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Chemical imaging of coal in micro-scale with Raman mapping technology
Fuel 264 (2020) 116826
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Full Length Article
Chemical imaging of coal in micro-scale with Raman mapping technology ⁎
T
Jun Xu, Jiawei Liu, Xin Zhang, Peng Ling, Kai Xu , Limo He, Sheng Su, Yi Wang, Song Hu, ⁎ Jun Xiang State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 430074 Wuhan, Hubei, China
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: Micro-Raman mapping Coal macerals Chemical structures Micro-scale In-situ
Raman mapping and petrography analysis of three two-dimensional planes including typical coal macerals in a lignite were done and coupled. The self-correlations between Raman spectral parameters and the relationship between Raman spectral parameters and random reflectivity (Rf) of coal maceral particles were set up. The results indicate that the area ratio of D band to G band (AD/AG) and intensity ratio of D band to G band (ID/IG) should not be equal, and they were not well related to Rf of coal maceral particles. Good self-correlations between full width at half maximum intensity of G band (G FWHM), the total area of first-order Raman spectrum (SAll) and fluorescence interference degree defined as drift coefficient α were found. G FWHM, SAll and α are also well related to Rf and can act as good indicators for chemical structure characteristics of coal maceral particles. With the increase of Rf, the relative amount of polyolefin, small aromatic rings, C–H, C-O, O–H etc. substitutional groups in vitrinite and liptinite decreases drastically, while the increase of aromatic ring size mainly takes place in inertinite. Chemical imaging of two-dimensional coal planes at a very high resolution was realized by drift coefficient α, and reproduction of the distribution of coal macerals in micro-scale is reasonable. The Raman mapping technology developed can be further used to deeply study the chemical structures of detailed coal macerals. This study can be further extended for rapid identification and detection of coal macerals by microRaman spectroscopy.
1. Introduction Chemical structure is the basic characteristics of coal [1–8]. The investigation of coal chemical structure has attracted researchers’
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attention for decades. Elemental analyzer, Fourier Transform Infrared spectroscopy (FT-IR), X-Ray Diffraction (XRD) and Nuclear Magnetic Resonance spectroscopy (NMR) etc. advanced chemical detection technologies have been developed for coal characterization
Corresponding authors. E-mail addresses: [email protected] (K. Xu), [email protected] (J. Xiang).
https://doi.org/10.1016/j.fuel.2019.116826 Received 29 July 2019; Received in revised form 2 November 2019; Accepted 4 December 2019 Available online 16 December 2019 0016-2361/ © 2019 Published by Elsevier Ltd.
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investigating the structure of coal macerals in micro-scale was attempted. However, Raman and petrography analysis were not coupled, which gives a barrier to identify the coal macerals accurately. Besides, just limited micro-particles of coal macerals were analyzed or the average results of Raman or petrography analysis were still used. It is still hard to reveal the chemical structure characteristics of coal macerals and coal heterogeneity comprehensively. Micro-Raman mapping technology, a micro-Raman spectrometer coupled with an automatic platform, can analyze a coal plane automatically with a set step distance rapidly, realizing the chemical structure analysis about two-dimensional plane of coal in micro-scale [13,24,30–32]. When comparing to the image of petrography analysis, the accurate identification of coal macerals can be realized. Besides, it can obtain large amount of Raman spectra in a short time, which can provide a chance for big data analysis and setting up universal laws about the Raman spectral characteristics of coal [33]. In this study, test method of micro-Raman mapping technology was developed to analyze the coal macerals in micro-scale in-situ. Raman and petrography analysis were coupled and the correlations between their results were set up. The chemical structures of coal macerals were discussed, and the chemical imaging of coal macerals in micro-scale was realized.
[1–5,7,9–12]. All these detection technologies analyze the coal particles as a whole, which means the coal is regarded as a homogeneity and the bulk or average chemical structure information is obtained [7,12,13]. However, the coal is significantly heterogeneous in various scales, which has been demonstrated to have a great influence on coal conversion process [6,7,14–16]. The heterogeneity of coal chemical structure cannot be ignored for fully understanding the characteristics of coal chemical structure [6,7]. In micro-scale, coal compositions can be mainly divided as vitrinite, inertinite and liptinite according to their coal-forming process and optical properties [15–22]. The optical properties of these coal macerals are relatively uniform. The content and distribution of coal macerals can determine coal heterogeneity in micro-scale, and they can be used as indicators for coal utilization [15,18,19]. With development of separation technology and characterization techniques, the structure characteristics of coal macerals have been revealed. Machnikowska et al. [23] used diffuse reflectance FT-IR to investigate the chemical structures of coal macerals. They found that the amount of C–H structure of aliphatic hydrocarbon in liptinite is highest and the aromatization degree of inertinite is highest. Yan et al. [17] used elemental analysis, FT-IR and 13C NMR to study the chemical structures of maceral separations from Victoria brown coal with the sink-float method. The results indicate that the liptinite-rich float fraction has higher aliphaticity degree and the vitrinite-rich sink fraction has higher aromatization degree than the corresponding parent coal. Michael et al. [19] used the fluorescence microscopy and image analysis to study the coal macerals. Their results indicated that from lignite to low rank bituminous coal, the fluorescence intensity of coal macerals is ordered as: liptinite > vitrinite > intertinite. They pointed out that fluorescence intensity can be used as the indicator to identify the coal macerals. Michael Cloke and Edward Lester [15] further concluded that the intertinite in coal has the highest carbon to hydrogen atom ratio, reflectance and lowest volatile content, while the liptinite has highest volatile content, hydrogen content and lowest reflectance. They further pointed out that the coal macerals can significantly affect the char morphology and coal combustion characteristics. These studies showed that there are significant differences for coal maceral structures, and these differences can determine the coal conversion process. In addition, these studies all first separated the coal macerals and then characterized the buck structures of coal macerals, which can be significantly influenced by the separation degree of coal macerals. Besides, whether the chemical structure of coal macerals in micro-scale is homogeneous or not is still unknown. The heterogeneous of coal cannot be revealed completely because the structure information about coal macerals in micro-scale is still lack. In-situ analysis of coal maceral chemical structure is very important for further understanding the coal chemical structure [7,13,20,21,24]. Raman spectroscopy has been demonstrated to be a powerful characterization tool for coal since it can reflect not only coal maturity, aromatization degree, but also amorphous carbon structure [25–29]. When Raman spectroscopy is coupled with microscope, it can further effectively analyze the chemical structures of coal macerals in microscale in-situ [16,21]. Morga [21] used the micro-Raman spectroscopy to investigate the reactivity of semifusinite and fusinite. The Raman test was performed on the intertinite concentrates, and also the mean reflectance of semifusinite and fusinite was test. The results indicated that the proportion of disordered carbon and mixed sp2-sp3 or sp3-rich carbon structure in semifusinite and fusinite decreased with the increase of the mean reflectance. Guedes et al. [16] used micro-Raman to investigate the chemical structure of collotelinite, fusinite and macrinite in nine coals. They performed Raman analysis on five grains of bulk coal samples and also five collotelinite, fusinite and macrinite on polished coal section for petrography analysis with air objectives for each coal. The results indicated that the structures of macrinite and fusinite were similar and the structure characteristics of bulk coal are similar to the collotelinite since the coals are vitrinite-rich. In these studies,
2. Experiments and data processing 2.1. Sample preparation and characterization One lignite from Ji coal mine area of Shan Dong province in China was studied. The proximate analysis of coal was done according to Chinese National Standard GB/T 212-2008 [34]. The elemental analysis of coal was done in a Vario Micro cube (Elementar, Germany) according to Chinese National Standard GB/T 31391-2015 [27]. The results (in air dry basis, wt. %) are as following: moisture content: 2.68, volatile content: 30.02, ash content: 25.87 and the fixed carbon content: 41.43, carbon content: 57.31, hydrogen content: 4.07, nitrogen content: 1.01. Coal blocks with resin were further prepared by automatic inlaying machine (Simplimet 1000, Buehler Ltd, American). The preparation temperature was set as 150 ℃ with limited altering about the coal [35]. The coal blocks were then polished using automatic grinding and polishing machine (Automet300, Buehler Ltd, American), after which gridding was sculptured and numbered on coal blocks by a sharp knife to record the position of coal macerals that would be analyzed. Raman mapping test of coal was done in a micro-Raman spectrometer (Jobin Yvon Lab RAM HR800) equipped with an Nd-YAG laser (532 nm). A 50× objective lens (Olympus BX41) was used to focus on the surface of coal macerals, and the laser spot was about 2 μm. The laser power was controlled as 0.5 mW to reduce the thermal damage on coal maceral particles. The point-by-point Raman mapping test was done in an automatic platform with a step space of 4 μm and integration time of 5 s. The Raman signal overlap from the adjacent point can be avoided and also a dense enough test can be done when the step space of 4 μm was set. The Raman spectrum between 600 and 2000 cm−1 was recorded. Different two-dimensional planes of coal were chosen and analyzed. The petrography analysis of coal blocks were done in Axio Scope. A1 (Carl Zeiss, Germany) according to the ISO 7404 [36,37]. The same two-dimensional planes of coal blocks that had been done Raman mapping test were found and analyzed under 50× oil-immersion objective. The morphology of the planes were identified. Specially, three two-dimensional planes including the typical coal macerals: vitrinite, inertinite and liptinite were selected and the random reflectivity of coal maceral particles in these areas was tested. The size of these two-dimensional planes were 26 *66 μm2, 26 *62 μm2 and 26 *74 μm2 respectively. A total of 364 coal maceral particles were analyzed during Raman mapping test.
