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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