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Principles and Procedures to Assess Nanomaterial Toxicity 213

in vitro and in vivo, is an example of a predictive test paradigm. This is
also compatible with the preferred NTP approach to chemical toxicity,
that is, using predictive scientific models that focus on target-specific,
mechanism-based biological outcomes rather than descriptive
approaches (http://ntp-server.niehs.nih.gov). We propose that the same
guidelines be followed for engineered NM.

Physico-chemical characterization of NM
Characterization should be done at the time of NM administration as well
as at the conclusion of biological studies. This would allow one to inter-
pret the physico-chemical changes that take place in the presence of pro-
teins, surfactants, or biological fluids. Any test paradigm should attempt
to characterize the test material with respect to size distribution, chem-
ical composition, surface area, crystallinity, electronic properties, shape,
inorganic/organic coatings, hydrophobicity, and aggregation (Table 6.2).
The possible relationship of these physical and chemical properties to bio-
logical outcomes could be premised on an interactive model such as
shown in Figure 6.1. Particle size, shape, surface area, chemical compo-
sition, and surface coatings are primary material characteristics that are
often provided by the manufacturer. These primary characteristics, which
are usually acquired under dry conditions, determine intermediary mate-
rial properties such as surface reactivity, catalytic properties, crys-
tallinity, and biopersistance (Figure 6.1). Surface chemistry and surface
coating/chemicals could, in turn, determine the hydrophobicity and
hydrophilicity of the particles, their surface charge in aqueous solution,

TABLE 6.2 Nanomaterial Characterization
Parameters Methods

Size distribution (primary particles) TEM, SEM, XRD
Shape TEM, SEM
Surface area BET
Composition Mass spectrometry,
spectroscopy
(UV, Vis, Raman, IR, NMR)
Hydrophobicity MATH
Surface charge—suspension/solution Zeta potential
Crystal structure TEM, XRD
Agglomeration state TEM, SEM, DLS
Porosity MIP
Heterogeneity TEM, SEM, spectroscopy
ROS generation capacity DTT, FFA assay, nanosensors
Adapted from [12].
TEM, transmission electron microscopy; SEM, scanning electron
microscopy; XRD, X-ray diffraction; BET, Brunauer, Emmett and Teller;
MATH, microbial adhesion to hydrocarbons; DLS, dynamic light scattering;
MIP, mercury intrusion porosimeter; FFA, furfuryl alcohol