Keywords |
|
Nanoparticle; Aggregation; Temperature; EDLVO |
|
Introduction |
|
Recently, engineered nanoparticles (NPs) have received
enormous attention owing to their potential commercial and
industrial applications in many sectors, such as cosmetics, textiles,
pharmaceutical, catalysts and electronics [1,2]. But meanwhile, the
release of NPs into the environment will very likely happen along with
their large-scale manufacture and wide use, which will subsequently
impose risks for ecosystems and human health [3]. It is thus important
to evaluate the environmental and health risks of NPs before their mass
production. Since the toxicological testing’s of NPs are expensive and
time-consuming, researchers are developing theoretical models to
evaluate and predict the behavior and risks of NPs in environmental
systems [4-6]. |
|
Previous studies have shown that the aggregation of NPs plays
an important role in their environmental risks by influencing
their transport, fate, bioavailability and biological effects [7-11].
Understanding the fundamental principles underlying the aggregation
process of NPs and quantitatively describing this process are essential
prerequisites for characterizing the environmental behavior of NPs and
further quantifying the risk. The aggregation of NPs is fundamentally
governed by the interfacial force between interacting particles,
which includes several either attractive or repulsive forces. When
the attractive force is greater than the repulsive force, NPs approach
each other and aggregate; otherwise, NPs stay stable. The famous
Derjaguin-Landau-Verwey-Overbeek (DLVO) theory has been widely
used to characterize the interfacial force between particles [12,13].
According to it, the vander Waals (vdW) force and electrostatic (EL)
force compose the interfacial force. The DLVO theory achieved great
success in explaining the stability of colloids in salt solutions. But for
NP aggregation, many studies have found that a discrepancy exists
between DLVO predictions and experimental observations [14]. This
problem might be overcome by taking non-DLVO forces into account,
such as the polar Lewis acid/base (AB) force [15] and steric force [16].
Here the AB force is the sum of the hydrophobic interaction force,
hydrogen-bonding force and hydration force [15-17]. The precise
theoretical analysis of NP interaction and quantitative description of NP aggregation can be obtained by incorporating those non-DLVO
forces into the DLVO theory, which is known as the extended DLVO
(EDLVO or XDLVO) theory [15]. |
|
On the basis of EDLVO theory, our previous studies have
addressed the effects of ionic strength and natural organic matter on
NP aggregation with modeling approaches [5,18]. It is well known
that temperature also greatly influences the aggregation of NPs.
Understanding the temperature effect is important for environmental
and health risk assessments of NPs, as both natural water and human
body fluids can be at temperatures that are remarkably different from
the typically used room temperature. For example, river waters in
some cold areas may be only 4°C, whereas the temperature of blood
in the human body is as high as 37°C. NPs in these solutions would
undergo different aggregation processes. The temperature effect,
however, has not gained much attention in NP aggregation studies. In
this study, we investigated the temperature effect on the aggregation
of NPs in KCl and CaCl2 solutions using time-resolved dynamic light
scattering (TR-DLS). We selected CeO2 NP as a model NP owing to
its extensive commercial applications [19-21]. It has been listed as a
priority nanomaterial for immediate testing by the Organization for
Economic Co-operation and Development (OECD) [22]. We used
the EDLVO theory to interpret the fundamentals of the temperature
effect on NP aggregation. Furthermore, a kinetic model developed
on the basis of EDLVO theory and von Smoluchowski’s population
balance equation was used to predict the aggregation kinetics of CeO2 NPs, which were then compared with experimental observations. Our
aim was to fundamentally understand the temperature effect on NP
aggregation and theoretically predict the aggregation kinetics of NPs
under different temperature, which were anticipated to benefit the
predictive modeling research of environmental behavior and toxicity
assessment of NPs. |
|
Materials and Methods |
|
Materials |
|
CeO2 NPs with a nominal diameter of 25 nm were purchased from
Sigma-Aldrich. The atomic composition of the sample was verified using
X-ray diffraction (data not shown). The pH of the stock suspension was
measured to be 4.5 by pH meter (Accumet model 15, Fisher Scientific
Co., USA). KCl and CaCl2 stock solutions were prepared using ACS
