Enhanced Transverse Migration ofBacteria by Chemotaxis in a PorousT-SensorT A O L O N G A N D R O S E A N N E M . F O R D *

Department of Chemical Engineering, University of Virginia,Charlottesville, Virginia 22904

Received September 30, 2008. Revised manuscript receivedDecember 15, 2008. Accepted December 23, 2008.

Subsurface bioremediation is often hindered by the inabilityto achieve good mixing between injected bacteria and residualcontaminants. Chemotaxis, which is the ability of bacteria tomigrate preferentially toward higher concentrations of certainchemical attractants, could potentially increase bacterialtransport into the contaminated zone. To observe and quantifythis chemotactic enhancement to bacterial dispersiontransverse to groundwater flow, a microfluidic devicesaporous T-sensorswas created. It allowed two streams of equalflow rate to enter side-by-side into a porous channel; thetransverse mixing of the two streams was controlled primarilyby dispersion. When a suspension of the chemotacticbacteria Escherichia coli HCB1 and a solution of chemicalattractant R-methylaspartate were injected as the two incomingstreams, enhanced bacterial migration into the attractantstream was observed relative to a control experiment withdispersion alone. Chemotaxis was observed under lower flowratescomparable tonaturalgroundwaterflow.Thechemotacticresponse was greater than that predicted by an advection-dispersionequationmodelusingachemotacticcoefficientderivedunder quiescent experimental conditions, which suggeststhat flow in porous media may further enhance transversemigration for chemotactic bacteria. This study provided directevidence of the significance of bacterial chemotactictransverse migration at groundwater flow rates.

1. Introduction

Groundwater pollution by chemical waste has been discov-ered at hundreds of thousands of sites throughout the UnitedStates (1). In situ bioremediation has demonstrated greatpotential to remediate organic contamination in groundwaterand deep soil effectively and economically (2, 3). Thistechnology uses engineered systems to deliver microorgan-isms directly into the contaminated zone (bioaugmentation)or to stimulate the microbial activities at the contaminatesource (biostimulation), optimizing the subsurface hydro-logical and chemical conditions to achieve maximum ratesof contaminant degradation.

Enhancing the pore-scale mixing that brings microorgan-isms in contact with the contaminants remains a challengingproblem. Chemotaxis is the ability of bacteria to swim towardhigher concentration of potential substrates. It has beenobserved in many bacterial strains in response to variousenvironmental contaminants (4). When the bacterial flagella

rotate in a counterclockwise direction, the cell swims forward(runs); when one or more flagella reverse their rotation, thecell reorients (tumbles) (5). When sensing an increase inattractant concentration in the ambient environment, thebacterium reduces its tumble frequency, thereby prolongingthe course of straight swimming, which results in chemotaxisat population scale (5). If chemotactic transport transverseto groundwater flow is significant, migration of microorgan-isms into the low permeability zones or contaminant plumesmay be greatly enhanced, resulting in faster degradation ofthe residual contaminants (6).

However, bacterial motility and chemotaxis in porousmedia is still a field full of unknowns. Experimental data setsare limited, including many earlier works that reported onlythe “penetration rate” rather than effective diffusion coef-ficients or random motility, which are more suitable forquantitative analysis. In static saturated columns in which1D temporal diffusion profiles of bacteria were measured,Barton and Ford (7) found chemotaxis was insignificant, andOlson et al. (8) reported a reduced extent of chemotaxis,which could be due to the obstructed bacterial motility inthe pore space. In swarming plate assays packed with asection of porous media, Roush et al. (9) estimated a 100-fold increase in bacterial chemotactic sensitivity, which mightbe a result of increased attractant gradient in pore water asa result of consumption. When pore water flow was present,chemotaxis was found to reduce bacterial deposition in acolumn study (10), but detailed transport modeling was notattempted. No previous studies directly quantified bacterialchemotactic transport in pore water flow. In bioremediation,transverse mixing becomes an important factor, and mea-suring bacterial transport in 2D is critical. Transport modelsin the format of advection dispersion equations (ADE) havebeen used to simulate chemotaxis in groundwater (11) andchemotaxis involved in bioremediation (12), but no existingexperimental data were available to test these models.

