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Transport and viability of Escherichia coli cells in clean and iron oxide coated sand following coating with silver nanoparticles.
Bryne T. Ngwenya1*, Philip Curry1, Leon Kapetas2.
1School of Geosciences, University of Edinburgh, John Murray Building, James Hutton Road, Edinburgh EH9 3FE2Department of Geoscience & Engineering, Technical University of Delft, Stevinweg 1/ PO-box 5048 2628 CN Delft / 2600 GA Delft, The Netherlands.
*Corresponding author (bryne.ngwenya@ed.ac.uk)
As accepted on13th May 2015
Journal of Contaminant Hydrology
1
Abstract: A mechanistic understanding of processes controlling the transport and viability of
bacteria in porous media is critical for designing in situ bioremediation and microbiological
water decontamination programs. We investigated the combined influence of coating sand
with iron oxide and silver nanoparticles on the transport and viability of Escherichia coli cells
under saturated conditions. Results showed that iron oxide coatings increase cell deposition
which was generally reversed by silver nanoparticle coatings in the early stages of injection.
These observations are consistent with short-term, particle surface charge controls on bacteria
transport, where a negatively charged surface induced by silver nanoparticles reverses the
positive charge due to iron oxide coatings, but columns eventually recovered irreversible cell
deposition. Silver nanoparticle coatings significantly increased cell inactivation during transit
through the columns. However, when viability data is normalized to volume throughput, only
a small improvement in cell inactivation is observed for silver nanoparticle coated sands
relative to iron oxide coating alone. This counterintuitive result underscores the importance
of net surface charge in controlling cell transport and inactivation, and implies that the extra
cost for implementing silver nanoparticle coatings on porous beds coated with iron oxides
may not be justified in designing point of use water filters in low income countries.
Keywords: Bacteria transport, silver nanoparticles, iron oxide coated sand, retention, zeta
potential.
1. Introduction
A thorough understanding of the processes affecting the transport and viability of bacteria in
porous media is critical in solving practical problems ranging from in situ bioremediation to
water supply and health (Sayler et al., 2000; Weiss et al., 2005; Mthombeni et al., 2012).
Research efforts to date have shown that the transport of bacteria in porous media is affected
by a variety of physical, chemical and biological factors. Chemical factors relate mainly to
2
fluid composition, and include ionic strength (Kim et al., 2009; Wang et al., 2011), type and
content of natural organic matter (Yang et al., 2012), presence of clay particles (Vasiliadou &
Chrysikopolous, 2011) and nature of dissolved metal ions (Kim & Walker, 2009; Kapetas et
al., 2012). Properties of the porous medium are dominated by grain shape, size, composition
and surface charge (Dong et al., 2002; Syngouma & Chrysikopolous, 2011; Kapetas et al.,
2012). Biological properties relate to those of the bacteria and include cell types (Chen &
Walker, 2012), motility (Camesano & Logan, 1998; De Kerchove & Elimelech,
2008), growth phase (Walker et al., 2005), surface macromolecules (Liu et al., 2007; Tong et
al., 2010) and cell viability (Kuzmar & Elimelech, 2005; Asadishad et al., 2013a). The
overarching conclusion that arises from studying all these factors is that for a given set of
physical parameters of the porous medium (grain shape, size and packing), transport of
bacteria is controlled by the magnitude of surface charge of the bacterial cell relative to that
of the porous medium grains.
Meanwhile, lack of access to clean, potable drinking water is a worldwide issue, with the
World Health Organisation estimating at least 1.8million deaths a year due to drinking water
contaminated with bacteria (WHO, 2004). These problems are particularly acute in rural
areas of developing countries lacking large-scale water treatment infrastructure (Grabow,
1996), as well as during disasters when potable water supplies are in short supply due to
contamination and damage to infrastructure (Faruque et al., 2005; Roig et al., 2011). In both
cases, small scale, potable water treatment technologies under the banner of point of use
technologies (Sobsey et al., 2008) have become the default choice for providing access to
clean drinking water (Loo et al., 2012).
Despite their difficulties with deployment, biosand filters, constructed by filling a container
with sand and/or gravel (Mahmood et al., 2011) constitute one of the cheapest such
technologies (Loo et al., 2012). It has been shown that their microbiological removal
3
performance can be improved by coating the sand with iron oxides (Murphy et al., 2010;
Ahammed & Davra, 2011), which increases attachment efficiencies of cells (Ahammed &
Davra, 2011). Similar improvements in performance were also shown by coating the sand
with silver nanoparticles (Mahmood et al., 1993), while alternative filtration devices
incorporating silver nanoparticles ((Jain and Pradeep, 2005; Oyanedel-Craver and Smith,
2008; Lv et al, 2009; Dankovich and Gray, 2011; Lin et al, 2013) have also been shown to
provide improved bactericidal properties by supplying ionic silver and through production of
reactive oxygen species (Jain and Pradeep, 2005; Savage and Diallo, 2005; Kim et al, 2007;
Lv et al, 2009; Marambio-Jones and Hoek, 2010; Lin et al, 2013; Mpenyana-Monyatsi et al,
2012).