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2.2. Data processing In Raman spectra of carbon materials, two main bands: G band located at about 1590 cm−1 and D band located at about 1350 cm−1 mainly exist [5,25,27]. Some minor bands including bands at about 1080 cm−1, 1160 cm−1, 1230 cm−1, 1380 cm−1, 1500 cm−1, 1540 cm−1 and 1620 cm−1 etc. were also found in some researches [5,16,27–29]. Generally, for highly ordered carbon materials such as graphite, graphene etc., the G band can be mainly attributed to the E22g mode of graphite, and the D band can be assigned to the A1g symmetry mode of graphite [25,26]. Minor band at about 1620 cm−1 was attributed to the defect of graphite, and minor bands at about 1080 cm−1, 1160 cm−1, 1500 cm−1 etc. were mainly assigned to the substituent or amorphous carbon structures [26,27]. For highly disordered carbon materials such as low rank coals, Li et al. [28,29] proposed that the D band should be more attributed to the C–C between aromatic rings especially the aromatics with not less than 6 rings, and the G band should be more attributed to the aromatic ring quadrant breathing rather than the structures that were associated to graphitic. Minor bands including 1060 cm−1, 1160 cm−1, 1230 cm−1, 1380 cm−1, 1450 cm−1, 1540 cm−1, 1680 cm−1 were proposed to exist in Raman spectrum, and they were mainly attributed to the small aromatic rings or C–H, C-O, C]O etc. substituent [5,16,28,29]. For a coal, it is known that the substituted groups are more active than the small aromatic rings, which are more active than the large ones [5,11,28]. The large aromatic rings are more active than the semi-graphite or graphite structures [27,28]. Therefore, no matter what is the graphitization degree of coal, the minor bands in Raman spectrum of coal can be mainly attributed to the most active structures [5,27,28]. The D band can be attributed to the next active structures and the G band can be attributed to the most stable structures [5,27,28]. Therefore, the methods for resolving the Raman spectrum can be mainly divided into two kinds. One kind is that only using D band and G band to curve-fit the spectrum [38]. Another kind is the method including the minor bands to curve-fit the spectrum, such as three bands method [39], five bands method [27] and ten bands method [5,28]. It is known that more bands were used to resolve the spectrum, more detailed structure information can be revealed, but also more parameters were needed to be set and fixed, which can result in the confusion that different researchers can obtain different results using the same bands to curve-fit the same spectrum [38]. In this study, the first-order Raman spectrum between 800 and 1800 cm−1 was selected to be analyzed. The method only using D band and G band to resolve the spectrum was adopted because this method is simplest and suitable for large amount of data. In addition, it has no variable parameter when curve-fitting the spectrum, which is beneficial to establish a common approach based on large data base of Raman mapping. During the curve-fitting, the baseline was firstly corrected with the linear baseline as shown in Fig. 1. Two Lorentzian curves were used to curve-fit the spectrum. The positions of D band and G band were initialized as 1350 cm−1 and 1590 cm−1. The iteration computation of curve-fitting was not finished until the R-squared of the curve-fitting was higher than 0.99. The D band FWHM and G band FWHM were recorded and their combination including the ratio (D FWHM/G FWHM) and summation (D + G FWHM) were calculated. The ID/IG (the ratio of D band intensity to G band intensity), the AD/AG (the ratio of D band area to G band area) and SAll (the total area of Raman spectrum) were also calculated. In addition, considering that different coal macerals have different fluorescence characteristics, the degree of fluorescence interference in Raman spectrum was defined as drift coefficient α according to our previous study and used to reflect the fluorescence intensity of coal maceral particles [40]. The drift coefficient α is calculated as following:
α=
yB − yA yD − yA
The yB, yA and yD were Raman intensity of endpoints at 1800 cm
Fig. 1. A sample of data processing of the Raman spectrum.
800 cm−1, and D band at about 1350 cm−1, which are shown in Fig. 1. 3. Results and discussion 3.1. Self-correlations between the Raman spectral characteristic parameters Raman spectrum of coal is overlapped by D band, G band and some minor bands. With the increase of coalification degree of coal, the carbon structures of coal change obviously [5,34]. Besides, different coal maceral particles in same coal can also have significant different chemical structure, which can further led to the complexity of Raman spectrum of coal in micro-scale [7,12,16,18,21]. Therefore, confirming the change characteristics of Raman spectral parameters and the structure information represented by them are key issues before using the Raman spectrum to reveal coal chemical structure. Firstly, the selfcorrelations between ID/IG and AD/AG, G FWHM, D FWHM and D + G FWHM were set up and shown in Fig. 2 and Fig. S1 (in Supplemental materials). It can be seen that the values of all Raman spectral parameters have a large interval value. From the test of Raman spectra, it is known that all the data points are from the same coal and every point represents a coal maceral particle with a dimeter about 2 μm. So, a large interval value of Raman spectral parameters indicates great heterogeneity about the chemical structures of coal maceral particles in micro-scale. The chemical structures of coal maceral particles in same coal can be even significantly different from each other. For a long time, ID/IG is considered to monotonously decrease with the increase of carbon order degree, and the AD/AG is generally equal to ID/IG [25–27]. However, for coal maceral particles in this study, there are no obvious correlativity between them as shown in Fig. 2(a), indicating that AD/AG should not be easily equal to ID/IG. For raw coals, with the increase of order degree of coal maceral particles, the amount of aliphatic structures, C-H, C-O and C]O etc. substituted structures in aromatic rings decreases, and the aromatic rings become larger and larger, resulting in the weaken or even disappearance of minor bands and sharpness of D band and G band [5]. As the D band and G band were used to curve-fit the spectrum, the D band and G band partly covered the minor bands on either side. So, the increase of order degree of coal maceral particles, the intensity, FWHM and also the area of D band and G band all decrease. However, the ID/IG and AD/AG may not change monotonously since it is a ratio of ID and IG or AD and AG and the proportion of the contributions from side minor bands to D band and G band were not certain. Therefore, ID/IG and AD/AG cannot be loosely used to reveal the chemical structures or the carbon order degree when only D band and G band were used to curve-fit the spectrum. As shown in Fig. 2(b), there is a inflection point for the correlation
(1) −1
, 3
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Fig. 3. Correlations between the G FWHM and D FWHM, SAll.
