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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 12, No. 10, 2015, pp. 3373-3384
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Bioline Code: st15316
Full paper language: English
Document type: Research Article
Document available free of charge
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International Journal of Environment Science and Technology, Vol. 12, No. 10, 2015, pp. 3373-3384
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Characterization of nanoparticle transport through quartz and dolomite gravels by magnetic resonance imaging
Lakshmanan, S.; Holmes, W. M.; Sloan, W. T. & Phoenix, V. R.
Abstract
Magnetic resonance imaging (MRI) has tremendous
potential for revealing transport processes in
engineered and geologic systems. Here, we utilize MRI to
image nanoparticle (NP) transport through a saturated
coarse-grained system. Commercially available paramagnetically
tagged NPs are used; the paramagnetic tag making
the NP visible to MRI. NP transport was imaged as NPs
migrated through packed columns of quartz and dolomite
gravel. Changes in T2-weighted image intensity were calibrated
to provide fully quantitative maps of NP concentration
at regular time intervals (T2 being the spin–spin
relaxation time of 1H nuclei). Transport of nanoparticles
was significantly retarded in dolomite compared to quartz
due to electrostatic attraction between nanoparticle and
dolomite surfaces. NP concentration profiles were evaluated
with the CXTFIT computer package to estimate
nanoparticle transport parameters at multiple points along
the length of the column. This provided temporally
resolved parameters that standard breakthrough curve
analysis cannot provide. Particle–surface interaction
energy profiles were described through Derjaguin-Landau-
Verwey-Overbeek (DLVO) theory. While dispersion
coefficients and fast deposition rate constant (kfast) were
found to increase with distance, deposition rate constant
(k) and collision efficiency (α) were found to decrease with
distance. These length-dependant variations have significant
scaling-up implications for transport models used to
predict NP transport in natural and engineered coarsegrained
systems, such as sustainable urban drainage systems
and river beds.
Keywords
Porous media; Nanoparticles; Magnetic resonance imaging; Transport; DLVO; Environment
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