In this study, we tested whether coating iron oxide coated sand with silver nanoparticles
could improve its bactericidal properties, based on the hypothesis that the improved cell
attachment due to iron oxide coating (Johnson et al., 1996; Ryan et al., 1999; Li et al., 2004;
Abudalo et al., 2005; Ahamed & Davra, 2011; Metge et al., 2011; Kapetas et al., 2012)
increases the effective exposure time of cells to nanoparticles. We used breakthrough analysis
of Escherichia coli JM109 cell transport in columns filled with sand containing different
coatings, with clean sand as control, coupled with viability assessment of effluent cells.
2. Methodology
2.1 Sand preparation and characterisation
General-purpose silica sand from Fisher Scientific was sieved to collect the 120-350µM
fraction and first heated in an oven at 450ºC for 4 hours to remove organic matter, followed
by soaking in 20% v/v nitric acid to desorb trace metals (Mpenyana-Monyatsi et al., 2012).
The sand was then rinsed in deionized water repeatedly to remove any fine sediment and
raise the pH back to neutral. The sand was dried overnight at 60ºC and a portion kept for use
4
in control experiments (CS). The rest was treated by coating with iron oxide and silver
nanoparticles as described below.
A portion of the cleaned sand was coated with silver nanoparticles (CS-NP) by first soaking
in 1M ammonia solution to raise the pH to above 9 and hence deprotonate silanol functional
groups on the surface ((Kim et al, 2007). The deprotonated sand was soaked in 5mM silver
nitrate solution at a mass to volume ratio of 1 to 8 overnight, which led to silver ions
adsorbing to the deprotonated sand (Dankovich and Gray, 2011; Mpenyana-Monyatsi et al,
2012). The silver adsorbed to the sand was then reduced to nanoparticulate silver by exposure
to UV light overnight (Huang et al., 1996; Spadaro et al., 2010) while still soaked in silver
nitrate solution. Subsequently, the solution was decanted and unadsorbed nanoparticles were
removed by repeated washing in deionized water until the rinses were clear before drying the
coated sand overnight at 60ºC.
Iron oxide coated sand (IOCS) was made following Kapetas et al (2012), by mixing 30g of
Fe(NO3)3.9H2O in 300ml of deionized water and titrating drop wise with NaOH to pH of 6
(Yee & Fein, 2002). The suspension was left to mix overnight, after which the supernatant
was drained and the sand washed with deionized water until the supernatant was clear, before
drying the coated sand at 60ºC overnight.
Two further portions of sand were prepared. In one (IOCS-NP), silver nanoparticles were
precipitated on a portion of the iron oxide coated sand by the same treatment as that for
nanoparticle coated clean sand. In another portion nanoparticle-coated clean sand was further
coated in iron oxides to produce NP-IOCS. The rationale behind this was to test whether the
order in which nanoparticles and iron oxides were coated affected both cell transport and
viability.
Coatings were characterized using a Field Emission Gun-scanning electron microscope
(FEG-SEM) on carbon coated samples mounted on conducting tape. The analysis was carried
5
out on a CarlZeiss Sigma HDVP microscope at an accelerating voltage of 5 kV. Silver
nanoparticle suspensions decanted from the UV-treated sands were subjected to zeta potential
measurements following dilution in 10mM NaCl at pH 7 using the Malvern Zetasizer Nano
ZS. The different types of sand were also analysed for their zeta potential on suspensions of
~300mg/L in 10mM NaCl adjusted to pH 7. The instrument was set up to measure two
readings, each of which consisted of 20 separate scans.
2.2. Column flow experiments and sampling
All packing and experimental manipulations were conducted in a laminar flow cabinet in
order to maintain sterile conditions. Column preparation and packing followed the method of
Kurlanda-Witek et al (2014). Glass columns (12 cm in length and 1 cm diameter) with
matching top and bottom end-caps and fittings (Diba Omnifit) were used. All tubing and
column parts used in the experiment were autoclaved and dried under UV light prior to
setting up the experiment. Fluid flowed in (bottom) and out of the column through 1/16 inch
outer diameter (OD) and 1/8 inch internal diameter (ID) PTFE tubing with ¼-28 tpi UNF
fittings (PP) in both end caps. The 1/16 inch tubing was further connected to L/S 13
platinum-cured silicone pump tubing, 5 mm OD and 0.8 mm ID (Masterflex). Columns were
packed with porous media in 10 mM NaCl electrolyte (adjusted to pH 7 using 0.1M
NaOH/HCl) using the wet packing method (Deshpande and Shonnard, 1999). Glass beads of
0.5 mm diameter were placed at the top and bottom to prevent the fine sand from clogging
the inflow and outflow of the column. Electrolyte and bacteria suspensions made in 10 mM
NaCl and adjusted to optical density (OD) of 0.2, chosen to prevent excessive pore clogging
in the columns, were injected at a constant flow rate of 0.4 ml/min. The column was primed
with the background electrolyte for an hour, equivalent to ~7 pore volumes (PV) before
initiating the injection of a bacteria suspension. E. coli was chosen as it is often used in
6
studies as an indicator of fecal contamination in water (Dankovich and Gray, 2011). Cells
were grown in 1L of nutrient broth at 30°C on a shaking table and washed after 24 hours
(early stationary phase). The input suspension was continuously mixed throughout each
experiment using a magnetic stirrer. Bacteria injection was restricted to less than 12 hours to
avoid having to constantly change the input suspension as cell death in the influent
suspension started around this time and replenishing influent solutions would have changed
the reference point for viability assessments.
