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Biointerfacial Characterization (125:583) Nanoparticles L. Anthony and P. Moghe. Lectures: Nov. 16th: Part I: Nanoparticle Characterization** Dec. 4th: Part II: Biological Characterization (Nanoparticles at Interfaces) Lab Demo:

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Biointerfacial Characterization (125:583)

Nanoparticles

L. Anthony and P. Moghe

Lectures:

Nov. 16th: Part I: Nanoparticle Characterization**

Dec. 4th: Part II: Biological Characterization

(Nanoparticles at Interfaces)

Lab Demo:

November 20th: Particle Size by DLS; Zeta Potential

[8:40 - 9:20 AM; Wright-Rieman Labs, Room 396]

** Slides in this set are for November 16th


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Outline for Nanoparticle Characterization: Part I

  • Introduction/context:

    Particles at biointerfaces

    Properties of particles in dispersions/emulsions

  • Particle Size and Particle Size Distribution

  • Surface Charge: Zeta Potential, Isoelectric Point, Electrophoretic Mobility

Assigned papers

  • Nanoscale anionic macromolecules for selective retention of low-density lipoproteins

  • Chnari E, Lari HB, Tian L, Uhrich KE, Moghe PV

  • BIOMATERIALS 26 (17): 3749-3758 JUN 2005

Optimization of the preparation process for human serum albumin (HSA) nanoparticles

K. Langer, S. Balthasar V. Vogel, N. Dinauer H. von Briesen D. Schubert

INT. J. PHARMACEUTICS 257 (2003) 169-18


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Other papers/information posted on class website:

  • Albumin-derived nanocarriers: Substrates for enhanced cell adhesive ligand display and cell motility

  • Sharma RI, Pereira M, Schwarzbauer JE, Moghe PV

  • BIOMATERIALS 27 (19): 3589-3598 JUL 2006

  • Quantum dot bioconjugates for imaging, labeling, and sensing

  • Medintz, IL, Uyeda, HT, Goldman, ER, Mattoussi, H

  • NATURE MATERIALS, 4, 435-446 (2005)

A Nanoparticle-Based Model Delivery System To Guide the Rational Design of Gene Delivery to the Liver. 1. Synthesis and Characterization

Stephen R. Popielarski, Suzie H. Pun,† and Mark E. Davis*

BIOCONJUGATE CHEM. 1063 2005, 16, 1063-1070

Vesicle Size Distributions Measured by Flow Field-Flow Fractionation Coupled with Multiangle Light Scattering

Brian A. Korgel, John van Zanten, Harold Monbouquette

BIOPHYSICAL JOURNAL 74 June 1998 3264–3272

  • Short Monographs by Vendors:

    • Basic Principles of Particle Size Analysis

    • Zeta Potential, a Complete Course in Five Minutes

    • The Importance of Sample Viscosity in Dynamic Light Scattering Measurements

    • A Guide to Choosing a Particle Sizer


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Outline for Part I: Nanoparticle Characterization

  • Introduction/context:

    Particles at biointerfaces

    Properties of particles in dispersions/emulsions

  • Particle Size and Particle Size Distribution

  • Surface Charge: Zeta Potential, Isoelectric Point, Electrophoretic Mobility


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Introduction/Context: Particles at biointerfaces

  • Some reasons for using engineered nanoparticles in

  • biointerfacial research:

    • to study/effect a cellular process

    • Example:Albumin-derived nanocarriers: Substrates for

      • enhanced cell adhesive ligand display and cell motility

      • Sharma RI, Pereira M, Schwarzbauer JE, Moghe PV

      • BIOMATERIALS 27 (19): 3589-3598 JUL 2006

  • to transport drugs/biomaterials or biomolecules

    • ***Example:Nanoscale anionic macromolecules for

    • selective retention of low-density lipoproteins

    • Chnari E, Lari HB, Tian L, Uhrich KE, Moghe PV

    • BIOMATERIALS 26 (17): 3749-3758 JUN 2005

  • to visualize phenomena such as transport, sequestration, etc

    • Example: Quantum dot bioconjugates for

    • imaging, labeling, and sensing

    • Medintz, IL, Uyeda, HT, Goldman, ER, Mattoussi, H

    • NATURE MATERIALS, 4, 435-446 (2005)

    • *** Paper for discussion; examples given later in talk


  • Slide6 l.jpg

    Further modify/engineer

    particles/dispersion

    >Conjugated molecules

    >Buffers/salts

    >etc

    Make (or purchase) the nanoparticles and/or the dispersion

    Introduce particles into tissue/cellular/subcellular system

    and

    Study the particles and/or the effects of the particles

    tissue, cells or sub-cellular system under study

    Introduction/Context: Particles at biointerfaces

    Generic view of experiments with particles at biointerfaces:


    Slide7 l.jpg

    Further modify/engineer

    particles/dispersion

    >Conjugated molecules

    >Buffers/salts

    >etc

    tissue, cells or sub-cellular system under study

    Introduction/Context: Properties of particles in dispersions/emulsions

    • Note: Properties of the particle at the biointerface depend on:

    • Intrinsic properties of particles AND

      • Mediating properties of the liquid phase(s)

    Introduce particles into tissue/cellular/subcellular system (in vitro usually)

    Study effect of particles

    Make (or purchase) the nanoparticles and/or dispersion

    Note: cellular milieu is a very complex dispersant!


