1 Department of Physics, Central University of Tamil Nadu, IndiaImportance of Nanoparticle distribution – selection, assembly, measurements Prof.P. Ravindran, Department of Physics, Central University of Tamil Nadu, India & Center for Materials Science and Nanotechnology, University of Oslo, Norway
2 Outline for Part I: Nanoparticle CharacterizationIntroduction/context: Particles at biointerfaces Properties of particles in dispersions/emulsions Particle Size and Particle Size Distribution Surface Charge: Zeta Potential, Isoelectric Point, Electrophoretic Mobility
3 The challenge of real materials: Knowing the local structureTraditional crystallographic approach to structure determination is insufficient or fails for Disordered materials: The interesting properties are often governed by the defects or local structure ! Nanostructures: Well defined local structure, but long-range order limited to few nanometers (-> badly defined Bragg peaks) A new approach to determine local and nano-scale structures is needed. Nanostructures: Science (290) 2000
4 How about powder diffraction ?
5 Finally the Pair Distribution Function (PDF)The PDF is the Fourier transform of the total scattering diffraction pattern ! Proffen, Z. Krist, 215, 661 (2000)
6 What is a PDF? Pair distribution function (PDF) gives the probability of finding an atom at a distance “r” from a given atom. 5.11Å 4.92Å 4.26Å 3.76Å 2.84Å 2.46Å 1.42Å
7 What is a PDF? Example: C60 - ‘Bucky balls’Intra-domain The PDF (similar to the Patterson) is obtained via Fourier transform of the normalized total scattering S(Q): Inter-domain
8 Au nanoparticles : Why PDF ?50 nm 2nm Nanoparticles often show different properties compared to the bulk. Difficult to study via Bragg diffraction (broadening of peaks). PDF reveals “complete” structural picture – core and surface. This study: 5nm monodisperse Au nanoparticles 1.5 grams of material Neutron measurements on NPDF
9 Au nanoparticles : Nano vs. bulk100Å Experimental PDFs of gold nanoparticles and bulk gold, measured on NPDF.
10 Au nanoparticles : Structural refinementsPDF from nano- and bulk gold refined using PDFFIT. Nanoparticles show “normal” gold structure. No indication of surface relaxations. abulk < anano Indication of Au-cap distances Au-capping layer distance (Au-S) K.L. Page, Th. Proffen, H. Terrones, M. Terrones, L. Lee, Y. Yang, S. Stemmer, R. Seshadri and A.K. Cheetham, Direct Observation of the Structure of Gold Nanoparticles by Total Scattering Powder Neutron Diffraction, Chem. Phys. Lett. , accepted (2004).
11 Particle Size and Distribution: “Families” of Measurement PrinciplesAlso: hyphenated methods: e.g. fractionation method with ensemble-method detection (e.g. FFF-DLS). Microscopy with image analysis is analogous ensemble fractionation counting/”sorting” All particles analyzed simultaneously Based on first principles (optics, acoustics, etc) Size obtained from curve fitting/matrix inversion Distribution data from further computations on data Dynamic Light Scattering Static Light Scattering (& Laser Diffraction) Acoustic Attenuation Particles separated/sorted in space/time Based on differential (zonal) migration Size obtained by comparison with calibration standards Distribution data from detector output (graphical trace) Size Exclusion Chromatogr. Capillary Hydrodynamic Flow Field Flow Fractionation Electrophoresis [Sedimentation Velocity] Particles separated (diluted) but not sorted Based on size-dependent change in electronic (V, I, R, C, L) or optical signal Size obtained from prior calibration (instrumental) Distribution data from electronic “binning” of detector signals Coulter & Elzone (r) Accusizer (r)
12 Ensemble Methods Dynamic Light ScatteringBrownian Motion of Particles; temporal fluctuations in intensity of scattered light Static Light Scattering (& Laser Diffraction) Angular dependence of intensity of scattered light (Rayleigh/Mie) Acoustic Attenuation (new, not widely used; not covered in this lecture) “Instrument as black box” approach: Put sample in cuvette (dilute if necessary per mfgr.’s instructions) Enter known constants or accept instrument defaults (e.g. refractive index, viscosity, etc) Choose adjustable parameters or accept instrument defaults Press GO; come back when done; pick up print out Now, let’s take a closer look…
13 Dynamic Light Scattering (DLS) aka Photon Correlation Spectroscopy (PCS) aka Quasielastic Light Scattering (QELS) Step 1: Make the optical measurement: Temporal fluctuations in intensity of scattered light (wavelength and angle are set by instrument; RI is input or assumed) “speckle pattern” Figures from:
14 Dynamic Light Scattering (DLS) aka Photon Correlation Spectroscopy (PCS) aka Quasielastic Light Scattering (QELS) Step 1: Make the optical measurement: Temporal fluctuations in intensity of scattered light (wavelength and angle are set by instrument; RI is input or assumed) **Step 2: Establish the correlation function and solve for Dr **Step 3: Use the Stokes-Einstein relationship to solve for Rh (Dh= 2Rh) **Step 4: Further process autocorrelation data to obtain distribution information **Done by the instrument, although user can adjust inputs and processing options