Fig. 2. Correlations between Raman spectral parameter ID/IG and AD/AG, G FWHM.
can significantly increase the total area of Raman spectrum [28,29]. Therefore, the total area of Raman spectrum can be mainly related to the amount of O-containing functional groups in coal maceral particles [28,29,41]. As described above, the G FWHM can reflect the order degree of coal maceral particles. The good correlation between the G FWHM and SAll further confirms that the O-containing functional groups in coal maceral particles is also linearly related to the order degree of coal maceral particles. It needs to point out that the SAll of Raman spectrum can be also significantly affected by the Raman laser power during test. In the micro-Raman test, the laser power sometimes would be affected by the confocal degree according to the operation of instrument operator. In this study, the micro-Raman mapping mode is used, the sample is block and polished for test. The confocal degree of the tested area can be guaranteed to be almost same, and thus the effects of laser power can be ignored and the SAll can be used to reflect the relative amount of O-containing functional groups in coal maceral particles. Fig. 4 shows the correlations between drift coefficient α and G FWHM and SAll. It is interesting to point out that there are good correlations between the drift coefficient α and G FWHM, SAll. According to the definition, the drift coefficient α can reflect the fluorescence interference degree in Raman spectrum, and reveal the fluorescence intensity of coal maceral particles. It is known that the structures have large conjugated double bonds such as conjugated polyolefin and small aromatic rings would have higher fluorescence. Besides, the substitutional groups on aromatic rings including C–H, C-O, O–H, etc. are fluorescence auxiliary groups, which can enhance the fluorescence intensity significantly [40]. Therefore, the value of drift coefficient α can directly reflect the relative amount of polyolefin, small aromatic rings, and C–H, C-O, O–H, etc. substitutional groups on aromatic rings in coal
between ID/IG and G FWHM, and similar results were also observed for D FWHM and D + G FWHM as shown in Fig. S1. Obviously, the ID/IG firstly decreases and then increases with the decrease of D FWHM, G FWHM and their summation. It is known that the FWHM of D band and G band changes monotonously with the increase of order degree of coal maceral particles [38]. In other words, the ID/IG firstly decreases and then increases with the increase of order degree of coal maceral particles. Fig. 3 shows the correlations between G FWHM and D FWHM and SAll. From Fig. 3(a), it can be seen that there is good correlation between G FWHM and D FWHM. The D FWHM firstly increases rapidly and then slowly with the increases of G FWHM. It indicates that the change trend is same for G FWHM and D FWHM with the change of chemical structures of coal maceral particles. In addition, the D FWHM is more sensitive to the change of structures when the carbon structures are more ordered, and G FWHM is more sensitive when the carbon structures are more disordered, which is further demonstrated by the nearly linear correlation between D FWHM/G FWHM and G FWHM as shown in Fig. S2. As shown in Fig. 3(b), there is a surprising linear correlation between the G FWHM and SAll. It is known that the total area of Raman spectrum can be mainly determined by intrinsic Raman scattering ability and its light absorptivity [28,41,42]. It has been demonstrated that the aromatization of coal maceral particles on the one hand can increase the Raman intensity due to the sp2 carbons have higher Raman intensity than sp3 carbons, and on the other hand can reduce the Raman intensity as it can increase the light absorbing ability of them [28]. So, generally, the aromatization of coal maceral particles has no significantly effects on SAll. In addition, the electron-rich atoms such as oxygen etc. tend to have high Raman scattering ability, and they 4
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spectrometer. The coal macerals can be more accurately identified under the oil-immersion objective, and in-situ coupling the Raman test and petrography analysis can enhance the accuracy of results. In order to further set up the correlations between the results of Raman test and petrography analysis, the random reflectivity of selected coal maceral particles (Rf) was related to the Raman results of corresponding coal maceral particles. The correlations between Rf and drift coefficient α, SAll, D FWHM, G FWHM, D FWHM/G FWHM, ID/IG and AD/AG were shown in Fig. 6. From Fig. 6(a), (b) and (c), it can be seen that the drift coefficient α, SAll and G FWHM all decrease with the increase of Rf, and the R-square of the correlations are all higher than 0.92, indicating very high relevance between them. It is known that Rf can reflect the coalification degree of coal maceral particles [18,21]. It indicates that the order degree of carbon structures is determined by the coalification degree of coal maceral particles, and the relative amount of active structures including small aromatic rings, C–H, C–O, O–H, etc. substitutional groups is also well related to the coalification degree of coal maceral particles [5,11,34]. These three Raman parameters are good indicators for the coalification degree of coal maceral particles. As shown in Fig. 6(c), (d) and (e), there are also some correlations between the D FWHM, D FWHM/G FWHM, ID/IG, AD/AG and Rf. The D FWHM decreases, D FWHM/G FWHM first increases and then decreases, AD/AG increases, and ID/IG first deceases and then increases with the increase of Rf. Nevertheless, the relationships are not so good as there are some fluctuations, especially when the Rf is lower than 1.5. Combining the morphology, when Rf is lower than 1.5, corresponding coal maceral particles were mainly vitrinite and liptinite. There are abundant active structures including small aromatic rings and substitutional groups in the coal maceral particles. The kinds of substitutional groups and their combination can be very different, resulting in some fluctuation about the intensity or FWHM of D band and G band. Besides, from Fig. 6(a), (b) and (c), it can be also seen that when Rf is lower than 1.5, the drift coefficient α, SAll and G FWHM decreases drastically with the increase of Rf. It indicates that the small aromatic rings, C–H, C-O, O–H, etc. substitutional groups in vitrinite and liptinite decrease drastically with the increase of Rf. Besides, SAll, G FWHM and AD/AG tend to change slowly and the change trend of ID/IG turns with the further increase of Rf. From the results of petrography analysis, it is known that when Rf is larger than 1.5, the coal maceral particles are mainly inertinite, which has a relative high coalification degree. The amount of small aromatic rings and substitutional groups is limited and the aromatization degree is relative high. With the further increase of Rf, the decrease of small aromatic rings and substitutional groups are limited, and the growth of aromatic rings mainly takes place, resulting in the increase of ID/IG and limited change of SAll and G FWHM. It needs to point out that when the Rf is larger than 1.5, the drift coefficient α continuously decreases with the increase of Rf. It is mainly because that the increase of aromatic rings size can further decrease the fluorescence intensity of coal maceral particles. It also indicates that the drift coefficient α can act as a good indicator for the whole coal macerals.
Fig. 4. Correlations between drift coefficient α and G FWHM and SAll.
maceral particles. Good correlations between drift coefficient α and G FWHM, SAll further indicate that the relative amount of polyolefin, small aromatic rings, and C–H, C-O, O–H, etc. substitutional groups are also well related to the order degree of coal maceral particles. In other words, the higher order degree of coal maceral particles are, the less Ocontaining functional groups and lower fluorescence of coal maceral particles is. 3.2. In-situ coupling the Raman test and petrography analysis Fig. 5 shows the pictures of coal macerals. The upper three pictures were obtained under the microscope in Raman spectrometer, and the bottom three pictures were taken under the oil-immersion objective during petrography analysis. The magnification times of them were same. The areas of lattices in pictures obtained under the microscope in Raman spectrometer were the two-dimensional planes that were selective analysis with Raman mapping. From Fig. 5, it can be seen that the morphology of coal macerals was similar under both microscope, and the areas that were analyzed in Raman mapping and petrography analysis were anastomotic. It means the petrography results and the Raman results can be coupled in-situ. Fig. 5 shows that the difference between the vitrinite, inertinite and liptinite is more obvious under the oil-immersion objective. The vitrinite showed the color of gray and the inertinite showed the color of white and gray white. The liptinite is darkest [36]. Under the microscope in Raman spectrometer, the color of all coal macerals tend to be gray white. The vitrinite is more dark gray, and the inertinite is more dark white. The liptinite is darker. In addition, some areas are very dark under the microscope in Raman spectrometer but virtual and not focused under the oil-immersion objective. They are actually pores among the coal macerals. These pores are very easily mistaken as liptinite under the microscope in Raman
3.3. Chemical imaging of coal macerals From the analysis above, it is known that the drift coefficient α, SAll and G FWHM are the Raman parameters that monotonously change with Rf of coal maceral particles and there are also good correlations between themselves. Therefore, the simplest parameter: drift coefficient α is selected as the parameter for chemical imaging of coal macerals. It can directly reflect the relative amount of active structures including small aromatic rings, C–H, C-O, O–H, etc. substitutional groups as well as the coalification degree of coal maceral particles [40], and using the drift coefficient α as the parameter to map the regions can realize the chemical imaging of coal maceral regions. Fig. 7 shows the figures of the three two-dimensional planes of coal macerals imaging by drift coefficient α (the figures in left). In order to compare the results, the images obtained under the oil-immersion 5
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Fig. 5. Pictures of the coal macerals. The upper three pictures were obtained under the microscope in Raman spectrometer, and the bottom three pictures were taken under the oil-immersion objective during petrography analysis.