reagent-grade chemicals (Fisher Scientific Co., USA) and were filtered
through 0.02 μm filters (VWR International, USA) before use. |
|
Characterization of CeO2 NPs |
|
The morphology and primary particle size of CeO2 NPs were
determined using transmission electron microscopy (TEM). 5 μL
of CeO2 NP suspensions were deposited on a copper grid (400 mesh
size) coated with carbon film (Ted Pella, Redding, CA, USA). A Philips
EM420 TEM was employed to acquire images. Particle size distribution
(PSD) was obtained with DLS on a Zetasizer Nano ZS instrument
(Malvern Instruments). Briefly, 1.5 mL of 10 mg/L CeO2 NP suspension
was injected into a clean cuvette; the DLS instrument was then operated
with a scattering angle of 173° from the incident laser beam, and the
autocorrelation function automatically accumulated at least 10 runs for
each sample. The electrophoretic motilities’ (EPMs) of 10 mg/L CeO2 NPs were measured for a range of K+ and Ca2+ concentrations under
different temperatures using the Zetasizer Nano ZS instrument. At
least four parallel measurements were made for each condition. The
measurement began immediately after the desired conditions were
achieved to minimize the interference of aggregation. |
|
Aggregation kinetics |
|
The aggregation kinetics experiments were carried out at pH 5.7,
at which the CeO2 NPs are stable for at least 24 h. The pH values of the
CeO2 NP, KCl and CaCl2 solutions were pre-adjusted to 5.7 to ensure
that each measurement could start immediately after addition of K+ and Ca2+. For the aggregation experiment, the sample holder of the Zeta
sizer Nano ZS instrument was preheated or precooled to the desired
temperature. A premeasured amount of KCl or CaCl2 was added to 1
mL of CeO2 NP suspension in a cuvette. The NP suspension was then
shaken slightly and placed in the sample holder. |
|
Modeling the aggregation kinetics |
|
According to the EDLVO theory, the total interfacial force between
two metal oxide NPs is comprised of the vdW force, EL force and AB
force [15]. The total interfacial energy (VT) between NPs is computed
by assuming that each force acts individually and is thus additive:
VT=VvdW+VEL+VAB. |
|
The vdW attractive energy (VvdW)between two identical spherical
particles, which considers the retardation effect, can be computed
using Equation (1) [23]: |
|
(1) |
|
where AH is the Hamaker constant, which is 5.57×10-20 J for CeO2 in water [24]. r is the particle radius. h is the separation distance between
the interacting surfaces. λc is the “characteristic wavelength” of the
interaction, which is often assumed to be 100 nm [25]. |
|
The EL repulsive energy (VEL) between two identical spheres of
radii r in 1-1 electrolyte solutions (e.g., KCl) is given by Equation (2ac).
In 2-1 electrolyte solutions (e.g., CaCl2), Equation (2a) and (2b) are
replaced by Equation (2d) and (2e), respectively [26-28]: |
|
(2a) |
|
(2b) |
|
(2c) |
|
(2d) |
|
(2e) |
|
where n is the concentration of electrolytes; kB is the Boltzmann
constant; T is absolute temperature; zi is the valency of the ith ion; e
is unit charge; ψSi is the surface potential of the interacting particles
in an aqueous medium, which can be calculated from the EPMs of
NPs (UE) (Figure 1), the solution viscosity (μ) and permittivity (ε.ε0)
of water by the Smoluchowski Equation: ψSi=(UE .μ)/(εε0) [27]; ε0 is the
vacuum permittivity; ε is the relative permittivity of water; κ-1
is the
Debye length; NA is Avogadro’s number; and I is the ionic strength (M),
I=0.5·Σcizi2, where ci is the molar concentration of the ith ion. |
|
Finally, the AB energy (VAB) between two identical spheres is
expressed by Equation (3): |
|
(3) |
|
where λ is the correlation length or decay length of the molecules of the
liquid medium, which is estimated to be 1 nm for pure water [29], and
ΔGh0AB is the polar or AB free interaction energy between particles at
the distance h0 [30], which is the minimum equilibrium distance due to
Born repulsion, 0.157 nm [29]. |
|
Upon computing the total interaction energy (VT), the aggregation
kinetics of CeO2 NPs can be obtained by Equation (4), which was
developed on the basis of the EDLVO theory and von Smoluchowski’s
population balance equation [31]: |
|
(4) |
|
Where rt is the particle radius at time t, a is the primary particle radius,
n0 is the initial number concentration of primary particles, μ is the
solution viscosity, and dF is the fractal dimension of aggregates. W is
the stability ratio, which can be expressed by Equation (5) [32,33]: |
|
(5) |
|
where u is the normalized surface-to-surface separation distance (h)
between two particles (u=h/a) and VA(u) is the attractive energy. Here,
vdW energy is the only contributing term to VA(u) and thus VA=VvdW.