Microfluidic devices are designed to control fluid flow ona length-scale smaller than 1 mm (13). In hydrologic studies,microfluidic devices (also termed micromodels) have beenwidely applied to provide direct visualization and quanti-fication of transport phenomena in porous media, includingtransport of colloids and bacteria (14-18). Optical techniquesare often used to achieve nondestructive, online measure-ment of several different length scales in microfluidic devices.One such device called the T-sensor (19) (or similar designs)succeeded in creating a stable chemical gradient for the studyof chemotaxis in bulk liquid (20-25).

The primary objective of this study was to directly observebacterial chemotactic transport in a model porous medium.A microfluidic devicesthe porous T-sensor (Figure 1)swasmodified from the original T-sensor design (19). Twoinjectates were injected into the two arms of the porousT-sensor under the same flow rate. When they flowed sideby side into the head of the main channel, mixing betweenthe two streams started. The mixing was driven primarily bytransverse dispersion, because the device was operatingunder low Reynolds numbers. In this study, the transversetransport properties of the solute and the bacteria werequantified. Then the attractant and bacteria were injected inthe two input streams to capture the impact on bacterialtransverse migration by chemotaxis. Finally, the experimentalresults were compared with the traditional ADE modelincorporating chemotactic terms.

* Corresponding author phone: (434)924-6283; fax: (434)982-2658;e-mail: rmf3f@virginia.edu.

Environ. Sci. Technol. 2009, 43, 1546–1552

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2. Materials and MethodsBacterial Strain and Chemicals. The bacterial strain usedin the experiments was Escherichia coli HCB1, a wild typestrain obtained from Dr. Howard Berg at Harvard University.The cells are rod-shaped, with a length of approximately 2µm and cross sectional diameter of 1 µm (25). A bundle offlagella located at one end of the cell provides motility to E.coli HCB1. A 100 µm aliquot of -70 °C frozen stock (40% v/vglycerol) of E. coli HCB1 (25) was used to inoculate 50 mLof autoclaved Luria broth (Fisher) in a sterile 250 mL baffledshake flask. Cells were cultured for ∼9 h in a LabLine Environ-shaker (model 3528-5) at 150 rpm under 30 °C to midex-ponential phase, where the optical density of the culturereached 1.0 at 590 nm (Beckman DU-7 Spectrophotometer).A 5 mL quantity of the culture was filtered on a 0.22 µm filter(Millipore GSWP14250) and resuspended in 5 mL of 10%RMB (random motility buffer, which consisted 11.2 g ofK2HPO4, 4.8 g of KH2PO4, and 0.029 g of EDTA per liter ofdistilled water) (25-27). The diluted RMB with an ionicstrength of 0.02 M (28) was used in this study to reducebacterial sorption to the microfluidic device. R-Methylas-partate (R-mASP, Sigma-Aldrich) was dissolved in 10% RMBto yield a 3 × 10-4 M solution, a concentration which elicited

observable chemotactic attraction of E. coli HCB1 in aprevious study (25) and was also confirmed in this studyusing an agarose plug assay (29). Fluorescein (Fisher) ata concentration of 1.2 × 10-4 M was used as the tracer. Allthe solutions were prepared with 10% RMB as the solvent.The experiments were performed under room temperature(∼20 °C).

Microfluidic Device Design and Operation. The designof the porous T-sensor is illustrated in Figure 1. Typical softlithography methods were followed to produce the microf-luidic device (30), and detailed procedures can be found inthe Supporting Information. The main channel of the devicehas a total length of 8.3 cm, width of 6 mm, depth of 13 µm,and porosity of 40%. Each impermeable cylinder has adiameter of ∼200 µm, and the dimension of pore throats is∼46 µm. The porosity and cylinder diameter were chosen toserve as a 2D simplification of a homogeneous sandy aqui-fer. Liquid was delivered into the porous T-sensor using100 µL GC microsyringes (Becton-Dickinson) and 1/16 in.Teflon tubing (Upchurch Scientific), driven by a syringepump (Harvard PHD 2000).