15 Dynamic Light Scattering (DLS) aka Photon Correlation Spectroscopy (PCS) aka Quasielastic Light Scattering (QELS) Step 1: Make the optical measurement: Temporal fluctuations in intensity of scattered light (wavelength and angle are set by instrument; RI is input or assumed) Step 2: Establish the correlation function and determine Dr Step 3: Use the Stokes-Einstein relationship to solve for Rh (Dh= 2Rh) Step 4: Further process autocorrelation data to obtain distribution information **Done by the instrument, although user can adjust inputs and processing options
16 Dynamic Light Scattering (DLS) aka Photon Correlation Spectroscopy (PCS) aka Quasielastic Light Scattering (QELS) Step 1: Make the optical measurement: Temporal fluctuations in intensity of scattered light (wavelength and angle are set by instrument; RI is input or assumed) Step 2: Establish the correlation function and determine Dr Step 3: Use the Stokes-Einstein relationship to solve for Rh (Dh= 2Rh) Step 4: Further process autocorrelation data to obtain distribution information The data reduction is done by the instrument, although user can adjust inputs and processing options
17 Static Light Scattering (and Laser Diffraction)Ensemble Methods Static Light Scattering (and Laser Diffraction) Analogous to dynamic light scattering, but different optical property measured: Step 1: Measure the intensity of scattered light as a function of scattering angle for a dispersion of particles, usually at high dilution ** Step 2: Process the data to obtain the mean particle size and the distribution The data reduction is done by the instrument, although user can adjust inputs and processing options
18 Example: Sizing of Nanoparticles with Dynamic Light Scattering:COOH functionalized nanocarriers + LDL Uncharged nanocarriers + LDL LDL control Uncharged nanocarrier control COOH functionalized nanocarrier control Charged nanocarriers + LDL
19 Fractionation Methods Size Exclusion and other chromatography Capillary Hydrodynamic Flow Field Flow Fractionation (several variants) Capillary Electophoresis Sedimentation/Centrifugation Instrument as black box approach: Prepare sample if needed (diluted ; buffers and electrolytes added) Prepare eluant and/or other media (gel, sucrose density gradient, etc) Enter known constants or accept instrument defaults Inject standard sample, start pump or rotor or turn on applied field Run Calibration Standards, Run Samples Analyze data (graphically and mathematically) The details are not important, but note that fractionation is much more time and labor intensive than the ensemble methods. User must establish calibration curve Now, let’s take a closer look…
20 Fractionation Methods: Measurement PrinciplesSeparation by differential (zonal) migration Mixture in > Peaks 0ut Carrier fluid pumped through column, channel, etc Analyte velocity is a characteristic fraction of carrier velocity, due to analyte interaction with (i) stationary phase in column or (ii) to an applied field
21 Fractionation Methods: Measurement PrinciplesAdsorption, partition, size exclusion Affinity, polarity,size Chromatography Electrophoretic mobility Ionic size, charge Electrophoresis Laminar flow profile (velocity gradient) Size (big first) Hydrodynamic flow Field-flow fractionation Laminar flow profile Particle mass Size (small first)
22 Example: Capillary Hydrodynamic FlowComparison of CMP polishing slurries from two vendors Both slurrries nominally 50 nm particle size One is much more monodisperse than the other L.J. Anthony et al., Lucent Technologies, Bell Laboratories, unpublished work
23 Example: Field Flow FractionationSame system as in DLS example: nanocarriers for low density lipoprotein(LDL) sequestration (Data below are unpublished work; Chnari, Moghe et al. ) Note: this is also an example of Hyphenated Methods: Field Flow Fractionation with Static Light Scattering Detection Particles separated in time/space for “real” distribution; Accurate size data without calibration of field flow fractionation (FFF), just from the DLS Nanocarrier + LDL Nanocarrier LDL
24 Counting/”Sorting” MethodsAccuSizer (r) Coulter(r) and Elzone(r) Particle displaces electrolyte in sampling cell; changes the signal across the electrodes Mature method, widely used - for cells and other “big” particles Particle obscures a light bean transmitted through the cell; changes the light intensity Newer method; excellent down to ~ 300 nm, but true “nano” range still being optimized “Instrument as black box” approach: Put sample in reservoir (diluting if necessary per mfg. instructions Enter known constants or accept instrument defaults (refractive index, viscosity, etc) Choose adjustable parameters or accept instrument defaults Press GO; come back when done; pick up print out Now, let’s take a closer look…
25 Counting/”Sorting” Methods: Measurement PrinciplesMeasurement is based on an experimentally determined relationship between “instrument response” and particle size After integration over the complete particle (i.e. over all the elements that contain the particle) one find that the instrument response is proportional to the volume ν of a spherical particle, modified by a function F: Function F can be found from the integration over the particle but other approaches, including a best fit from experimental results, are possible to find a proper equation for F. The following equation was found by De Blois and Bean using the experimental (best fit) approach: where d and D stand for the diameter of particle and orifice (opening).