coalification process. However, from the Raman results, typical Raman spectra of carbon structure have been found and the color of the pores among inertinite is obviously different from the inertinite. The drift coefficient α of the pores is obviously larger than inertinite, which indicates that there are also organic components in the pores (or cell lumen) and they contain more small aromatic rings, C–H, C–O, O–H, etc. substitutional groups. The organic components in the pores (or cell lumen) tend to be more active during thermal conversion processes. As discussed above, the Raman mapping technology developed in this study can realize the chemical imaging of coal macerals in microscale at a very high resolution. In order to investigate the effects of laser power during Raman mapping test on the results of chemical imaging by drift coefficient α, a two-dimensional plane was further selected and the Raman mapping test was repeated with the laser power of 0.1 mW. The image of the two-dimensional plane of coal macerals by drift coefficient α is shown in Fig. S3. When comparing it with the image when the laser power is 0.5 mW, it can be seen that except the slight difference caused by the selection of two-dimensional plane area, they are very similar. It reveals that changing the laser power during test led to limited effects on the chemical imaging. Besides, there may be slight difference about the laser power during Raman mapping test for different two-dimensional planes. However, the self-correlations between the Raman spectral parameters are still reasonable. It also indicates that the slight difference of laser power during test has no obvious effects on the results. This method can be further used to deeply study the chemical structures of detailed coal macerals including fusinite, semifusinite, secretinite, small sporinite, inhomogenous ulminite, gelinite etc in the scale of about 2 μm. Besides, as there are good correlations between the Raman spectral parameters and the random reflectivity of coal maceral particles and good reproduction of coal maceral
objective about the same regions were also shown in the right. The changing of color from green to red reflect the increase of drift coefficient α, shown as the color column in the middle of Fig. 7. When comparing the color distribution of the pictures about the three twodimensional planes, it is surprising that the pictures imaging by the drift coefficient α and the real pictures obtained under the oil-immersion objective are very similar. The white and grey-white regions under the oil-immersion objective that were attributed to the inertinite are green and white-green in the map of drift coefficient α. The grey regions under the oil-immersion objective that were attributed to the vitrinite are red in the map of drift coefficient α, and the dark black stripe that were attributed to the liptinite are dark red in the map of drift coefficient α. It indicates that different coal macerals can significantly differ from each other in the map of drift coefficient α. Combing the structural information that the drift coefficient α represents, it directly indicates that the relative amount of active structures including small aromatic rings, C–H, C-O, O–H, etc. substitutional groups in the coal macerals is ordered as liptinite > vitrinite > inertinite. For vitrinite in all the three regions, the color of them in the map of drift coefficient α is relative uniform, indicating the chemical structure of them is also uniform in micro-scale. For the liptinite, it is long stripe with a longitudinal dimension lower than 2 μm and mounted in the vitrinite. The map of drift coefficient α can still recognize it, though the reproduction by Raman mapping is not as well as vitrinite. For the inertinite, there are some pores among it and also fragmentation of inertinite with the size lower than 10 *10 μm2, and all of them can be discriminated from the surrounding coal macerals in the map of drift coefficient α. It needs to point out that the pores among the inertinite are mainly formed from the cell lumen and filled by minerals during the 6
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Fig. 6. Correlations between Rf and drift coefficient α, Sall, D FWHM, G FWHM, D FWHM/G FWHM, ID/IG, AD/AG.
amount of polyolefin, small aromatic rings, C–H, C–O, O–H, etc. substitutional groups in vitrinite and liptinite decrease drastically and the increase of aromatic rings in inertinite mainly takes place with the increase of Rf. The method of chemical imaging of two-dimensional coal planes in micro-scale was developed by Raman mapping. This method can be further used to deeply study the chemical structures of detailed coal macerals, and extended to realize rapid identification and quantification of coal macerals.
distribution in the two-dimensional planes, it can be further extended to develop rapid identification of coal maceral component and random reflectivity distribution by micro-Raman mapping technology. 4. Conclusion Raman mapping and petrography analysis of a lignite were done. Three two-dimensional planes including typical coal macerals: vitrinite, inertinite and liptinite were selected, and the results of Raman analysis and petrography analysis were coupled in-situ. The self-correlations between the Raman spectral parameters were set up, and the relationship of Raman spectral parameters and random reflectivity of coal maceral particles were discussed. The results indicate that the widely used Raman spectral parameters AD/AG and ID/IG should not be equal, and they were both not well related to the random reflectivity of coal maceral particles. Good self-correlations between G FWHM, SAll and drift coefficient α were found, and they are well related to the random reflectivity of coal maceral particles. They can act as good indicators for chemical structure characteristics of coal maceral particles. The relative
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by National Natural Science Foundation of China (Nos. 51806073, 51806079), China Postdoctoral Science 7
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Formation of the structure of chars during devolatilization of pulverized coal and its thermoproperties: A review. Prog Energy Combust Sci 2007;33:135–70. [7] Gupta R. Advanced coal characterization: a review. Energy Fuels 2007;21:451–60. [8] Borah RC, Ghosh P, Rao PG. A review on devolatilization of coal in fluidized bed. Int J Energy Res 2011;35:929–63. [9] Li K, Khanna R, Zhang J, Barati M, Liu Z, Xu T, et al. Comprehensive investigation of various structural features of bituminous coals using advanced analytical techniques. Energy Fuels 2015;29:7178–89. [10] Baysal M, Alp Yürüm, Burçin Yıldız, Yuda Yürüm. Structure of some western Anatolia coals investigated by FTIR, Raman, 13C solid state NMR spectroscopy and X-ray diffraction. Int J Coal Geol 2016;163:166–76. [11] JoséV Ibarra, Edgar Muñoz, Rafael Moliner. FTIR study of the evolution of coal structure during the coalification process. Org Geochem 1996;24:725–35. [12] Guo Y, Bustin RM. Micro-FTIR spectroscopy of liptinite macerals in coal. Int J Coal Geol 1998;36:259–75. [13] Tselev A, Ivanov IN, Lavrik NV, Belianinov A, Jesse S, Mathews JP, et al. Mapping internal structure of coal by confocal micro-Raman spectroscopy and scanning microwave microscopy. Fuel 2014;126:32–7. [14] Xie K, Zhang Y, Li C, Li D. Pyrolysis characteristics of macerals separated from a single coal and their artificial mixture. Fuel 1991;70:474–9. [15] Cloke M, Lester E. Characterization of coals for combustion using petrographic analysis: a review. Fuel 1994;73:315–20. [16] Guedes A, Valentim B, Prieto AC, Noronha F. Raman spectroscopy of coal macerals and fluidized bed char morphotypes. Fuel 2012;97:443–9. [17] Yan Y, Qi Y, Marshall M, Jackson WR, Chaffee AL. Separation and analysis of maceral concentrates from Victorian brown coal. Fuel 2019;242:232–42. [18] Chang H, Deng H, Yang Q, Du S, Hu F, Jia C. Interaction of vitrinite and inertinite of Bulianta coal in pyrolysis. Fuel 2017;207:643–9. [19] Cloke M, Lester E, Allen M, Miles N. Automated maceral analysis using fluorescence microscopy and image analysis. Fuel 1995;74:659–69. [20] Collins L, Tselev A, Okatan MB, Proksch R, Mathews JP, Mitchell GD, et al. Breaking the limits of structural and mechanical imaging of the heterogeneous structure of coal macerals. Nanotechnology 2014;25:43. [21] Morga R. Micro-Raman spectroscopy of carbonized semifusinite and fusinite. Int J Coal Geol 2011;87:253–67. [22] Elliott MA, Yohe GR. The coal industry and coal research and development in perspective. Chemistry of Coal Utilization: Second Supplementary Volume. New York: John Wiley and Sons; 1981. p. 54. [23] Machnikowska H, Krztoń A, Machnikowski J. The characterization of coal macerals by diffuse reflectance infrared spectroscopy. Fuel 2002;81:245–52. [24] Manjunath GL, Nair RR. Implications of the 3D micro scale coal characteristics along with Raman stress mapping of the scratch tracks. Int J Coal Geol 2015;141–142:13–22. [25] Tuinstra F, Koenig JL. Raman spectrum of graphite. J Chem Phys 1970;53:1126. [26] Ferrari AC. Raman spectroscopy of graphene and graphite: Disorder, electronphonon coupling, doping and nonadiabatic effects. Solid State Commun 2007;143:47–57. [27] Sheng C. Char structure characterised by Raman spectroscopy and its correlations with combustion reactivity. Fuel 2007;86:2316–24. [28] Li X, Hayashi J, Li C. FT-Raman spectroscopic study of the evolution of char structure during the pyrolysis of a Victorian brown coal. Fuel 2006;85:1700–7. [29] Li X, Hayashi J, Li C. Volatilisation and catalytic effects of alkali and alkaline earth metallic species during the pyrolysis and gasification of Victorian brown coal. Part VII. Raman spectroscopic study on the changes in char structure during the catalytic gasification in air. Fuel 2006;85:1509–17. [30] Dumpala R, Kumar N, Kumaran CR, Dash S, Ramamoorthy B, Ramachandra Rao MS. Adhesion characteristics of nano-and micro-crystalline diamond coatings: Raman stress mapping of the scratch tracks. Diam Relat Mater 2014;44:71–7. [31] Ma J, Zhou X, Ma J, Ji Z, Zhang X, Xu F. Raman Microspectroscopy Imaging Study on Topochemical Correlation Between Lignin and Hydroxycinnamic Acids in Miscanthus sinensis. Microsc Microanal 2014;20:956–63. [32] Sekine Y, Ishikawa K, Kikuchi E, Matsukata M, Akimoto A. Reactivity and structural change of coal char during steam gasification. Fuel 2006;85:122–6. [33] Campos JLE, Miranda H, Rabelo C, Sandoz-Rosado E, Pandey S, Riikonen J, et al. Applications of Raman spectroscopy in graphene-related materials and the
Fig. 7. Images of the three two-dimensional planes of coal macerals. The left ones were imaged by Raman spectral parameter drift coefficient α, and the right ones were obtained under the oil-immersion objective about the same regions.
