Adsorption of Carbon Dioxide and Nitrogen on Single-Layer

7846

J. Phys. Chem. C 2010, 114, 7846–7849

Adsorption of Carbon Dioxide and Nitrogen on Single-Layer Aluminum Nitride Nanostructures Studied by Density Functional Theory Yan Jiao,†,‡ Aijun Du,*,† Zhonghua Zhu,*,‡ Victor Rudolph,‡ and Sean C. Smith*,† Centre for Computational Molecular Science, Australian Institute for Bioengineering and Nanotechnology, The UniVersity of Queensland, QLD 4072, Brisbane, Australia, and School of Chemical Engineering, The UniVersity of Queensland, QLD 4072, Brisbane, Australia ReceiVed: December 2, 2009; ReVised Manuscript ReceiVed: March 5, 2010

The adsorption of carbon dioxide and nitrogen molecules on aluminum nitride (AlN) nanostructures has been explored using first-principle computational methods. Optimized configurations corresponding to physisorption and, subsequentially, chemisorption of CO2 are identified, in contrast to N2, for which only a physisorption structure is found. Transition-state searches imply a low energy barrier between the physisorption and chemisorption states for CO2 such that the latter is accessible and thermodynamically favored at room temperature. The effective binding energy of the optimized chemisorption structure is apparently larger than those for other CO2 adsorptive materials, suggesting the potential for application of aluminum nitride nanostructures for carbon dioxide capture and storage. Introduction In response to the increasing crisis of global warming, the capture and storage of carbon dioxide emitted from fossil-fuel power systems will be an essential technology in the context of energy and environment.1 Previously, a number of techniques have been adopted to separate carbon dioxide from fuel gas streams, such as cryogenic separation, membrane separation, and adsorption processes, such as pressure swing adsorption and temperature swing adsorption.2 The successful implementation of adsorption processes relies greatly on materials that selectively adsorb carbon dioxide molecules. An ideal candidate should possess not only a large accessible surface area but also a high free volume, a low framework density, and strong energetic interactions between the framework and the carbon dioxide molecule compared to other small gas molecules. Several novel materials have been proposed to capture CO2, including metal organic frameworks3 and zeolitic imidazolate frameworks.4 Simulation studies that effectively reproduced the experimental adsorption isotherm showed that the interaction between carbon dioxide and the atoms on those frameworks is mainly due to van der Waals and electrostatic interactions.5-7 It has recently been discovered that covalent bonds can form between the nitrogen atoms of azabenzene and the carbon atom of CO2 when extra electrons are present.8 The implication of this in the present context is that a structure with incorporated nitrogen atoms possessing a significant negative charge density might potentially form a chemical bond with carbon dioxideswith potential ramifications for CO2 capture applications. In recent years, various aluminum nitride nanostructures have been extensively studied by both experiment and theoretical simulations due to their importance as building blocks of nanomaterials.9-16 Like carbon nanotubes, these structures possess high surface/volume ratios, which may facilitate accommodation of large amounts of gas molecules. Unlike the * To whom correspondence should be addressed. E-mail: [email protected] (A.D.), [email protected] (Z.Z.), [email protected] (S.C.S.). † Center for Computational Molecular Science. ‡ School of Chemical Engineering.

Figure 1. Atomic configuration of a 16-ring aluminum nitride nanocluster. The bond length is in angstroms. Color code: blue, nitrogen; light gray, aluminum; white, hydrogen.

4-fold coordinated Al and N ions in the bulk wurtzite structure, the Al and N ions on the surface of as-synthesized AlN nanostructures are 3-fold coordinated, thus naturally inducing unsaturated Al and N sites that might play an important role in gas adsorption. In view of the potential for chemical bond formation between ionic nitrogen and CO2, we present herein a study of carbon dioxide adsorption on the surface of an aluminum nitride single layer compared with nitrogen adsorption in order to find out whether it could be a candidate CO2 capture material. Computational Methods Two models for a single-layer aluminum nitride structure, namely, a nanocluster and a nanosheet, are explored in our study. As shown in Figure 1, the nanocluster model consists of 16 AlN rings. Dangling bonds on the edge of the cluster are saturated by hydrogen atoms to satisfy the 3-fold coordinate configuration, which is the most stable state of an AlN single layer. After the initial geometry optimization of the nanocluster,