Three tests were performed in the porous T-sensor withdifferent groups of injectates. In test 1 (tracer test), the two

FIGURE 1. (a) Illustration of the porous T-sensor design. The main channel has a total length of 8.3 cm, width of 6 mm, depth of 13µm, and porosity of 40%. (b) An image of the inner structure taken under a bright field microscope (Carl Zeiss, 20× objective). Eachimpermeable cylinder had a diameter of ∼200 µm, and the dimension of pore throats was ∼46 µm. (c) Example of an image takenunder a wide field microscope (Olympus IX-70, 60× objective). The bright square shows the area chosen for bacteria counting. (d)Example of a transverse profile consists of 25 pore throats.

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injectates were 1.2 × 10-4 M fluorescein solution andphosphate buffer. In test 2 (bacterial control), the injectateswere bacterial suspension and phosphate buffer. In test 3(chemotaxis), bacterial suspension and 3 × 10-4 M R-mASPwere used. In each test, the linear velocity in the main channelwas adjusted to 20, 10, and 5 m/day, representing typicalgroundwater flow rates. Images were captured after thechannel had been flushed at the desired linear velocity for0.5-1 h to ensure steady state was achieved. In each test foreach velocity, images were recorded across three transverseprofiles at 2, 4, and 6 cm from the head of the main channel.Each profile had 25 pores separated by 24 impermeable grains(Figure 1d). Snapshots were taken in these pore bodies tomeasure fluorescein and bacterial concentration. Measure-ments from the first and the last pore were dropped due toaccumulation near the channel boundaries. In the tracertest, three snapshots of fluorescent light were taken at eachpore with an epifluorescent microscope (Carl-Zeiss Standard16, 20× objective, arc lamp; Hamamatsu 4742-95 digitalcamera) at 80 ms exposure time. The fluorescent intensityat the pore centers was converted to fluorescein concentra-tion with previously obtained standard curves. In the bacterialcontrol and chemotaxis tests, ten bright field snapshots weretaken at each pore with a wide field microscope (OlympusIX-70, 60× objective) equipped with a digital camera(Hamamatsu C4742-98) at 1 s intervals. A ∼50 × 50 µm squareregion at the center of each pore was selected (Figure 1c),and the number of bacteria in the square was countedmanually with ImageJ (NIH) and the point picker plug-in (byPhilippe Thevenaz, Ecole Polytechnique Federale de Lau-sanne). The sum of bacteria counted from the ten snapshotswas used to get the normalized bacterial concentration atthe specific pore body. In each group of ten snapshots, thestandard deviation of counted cell number was less than 5%of the averaged value.

Mathematical Modeling. The governing equation forbacteria transport including chemotaxis was provided byOlson et al. (12), which is

Rb∂b∂t

)-vf∂b∂x

+Dbx∂

2b

∂x2+Dby

∂2b

∂y2-

∂(vChxb)

∂x-

∂(vChyb)

∂y

(1)

where

Dbx )Rbxvf +µ0,eff

ε, Dby )Rbyvf +

µ0,eff

ε(2)

vChx )13

�0,eff

εKCh

(KCh +C)2

∂C∂x

, vChy )13

�0,eff

εKCh

(KCh +C)2

∂C∂y

(3)

and Rb is the bacterial retardation factor, b is the bacterialconcentration, vf is the averaged linear velocity of the flow,C is the concentration of the tracer, ε is the porosity, Dbx andDby are the longitudinal and transverse dispersion coefficientsfor bacteria, vChx and vChy are the longitudinal and transversechemotactic velocities describing the convective transportof bacteria caused by chemotaxis in shallow attractantgradients. In eq 2, µ0,eff is the effective random motilitycoefficient. The magnitude of the chemotactic velocities aredescribed by eq 3, where �0,eff is the chemotactic sensitivityand KCh is the constant describing the apparent binding rateof the chemotactic sensors (31). The coefficients µ0,eff and�0,eff are also expected to satisfy (8, 32)