26 Examples: Counting/”Sorting” submicron particles(NiComp-PSS Accusizer) Vendor’s data: Particles are ~245 and 380 nm “Real world” data: Oversized (> 1 um) particles in chemical mechanical polishing process steps (silica slurry, 50 nm nominal particle size) As-received After polisher set-up After normal polishing run After wafer broke on pad L.J. Anthony et al: Proc. 2nd Int. Symp. Chemical Planarization in Integrated Circuit Device Manufacturing, pp , The Electrochemical Society, 1998
27 Outline for Part I: Nanoparticle CharacterizationIntroduction/context: Particles at biointerfaces Properties of particles in dispersions/emulsions Particle Size and Particle Size Distribution Surface Charge: Zeta Potential, Isoelectric Point, Electrophoretic Mobility
28 Charged Species on SurfacesOrigins of Surface Charge Characteristics of Surface Charge: Definitions Zeta Potential and Electrophoretic Mobility Determination of Zeta Potential Zeta Potential vs pH
29 Origins of Surface ChargeIonization of surface functional groups Organic/molecular: e.g. RCOOH <--> RCOO-, RNH2<--> RNH3+, etc As in protein/peptide C-terminus, N-terminus, certain side groups (aspartic acid, etc.) Note: can be intrinsic to the particle and/or surface- functionalized/derivatized (biotin, etc.) Inorganic/ionic: e.g. SiOH <> SiO-) (For example, glass beads, hydroxyapatite) 2) Adsorption of charged species Charged/ionizable molecules: e.g. surfactants, phospholipids (For example: SDS, constituents of ECM) Small ions: e.g. Ca++, Mg++, etc. (For example in certain physiological processes)
30 Characteristics of Surface Charge: DefinitionsParticle surface Stern Layer: Rigid layer of ions tightly bound to particle; ions travel with the particle Plane of hydrodynamic shear: Also called Slipping Plane: Boundary of the Stern layer: ions beyond the shear plane do not travel with the particle Diffuse Layer: Also called Electrical Double Layer: Ionic concentration not the same as in bulk; there is a gradient in concentration of ions outward from the particle until it matches the bulk
31 Characteristics of Surface Charge: DefinitionsZeta potential: The electrical potential that exists at the slipping plane The magnitude of the zeta potential gives an indication of the potential stability of the colloidal system * If all the particles have a large zeta potential they will repel each other and there is dispersion stability * If the particles have low zeta potential values then there is no force to prevent the particles coming together and there is dispersion instability A dividing line between stable and unstable aqueous dispersions is generally taken at +30 or -30mV
32 Zeta Potential and Electrophoretic Mobility+ - In an applied electric field, charged particles travel toward the electrode of opposite charge. When attractive force of the electric field is balanced by the viscous drag on the particle, the particle travels with constant velocity. This velocity is the partlcle’s electrophoretic mobility, UE UE = 2 z f(Ka)/3 z = Zeta potential Note relationship of zeta potential and electrophoretic mobility; therefore… Zeta potential can be determined by measuring UE =dielectric constant (of electrolyte) =dielectric constant (of electrolyte) f(Ka) = Henry’s function = ~1.5 (Smoluchowski approximation) for particles >~ 200 nm and electrolyte ~> 1 x 10-3 M = ~1.0 (Huckel approximation) for smaller particles and/or dilute/non-aqueous dispersions
33 Determination of Zeta PotentialMeasure the Electrophoretic Mobility, UE (and know viscosity, dielectric constant; and choose a Henry function) Solve Smoluchowski/Huckel Equation for Zeta Potential Predominant Methods: Laser Doppler Velocimetry Phase Analysis Light Scattering (PALS) Method for particles with lower mobilities
34 Determination of Zeta PotentialPrinciples of Phase Analysis Light Scattering (PALS): Similar to particle sizing by dynamic light scattering I.e. what is measured is temporal fluctuations in intensity of light scattered by the particles in the dispersion. In light scattering, the fluctuations are related to Brownian motion of particles. In PALS for ZP, the fluctuations are related to the movement of the particle in the applied field, i.e. to UE; The ZP is then calculated from the UE that is determined by the PALS measurement. (As in light scattering, the instrument’s autocorrelator and software take care of the data reduction.)
