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Determining the composition of surfaces and nanomaterials , by theory and experiment

International Workshop on High-Volume Experimental Data, Computational Modeling and Visualization Xiangshan , 17-1 9 October 2011. Determining the composition of surfaces and nanomaterials , by theory and experiment . Michel A. Van Hove Department of Physics and Materials Science

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Determining the composition of surfaces and nanomaterials , by theory and experiment

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  1. International Workshop on High-Volume Experimental Data, Computational Modeling and VisualizationXiangshan, 17-19 October 2011 Determining the composition of surfaces and nanomaterials, by theory and experiment Michel A. Van Hove Department of Physics and Materials Science City University of Hong Kong

  2. Structure determination • Optimize atomic-level structure (from theory or from experiment) at three levels • local optimization: • easiest – "descend" (lowest energy, best fit) • global optimization with fixed composition: • harder – "exchange pairs", "break and make bonds", "move far" • optimization of composition: • both number and identities of atoms can vary • adds – "exchange with external supply" or "exchange with internal supply" • but must respect experimental reality

  3. Graphene: vacancies, added C atoms How many atoms to include for best energy? "Magic" numbers

  4. Fullerenes: C60, C60n • 60 carbon atoms are initially placed randomly • a Genetic Algorithm changes their positions by: • recombining top and bottom halves • each GA step is followed by a local optimization by conjugate-gradient minimization or molecular dynamics quenching • 15-30 steps are needed for local optimization D.M. Deaven and K.M. Ho, Phys. Rev. Lett. 75, 288 (1995)

  5. bimetallic alloy nanocrystals G.F. Wang, M.A. Van Hove, P.N. Ross and M.I. Baskes, Prog. Surf. Sci. 79, 28-45 (2005).

  6. Nanoparticles: pure, alloys HREM micrographs of a C-supported Pt-Ni nanoparticle catalyst U.A. Paulus et al., J. Phys. Chem. B, 106, 4181 (2002)

  7. Exp. “Magic” cubo-octahedral nanoparticles (100) (111) 1: Vertices (6nn) 2: {111}/{111} edges (7nn) 3. {111}/{100} edges (7nn) 4. {100} facets (8nn) 5. {111} facets (9nn) Dispersion is the fraction of all atoms that are on the surface

  8. Modified cubo-octahedral fcc nanoparticles: “non-magic” By adding atoms on the facets or removing atoms from the edges, we constructed non-perfect cubo-octahedral nanoparticles with troughs. These ridges contain new B5 sites (5-fold coordinated adsorption sites), similar to those on fcc (110) and (311) surfaces. Remember: segregation reversal at PtNi(110) surface. (110) geometry (311) geometry

  9. Nanoparticle structures and order-disorder transitions: size effect inPtNinanoparticles Segregation profiles (C as % Pt by shell and in core) of equilibrium cubo-octahedral Pt50Ni50 nanoparticles with N atoms, simulated at T=600K Surface-sandwich structure with a disordered core for smaller nanoparticles cross-sections Core-shell structure with an ordered core for larger nanoparticles G.F. Wang, M.A. Van Hove, P.N. Ross and M.I. Baskes, Prog. Surf. Sci. 79, 28-45 (2005).

  10. {100} facet reconstruction: no more "magic" Pt75Re25 Re Pt exteriors initial after 5 million MC steps after 20 million MC steps square lattice of {100} facets hexagonal lattice on {100} facets

  11. Complexity: Ni(100)+(5x5)-xLi solved by LEED Ni atoms are missing (9 of 25 per 5x5 cell) 45 structural models H. Tochihara, S. Mizuno et al

  12. C60 / Cu(111)

  13. STM imaging of Cu(111)+(4x4)-C60 • Annealing to ~340K appears to cause a reconstruction of the surface: 7 Cu atoms are expelled for each C60, forming bare Cu islands regions A: low T (4x4)C60 on simple Cu(111)? region B: high T (4x4)C60 ~2.1Å lower sunk in Cu holes? A B W.W. Pai, C.L. Hsu, M.C. Lin, K.C. Lin, and T.B. Tang, Phys. Rev. B 69, 125405 (2004)

  14. Proposed adsorption geometry of C60 on Cu(111): C60 over 7-Cu holes (based on STM and theory) C60 on unreconstructed Cu(111) C60 on reconstructed Cu(111) side views top views

  15. Cu(111)+(4x4)-C60: Low-Energy Electron Diffraction • Tensor LEED • allows automated optimization of 102 parameters (C60 layer + 2 Cu layers) • 42 independent beams, 50-380 eV (total 7111 eV) • still fairly efficient computation because of 3-fold rotational symmetry • We tested several models: • unreconstructed: RP = 0.671 (fcc on-top), 0.536 (fcchcp-site) • reconstructed: RP = 0.376 (7-atom hole), 0.608 (1-atom hole) • relaxations from theoretical optimum structure: ~< 0.1Å  surface, ~< 0.2Å // surface: quite good!

  16. LEED: Cu(111)+(4x4)-C60 – Cu-C interface G. Xu, X.Q. Shi, R.Q. Zhang, W.W. Pai, M.A. Van Hove, in prep.

  17. C60 / Pt(111)

  18. Pt(111)+(13x13)R13.9°-C60 & Pt(111)+(23x23)R30°-C60 ( = 12x12 ) Unique case: two structures, coverages 1/13 vs 1/12 Prior XRD (Felici et al) for (13x13)R13.9°: 1 missing Ptatom / C60 LEED pattern for (23x23)R30° LEED pattern for (13x13)R13.9° R. Felici, M. Pedio, F. Borgatti, S. Iannotta, M. Capozi, G. Ciullo and A. Stierle, Nature Mat. 4, 688 (2005)

  19. Modeling "missing atoms" in DFT top of metal slab bottom of metal slab Moving "missing atoms" elsewhere in 3D unit cell preserves number of atoms: move to realistic step on back of slab no hole 1 atom "missing" 7 atoms "missing"

  20. Pt(111)+(13x13)R13.9° & (12x12)R30°-C60: DFT DFT analysis: comparing models with different numbers of atoms! where do the missing atoms go? DFT favors 1 missing atom, if vacancy atoms move to step sites * = like comparing O3 vs. O2 vs. O (3 vs. 2 vs. 1 atom) ** = like comparing 2O3 vs. 3O2 vs. 6O (6 atoms each) X.Q. Shi, A.B. Pang, K.L. Man, R.Q. Zhang, C. Minot, M.S. Altman, M.A. Van Hove, submitted

  21. Pt(111)+(13x13)R13.9° & (12x12)R30°-C60: LEED LEED analysis (total E range 4860 eV for 13, 3080 eV for 12): 1 missing Pt atom for both 13 and 12 Comparison of models by Pendry R-factor fit: Comparison with C60 on other metals by LEED: Cu(111)+(4x4)-C60: RP = 0.38 – 7 missing Cu atoms Ag(111)+(12x12)R30°-C60: RP = 0.36 – 1 missing Ag atom

  22. Conclusions: composition determination • To optimize composition • both number and identities of atoms may vary • many models must be tested • from theory • conserve total number of atoms among models • so "exchange with external supply" or "exchange with internal supply" • respect experimental reality (where can atoms go to / come from?) • from experiment • XRD, LEED, PED, STM, … • comparison of models does not require conserving number of atoms, if “missing” atoms do not contribute

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