λ(u) is the correction factor for the diffusion coefficient, which is
related to the separation distance by Equation (6) [34]: |
|
(6) |
|
The number concentration of CeO2 NPs is determined from the
mass concentration. The lattice parameter (al) of CeO2 unit cells is
5.4087 Å [35], and each unit cell contains four Ce atoms and eight O
atoms. The number of Ce atoms (N) per CeO2 NP with radius r can be
calculated by N=16π (r/al)3/3. The mass of a single CeO2 NP is then
obtained, and the number concentration of NPs can be computed. |
|
Results and Discussion |
|
Characterization of CeO2 NPs |
|
A TEM image of CeO2 NPs is presented in figure 1. The NPs have
a relatively uniform size distribution. The inset in figure 1 shows the
PSD diagram of CeO2 NPs, which was measured by DLS. Consistent
with previous studies, the DLS-measured NP size is larger than that
determined by TEM [36,37]. This is probably owing to particle
aggregation and the water layer surrounding the NP surface. The
polydispersivity index (PDI) is quite small (∼0.1), indicating that
CeO2 NPs are relatively monodispersed in solution. Figure 2 shows the
zeta potentials of CeO2 NPs under different temperatures in KCl and
CaCl2 solutions. The CeO2 NPs are positively charged under all tested
conditions. The divalent ion (Ca2+) is more effective than the monovalent
ion (K+) in screening the surface charge of NPs. As ionic strength
increased, the zeta potential became smaller due to the compression of
the electrical double layer surrounding the NP. The temperature effect
is apparent; as the temperature increased, the zeta potential became
less positive, which was consistent with previous studies [38,39]. The
reason could be that increasing temperature favors proton desorption
from the particle surface [38]. At higher temperature, the lower zeta
potential of CeO2 NPs implies that the electrostatic repulsion force
between particles is weaker, and this probably promotes the particle
aggregation. |
|
Effect of temperature on the aggregation of CeO2 NPs in KCl
and CaCl2 |
|
The representative aggregation kinetics profile of CeO2 NPs in
KCl and CaCl2 solutions under different temperatures were presented
in figure 2. As the temperature increased, the NP aggregation became
faster. The attachment efficiency (α), or inverse stability ratio (1/W), was calculated by normalizing the initial slopes of aggregation kinetics
curves with the slopes obtained in the diffusion-limited aggregation
regime (Figure 3). The critical coagulation concentration (CCC)
for CeO2 NPs in KCl was ca. 100, 40 and 10 mM at 4, 25 and 37°C,
respectively. In CaCl2, CCCs were ca. 10, 10 and 2 mM at 4, 25 and
37°C, respectively. The substantially lower CCCs for CeO2 NPs in Ca2+ solutions than those in K+ solutions is because divalent ions more
effectively screen the surface charge of NPs and subsequently enhance
the aggregation. Higher temperature leads to a smaller CCC and thus
promotes NP aggregation. |
|
Higher temperature promotes NP aggregation for two reasons.
First, the solution viscosity μ was smaller at higher temperature;
according to Equation (4), the particle aggregation was thus enhanced.
Second, the interaction energy between NPs also changes as the
temperature increases. The total interfacial energy VT can be calculated
using Equation (1)-(3). Parameters involved in these equations could
be either measured or computed. Surface potentials (ψS) of CeO2NPs
under different temperatures were calculated from the EPMs with the
Smoluchowski equation [27]. The other major parameters are listed in
table S1. |
|
The interaction energies for CeO2 NPs under different temperatures
were computed and are presented in figure 2, which shows that the
interaction energy between NPs is lower at a higher temperature in both
KCl and CaCl2 solutions. The energy barrier reflects the aggregation
tendency. The energy barrier diminished as the temperature increased.