µ0,eff

ε)

µ0

τb) µa (4a)

�0,eff

ε)

�0

τb) �a (4b)

where µ0 and �0 are random motility and chemotacticsensitivity coefficients in bulk aqueous phase under quiescentconditions, µa and �a are defined as the apparent coefficientsin porous media, and τb is the effective tortuosity for bacteriato transport through the porous medium. Values of µ0, �0,and KCh are available for several bacterial species (6), includingE. coli HCB1. Olson et al. (33) observed that under quiescentnonchemotactic conditions, the bacterial τb was much greaterthan the tortuosity experienced by a solute, which may bebecause bacterial runs were continuously restricted by thepore space. The value of τb may depend on porous mediaproperties, flow field characteristics, and bacterial swimmingproperties, although the specific form of the dependencewas not quantified. In static pore water, accounting for onlythe restricted run lengths due to the solid phase, bacterialmovement resembles Knudson diffusion and τb can beestimated by

τb ) τ(1+3µ0

vbdp) (5)

where vb is the bacterial swimming speed in bulk liquid, dp

is the pore diameter (33), and τ is the tortuosity. The tortuosityfor transverse dispersion in hexagonal cylinder arrays isapproximately 2 based on numerical simulations by Acharyaet al. (34). In static saturated soil columns, incorporating theKnudson diffusion still significantly underestimated theeffective tortuosity experienced by bacteria (33). However,we note that this expression may not be valid when porewater flow exists, as will be discussed later. The tortuosity forbacteria was 3.7, as calculated from eq 5, given an averagedswimming velocity for E. coli HCB1 of 22.8 µm/s and porediameter dp of 46 µm (6).

The channel walls were impermeable boundaries (y ) 0,y ) 6 mm). A step change of bacterial or attractantconcentration was assumed at the head of the channel [e.g.,C(x)0,0ey<3 mm) ) 1, C(x)0,3 mmeye6 mm) ) 0]. Thetransverse dispersion profiles of the tracer (test 1) and bacteria(test 2) were modeled with methods provided in theSupporting Information to obtain Dcy and Rcy (transversediffusion coefficient and dispersivity for the solute), as wellas Dbx and Rby in the main channel (35). Finally, test 3 wassimulated by first solving the 2D concentration distributionof the chemical attractant, followed by that of chemotacticbacteria (eq 2) with a MATLAB finite difference code, usingthe transport parameters obtained from the previous tests.The longitudinal dispersivities were assumed to be 10 timesthe value of the corresponding transverse dispersivities, whichintroduced only negligible error due to the shallow concen-tration gradients in the x direction.

3. Results and DiscussionTransverse Dispersivities. The diffusion coefficient D0 offluorescein in water at 21.5 °C was calculated as 4.9 × 10-6

cm2 s-1 (36). For E. coli HCB1, its random motility coefficientµ0 is 3.0 × 10-6 cm2 s-1 (37, 38). The best fitting curves forthe tracer test and control test data yielded averageddispersivitiesRcy of 0.004 cm andRby of 0.0008 cm (see Figure2). The dispersion coefficient values for Dcy were 6-18 timeslarger than D0, and the Dby values were 3-4 times larger thanµ0. Acharya et al. (34) performed numerical simulations onhexagonal arrays of cylinders with higher porosities thanours (45.6% and 49.7%) and found the ratio of Dcy/D0 was3-7 when the Peclet number was in the range used in thisstudy (Pe) 20-150). The lower porosity (40%) in this porousT-sensor design and the difference of pore space geometriesmay have contributed to the higher Dcy values observed forthe solute, as narrower pore throats can bring initially distantstreamlines into closer proximity and enhance transversedispersion (39).

