a stack with the reference layer. This is done by drawing perpendicular lines from the two edges of the reference layer and selecting the layers which are encountered by either of these perpendicular lines. 4. Interlayer spacing: This parameter identifies layers whose perpendicular distances are close to that of typical turbostatic carbon, i.e. 0.344 nm. We selected those layers whose interlayer spacing falls between 0.33 and 0.38 nm for our calculations. Fig. 3. Validity check of the present method: computer versus manual counting.
algorithm makes use of four parameters: aspect ratio, parallelism, overlap view parameter and the interlayer spacing to evaluate the structural parameters. 1. Aspect ratio: The aspect ratio is defined as the ratio of the major axis to the minor axis of the fitted ellipse. This parameter defines a limit for curvilinearity in the fringes for calculation purposes. We took fringes with an aspect ratio of 2 or more into consideration as constituents of the carbon structure. 2. Parallelism: This parameter identifies layers that are parallel to the reference layer. A layer with an angle of inclination within ^ 108 is considered to be parallel to the reference layer. The perpendicular distance between two layers can only be obtained if they satisfy this criterion.
The flow diagram of the image algorithm is shown in Fig. 2. The source code is written in Fortran 77 and was compiled and executed on a Power Challenge XL (Silicon Graphics, Inc.). The validity of the algorithm was established as shown in Fig. 3 by using a rather simple image and comparing the parameters obtained manually by four persons and that from the algorithm. A good agreement between the computed parameters and those counted manually was obtained. Fig. 3 also shows the human subjectivity involved in the computation of parameters as the results obtained by the four persons are different. The advantage of this new image analysis algorithm is that it removes human subjectivity while giving quite reliable results. Using this algorithm and the computerized imaging system it was possible to analyze many pictures to obtain general information from TEM images. The unique points of the present method can be summarized as follows: 1. In addition to the use of a computerized imaging system, we developed a two-step image extraction method: noise reduction using the step filter, followed by the mathematical separation and reconnection of fringe layers 2. An image analysis algorithm free from human subjectivity was developed.
3. Results 3.1. Analysis of TEM results
Fig. 4. Digitized TEM images and the corresponding extracted images: (a) and (c) POC 0; (b) and (d) POC 92.
In Fig. 4, the TEM images (a) and (b) and the extracted fringe images after image filtration and processing (c) and (d) are shown for POC 0 and POC 92 char samples. The two images show a striking difference in the shape, size, and orientation of the layers. The layers in Fig. 4(c) are small, twisted and lack in orientation. With increased conversion to 92%, a very clear orientation and an increase in layer size were observed as shown in Fig. 4(d). An increase in crystallinity in char structure with gasification could be inferred from these pictures. To obtain quantitative information, the extracted lattice images were subjected to the image analysis algorithm to evaluate the graphene layer size, interlayer spacing, the number of layers per stack, and their distribution.
3
NC 2 × L=0:2462 :
4
Then the number of carbons which belong to the stacks with n layers, NC(n), was calculated by adding all the number of carbons for each layer (Column 7 in Table 1). The total number of carbons was 96 466. It should be noted that the total number of carbons evaluated here are that extended to 3-dimension from a 2-dimensional information (lattice fringe), and hence it does not represent the absolute number of carbons. The fraction of carbons belonging to n layers’ stack can be calculated as fC n NC n=
∞ X
NC n NC n=96 466
5
n1
and the results are shown in the eighth column in Table 1. The average interlayer spacing (d-spacing) of the stacks with n layers were also calculated, and the results are shown in the ninth column in Table 1 together with column 10.
Fraction of layers fL(n) Table 1 HRTEM image analysis of one particular area (56 × 56 nm 2) for POC 42
These values are shown in the fourth and fifth columns, respectively. Not only the number of layer, but the size of layer is also important. From the lattice image, we know the projected length, L, of all the layers which may be regarded as representing the diameter of the graphene layer as a first approximation. The average length of the lattice fringes which make the stacks with n layers, L(n), were calculated and listed in the sixth column. The number of carbon atoms per layer, NC, was estimated by assuming that L (in nm) stands for the length of an edge of a rhombic crystallite as described by Diamond [20]:
– 0.34 0.37 0.39 0.33 0.49 0.35 0.11 0.02 0.03 1.00 46 885 33 982 10 192 1628 3779 96 466 2.70 2.88 3.15 3.43 2.94
NL n NL n=341:
n1
203 50 8 1 2 264
∞ X
Average diameter of layer L (nm)
fL n NL n=
0.60 0.30 0.07 0.01 0.02 1.00
and its fraction out of the total number of layers is:
203 100 24 4 10 341
2
1 2 3 4 5 Total
NL n n × NS n
Number of carbons NC(n)
With regard to the number of layers, there are 341 layers including 203 singly occurring layers and 138 stacked layers in this area. The total number of layers occurring in groups of n layers, NL(n), would be:
0.77 0.19 0.03 0.003 0.007 1.00
Average dspacing (nm) Fraction of carbons fC(n)
1
Number of layers NL(n)
NS n NS n=264:
n1
Fraction of stacks fS(n)
∞ X
Number of stacks NS(n)
fS n NS n=
Standard deviation
The definition of terms used in this article will be explained as follows by using an example. Table 1 shows the result for one particular TEM image of the POC 42 sample. The observed area where we found 264 stacks was 56 × 56 nm 2. These stacks were classified according to the number of layers per stack or stacking number. The stacking number, n, and the number of stacks having n layers, NS(n), are listed in the first and second columns of Table 1, respectively. The third column shows the fraction of stacks having n layers out of all the stacks and is defined as follows:
– 0.07 0.05 0.00 0.01
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Stacking number n
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Fig. 5. Fraction of number of layers occurring in the stacks with n layers: Distribution for four different fields of POC 42.