When the temperature increased from 4 to 37°C, the magnitude of
the energy barrier decreased from 11 to 4 kBT and from 7 to 1 kBT
in 0.01 M of KCl and 0.002 M CaCl2, respectively. This suggests that
NPs more easily overcome the energy barrier and aggregate at high
temperatures. Moreover, according to Equation (1)-(3), the EL force is
the only force that is influenced by the change in temperature (Figure
S4). Parameters in Equation (3), such as the surface potential of NPs, solution permittivity and Debye length, are affected by temperature.
The temperature has no impact on vdW and AB forces. |
|
For a better understanding of the contribution of each energy term
to the total interaction, the representative energy profiles are presented
in figure 3 and figure S5 in the supporting information. Apparently, the
AB repulsion energy contributes more relative to EL repulsion energy.
This indicates that, compared with EDLVO theory, the conventional
DLVO theory, which considers only EL and vdW energy, provides a
less accurate description of the interfacial energy between CeO2 NPs. |
|
Modeling the aggregation kinetics of CeO2 NPs |
|
Equation (4) was used to model the aggregation kinetics of CeO2 NPs. The initial number concentration of CeO2NPs is approximately
2.35×1015 particles/m3 in all aggregation experiments. The fractal
dimension dF was reported to be ca. 1.8 [40-43]. The total interaction
energy VT was computed according to Equation (1)-(3). The attractive
energy, VA, equals the vdW energy (VvdW). The AB free interaction energy
between particles at the distance h0, ΔGh0AB, was consistent with our
previous studies. Other parameters are listed in table S1. The modeling
results were further compared with experimental observations, and
representative comparisons are presented in figure 4 and figure S6 in
the supporting information. At all temperatures, model predictions
agreed well with experimental data. Some minor discrepancies between
model predictions and experimental observations may be attributed to
deviations in the surface potential of NPs and the size distribution of
particles. |
|
Conclusion |
|
In conclusion, this work investigated the temperature effect on
the aggregation of CeO2 NPs with both experimental and modeling
approaches. As the temperature increased from 4°C to 37°C, the CCCs
for CeO2 NPs decreased from ca. 100 to 10 mM in KCl and from ca. 10 to
2 mM in CaCl2. The promotive effect of temperature on NP aggregation
is ascribed to the smaller solution viscosity and lower interfacial energy
barrier at higher temperature. For instance, the energy barrier height
decreased from 11 to 4 kBT in 0.01 M KCl and from 7 to 1 kBT in
0.002 M CaCl2, which resulted from the smaller repulsive EL energy
at a higher temperature. The aggregation model based on the EDLVO
theory gave fairly good predictions of NP aggregation under different
temperatures. To the best of our knowledge, this is the first study to
research the temperature effect on NP aggregation with modeling
approaches, which is expected to benefit the theoretical predictions of
the environmental behavior and biological effects of NPs and to further
contribute to the environmental and health risk assessment of NPs. |
|
Acknowledgments |
|
This study was partially supported by the U.S. Environmental Protection
Agency Science to Achieve Results Program Grant RD-83385601 and Engineering
Research Center (ERC)/Semiconductor Research Corporation (SRC)/ESH grant
(425.025). |
|
Supporting Information |
|
Details about the EPMs of CeO2 NPs, representative aggregation kinetics
profiles, attachment efficiencies, model parameters, interaction energy profiles,
and other aggregation modeling profiles. This material is available free of charge
via the Internet. |
|
References |
|
- Stamper RL, Liberman MF, Drake MV (1999) Becker & Shaffer’s Diagnosis and Therapy of the Glaucomas. (7th Edn), Mosby, St Louis-Missouri.
- Mansoori T, Viswanath K, Balakrishna N (2011) Reproducibility of peripapillary retinal nerve fiber layer thickness measurements with spectral domain optical coherence tomography in normal and glaucomatous eyes. Br J Ophthalmol 95: 685-688.