10.1021/jp911419k  2010 American Chemical Society Published on Web 04/09/2010

Adsorption of CO2 and N2 on Single-Layer AlN

J. Phys. Chem. C, Vol. 114, No. 17, 2010 7847

a carbon dioxide molecule is positioned above at different initial separations in order to ensure adequate exploration of possible stable configurations. The AlN cluster calculations are carried out utilizing the Gaussian 03 program17 at two levels of model chemistry, B3LYP/6-31G(d)18-20 and LSDA/6-31G(d,p),21 with all atoms fully relaxed during the simulation. The adsorption energy of carbon dioxide onto the AlN surface is computed as

Ead ) EAlN+CO2 - (EAlN + ECO2)

(1)

where EAlN+CO2 is the total energy of the system after gas adsorption, EAlN is the energy of pure AlN nanostructures without the gas molecule, and ECO2 is the total energy of the isolated carbon dioxide molecule. Transition-state searching is conducted using the STQN method22,23 in order to determine the barrier (if any) to chemisorption after physisorption has occurred. All electron DFT computations are conducted for the AlN nanosheet model using the DMol3 code.24,25 The electronic exchange correlation is treated using two functionals, PWC26 and PW91.27 The convergence criterion of self-consistent field computations is chosen to be 10-5 Ha of the total energy, 0.002 Ha/Å for force and 0.005 Å for displacement. The electronic wave functions are expanded in a DNP basis set truncated at a real space cutoff of 4.8 Å. During the geometry optimization, the positions of all the atoms are fully relaxed. For the initial model optimization, periodic boundary conditions on three directions are applied to the whole system to simulate the infinitely large system and a vacuum slab of 20 Å is put between two layers, so the atoms on one layer do not interact with those on the other layer. The optimized length of the Al-N bond on the single-layer nanosheet is 1.79 and 1.81 Å using the LDA and GGA methods, respectively. After optimization, a large supercell consisting of 25 primitive cells (each primitive cell containing one Al atom and one N atom) is used so that a gas molecule will not interact with its periodic images during the following calculations. 2 ×2 × 1 K points are used to sample the 3D Brillouin zone. The energy of the transition state between chemisorption and physisorption of CO2 is determined using the complete LST/QST method28 as implemented in the DMol3 code, wherein the reactant and product configurations correspond, respectively, to the optimized structures for CO2 physisorption and chemisorption onto the AlN nanosheet. Calculations for nitrogen molecules are carried out using the same AlN structures and computational methods to facilitate reliable comparison between the two gas species. Results and Discussion The calculations carried out using the B3LYP method indicate that there are two stationary states for CO2 adsorption onto the aluminum nitride nanocluster, corresponding to physisorption and chemisorption. If CO2 is located initially 1.5 Å away from the plane of the nanocluster at the starting point, optimization results in the chemisorption structure with a bond length of 1.42 Å between the carbon atom and the nitrogen atom (i.e., the adsorption site) and an adsorption energy of -0.89 eV relative to free CO2 and the AlN nanocluster. The C-O bond length stretches from 1.17 to 1.28 Å due to the bonding interactions that are formed between C-N and O-Al in the chemisorption structure. In contrast, if the CO2 is positioned initially further away, for example, at a 3.0 Å separation from the cluster, then

Figure 2. Computed minimum energy path by transition-state search of carbon dioxide adsorption on an aluminum nitride nanocluster using the B3LYP method. Color code: blue, nitrogen; light gray, aluminum; red, oxygen; dark gray, carbon; white, hydrogen. Energy levels are not drawn to scale.