Effluents were collected every 4 minutes. The absorbance was measured on 1 ml of the
effluent using a Camspec M501 single beam scanning UV/visible spectrophotometer at a
wavelength of 600 nm to determine optical density of the bacteria suspension as a basis for
determining bacterial cell breakthrough. Due to the large number of effluent samples
generated in each experiment (100+), we used the bacteria breakthrough curve to select
samples at three critical time points for analysis: at time zero when flow switched to cell
suspension (to), when OD was ~50% of the influent (t0.5) and at end of the experiment when
OD exceeded 70% of the influent end (tend). The last criterion was used because for the iron
oxide coated sands, we were not able to attain full breakthrough of bacteria within the 12-
hour time window constrained by influent cell viability. Influent suspensions were also
collected and analysed at the same time points to monitor temporal changes in input cell
viability. Column parameters were calculated from the breakthrough curve of a conservative
tracer injected at the same flow rate as for bacteria suspensions. Bromothymol blue dye was
used as a tracer with its breakthrough measured using the same UV-Visible spectrometer at
550 nm (Kurlanda-Witek et al., 2014).
Since the focus of our study was to characterise the viability of cells in the context of water
disinfection through filters containing sand doped with iron oxides and silver nanoparticles,
all our experiments were run with only the rising limb (step input) of the breakthrough curve
7
(see also Mthombeni et al., 2012). However, we run an additional experiment with one of the
iron oxide coated sands containing silver nanoparticles (IOCS-NP) that included the falling
limb in order to investigate whether cell attachment in these columns was also irreversible, as
observed for iron oxide coated sand alone (e.g. Scholl & Harvey, 1992; Abudalo et al., 2005;
Metge et al., 2011). For this experiment, influent injection was switched back to the pure
electrolyte immediately after the rising limb breakthrough curve attained C/Co = 0.5.
Subsequently, sampling continued as for the other tests but cell viability tests were not
carried out on effluent suspensions.
2.3. Cell viability in influent and effluents
Cell viability in influent and effluent fluids was determined by plating on agar to measure
colony forming units (CFU/ml) and by fluorescence microscopy following LIVE/DEAD
staining. To determine CFU, 0.1ml of each sample was diluted serially to 10-4 and 20 l of
the diluted suspension was plated in duplicate on nutrient agar. The plates were incubated
overnight at 37°C. Fluorescence microscopy was carried out using a Zeiss AxioImager Z1
microscope following staining of samples with Cyto9 and propidium iodide (Invitrogen),
using FITC (490/530 Ex/Em) and TRITC (547/572 Ex/Em) filters. Samples were prepared by
mixing 1 ml of the suspension with 3μl of the combined (premixed) stain in sterile Eppendorf
tubes and incubating for 15 minutes (Zhu and Xu, 2013). Stained suspensions were
centrifuged for 10 minutes and rinsed three times in 10 mM NaCl in order to remove the stain
and hence avoid artefacts due to cell death post-staining. Centrifuging also generated a dense
pellet for spreading on a glass slide coated with 1% agarose gel to immobilize cells during
imaging.
2.4. Cell transport modelling
8
We used HYDRUS-1D (Šimůnek et al., 2009) to model the transport of E. coli cells in the
different sand columns using the OD data. The data was fitted to an advection-dispersion
equation (ADE) including sorption terms and attempts were made to account for different
processes that may contribute to the shape of the breakthrough curve (Bradford et al., 2007;
Foppen et al., 2007; Tufenkji, 2007). One or two site models were tested including different
bacterial removal processes such as straining, depth dependent sorption, ripening or
Langmuir-dynamics adsorption. Based on the lowest objective function and minimal amount
of optimized parameters (most parsimonious model), we found that we needed two different
models to fit the different types of sands. Transport in clean sands (CS and CS-NP) was
modelled with a reversible (attachment-detachment) interaction with one site, hence
(Tufenkji, 2007):
∂C∂ t
=D ∂2 C∂ x2 −v ∂C
∂ x−
ρb
ε∂ S∂ t (1)
∂ S∂ t
=ka C−ρb
εkd S
(2)
Data for iron oxide-coated sands (IOCS, IOCS-NP and NP-IOCS) required a two site model
involving irreversible attachment to one site (site 2, S2) and a Langmuir adsorption process
(Tobler et al., 2014), thus:
∂C∂ t
=D ∂2 C∂ x2 −v ∂C
∂ x−
ρb
εk2 C−
ρb
ε∂S1
∂ t (3)
∂ S1
∂ t=k1( Smax1−S
Smax 1)C
(4)
In these formulations, C is the concentration (normalised OD) in the fluid, x is distance
(outlet) along the column, v is the average linear flow velocity, ka is the attachment rate
coefficient, kd is the detachment rate coefficient, S is the concentration of cells on the porous
medium (with those on site 1 designated as S1 where Smax1 is the maximum permissible
9
adsorbed cells to S1), k1 is the Langmuir coefficient, k2 is the attachment rate coefficient for
S2, ρb is the dry bulk density of the porous medium, ε is the porosity and D is the
hydrodynamic dispersion coefficient, defined thus:
D=αv+Do (3)
where is the dispersivity and D0 is the molecular diffusion or bacterial motility coefficient
(Tobler et al., 2014). Based on the ADE model fit to the tracer data, the value of the
hydrodynamic dispersion coefficient was calculated and was kept constant across models. A
porosity value of 40% was calculated experimentally and confirmed during the simulation of
the dye tracer breakthrough curve. During modelling, the boundary conditions were set to
constant flux.