- Schuman JS (2008) Spectral domain optical coherence tomography for glaucoma (an AOS thesis). Trans Am Ophthalmol Soc 106: 426-458.
- Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, et al. (1991) Optical coherence tomography. Science 254: 1178-1181.
- Sakata LM, Deleon-Ortega J, Sakata V, Girkin CA (2009) Optical coherence tomography of the retina and optic nerve - a review. Clin Experiment Ophthalmol 37: 90-99.
- Chen TC (2009) Spectral domain optical coherence tomography in glaucoma: qualitative and quantitative analysis of the optic nerve head and retinal nerve fiber layer (an AOS thesis). Trans Am Ophthalmol Soc 107: 254-281.
- Sung KR, Kim JS, Wollstein G, Folio L, Kook MS, et al. (2011) Imaging of the retinal nerve fibre layer with spectral domain optical coherence tomography for glaucoma diagnosis. Br J Ophthalmol 95: 909-914.
- Leung CK, Cheung CY, Weinreb RN, Qiu Q, Liu S, et al. (2009) Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology 116: 1257-1263.
- Vizzeri G, Weinreb RN, Gonzalez-Garcia AO, Bowd C, Medeiros FA, et al. (2009) Agreement between spectral-domain and time-domain OCT for measuring RNFL thickness. Br J Ophthalmol 93: 775-781.
- Kim JS, Ishikawa H, Sung KR, Xu J, Wollstein G, et al. (2009) Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography. Br J Ophthalmol 93: 1057-1063.
- Hong S, Kim CY, Lee WS, Seong GJ (2010) Reproducibility of peripapillary retinal nerve fiber layer thickness with spectral domain cirrus high-definition optical coherence tomography in normal eyes. Jpn J Ophthalmol 54: 43-47.
- Schulze A, Lamparter J, Pfeiffer N, Berisha F, Schmidtmann I, et al. (2011) Diagnostic ability of retinal ganglion cell complex, retinal nerve fiber layer, and optic nerve head measurements by Fourier-domain optical coherence tomography. Graefes Arch Clin Exp Ophthalmol 249: 1039-1045.
- Quigley HA, Dunkelberger GR, Green WR (1989) Retinal ganglion cell atrophy correlated with automated perimetry in human eyes with glaucoma. Am J Ophthalmol 107: 453-464.
- Kerrigan-Baumrind LA, Quigley HA, Pease ME, Kerrigan DF, Mitchell RS (2000) Number of ganglion cells in glaucoma eyes compared with threshold visual field tests in the same persons. Invest Ophthalmol Vis Sci 41: 741-748.
- Schuman JS, Pedut-Kloizman T, Hertzmark E, Hee MR, Wilkins JR, et al. (1996) Reproducibility of nerve fiber layer thickness measurements using optical coherence tomography. Ophthalmology 103: 1889-1898.
- Carpineto P, Ciancaglini M, Zuppardi E, Falconio G, Doronzo E, et al. (2003) Reliability of nerve fiber layer thickness measurements using optical coherence tomography in normal and glaucomatous eyes. Ophthalmology 110: 190-195.
- Blumenthal EZ, Williams JM, Weinreb RN, Girkin CA, Berry CC, et al. (2000) Reproducibility of nerve fiber layer thickness measurements by use of optical coherence tomography. Ophthalmology 107: 2278-2282.
- Jones AL, Sheen NJ, North RV, Morgan JE (2001) The Humphrey optical coherence tomography scanner: quantitative analysis and reproducibility study of the normal human retinal nerve fibre layer. Br J Ophthalmol 85: 673-677.
- Paunescu LA, Schuman JS, Price LL, Stark PC, Beaton S, et al. (2004) Reproducibility of nerve fiber thickness, macular thickness, and optic nerve head measurements using StratusOCT. Invest Ophthalmol Vis Sci 45: 1716-1724.
- Budenz DL, Chang RT, Huang X, Knighton RW, Tielsch JM (2005) Reproducibility of retinal nerve fiber thickness measurements using the stratus OCT in normal and glaucomatous eyes. Invest Ophthalmol Vis Sci 46: 2440-2443.