the equilibrium distance between the adsorbate and the substrate yielded by optimization is 3.21 Å with an adsorption energy of -0.15 eV relative to free CO2 and the AlN nanocluster, corresponding to a physisorption state. The computed minimum energy path from physisorption to chemisorption configurations is shown in Figure 2. Examination of the corresponding structures implies that the low energy barrier may be ascribed to an activation energy needed to start bending the carbon dioxide molecule as the chemical bond formation begins to develop. The B3LYP method is well-known to underestimate the dispersion that is the major source of attraction of nonbonded interaction; hence, the description of the interacting AlN nanocluster and CO2 molecule may be unreliable when the distance between the two is largesclearly this would affect the energetics of the physisorption state as well as possibly the chemisorption state. To address this potential problem, the previous calculation is compared with calculations using the local spin-density approximation (LSDA) method, which could more effectively approximate the dispersion forces.29 The LSDA calculations indicate that the carbon dioxide is attracted largely by electrostatic forces to the AlN nanocluster to form the C-N bonding interaction with no energy barrier at all. The lack of a reaction barrier is understandable because LDA is known to underestimate reaction barrier heights owing to its overbinding effect.30 The adsorption site of CO2 is similarly on the top of a nitrogen atom with an adsorption energy of -1.94 eV and the bond length between carbon and nitrogen is 1.42 Å. Also, the C-O bond elongates to 1.27 Å from 1.17 Å. For the chemisorption equilibrium configuration computed using LSDA, the nitrogen atom at the adsorption site forms a bond with carbon as the CO2 moves closer. This stronger interaction predicted from the LSDA method causes significant buckling of the nanocluster in the chemisorption state. Mulliken population analysis31 and natural population analysis32 reveals the electron transfer associated with this strong adsorption effect. The total electronic charge that transfers from the AlN nanosheet to the CO2 molecule is 0.13 and 0.55 e, respectively. Only the oxygen atoms of CO2 acquire electrons, while the carbon atom itself loses electron density. Meanwhile, the nitrogen atom at the adsorption site gains electron density. The evolution of absolute charges on the nitrogen and carbon atoms during adsorption indicates a stronger electrostatic interaction, leading to the bond formation between them. This charge-transfer mechanism is

7848

J. Phys. Chem. C, Vol. 114, No. 17, 2010

Jiao et al.

Figure 3. Optimized configuration (a) and electron charge transfer (b) using LSDA on the nanocluster model. The isosurface cutoff value is chosen to be 0.015. Color code: blue, nitrogen; light gray, aluminum; red, oxygen; dark gray, carbon; white, hydrogen.

TABLE 1: Summary of CO2 Adsorption on the AlN Nanocluster Model functional B3LYP

LSDA

physisorption adsorption energy (eV) C-N distance (Å) C-O bond length (Å) adsorption energy (eV) C-N distance (Å) C-O bond length (Å)

-0.15 3.21 1.17

transition state chemisorption 0.06 2.24 1.17

-0.89 1.42 1.28 -1.94 1.42 1.27

TABLE 2: Summary of CO2 Adsorption on the AlN Nanosheet Model with Periodic Boundary Conditions functional PW91

PWC

physisorption adsorption energy (eV) C-N distance (Å) C-O bond length (Å) adsorption energy (eV) C-N distance (Å) C-O bond length (Å)

-0.09 3.45 1.17 -0.27 2.76 1.17

transition state chemisorption 0.25 2.15 1.17 -0.24 2.22 1.17

-0.61 1.44 1.27 -1.37 1.42 1.28

illustrated in Figure 3, which was generated by an electron density distribution method using Molekel.33 The numerical results from the cluster calculations are summarized in Table 1. The above cluster calculations may potentially suffer from artifacts due to the limited size of the cluster and the associated edge effects. For example, the buckling of the cluster upon chemisorption may be overestimated in comparison with an extended AlN sheet. To further confirm the comparative adsorption behavior found for the cluster model, we have also implemented periodic calculations for a nanosheet model. Gratifyingly, we find that similar results are obtained and the main conclusions hold across both types of model. Two adsorption states for CO2 are also found in the nanosheet model, corresponding to chemisorption and physisorption. The results are summarized in Table 2. The reaction (minimum energy) profile calculated using the PWC functional for the sheet model is presented in Figure 4. In the structure of the product (i.e., the chemisorbed configuration), a bond forms between the carbon of CO2 and the nitrogen atom of the AlN nanosheet. Analogous to the results for the cluster model, the planar surface of the AlN nanosheet is deformed by the presence of a chemisorbed carbon dioxide molecule. The activation barrier of the system to form chemisorption using the nanosheet model is quite small; hence, the chemical bond formation is expected to be thermodynamically favored as well as kinetically accessible at room temperature. On the other hand, the energy and temperature needed to liberate

Figure 4. Comparison of nitrogen (a) and carbon dioxide (b) adsorption on an aluminum nitride plane sheet model using the PWC functional. Color code: blue, nitrogen; light gray, aluminum; red, oxygen; dark gray, carbon. Energy levels are not drawn to scale.