3.2. Change of structural parameter with char conversion The calculation described in the preceding section was repeated for all observed images of all the char samples. The results are summarized in this section as a function of the char conversion. To begin with, it would be necessary to show how this data processing method is reproducible. Fig. 5 shows the relationship between the fractional number of layers, fL(n), and the stacking number, n, for randomly selected four fields of POC 42 sample. Around 350 layers were identified in each field. Although there are some variations, Fig. 5 shows some consistency of the distribution pattern. The reliability of the present method can be judged from this figure. Thus the data shown below is not a specific one for the particular area analyzed, but a rather general one for the whole sample examined. Fig. 6 summarizes the preceding relationship for the four char samples. For each sample nearly 10–11 different fields were observed, and the data from all these fields are summarized. The fraction of single layers is always higher
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than the stacked layers. The fL(1) and fL(2) are almost the same in POC 0, POC 42 and POC 75 samples. Although POC 42 shows a decrease in stacking number and POC 75 an increase in stacking number, it may be concluded that these three samples have essentially a similar stacking structure. However, when the char is gasified up to 92%, an appreciable change was observed. Compared with POC 75, fL(1) and fL(2) decreased while layers belonging to stacks with n from four to eight considerably increased. Also some new stacks with n of 9–11 were observed which were not present in the other chars. This suggests the highly crystalline nature of POC 92. The layer size, L, was determined from the length of the fringe observed. The average size of the layers belonging to the stacks of n layers are shown in Fig. 7. The difference between POC 0 and POC 42 samples is not significant, the average layer size being around 3.0 nm. The size for POC 75 is slightly larger than these two, but the difference is not so large. At 92% conversion, however, the average size of the layers increased to a large extent. The average size of the newly formed stacks with 9–11 layers was around 4.0 nm. In order to correlate the structural change with char reactivity, it would be helpful to know the relative number of carbon atoms present in each group of stacks. Fig. 8 shows the fraction of carbon atoms, fC(n), as a function of n. It is observed that the relative ratio of carbon atoms belonging to single layers decreased as gasification proceeded. The ratio of carbons in highly stacked layers was large for POC 92, where carbon atoms are almost equally distributed in stacks with one to seven layers. Further, the contribution from stacks with more than eight layers became appreciable. In other words, as gasification proceeds the carbon atoms available for the reaction would be more crystalline. To simulate how the carbon atoms are lost from these layers upon gasification, the sets of data in Fig. 8 were converted to those evaluated on the basis of the initial number of carbon atoms. This was simply done by multiplying the fraction of
Fig. 6. Fraction of number of layers occurring in the stacks with n layers.
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Fig. 7. Average diameter of layers occurring in the stacks with n layers.
the residual char (Fig. 9). Fig. 9(a) is naturally the same as Fig. 8(a). In other figures (Fig. 9(b)–(d)), the absolute number of total carbons decreased with gasification. The average d-spacing was found to decrease from 0.36 nm for POC 0 to 0.34 nm for the POC 92 char sample.
4. Discussion 4.1. Comparison of the present method with the previous ones Most of the earlier studies have attempted to draw qualitative information on the char structure during gasification or combustion with the aid of HRTEM observation [8,11,12,15]. However, recently many attempts have been made to analyze HRTEM data more quantitatively as discussed in Section 1. Although those studies used different
parameters to examine structural changes, the analytical methods have been essentially based on the same methodology for the extraction of the final image for statistical analysis: TEM observation, FT, filtration, followed by inverse FT. The statistical analysis is very sensitive to the final extracted image from the TEM micrographs. The image filtration technique developed and used in the present study to extract the final image for statistical analysis is different from those previously adopted ones as described before. It should be mentioned that this study for the first time evaluates the contribution of single layers from TEM analysis which was made possible because of the use of a new step filter and the concept of mathematical subtraction of images. In this study, we analyzed the structural changes using parameters such as the layer size, d-spacing, the distribution of the number of layers, layer size diameter, and carbon atoms, as a function of the stacking number. It is easy to
Fig. 8. Fraction of number of carbons belonging to the stacks with n layers.
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Fig. 9. Fraction of number of carbons belonging to the stacks with n layers on the initial carbon number basis.