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The data fitting results showed the transverse dispersivityof bacteria was much smaller than that of the solute. Althoughdispersivity is typically considered an intrinsic property ofporous media, several previous studies showed the longi-tudinal dispersivity of bacteria and conservative tracers weredifferent in the same porous media (40, 41). There are fewstudies that directly measured the transverse dispersivity ofbacteria. A double-layered sand column study by Morley etal. (42) suggested that applying the value of the tracer’stransverse dispersivity for bacteria could yield a satisfactorymodel fitting for their experimental system. However, a recentstudy by Auset and Keller (16) that directly visualized colloidaltransport in PDMS microfluidics etched with porous patternsshowed that dispersivities of colloids were not controlled bypore geometries alone but were also a function of colloidalsize. Although transverse dispersivity was not directly ad-dressed by Auset and Keller, their discovery implied itsdependency on particle size, as smaller colloids made moredetours and hence might exhibit greater transverse disper-sivities than the larger colloids. This size-dependent phe-nomenon may partly explain the difference between Rcy andRby, due to the large difference in the size of fluoresceinmolecule and E. coli HCB1 bacterium. Other factors, including

the rod shape of E. coli HCB1 cells, the existence of flagella,the bacterial swimming properties, and the cell-surfaceinteractions could also contribute to the observed differencesin dispersivity.

Chemotaxis Transverse to Flow. Results from the chemo-taxis test are presented in Figure 3, plotted together withdata from the control test without a chemical attractant. Asignificant difference between the chemotaxis and the controltest appeared under the lowest fluid velocity (5 m/day). Thetransverse bacterial profiles at 4 and 6 cm in the chemotaxistest showed considerable deviation from the typical disper-sion-driven profiles in the control test. Peaks of bacterialconcentration were formed in the attractant side of the mainchannel and migrated further into the attractant side as thelongitudinal transport distance increased, resembling thechemotactic bands previously observed in a T-sensor channelwithout porous structures (25). The percentages of bacteriatransported to the other half of the main channel aresummarized in Table S1 (Supporting Information). Under a5 m/day flow rate, 60% of the chemotactic bacterial popula-tion was transported to the attractant side within a longi-tudinal distance of 6 cm, while only 20% was transported bydispersion alone when the attractant was not present. This

FIGURE 2. Distribution of fluorescein concentration (left column) and normalized bacterial concentration (right column) at crosssections 2, 4, and 6 cm from the head of the main channel in tests 1 and 2 (solute dispersion and bacterial dispersion tests). Thecurves represent best fitting results using eq S3 (Supporting Information).

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significant difference reflected the influence of chemotaxison the bacterial transport transverse to flow. Under the higherflow rates, the difference between the control and chemotaxisprofiles was not as great; peaks in the bacterial concentrationwere not observable (except at 6 cm under 10 m/day). Inother words, distinct chemotactic peaks were only observedwhen the residence time was greater than 5.8 min. Never-theless, over the range of flow rates, all chemotaxis profilesindicated enhanced transverse transport of bacteria into theother half of the main channel compared with the controlexperiments.

Modeling Chemotaxis Transverse to Flow with ADE. Theexperimental scenarios in test 3 were simulated numericallywith the ADE modeling framework described previously.Except for �a, other parameters required to solve the equationswere all obtained from tests 1 and 2 (solute dispersivitiesand bacterial dispersion coefficients under the tested veloci-ties) or reported data [diffusion coefficient for the attractantR-mASP D0,R-mASP)8.6 × 10-6 cm2 s-1, chemotactic sensitivitycoefficient �0 ) 2.4 × 10-4cm2 s-1, and the chemotacticreceptor constant KCh ) 1.25 × 10-4 M were used (25, 37, 43)].Equation 3 indicates that the magnitude of �a is directlyproportional to the chemotactic velocity. Assuming eq 4b isvalid, as τb was estimated as 3.7, the value for �a is �0/τb.However, with this input, the model did not produce anoticeable difference between chemotactic and pure disper-sion profiles (Figure S1, Supporting Information). Thus, twomore scenarios, �a) 50 × �0/τb and �a) 100 × �0/τb were alsotested (Figures 3 and S1, Supporting Information). For the5 m/day experiment, a distinctive feature we observed wasthe formation of peaks of bacterial concentration that movedfar into the attractant side of the channel at 4 and 6 cmprofiles. This feature could only be qualitatively captured byincreasing the value of �a in the model (e.g., �a ) 100 × �0/τb;see Figure 3). However, the increased �a resulted in over-