35 Zeta Potential vs pH pH dependency of ZP is very important!Remember, dispersion stability (or conversely, ability of particles to approach each other) is determined by ZP, with ~ 30 mV being the approximate cutoff. [In this example, the dispersion is stable below pH ~4 and above pH ~7.5] Typical plot of Zeta Potential vs pH. Zeta Potential, mV pH At ZP=0, net charge on particle is 0. This is called the isoelectric point
36 Zeta Potential and Electrolyte ConcentrationZeta potential also depends on electrolyte concentration! Remember that the ionic environment of the particle exists as a gradient that eventually equilibrates with the bulk solution. Too few ions: not enough charge to stabilize the particles Too many ions: the double layer is compressed and the particles can approach (“salting out”)
37 Example: Zeta Potential MeasurementsParticle diameter Optimizing a process for preparing human serum albumin nanoparticles (from, K.Langer et al.) At low values of Zeta potential (near pH 6), the dispersion de-stabilizes and the particles agglomerate
38 Self-assembly Cheap, atomically-precise at small sizes (< 5 nm), but poor positioning at large distances (> 50 nm) Nanotechnology is continuously evolving. It started out with the goal to mass-produce nanometer scale structures by simple methods, such as self-assembly. That brought in biotechnology, where we use nature’s methods of self-replication to obtain copious amounts of small, but complex molecules and structures. The huge amount of information generated by biotechnology (such as protein crystallography data base and the genome projects) in turn brings in information technology for optimally mining and connecting all these data. The newly-built Discovery Institutes in Madison are following this approach (see next slide). Another type evolution happened when people realized that making huge amounts of small objects alone did not produce a sophisticated structure. A theorist once suggested jokingly that one just needs to put a pile of semiconducting and metallic nanotubes into a beaker, and they will eventually self-assemble into a supercomputer with semiconducting transistors and metallic wires. That could take many ages of the universe. One needs to put these objects onto a well-defined grid with well-defined interconnects to produce a microprocessor or a memory. That needs some “top-down” approach for defining the grid, such as lithography, in addition to the “bottom up” self-assembly. Such a hybrid technology is driving the current generation of nano-centers, such as the NSEC in Madison.
39 Synthesis of Nanocrystals in Inverse Micelles ISurfactant: Hydrophilic Head Example: Phospholipid + Hydrophobic Tail Micelle: Inverse Micelle: Heads outside, Water outside Heads inside, Water inside Nanocrystals demonstrate how the properties of a material change, when the electrons and their wave functions are boxed into a region comparable to their wavelength. The band gap changes, and with it the color of the emitted light. Smaller particles have a larger band gap and emit blue light, while large particles emit red light, which is characteristic of normal CdSe. A nanoscale chemical beaker with aqueous solution inside
40 Synthesis of Nanocrystals in Inverse Micelles IIRecipe: Fill inverse micelles with an ionic solution of the desired material. Add a reducing agent to precipitate the neutral material. Narrow the size distribution further by additional tricks. Nanocrystals demonstrate how the properties of a material change, when the electrons and their wave functions are boxed into a region comparable to their wavelength. The band gap changes, and with it the color of the emitted light. Smaller particles have a larger band gap and emit blue light, while large particles emit red light, which is characteristic of normal CdSe.