identify and store the characteristics of the lattice fringes from the extracted images using various commercially available image analysis software. So far, to our knowledge, the preceding structural parameters specially stack and layer size distributions have been evaluated manually, which is not only very tedious and time consuming but is also very subjective depending from person to person. To remove the impact of human subjectivity on statistical analysis results, we developed the computer algorithm as described in the previous section, specifically to evaluate the foregoing structural parameters. Although this algorithm is based on some assumptions, the validity of this has been established before its application. The use of this algorithm also reduces the processing and analysis time. Another point worth mentioning here is that the average layer size distribution with the stacking number (Fig. 7) obtained from this technique, has neither been reported so far from any technique nor can be obtained from any commercially available software. The automated calculation of layer statistics, such as length, are standard features of many commercially available analysis routines but it is not possible to know from these softwares as to which layer belongs to which stacking number. One can obtain the stack distribution similar to the one shown in Fig. 6 from XRD analysis, but it is not possible to obtain a layer size distribution similar to the one shown in Fig. 7 from XRD analysis. From such a distribution, we can discuss the char gasification profile in terms of the fraction of the number of carbons belonging to stacks with different stacking numbers. 4.2. Application of the proposed technique–structure change due to gasification In this section we will discuss the char structure transformations during gasification based on the structural parameters obtained from the proposed method. The result from the foregoing analysis shows that the char structure
undergoes transformation during gasification. The char structure initially contains a higher fraction of less crystalline portions, and it becomes more crystalline as gasification proceeds especially at the later stages. It has been frequently suggested that both less crystalline and highly crystalline portions are present in the raw char and that the less crystalline part would be preferentially lost in the earlier stage while the highly crystalline part remains until the later stage [1–3,6–8]. However, the present observation (Fig. 6) clearly indicates that the highly crystalline portions which were absent in the initial stages were created during the gasification. The development of these highly crystalline portions may not be attributed only due to the effect of gasification, but the thermal annealing effect must contribute to some extent. Fig. 9(b)–(d) shows the fraction of carbon atoms in the residual char samples as gasification progresses. Up to 42% conversion, carbons from one or two layers contribute most of the total carbon loss. Other layers also show the loss of carbon to different degrees. With further gasification up to 75%, the carbon loss from one to four layers remains significant, while the contribution of stacks with a higher stacking number is marginal (Fig. 9(c)). This is also observed when the char was gasified to 92% conversion where most of the carbon loss comes from the layers belonging to stacks with one to four layers. It can be inferred from the present analysis that the carbon present in less crystalline form contributes more to the carbon loss during gasification than do more crystalline carbons. Most studies, qualitative or quantitative, concluded that the carbon structure becomes more ordered with increased conversion. The results of the present analysis are consistent with their conclusions. However, the mere information that char crystallinity increases with gasification is not sufficient to establish any meaningful relationship between reactivity and an ordered structure. Our approach in the present study for such a correlation is a step ahead of the previous
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approaches in a sense that here we have discussed the gasification process on the basis of change in the relative number of carbon atoms belonging to stacks with different stacking numbers. This is more rational if one intends to establish, for example, a relationship between reactivity and an ordered structure. However, this relationship is not simple but rather very complex, as the reactivity of char depends on multiple factors of which the carbon structure is one. Effects of other factors, such as catalytic activity [3] of mineral matters present in coal char, are also important and should be investigated in the future.
Acknowledgements This study was partly supported by “Research for the Future Program-Molecular Engineering of Coal” which was sponsored by the Japan Society for the Promotion of Science. The helpful discussion with Prof M. Shiraishi of Tokai University is gratefully acknowledged. One of the authors, A.S., wishes to thank Harish Hirani of IIT, Delhi for his useful suggestions in the development of the image algorithm. We would like to express our appreciation to the High Voltage Electron Microscope Laboratory of the Tohoku University for the microscope observation.
5. Conclusions The development of a new filtration technique made it possible to obtain more information from TEM micrographs than conventional techniques, such as the fraction of single layers. The image analysis algorithm makes the structural analysis procedure less time consuming and thus many pictures can be quickly analyzed to obtain a more general view of the char structure. It also removes the problem of human subjectivity involved in computational analysis. The proposed filtration and statistical analysis technique can be applied to any carbonaceous materials to analyze quantitatively the structure from their HRTEM images. When applied to coal chars, this technique provides valuable information on the development of the crystalline structure of chars during gasification. TEM analysis revealed that the crystallinity of residual char increases with gasification especially at a very later stage. The average layer size distribution obtained from this technique made it possible to evaluate the fraction of number of carbons belonging to stacks with different stacking numbers. This provides a new approach to discuss the char gasification profile. With increasing conversion, the carbon atoms available for gasification are in a more crystalline form. The carbons present in a less crystalline form contribute more to the carbon loss during gasification than do crystalline carbons. The analysis also shows that some highly crystalline stacks, not present initially, have developed during the course of gasification. The development of these highly crystalline stacks during gasification may be partly due to the thermal annealing effect, which we are currently investigating.
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Shibaoka M. Fuel 1985;64:263. Vleeskens JM, Nandi BN. Fuel 1986;65:797. Sha X-Z, Kyotani T, Tomita A. Fuel 1990;69:1564. Lin SY, Hirato M, Horio M. Energy Fuels 1994;8:598. Beeley TJ, Gibbens JR, Hurt R, Man CK, Pendlebury KJ, Williamson J. Prep Am Chem Soc Div Fuel Chem 1994;39(2):564. Davis KA, Hurt RH, Yang NYC, Headley TH. Combust Flame 1995;100:31. Hurt RH, Gibbins JR. Fuel 1995;74:471. Hurt RH, Davis KA, Yang NYC, Headley TJ, Mitchell GD. Fuel 1995;74:1297. Oberlin A. In: Thrower PA, editor. Chemistry and physics of carbon, 22. New York: Marcel Dekker, 1989. pp. 1. Thomas JM. In: Walker Jr. PL, editor. Chemistry and physics of carbon, 1. New York: Marcel Dekker, 1965. pp. 122. Furuta T, Yamashita Y, Shiraishi M. Tanso 1989;140:241. Wornat MJ, Hurt RH, Yang NYC, Headley TJ. Combust Flame 1995;100:131. ´ B, Rainey LC, Sarofim AF, Sande JBV, Ciambelli P. Energy Palota´s A Fuels 1996;10:254. Shim H-S, Hurt R, Proc 23rd Carbon Conf, Penn State Univ, 1997: 438. Zielin˜ska-Blajet M, Yoshizawa N, Furuta T, Maruyama K, Yamada Y, Proc Int Conf Coal Sci, Essen 1997: 793. Endo M, Oshida K, Kogiso K, Matsubayashi K, Takeuchi K, Dresselhaus MS. Proc Mat Res Soc Symp 1995;371:511. Endo M, Furuta T, Minoura F, Kim C, Oshida K, Dresselhaus G, Dresselhaus MS. Supramol Sci 1998;66(5):261. Yoshizawa N, Yamada Y, Shiraishi M. J Mater Sci 1998;33:199. Millward GR, Jefferson DA. In: Walker Jr. PL, editor. Chemistry and physics of carbon, 14. New York: Marcel Dekker, 1978. pp. 37. Diamond R. Acta Cryst 1957;10:359.
