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Nanoparticle analysis and characterization methodologies
in environmental risk assessment of engineered nanoparticles
Martin Hassello
¨
v Æ James W. Readman Æ
James F. Ranville Æ Karen Tiede
Accepted: 28 April 2008 / Published online: 16 May 2008
Ó Springer Science+Business Media, LLC 2008
Abstract Environmental risk assessments of engineered
nanoparticles require thorough characterization of nano-
particles and their aggregates. Furthermore, quantitative
analytical methods are required to determine environmen-
tal concentrations and enable both effect and exposure
assessments. Many methods still need optimization and
development, especially for new types of nanoparticles in
water, but extensive experience can be gained from the
fields of environmental chemistry of natural nanomaterials
and from fundamental colloid chemistry. This review
briefly describes most methods that are being exploited in
nanoecotoxicology for analysis and characterization of
nanomaterials. Methodological aspects are discussed in
relation to the fields of nanometrology, particle size anal-
ysis and analytical chemistry. Differences in both the type
of size measures (length, radius, aspect ratio, etc.), and the
type of average or distributions afforded by the specific
measures are compared. The strengths of single particle
methods, such as electron microscopy and atomic force
microscopy, with respect to imaging, shape determinations
and application to particle process studies are discussed,
together with their limitations in terms of counting statis-
tics and sample preparation. Methods based on the
measurement of particle populations are discussed in terms
of their quantitative analyses, but the necessity of knowing
their limitations in size range and concentration range is
also considered. The advantage of combining comple-
mentary methods is highlighted.
Keywords Nanoparticles Nanoaggregates
Nanometrology Analytical chemistry
Particle size analysis
Introduction
Due to the extensive current, and foreseen future invest-
ments, in nanotechnology, nanoparticles used in consumer
products, industrial applications and health care technology
are likely to enter the environment (Aitken et al. 2006;
Roco 2005). To ensure sustainable development of nano-
technology, there is a need for risk assessments of
engineered nanoparticles (ENP) introduced from various
applications (Colvin 2003; Maynard et al. 2006). Such risk
assessments, require proper tools and methodologies to
carry out both effect and exposure assessments (EPA 2007;
Maynard et al. 2006; SCENIHR 2005; Crane and Handy
2007). Conventionally, exposure assessment is recom-
mended to include both a modeling and a measurement
approach (Holt et al. 2000); both approaches require
instrumentation and analytical methods. Prediction of
environmental concentrations of ENP through modeling is
based on emission scenarios (from production volumes and
M. Hassello
¨
v(&)
Department of Chemistry, University of Gothenburg,
Gothenburg 41296, Sweden
e-mail: martin.hassellov@chem.gu.se
J. W. Readman
Plymouth Marine Laboratory, Prospect Place,
Plymouth PL1 3DH, UK
J. F. Ranville
Department of Chemistry & Geochemistry, Colorado School of
Mines, Golden, CO 80401, USA
K. Tiede
Central Science Laboratory, Sand Hutton, York YO41 1LZ, UK
K. Tiede
Environment Department, University of York, Heslington,
York YO10 5DD, UK
123
Ecotoxicology (2008) 17:344–361
DOI 10.1007/s10646-008-0225-x
life cycle assessments) and partitioning parameters (fate
and behavior). Presently, little is known about the fate and
behavior parameters of ENP. Hence, development of suit-
able analytical methods are required to determine
concentrations and nanoparticle characteristics in complex
environmental matrices such as water, soil, sediment,
sewage sludge and biological specimens. The approach for
prediction of environmental concentrations through mod-
eling requires validation through measurement of actual
environmental concentrations. For ENPs that are only
recently being introduced into the environment, extremely
sensitive methods are required. Although direct observa-
tions are not hampered by the underlying assumptions of
exposure modeling, it is very important to assure that direct
observations are representative in time and space for the
regional setting to which the observation will be allocated
(local or regional).
ENP differ from most conventional ‘‘dissolved’’ chem-
icals in terms of their heterogeneous distributions in size,
shape, surface charge, composition, degree of dispersion,
etc. Therefore, it is not only important to determine their
concentrations, but also several other metrics.
In addition to exposure assessment requirements, it is
essential that characterization of ENP dispersion states (i.e.
aggregated or dispersed), and measurements of ‘‘steady-
state’’ concentrations are used in effect assessment test
systems (e.g., toxicity testing). It has been found that the
ENP concentrations are often not sustained in dispersions
throughout an experiment (Federici et al. 2007). Although
this need was not recognized in the pioneer studies in
nanoecotoxicology, it is now starting to be implemented in
most effects experiments. In a recent review (Hansen et al.
2007), it was shown that although size determinations are
becoming more common (17–96% of exposure and effects
studies), other relevant characterization properties are
rarely determined (e.g., surface area in only 6–33% of
studies). An additional complication relates to stability. For
example, Fig. 1 demonstrates that Buckminster fullerenes
readily degrade and are highly reactive (Taylor et al.
1991). Indeed, it is the reactions of Buckminster fullerenes
that render them of particular interest when investigating
their potential applications in nanotechnology (Taylor
2006). This reactivity has substantial implications in
interpretation of environmental behaviour and ecotoxico-
logical impact.
Assessing uptake and bioaccumulation in biological
matrices are essential and will be equally as challenging as
analyses of complex environmental media. Furthermore,
some good laboratory practices and harmonized methods
still need to be developed. Due to both the complexity of
the behavior of nanomaterials in dispersions and the
requirements for expertise in state-of-the-art methods in
ecotoxicology testing and nanoparticle characterization, the
necessity for interdisciplinary collaboration has been
highlighted (Crane and Handy 2007; Handy et al. 2008).
This paper focuses on mature and validated methods that
are commercially available and/or fairly easy to setup.
Consequently, highly specialized methods in the develop-
ment phase, or methods requiring large-scale facilities such
as synchrotron sources, are not discussed.
Nanometrology, analytical chemistry and particle
size analysis
The physical properties of nanoparticles are referred to as
metrics (Table 1) and the field of science which aims to
standardize physical measurements at the nanometer scale,
is called nanometrology. Even though nanometrology is a
young field regarding definitions and terminology, many
concepts are borrowed and adopted from the fields of
particle size analysis (Barth and Flippen 1995) and physi-
cal chemistry. In addition to the physical properties,
nanoparticles can be described by their chemical compo-
sition where the compound or species determined is called
the analyte (Table 1).
The metric ‘‘particle diameter’’ is probably the most
commonly used descriptor of particle size, but a single
diameter value is only enough to describe a perfect
spherical particle. Non-spherical nanoparticles (or colloids)
are, however, common in the environment and it is actually
common for nanoparticles to have very large aspect ratios
(e.g., clay platelets, rods or fibrils). Many engineered
nanoparticles share these features (e.g., carbon nanotubes,
nanowires, nanoclays, nanorods). It has been shown that
the toxicity can be shape dependent (Pal et al. 2007), and
Fig. 1 C60 fullerene solutions (in toluene) stored under dark and
light conditions (Photograph courtesy of P. Frickers and J.W.
