Construction Strategy for Atomistic Models of Coal Chars Capturing Stacking Diversity and Pore Size Distribution Chang’an Wang,†,‡ Justin K. Watson,§ Enette Louw,‡ and Jonathan P. Mathews*,‡ †
School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, People’s Republic of China The EMS Energy Institute and Department of Energy and Mineral Engineering, and §Applied Research Laboratory, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
‡
S Supporting Information *
ABSTRACT: While there are efficient construction strategies for crystalline, amorphous, and polymeric structures, there are few that enable construction where there are distributions of properties within structures of various order/disorder. Coal chars are one example and are particularly challenging. Chemical and physical properties that vary over length scales along with the distributions of the stacking extent impact the local domain size and orientation. These properties influence char reactivity. Similarly, the pore size distribution imparts access to the reactive surface. Thus, relatively large-scale structures (50 000s of atoms) are needed with control of the distributions of structural features and to allow for incorporation of mesoporosity. These challenges limit the size and availability of coal char models limiting the effectiveness of atomistic simulations. Here, a highly
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experimental adsorption isotherms of several gases (such as Ar, CO2, and N2). Advancements in the high-resolution transmission electron microscopy (HRTEM) technique and in image processing, to create lattice fringe images, allow for the direct determination of the distribution of structural features (fringe lengths, stacking degree, and orientations).36−42 If this wealth of structural features could be duplicated, it would add a new level of constraint to char construction strategies, resulting in better representation of the structure−property and behavior relationships. A computational lattice fringe generator Fringe3D43 was developed to create 3D aromatic structural features directly from HRTEM lattice fringe micrographs and populated the appropriate aromatic structures in a 3D molecular space. Image analysis of lattice fringe data has contributed to the quantitative evaluation of the lattice size and spatial orientation features of coals, chars, soot, and an activated carbon.10,11,44−47 FernandezAlos43 demonstrated that the Fringe3D approach decreased the construction time while increasing the accuracy and complexity of the produced “slice” models, thus increasing the accessibility to the creation and use of these atomistic representations. Molecules are assumed to be flat graphene molecules of various sizes (no curvature), and the fringes are assumed to be as deep as they are wide. Thus, from the fringe length, the appropriately sized graphene sheet is “grown” from a large graphene sheet source file (numbered from the interior revolving outward) in an assumed catenation and placed at appropriate Cartesian locations with appropriate orientations from image analysis data. The resultant structures were similar to the lattice micrograph in capturing order and distributions. However, the structure produced is a “slice” model and not particularly useful for structure property relations or reactivity simulations. A 3D model of Illinois no. 6 coal char atomistic representation was generated by Castro-Marcano et al.48 based on HRTEM lattice fringe image analyses. The atomistic representation of the devolatilized Illinois no. 6 coal char was composed of 7458 atoms within 66 polyaromatic layers, which was used to perform combustion simulation integrating with ReaxFF. While diversity was captured with the aid of Fringe3D, the final structure was created using the amorphous construction protocols in Materials Studio, thus losing control over order/disorder and pore size distribution. Louw49 developed a new semi-automated approach for HRTEM image analysis and a new technique for quantifying stack distributions through Photoshop and MATLAB, respectively, which enabled the quantitative characterization of the diversity within the crystalline char structure. Louw et al. also used a new Perl script Vol3D to populate a specified cuboid volume with the distribution of structural features.49,50 Two large-scale molecular representations of char were rapidly generated with the Perl script Vol3D, including a vitrinite-rich char (42 703 atoms) and an inertinite-rich char (32 206 atoms). A highly automated approach that captures the distribution of structural features on a large scale, especially simultaneously incorporating distributions of fringe lengths, their orientations, stacking extents, and the pore size distributions for coal chars, remains challenging. These structural differences and the pore size distribution have a profound influence on char reactivity in the structure-controlled regime.51 Coal char reactivity is dependent upon its time−temperature history, rank, maceral contribution (organic input and depositional influences), ash, and porosity. It is challenging to individually explore these contributions and, in some cases, impossible using experimental
The reaction of molecular oxygen with metal-catalyzed char and an analogous biomass char were investigated by Backreedy et al.23 through a simplistic small-scale structure. Similarly small-scale (50 000 atoms) atomistic representation of coal char with a degree of local order/disorder using Fringe3D and Vol3D scripts. Specifically, our goal was a rapid automated approach with a high level of control over distributions of structural features that is not available with current construction strategies. In the work presented here, the stacks are directly derived from HRTEM micrographs, as is their inherent PAH distribution and regional order/orientation. One structure was produced with limited inherent microporosity and one larger structure with added micro- to mesoporosity to reproduce the desired distribution.