A new quantitative approach for microstructural analysis of coal char using HRTEM images A. Sharma*, T. Kyotani, A. Tomita Institute for Chemical Reaction Science, Tohoku University, 2-1-1 Katahira, Sendai 980-8577, Japan Accepted 4 March 1999
Abstract HRTEM is a useful technique to observe the structure of coal char at an atomic level. To analyze the char structure quantitatively, we developed a new filtration technique for HRTEM images and a computer algorithm to obtain information such as the graphene layer size, interlayer spacing, the number of layers per stack, and its distribution from the post-filtered extracted HRTEM images. By using a computerized imaging system and image analysis algorithm, it is possible to analyze many images from different sample spots, which helps to make the analyzed result as general as possible. The aim of this article is to describe the usefulness of the technique to analyze the structure of carbonaceous materials. We applied this technique to study quantitatively the crystallinity of Pocahontas coal chars gasified in CO2 up to various conversion levels at 12008C. TEM observation showed a remarkable structural change during the gasification. q 1999 Elsevier Science Ltd. All rights reserved. Keywords: Char; Structure; Gasification; Transmission electron microscopy
1. Introduction There has been considerable interest in the study of structural change of coal char during combustion or gasification. Over the years, the presence of residual or unburned carbon in fly ash during pulverized coal combustion and high temperature coal gasification has attracted much attention, as it is directly related to the efficiency of combustion as well as to the quality of ash in its post-utilization in the cement industry [1–8]. The reactivity of the residual char generally becomes very low, and this has been attributed to the change in carbon structure and/or to the loss of catalytic activity of mineral matter. The relative importance of these factors depends on the nature of coal sample as well as on the reaction conditions. The structural change of coal chars due to gasification has been studied by using various techniques. Among all, X-ray diffraction (XRD) and microscopy (high resolution transmission electron microscopy (HRTEM), scanning electron microscopy and optical microscopy) are the most powerful techniques. The lattice fringe image detected by HRTEM gives direct information on the structure of char at an atomic level, although we need to keep in mind that “things are seldom what they seem [9]”. Also we should try to avoid dangers * Corresponding author. Tel.: 1 81-22-217-5627; fax: 1 81-22-2175626.
implicit in the use of microscopy such as eclecticism and tendentiousness [10]. So far several qualitative to quantitative analyses through TEM observation have been proposed. Furuta et al. [11] reported the structural change of resin char during CO2 gasification at 9008C. Davis et al. [6] developed a quantitative analytical method which consists of the filtration of raw image using Fourier transform (FT) to eliminate the non-periodical structure and the analysis of the filtered image. From this analysis they obtained average fringe lengths and relative amounts of crystalline structure. Wornat et al. [12] used HRTEM to study the structural transformation of biomass chars during combustion. Palota´s et al. [13] used HRTEM and an image analysis system to study quantitatively the structural changes of soot and carbon black particles during combustion. Their method was also the use of FT of the TEM image for extraction of structural data, followed by reverse transform. They concluded that the changing structure of the carbon black during oxidation influences its rate of oxidation. Shim and Hurt [14] further developed their algorithm to determine the mean crystallite diameter, non-graphitic amorphous index, orientational order parameter, and tortuosity. In these studies, they correlated the crystalline transformation of coal chars with the decrease in global oxidation reactivity. Zielin˜ska-Blajet et al. [15] characterized the structure of activated carbons using XRD and TEM techniques, and observed a qualitative agreement between the results from the two techniques. In
0016-2361/99/$ - see front matter q 1999 Elsevier Science Ltd. All rights reserved. PII: S0016-236 1(99)00046-0
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Fig. 1. Comparison of the conventional and the present filtration technique.
another study, Endo et al. [16,17] used HRTEM combined with an image processor for observation and analysis of pores in activated carbon fibres. Very recently, Yoshizawa et al. [18] used a similar FT technique to analyze the HRTEM pattern and evaluate the layer length and stacking number distribution of KOH-activated carbons prepared at various activation temperatures. The main aim of the present article is to introduce a new filtration and statistical analysis technique to analyze the coal char structure quantitatively from HRTEM images. The results from such statistical analyses are very sensitive to the filtration procedure applied for the processing of the HRTEM images. The filtration technique introduced here is quite different from those reported previously. For statistical analysis, we developed a new algorithm in Fortran 77 to extract quantitative structural information from TEM micrographs. We determined by this method not only the graphene layer size, but also the interlayer spacing, the number of layers per stack (or the stacking number), and
their distributions. To demonstrate the ability of the foregoing technique to analyze the structure of carbonaceous materials quantitatively, we have studied the transformations in the crystallinity of Pocahontas No. 3 coal chars (POC) due to high temperature gasification. Three coal char samples were prepared by gasifying in CO2 at 12008C up to conversion of 42, 75 and 92%. In this study we have also attempted to correlate the char gasification profile with the change in the carbon structure based on quantitative HRTEM image analysis technique.