Readman). The photo illustrates the potential of using spectroscopic
methods to study these photochemical changes in structure or surface
chemistry
Nanoparticle analysis and characterization methods 345
123
nanoparticle reactivity can be dependent both on size and
shape (Madden and Hochella 2005). There are several
different diameter measures that correspond to an equiva-
lent size of a specific type (Table 2). Different particle size
analysis methods also yield different equivalent sizes
(Table 2), which is important to consider when comparing
size values obtained using different methods. Another
important feature in method comparisons is that different
techniques give different size averages, depending on if
they fundamentally rely on an instrument response to:
particle numbers, volume, mass or optical property (e.g.,
light scattering) (Table 3). These averages can be the same
for spherical, monodisperse particles (with an infinitely
narrow size distribution) This, however, is usually not the
Table 1 A list of physical
properties (metrics), and a list
chemical compositions, analytes
and respective associated
methods and instruments
a
For abbreviations see text
Instruments and methods
a
Physical properties/metrics
Diameter EM, AFM, Flow-FFF, DLS,
Volume Sed-FFF
Area EM, AFM
Mass LC-ESMS
Surface charge z-Potential, electrophoretic mobility
Crystal structure XRD, TEM-XRD (SAED)
Aspect ratio or other shape factor
Chemical composition/analytes
Elemental composition Bulk: ICP-MS, ICP-OES, single nanoparticle:
TEM-EDX, particle population: FFF-ICP-MS
Fluorophores Fluorescence spectroscopy
Fullerene (‘‘molecules’’) UV–vis, IR, NMR, MS, HPLC
Total organic carbon High temp chemical oxidation
Other properties not falling within the above classes
Aggregation state DLS, AFM, ESEM, etc.
Hydrophobicity Liquid–liquid extraction chromatography
Dissolution rate Dialysis or voltammetry or spectrometry
Surface chemistry, coating composition,
# of proton exchanging surface sites
Optical or X-ray spectroscopic methods,
acid–base titrations
Table 2 Different equivalent
sizes measured by different
methods
Equivalent spherical
size measures
Applies to method
Hydrodynamic diameter Flow-FFF, DLS Calculated from the measured
diffusion coefficient, using
Stokes–Einstein equation
Equivalent spherical volume
diameter
Sed-FFF (if known density),
LIBD, electrozone sensing
Buoyant mass Sed-FFF SedFFF µ Dd*V
Equivalent spherical mass
diameter
MS Assume a certain structure
Projected area Microscopy
Equivalent molar mass Ultrafiltration Molecular weight cutoff (MWCO),
defined from retention of proteins
Equivalent poresize diameter Particle filtration Filter poresize often defined as
maximum size that penetrates filter
Root mean square radius of
gyration
SLS mean square distances from center
of mass of point masses within the
particle
Aspect ratio Microscopy, combination
of light scattering
methods or different
FFF methods
The longest dimension divided by the
shortest for symmetrical particles
(e.g., rods & ellipsoids)
346 M. Hassello
¨
v et al.
123
case. Each method also has its limitations in applicable size
and concentration ranges (Table 4). Therefore, it has to be
taken into account that there may be part of the nanopar-
ticle (or nanoparticle-aggregate) size distribution that is
‘‘hidden’’ for the applied method. Some relevant terms and
definitions from analytic chemistry, nanometrology and
particle size analysis is given in Table 5.
There are some special challenges for studies of ENPs in
environmental samples. The first challenge is that for envi-
ronmentally relevant concentrations (ng l
-1
–pg l
-1
), the
detection limits for most methods are not sufficiently low.
The second challenge is that in environmental samples there
is a high background of natural and unintentionally produced
nanoparticles (Banfield and Navrotsky 2001; Filella 2007;
Hochella and Madden 2005; Lead and Wilkinson 2006;
Waychunas et al. 2005; Wigginton et al. 2007).
A strategy for coping with these challenges may be to
combine existing and new methods that afford both a
screening capability and a highly selective detection. These
techniques, however, can be developed and tested under less
stringent experimental conditions (with higher concentra-
tions) to investigate behaviors, fates and effects.
Table 3 Description of different types of size averages, with equations defining them and methods that are deriving such average sizes
Type of size average Applies to method Equation
Number average: size average of numbers of particles
within a certain size class
Microscopy, LIBD
d
n
¼
P
i
n
i
d
i
P
i
n
i
Mass or volume average: size average of volume of particles
within a certain size class
FFF and SEC with most detection
methods, CFF
d
v
¼
P
i
V
i
d
i
P
i
V
i
Z-average size, an intensity weighted average attributed to certain methods Dynamic light scattering
d
n
¼
P
i
n
i
d
6
i
P
i
n
i
d
5
i
Table 4 Specifications of methods for analysis and characterization of nanoparticles
Method Approximate size
range (nm)
Limit of detection
a
Single particle or particle
population methods
Level of sample
perturbation
AFM 0.5 to [1000 ppb–ppm sp Medium
BET 1 to [1000 Dry powder pp High
Centrifugation 10 to [1000 Detection dependant pp Low
Dialysis 0.5–100 Detection dependant pp Low
DLS 3 to [1000 ppm pp Minimum
Electrophoresis 3 to [1000 ppm pp Minimum
EM-EELS/-EDX Analysis spot size: *1 nm ppm in single particle sp High
ESEM 40 to [1000 ppb–ppm sp Medium
ES-MS \3 ppb pp Medium
FFF Flow FFF: 1–1000 Detection dependant; UV: ppm,
Fluo&ICP-MS: ppb
pp Low
Sed FFF: 50–1000
HDC 5–1200 Detection dependant pp Low
ICP-MS Depends on fractionation ppt–ppb pp
LIBD 5 to [1000 ppt sp Minimum
Microfiltration 100 to [1000 Detection dependant pp Low-medium
SEC 0.5–10 Detection dependant pp Medium
SEM 10 to [1000 ppb–ppm sp High
SLS 50 to [1000 pp Minimum
TEM/HR-TEM 1 to [1000 ppb–ppm sp High
TEM-SAED Analysis spot size: 1 nm sp High
Spectrometry ppb–ppm pp Minimum
Turbidimetry/nephelometry 50 to [1000 ppb–ppm pp Minimum
Ultrafiltration 1–30 Detection dependant pp Medium
WetSEM 50 to [1000 ppm sp Low
WetSTEM ppm sp Low
XRD 0.5 to [1000 Dry powder pp High
a
For comparison mass concentration limit of detection for 100 nm particles are estimated
Nanoparticle analysis and characterization methods 347
123
Dispersion, sampling and sample handling
Dispersion of nanoparticles for both exposure
and effect assessments
Colloidal systems are dynamic non-equilibrium systems and
are often sensitive to physical or chemical disturbances
(Filella 2007). Sampling and laboratory procedures (e.g.,
pumping, mixing, etc.) that introduce shear forces are likely
to perturb the dispersion state of ENPs, possibly leading to
either further aggregation, or to partial disruption of existing
aggregation. The presence of natural organic matter and
natural nanoparticles further complicates the situation. It is
important to be aware of and characterize the interaction of
the ENP with the natural material. It is equally important to
compensate for any background material of the same com-
position as the ENP. Background levels of identical
composition can be present for TiO
2
, SiO
2
but also for car-
bon-based nanoparticles. Geological studies, using primarily
transmission electron microscopy (TEM) to visualise the
materials, have reported fullerenes in geological formations
dating back 1.85 billion years (Becker et al. 1994), and
CNTs together with fullerene–like structures in a Greenland
ice core dated at approximately 10,000 years old (Murr et al.
2004). Given their reactivity, this is surprising (Taylor 2006),
but infers that these carbon-based nanoparticles have natural
as well as engineered origins.
In the case of ecotoxicological exposures to carbon
nanoparticles, the preparation and characterisation of aque-
ous fullerene suspensions is especially challenging owing to
their low solubilities. Fortner et al. (2005) describe nano-
aggregate formation of C60 fullerenes in water. Particle sizes
within the aggregates are, however, dependant on formation
parameters including pH, ionic strength and even the mixing
rates. The properties of the aggregates are different from the
pristine particles. Coupled with the fact that fullerenes oxi-
dise (Fig. 1), ecotoxicological exposure techniques are
rendered highly complex. For carbon nanotubes (CNTs),
their extremely low solubility in water, variable sizes of the
particles, small diameters and the complexity of aggregates
formed render dosing and particulate characterisations
extremely difficult in aqueous exposure experiments. Now-
ack and Bucheli (2007) describe a standard procedure for
solubilising CNT through cutting the tubes by sonication,
and hydroxylation of the ends and damaged regions using
strong acid. Other treatments to disperse the materials are
reported using surfactants (Jiang et al. 2003) and biopoly-
mers, including humic and fulvic acids (Hyung et al. 2007).