- Budenz DL, Fredette MJ, Feuer WJ, Anderson DR (2008) Reproducibility of peripapillary retinal nerve fiber thickness measurements with Stratus OCT in glaucomatous eyes. Ophthalmology 115: 661-666.
- Mwanza JC, Chang RT, Budenz DL, Durbin MK, Gendy MG, et al. (2010) Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus HD-OCT in glaucomatous eyes. Invest Ophthalmol Vis Sci 51: 5724-5730.
- Manassakorn A, Aupapong S (2011) Retinal nerve fiber layer defect patterns in primary angle-closure and open-angle glaucoma: a comparison using optical coherence tomography. Jpn J Ophthalmol 55: 28-34.
- Leung CK, Choi N, Weinreb RN, Liu S, Ye C, et al. (2010) Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: pattern of RNFL defects in glaucoma. Ophthalmology 117: 2337-2344.
- Nouri-Mahdavi K, Nikkhou K, Hoffman DC, Law SK, Caprioli J (2008) Detection of early glaucoma with optical coherence tomography (StratusOCT). J Glaucoma 17: 183-188.
- Naithani P, Sihota R, Sony P, Dada T, Gupta V, et al. (2007) Evaluation of optical coherence tomography and heidelberg retinal tomography parameters in detecting early and moderate glaucoma. Invest Ophthalmol Vis Sci 48: 3138-3145.
- Badalà F, Nouri-Mahdavi K, Raoof DA, Leeprechanon N, Law SK, et al. (2007) Optic disk and nerve fiber layer imaging to detect glaucoma. Am J Ophthalmol 144: 724-732.
- Pablo LE, Ferreras A, Schlottmann PG (2011) Retinal nerve fibre layer evaluation in ocular hypertensive eyes using optical coherence tomography and scanning laser polarimetry in the diagnosis of early glaucomatous defects. Br J Ophthalmol 95: 51-55.
- Yalvac IS, Kulacoglu DN, Satana B, Eksioglu U, Duman S (2010) Correlation between optical coherence tomography results and the scoring tool for assessing risk (STAR) score in patients with ocular hypertension. Eur J Ophthalmol 20: 1018-1025.
- Huang JY, Pekmezci M, Mesiwala N, Kao A, Lin S (2011) Diagnostic power of optic disc morphology, peripapillary retinal nerve fiber layer thickness, and macular inner retinal layer thickness in glaucoma diagnosis with fourier-domain optical coherence tomography. J Glaucoma 20: 87-94.
- Li S, Wang X, Li S, Wu G, Wang N (2010) Evaluation of optic nerve head and retinal nerve fiber layer in early and advance glaucoma using frequency-domain optical coherence tomography. Graefes Arch Clin Exp Ophthalmol 248: 429-434.
- Leite MT, Zangwill LM, Weinreb RN, Rao HL, Alencar LM, et al. (2010) Effect of disease severity on the performance of Cirrus spectral-domain OCT for glaucoma diagnosis. Invest Ophthalmol Vis Sci 51: 4104-4109.
- Jeoung JW, Park KH (2010) Comparison of Cirrus OCT and Stratus OCT on the ability to detect localized retinal nerve fiber layer defects in preperimetric glaucoma. Invest Ophthalmol Vis Sci 51: 938-945.
- Lee EJ, Kim TW, Weinreb RN, Park KH, Kim SH, et al. (2011) Trend-based analysis of retinal nerve fiber layer thickness measured by optical coherence tomography in eyes with localized nerve fiber layer defects. Invest Ophthalmol Vis Sci 52: 1138-1144.
- Taliantzis S, Papaconstantinou D, Koutsandrea C, Moschos M, Apostolopoulos M, et al. (2009) Comparative studies of RNFL thickness measured by OCT with global index of visual fields in patients with ocular hypertension and early open angle glaucoma. Clin Ophthalmol 3: 373-379.
- Dersu I, Wiggins MN (2006) Understanding visual fields, Part II; Humprey Visual Fields. Journal of Ophthalmic Medical Technology 2.
- Budenz DL, Rhee P, Feuer WJ, McSoley J, Johnson CA, et al. (2002) Sensitivity and specificity of the Swedish interactive threshold algorithm for glaucomatous visual field defects. Ophthalmology 109: 1052-1058.
|
|