2. Experimental 2.1. Coal char samples Argonne Premium Coal Sample, Pocahontas no. 3, was selected for the investigation. It is a low volatile bituminous coal, and the elemental analysis was Cmaf: 91.1%, Hmaf:
A. Sharma et al. / Fuel 78 (1999) 1203–1212
4.4%, Smf: 0.7% and Omaf: 2.5% (by difference). The ash content on a dry basis was 4.8%, and the ash analysis was SiO2: 32.0%, Al2O3: 20.1%, Fe2O3: 15.8%, CaO: 12.8% and SO3: 12.4%. The coal with a particle size less than 100 mesh, was devolatilized at 8008C for 5 min in a fluidized bed reactor with He gas as the fluidizing agent. The char obtained was then heated in an infrared image furnace from ambient temperature to 12008C at 1008C/min in a high purity He gas. A reference char sample was prepared by keeping the char for 5 min at 12008C in He and then cooling it down to ambient temperature. This char sample was called POC 0. To prepare gasified char samples with different conversions, char samples were kept for 5 min at 12008C in He and then switched over to 1% CO2 –He mixture for 120, 300 and 480 min to achieve 42, 75, and 92% (daf) conversions on char weight basis. The residual char samples were referred to as POC 42, POC 75 and POC 92, respectively. 2.2. Characterization of char The char samples were then subjected to TEM observation and XRD analysis. For TEM observation, they were ground in ethanol and sprayed over a copper microgrid. The char samples on the microgrid were observed under a 200 keV transmission electron microscope (JEOL, JEM2010) equipped with a computerized imaging system for high resolution pictures. For disordered structures or small crystallite sizes, spherical aberration influences on TEM lattice images must be considered. This has been discussed in terms of the linear transfer theory [19]. The phase transfer function was calculated for l 0.00295 nm (electron beam wavelength) and CS 0.5 mm (spherical aberration coefficient) which are the conditions for the present observations. The transfer function is used to obtain the defocus position at which smoothness in contrast was guaranteed for 0.3 nm and a higher basal spacing. To get a general view of the char structure, 10–15 photographs were taken for each sample from different spots. 2.3. Image analysis technique The image analysis technique consists of: (1) filtration of TEM micrographs for noise reduction without losing an appreciable information, (2) identification and reconnection of the fringe layers, which were disconnected during the filtration step to obtain the extracted image for statistical analysis, and (3) the statistical analysis to evaluate the quantifiable structural parameters. Most of the previous studies have essentially used the same filtration and processing technique. We developed a new filtration technique and an image analysis computer algorithm to obtain the structural parameters as described in the following sections. 2.4. New image filtration and processing technique The first step in an image analysis technique is digitization of the TEM micrographs. However, we bypassed this
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step as we used a computerized imaging system by which we stored the image as a digitized image as shown in Fig. 1(a). This direct digitization of the image eliminates the noise and errors involved during the development and the scanning of the picture. The digitized image is then subjected to a filtration procedure. We have developed a new filtration procedure to extract the final image from the TEM image. All filtration procedures use essentially the same methodology of Fourier transform (FT) of the raw image, noise reduction followed by inverse FT, of which the most important step is the noise reduction. The conventional method uses a square filter to take out the frequencies in the frequency domain image which fall in the square filter range for example 0.30–0.50 nm 21 range in the FT domain. The new filter which we have used is a step filter which has only the lower limit which in our case is 0.1 nm 21 in the FT domain. The advantage of a step filter over a square filter is as follows: While the latter extracts structures that occur periodically with a separation distance characteristic of turbostatic carbons, the former extracts not only periodically occurring stacks, but also non-periodically occurring single layers. As a result, a lot of information concerning single layers is lost along with the noise when a square filter is used. The filtered image is then inverse fourier transformed and shows a network of fringes connected by Y and/or T shaped links. The conventional method uses the variable threshold value criterion to separate the fringes which is a subjective criterion. In this process not only small fringe layers are lost, but also the layer size of large fringe layers is reduced. This can be better understood from the fact that different extracted images can be obtained from the same filtered TEM image by changing the threshold value. Thus the statistical analysis of all these extracted images will give different structural parameters though they all come from the same TEM image. We developed a new unique method which not only retains the small layers, but also keeps the size of large fringe layers intact while separating the fringes. This is done, first by separating the fringe layers followed by reconnection of disconnected layers. The separation of the fringe layers is based on the fact that the layers and the Y and/or T shaped links have different brightness and hence can be mathematically separated. Once the fringe layers are separated, we reconnected the disconnected layers using the geometrical parameters of these layers. A comparative description of the preceding two methods has been given in the following section with the help of an example. The digitized TEM image as shown in Fig. 1(a) was first fourier transformed to get the power spectrum as shown in Fig. 1(b). The conventional method uses a square filter as shown in Fig. 1(c) while the step filter used in our method is shown in Fig. 1(d). The step filter can be imagined as a circular disc of radius 0.1 nm 21 placed at the center of the power spectrum shown in Fig. 1(b) while a square filter will be an annular disc of an outer radius 0.5 nm 21 and an inner radius 0.3 nm 21 from the center of the power spectrum. The
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Fig. 2. Flow diagram of the image algorithm for image analysis.