Table 5 Analytical chemistry, metrology and particle size analysis definitions
Term Definition
Metric The property that is being quantified
Analyte The compound or specie that is being quantified
Limit of detection The lowest concentration that can be distinguished from the background, typ defined as 3*Stdev (blank measurements)
Precision The statistical spread of values in a measurement series
Accuracy The exactness of the averaged measurements related to the true value
Measurement
uncertainty
The accumulated uncertainty incl. method, lab, between days and between lab biases
Method validation Experimental proof that the method conforms according to the specifications
Reference material A material or substance that is sufficiently homogeneous for its property values to be used for calibration of
instruments or assessment of methods
Certified reference
material
A reference material that is accompanied by a certificate that specifies the traceability of the CRM and associated
uncertainty
Control sample Within laboratory quality control over time and between interlaboratory comparisons
Interlaboratory
comparison
A blind test between participating laboratories to quantify deviation from true or reference value
Number based
concentration
Determinations of number of particles per unit volume or mass
Mass based
concentration
Determinations of mass of particles per unit volume or mass
Number average based
size
The size average of numbers of particles within a certain size class:
d
n
¼
P
i
n
i
d
i
P
i
n
i
Volume average based
size
The size average of volume of particles within a certain size class:
d
v
¼
P
i
V
i
d
i
P
i
V
i
Z-average based size A light scattering based average:
d
n
¼
P
i
n
i
d
3
i
P
i
n
i
d
2
i
Polydispersity index Weight average size/number average size
348 M. Hassello
¨
v et al.
123
Treatments to facilitate dispersion must, however, be
accounted for in interpretation of toxic response and how
environmental relevance may be affected.
Sampling
Due to the unstable nature of colloidal nanoparticle dis-
persions it is preferable to use in situ analyses, but these
methods are rarely available (Lead and Wilkinson 2006).
The second choice is to apply methodologies that cause
minimum perturbation from sampling to analysis. An
example of such techniques are the probing of dispersions
with electromagnetic radiation (e.g., light, X-rays or neu-
trons) where the scattering/absorption patterns can be
related to physical properties of the particles, as will be
described below.
Sample contamination and loss
Sampling of nanoparticles should generally be feasible
with most standard sampling protocols, but the handling
procedures differ from many other chemicals. Samples of
colloids from surface waters are often collected in bottles
that have been selected for minimum adsorption and con-
tamination, e.g., plastics, especially fluoroplastics, for
inorganic colloids or metal analysis and glass for analysis
of organic trace constituents (Hall 1998). Since engineered
nanoparticles may consist of e.g., an inorganic core with an
organic coating or surfactants, conventional material
selections may have to be revised. Further, the nanoparticle
surface charge and possible charges on the bottle walls of
both plastic and glass at the specific pH should be taken
into account. Consequently, for engineered nanoparticles,
adsorption to sample bottles needs to be investigated for
both inorganic and carbon-based nanoparticles on a case-
by-case basis until new experience-based knowledge has
been accrued. Similar concerns apply to all other materials
to which the sample is being exposed (e.g., tubing, filter
materials, pipettes, amongst others).
Extracting inorganic nanoparticles from soil and
sediment
Examining ENP in soils and sediments have the same
limitations as for water samples, with the additional com-
plication of much higher quantities of natural solids, many
of which are in the same size range as the ENP. Dispersion
methods for releasing natural nanomaterials from the solid
matrix, such as sonication and chemical dispersants
(hexametaphosphate, detergents, etc.) will likely release
the ENP to the solution phase, but the physicochemical
state of ENP will be likely to change (e.g., break-up of
flocs). These protocols are reported in the soil literature
(Gee and Bauder 1986). The separation of nanoparticles
from soil suspensions or sediment slurries are difficult, and
are prone to artifacts. As a general suggestion, centrifu-
gation is generally less perturbing than filtration (Gimbert
et al. 2005, 2006), but the differential settling during cen-
trifugation can also induce aggregation. This is further
discussed in the ‘‘Prefractionation’’ section below. The
challenge then remains to discriminate between natural and
engineered nanoparticles.
Extracting carbon-based nanoparticles from water, soil
and sediment
Pristine fullerenes are comparatively soluble in organic
solvents such as toluene and can be extracted from media
(including water) into solvent (Fortner et al. 2005). In the
case of CNTs (both single and multi-walled), Nowack and
Bucheli (2007) summarise that no method currently exists
for their quantification in natural media. Indeed, CNTs
have low solubility, even in organic solvents.
Prefractionation
Environmental samples often contain complex mixtures of
particles of different size classes, composition, shapes and
are of biotic and/or abiotic origin. In order to study nano-
particles, it is often necessary to first reduce the complexity
using a course prefractionation. The prefractionation can be
based on settling, centrifugation or filtration. Settling or
centrifugation is only effective in removing particles that
have a settling velocity that dominates over their Brownian
motion. The settling velocity depends on the particle vol-
ume, shape, and their density difference with respect to
water. Therefore, settling or centrifugation is more efficient
in removing more dense mineral particles than it is for
algae and other organic particles. Centrifugation is a min-
imum perturbation prefractionation technique, but settling
particles can scavenge other smaller particles due to the
differential settling velocities.
Microfiltration, with pore sizes generally greater than
0.1 lm, is the most common prefractionation technique,
due to its simplicity of operation. However, common
‘‘dead-end’’ filtration is prone to many artifacts, e.g.,
nanoparticle deposition, membrane concentration polari-
zation, and filter cake formation (Buffle et al. 1992;
Morrison and Benoit 2001).
Nanoparticles can be deposited on the membrane sur-
face due to collision or electrostatic attraction. Particles
smaller than the pore size can be transported through the
membrane more slowly than the liquid, due to electrostatic
repulsion within the pores. This causes concentration
polarization (build up of higher particle concentration in
the membranes diffusive boundary layer) which leads to
Nanoparticle analysis and characterization methods 349
123
higher collision rates between particles and consequently
aggregation. Aggregates or attached particles on the
membranes, provides more efficient trapping of nanopar-
ticles and their aggregates. This leads to formation of a
filter cake and the effective pore size decreases severely; in
other words the filter clogs.
These problems are especially severe for non-stabilized
nanoparticles, e.g., those that lack hydrophilic surfaces.
Therefore, filtration of engineered nanoparticle suspensions
should be critically evaluated in terms of the scavenging of
nanoparticles and, as a consequence, changing the size
distribution.
Fractionation by ultrafiltration, nanofiltration
and dialysis
Fractionation by membranes can either be done by apply-
ing a pressure to overcome the pressure drop across a
membrane that sieves molecules or particles according to
their size as in ultrafiltration or it can be done by letting
solutes equilibrate across the membrane as in dialysis. The
microfiltration artifacts mentioned above become greater as
the pore size of the filter decreases (ultrafiltration and
nanofiltration). This is especially critical where membranes
are used as macromolecular sieves. In order to reduce the
diffusive boundary layer over the membrane, and thereby
minimize the concentration polarization over the mem-
brane, cross-flow (or tangential) filtration (CFF) has been
developed. In CFF the sample is recirculated (or stirred) in
a reservoir on top of the membrane. A fraction of the
sample with components smaller than the pore size, will
pass through the membrane (to yield the permeate) in each
cycle. By measuring the concentration of analyte in both
the initial sample, the retentate (the fraction not passing
through the membrane) and the permeate, it is possible to
calculate the concentrations of analyte in the fractions
smaller and larger than the membrane pore size. The per-
formance of crossflow ultrafiltration has been extensively
evaluated for natural colloids and reveals that the mem-
brane type, membrane manufacturer, and operating
conditions, have large influences on the fractionation
results and recoveries obtained (Guo et al. 2000; Larsson
et al. 2002; Liu and Lead 2006). Therefore, crossflow
ultrafiltration should be appropriately tested and evaluated
prior to application to ENPs. Ultrafiltration is a preparative
size fractionation method that can be scaled to process
large sample volumes and produce large quantities of iso-
lated nanomaterials. Although it is limited to two fractions
(above and below the membrane pore size), multi stage
filtrations can allow for a crude size fractionation, however,
this is extremely labor and time intensive. When the
membrane pore size is below *1 nm, the method is
typically defined as nanofiltration. Nanofiltration is usually
applied to the separation of molecules from salts and could
potentially be applied to separate nanoparticles from their
dissolved counterparts.