2. METHODS 2.1. Model Generation of Stacks and Pores. A Perl script named Fringe3D was developed to automatically generate atomistic representations of the PAH structures by directly duplicating the lattice fringe micrographs from HRTEM analysis.43 This approach enabled the models to capture crystallite parameters, such as stacking height, layer size, interlayer spacing, and number of layers in each stack. The application of Fringe3D to populate 3D molecular structures for PAHs has been employed in previous studies.36,43,48,63 The construction procedure of large-scale atomistic representation of coal char incorporated with distributions of stacks and pores is shown in Figure 1. HRTEM micrographs coupled with image processing and statistical analyses provide distribution data of fringe length, position, orientation, and stacking parameters. The center of mass for each fringe is known in x and y coordinates. The average number of carbon atoms corresponding to each fringe was estimated from its length by assuming that the width was the same as its length with an assumed catenation.36 The stacking parameters include the number of layers per stack, the length of each layer, and the angle variation of layers in each stack.45,49,50 Thus, the stacking diversity here also captures multiple structural features. The Fringe3D script was used to produce multiple Protein Data Bank (PDB) files, where each file contains a stack (directly obtained from HRTEM micrographs) ranging from a single aromatic molecule to multiple layers. Each molecule was built to need from a source file with circular catenation.49 Alternative catenation methods, such as parallelogram and square, can also be included to enhance the structural diversity. These source files had the atoms numbered, so that every 2−4 carbon atoms created a new ring in the graphene sheet such that the desired fringe size and stack can be duplicated rapidly. The desired stack distributions were constructed in 3D molecular modeling space (with the center of mass at 0, 0, and 0 Cartesian coordinates) using Fringe3D for subsequent use by Vol3D. The HRTEM image-guided atomistic reconstruction approach of Leyssale et al.41,42,64 and Leyssale and colleagues65−67 is also similar. There, however, the micrograph is used to guide (increase the probability of atom placement) assuming transverse isotropy with additional constrains. Simulated annealing or Monte Carlo sampling reduce the energy of the system, with the final form being PAH molecules often with five- and seven-membered rings, a low percentage of sp3-hybridized carbons with cross-links, and screw disclinations reducing structure freedom. While currently superior in gaining curvature, matching tortuosity, and locking structural components, there is a lack of control over the distributions of structural features. Our approach may be superior in the case of chars where smaller domains (and a higher hydrogen content) may be observed in comparison to carbon structures. Similar to the approach to produce individual graphene molecules of the desired size, pores can be similarly constructed. Initially, the structure is created without added porosity to establish a porosity baseline. Additional pores can then be added to meet the desired pore size distribution. Here, the added pores were assumed to be cubic. Other shapes, such as slit, bar, tetrahedral, or spherical, can be included as appropriate (see the Supporting Information). The actual pore D
DOI: 10.1021/acs.energyfuels.5b00816 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 2. Generation of aromatic structures via the source file of circular catenation and construction of stacks (the central benzene ring is colored green, and the placement of each additional carbon atom is determined by the catenation rule in the source file, with hydrogen atoms not shown to aid viewing): (a) aromatic structure growth and (b) example of stack construction with the centroid in red.