difference in the information retained from these two filters is that using a step filter all the information except that covered by a circular disc is retained while using a square filter only the information contained in the annular disc is retained as shown by shaded portions in Fig. 1(c) and (d). One can well imagine the difference in the magnitude of the information thus obtained by these two filters. The inverse FT filtered images from the conventional and our methods are converted to two color images as shown in Fig. 1(e) and (f), respectively. The images are very different from each other in terms of fringe layer content though both show fringes connected by Y and T shaped links. To obtain the extracted image, the conventional method changes the threshold value of the image (Fig. 1(e)) to such a value that the fringes are separated, followed by skeletonization. In contrast, our method only skeletonizes the image (Fig. 1(f)) without altering the threshold value as shown in Fig. 1(g). As a result, this skeletonized image shows a network of
layers connected to each other by Y or T shaped links, which are now mathematically separated in the next step. First the threshold value of the skeletonized image is increased to such a value that fringes disappear and only nodes remain. This image containing only nodes (Fig. 1(h)) is then subtracted from the original skeletonized image (Fig. 1(g)) to get the image with separated fringes free from the Y and T shaped links. The extracted images from the two techniques are shown in Fig. 1(i) and (j). The pixel information of the image shown in Fig. 1(j) is then stored and subjected to the reconnection algorithm. The algorithm reconnects the fringe layers using several geometrical parameters of these layers and the fact that the nodes are typically of 2–4 pixel length. The input file contains the information of the image in binary mode. The algorithm first searches for a pixel with a value 1 (dark) and then finds whether or not there is a pixel adjacent to it with a value 1. When there is break in this search i.e. when it encounters a pixel with a value 0 (white), it looks for the next 2–4 pixels in the direction of its search. If it finds a pixel with a value 1 within the next 2–4 pixels, it replaces the in-between pixels having a value 0 with 1 and thus connects the two layers. The final image is regenerated using the NIH Image from the output file which contains the pixel information of the image. The final image is shown in Fig. 1(k). The two images, Fig. 1(i) and (k) give very different information about the fringes. The extracted images such as those in Fig. 1(k) were then subjected to analysis to identify and characterize the fringes. The filtration and characterization processes were done on a Power Macintosh model 7300/180 computer using the public domain NIH Image program (developed at the US National Institutes of Health and available in the Internet at http://rsb.info.nih. gov/nih-image) while the reconnection algorithm, written in Fortran 77, was compiled and executed on a Power Challenge XL (Silicon Graphics, Inc.). The characteristics of the fringes obtained using the NIH Image, were stored in the computer and then processed using the image analysis algorithm developed by us which gives a graphene layer size, interlayer spacing and the number of layers per stack and their distribution. 2.5. New image analysis algorithm The extracted image shows fringes as black lines. Some of these fringes show curvilinearity. The NIH Image software is able to characterize these layers by first identifying a layer and then fitting an ellipse to the layer. The midpoint coordinates of the major axis of the fitted ellipse are assigned as the X-, Y-coordinates and the angle of inclination of the major axis to the X-axis is assigned as the angle of inclination of the layer. By counting the number of pixels, the actual length of the layer can be obtained. These total number of layers (NL), angle of inclination (u ), X-, Y-coordinates (x,y), major and minor axes (rx, ry) of the fitted ellipse and the layer length, L, become the input data for the image analysis algorithm for statistical analysis. The
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3. Overlap view parameter: This parameter identifies the layers that can form a stack with the reference layer. This is done by drawing perpendicular lines from the two edges of the reference layer and selecting the layers which are encountered by either of these perpendicular lines. 4. Interlayer spacing: This parameter identifies layers whose perpendicular distances are close to that of typical turbostatic carbon, i.e. 0.344 nm. We selected those layers whose interlayer spacing falls between 0.33 and 0.38 nm for our calculations. Fig. 3. Validity check of the present method: computer versus manual counting.
algorithm makes use of four parameters: aspect ratio, parallelism, overlap view parameter and the interlayer spacing to evaluate the structural parameters. 1. Aspect ratio: The aspect ratio is defined as the ratio of the major axis to the minor axis of the fitted ellipse. This parameter defines a limit for curvilinearity in the fringes for calculation purposes. We took fringes with an aspect ratio of 2 or more into consideration as constituents of the carbon structure. 2. Parallelism: This parameter identifies layers that are parallel to the reference layer. A layer with an angle of inclination within ^ 108 is considered to be parallel to the reference layer. The perpendicular distance between two layers can only be obtained if they satisfy this criterion.
The flow diagram of the image algorithm is shown in Fig. 2. The source code is written in Fortran 77 and was compiled and executed on a Power Challenge XL (Silicon Graphics, Inc.). The validity of the algorithm was established as shown in Fig. 3 by using a rather simple image and comparing the parameters obtained manually by four persons and that from the algorithm. A good agreement between the computed parameters and those counted manually was obtained. Fig. 3 also shows the human subjectivity involved in the computation of parameters as the results obtained by the four persons are different. The advantage of this new image analysis algorithm is that it removes human subjectivity while giving quite reliable results. Using this algorithm and the computerized imaging system it was possible to analyze many pictures to obtain general information from TEM images. The unique points of the present method can be summarized as follows: 1. In addition to the use of a computerized imaging system, we developed a two-step image extraction method: noise reduction using the step filter, followed by the mathematical separation and reconnection of fringe layers 2. An image analysis algorithm free from human subjectivity was developed.
3. Results 3.1. Analysis of TEM results
Fig. 4. Digitized TEM images and the corresponding extracted images: (a) and (c) POC 0; (b) and (d) POC 92.
In Fig. 4, the TEM images (a) and (b) and the extracted fringe images after image filtration and processing (c) and (d) are shown for POC 0 and POC 92 char samples. The two images show a striking difference in the shape, size, and orientation of the layers. The layers in Fig. 4(c) are small, twisted and lack in orientation. With increased conversion to 92%, a very clear orientation and an increase in layer size were observed as shown in Fig. 4(d). An increase in crystallinity in char structure with gasification could be inferred from these pictures. To obtain quantitative information, the extracted lattice images were subjected to the image analysis algorithm to evaluate the graphene layer size, interlayer spacing, the number of layers per stack, and their distribution.
3
NC 2 × L=0:2462 :
4
Then the number of carbons which belong to the stacks with n layers, NC(n), was calculated by adding all the number of carbons for each layer (Column 7 in Table 1). The total number of carbons was 96 466. It should be noted that the total number of carbons evaluated here are that extended to 3-dimension from a 2-dimensional information (lattice fringe), and hence it does not represent the absolute number of carbons. The fraction of carbons belonging to n layers’ stack can be calculated as fC n NC n=
∞ X
NC n NC n=96 466
5
n1
and the results are shown in the eighth column in Table 1. The average interlayer spacing (d-spacing) of the stacks with n layers were also calculated, and the results are shown in the ninth column in Table 1 together with column 10.