Dialysis is an ultra- or nanofiltration method that oper-
ates on diffusion of solutes across a membrane that arises
from concentration gradients and osmotic pressure instead
of pressure driven filtration (as is the case in CFF). Dialysis
is a very mild fractionation method and it can be used to
separate truly dissolved components (ions and small mol-
ecules) from their nanoparticle counterparts. Dialysis has
been used to study nanoparticle-solute sorption behavior as
well as nanoparticle dissolution, where the aqueous coun-
terparts will diffuse across the dialysis membrane (Franklin
et al. 2007). However, dialysis usually utilizes deionized or
distilled water as an acceptor solution. This may promote
dissolution or ionic strength changes which will lead to
changes in dispersion state.
Field-flow fractionation, size exclusion
and hydrodynamic chromatography
Field-Flow Fractionation (FFF) is a mild chromatography-
like size-fractionating method that differs from chroma-
tography in that it does not utilize a stationary phase. The
most common FFF sub-technique is Flow FFF, which is
discussed here. Flow FFF separates nanoparticles accord-
ing to their particle size by virtue of their diffusion
coefficients in a very thin open channel (Giddings 1993;
Hassello
¨
v et al. 2007; Schimpf et al. 2000). The separation
principle relies on the combination of an applied field and
longitudinal carrier flow. The field acts perpendicular to the
length of the separation channel and causes the nanopar-
ticles to move towards the accumulation wall.
Nanoparticles form a cloud whose thickness is given by the
particles’ ability to oppose (generally through diffusion)
the force of the field. Smaller particles will not be affected
to the same extent as larger particles, and hence the smaller
particles elevate higher in the channel. Perpendicular to the
field, along the channel, the laminar separation flow is
acting on the nanoparticles. The parabolic shape of the
laminar flow velocity in the channel implies that particles
traveling nearer to the middle of the channel move faster
than particles traveling closer to the channel walls. Con-
sequently, the smaller particles, having higher extending
clouds, on average, travel faster than the larger particles,
resulting in fractionation of the sample that provides a
continuous size distribution. To monitor the size distribu-
tions, the FFF needs to be coupled to a detector that
responds to the nanoparticle number or mass concentration.
Examples include: UV absorbance, light scattering (von
der Kammer et al. 2005b), or elemental detectors such as
350 M. Hassello
¨
v et al.
123
ICP-MS (Hassello
¨
v et al. 1999; Ranville et al. 1999;
Jackson et al. 2005). The latter detector is very useful for
characterizing metal-containing nanoparticles, an example
being given in Fig. 2. Depending on the type of detector
used, different kinds of size dependant information of the
sample is achieved. One great advantage with FFF, com-
pared to other fractionation methods, is that the retention
time is directly proportional to nanoparticle physical
properties. Retention in FFF is expressed as the retention
ratio (R) given by
R ¼
t
0
t
r
ð1Þ
where t
0
is the void time and t
r
is the sample retention time.
For highly retained components, R can be approximated by
R 6k ð2Þ
while R can be estimated as follows for intermediate
retention
R ¼ 6k coth
1
2k
2k
ð3Þ
The fundamental retention parameter (k) is defined as
the mean distance of the component from the wall (l)
divided by the channel thickness (w).
k ¼
l
w
¼
D
Uw
ð4Þ
Channel thickness is calculated from experimentally
determined channel volumes, since the actual channel
thickness may differ from the manufacturer’s specifica-
tions. Estimates of k from experimental determinations of
R allow calculation of the diffusion coefficient (D). It is
important to note that the fundamental measurement made
by Flow FFF is the diffusion coefficient. In the techniques
of Flow FFF, diffusion coefficients can be used to deter-
mine hydrodynamic diameter. In Sedimentation FFF
buoyant mass or equivalent spherical diameter can be
determined (Giddings 1993).
The most critical factor in Flow FFF analysis is the
choice of membrane and the carrier composition optimi-
zation. The particles should travel through the fractionation
channel in close vicinity to the membrane without aggre-
gating, adsorbing to the membrane or having inter-particle
repulsion. This is generally accomplished for the complex
natural samples by controlling the electrostatic repulsion
and steric stabilization by a combination of suitable ionic
strength (typically 0–20 mM monovalent salt) and a sur-
factant (e.g., 0.05% sodium dodecyl sulphate) (Hassello
¨
v
et al. 2007). FFF has been successfully applied to a wide
range of synthetic nanoparticles (e.g., SiO
2
, TiO
2
, ZrO
2
,
Au, Ag, carbon black, pigments, Teflon, carbon nanotubes,
soot particles) (Schimpf et al. 2000).
Another size fractionation method is size exclusion
chromatography (SEC) where a particle or macromolecule
mixture is passed through a column with a porous packing
material with a distribution of pore sizes in the range of
particles to be fractionated (Barth and Boyes 1992). The
particles are separated according to their hydrodynamic
volume (size and shape) by their ability to enter the porous
structure of the packing materials. Particles that are larger
enter pores to a lesser extent than the smaller particles.
Each SEC column has a certain operating size (or molar
mass) window, and the first eluting larger particles (all at
once) are those outside the operating window, then come
the fractionated particles and then the ‘‘salt peak’’ ions and
molecules that have passed through the complete pore
volume. Size exclusion chromatography has been applied
to both carbon nanotubes and fullerenes, as described in a
later section, to natural organic and inorganic nanomate-
rials (Perminova et al. 2003; Vogl and Heumann 1997;
Jackson et al. 2005).
Hydrodynamic chromatography (HDC) is another size
fractionation method that is carried out in narrow open
capillaries, or in wider capillaries with non-porous packing
materials that essentially form capillary routes. Due to the
size, the center of mass cannot approach the walls infinitely
and therefore a smaller particle can approach the wall to a
larger extent than can a large one. Therefore, the elution
order is the same as in SEC and also in the steric mode of
FFF. The separation efficiency of HDC is very poor, but
the operating size range is very good. HDC has been suc-
cessfully applied for the fractionation of nanoparticles
(Williams et al. 2002; Tiede unpublished results).
Chromatographic analyses of carbon nanoparticles
Many conventional techniques have been used to analyse
fullerene solutions including UV–vis spectrophotometry,
0
10
20
30
40
50
60
2400180012006000
Retention time (sec.)
metal (µg l
-1
)
0
Hydrodynamic diameter (nm)
Relative Fluoresence
82Se
111Cd
Fl
40 80 120 160
Fig. 2 Representative FFF fractogram of a CdSe quantum dot using
on-line fluorescence and ICP-MS detection
Nanoparticle analysis and characterization methods 351
123
infrared spectroscopy, nuclear magnetic resonance, and
mass spectrometry, frequently coupled to high performance
liquid chromatography (HPLC) (Andrievsky et al. 2002;
Fortner et al. 2005; Isaacson et al. 2007; Nowack and Buc-
heli 2007; Treubig and Brown 2002). For HPLC, octadecyl
silane (ODS) stationary phases are most commonly selected
with elution using solvents such as toluene or toluene:ace-
tonitrile mixtures (Treubig and Brown 2002). When UV–vis
absorbance detection is used, 325 nm is the wavelength
typically selected. Alternatively, gel permeation chroma-
tography can be used, for example using Agilent PL gel
10 lm 50 A with toluene elution (Readman and Frickers,
unpublished data). Size exclusion chromatography has also
been applied to characterise CNTs (Duesberg et al. 1998).