estimates of the chemotactic response at 2 cm, which inthe experiments did not deviate very much from thecorresponding dispersion profile (see Figure 3). In general,the current ADE model failed to provide quantitativelyaccurate simulations of the chemotaxis test results.Qualitatively, the model captured some features andtendencies of chemotactic transport observed in theexperiments after increasing �a more than 1 order ofmagnitude beyond its theoretical maximum value of �0/τb. As natural groundwater flow is typically slower than 10m/day, the current model would significantly underesti-mate chemotactic transport transverse to groundwaterflow.

Several reasons may explain the inability of the ADE modelsimulations to adequately represent the experimental ob-servations. The chemotactic velocity terms in the model (eq3) were developed on the basis of bacterial chemotaxis instatic pore water (8). Because the run length of a bacteriumis comparable to the pore body dimensions, linear movementduring each “run” is interrupted as the bacterium frequentlyencounters the solids. Compared with bacterial swimmingin the bulk liquid, effective run lengths are reduced andresidence times on the surface of solids are expected toincrease (32, 44), resulting in decreased motility and chemo-tactic velocity or increased effective tortuosity for bacteria(32). Incorporating eq 3 into the ADE model assumed thatfluid flow would not change the bacterial transport properties.Although the previous study by Lanning et al. (25) did notobserve any significant impact of flow on transverse chemo-tactic transport in bulk liquid, the conclusion cannot beextrapolated to porous media systems where much greatershear rates exist. Marcos and Stocker (45) found that understrong shear flow, Pseudoalteromonas haloplanktis, a bac-terium with similar size, shape, and swimming behavior toE. coli, tended to transport and align its major axis along the

FIGURE 3. Left column: distribution of normalized bacterial concentration at cross sections 2, 4, and 6 cm from the head of the mainchannel in the chemotaxis test (connected open squares). As a comparison, the data from the control test are also plotted ascrosshairs. Right column: results of numerical simulations resembling the chemotactic test (test 3), with �a ) 100�0/τb.

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streamline of flow. When the flow was stopped, bacteriaresumed their normal run-and-tumble pattern. Their ob-servations suggested a dependency of bacterial motility onlocal shear rates. Thus, parameters in the ADE model relatedto bacterial motility (i.e., µ0,eff, �0,eff, and �a) are expected todecrease with increased shear. This argument is partlysupported by the experimental evidence that the chemotacticresponse was most significant under the lowest flow ratestested. The bacterial tortuosity τb is also affected, as itapproaches τ for a solute under high shear rates when thebacteria primarily follow streamlines. However, furtherinvestigation is needed to quantify the dependence of theseparameters on shear.

For the model predictions to match the magnitudes oftransverse chemotactic transport under low flow rates, the�a value needed to be more than 1 order of magnitude greaterthan �0/τb. In other words, bacteria in the porous mediumdemonstrated much greater transverse chemotactic transportthan the ADE model had predicted. This result was contraryto that suggested by eq 4a, which was only tested in staticpore water (8). While Roush et al. (9) also reported that thechemotactic sensitivity coefficient of Pseudomonas putidaKC in porous media was 100 times higher than in bulk liquid,their observation was made in a static swarming plate systemwith consumable attractants, and consumption within therestricted pore space was believed to enhance chemotaxisby generating sharper local gradients. However, as noconsumption of attractant took place in this study, theenhanced chemotaxis must be due to other reasons.