Fraction of layers fL(n) Table 1 HRTEM image analysis of one particular area (56 × 56 nm 2) for POC 42
These values are shown in the fourth and fifth columns, respectively. Not only the number of layer, but the size of layer is also important. From the lattice image, we know the projected length, L, of all the layers which may be regarded as representing the diameter of the graphene layer as a first approximation. The average length of the lattice fringes which make the stacks with n layers, L(n), were calculated and listed in the sixth column. The number of carbon atoms per layer, NC, was estimated by assuming that L (in nm) stands for the length of an edge of a rhombic crystallite as described by Diamond [20]:
– 0.34 0.37 0.39 0.33 0.49 0.35 0.11 0.02 0.03 1.00 46 885 33 982 10 192 1628 3779 96 466 2.70 2.88 3.15 3.43 2.94
NL n NL n=341:
n1
203 50 8 1 2 264
∞ X
Average diameter of layer L (nm)
fL n NL n=
0.60 0.30 0.07 0.01 0.02 1.00
and its fraction out of the total number of layers is:
203 100 24 4 10 341
2
1 2 3 4 5 Total
NL n n × NS n
Number of carbons NC(n)
With regard to the number of layers, there are 341 layers including 203 singly occurring layers and 138 stacked layers in this area. The total number of layers occurring in groups of n layers, NL(n), would be:
0.77 0.19 0.03 0.003 0.007 1.00
Average dspacing (nm) Fraction of carbons fC(n)
1
Number of layers NL(n)
NS n NS n=264:
n1
Fraction of stacks fS(n)
∞ X
Number of stacks NS(n)
fS n NS n=
Standard deviation
The definition of terms used in this article will be explained as follows by using an example. Table 1 shows the result for one particular TEM image of the POC 42 sample. The observed area where we found 264 stacks was 56 × 56 nm 2. These stacks were classified according to the number of layers per stack or stacking number. The stacking number, n, and the number of stacks having n layers, NS(n), are listed in the first and second columns of Table 1, respectively. The third column shows the fraction of stacks having n layers out of all the stacks and is defined as follows:
– 0.07 0.05 0.00 0.01
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Stacking number n
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Fig. 5. Fraction of number of layers occurring in the stacks with n layers: Distribution for four different fields of POC 42.
3.2. Change of structural parameter with char conversion The calculation described in the preceding section was repeated for all observed images of all the char samples. The results are summarized in this section as a function of the char conversion. To begin with, it would be necessary to show how this data processing method is reproducible. Fig. 5 shows the relationship between the fractional number of layers, fL(n), and the stacking number, n, for randomly selected four fields of POC 42 sample. Around 350 layers were identified in each field. Although there are some variations, Fig. 5 shows some consistency of the distribution pattern. The reliability of the present method can be judged from this figure. Thus the data shown below is not a specific one for the particular area analyzed, but a rather general one for the whole sample examined. Fig. 6 summarizes the preceding relationship for the four char samples. For each sample nearly 10–11 different fields were observed, and the data from all these fields are summarized. The fraction of single layers is always higher
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than the stacked layers. The fL(1) and fL(2) are almost the same in POC 0, POC 42 and POC 75 samples. Although POC 42 shows a decrease in stacking number and POC 75 an increase in stacking number, it may be concluded that these three samples have essentially a similar stacking structure. However, when the char is gasified up to 92%, an appreciable change was observed. Compared with POC 75, fL(1) and fL(2) decreased while layers belonging to stacks with n from four to eight considerably increased. Also some new stacks with n of 9–11 were observed which were not present in the other chars. This suggests the highly crystalline nature of POC 92. The layer size, L, was determined from the length of the fringe observed. The average size of the layers belonging to the stacks of n layers are shown in Fig. 7. The difference between POC 0 and POC 42 samples is not significant, the average layer size being around 3.0 nm. The size for POC 75 is slightly larger than these two, but the difference is not so large. At 92% conversion, however, the average size of the layers increased to a large extent. The average size of the newly formed stacks with 9–11 layers was around 4.0 nm. In order to correlate the structural change with char reactivity, it would be helpful to know the relative number of carbon atoms present in each group of stacks. Fig. 8 shows the fraction of carbon atoms, fC(n), as a function of n. It is observed that the relative ratio of carbon atoms belonging to single layers decreased as gasification proceeded. The ratio of carbons in highly stacked layers was large for POC 92, where carbon atoms are almost equally distributed in stacks with one to seven layers. Further, the contribution from stacks with more than eight layers became appreciable. In other words, as gasification proceeds the carbon atoms available for the reaction would be more crystalline. To simulate how the carbon atoms are lost from these layers upon gasification, the sets of data in Fig. 8 were converted to those evaluated on the basis of the initial number of carbon atoms. This was simply done by multiplying the fraction of
Fig. 6. Fraction of number of layers occurring in the stacks with n layers.
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Fig. 7. Average diameter of layers occurring in the stacks with n layers.
the residual char (Fig. 9). Fig. 9(a) is naturally the same as Fig. 8(a). In other figures (Fig. 9(b)–(d)), the absolute number of total carbons decreased with gasification. The average d-spacing was found to decrease from 0.36 nm for POC 0 to 0.34 nm for the POC 92 char sample.