Light scattering techniques
Light scattering is a very commonly used method to
determine particle size (Schurtenberger and Newman
1993). The electromagnetic radiation of the incident pho-
tons induces an oscillating dipole in the particle electron
cloud. As the dipole changes, electromagnetic radiation is
scattered in all directions. The light source could be laser
light, X-rays or neutrons, each of which enables probing at
different size ranges and particle compositions. Discussion
will mainly be limited to describing methods utilizing laser
light, since these are the most readily available methods to
be used in particle characterization for ecotoxicology.
Dynamic light scattering
In dynamic light scattering (DLS), also called photon
correlation spectroscopy or quasielastic light scattering,
fluctuations in the scattered light that depend on particle
diffusion is utilized. The fluctuations originate from the
Brownian motion of the particles and from the fact that
neighboring particles can have constructive or destructive
interference of the scattered light intensity in a certain
direction. In the DLS instrument the intensity is measured
over very short time periods (dt) and then it is possible to
compare (correlate) the intensity at time t0 with time
t0 + dt (in the order of micro-milliseconds). Smaller par-
ticles (with faster diffusion) lose the correlation (the
memory of their previous position) more rapidly than lar-
ger particles. The scattering intensity is plotted as an
autocorrelation function:
g sðÞ¼G sðÞI
hi
2
=c
1=2
¼ Ae
2Cs
ð5Þ
where G(s) is the field autocorrelation function, I
hi
2
is the
base line and c is the coherence factor, expressing the
efficiency of the photon collection. A is an instrument-
specific constant, C is the decay rate and s the delay time.
C can be converted to the diffusion coefficient, D, using the
relation:
D ¼ C/q
2
ð6Þ
where q is the wave vector, which can be described by the
following relation:
q ¼ 4pg sin p=4ðÞ=k ð7Þ
where g is the refractive index of the solvent and k is the
wavelength of the incident light. If the diffusion coefficient
is known, the hydrodynamic radius, R
h
, can be calculated
from the Stokes–Einstein equation:
R
h
¼ kT= 6pgD ð8Þ
where k is Bolzmann’s constant and T is the absolute
temperature.
The advantages of DLS are: the rapid and simple
operation, readily available equipment, and minimum
perturbation of the sample (Ledin et al. 1994). The limi-
tations are the interpretation, especially for polydisperse
systems, and critical review of the data obtained (Filella
et al. 1997). DLS gives an intensity weighted correlation
function that can be converted to an intensity weighted
(z-average) diffusion coefficient.
For d \ k/20, then the scattering intensity, I * d
6
,
according to the Rayleigh approximation, while for k/
20 \ d [ *k then I * d
2
(Debye approximation). The
strong particle size dependence of the scattering intensity
will bias the measured size, as a small amount of large
particles will have such a large influence that smaller
particles will be neglected. Consider a sample with two
particle sizes, d: 3 and 30 nm, of equal particle number
concentrations. The volume concentration will be 1,000
times larger in the 30 nm particles due to the geometrical
formula of a sphere, but according to the Rayleigh
approximation, the scattering intensity will be 10
6
times
stronger for the 30 nm particle compared to the 3 nm
particle. For even larger particles, the response difference
will be enormous. Consequently, even the smallest fraction
of dust or other micrometer-sized particles will ruin the
signal from the nanoparticles.
For multimodal size distributions (multi component
mixtures), the conversion of the autocorrelation function to
diffusion coefficient is an ill-posed mathematical problem,
where small variations can give large deviations in the
output. For this reason, but more importantly due to the fact
that the signal from larger particles dominates over smaller
ones, a general rule is that DLS is not suitable for samples
with polydispersity index above *1.5–1.7.
Since DLS measures diffusion coefficients, and that all
size calculations are based on assumptions that the Stokes–
Einstein relation (Eq. 8) holds, it is essential to validate that
352 M. Hassello
¨
v et al.
123
the diffusion coefficient measured is the undisturbed self-
diffusion coefficient. For charged nanoparticles, electro-
static forces between particles have an effect on the
diffusive behavior. This effect is concentration dependant,
and the upper boundary occurs when the nanoparticle gets
entrapped by forces from their close neighbors, the point of
so-called gel-formation. By dilution of the sample to the
greatest possible extent, while remaining above the detec-
tion limit, and extrapolation of the measured diffusion
coefficient to infinite dilution, the unperturbed diffusion
coefficient can be estimated. This value is one that can
most reliably be used to calculate size in the Stoke Einstein
equation. However, dilution of a sample will change its
diffusion behavior and aggregation state. If primary parti-
cle size is not the goal, but rather to characterize the
dispersion state in a sample, then it is more relevant to not
dilute the sample, reporting diffusion coefficients only,
rather than size.
It should also be noted that the derived data from DLS
are intensity based distributions or averages, and mathe-
matical conversions to volume or number distributions
should only be provided with good knowledge of the par-
ticle shapes, polydispersity and underlying assumptions
(Finsy 1994). Although dynamic light scattering does not
provide full characterization of nanoparticle dispersion, it
is very valuable to, for example, monitor aggregation
behavior.
Static light scattering
Static light scattering (SLS), also called multi angle (laser)
light scattering (MALS or MALLS), provides measure-
ment of physical properties that are derived from the
angular dependency of light scattered by a particle. This is
due to the fact that a particle of a certain size generates
destructive and constructive interferences at certain angles.
Time averaged scattering intensities are measured at sev-
eral angles to derive any number of several size parameters
including the particle size, root mean square radius of
gyration (R
g
.), which is the root mean square distance of
point masses in a particle from its center of gravity. Con-
sequently SLS relates to the particle structure and
morphology and can therefore be used in combination with
DLS to give information of particle shape factors. There
are several important assumptions in SLS theory for dif-
ferent analytical solutions. The most used is called
Rayleigh–Gans–Debye approximation (Schurtenberger and
Newman 1993). For these approximations, the refractive
index difference between the particle and solvent should be
negligible, the concentration of particles approaches zero,
and no light absorption by the particles occurs.
Both dynamic and static light scattering polydisperse
samples impose limitations on these methods. Therefore,
it has been shown to be beneficial to couple light scat-
tering detectors online to a fractionation method such as
FFF or SEC (von der Kammer et al. 2005b; Wyatt
1998). With this combination, independent size distribu-
tions can be derived from the two methods and thereby,
from comparison of the two results, distributions of
particle shape factors can be estimated (von der Kammer
2005).
Nephelometry
Turbidity, or nephelometry, is a particle concentration
measurement that utilizes scattering of light at 90° or
sometimes 180°, with respect to the light source. The light
source can be a laser or monochromatic light. The equip-
ment is very simple and can be portable or even in situ, but
the relationship between the concentration and particle
concentration is not trivial. The light scattering intensity is,
as mentioned above, strongly dependent on particle size,
and also on other parameters such as the refractive index
difference between the particles and the suspension media.
Therefore, in quantitative analysis, turbidity measurements
should only be used for well-defined particles of fairly
narrow size distributions and complemented by calibration
with other techniques (e.g., gravimetry). For dispersed
nanoparticles, turbidity is fairly insensitive, and is less
suitable than for monitoring aggregation.
Nephelometry has also been used as a chromatographic
particle concentration detector (von der Kammer et al.
2005a).