Foundation Funded Project (M2018630858). The authors also acknowledge the extended help from the Analytical and Testing Center of Huazhong University of Science and Technology.
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Coal Geol 2014;136:52–8. [39] Li G, Lu Z, Huang B, Wang Z, Huang H, Xue R, et al. Raman scattering investigation of carbons obtained by heat treatment of a polyfurfuryl alcohol. Solid State Ionics 1996;89:327–31. [40] Xiang J, Liu J, Xu J, Su S, Tang H, Hu Y, et al. The fluorescence interference in Raman spectrum of raw coals and its application for evaluating coal property and combustion characteristics. Proc Combust Inst 2018;37:3053–60. [41] Xu J, Su S, Sun Z, Qing M, Xiong Z, Wang Y, et al. Effects of steam and CO2 on the characteristics of chars during devolatilization in oxy-steam combustion process. Appl Energy 2016;182:20–8. [42] Xu J, Su S, Sun Z, Si N, Qing M, Liu L, et al. Effects of H2O gasification reaction on the characteristics of chars under Oxy-fuel combustion conditions with wet recycle. Energy Fuels 2016;30:9071–9.
development of parameterized PCA for large-scale data analysis. J Raman Spectrosc 2018;49:54–65. Xu J, Tang H, Su S, Liu J, Han H, Zhang L, et al. Micro-raman spectroscopy study of 32 Kinds of Chinese coals: second-order raman spectrum and its correlations with coal properties. Energy Fuels 2017;31:7884–93. Yu J, Tahmasebi A, Han Y, Yin F, Li X. A review on water in low rank coals: The existence, interaction with coal structure and effects on coal utilization. Fuel Process Technol 2013;106:9–20. ISO 7404-3. Methods for the petrographic analysis of coals-Part 3: method of determining maceral group composition; 2009. ISO 7404-5. Methods for the petrographic analysis of coals-Part 5: method of determining microscopically the reflectance of vitrinite; 2009. Hinrichs R, Brown MT, Vasconcellos MAZ, Abrashev MV, Kalkreuth W. Simple procedure for an estimation of the coal rank using micro-Raman spectroscopy. Int J
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Full Length Article
Chemical imaging of coal in micro-scale with Raman mapping technology ⁎
T
Jun Xu, Jiawei Liu, Xin Zhang, Peng Ling, Kai Xu , Limo He, Sheng Su, Yi Wang, Song Hu, ⁎ Jun Xiang State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 430074 Wuhan, Hubei, China
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: Micro-Raman mapping Coal macerals Chemical structures Micro-scale In-situ
Raman mapping and petrography analysis of three two-dimensional planes including typical coal macerals in a lignite were done and coupled. The self-correlations between Raman spectral parameters and the relationship between Raman spectral parameters and random reflectivity (Rf) of coal maceral particles were set up. The results indicate that the area ratio of D band to G band (AD/AG) and intensity ratio of D band to G band (ID/IG) should not be equal, and they were not well related to Rf of coal maceral particles. Good self-correlations between full width at half maximum intensity of G band (G FWHM), the total area of first-order Raman spectrum (SAll) and fluorescence interference degree defined as drift coefficient α were found. G FWHM, SAll and α are also well related to Rf and can act as good indicators for chemical structure characteristics of coal maceral particles. With the increase of Rf, the relative amount of polyolefin, small aromatic rings, C–H, C-O, O–H etc. substitutional groups in vitrinite and liptinite decreases drastically, while the increase of aromatic ring size mainly takes place in inertinite. Chemical imaging of two-dimensional coal planes at a very high resolution was realized by drift coefficient α, and reproduction of the distribution of coal macerals in micro-scale is reasonable. The Raman mapping technology developed can be further used to deeply study the chemical structures of detailed coal macerals. This study can be further extended for rapid identification and detection of coal macerals by microRaman spectroscopy.
1. Introduction Chemical structure is the basic characteristics of coal [1–8]. The investigation of coal chemical structure has attracted researchers’
⁎
attention for decades. Elemental analyzer, Fourier Transform Infrared spectroscopy (FT-IR), X-Ray Diffraction (XRD) and Nuclear Magnetic Resonance spectroscopy (NMR) etc. advanced chemical detection technologies have been developed for coal characterization
Corresponding authors. E-mail addresses: [email protected] (K. Xu), [email protected] (J. Xiang).
https://doi.org/10.1016/j.fuel.2019.116826 Received 29 July 2019; Received in revised form 2 November 2019; Accepted 4 December 2019 Available online 16 December 2019 0016-2361/ © 2019 Published by Elsevier Ltd.
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investigating the structure of coal macerals in micro-scale was attempted. However, Raman and petrography analysis were not coupled, which gives a barrier to identify the coal macerals accurately. Besides, just limited micro-particles of coal macerals were analyzed or the average results of Raman or petrography analysis were still used. It is still hard to reveal the chemical structure characteristics of coal macerals and coal heterogeneity comprehensively. Micro-Raman mapping technology, a micro-Raman spectrometer coupled with an automatic platform, can analyze a coal plane automatically with a set step distance rapidly, realizing the chemical structure analysis about two-dimensional plane of coal in micro-scale [13,24,30–32]. When comparing to the image of petrography analysis, the accurate identification of coal macerals can be realized. Besides, it can obtain large amount of Raman spectra in a short time, which can provide a chance for big data analysis and setting up universal laws about the Raman spectral characteristics of coal [33]. In this study, test method of micro-Raman mapping technology was developed to analyze the coal macerals in micro-scale in-situ. Raman and petrography analysis were coupled and the correlations between their results were set up. The chemical structures of coal macerals were discussed, and the chemical imaging of coal macerals in micro-scale was realized.