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    tissue, cells or sub-cellular system under study

    Introduction/Context: Properties of particles in dispersions/emulsions

    Properties of Particles in dispersion or cellular milieu

    Average size (diameter)

    Size distribution

    Shape

    Bulk chemical composition

    Surface composition

    covalently linked molecules

    adsorbed species

    surface charge (zeta potential)

    Compliance/modulus, etc

    Pore size/roughness

    Crystalline/amorphous

    Refractive index

    Density

    Cover up

    Properties of Particles

    Average size (diameter)

    Size distribution

    Shape

    Bulk chemical composition

    Crystalline/amorphous

    Pore size/roughness

    Surface composition

    covalently linked molecules

    adsorbed species

    Compliance/modulus, etc

    Refractive index

    Density

    Properties of Dispersant (s)

    Aqueous or organic? (or mix?)

    Bulk composition

    Additives (what?, how much?)

    Ionic or non-charged

    Small molecules

    Bio and macro molecules

    pH, conductivity

    Viscosity

    Refractive index

    Density


    Slide9 l.jpg

    tissue, cells or sub-cellular system under study

    Introduction/Context: Properties of particles in dispersions/emulsions

    Properties of Particles in dispersion or cellular milieu

    Average size (diameter)

    Size distribution

    Shape

    Bulk chemical composition

    Surface composition

    covalently linked molecules

    adsorbed species

    surface charge (zeta potential)

    Compliance/modulus, etc

    Pore size/roughness

    Crystalline/amorphous

    Refractive index

    Density

    Cover up

    Properties of Particles

    Average size (diameter)

    Size distribution

    Shape

    Bulk chemical composition

    Crystalline/amorphous

    Pore size/roughness

    Surface composition

    covalently linked molecules

    adsorbed species

    Compliance/modulus, etc

    Refractive index

    Density

    Properties of Dispersant (s)

    Aqueous or organic? (or mix?)

    Bulk composition

    Additives (what?, how much?)

    Ionic or non-charged

    Small molecules

    Bio and macro molecules

    pH, conductivity

    Viscosity

    Refractive index

    Density


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    Outline for Part I: Nanoparticle Characterization

    • Introduction/context:

      Particles at biointerfaces

      Properties of particles in dispersions/emulsions

    • Particle Size and Particle Size Distribution

    • Surface Charge: Zeta Potential, Isoelectric Point, Electrophoretic Mobility


    Slide11 l.jpg

    ensemble

    fractionation

    counting/”sorting”

    Particle Size and Distribution: “Families” of Measurement Principles

    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)

    Microscopy with image analysis is analogous

    Also: hyphenated methods: e.g. fractionation method with ensemble-method detection (e.g. FFF-DLS).


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    Ensemble Methods

    Dynamic Light Scattering

    Brownian Motion of Particles; temporal fluctuations in intensity of scattered light

    Static Light Scattering

    (& Laser Diffraction)

    Angular dependence of intensity of scattered light (Rayleigh/Mie)

    (new, not widely used; not covered in this lecture)

    Acoustic Attenuation

    • “Instrument as black box” approach:

      • Put sample in cuvette (diluting 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…


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    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: http://www.science.uva.nl/~sprik/masterlaser/dlsexp/dls.html


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    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


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    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


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    Ensemble Methods

    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


    Slide17 l.jpg

    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


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    Uncharged nanocarrier control

    COOH functionalized nanocarrier control

    LDL control

    Uncharged nanocarrers + LDL

    Charged nanocarrers + LDL

    COOH functionalized nanocarriers + LDL

    Example: Sizing of Nanoparticles with Dynamic Light Scattering:


    Slide19 l.jpg

    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…


    Slide20 l.jpg

    Fractionation Methods: Measurement Principles

    • Separation by differential (zonal) migration

    • Mixture in -------> Peaks 0ut

    • Carrier fluid pumped through column, channel, etc

    • Analye velocity is a characteristic fraction of carrier velocity, due to analyte interaction with (i) stationary phase in column

    • or (ii) to an applied field

    Xyz


    Slide21 l.jpg

    Fractionation Methods: Measurement Principles

    Adsorption, 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)


    Slide22 l.jpg

    Example: Capillary Hydrodynamic Flow

    Comparison 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


    Slide23 l.jpg

    Nanocarrier + LDL

    Nanocarrier

    LDL

    Example: Field Flow Fractionation

    Same system as in DLS example: nanocarriers for 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 FFF, just from the DLS


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    Example: Sedimentation Velocity Analysis

    Optimizing a process for preparing human serum albumin nanoparticles

    (from the assigned paper, K.Langer et al.)