Laser induced breakdown detection
Laser induced breakdown detection (LIBD) is based on the
fact that when a solid nanoparticle passes through the focal
volume of a focused, pulsed laser, the power density
required to induce breakdown of the dielectric properties of
the water is lower than for pure water (Kim and Walther
2007). If the laser energy is correctly tuned, plasma for-
mation will only occur when a nanoparticle passes through
the focal volume of the optical cell. The plasma formation,
or breakdown, is detected with either a piezo-electric
crystal attached to the cuvette, or with a CCD camera
synchronized with the laser pulse. The parameter measured
is the breakdown probability (BP). Since BP for a given
laser energy depends both on particle concentration and on
size, it is necessary to elucidate both. The most common
mode is to tune the laser pulse energy and measure the BP
of the sample, and do the same for a set of calibration
standards of known size at different concentrations. The BP
for larger nanoparticles has a threshold (increased from
zero probability) at lower laser energies than smaller
nanoparticles. The BP-laser energy curves have different
Nanoparticle analysis and characterization methods 353
123
slopes depending on the concentrations that are also given
from the calibration standards.
The main advantage of LIBD is that it is extremely
sensitive even to small nanoparticles with detection limits
in the ppt (ng dm
-3
) range. In fact, LIBD is so sensitive
that most samples have to be diluted in order to not saturate
the breakdown probabilities.
The main disadvantages are that LIBD cannot discrim-
inate between different types of nanoparticles and even
more seriously, that different nanoparticle compositions
have different breakdown probabilities (instrument
responses). Therefore it is not possible to use one set of
calibration standards for different types of nanoparticles.
LIBD is a specialized technique that is not yet commer-
cially available.
Spectroscopic analysis and characterization
Certain classes of nanoparticles demonstrate strong fluo-
rescence and this property is utilized in many fields such as
medical imaging, immunoassays, photonics, amongst oth-
ers (Bailey et al. 2004). Quantum dots (QDs) are composed
of semi-conductor materials, for example CdSe, CdS,
CdTe, and are highly fluorescent. These particles can be
characterized by either their absorption or fluorescence
emission spectra. The absorption spectra is broad over low
wavelengths but displays a sharp peak, called the first
exciton peak at the upper wavelength of the absorption
spectra. This peak is generally in the order of 20–50 nm
lower in wavelength than the emission peak. The position
of this absorption peak can be correlated to the particle size
and is commonly used to monitor size in QD synthesis (Yu
et al. 2003). The emission peak tends to be fairly narrow,
on the order of 50 nm, with the wavelength being highly
sensitive to nanoparticle size. Measurement of fluorescence
spectra can thus also be used to determine particle size. In
natural systems, natural fluorophores contained in humic
substances and biological cells may interfere with these
determinations. Non-fluorescent nanoparticles such as sil-
ica can be labeled with dyes to impart fluorescence. In
some cases the fluorescence of the dye can be enhanced by
the presence of a second dye that can contribute its exciton
energy through a radiation-less transfer.
Quantitation of particle concentrations can be performed
using absorption or fluorescence if the optical constants of
the particles are known. For example, extinction coeffi-
cients for the first exciton peak of some QDs were
determined by Yu et al. (2003). It is yet to be determined
how significantly background absorption from natural
occurring materials in water will limit the usefulness of
UV–vis absorption for nanoparticle quantitation in aquatic
systems.
Both UV–vis absorption and fluorescence can be used as
online detectors for chromatography and FFF systems. The
extremely bright fluorescence of some nanoparticles should
provide low detection limits for these techniques. Figure 2
shows an example of the use of online fluorescence
detection with FFF for a CdSe quantum dot.
Fluorescence microscopy gives spatial information and
has been very useful in looking at the distribution of
nanoparticles in cells and organisms. For example, uptake
of QDs into the guts of filter feeding organisms is clearly
observable using fluorescence microscopy.
For naturally fluorescent material or labeled macro-
molecules, fluorescence correlation spectroscopy within
the focal point of a laser confocal microscope, have been
successfully applied to determine the diffusion coefficients
of these materials (Lead et al. 2000b). The principle is
similar to dynamic light scattering (also called photon
correlation spectroscopy), but the sensitivity is much better
for small (fluorescent) particles. The method should be
very suitable for studies of QDs in environmental media.
In describing the UV–vis absorption spectra of metal
NPs, the term surface plasmon is used, which describes the
oscillating electron clouds present at the metal-solution
interface. Particle size strongly affects the absorption
spectra through quantum confinement effects that are
important at the nanometer scale of materials. The smaller
the particle size, the lower the wavelength of light absor-
bed. Aggregation of NP results in band broadening and red
shifting of surface plasmon band and has been used to
study the effect of electrolytes on metal NP stability (Aryal
et al. 2006).
Particle shape characterization of metal NP is also
possible from examination of surface plasmons. While
spherical gold and silver NP have strong surface plasmon
bands at about 520 and 400 nm respectively, nanorods of
these metals show two bands, a red-shifted long-axis band
and a blue shifted short-axis band. The wavelength of the
long axis band is particularly sensitive to particle aspect
ratio. It has also been noted that Au nanorods have 106
stronger fluorescence than spherical Au NPs (Link and El-
Sayed 1999). Consequently, surface plasmon effects can be
used to study particle-particle interactions since the aspect
ratio changes when to single particles come close together.
Electron microscopy and atomic force microscopy
There are several powerful microscopy techniques that can
provide images of nanoparticle systems as well as addi-
tional information on elemental composition, structure and
even charges or force measurements. Microscopy methods
are all single particle methods, that is the data does not
arise from an ensemble of particles such as is the case with
354 M. Hassello
¨
v et al.
123
light scattering. This enables information to be collected on
each particle free from interferences from other particles or
background solutes. This gives good information on par-
ticle processes that sometimes cannot be obtained with
bulk analysis (Mavrocordatos et al. 2007). However, it also
means that even though a quantitative measurement with
sometimes fairly good accuracy can be achieved on a
single particle, it is only by counting and measuring
enough particles (of a certain type or in a certain size
range) that good enough counting statistics of the complete
sample can be obtained. This is needed in order to deliver a
quantitative analysis or characterization of the sample.
Sizing with microscopy means that an average size mea-
sured on a certain number of particles are a number
average, and in order to measure an accurate size distri-
bution of nanoparticles it is necessary to count and measure
thousands of particles in order to obtain a reliable counting
statistics of the very few larger nanoparticles in the size
distribution. The large particles in the distributions (or
aggregates), even if very few, can contribute substantially
to the volume or mass based distributions. In nanotech-
nology or material science this is not a problem since the
particles to be measured are of the same type and are of
similar size but when dispersed in water and mixed with
natural organic matter and natural nanoparticles it is
another story. Therefore we see a big need for automation
in electron microscopy and development of ‘‘smart’’ image
analysis software that enables characterization of the mil-
lions of particles needed in each sample (Mavrocordatos
et al. 2007). With this said microscopy methods are very
powerful for imaging and process understanding but it
should be complemented with a particle population method
that is giving quantitative information on the sample.
Another common feature for all microscopy techniques
is that they require different levels of sample preparation. It
ranges from the mildest being drying of the particles to a
moist condition (AFM and ESEM) to a high-vacuum in
SEM and TEM. In some methods coating or staining the
sample is used. The transfer of the sample from its dis-
persed hydrated state to a dried high vacuum state often
means that the particle size distribution changes dramati-
cally. For example by evaporating a sample drop into
dryness (a common method) the particle concentration and
solute concentration increases drastically in the decreasing
volume of the drop before it finally evaporates. This leads
to aggregation of particles and precipitation of salts. Some
methods are used to preserve the hydrated state of particles
either by cryofixation, which is a rapid freezing so that the
water forms non-crystalline ice. Another method is
embedding the particles in some water-soluble resin that
fixes the water when it cures.
The three most common sample preparation methods for
natural colloids is drop deposition, adsorption deposition or
ultracentrifugation harvesting, and the methods have been
compared for AFM and electron microscopy respectively
(Balnois and Wilkinson 2002; Mavrocordatos et al. 2007).