3. Results
3.1 Physicochemical properties of sands
Examination of the sand showed that coatings were present on grain surfaces as manifest by
differences in colour (Figure SI.1),
although it is also apparent that some of
the iron oxides and silver particles are
not attached to grains. The SEM results
show that unlike in clean sand controls
(Figure 1a and 1ai, note that for all
images in Figure 1, larger versions are
shown in Figure SI.2 of the supporting
information to highlight features
described herein) lacking bright spots,
silver particles of variable sizes ranging
10
from nanoparticulate to microparticulate aggregates were present on sand grains. However,
the distribution was patchy and most particles were associated with pitted grains and rough
surfaces (Figure 1b and bi), suggesting that the surface particles represent trapped particles
which likely precipitated homogeneously rather than through the nucleophilic substitution
mechanism proposed by Kim et al (2009). Iron oxide coatings were equally patchy (Figure 1c
and ci, d and di) but generally resulted in a rough surface that trapped more silver particles
(Figure 1e and 1i). The presence of silver and iron coatings was confirmed by qualitative
EDX analysis (Figure SI.3).
Measured zeta potentials were negative for all sands produced (Figure SI.4), although values
were statistically different amongst clean sand (CS = -30.2±1.2 mV), nanoparticle coated
clean sand (CS-NP = -14±2.5 mV) and iron oxide coated sand (IOCS = -5.98±0.7 mV). The
transition to more positive values when sand is coated in iron oxides is consistent with other
studies (e.g. Abudalo et al., 2005). As expected (e.g. Terada et al., 2012), suspensions of E.
coli have negative zeta potentials (-33.4±1.13 mV), as are silver nanoparticle suspensions (-
17.3±6.3 mV). The latter imparts a more negative zeta potential to iron oxide coated sands
whereas clean sand is rendered slightly more positive by silver nanoparticle coatings. These
changes are likely to have significant impact on cell transport.
3.2. Cell breakthrough curves
Optical densities of the individual effluent samples (symbols) normalized to the mean of the
influent samples at the three different time points are plotted against pore volume (PV) in
Figure 2. Lines are fitted curves based on transport modelling (section 3.4), except for the
column that included the falling limb (IOCS-NP-R) which was not modelled and where the
line connects the data points. E. coli transport in clean sand is only marginally slower than the
conservative tracer with breakthrough at 1.6PV, breakthrough being defined as pore volume
11
at 50% of signal and equals 1 for the conservative tracer (Fetter, 1998).
The introduction of iron oxide slows E. coli
breakthrough by as much as 33PV relative to
the tracer and by 21 PV relative to cell
transport in clean sand. This observation is
qualitatively similar to other studies showing
that iron oxide coatings on porous media
grains can significantly retard bacterial cell
transport (e.g. Fletcher & Loeb, 1979; van
Loosdrecht et al., 1989; Scholl & Harvey,
1992; Yee & Fein., 2002; Abudalo et al.,
2005; Metge et al., 2011; Kapetas et al., 1012;
Mohanty et al., 2013).
The presence of silver nanoparticles decreases
cell transport slightly for clean sand, with
modelled breakthrough at 2.2PV, although the
data in the early part of the curve is coeval
with that of clean sand. By contrast, silver nanoparticle coatings on iron oxide coated sand
increase cell transport, although the detailed profile of the curves depends on the order of
coating of iron oxides relative to silver nanoparticles. When iron oxides were deposited on
nanoparticle coated clean sand (NP-IOCS), cell breakthrough occurred at 25PV, 8PV earlier
than in IOCS alone. However, when nanoparticles where deposited on IOCS (IOCS-NP),
cells breakthrough at the same time as for IOCS alone. Nevertheless, the early part of the
breakthrough curve was experimentally identical to NP-IOCS, consistent with silver
nanoparticles reducing cell deposition relative to IOCS regardless of coating order. Finally,
12
the column that included the falling limb (IOCS-NP-R) shows that bacteria attachment to iron
oxide coated sands containing silver nanoparticles is also irreversible, as shown by the
sudden drop in optical density following the switch from injecting a cell suspension to pure
electrolyte.