    Assessing the process steps in particle synthesis and purification

    (note removal of fines)

    Assessing reproducibility of three identical runs


    Slide25 l.jpg

    Caveat with all fractionation methods: Dispersion

    • Band-brodening in time:

      • later eluting zones are inherently broader

      • does not in itself reflect more polydispersity;

      • effects need to be deconvoluted to determine polydispersity


    Slide26 l.jpg

    Counting/”Sorting” Methods

    AccuSizer (r)

    Coulter(r) and Elzone(r)

    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

    Particle displaces electrolyte in sampling cell; changes the signal across the electrodes

    Mature method, widely used - for cells and other “big” particles

    • “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…

    http://openchemist.net/chemistry/coulter/node3.html


    Slide27 l.jpg

    Counting/”Sorting” Methods: Measurement Principles

    After integration over the complete particle (i.e. over all the elements that contain the particle) we 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.

    Measurement is based on an experimentally determined relationship between “instrument response” and particle size

    http://openchemist.net/chemistry/coulter/node3.html


    Slide28 l.jpg

    Examples: Counting/”Sorting” submicron particles

    (NiComp-PSS Accusizer)

    “Real world” data:

    Oversized (> 1 um) particles in chemical mechanical polishing process steps

    (silica slurry, 50 nm nominal particle size)

    Vendor’s data:

    Particles are ~245 and 380 nm

    As-received

    After polisher

    set-up

    After normal polishing run

    After wafer broke on pad

    http://www.shjnj.cn/CPJS-2.htm

    L.J. Anthony et al: Proc. 2nd Int. Symp. Chemical Planarization in Integrated Circuit Device Manufacturing, pp 181-196, The Electrochemical Society, 1998


    Slide29 l.jpg

    Outline for Part I: Nanoparticle Characterization

    • Introduction/context:

      Particles at biointerfaces

      Properties of particles in dispersions/emulsions

    • Particle Size and Particle Size Distribution

    • Surface Charge:

      Zeta Potential, Isoelectric Point, Electrophoretic Mobility


    Slide30 l.jpg

    Charged Species on Surfaces

    • Origins of Surface Charge

    • Characteristics of Surface Charge: Definitions

    • Zeta Potential and Electrophoretic Mobility

    • Determination of Zeta Potential

    • Zeta Potential vs pH

    • Assigned paper and other examples


    Slide31 l.jpg

    Origins of Surface Charge

    Ionization 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)


    Slide32 l.jpg

    Characteristics of Surface Charge: Definitions

    Particle 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


    Slide33 l.jpg

    Characteristics of Surface Charge: Definitions

    Zeta 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


    Slide34 l.jpg

    +

    -

    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zf(Ka)/3

    Note relationship of zeta potential and electrophoretic mobility; therefore…

    Zeta potential can be determined

    by measuring UE

    z=Zeta potential

    =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


    Slide35 l.jpg

    Determination of Zeta Potential

    • Measure 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


    Slide36 l.jpg

    Determination of Zeta Potential

    Principles of 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.)


    Slide37 l.jpg

    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


    Slide38 l.jpg

    Zeta Potential and Electrolyte Concentration

    • Zeta potential also depends on electrolyte concentration! Remember that the ionic environment of the particle exists as a gradient that 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”)


    Slide39 l.jpg

    Example: Zeta Potential Measurements

    Optimizing a process for preparing human serum albumin nanoparticles

    (from the assigned paper, K.Langer et al.)

    Zeta potential

    Particle diameter

    At low values of Zeta potential (near pH 6), the dispersion de-stabilizes and the particles agglomerate


    Slide40 l.jpg

    Biointerfacial Characterization (125:583)

    Nanoparticles

    L. Anthony and P. Moghe

    Lectures:

    Nov. 16th: Part I: Nanoparticle Characterization**

    Dec. 4th: Part II: Biological Characterization

    (Nanoparticles at Interfaces)

    Lab Demo:

    November 20th: Particle Size by DLS; Zeta Potential

    [8:40 - 9:20 AM; Wright-Rieman Labs, Room 396]

    Coming attractions!


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