Scanning electron microscopy
In the family of electron microscopy techniques the sample
is exposed to a high energy focused beam of electrons. In
scanning electron microscopy (SEM) the interaction of the
beam with the particle surface are scanned over the sample
and measured as secondary electrons (most common), or
backscattered electrons or X-ray photons. Due to the high
depth of field in SEM a three dimensional appearance can
be obtained. The sample needs to be conductively coated
with gold or graphite and maintained under ultrahigh
vacuum in order not to have the secondary electrons
interact with gas molecules. The substrate is typically a
filter membrane or a conducting grid.
Environmental scanning electron microscopy and
related techniques
Due to the problems with morphological changes of the
particles associated with the transfer to high vacuum state,
environmental scanning electron microscopy (ESEM) was
developed, where the sample cell is separated from the
detector cell. This allows the sample to be measured under
variable pressure and humidity (in theory up to 100%) with
residual hydration water still on the particles. This water
layer also serves as a conductor on the surface so the
sample does not need to be conductively coated. The res-
olution is decreased (from *10 to *100 nm) due to the
interactions of the secondary electrons with the water vapor
molecules but there are less sample artifacts for example
from natural colloids (Doucet et al. 2005). ESEM still
allows analysis of the emitted X-rays. Wet STEM is a
method for scanning TEM analysis of a wet sample on a
TEM grid in an ESEM microscope utilizing dark-field
imaging conditions with a resolution of a few tenths of nm
(Bogner et al. 2005). A new sample capsule (WetSEM
TM
)
with electron transparent membranes provides an alterna-
tive to ESEM in ordinary SEM microscopes. The WetSEM
capsules allow imaging under liquid or moist conditions
(Thiberge et al. 2004). However, the loss of resolution is
considerable (partly due to diffusion of the particles), and
the membrane is sensitive to radiation damage, and only
particles close to the membranes are in focus.
Transmission electron microscopy
In transmission electron microscopy (TEM) the electron
beam is transmitted through a very thin specimen on a
conducting grid (e.g., copper grid with a thin resin,
Nanoparticle analysis and characterization methods 355
123
e.g., formvar). After the beam has been transmitted through
the sample and has interacted with the particles the non-
absorbed electrons are focused onto an imaging detector
(fluorescence screen or CCD camera). In TEM the particles
are shined through by the electron beam and the absor-
bance (image contrast) is both a function of the electron
density of the elements in a particle and the thickness of the
particles. Organic matter with only light elements needs to
be stained by a heavy metal cocktail in order to be visible.
High-resolution TEM is a method that can give subna-
nometer resolution and is used in material science to study
atom-by-atom structure. HR-TEM is a very demanding and
time-consuming method but it has been applied to detect
nanoparticle formation by bacteria or in geochemical pro-
cesses (Banfield and Navrotsky 2001; Suzuki et al. 2002).
TEM has also been applied to characterize carbon
nanoparticle dispersions in ecotoxicological exposure
experiments (Smith et al. 2007).
Electron microscopy microanalysis
For all electron microscopy methods mentioned here
analysis of spectral patterns of emitted X-rays (K, L & M
lines) for elemental composition of the particles can be
utilized if the microscopes are fitted with an energy dis-
persive X-ray spectrometer (EDX or sometimes EDS). The
spatial resolution can be even less than10 nm. The sensi-
tivity is best for heavier elements, so in reality it works best
for major elements of the particles and associated heavy
metals in fairly high concentrations. The measurement
uncertainty of EDX is generally *20% (Mavrocordatos
et al. 2004, 2007).
Electron energy loss spectrometry (EELS) is another
elemental composition method that can be applied in either
spectrometric mode or in imaging mode in TEM. In EELS
the loss of energies due to inelastic scattering processes
(e.g., inner shell ionizations) can be interpreted to which
elements that were causing the scattering. The energies lost
are specific for each element. The EELS results are more
difficult to interpret than EDX and works best for the
lighter elements (from carbon and up to zinc). EELS can
also be used to obtain additional chemical information
(e.g., redox states of transition metals).
Atomic force microscopy
Atomic Force Microscopy (AFM) is a subnanometer res-
olution method in the family of scanning probe
microscopy. It utilizes a cantilever with a very thin tip (tens
of nm), that is oscillating over the surface of the sample.
The oscillating movement (Z-axis) and the scanning over
the surface (X and Y-axis) is controlled by piezoelectric
actuators.
A laser-based balance can measure both repulsive (Pauli
principle) and attractive (van der Waals) forces between
the tip and the sample in the range 10
-7
to 10
-12
N. The
occurrence of these forces at different stages of the canti-
lever oscillation can be used to derive a separating distance
between the tip and the particles. The resulting images are
an atomic force topography. The substrates that the particle
samples are prepared on should be atomically flat (mica,
graphite or silicon wafers are examples of suitable sub-
strates). The preparation methods are typically drop
deposition, adsorption deposition or ultracentrifugation as
for electron microscopy, but in addition it is possible to
analyze samples under moist conditions or even in liquids,
which affords minimum perturbation. However, under
liquid conditions the particles are sometimes attracted to
the substrate (very weakly) and are moved around dis-
turbing the images. Another feature in AFM is that the
geometry of the tip compared to particle size gives that the
tip is starting to ‘‘feel’’ the particle significantly before its
center has approached the particle periphery, and analo-
gously when the scanning tip is leaving the particle it feels
the particle forces too long. Therefore the lateral dimen-
sions are greatly overestimated, while the height
measurements are very accurate. This should be kept in
mind when interpreting AFM images, which means, e.g., a
carbon nanotube can give a height of 1 nm but a width of
up to 50 nm even though these should be the same. The
geometry of the tip should be decreased if small particles
are to be more accurately probed. The cantilever tip can be
set to contact the particles but lateral forces lead to
movement of particles, so a tapping mode or a non-contact
mode has been developed to just feel the forces above the
particles (Balnois et al. 2007). The latter has shown to be
more accurate for soft, compressible particles such as
humic acids. AFM is one of the most common nanome-
trology methods and has numerous applications (e.g., Lead
et al. 2005; Viguie et al. 2007).
In Fig. 3, a dispersed ZnO nanopowder sample (with
manufacturer stated size 50–70 nm) has been prepared
with adsorption deposition and analyzed with AFM, TEM
and SEM. The difference in visualization and size mea-
surements is clear. AFM and TEM show sintered
aggregates with primary particles in the size range pro-
vided by manufacturers, whilst SEM shows mainly larger
flakes of material with some nanoparticles on top. It is
likely that the sample preparation and vacuum-induced
changes can explain these differences.
Surface charge measurements
Colloidal nanoparticles develop surface charges in aqueous
solutions. The net surface charge, or surface potential, is
356 M. Hassello
¨
v et al.
123
one of the most important nanoparticle characteristics since
it describes to what extent the nanoparticle dispersion is
electrostatically stabilized by interparticle repulsion. Con-
sequently, ENP surface potential will have major influence
on their fate and behavior (Guzman et al. 2006; Hunter and
Liss 1979). However, it is not easy to directly measure the
surface potential but there is a simple method that measures
the so-called zeta potential, which is the potential at a
hydrodynamic slipping plane in the electrostatic double
layer of the particles as measured by electrophoresis. The
measured electrophoretic mobility can be converted to z
potential through Smoluchowski’s theories. The point of
zero charge (PZC) is the pH where negative and positive
charges are balanced, so there is no net charge on the
nanoparticles. At PZC there is generally maximum aggre-
gation taking place since the particles are allowed to come
in close contact so that attractive van der Waals forces can
act.
Surface area measurement
The Brunauer, Emmett, Teller (BET) (Brunauer et al.
1938) method is used to measure the specific surface area
of solids, which involves drying of a powder in vacuum
and then measuring (using a microbalance) the adsorption
of dinitrogen gas (assumed as a monolayer) on the surface
and in micropores. The BET method builds on the
assumption that N
2
has access to the complete surface of
the particles. Other variants of this method based on
adsorption of organic molecules (e.g., ethylene glycol
monoethyl ether, EGME) can be used (Hassello
¨
v et al.