4. Discussion 4.1. Comparison of the present method with the previous ones Most of the earlier studies have attempted to draw qualitative information on the char structure during gasification or combustion with the aid of HRTEM observation [8,11,12,15]. However, recently many attempts have been made to analyze HRTEM data more quantitatively as discussed in Section 1. Although those studies used different
parameters to examine structural changes, the analytical methods have been essentially based on the same methodology for the extraction of the final image for statistical analysis: TEM observation, FT, filtration, followed by inverse FT. The statistical analysis is very sensitive to the final extracted image from the TEM micrographs. The image filtration technique developed and used in the present study to extract the final image for statistical analysis is different from those previously adopted ones as described before. It should be mentioned that this study for the first time evaluates the contribution of single layers from TEM analysis which was made possible because of the use of a new step filter and the concept of mathematical subtraction of images. In this study, we analyzed the structural changes using parameters such as the layer size, d-spacing, the distribution of the number of layers, layer size diameter, and carbon atoms, as a function of the stacking number. It is easy to
Fig. 8. Fraction of number of carbons belonging to the stacks with n layers.
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Fig. 9. Fraction of number of carbons belonging to the stacks with n layers on the initial carbon number basis.
identify and store the characteristics of the lattice fringes from the extracted images using various commercially available image analysis software. So far, to our knowledge, the preceding structural parameters specially stack and layer size distributions have been evaluated manually, which is not only very tedious and time consuming but is also very subjective depending from person to person. To remove the impact of human subjectivity on statistical analysis results, we developed the computer algorithm as described in the previous section, specifically to evaluate the foregoing structural parameters. Although this algorithm is based on some assumptions, the validity of this has been established before its application. The use of this algorithm also reduces the processing and analysis time. Another point worth mentioning here is that the average layer size distribution with the stacking number (Fig. 7) obtained from this technique, has neither been reported so far from any technique nor can be obtained from any commercially available software. The automated calculation of layer statistics, such as length, are standard features of many commercially available analysis routines but it is not possible to know from these softwares as to which layer belongs to which stacking number. One can obtain the stack distribution similar to the one shown in Fig. 6 from XRD analysis, but it is not possible to obtain a layer size distribution similar to the one shown in Fig. 7 from XRD analysis. From such a distribution, we can discuss the char gasification profile in terms of the fraction of the number of carbons belonging to stacks with different stacking numbers. 4.2. Application of the proposed technique–structure change due to gasification In this section we will discuss the char structure transformations during gasification based on the structural parameters obtained from the proposed method. The result from the foregoing analysis shows that the char structure
undergoes transformation during gasification. The char structure initially contains a higher fraction of less crystalline portions, and it becomes more crystalline as gasification proceeds especially at the later stages. It has been frequently suggested that both less crystalline and highly crystalline portions are present in the raw char and that the less crystalline part would be preferentially lost in the earlier stage while the highly crystalline part remains until the later stage [1–3,6–8]. However, the present observation (Fig. 6) clearly indicates that the highly crystalline portions which were absent in the initial stages were created during the gasification. The development of these highly crystalline portions may not be attributed only due to the effect of gasification, but the thermal annealing effect must contribute to some extent. Fig. 9(b)–(d) shows the fraction of carbon atoms in the residual char samples as gasification progresses. Up to 42% conversion, carbons from one or two layers contribute most of the total carbon loss. Other layers also show the loss of carbon to different degrees. With further gasification up to 75%, the carbon loss from one to four layers remains significant, while the contribution of stacks with a higher stacking number is marginal (Fig. 9(c)). This is also observed when the char was gasified to 92% conversion where most of the carbon loss comes from the layers belonging to stacks with one to four layers. It can be inferred from the present analysis that the carbon present in less crystalline form contributes more to the carbon loss during gasification than do more crystalline carbons. Most studies, qualitative or quantitative, concluded that the carbon structure becomes more ordered with increased conversion. The results of the present analysis are consistent with their conclusions. However, the mere information that char crystallinity increases with gasification is not sufficient to establish any meaningful relationship between reactivity and an ordered structure. Our approach in the present study for such a correlation is a step ahead of the previous
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approaches in a sense that here we have discussed the gasification process on the basis of change in the relative number of carbon atoms belonging to stacks with different stacking numbers. This is more rational if one intends to establish, for example, a relationship between reactivity and an ordered structure. However, this relationship is not simple but rather very complex, as the reactivity of char depends on multiple factors of which the carbon structure is one. Effects of other factors, such as catalytic activity [3] of mineral matters present in coal char, are also important and should be investigated in the future.
Acknowledgements This study was partly supported by “Research for the Future Program-Molecular Engineering of Coal” which was sponsored by the Japan Society for the Promotion of Science. The helpful discussion with Prof M. Shiraishi of Tokai University is gratefully acknowledged. One of the authors, A.S., wishes to thank Harish Hirani of IIT, Delhi for his useful suggestions in the development of the image algorithm. We would like to express our appreciation to the High Voltage Electron Microscope Laboratory of the Tohoku University for the microscope observation.
5. Conclusions The development of a new filtration technique made it possible to obtain more information from TEM micrographs than conventional techniques, such as the fraction of single layers. The image analysis algorithm makes the structural analysis procedure less time consuming and thus many pictures can be quickly analyzed to obtain a more general view of the char structure. It also removes the problem of human subjectivity involved in computational analysis. The proposed filtration and statistical analysis technique can be applied to any carbonaceous materials to analyze quantitatively the structure from their HRTEM images. When applied to coal chars, this technique provides valuable information on the development of the crystalline structure of chars during gasification. TEM analysis revealed that the crystallinity of residual char increases with gasification especially at a very later stage. The average layer size distribution obtained from this technique made it possible to evaluate the fraction of number of carbons belonging to stacks with different stacking numbers. This provides a new approach to discuss the char gasification profile. With increasing conversion, the carbon atoms available for gasification are in a more crystalline form. The carbons present in a less crystalline form contribute more to the carbon loss during gasification than do crystalline carbons. The analysis also shows that some highly crystalline stacks, not present initially, have developed during the course of gasification. The development of these highly crystalline stacks during gasification may be partly due to the thermal annealing effect, which we are currently investigating.
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