TABLE 3: Comparison of Binding Energies of CO2 and N2 Adsorbed on AlN Nanostructures and Equilibrium Distance Using Different Methods CO2

N2

physisorption chemisorption adsorption equilibrium AlN model functional energy (eV) energy (eV) energy (eV) distance (Å)a nanocluster B3LYP LSDA nanosheet PW91 PWC

-0.14 -0.09 -0.27

-0.89 -1.94 -0.61 -1.37

-0.06 -0.59 -0.10 -0.29

3.17 2.14 3.17 2.23

a Distance between the aluminum atom at the adsorption site and the nearest nitrogen atom from N2.

the carbon dioxide molecule are quite large and should not occur spontaneously at ambient temperatures. The significant binding of CO2 to AlN sheets here suggests a possible avenue for carbon dioxide capture applications. With this in mind, it is important to check the relative strength of adsorption of other small gas molecules on AlN single-layer nanostructures. Nitrogen is selected as the most relevant point of comparison because it is abundant in the atmosphere and also in factory flu gas. The most stable adsorption configuration of a nitrogen molecule is perpendicular to the AlN plane with the axis of the molecule pointing at an aluminum atom. The adsorption energies are comparable to the physisorption energies of CO2 but are much weaker than the chemisorption energies of CO2. Also, the bond length in the nitrogen molecule is barely changed after the occurrence of adsorption. The indication is, therefore, that N2 experiences only a physisorption interaction with the AlN layer structure. The CO2 adsorption data and equilibrium distances as well as those for N2 are presented together in Table 3 for comparison. Although the existence and synthesis of a graphene-like single-layer AlN structure is still an open question,34,35 our calculation indicates the application potential of AlN-based materials in carbon dioxide capture and storage. In view of the fact that carbon dioxide apparently chemisorbs at ambient temperatures, whereas nitrogen does not, it may be concluded that the AlN single-layer sheets may display considerable selectivity for adsorption of CO2 from air or exhaust flu gases. Here, we should also note that the interaction of AlN with other contaminants, such as H2O, HCl, NH3, H2S, etc., is also very interesting and such calculations are intended for a future publication. Conclusion In summary, the chemisorption of carbon dioxide on singlelayer aluminum nitride nanostructures has been confirmed by

Adsorption of CO2 and N2 on Single-Layer AlN calculations for two alternative models using different DFT methods. The computed energy barrier of CO2 chemisorption on the AlN single layer is 0.03-0.34 eV using PWC, PW91, and B3LYP functionals or even without any barrier using LSDA. The comparatively small energy barrier indicates that the chemisorption structure is both thermodynamically favorable and kinetically accessible at ambient temperatures. Compared with the computed adsorption characteristics of N2, the strong adsorption of CO2 onto AlN single-layer nanostructures indicates the potential for application of AlN-based materials in carbon dioxide capture and storage. The inorganic nature of this material is potentially advantageous in industry becausesin contrast to carbon nanotubes or graphenessit can withstand aggressive chemicals as well as high temperatures. Acknowledgment. We acknowledge generous grants of highperformance computer time from the Centre for Computational Molecular Science cluster computing facility at The University of Queensland and the Australian Research Council (LIEF grant LE0882357: A Computational Facility for Multiscale Modeling in Computational Bio and Nanotechnology). The authors also greatly appreciate the financial support by the ARC discovery project. References and Notes (1) Metz, B., Davidson, O., De Coninck, H. C., Loos, M., Meyer, L. A., Eds. IPCC, 2005: IPCC Special Report on Carbon Dioxide Capture and Storage; Cambridge University Press: Cambridge, U.K., 2005. (2) Aaron, D.; Tsouris, C. Sep. Sci. Technol. 2005, 40, 321. (3) Millward, A. R.; Yaghi, O. M. J. Am. Chem. Soc. 2005, 127, 17998. (4) Banerjee, R.; Phan, A.; Wang, B.; Knobler, C.; Furukawa, H.; O’Keeffe, M.; Yaghi, O. M. Science 2008, 319, 939–943. (5) Liu, D.; Zheng, C.; Yang, Q.; Zhong, C. J. Phys. Chem. C 2009, 113, 5004–5009. (6) Yang, Q.; Zhong, C. J. Phys. Chem. B 2006, 110, 17776–17783. (7) Farrusseng, D.; Daniel, C.; Gaudille`re, C.; Ravon, U.; Schuurman, Y.; Mirodatos, C.; Dubbeldam, D.; Frost, H.; Snurr, R. Q. Langmuir 2009, 25, 7383–7388. (8) Lee, S. K.; Kim, N.; Ha, D. G.; Kim, S. K. J. Am. Chem. Soc. 2008, 130, 16241–16244. (9) Keller, S.; Heikman, S.; Ben-Yaacov, I.; Shen, L.; DenBaars, S. P.; Mishra, U. K. Appl. Phys. Lett. 2001, 79, 3449–3451. (10) Zhang, D.; Zhang, R. Q. Chem. Phys. Lett. 2003, 371, 426–432. (11) Zhao, M.; Xia, Y.; Liu, X.; Tan, Z.; Huang, B.; Song, C.; Mei, L. J. Phys. Chem. B 2006, 110, 8764–8768. (12) Wang, H.; Xie, Z.; Wang, Y.; Yang, W.; Zeng, Q.; Xing, F.; An, L. Nanotechnology 2009, 20, 025611.