41 Nanocrystals with equal size form perfect arraysLin, Jaeger, Sorensen, Klabunde, J. Phys. Chem B105, 3353 (2001)
42 Oleic acid spacer ad-justs the distance"Perfect" Magnetic Particles: FePt (4nm) 3D array D array Oleic acid spacer ad-justs the distance Sun, Murray , Weller, Folks, Moser, Science 287, 1989 (2000)
43 Manna, Scher, Alivisatos, JACS 122, 12700 (2000)Shape control of nanocrystals via selective surface passivation by adsorbed molecules. Only the clean surface facets will grow. Manna, Scher, Alivisatos, JACS 122, (2000)
44 Rhodium nanoparticles on a TiO2 supportSupported Catalysts Rhodium nanoparticles on a TiO2 support
45 Zeolites O Si,Al TetrahedraChannels for incorporating catalysts or filtering ions O Si,Al Tetrahedra
46 Self-assembled Nanostructures at SurfacesPush Nanostructures to the Atomic Limit Reach Atomic Precision
47 Most stable silicon surfaceSi(111)7x7 Hexagonal fcc (diamond) (eclipsed) (staggered) Most stable silicon surface > 100 atoms rearrange themselves to minimize broken bonds.
48 Si(111)7x7 as 2D Template One of the two 7x7 triangles is more reactive. Aluminum sticks there. Jia et al., APL 80, 3186 (2002)
49 Stepped Si(111)7x7 1 kink in 20 000 atomsStraight steps because of the large 7x7 cell. Wide kinks cost energy. Viernow et al., APL 72, 948 (1998) 15 nm
50 Stepped Si(111)7x7 as 1D TemplateThe 7x7 unit cell provides a precise 2.3 nm building block x-derivative of the topography “ illumination from the left ”
51 Atomic Perfection by Self-AssemblyWorks up to 10 nm nm One 7x7 unit cell per terrace Kirakosian et al., APL 79, 1608 (2001)
52 Sweep out Kinks into Bunches by ElectromigrationYoshida et al., APL 87, (2005)
53 "Decoration" of Steps 1D Atomic ChainsSi chain Si dopant Clean Triple step + 7x7 facet With Gold 1/5 monolayer
54 One-Dimensional Growth of Atom Chains0.02 monolayer below optimum Au coverage Chains Clean 77
55 Gold at the center, not the edge ! First Principles Calculations:Unexpected Structures : Gold at the center, not the edge ! Graphitic silicon ribbon ! First Principles Calculations: Sanchez-Portal et al., PRB 65, (2002) Crain, Erwin, et al., PRB 69, (2004) X-Ray Diffraction: Robinson et al., PRL 88, (2002) Graphitic Silicon Gold chain Working on structural model s to explain fractional filling. Previous x-ray diffraction showed gold sat in the middle of the terrace (for 557) rather than at the step edge. One possible model adds 2 extra silicon adatoms every 3 unit cells. Such a model gives the correct electron count, explains the x3 periodicity as observed by STM. Also give favorable energy in LDA calculations by Steve Erwin as compared to say surface without adatoms or x2 adatom chains Si(557) - Au
56 Carbon Nanowire inside a NanotubeZhao et al., PRL 90, (2003)
57 Silicon Nanowire GrowthWorks also for carbon nanotubes with Co, Ni as catalytic metal clusters. Wu et al., Chem. Eur. J. 8, 1261 (2002)
58 Catalytic Nanowire Growth of Ge by Precipitation from Solution in AuPhase diagram for immiscible solids : The melting temperature of a mixture is lower than for the pure elements. (L = liquid region) Wu and Yang, JACS 123, 3165 (2001)
59 ZnO Nanowires Grown by Precipitation from a SolutionSEM images of ZnO nanowire arrays grown on sapphire substrates. A top view of the well-faceted hexagonal nanowire tips is shown in (E). (F) High-resolution TEM image of an individual ZnO nanowire showing its <0001> growth direction. For the nanowire growth, clean (110) sapphire substrates were coated with a 10 to 35 Å thick layer of Au, with or without using TEM grids as shadow masks. Peidong Yang et al., Science 292, 1897 (2001) and Int. J. of Nanoscience 1, 1 (2002)
60 ZnO Nanowires for Solar CellsNeed to collect the electrons quickly in a solar cell to prevent losses. This can be achieved by running many nanowires to the places where electrons are created (here in CdSe dots which coat the ZnO wires). Leschkies et al., Nano Letters 7, 1793 (2007)
61 Striped Cu/Co Nanowires Grown by Electroplating into Etched Pores (Superlattices for efficient sensors) Ohgai, … , Ansermet, Nanotechnology 14, 978 (2003)
62 Bottom Up Nanomanufacturing – Self AssemblySpontaneous organization of building blocks with dimensions ranging from nanometers to microns. Two prominent components: Building blocks -- size, shape, surface structure Interactive forces between building blocks
63 Bottom Up Nanomanufacturing – Self AssemblyA challenge for perfecting structures made by self-assembly chemistry is to find ways of synthesizing building blocks (BBs) not only with the right composition but also having the same size and shape. Ideally, BBs should be monodisperse. Most BBs, however, have some degree of polydispersity. Any deviation from monodispersity in size and shape would lead to defects in the assembled system. Equally demanding is to control surface structure of BBs, including charge and functionality. Surface properties will control the inter actions between BBs.