Another fact that has not been included in the ADE modelis the complexity of bacteria-solid surface interactions.Because the swimming bacteria are propelled by flagellaextending far from the cell wall, when the movements of thecell and flagella are restrained by the existence of the solidwall, the near surface swimming behaviors are quite differentfrom the random walk pattern in bulk liquid (46-48). Whenflow is introduced near solid surfaces, shear forces contributeto the complexity, sometimes resulting in bacterial transportpatterns not predicted by the traditional models such asmoving upstream (23). As the understanding of bacteria-solidsurface interaction is still in progress at the pore and subporescale, an accurate expression of this effect in larger scaleADE models is premature, and further investigation iswarranted.

AcknowledgmentsThe authors gratefully acknowledge support from the Na-tional Scientific Foundation (Hydrologic Sciences, Grant0711377). We thank Dr. James Landers, Dr. Jerome Ferrance,and Mr. Ling Huang for their help to produce the microfluidicdevice. We thank Dr. Ammasi Periasamy and Dr. YuanshengSun for their support with microscopy techniques and thethree anonymous reviewers for their valuable comments.We also appreciate Ms. Kathryn Strobel and Mr. Xiaopu Wangfor their assistance with cell counting.

Supporting Information AvailableDetailed methods of the micromodel fabrication and themathematical models used to fit transverse concentrationprofiles of tracer and bacteria are provided; the ADE modelingresults for scenarios �a ) �0/τb and �a ) 50 × �0/τb are givenin Figure S1, and the percentages of bacteria transported tothe other half of the main channel are summarized in TableS1. This information is available free of charge via the Internetat http://pubs.acs.org

Literature Cited(1) NRC. Contaminants in the Subsurface: Source Zone Assessment

and Remediation; The National Academies Press: Washington,DC,2005.

(2) Jogensen, K. S. In Situ Bioremediation. In Advances in AppliedMicrobiology; Laskin, A. I., SariaslanI, S., Gadd, G. M., Eds.;Academic Press: New York, 2007; Vol. 61, pp 285-305.

(3) Scow, K. M.; Hicks, K. A. Natural Attenuation and EnhancedBioremediation of Organic Contaminants in Groundwater. Curr.Opin. Biotechnol. 2005, 16, 246–253.

(4) Harms, H.; Wick, L. Y. Dispersing Pollutant-Degrading Bacteriain Contaminated Soil without Touching it. Eng. Life Sci. 2006,6, 252–260.

(5) Berg, H. C.; Brown, D. A. Chemotaxis in Escherichia coli Analyzedby 3-Dimensional Tracking. Nature 1972, 239, 500–504.

(6) Ford, R. M.; Harvey, R. W. Role of Chemotaxis in the Transportof Bacteria through Saturated Porous Media. Adv. Water Resour.2007, 30, 1608–1617.

(7) Barton, J. W.; Ford, R. M. Determination of Effective TransportCoefficients for Bacterial Migration in Sand Columns. Appl.Environ. Microbiol. 1995, 61, 3329–3335.

(8) Olson, M. S.; Ford, R. M.; Smith, J. A.; Fernandev, E. J.Quantification of Bacterial Chemotaxis in Porous Media UsingMagnetic Resonance Imaging. Environ. Sci. Technol. 2004, 38,3864–3870.

(9) Roush, C. J.; Lastoskie, C. M.; Worden, R. M. Denitrification andChemotaxis of Pseudomonas stutzeri KC in Porous Media. J.Environ. Sci. Health A 2006, 41, 967–983.

(10) Velasco-Casal, P.; Wick, L. Y.; Ortega-Calvo, J.-J. ChemoeffectorsDecrease the Deposition of Chemotactic Bacteria duringTransport in Porous Media. Environ. Sci. Technol. 2008, 42,1131–1137.

(11) Sen, T. K.; Das, D.; Khilar, K. C.; Suraishkumar, G. K. BacterialTransport in Porous Media: New Aspects of the MathematicalModel. Colloids Surf. A 2005, 260, 53–62.