J. Phys. Chem. C, Vol. 114, No. 17, 2010 7849 (13) Mei, Y.; Thurmer, D. J.; Deneke, C.; Kiravittaya, S.; Chen, Y.-F.; Dadgar, A.; Bertram, F.; Bastek, B.; Krost, A.; Christen, J.; Reindl, T.; Stoffel, M.; Coric, E.; Schmidt, O. G. ACS Nano 2009, 3, 1663–1668. (14) Liu, C.; Hu, Z.; Wu, Q.; Wang, X.; Chen, Y.; Sang, H.; Zhu, J.; Deng, S.; Xu, N. J. Am. Chem. Soc. 2005, 127, 1318–1322. (15) Balasubramanian, C.; Codbole, V. P.; Rohatgi, V. K.; Das, A. K.; Bhoraskar, S. V. Nanotechnology 2004, 15, 370–373. (16) Du, A. J.; Zhu, Z. H.; Chen, Y.; Lu, G. Q.; Smith, S. C. Chem. Phys. Lett. 2009, 469, 183–185. (17) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J. A., Jr.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G. A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.; Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A. D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski, J.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.; Gonzalez, C.; Pople, J. A. Gaussian 03, revision E.01; Gaussian, Inc.: Wallingford, CT, 2004. (18) Becke, A. D. J. Chem. Phys. 1993, 98, 5648–5652. (19) Lee, C.; Yang, W.; Parr, R. G. Phys. ReV. B 1988, 37, 785–789. (20) Miehlich, B.; Savin, A.; Stoll, H.; Preuss, H. Chem. Phys. Lett. 1989, 157, 200–203. (21) Vosko, S. H.; Wilk, L.; Nusair, M. Can. J. Phys. 1980, 58, 1200– 1211. (22) Peng, C.; Schlegel, H. B. Isr. J. Chem. 1993, 33, 449–454. (23) Peng, C.; Ayala, P. Y.; Schlegel, H. B.; Frisch, M. J. J. Comput. Chem. 1996, 17, 49–56. (24) Delley, B. J. Chem. Phys. 1990, 92, 508–517. (25) Delley, B. J. Chem. Phys. 2000, 113, 7756–7764. (26) Perdew, J. P.; Wang, Y. Phys. ReV. B 1992, 45, 13244–13249. (27) Perdew, J. P.; Chevary, J. A.; Vosko, S. H.; Jackson, K. A.; Pederson, M. R.; Singh, D. J.; Fiolhais, C. Phys. ReV. B 1992, 46, 6671– 6687. (28) Halgren, T. A.; Lipscomb, W. N. Chem. Phys. Lett. 1977, 49, 225– 232. (29) Wang, Y. G. J. Phys. Chem. A 2009, 113, 10873–10879. (30) Andersson, S.; Gru¨ning, M. J. Phys. Chem. A 2004, 108, 7621– 7636. (31) Mulliken, R. S. I. J. Chem. Phys. 1955, 23, 1833–1840. (32) Glendening, E. D.; Reed, A. E.; Carpenter, J. E.; Weinhold, F. NBO, version 3.1. (33) Flu¨kiger, P.; Lu¨thi, H. P.; Portmann, S.; Weber, J. MOLEKEL 4.3; Swiss Center for Scientific Computing: Manno, Switzerland, 2000-2002. (34) Zhao, M.; Xia, Y.; Zhang, D.; Mei, L. Phys. ReV. B 2003, 68, 235415. (35) Zhao, M.; Xia, Y.; Tan, Z.; Liu, X.; Li, F.; Huang, B.; Ji, Y.; Mei, L. Chem. Phys. Lett. 2004, 389, 160–164.

JP911419K