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Shaping the Morphology of Gold Nanoparticles by CO Adsorption Keith McKenna* and Alex Shluger London Centre for Nanotechnology 17-19 Gordon Street London.

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Presentation on theme: "Shaping the Morphology of Gold Nanoparticles by CO Adsorption Keith McKenna* and Alex Shluger London Centre for Nanotechnology 17-19 Gordon Street London."— Presentation transcript:

1 Shaping the Morphology of Gold Nanoparticles by CO Adsorption Keith McKenna* and Alex Shluger London Centre for Nanotechnology Gordon Street London WC1E 6BT UK Department of Physics and Astronomy University College London Gower Street London WC1 0AH UK modelling the structure of nanoparticles at finite temperature and pressure *

2 Outline Introduction Experimental probes of structure and dynamics 1.Atomic scale dynamics empirical potentials + Monte Carlo 2.Au nanoparticle interacting with CO DFT + statistical mechanics 3.Atomistic models multiscale (P,T,N,t) Summary

3 Introduction Why is the modification of the structure of nanoparticles by molecules interesting? –because NP properties are very sensitive to their structure –deliberate e.g. SAM passivation, molecular electronics, plasmonic waveguides, biological markers... –environment e.g. catalysis, gas sensing, nanotoxicology, earth sciences... Pablo D. Jadzinsky et al, Science 318, 430 (2007) Artist's impression of a molecular electronics device G. Rupprechter, Annual Reports on the Progress of Chemistry (2004)

4 Molecule-induced structural transformations –transformation in average morphology –atomic scale fluctuations at finite temperatures –molecules are significant perturbation (large surface/volume ratio) –properties modified: electronic, chemical, optical, magnetic... Theoretical models –state of the art is ab inito: models often assume rigidity of the NP –timescale gap (MD) Experimental characterization –in situ probes –difficult to uniquely identify origin of effects However...

5 Metallic nanoparticles in atmosphere Catalysis –pollution filtering in automobiles and industry (e.g. CO oxidation) –CNT growth Chemical sensors –modification of electronic or optical properties Molecular electronics –noise, reliability issues N. Lopez et al., Journal of Catalysis, (2004) S. V. Ryabtsev et al., Semiconductors (2001) Pd/SnO 2

6 Experimental probes Scanning probes –STM, AFM... –topographic and spectroscopic –in situ rare Temperature programmed desorption –adsorbed molecules - coverage and energy –ex situ, non-equilibrium Photoelectron spectroscopy –direct probe of electronic structure –indirect probe of morphology STM - G. Yang et al - Surface Science (2005) Au TPD - C. Lemire et al, Surface Science (2004)

7 Transmission electron microscopy –atomic resolution possible –e.g. Pd NPs on MgO(100) exposed to oxygen and annealed –interpreted in terms of O modified surface energies (Wulff construction – large particles) –possible role of electrons (metallic clusters have positive electron affinity) H. Graoui, S. Giorgio and C.R. Henry, Surface Science 417, 350–360 (1998) B. Pauwels et al., PRB 62(15) (2000) Pd/MgO O 2 and anneal Au/MgO

8 X-ray Absorption Fine Structure (XAFS) –probe local structure (coordination) –timescale ~ 1-10Hz –e.g. Pd and cycled CO/NO –also used in situ IR spectroscopy –not just oxidation structural change M. A. Newton et al, Nature Materials (2007)

9 IR spectroscopy –e.g. Au/TiO 2 in CO pressures –appearance of additional IR band on increasing pressure –persists to low pressure (hysteresis) –flattening of particle shape T. Diemant et al, Topics in Catalysis 44, 83 (2007) It can be very difficult to uniquely interpret what is happening using a single technique

10 Theoretical models What is the favoured nanoparticle structure in vacuum? –thermodynamic equilibrium –empirical potentials (Sutton-Chen, TB- SMA...) –can also include NP-support interactions –DFT for small clusters (static) –global minimisation (genetic, simulated annealing...) –dynamics (MD - small timescales) many-body attraction short-range repulsion

11 Nanoparticles of different symmetry preferred as a function of size (e.g fcc): Icosahedron (strained in bulk) Octahedron Truncated octahedron Wulff construction

12 Compare the energy of clusters with different symmetry –average excess energy per surface atom –e.g. Baletto et al, J. Chem. Phys (2002) –icosohedral - small N –truncated octahedral - large N bulk strain N

13 Atomic scale dynamics Finite temperature –surface diffusion –kinetic barriers (lower at surface) –metals (E= eV) (ms at RT) –transient configurations –low probability configurations may be important (t> 10 3 s) Monte Carlo approach –probability to find a given structure (equilibrium) –statistical distributions of properties –average properties of set of configurations from NPT ensemble (P=0) –surface atom trial move –embedded atom model potentials C. L. Cleveland et al, PRL 81 (1998) K. P. McKenna et al, J. Phys. Chem. C Lett. 111, (2007) K. P. McKenna et al, J. Chem. Phys. 126, (2007)

14 Free Au nanoparticle Effect of temperature structure (P=0) – Au NP with 1152 atoms (size ~3nm) – magic number truncated octahedron – full exploration of configurational space Results K. P. McKenna et al, J. Chem. Phys. 126, (2007) Typical room temperature morphology Increasing concentration of low coordinated atoms with temperature (3C)