2001). Dinitrogen gas gives higher surface areas than
EGME, probably due to greater access to smaller pores.
Crystal structure
X-ray diffraction (XRD) is a method of measuring inter-
particle spacings resulting from interference between
waves reflecting from different crystal planes. It is used in
mineralogy to determine crystal structure of mineral par-
ticles. For example XRD can be used to distinguish
between the anatase and rutile and amorphous phases of
TiO
2
nanoparticles. A dry sample needs to be prepared as a
thin film. Elemental composition of major elements can
also be obtained although the sensitivity is low compared
to other elemental analysis methods (e.g., ICPMS or AES).
Fig. 3 ZnO nanoparticle
powder (50–70 nm, Sigma
Aldrich UK), dispersed in
distilled water (*5mgl
-1
),
allowed to dry on silica and
imaged by AFM (1a and b),
TEM (2) and SEM (3) under
standard conditions
Nanoparticle analysis and characterization methods 357
123
It is also possible in TEM to measure the diffraction
patterns of single particles using a method called ‘‘Selected
area electron diffraction’’ (SAD, or SAED). In SAD the user
can select an area of the sample with a small aperture and
only the electron diffraction pattern from that area will be
measured. This has benefits over XRD for heterogeneous
samples because it allows single particle characterization.
Difference in analysis of particulate
and nanoparticulate assemblages compared
to conventional analysis of solutes
For analysis of nanoparticle assemblages by bulk analytical
methods (in contrast to single particle analysis methods, e.g.,
microscopy) in whole samples or on fractions after sample
treatment (e.g., filtration or Field-Flow Fractionation), it is
necessary to recognize that for certain methods there may be
differences compared to more common analysis of dissolved
solutes (e.g., ions or molecules). In bulk analysis of a
nanoparticle dispersion the analytes mass concentration are
not homogeneously distributed, but rather as uniformly
distributed point masses. This is not a problem providing the
probed sample volume of the method is not approaching that
of single nanoparticles. But when analyzing samples with
environmentally relevant concentrations, with methods that
are probing a very small samples volume (e.g., a very rapid
measurement in a capillary or a fast flowing sample stream
such as in mass spectrometers) the measurement may
approach or enter a domain of single nanoparticle events.
The consequence is a noisier signal and if there is statistically
less than one particle per measurement then the recovery of
the determination decreases, which gives an erroneous
determination. Since the particle numbers (for the same
mass) decreases rapidly for larger particles, this issue is more
severe for them than for smaller particles. This is a well-
known phenomena in e.g., ICPMS analysis of micrometer
sized particles, and is called slurry nebulization. It needs to
be considered when the number concentration is low. Other
problems may be non-quantitative measurement of the par-
ticles, for example through incomplete atomization in
elemental analyses or non-transparent or shading effects in
spectroscopy. For slurry nebulization in ICP-AES or ICP-
MS, it has been found that the particle size is the dominating
factor to obtain complete atomization, where particles below
3–5 lm have been found to yield quantitative recoveries
compared to solutions (Ebdon et al. 1997; Santos and Nob-
rega 2006). The main reason for decreasing recoveries was
poor transport efficiencies in the nebulizer-spraychamber
system. This implies that for nanoparticles, incomplete
atomization should not be a problem but maybe for aggre-
gates particularly refractory materials such as carbides and
some oxides may also present problems.
Validation, measurement uncertainty and good
laboratory practices
In metrology and analytical chemistry, it is fundamental to
be able to report on the traceability of the acquired results.
Calibration standards used for quantification are generally
traceable to a primary national or international standard.
However, for nanoparticles the validity of these standards
has a shorter lifetime than most other standards and is more
sensitive to operating conditions. Nanoparticle standards,
or reference materials, exist both as suspensions and as
powders. Nanoparticle standards in suspension are gener-
ally labeled with expiry dates and instructions for storage.
Sometimes there are also instructions on how to further
dilute the standard in order to maintain its integrity. The
use of powdered nanoparticle standards does not include a
standardized procedure for dispersion of the nanoparticles.
To make the dispersion in each individual laboratory
increases the uncertainty of the original metric stated by
the manufacturer. Indeed, many metrics (e.g., size distri-
bution) are strongly dependant on how the dispersion was
made and in which media (pH, ionic strength and com-
position and presence of organic matter).
In addition to the nanometrology specific issues of method
validation relates to the normal quality control (QC) of any
analytical method (Table 5). The most important steps in
analytical QC are method validation and quantification of
measurement uncertainty. The method validation is simply
an experimental procedure to determine that the method and
procedures (standard or in-house developed) are complying
with the documented specifications (e.g., limit of detection,
linearity, determination of precision and accuracy and
robustness). One way of determining the accuracy is to use a
certified reference material (CRM) of the same type as the
samples and with documented property values within the
range of the method (Table 5). CRMs or NIST traceable size
standards are however very rare for nanoparticles as yet.
There exist reference materials with certified sizes for gold
and polystyrene colloids in the nanometer size range. More
reference materials are under development through inter-
national efforts. Testing the homogeneity, shelf life of a
reference material and carrying out all the analysis in order to
certify the material is very elaborate and expensive. In the
absence of CRMs there is also the possibility to use non-
certified materials to (test materials) to benchmark analytical
procedures and toxicity testing (Aitken et al. 2007).
Another option is to participate in interlaboratory compar-
isons were a blind sample is sent to many laboratories for
analysis, thereby affording a good indication of accuracy and
precision in the results. Interlaboratory comparisons are not yet
as common in nanometrology as in conventional analytical
chemistry where rigorous quality assurance protocols are fol-
lowed in order to achieve and maintain certified accreditation.
358 M. Hassello
¨
v et al.
123
There are, however, a few examples of informal interlabora-
tory comparisons on natural nanoparticles (Lead et al. 2000a)
and on engineered nanomaterials (Breil et al. 2002)which
have proved highly informative to the participants and for other
users of the same methodologies. A good daily routine is to
analyze a QC sample and plot that value into a control diagram
to monitor measurement uncertainty between interlaboratory
comparisons. The QC sample should be a sample that is stable
over time and that is as similar to the usual samples as possible.
Thus can method or instrument related problems in the labo-
ratory can be easily and quickly discovered.
Good laboratory practices in characterization of exposure/
effect experiments should include minimal sample perturba-
tion and determination of the dispersion-agglomeration state.
Dynamic light scattering fulfills these criteria, is a simple
measurement to perform, and is available in most academic
institutions. It is also a simple measurement to perform.
However, for the reasons described previously, the results
from DLS should not be over interpreted. DLS is primarily not
a size determination method as it measures scattering intensity
weighted diffusion coefficients. Thus, it is well suited to fol-
low initial stages of aggregation, but not to provide
nanoparticle sizes. For toxicity tests of nanoparticles, we
suggest to conduct a separate dispersion experiment under
optimum conditions as a reference to the dispersion behavior
in the effect media and during the course of the effect exper-
iment. This reference experiment with maximum dispersion
may include surfactants, co-solvents, certain ionic strength
and sonication. By comparing the results in the realistic effect/
exposure experiments with this reference experiment one can
obtain information on the degree of aggregation.
If competence and equipment is available a less biased
(but with slightly more perturbation) determination of the
size distribution can be achieved using e.g., Field-Flow
Fractionation. Microscopy (e.g., AFM, SEM or TEM) is
very powerful in imaging nanoparticles and aggregates, but
the aggregation state of the sample may have changed
during sample preparation.
Acknowledgements Hassello
¨
v thanks the Swedish Environmental
Research Council FORMAS and University of Gothenburg Nano-
particle platform for financial support. J. Readman acknowledges
partial support of his contribution through the UK Natural Environ-
ment Research Council Environmental Nanoscience Initiative (Grant
Reference Number: NE/E014321/1). Ranville acknowledges partial
support through EPA STAR Grant RD-83332401-0
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