3.3 Cell viability
13
Figure 3a shows a bar chart of viable cells (CFU) collected from each experiment normalised
to the initial input CFU, plotted for t0.5 and tend time points only. The t0.5 is an important
reference point since it represents the time at which 50% of the injected cells elute through
the column. In the context of viability, one would expect therefore to recover the full 50% of
the influent CFU. The lowest cell survival was recorded in columns containing silver
nanoparticle coatings as the outermost layer (CS-NP and IOCS-NP). This is consistent with
other studies showing that coating
porous media with silver
nanoparticles increases bactericidal
properties of the porous media
(Mahmood et al., 1993; Jain and
Pradeep, 2005; Marambio-Jones and
Hoek, 2010; Mpenyana-Monyatsi et
al, 2012; Lin et al, 2013) and also
with the known antibacterial
properties of silver nanoparticles
(Sondi and Salopek-Sondi, 2004,
Morones et al., 2005; Choi and Hu,
2008; Dimpka et al., 2011; Joshi et
al., 2012). Meanwhile, more cells
survived when iron oxides formed
the outermost coating (IOCS and NP-IOCS), with no statistically significant decrease in
survival in NP-IOCS columns relative to IOCS columns. This result is counter-intuitive since
one would still expect some nanoparticle toxicity to occur via diffusion of dissolved silver
14
through the iron oxide coating. It is also notable that both sands without silver coatings
display some bactericidal activity.
As cells approach maximum breakthrough, however, there was an increase in the proportion
of viable cells, with no statistically significant difference amongst CS, CS-NP and IOCS.
Smaller increases in cell
viability were recorded for NP-
IOCS and IOCS-NP, with
IOCS-NP showing marginally
lower viability than NP-IOCS,
consistent with direct contact
between cells and silver
nanoparticle coatings being
critical to the bactericidal
properties of the porous
medium. As expected, the
highest survival occurred in
clean sand columns. The
increase in viability relative to
values measured at t0.5 implies
loss of antibacterial potency
either due to site blocking on the
surface of the porous medium (Camesano and Logan, 1998; Tufenkji, 2007), or exhaustion of
dissolved silver from nanoparticle coatings. To examine the relative importance of these two
possibilities, we re-plotted the data in terms of the proportion of dead cells in the effluent by
re-normalising the data to the proportion of cells that should be viable if the porous medium
15
has no effect on cell viability. In other words, all 50% of cells exiting the column should be
viable at t0.5 while the values for tend should correspond to those expected when the
experiment was terminated. These tend values are ~88% (C/Co = 0.88) for CS, ~93% for CS-
NP, ~76% for IOCS, ~70% for NP-IOCS and ~76 for IOCS-NP. It becomes clear that sands
coated with silver nanoparticles kill more cells generally (Figure 3b), especially at 50%
breakthrough. IOCS performs worst at both sampling points. Figure 3b also yields the
unexpected result that sand columns containing silver nanoparticles in the outermost layer
(CS-NP and IOCS-NP) become less effective at killing cells as full breakthrough is
approached, whilst those with iron oxides in the outer layer (IOCS and NP-IOCS) maintain,
and in the case of NP-IOCS improve their antimicrobial efficacy. This suggests that the
decrease in antimicrobial efficacy might be due to exhaustion of silver nanoparticle coatings.
Viability trends observed from CFU data were broadly confirmed by fluorescence
microscopy imaging, where cells sampled upon approach to full breakthrough (tend) showed
higher viability after passing through clean sand and iron oxide coated sand without
nanoparticles (Figure 4). Significantly more cells perish after passing through clean sand
coated with silver nanoparticles compared with uncoated sand. Similarly, more cells die after
injection through nanoparticle-coated IOCS relative to IOCS alone, while there appears to be
relatively little difference between clean sand and IOCS. The only deviation between CFU
data and fluorescence imaging is that viability is apparently higher in IOCS-NP than in NP-
IOCS. These differences are not surprising given that the comparison of fluorescence images
is only qualitative since the proportion of viable cells in input solutions varied slightly
between batches. It is clear that there are fewer dead cells in the input suspension for IOCS-
NP than NP-IOCS. Unfortunately, cell densities were too high to quantify accurately in the
images.
16
3.4 Cell transport modelling
As detailed in section 2.4, breakthrough curves for clean sand columns were best fitted using
a model involving reversible attachment of cells to a single surface, as shown by model
curves in Figure 2a. This entailed optimisation of only two parameters, the attachment and
detachment rate coefficients. Ratios of the attachment to detachment rate coefficients are
plotted in Figure 5a and show that the effect of nanoparticle coating is to marginally increase
the attachment to detachment ratio, mainly due to a higher detachment rate coefficient in
clean sand.