15 Roughening transition associated with (111) facets surface melting may occur at lower T than roughening phase transition Size of (111) facets Energy

16 Au NP supported on MgO(100) surface The system: –1-2nm diameter –Au binds to O preferentially –3% lattice mismatch –Epitaxial structure –N= –T=250K - 800K B. Pauwels et al, PRB 62 (2000) K. P. McKenna et al, J. Phys. Chem. C Lett. 111, (2007)

17 Expectation energy Discontinuity in configurational contribution to specific heat Small compared to vibrational and electronic contributions Second order phase transition 9C sites Correspond to ideal Au(111) facets Almost independent of temperature below 500K Rapid decrease in size of ideal facets after 500K Phase transition is associated with roughening of the (111) facets

18 Au-MgO Interface layer 8C sites in the interface layer are fully coordinated Probability distribution indicate magic numbers nm Above 500K also get appreciable non-magic numbers Disordered interface layer 7C interface layer sites correspond to the perimeter sites The number of these decreases sharply after 500K Therefore the roughening transition is a complex one involving Au(111) facets and the perimeter of the Au-MgO interface layer

19 4C 3C Increasing concentration of low coordinated atoms with temperature (3C)

20 Effects of pressure: CO and Au NPs Molecules –adsorb and desorb from the NP –may also react - catalysis –can change NP morphology Thermodynamic equilibrium –equilibrium of molecular adsorption/desorption –equilibrium of NP configuration –very large configuration space to investigate Constrained equilibrium –consider various possible structures –for each look at equilibrium with ambient –configuration with lowest Gibbs' free energy of adsorption is favoured statistical mechanics: equate chemical potentials of gas and adsorbed phase K. P. McKenna et al, J. Phys. Chem. C Lett (2007)

21 CO on an Au nanoparticle –NP active for CO CO 2 –CO adsorbs in the top position –increased adsorption for low coordinated sites (cluster study) N. Lopez et al., Journal of Catalysis, (2004) L. M. Molina and B. Hammer (2004) COCO 2 (Au/MgO(100))

22 79 atom Au neutral cluster – agreement on truncated octahedron structure for many different empirical models (SC, EAM, etc) – optimise using DFT – GGA PAW method (VASP) – 400 eV cut off – 21Å 3 cubic cell DFT provides: –energy of NP configurations (without molecules) –diffusion barriers between configurations –adsorption energy for molecules on different sites, i

23 Alternative NP configurations 3C 4C

24 CO adsorption –overestimation by DFT but trends reliable –adsorption energies increase with decreasing coordination –always Au-C-O –localised relaxation of Au NP –correlation with E comd –3/4C - similar adsorption energies (both adatoms on (111) and (100) surfaces

25 IR spectra Bonding transition –physisorption to chemisorption Z < 7 –Au-C bond length changes –Au-C vibrational mode calculated by finite differences –two distinct bands –provides a measure of relative population of sites CO stretching mode – cm -1 –less distinct bands

26 Ab inito thermodynamics Average coverage of CO –NP with low coordinated sites gain energy compared to more truncated structures –balance of reorganisation energy and adsorption energy

27 Proposed configuration change –exposes 4C Au atoms while remaining truncated –tendency towards octahedral –which configurations are favoured at different T and P

28 Atomistic models Why atomistic? –DFT calculations expensive –consider dependence on N for different systems (multiscale) –DFT to parameterise atomistic models Energetics –lattice model based on TB-SMA + 2% E coh strain for Ico

29 Compare Icosahedral, octahedral and truncated octahedral –can analytically determine N z for different symmetry types –directly related to XAFS e.g. D. Glasner and A. Frenkel, XAFS13 processings Mean Z higher but at the expense of bulk strain

30 Compare energy of different clusters: –first in vacuum –simple model is qualitatively reasonable –icosahedral truncated octahedral Ico Octa TO

31 Dependence on pressure and temperature for Au –icosahedral octahedral truncated octahedral Octa Ico TO

32 Pd Ag Ico TO Octa Phase diagrams Au Al 17,000 33,000 24,000 30, atoms (1-2 nm) 5 decades of pressure

33 Summary Structural trends modified by molecules at finite P and T Even low pressure can be different to vacuum Influences properties: optical, electronic, chemical... Transformations depends upon: –composition of NP –adsorption properties of molecules –interactions with substrate Connect to measurable properties –XAFS (rdfs) –electronic structure - spectroscopies –optical properties - plasmon spectra –topographical probes –IR, TPD,... Future developments –non-equilibrium dynamics by kinetic MC simulations –effect of substrates, interactions between nanoparticles (grand canonical) –combine theory and experiment to understand transformations

34 Acknowledgements This work funded by the EPSRC Materials Modelling Initiative grant GR/S8000/01. Computational time on HPCx provided by the Materials Chemistry Consortium through EPSRC grant EP/D504872/1 and on C 3 through UCL research computing. Thanks to the following people for useful discussions: Peter Sushko, John Harding, Oliver Diwald, John Venables & Marshall Stoneham.


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