64 Benefits of Self Assembly
65 Building Blocks (BBs) and Self AssemblyMany factors must be considered when we approach the bottom-up nanomanufacturing by self assembly – including BBs, forces on BBs, and functional nanotechnological applications. Forces on BBs
66 Strategies for Nanostructure FabricationBottom-up approach for nanostructures using nano- particles as building blocks Example: Opals: The fascinating interference colors stems from Bragg diffraction of light by the regular lattice of silica particles nm in diameter.
67 Attractive Features of Self-AssemblySelf-assembly proceeds spontaneously The self-assembled structure is close to thermodynamic equilibrium Self-assembly tends to have less defects, with self-healing capability
68 Why Should We Deal With Self Assembly?Like atoms or molecules, nanocrystals can be treated as artificial atoms and used as the building blocks of condensed matter. Assembling nanocrystals into solids opens up the possibilities of fabricating new solid-state materials and devices with novel or enhanced physical and chemical properties, as interactions between proximal nano crystals give rise to new collective phenomena.
69 Stabilization Of ColloidsFundamental problem: The thermodynamically stable state of metals, semiconductors, and polymers is bulk material, not colloidal particles. Stable colloidal dispersions require an interfacial stabilizer, which is a chemical that reduces the interfacial free energy between the particle and the solvent and makes short range forces between the particles repulsive. R. P. Andres Science (1996)
70 Gold Colloidal NanoparticlesIn the case of our gold nanoparticles, the stabilizer is citrate ion, whose negative charge is opposite to that of positive gold ions on the particle surface. The excess negative charge due to adsorption of citrate on the surface of the particles makes the particles repel one another. Our polystyrene latex also is charge stabilized. Dissociation of a fraction of the sodium ions of the sodium 4-styrenesulfonate units of the polymer leaves the particles with a negative charge. The stabilizer often is a surfactant, which is a chemical compound such as sodium dodecyl sulfate (SDS) whose structure has one end that is chemically attracted to the particle and the other end chemically attracted to the solvent. However, there are no surfactants in our gold nanoparticle and polystyrene latex preparations. R. P. Andres, Science (1996)
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72 Self-Assembled Monolayers (SAMs)Ordered molecular aggregates that form a monolayer of material on a surface. Formation of SAMs: Alkyl thiols (RSH) react with Au(0) surface, forming RS-Au(I) adducts: If R is a long chain, van der Waals interactions between the RS units lead to the formation of a highly ordered monolayer on the surface. The thermodynamic stability of SAMs increases with the length of the alkyl chain. RSH – alkyl sulfhydryl group
73 Substrate and Ligand Pairs for Forming SAMs
74 Alkanethiolate SAMs on Gold Surfaces
75 SAMs Based on Polymer BBsA film formed by the triblock molecules, revealing regularly sized and shaped aggregates that self assemble into monolayer nanostructures. Stupp et al, Science( 97)
76 Solution-based Molecular Manipulation for BBs SynthesisMesoporous molecular nanostructures are used as templates for nanocrystal synthesis. Phase sequence of surfactant-water binary system
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78 Self-Assembly of Surfactant (Soap) Molecules
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80 Self-Organized NanostructuresRegularly sized and shaped nanostructures can be tiled into superlattices of varying geometries and symmetries. Stupp et al, Science(97)
81 What is Hierarchical Assembly?A characteristic feature of self-assembly is hierarchy. Primary building blocks associate into more complex secondary structures that are integrated into the next size level in the hierarchy. This organizational scheme continues until the highest level in the hierarchy is reached.