Modelling of iron oxide coated
sand columns with a two site
scheme involving irreversible
attachment to site 2 and a
Langmuir adsorption to site 1
yielded a higher attachment rate
coefficient (k2) for IOCS-NP
whereas those for the two
columns containing sand with
an outer coating of iron oxides (IOCS and NP-IOCS) had similar values (Figure 5b). This
result is counter-intuitive based on breakthrough times but may reflect a switch in
attachment/adsorption during injection progress, as we explain in the discussion. The model
also predicts lower Langmuir adsorption maxima (Smax1, Figure 5c) to site 1 for iron oxides
coated sands containing nanoparticles (NP-IOCS and IOCS-NP) relative to IOCS alone,
consistent with lower Langmuir adsorption coefficients (k1, Figure 5d).
Cell breakthrough curves as a function of depth (Figure SI.5) generated from transport
modelling are generally consistent with breakthrough data. Thus, for clean sand and silver
17
nanoparticle coated clean sands, the retention capacity is exhausted within 50-70 minutes.
The presence of iron oxide coatings significantly increases retention capacity and generally
leads to steeper (i.e. rapid drop in breakthrough along the column) profiles, consistent with
the incomplete cell breakthrough even after 35PV. Notable differences amongst the iron
oxide coated sands include (i) less steep total breakthrough curves for IOCS-NP relative to
IOCS and NP-IOCS but becoming steeper as injection progresses (Figure SI.5b), (ii) higher
Langmuir adsorption to IOCS compared to both IOCS-NP and NP-IOCS, particularly in the
early stages of injection (Figure SI.5c), and (iii) adsorption to site 2 becoming dominant in
IOCS-NP, especially in the later stages of injection (Figure SI.5d).
4. Discussion and conclusions
The transport of E. coli cells through columns with different sand treatments can be
summarized into three findings: (i) silver nanoparticle coatings decrease both attachment and
detachment rate coefficients relative to clean sand but a higher detachment rate coefficient in
clean sand results in relative increase in cell deposition in the presence of nanoparticles in the
long term; (ii) the presence of iron oxide coatings increases cell attachment/adsorption to
sand surfaces, which is reversed by silver nanoparticle coatings regardless of the order of
coating nanoparticles relative to iron oxides; and (iii) silver nanoparticle coatings kill
significantly more cells initially but there is a tendency for this difference to disappear as full
breakthrough is approached.
Transport of bacteria in porous media is known to be affected by a variety of physical,
chemical and biological factors. In our study, chemical (fluid composition) and biological
properties were kept constant since cells were harvested and prepared in similar manner
across all flow experiments. Meanwhile, E. coli cells typically acquire a negative surface
charge, in the region of -40 to -60 mV (Shwegmann et al., 2010; Terada et al., 2012) around
18
neutral pH, although we measured a slightly more positive value of -33.4± 1.12 mV for our
culture. The measured surface charge of the sand used in our study is also negative. Not
surprisingly, the ensuing electrostatic repulsions lead to minimal retardation of cell transport
in clean sand columns (Figure 2a). These observations are consistent with a large number of
studies on bacterial transport in clean sand and natural sand columns to date (e.g. Fletcher
and Loeb, 1979; Abudalo et al., 2005; Kapetas et al., 2012, Mohanty et al., 2013). The critical
importance of surface charge magnitude of the bacterial cell relative to the porous medium
has been aptly demonstrated by studies in which cell surface charge is varied, either through
changes in surface macromolecule composition (e.g. Kim et al., 2010; ), use of gram-positive
versus gram-negative cells (e.g. Chen & Walker, 2012) where the former contain higher
surface charge density (Ngwenya et al., 2003), or through surface modification using
hydrogen-bonded organic compounds (e.g. Sharma et al., 1985).
Coatings on mineral surfaces modify the mineral surface charge in the direction of the
coatings’ charge (Sheng et al., 2008; Asadishad et al., 2013b). In the case of iron oxide
coatings, their mineralogy is dominated by hydrous ferric oxides which have an isoelectric
point of about 8 (Kosmulski, 2009; Asadishad et al., 2013b). Therefore, full surface coverage
by iron oxide coatings transforms the silica surface into a positively charged surface at pH
values below the isoelectric point, although the exact magnitude and charge reversal will
depend on the thickness of the oxide layer (Abudalo et al., 2005; Sheng et al., 2008). Our
measured value of -5.98±0.7 mV for iron coated sand is thus consistent with thin and/or
heterogeneous coverage. In any case, iron oxide coatings promoted cell deposition (Fig. 2b)
relative to clean sand, consistent with previous studies (Fletcher & Loeb, 1979; van
Loosdrecht et al., 1989; Scholl & Harvey, 1992; Johnson et al., 1996; Ryan et al., 1999; Yee
& Fein., 2002; Abudalo et al., 2005; Metge et al., 2011; Kapetas et al., 1012; Mohanty et al.,
2013).