Figure 3. Pore generation from the source file for the cubic pore (the central initial pore is phosphorus default-colored pink, and bonds are added to aid visibility). which is calculated through determining the largest sphere for every grid point in the void volume, while the pore size distribution was determined from the plot of −dVpore(r)/dr versus r. The program was modified to visualize the pore locations. Pore sizes were rounded to the nearest angstrom and then classified into several size bins. The Fringe3D script was used to populate the desired pore distribution and positioning into separate files that were colored and combined using Materials Studio to create the color pore maps. Pores were visualized as partially transparent spheres.
desired size (number of carbon atoms) was achieved according to the number of carbon atoms using a circular catenation file. These structures coupled with stacking parameters included in the input of Fringe3D generate a desired distribution. Rapid and controllable construction of pores is of importance for incorporation of pores within a large-scale molecular char model. Figure 3 shows the growth of the pore size generated from the source file for cubic pores. Structures simulating different pore sizes are built with a specific element to aid removal, leaving pores (empty spaces) of various sizes distributed randomly throughout the atomistic representation structure. A matrix of the relative position of atoms forming the central initial pore was determined by calculation. Pores of various shapes and the desired distribution of sizes can be rapidly constructed without bias using Fringe3D by modifying the central initial pore and the transition step size in the source file. The desired pores are produced, as separate files, for subsequent use by Vol3D. Therefore, pores can be created over the desired size range and with the user-desired fidelity. As shown in Figure 3, the addition of phosphorus atoms around the central initial pore is represented to show the growth of sizes. No atom bonding is considered because these atoms act as placeholders to ensure that the desired porosity is captured. For cubic pores, the eight atoms at the outermost shell are used for ease of viewing the size and shape, although only four atoms (opposing corners) are needed to define the Vol3D volume.
3. RESULTS AND DISCUSSION 3.1. Construction of Stacks and Pores. Automated methods are required to populate various stacks, simultaneously capturing diverse distribution data obtained from HRTEM analysis and other experimental methods. Figure 2a shows the generation of aromatic structures via the source file of circular catenation, illustrating aromatic structure growth from C6 to C54 with the fringe length increasing from 3 to 12.5 Å (estimated by averaging the maximum and minimum lengths of the aromatic fringes). The central benzene was composed of the first 6 carbon atoms, with every 2−4 carbon atoms generating another six-membered polyaromatic structure.36 The addition of six-member rings around the central ring can accommodate the polyaromatic structure size to represent the fringe length. The number of carbon atoms was varied using Fringe3D until the desired layer size was obtained. An example of stack construction is shown in Figure 2b. A PAH molecule of E
DOI: 10.1021/acs.energyfuels.5b00816 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 4. Examples of stacks and pores for Vol3D: (a) stack example and (b) pore example.
Figure 5. Comparison between lower and higher porosity models of coal char, with sizes of 100 × 100 × 100 Å and 104 × 104 × 104 Å, respectively (carbon atoms are represented in green color; hydrogen atoms are not shown to aid viewing; and added porosity is shown by blue and purple spheres): (a and b) plan and side views of the lower porosity model, respectively, and (c and d) plan and side views of the higher porosity model, respectively.
One source file for cubic pores is supplied in the Supporting Information (original Excel and produced PDB source file). 3.2. Atomistic Models Incorporating with Pore Distribution. Figure 4 shows representative input files of stacks and pores for Vol3D to populate a 3D molecular char model. Here, only cubic pores are involved in the char model
for simplifying the construction process. Each stack or pore is considered as a cuboid in Vol3D and used to calculate volume and place stacks and pores. An individual input file contains a single stack or pore being centered at the origin location (0, 0, and 0). All fringes are currently flat molecules. The contribution of heteroatoms and ash is ignored here, with the hydrocarbon F
DOI: 10.1021/acs.energyfuels.5b00816 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 6. Construction approach for char model with a larger scale and more complicated structure, with added pores viewed with spheres of partial transparency (blue and purple spheres represent micro- and mesopores, respectively).