[1–5,7,9–12]. All these detection technologies analyze the coal particles as a whole, which means the coal is regarded as a homogeneity and the bulk or average chemical structure information is obtained [7,12,13]. However, the coal is significantly heterogeneous in various scales, which has been demonstrated to have a great influence on coal conversion process [6,7,14–16]. The heterogeneity of coal chemical structure cannot be ignored for fully understanding the characteristics of coal chemical structure [6,7]. In micro-scale, coal compositions can be mainly divided as vitrinite, inertinite and liptinite according to their coal-forming process and optical properties [15–22]. The optical properties of these coal macerals are relatively uniform. The content and distribution of coal macerals can determine coal heterogeneity in micro-scale, and they can be used as indicators for coal utilization [15,18,19]. With development of separation technology and characterization techniques, the structure characteristics of coal macerals have been revealed. Machnikowska et al. [23] used diffuse reflectance FT-IR to investigate the chemical structures of coal macerals. They found that the amount of C–H structure of aliphatic hydrocarbon in liptinite is highest and the aromatization degree of inertinite is highest. Yan et al. [17] used elemental analysis, FT-IR and 13C NMR to study the chemical structures of maceral separations from Victoria brown coal with the sink-float method. The results indicate that the liptinite-rich float fraction has higher aliphaticity degree and the vitrinite-rich sink fraction has higher aromatization degree than the corresponding parent coal. Michael et al. [19] used the fluorescence microscopy and image analysis to study the coal macerals. Their results indicated that from lignite to low rank bituminous coal, the fluorescence intensity of coal macerals is ordered as: liptinite > vitrinite > intertinite. They pointed out that fluorescence intensity can be used as the indicator to identify the coal macerals. Michael Cloke and Edward Lester [15] further concluded that the intertinite in coal has the highest carbon to hydrogen atom ratio, reflectance and lowest volatile content, while the liptinite has highest volatile content, hydrogen content and lowest reflectance. They further pointed out that the coal macerals can significantly affect the char morphology and coal combustion characteristics. These studies showed that there are significant differences for coal maceral structures, and these differences can determine the coal conversion process. In addition, these studies all first separated the coal macerals and then characterized the buck structures of coal macerals, which can be significantly influenced by the separation degree of coal macerals. Besides, whether the chemical structure of coal macerals in micro-scale is homogeneous or not is still unknown. The heterogeneous of coal cannot be revealed completely because the structure information about coal macerals in micro-scale is still lack. In-situ analysis of coal maceral chemical structure is very important for further understanding the coal chemical structure [7,13,20,21,24]. Raman spectroscopy has been demonstrated to be a powerful characterization tool for coal since it can reflect not only coal maturity, aromatization degree, but also amorphous carbon structure [25–29]. When Raman spectroscopy is coupled with microscope, it can further effectively analyze the chemical structures of coal macerals in microscale in-situ [16,21]. Morga [21] used the micro-Raman spectroscopy to investigate the reactivity of semifusinite and fusinite. The Raman test was performed on the intertinite concentrates, and also the mean reflectance of semifusinite and fusinite was test. The results indicated that the proportion of disordered carbon and mixed sp2-sp3 or sp3-rich carbon structure in semifusinite and fusinite decreased with the increase of the mean reflectance. Guedes et al. [16] used micro-Raman to investigate the chemical structure of collotelinite, fusinite and macrinite in nine coals. They performed Raman analysis on five grains of bulk coal samples and also five collotelinite, fusinite and macrinite on polished coal section for petrography analysis with air objectives for each coal. The results indicated that the structures of macrinite and fusinite were similar and the structure characteristics of bulk coal are similar to the collotelinite since the coals are vitrinite-rich. In these studies,
2. Experiments and data processing 2.1. Sample preparation and characterization One lignite from Ji coal mine area of Shan Dong province in China was studied. The proximate analysis of coal was done according to Chinese National Standard GB/T 212-2008 [34]. The elemental analysis of coal was done in a Vario Micro cube (Elementar, Germany) according to Chinese National Standard GB/T 31391-2015 [27]. The results (in air dry basis, wt. %) are as following: moisture content: 2.68, volatile content: 30.02, ash content: 25.87 and the fixed carbon content: 41.43, carbon content: 57.31, hydrogen content: 4.07, nitrogen content: 1.01. Coal blocks with resin were further prepared by automatic inlaying machine (Simplimet 1000, Buehler Ltd, American). The preparation temperature was set as 150 ℃ with limited altering about the coal [35]. The coal blocks were then polished using automatic grinding and polishing machine (Automet300, Buehler Ltd, American), after which gridding was sculptured and numbered on coal blocks by a sharp knife to record the position of coal macerals that would be analyzed. Raman mapping test of coal was done in a micro-Raman spectrometer (Jobin Yvon Lab RAM HR800) equipped with an Nd-YAG laser (532 nm). A 50× objective lens (Olympus BX41) was used to focus on the surface of coal macerals, and the laser spot was about 2 μm. The laser power was controlled as 0.5 mW to reduce the thermal damage on coal maceral particles. The point-by-point Raman mapping test was done in an automatic platform with a step space of 4 μm and integration time of 5 s. The Raman signal overlap from the adjacent point can be avoided and also a dense enough test can be done when the step space of 4 μm was set. The Raman spectrum between 600 and 2000 cm−1 was recorded. Different two-dimensional planes of coal were chosen and analyzed. The petrography analysis of coal blocks were done in Axio Scope. A1 (Carl Zeiss, Germany) according to the ISO 7404 [36,37]. The same two-dimensional planes of coal blocks that had been done Raman mapping test were found and analyzed under 50× oil-immersion objective. The morphology of the planes were identified. Specially, three two-dimensional planes including the typical coal macerals: vitrinite, inertinite and liptinite were selected and the random reflectivity of coal maceral particles in these areas was tested. The size of these two-dimensional planes were 26 *66 μm2, 26 *62 μm2 and 26 *74 μm2 respectively. A total of 364 coal maceral particles were analyzed during Raman mapping test.
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2.2. Data processing In Raman spectra of carbon materials, two main bands: G band located at about 1590 cm−1 and D band located at about 1350 cm−1 mainly exist [5,25,27]. Some minor bands including bands at about 1080 cm−1, 1160 cm−1, 1230 cm−1, 1380 cm−1, 1500 cm−1, 1540 cm−1 and 1620 cm−1 etc. were also found in some researches [5,16,27–29]. Generally, for highly ordered carbon materials such as graphite, graphene etc., the G band can be mainly attributed to the E22g mode of graphite, and the D band can be assigned to the A1g symmetry mode of graphite [25,26]. Minor band at about 1620 cm−1 was attributed to the defect of graphite, and minor bands at about 1080 cm−1, 1160 cm−1, 1500 cm−1 etc. were mainly assigned to the substituent or amorphous carbon structures [26,27]. For highly disordered carbon materials such as low rank coals, Li et al. [28,29] proposed that the D band should be more attributed to the C–C between aromatic rings especially the aromatics with not less than 6 rings, and the G band should be more attributed to the aromatic ring quadrant breathing rather than the structures that were associated to graphitic. Minor bands including 1060 cm−1, 1160 cm−1, 1230 cm−1, 1380 cm−1, 1450 cm−1, 1540 cm−1, 1680 cm−1 were proposed to exist in Raman spectrum, and they were mainly attributed to the small aromatic rings or C–H, C-O, C]O etc. substituent [5,16,28,29]. For a coal, it is known that the substituted groups are more active than the small aromatic rings, which are more active than the large ones [5,11,28]. The large aromatic rings are more active than the semi-graphite or graphite structures [27,28]. Therefore, no matter what is the graphitization degree of coal, the minor bands in Raman spectrum of coal can be mainly attributed to the most active structures [5,27,28]. The D band can be attributed to the next active structures and the G band can be attributed to the most stable structures [5,27,28]. Therefore, the methods for resolving the Raman spectrum can be mainly divided into two kinds. One kind is that only using D band and G band to curve-fit the spectrum [38]. Another kind is the method including the minor bands to curve-fit the spectrum, such as three bands method [39], five bands method [27] and ten bands method [5,28]. It is known that more bands were used to resolve the spectrum, more detailed structure information can be revealed, but also more parameters were needed to be set and fixed, which can result in the confusion that different researchers can obtain different results using the same bands to curve-fit the same spectrum [38]. In this study, the first-order Raman spectrum between 800 and 1800 cm−1 was selected to be analyzed. The method only using D band and G band to resolve the spectrum was adopted because this method is simplest and suitable for large amount of data. In addition, it has no variable parameter when curve-fitting the spectrum, which is beneficial to establish a common approach based on large data base of Raman mapping. During the curve-fitting, the baseline was firstly corrected with the linear baseline as shown in Fig. 1. Two Lorentzian curves were used to curve-fit the spectrum. The positions of D band and G band were initialized as 1350 cm−1 and 1590 cm−1. The iteration computation of curve-fitting was not finished until the R-squared of the curve-fitting was higher than 0.99. The D band FWHM and G band FWHM were recorded and their combination including the ratio (D FWHM/G FWHM) and summation (D + G FWHM) were calculated. The ID/IG (the ratio of D band intensity to G band intensity), the AD/AG (the ratio of D band area to G band area) and SAll (the total area of Raman spectrum) were also calculated. In addition, considering that different coal macerals have different fluorescence characteristics, the degree of fluorescence interference in Raman spectrum was defined as drift coefficient α according to our previous study and used to reflect the fluorescence intensity of coal maceral particles [40]. The drift coefficient α is calculated as following:
α=
yB − yA yD − yA
The yB, yA and yD were Raman intensity of endpoints at 1800 cm
Fig. 1. A sample of data processing of the Raman spectrum.