19
There are conflicting reports on the surface charge of silver nanoparticles, with some studies
reporting positive zeta potentials (e.g. Khan et al., 2011), while the majority report negative
zeta potentials (e.g. Fabrega et al., 2009; Dimpka et al., 2011; Yin et al., 2014). One possible
reason is that the zeta potential of silver nanoparticles can vary depending on synthesis
method as well as electrolyte composition, including pH and ionic strength (e.g. Hedberg et
al., 2012). The zeta potential of the silver nanoparticles coating our sands measured following
dilution in 10 mM NaCl was –17.3±6.3 mV at pH 7 (Figure SI.4). This value is less negative
than that of clean sand and hence reversed the charge on iron oxide coated sand towards more
negative values. In fact, all silver nanoparticle coated sands yielded statistically similar zeta
potentials close to that of silver nanoparticles alone (Figure SI.4), although these values likely
represent the averaged effect of coatings and the underlying sand surface. Alternatively, our
measurements may have been dominated by silver nanoparticles dislodged from grain
surfaces, which are likely to stay in suspension longer than larger sand grains during
measurement. This charge reversal with respect to iron oxide coated sand has a significant
effect on the transport of E. coli cells, which was reflected in reduction in breakthrough pore
volume (at C/Co = 0.5) for NP-IOCS by about 8 PV while apparently not affecting that of
IOCS-NP, although early breakthrough was also observed. We note also that site 1 adsorption
maxima decreased in both cases relative to IOCS alone (Figure 5). All these observations
point to the important role of surface charge on bacteria transport. While mineral (see above)
and organic (e.g. Abudalo et al., 2010) coatings have been shown to affect surface charge and
consequently transport dynamics of bacteria in porous media, we are aware of only one
previous study (Mthombeni et al., 2012) on systematic analysis of bacterial breakthrough
behaviour in nanoparticle-coated porous media. As emphasized by Mthombeni et al. (2012),
such data is critical to optimize porous bed systems for microbiological water disinfection.
20
Paradoxically, these surface charge effects in model parameters due to silver nanoparticle
coating are apparent despite zeta potential measurements of all nanoparticle-coated sands
(CS-NP, IOCS-NP and NP-IOCS) being similar (Figure SI. 4). As suggested above, the
measured values may simply reflect dislodged silver nanoparticles in suspension, but it is also
possible that surface charge is not the only parameter controlling cell deposition in the
presence of silver nanoparticle coatings. We noted in section 3.2 that when silver
nanoparticles are external (CS-NP and IOCS-NP), early cell breakthrough was followed by
enhanced retardation in later stages of injection (Figure 2). Furthermore, depth-dependent
breakthrough profiles (section 3.4) suggested there may be a switch in cell deposition from
site 1 in early stages to site 2 in later stages of injection (Figure SI.5c-d). Another factor that
may have affected cell deposition is viability since a proportion of cells die during transport,
especially in silver nanoparticle coated sands. As far as we can gather, the role of viability in
cell deposition is equivocal, with some studies reporting increased deposition for inactivated
cells (Kuznar and Elimelech, 2005; Asadishad et al., 2013a). However, Jimenez-Sanchez et al
(2012) reported faster breakthrough for dead cells relative to live cells, whilst Yang et al
(2013) reported no significant difference between viable and inactivated cells. In our study,
columns containing sand with outside silver nanoparticle coatings showed lower cell
viability, led to faster breakthrough followed by increased retention. These complex
interactions may be reconciled by invoking silver nanoparticle-induced ripening due to cell
death as a mechanism for increasing cell deposition in later stages. Although attempts to
model the data with a ripening term did not improve data fitting and was therefore rejected on
parsimonious grounds, it is consistent with a switch from a charge-dominated adsorption in
early stages to irreversible attachment to site 2, which we assume to be due to iron oxides.
One motivation for coating porous media with silver nanoparticles is the potential to use them
as biofilters with improved bactericidal properties for microbiological water purification.
21
Based on the latest WHO-UNICEF Joint Monitoring Programme for Water Supply and
Sanitation update (JMP 13 update), there were still 768 million and 2.5 billion people without
access to improved drinking water supply and sanitation respectively in 2011 (WHO and
UNICEF 2013). The greatest disease risk is associated with microbial contamination of
drinking water, and this problem is particularly acute in rural areas of developing countries
lacking large-scale water treatment infrastructure (Sobsey et al., 2008). Porous bed filtration
devices fabricated using locally available material impregnated with silver nanoparticles (Jain
and Pradeep, 2005; Oyanedel-Craver and Smith, 2008; Lv et al, 2009; Dankovich and Gray,
2011; Lin et al, 2013) are considered potential candidates for point of use (PoU) filtration
devices.
Sand-based (biosand) filters, including those coated with iron oxides with or without silver
nanoparticles are one of the cheapest (Mahmood et al., 2011; Loo et al., 2012). While it is
clear from CFU data and fluorescence imaging that sands coated with silver nanoparticles are
better at inactivating bacteria, it is instructive to bear in mind that these CFU numbers
represent different time points
amongst the columns associated
with widely divergent water
throughput. The significance of this
can be gleaned from normalizing
the viability data to their respective
pore volumes at t0.5 (Figure 6),
which clearly shows that (a) iron
oxide coatings are critical to
improve bactericidal properties of sand filters, consistent with previous studies reporting up
to 2 log reduction values for bacteria (Murphy et al., 2010; Ahamed & Davra, 2011), perhaps
22
in part due to irreversible bacteria attachment and (b) silver nanoparticle coatings do not
significantly improve on the performance of iron oxide coated sand filters. As expected, clean
sand performs poorly with similarly marginal improvement afforded by nanoparticle coating.