skeleton structure depicted as aromatic linear molecules within stacks assuming no cross-linking. The porosity characterization incorporated in the molecular char model can be obtained from SAXS or combinations of other approaches. Char models with the limited inherent microporosity and micro- and mesoporosity equivalents were constructed using the present approach, representing chars of lower and higher porosity, respectively. Such structures are expected to allow for exploration of the contribution of porosity to the reactivity as an independent parameter. Atomistic representations of the aromatic structure of chars with only inherent porosity and incorporation with added pores are shown in panels a−d of Figure 5. Only aromatic carbon atoms are involved in the atomistic representations of mature chars. While curvature can be included through manual construction approaches, our desire is to produce meaningful structures that have appropriate behavior. Here, the curvature of fringes, observed from HRTEM, is not addressed; however, with advances in image analysis (specifically related to curvature inflection locations,
frequency, and degree of curvature) and in our construction protocol (Fringe3D), this issue can be addressed. For this structure, image analysis indicated that ∼40% of the fringes was linear. The heteroatoms and functional groups can be conveniently incorporated using Perl scripts by substituting specific atoms in a subsequent stage.71 Panels a−d of Figure 5 show the plan and side views of lower and higher porosity models of coal chars, which distinctly exhibit the aromatic structure and preferential alignment of stacks, respectively. Panels c and d of Figure 5 show the char model incorporated with added cubic micro- and mesoporosities. Partial transparent spheres are used to aid viewing of the added porosity included in the higher porosity model of coal char, with blue spheres representing micropores and purple spheres representing mesopores. The char model of lower porosity was first constructed at the scale of 100 × 100 × 100 Å to obtain the desired stacking distribution, while the scale of the equivalent char model of higher porosity was determined subsequently by minimizing the difference of stack frequency from the lower G
DOI: 10.1021/acs.energyfuels.5b00816 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 7. Diagonal views and pore distributions for both lower and higher porosity atomistic representations of coal chars with length scales of 100 and 104 Å, respectively (carbon atoms are represented in green; hydrogen atoms are represented in gray; and pore distribution is color-coded by pore size): (a and b) diagonal views of lower and higher porosity models, respectively, and (c and d) pore distributions of lower and higher porosity models, respectively.
contained in the component char model, the distributions of added pores within the higher porosity char model are extracted from char models. It can be seen that char models with the same size distribution but different location distribution of pores can be generated using the present approach. Therefore, we have the capability to simultaneously incorporate microporosity and smaller mesopores within the large-scale char model trivially. 3.3. Evaluation of Molecular Char Models. Various modules and scripts were applied to evaluate molecular char models incorporated with pore distribution. Evaluation of char models includes calculation of the helium density, H/C ratio, pore size distribution, stacking distribution, etc. Figure 7 shows the pore distributions for both lower and higher porosity atomistic representations of coal chars with a length scale of ∼100 and 104 Å, respectively. These two models correspond to the char models of lower and higher porosity in Figure 5. The stacking and layer size distributions of these two char models refer to the experimental work of Sharma et al.,45 while the pore size distribution for the char model with micro- and mesoporosities was assumed. Molecular char models with inherent porosity and incorporated with added pores are evaluated and compared, as shown in panels a and b of Figure 7, with phosphorus atoms (adding pore construction) removed and hydrogen atoms adjusted according to the hybridization, formal charge, and common oxidation state of carbon. Only microporosity and small mesopores are included in models because of the scale of the char model described here (∼100
porosity char. Distributions of stacks are illustrated obviously in Figure 5. The structure comprises an ordered domain consistent with region-specific HRTEM observations. Such structures are likely to be graphitizable if exposed to high temperatures over an extended period of time as would be expected for a bituminous coal. The least square method using the evaluation function of the square sum of the relative deviation between numbers of individual stack in two models was employed to identify similar structures (stack distribution). The difference between these two models is primarily the pore size distribution. The char model incorporated with added pores shows a visible “looser” structure, and more pores with specific sizes are distributed randomly. The diversity of the pore size and shape should be easily enhanced using various input files of pores. The atomistic representations of large-scale (>50 000 atoms) coal chars were constructed at a size scale of 100 Å3. For char models with a larger scale and more complicated structure, the construction approach as illustrated in Figure 6 can be followed. Here, eight example structures of 100 × 100 × 100 Å are combined to create a 200 × 200 × 200 Å structure (>500 000 atoms), while the component structures can be constructed individually to capture the various structure features. Both higher and lower porosity components can be included in the combined char model to capture anisotropy in the char macrostructure. The partial diversities of char morphology are also captured and manifested in atomistic representations and not only “average” structures. To view internal porosity H
DOI: 10.1021/acs.energyfuels.5b00816 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 8. Distribution of stacking and layer length for both the lower and higher porosity models of coal chars.