800 cm−1, and D band at about 1350 cm−1, which are shown in Fig. 1. 3. Results and discussion 3.1. Self-correlations between the Raman spectral characteristic parameters Raman spectrum of coal is overlapped by D band, G band and some minor bands. With the increase of coalification degree of coal, the carbon structures of coal change obviously [5,34]. Besides, different coal maceral particles in same coal can also have significant different chemical structure, which can further led to the complexity of Raman spectrum of coal in micro-scale [7,12,16,18,21]. Therefore, confirming the change characteristics of Raman spectral parameters and the structure information represented by them are key issues before using the Raman spectrum to reveal coal chemical structure. Firstly, the selfcorrelations between ID/IG and AD/AG, G FWHM, D FWHM and D + G FWHM were set up and shown in Fig. 2 and Fig. S1 (in Supplemental materials). It can be seen that the values of all Raman spectral parameters have a large interval value. From the test of Raman spectra, it is known that all the data points are from the same coal and every point represents a coal maceral particle with a dimeter about 2 μm. So, a large interval value of Raman spectral parameters indicates great heterogeneity about the chemical structures of coal maceral particles in micro-scale. The chemical structures of coal maceral particles in same coal can be even significantly different from each other. For a long time, ID/IG is considered to monotonously decrease with the increase of carbon order degree, and the AD/AG is generally equal to ID/IG [25–27]. However, for coal maceral particles in this study, there are no obvious correlativity between them as shown in Fig. 2(a), indicating that AD/AG should not be easily equal to ID/IG. For raw coals, with the increase of order degree of coal maceral particles, the amount of aliphatic structures, C-H, C-O and C]O etc. substituted structures in aromatic rings decreases, and the aromatic rings become larger and larger, resulting in the weaken or even disappearance of minor bands and sharpness of D band and G band [5]. As the D band and G band were used to curve-fit the spectrum, the D band and G band partly covered the minor bands on either side. So, the increase of order degree of coal maceral particles, the intensity, FWHM and also the area of D band and G band all decrease. However, the ID/IG and AD/AG may not change monotonously since it is a ratio of ID and IG or AD and AG and the proportion of the contributions from side minor bands to D band and G band were not certain. Therefore, ID/IG and AD/AG cannot be loosely used to reveal the chemical structures or the carbon order degree when only D band and G band were used to curve-fit the spectrum. As shown in Fig. 2(b), there is a inflection point for the correlation
(1) −1
, 3
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Fig. 3. Correlations between the G FWHM and D FWHM, SAll.
Fig. 2. Correlations between Raman spectral parameter ID/IG and AD/AG, G FWHM.
can significantly increase the total area of Raman spectrum [28,29]. Therefore, the total area of Raman spectrum can be mainly related to the amount of O-containing functional groups in coal maceral particles [28,29,41]. As described above, the G FWHM can reflect the order degree of coal maceral particles. The good correlation between the G FWHM and SAll further confirms that the O-containing functional groups in coal maceral particles is also linearly related to the order degree of coal maceral particles. It needs to point out that the SAll of Raman spectrum can be also significantly affected by the Raman laser power during test. In the micro-Raman test, the laser power sometimes would be affected by the confocal degree according to the operation of instrument operator. In this study, the micro-Raman mapping mode is used, the sample is block and polished for test. The confocal degree of the tested area can be guaranteed to be almost same, and thus the effects of laser power can be ignored and the SAll can be used to reflect the relative amount of O-containing functional groups in coal maceral particles. Fig. 4 shows the correlations between drift coefficient α and G FWHM and SAll. It is interesting to point out that there are good correlations between the drift coefficient α and G FWHM, SAll. According to the definition, the drift coefficient α can reflect the fluorescence interference degree in Raman spectrum, and reveal the fluorescence intensity of coal maceral particles. It is known that the structures have large conjugated double bonds such as conjugated polyolefin and small aromatic rings would have higher fluorescence. Besides, the substitutional groups on aromatic rings including C–H, C-O, O–H, etc. are fluorescence auxiliary groups, which can enhance the fluorescence intensity significantly [40]. Therefore, the value of drift coefficient α can directly reflect the relative amount of polyolefin, small aromatic rings, and C–H, C-O, O–H, etc. substitutional groups on aromatic rings in coal
between ID/IG and G FWHM, and similar results were also observed for D FWHM and D + G FWHM as shown in Fig. S1. Obviously, the ID/IG firstly decreases and then increases with the decrease of D FWHM, G FWHM and their summation. It is known that the FWHM of D band and G band changes monotonously with the increase of order degree of coal maceral particles [38]. In other words, the ID/IG firstly decreases and then increases with the increase of order degree of coal maceral particles. Fig. 3 shows the correlations between G FWHM and D FWHM and SAll. From Fig. 3(a), it can be seen that there is good correlation between G FWHM and D FWHM. The D FWHM firstly increases rapidly and then slowly with the increases of G FWHM. It indicates that the change trend is same for G FWHM and D FWHM with the change of chemical structures of coal maceral particles. In addition, the D FWHM is more sensitive to the change of structures when the carbon structures are more ordered, and G FWHM is more sensitive when the carbon structures are more disordered, which is further demonstrated by the nearly linear correlation between D FWHM/G FWHM and G FWHM as shown in Fig. S2. As shown in Fig. 3(b), there is a surprising linear correlation between the G FWHM and SAll. It is known that the total area of Raman spectrum can be mainly determined by intrinsic Raman scattering ability and its light absorptivity [28,41,42]. It has been demonstrated that the aromatization of coal maceral particles on the one hand can increase the Raman intensity due to the sp2 carbons have higher Raman intensity than sp3 carbons, and on the other hand can reduce the Raman intensity as it can increase the light absorbing ability of them [28]. So, generally, the aromatization of coal maceral particles has no significantly effects on SAll. In addition, the electron-rich atoms such as oxygen etc. tend to have high Raman scattering ability, and they 4
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spectrometer. The coal macerals can be more accurately identified under the oil-immersion objective, and in-situ coupling the Raman test and petrography analysis can enhance the accuracy of results. In order to further set up the correlations between the results of Raman test and petrography analysis, the random reflectivity of selected coal maceral particles (Rf) was related to the Raman results of corresponding coal maceral particles. The correlations between Rf and drift coefficient α, SAll, D FWHM, G FWHM, D FWHM/G FWHM, ID/IG and AD/AG were shown in Fig. 6. From Fig. 6(a), (b) and (c), it can be seen that the drift coefficient α, SAll and G FWHM all decrease with the increase of Rf, and the R-square of the correlations are all higher than 0.92, indicating very high relevance between them. It is known that Rf can reflect the coalification degree of coal maceral particles [18,21]. It indicates that the order degree of carbon structures is determined by the coalification degree of coal maceral particles, and the relative amount of active structures including small aromatic rings, C–H, C–O, O–H, etc. substitutional groups is also well related to the coalification degree of coal maceral particles [5,11,34]. These three Raman parameters are good indicators for the coalification degree of coal maceral particles. As shown in Fig. 6(c), (d) and (e), there are also some correlations between the D FWHM, D FWHM/G FWHM, ID/IG, AD/AG and Rf. The D FWHM decreases, D FWHM/G FWHM first increases and then decreases, AD/AG increases, and ID/IG first deceases and then increases with the increase of Rf. Nevertheless, the relationships are not so good as there are some fluctuations, especially when the Rf is lower than 1.5. Combining the morphology, when Rf is lower than 1.5, corresponding coal maceral particles were mainly vitrinite and liptinite. There are abundant active structures including small aromatic rings and substitutional groups in the coal maceral particles. The kinds of substitutional groups and their combination can be very different, resulting in some fluctuation about the intensity or FWHM of D band and G band. Besides, from Fig. 6(a), (b) and (c), it can be also seen that when Rf is lower than 1.5, the drift coefficient α, SAll and G FWHM decreases drastically with the increase of Rf. It indicates that the small aromatic rings, C–H, C-O, O–H, etc. substitutional groups in vitrinite and liptinite decrease drastically with the increase of Rf. Besides, SAll, G FWHM and AD/AG tend to change slowly and the change trend of ID/IG turns with the further increase of Rf. From the results of petrography analysis, it is known that when Rf is larger than 1.5, the coal maceral particles are mainly inertinite, which has a relative high coalification degree. The amount of small aromatic rings and substitutional groups is limited and the aromatization degree is relative high. With the further increase of Rf, the decrease of small aromatic rings and substitutional groups are limited, and the growth of aromatic rings mainly takes place, resulting in the increase of ID/IG and limited change of SAll and G FWHM. It needs to point out that when the Rf is larger than 1.5, the drift coefficient α continuously decreases with the increase of Rf. It is mainly because that the increase of aromatic rings size can further decrease the fluorescence intensity of coal maceral particles. It also indicates that the drift coefficient α can act as a good indicator for the whole coal macerals.