The lack of significant improvement in bactericidal effects from silver nanoparticle coatings
is counter-intuitive given that (a) several studies have demonstrated antimicrobial effects of
silver nanoparticle coated media, including sand (e.g. Mahmood et al., 1993), alginate beads
(Lin et al., 2013), amberlite cationic resin beads (Mthombeni et al., 2012) and other similarly
coated materials (Mpenyana-Monyatsi et al, 2012). However, it is entirely consistent with
the importance of surface charge on bacterial transport and viability. Specifically, iron oxide
coatings impart a positive charge to sand surfaces and hence improve bacteria removal
relative to plain sand by irreversible attachment of cells (Ahamed & Davra, 2011) as shown
by the test with a falling limb in Figure 2. By contrast, silver nanoparticles reverse the
positive charge and decrease attachment efficiencies. More importantly, surface charge has
been demonstrated to affect bacterial inactivation rates, with positively charged surfaces
having higher cell inactivation rates (Gottenbos et al., 1999; Gottenbos et al., 2001; Yamada
et al., 2010; Shwegmann et al., 2010; Terada et al., 2012; Asadishad et al., 2013a, Cai et al.,
2013). Since attachment is critical for surface inactivation of cells (Asadishad et al., 2013a;
Agnihotri et al., 2013), the negative surface charge induced by the presence of silver
nanoparticles has the effect of reducing the bactericidal efficiency of silver nanoparticles
during bacterial transit through the porous medium.
In conclusion, we note that in most of the studies demonstrating antibacterial properties of
silver nanoparticle coatings, viability tests were carried out after only limited throughput of
cell contaminated fluids with a maximum pore volume of ~5 in the case of Mahmood et al
(1993) based on their column dimensions and packing parameters. Even then, significant
recovery in cell viability was noted as a function of pore volume eluted with, in some cases
23
complete recovery. Preliminary tests using our nanoparticle-coated sand also showed
complete cell death during short term column flow tests. Taken together, these observations
underscore the importance of performing breakthrough experiments and analysis to aid
optimal choice of potential materials for point of use water purification devices (Mthombeni
et al., 2012).
Acknowledgements: The work reported in this paper was supported by a summer research
grant from the School of Geosciences, University of Edinburgh to PC. Technical help with
SEM from Nicola Cayser is gratefully acknowledged, as is imaging support provided by
Dave Kelly at the Wellcome Trust Centre for Optical Imaging Laboratory (COIL), University
of Edinburgh.
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Figure captions
Figure 1: Backscattered SEM images of sand grains exposed to different treatments with
panels on the left at 200x magnification and those on the right at 5000x magnification. Very
bright spots contain Ag whilst large bright patches are iron oxides. Pictures are for (a and ai)
clean sand, (b and bi) silver nanoparticle coated sand, (c and ci) iron oxide coated sand, (d
and di) nanoparticle coated sand with iron oxide on top and (e and ei) iron oxide coated sand
with nanoparticles on top. Note the patchy silver in most treatments, except for IOCS-NP
where most grains contain Ag.
Figure 2. Breakthrough curves plotted as optical density of the effluent suspension
normalized to the optical density of the input suspension, which was ~0.2. Most experiments
were run as a step function with only the loading limb but in one case, we included a removal
arm to check reversibility of attachment for iron oxide coated sands. Curves are model fits
generated fitting the data to different transport models (see section 3.4), except for the
experiment with the removal arm which was not fitted. Note the increase in pore volume
required for bacteria to break through in iron oxide coated sands compared to clean sand. Dye
tracer data is added for reference.
Figure 3. (a)Bar charts showing cell viability as colony forming units (CFU) normalized to
number of viable cells in the influent suspension. Data shown for input (100%), at 50%
34
breakthrough (t0.5) and at the end of each column experiment when the breakthrough curve
exceeded 70% (tend) of the input suspension’s optical density. (b) Bar charts showing
percentage dead cells in the effluent after re-normalisation assuming that the optical density
(e.g. C/Co = 0.5 at t0.5) represents 100% viable cells.
Figure 4. Fluorescence images taken on influent suspensions (left) and effluent samples
when the breakthrough curve exceeded 75% of the influent optical density (right).
Suspensions where stained using LIVE/DEAD stains (Green fluorescent Syto9 and red
fluorescent Propidium iodide). Dead cells stain red and labels in the middle represent
different sand types.
Figure 5. Bar charts showing parameters obtained by fitting transport equations to bacteria
breakthrough curves for (a) clean sand columns and (b-d) iron oxide-coated sand columns.
For clean sand columns, the charts plot the ratio of the attachment to detachment rate
coefficients obtained by fitting data to a single site with reversible attachment. For all iron
oxide coated sands, the model parameters are for a two site model, one of which involves
irreversible attachment while the other involves Langmuir adsorption.
Figure 6. Bar charts of viability (CFU) data after normalizing by respective pore volumes at
t0.5, showing that IOCS is just as effective antimicrobial surface as IOCS coated with silver
nanoparticles.
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