82 Driving Forces on Various ScalesMolecular Scale: H-bonding, hydrophoic interaction, electrostatic forces, “lock-key” type interactions, and van der Waals forces Nano- and Mesoscale: capillary forces, external fields (gravitational, centrifugal, magnetic, electric, optical, …… ), surface tension, electrostatic forces, shear forces, and molecule-based interactions.
83 Driving Forces: Attractive vs. Repulsive
84 Template-Assisted AssemblyAqueous dispersion of colloidal polystyrene or silica particles are assembled on a solid surface patterned with relief structures. These patterned structures are used as templates for assembling of a variety of nano-particles Yin et al., J. Am. Chem. Soc. 2001, 123, 8718
85 Surfactant-Assisted AssemblyAssembly of CeO2 nanoparticles (5 nm) into hierarchically structured nanoporous materials using block copolymers. The force between particles (Van der Waals force) is weak, surfactants are used to provide the necessary bonding to form self-assembled nanoporous materials. Corma et al., Nat. Mater. 2004,3, 394
86 Charge-Driven AssemblyAssembly of negatively charged gold and silica nanoparticles into hollow microspheres directed by positively charged poly (L-lysine) Murthy et al., J. Am. Chem. Soc. 2004,126, 5292
87 Self-Assembly of Nanoparticles to SuperlatticesNanocrystals are able to assemble into close-packed ordered superlattices under the following conditions: narrow size distribution (< 5%) surfactant that is strong enough to separate the individual nanocrystals slow drying rate so that the nanocrystals can move to suitable positions Schematic illustration of self-assembled, passivated nanocrystal superlattices of spherical (a) and faceted (b) particles Wang, Adv. Mater.1998,10,13-30
88 Self-Assembled Nanocrystal SuperlatticesSolid, periodic arrays composed of nanocrystals and surfactants have been synthesized into one-, two and three-D superlattices. Very narrow size distribution of weakly interacting nanocrystals: The narrower the particle size distribution, the easier it is to obtain long-range superlattice ordering. Delicate interplay between interparticle attractions strong enough to drive superlattice crystallization, yet weak enough to allow annealing. The macroscopic properties of the nanocrystal super-lattices are determined not only by the properties of each individual particle, but also the interaction/coup-ling between the nanocrystals interconnected and isolated by a monolayer of thin organic molecules. Wang, Adv. Mater.1998,10,13-30
89 “Lock-and-key” AssemblySchematic representation showing possible approaches to the directed self-assembly of metallic (1. and 2.), and bimetallic (3.) macroscopic materials using antibody/anti-gen cross-linking of inorganic nanoparticles. Shenton et al, Adv Mater(1999)11,
90 Synthesis of One-Dimensional NanostructuresSix strategies for achieving one-D growth of wires, rods, belts and tubes Self-assembly of 0D nano- structures Dictation by the anisotropic crystallographic structure of a solid Confinement by a liquid droplet Direction through the use of a template Kinetic control provided by capping reagent Size reduction of a 1D microstructure Y. N. Xia et al, Adv Mater15,353(03)
91 Self Assembly of Nanoparticles into 1D NanostructuresA, B) Structures that were assembled from 150 nm poly styrene beads, and 0 nm Au colloids, respectively, by templating against 120nm-wide channel An L-shaped chain of spheres assembled against the template. D) Template-based self-assembled spiral chain of polystyrene beads
92 Growth of TiO2 Self-Assembled Nanocrystals(a)-(d): Progression of chain development: a) single primary crystallite; (b) four primary crystallites forming a single crystal via oriented attachment; (c) five primary crystallites forming a single crystal via oriented attachment;(d) single crystals of anatase with magnified attachment interfaces. L. Penn et al, Geochim. Cosmochim. Acta, 63,1549 (99)
93 Spontaneous Organization of Single CdTe Nanoparticles into Luminescent NanowiresTang et al., Science,297, 237 (02)
94 Self-Assembled In2O3 Nanowire NetworksThe nanostructures were synthesized by a vapor transport and condensation method SEM and TEM images of the In2O3 nanowire and nanocrystal chains. Big crystals are part of the network. a) SEM image showing the nanocrystal chains. b) SEM image showing the network junctions. c) SEM image showing the nanowire and nanocrystal chains. d) TEM bright field image of part of a nanocrystal chain.