Fig. 4. Correlations between drift coefficient α and G FWHM and SAll.
maceral particles. Good correlations between drift coefficient α and G FWHM, SAll further indicate that the relative amount of polyolefin, small aromatic rings, and C–H, C-O, O–H, etc. substitutional groups are also well related to the order degree of coal maceral particles. In other words, the higher order degree of coal maceral particles are, the less Ocontaining functional groups and lower fluorescence of coal maceral particles is. 3.2. In-situ coupling the Raman test and petrography analysis Fig. 5 shows the pictures of coal macerals. The upper three pictures were obtained under the microscope in Raman spectrometer, and the bottom three pictures were taken under the oil-immersion objective during petrography analysis. The magnification times of them were same. The areas of lattices in pictures obtained under the microscope in Raman spectrometer were the two-dimensional planes that were selective analysis with Raman mapping. From Fig. 5, it can be seen that the morphology of coal macerals was similar under both microscope, and the areas that were analyzed in Raman mapping and petrography analysis were anastomotic. It means the petrography results and the Raman results can be coupled in-situ. Fig. 5 shows that the difference between the vitrinite, inertinite and liptinite is more obvious under the oil-immersion objective. The vitrinite showed the color of gray and the inertinite showed the color of white and gray white. The liptinite is darkest [36]. Under the microscope in Raman spectrometer, the color of all coal macerals tend to be gray white. The vitrinite is more dark gray, and the inertinite is more dark white. The liptinite is darker. In addition, some areas are very dark under the microscope in Raman spectrometer but virtual and not focused under the oil-immersion objective. They are actually pores among the coal macerals. These pores are very easily mistaken as liptinite under the microscope in Raman
3.3. Chemical imaging of coal macerals From the analysis above, it is known that the drift coefficient α, SAll and G FWHM are the Raman parameters that monotonously change with Rf of coal maceral particles and there are also good correlations between themselves. Therefore, the simplest parameter: drift coefficient α is selected as the parameter for chemical imaging of coal macerals. It can directly reflect the relative amount of active structures including small aromatic rings, C–H, C-O, O–H, etc. substitutional groups as well as the coalification degree of coal maceral particles [40], and using the drift coefficient α as the parameter to map the regions can realize the chemical imaging of coal maceral regions. Fig. 7 shows the figures of the three two-dimensional planes of coal macerals imaging by drift coefficient α (the figures in left). In order to compare the results, the images obtained under the oil-immersion 5
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Fig. 5. Pictures of the coal macerals. The upper three pictures were obtained under the microscope in Raman spectrometer, and the bottom three pictures were taken under the oil-immersion objective during petrography analysis.
coalification process. However, from the Raman results, typical Raman spectra of carbon structure have been found and the color of the pores among inertinite is obviously different from the inertinite. The drift coefficient α of the pores is obviously larger than inertinite, which indicates that there are also organic components in the pores (or cell lumen) and they contain more small aromatic rings, C–H, C–O, O–H, etc. substitutional groups. The organic components in the pores (or cell lumen) tend to be more active during thermal conversion processes. As discussed above, the Raman mapping technology developed in this study can realize the chemical imaging of coal macerals in microscale at a very high resolution. In order to investigate the effects of laser power during Raman mapping test on the results of chemical imaging by drift coefficient α, a two-dimensional plane was further selected and the Raman mapping test was repeated with the laser power of 0.1 mW. The image of the two-dimensional plane of coal macerals by drift coefficient α is shown in Fig. S3. When comparing it with the image when the laser power is 0.5 mW, it can be seen that except the slight difference caused by the selection of two-dimensional plane area, they are very similar. It reveals that changing the laser power during test led to limited effects on the chemical imaging. Besides, there may be slight difference about the laser power during Raman mapping test for different two-dimensional planes. However, the self-correlations between the Raman spectral parameters are still reasonable. It also indicates that the slight difference of laser power during test has no obvious effects on the results. This method can be further used to deeply study the chemical structures of detailed coal macerals including fusinite, semifusinite, secretinite, small sporinite, inhomogenous ulminite, gelinite etc in the scale of about 2 μm. Besides, as there are good correlations between the Raman spectral parameters and the random reflectivity of coal maceral particles and good reproduction of coal maceral
objective about the same regions were also shown in the right. The changing of color from green to red reflect the increase of drift coefficient α, shown as the color column in the middle of Fig. 7. When comparing the color distribution of the pictures about the three twodimensional planes, it is surprising that the pictures imaging by the drift coefficient α and the real pictures obtained under the oil-immersion objective are very similar. The white and grey-white regions under the oil-immersion objective that were attributed to the inertinite are green and white-green in the map of drift coefficient α. The grey regions under the oil-immersion objective that were attributed to the vitrinite are red in the map of drift coefficient α, and the dark black stripe that were attributed to the liptinite are dark red in the map of drift coefficient α. It indicates that different coal macerals can significantly differ from each other in the map of drift coefficient α. Combing the structural information that the drift coefficient α represents, it directly indicates that the relative amount of active structures including small aromatic rings, C–H, C-O, O–H, etc. substitutional groups in the coal macerals is ordered as liptinite > vitrinite > inertinite. For vitrinite in all the three regions, the color of them in the map of drift coefficient α is relative uniform, indicating the chemical structure of them is also uniform in micro-scale. For the liptinite, it is long stripe with a longitudinal dimension lower than 2 μm and mounted in the vitrinite. The map of drift coefficient α can still recognize it, though the reproduction by Raman mapping is not as well as vitrinite. For the inertinite, there are some pores among it and also fragmentation of inertinite with the size lower than 10 *10 μm2, and all of them can be discriminated from the surrounding coal macerals in the map of drift coefficient α. It needs to point out that the pores among the inertinite are mainly formed from the cell lumen and filled by minerals during the 6
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Fig. 6. Correlations between Rf and drift coefficient α, Sall, D FWHM, G FWHM, D FWHM/G FWHM, ID/IG, AD/AG.
amount of polyolefin, small aromatic rings, C–H, C–O, O–H, etc. substitutional groups in vitrinite and liptinite decrease drastically and the increase of aromatic rings in inertinite mainly takes place with the increase of Rf. The method of chemical imaging of two-dimensional coal planes in micro-scale was developed by Raman mapping. This method can be further used to deeply study the chemical structures of detailed coal macerals, and extended to realize rapid identification and quantification of coal macerals.
distribution in the two-dimensional planes, it can be further extended to develop rapid identification of coal maceral component and random reflectivity distribution by micro-Raman mapping technology. 4. Conclusion Raman mapping and petrography analysis of a lignite were done. Three two-dimensional planes including typical coal macerals: vitrinite, inertinite and liptinite were selected, and the results of Raman analysis and petrography analysis were coupled in-situ. The self-correlations between the Raman spectral parameters were set up, and the relationship of Raman spectral parameters and random reflectivity of coal maceral particles were discussed. The results indicate that the widely used Raman spectral parameters AD/AG and ID/IG should not be equal, and they were both not well related to the random reflectivity of coal maceral particles. Good self-correlations between G FWHM, SAll and drift coefficient α were found, and they are well related to the random reflectivity of coal maceral particles. They can act as good indicators for chemical structure characteristics of coal maceral particles. The relative
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by National Natural Science Foundation of China (Nos. 51806073, 51806079), China Postdoctoral Science 7
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Fig. 7. Images of the three two-dimensional planes of coal macerals. The left ones were imaged by Raman spectral parameter drift coefficient α, and the right ones were obtained under the oil-immersion objective about the same regions.
Foundation Funded Project (M2018630858). The authors also acknowledge the extended help from the Analytical and Testing Center of Huazhong University of Science and Technology.
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