Å3). Larger meso- and macropores are expected to influence mass transfer and char reactivity during combustion and gasification. The methodology of incorporating larger mesoand macropores is similar to that involved in construction of micro- and small mesopores. Simultaneous incorporation of micro-, meso-, and macropores within a larger scale molecular char model can be obtained by the approach developed here. Because the data of pore size and position are available from the Poreblazer program,68 visualization of these properties in the 3D framework was enabled. The pore distributions colorcoded by pore size in char models of both lower and higher porosities are shown in panels c and d of Figure 7. Here, partially transparent spheres are used to represent pore shapes and to aid viewing the mergence and interlinkage of the various pores. The quantity and size range of pores involved in the atomistic representation of higher porosity char are larger than those of limited porosity equivalent. Most large micropores in the lower porosity model are distributed in the periphery of the cuboid, while the case for the higher porosity model is quite different because of the incorporation of added pores. In addition, both the distribution of the pore size and the interconnectedness of the pore network in atomistic representation of chars are visualized in panels c and d of Figure 7, which are also an important characteristic in porous carbon.9 The real structural features of the pore can be illustrated in the atomistic representations of coal chars constructed here. The distribution of mesopores and large micropores is consistent with incorporated pores in the char model of higher porosity. The equivalent structures are necessary to be demonstrated for the higher and lower porosity char models to allow for exploration of the contribution of porosity to the reactivity as an independent parameter. Therefore, the differences of the model structure were evaluated. The char model of lower porosity has dimensions of 100 × 100 × 100 Å, while the higher porosity char model has dimensions of 104 × 104 × 104 Å to obtain the equivalent stack features with additional porosity. Figure 8 shows the comparison of layer length and stack distributions involved in these two models. The experimental data of stacking and layer size distributions refer to Upper Freeport coal char heat-treated at 800 °C, while the stacking diversity was assumed by analyzing the HRTEM image obtained by Sharma et al.45 It is desirable to ensure a statistically representative suite of HRTEM micrographs to ensure that the range of structural features is captured. Only 5− 6 micrographs were used in the stacking quantification, and only 1 micrograph was used for stack construction. Thus, it is not claimed that this structure faithfully captures the range of
structural features but does demonstrate an approach to incorporate distributions in a rapid construction protocol. It is also likely that more crystalline regions are sampled; thus, it is more appropriate to indicate that this structure is a crystalline region of the char. In comparison to experimental results, similar distributions of stacking and layer size were obtained for both lower and higher porosity char models, with only a small difference between two char models. In addition, Figure 8 shows that a large fraction of the aromatic layers do not form part of a stack but are present as individual layers (as represented as a “stack” of a single layer here), which indicates that a similar trend of stacking sizes was observed in the HRTEM analysis on the char presented by Louw.49 The char model with inherent porosity contains a total of 293 stacks (from a distribution of 22 unique stacks with one or multiple layers) with 556 aromatic structures, 54 476 atoms, and a mass of 528 389 Da. The char model of higher porosity contains 302 stacks with 548 aromatic structures, 54 726 atoms, and a mass of 531 939 Da. Furthermore, a similar H/C ratio (∼0.26) was obtained for lower and higher porosity models. The H/C ratio of char molecular models obtained here is smaller than experimental data from demineralized char of Upper Freeport coal (∼0.24).72 No cross-linking or oxygenic functional groups involved in the present atomistic models of coal chars have contribution to the H/C difference. The deviations between two models at the aspect of the number of each molecule, the number of carbon and hydrogen atoms, and the molecular mass are