(12) Olson, M.; Ford, R.; Smith, J.; Fernandez, E. MathematicalModeling of Chemotactic Bacterial Transport through a Two-Dimensional Heterogeneous Porous Medium. Bioremed. J. 2006,10, 13–23.

(13) Stone, H. A.; Stroock, A. D.; Ajdari, A. Engineering Flows inSmall Devices: Microfluidics toward a Lab-on-a-Chip. Annu.Rev. Fluid Mech. 2004, 36, 381–411.

(14) Wan, J. M.; Wilson, J. L. Visualization of the Role of the Gas-Water Interface on the Fate and Transport of Colloids in Porous-Media. Water Resour. Res. 1994, 30, 11–23.

(15) Baumann, T.; Werth, C. J. Visualization and Modeling ofPolystyrol Colloid Transport in a Silicon Micromodel. VadoseZone J. 2004, 3, 434–443.

(16) Auset, M.; Keller, A. A. Pore-Scale Processes That ControlDispersion of Colloids in Saturated Porous Media. Water Resour.Res. 2004, 40.

(17) Dupin, H. J.; McCarty, P. L. Impact of Colony Morphologies andDisinfection on Biological Clogging in Porous Media. Environ.Sci. Technol. 2000, 34, 1513–1520.

(18) Lanning, L. M.; Ford, R. M. Glass Micromodel Study of BacterialDispersion in Spatially Periodic Porous Networks. Biotechnol.Bioeng. 2002, 78, 556–566.

(19) Kamholz, A. E.; Weigl, B. H.; Finlayson, B. A.; Yager, P.Quantitative Analysis of Molecular Interaction in a MicrofluidicChannel: The T-Sensor. Anal. Chem. 1999, 71, 5340–5347.

(20) Lin, F.; Butcher, E. C. T Cell Chemotaxis in a Simple MicrofluidicDevice. Lab Chip 2006, 6, 1462–1469.

(21) Koyama, S.; Amarie, D.; Soini, H. A.; Novotny, M. V.; Jacobson,S. C. Chemotaxis Assays of Mouse Sperm on MicrofluidicDevices. Anal. Chem. 2006, 78, 3354–3359.

(22) Mao, H. B.; Cremer, P. S.; Manson, M. D. A Sensitive, VersatileMicrofluidic Assay for Bacterial Chemotaxis. Proc. Natl. Acad.Sci. U.S.A. 2003, 100, 5449–5454.

(23) Hill, J.; Kalkanci, O.; McMurry, J. L.; Koser, H. HydrodynamicSurface Interactions Enable Escherichia coli To Seek EfficientRoutes To Swim Upstream. Phys. Rev. Lett. 2007, 98.

(24) Kim, M. J.; Breuer, K. S. Enhanced Diffusion Due to MotileBacteria. Phys. Fluids. 2004, 16, L78-L81.

(25) Lanning, L. M.; Ford, R. M.; Long, T. Bacterial ChemotaxisTransverse to Axial Flow in a Microfluidic Channel. Biotechnol.Bioeng. 2008, 100, 653–663.

(26) Alder, J.; Templeton, B. The Effect of Environmental Conditionson the Motility of Escherichia coli. J. Gen. Microbiol. 1967, 46.

(27) Wang, M.; Ford, R. M.; Harvey, R. W. Coupled Effect ofChemotaxis and Growth on Microbial Distributions in Organic-Amended Aquifer Sediments: Observations from Laboratory andField Studies. Environ. Sci. Technol. 2008, 42, 3556–3562.

(28) Vigeant, M. A.; Ford, R. M. Interactions between MotileEscherichia coli and Glass in Media with Various Ionic Strengths,As Observed with a Three-Dimensional-Tracking Microscope.Appl. Environ. Microbiol. 1997, 63, 3474–3479.

VOL. 43, NO. 5, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 1551

(29) Parales, R. E.; Ditty, J. L.; Harwood, C. S. Toluene-DegradingBacteria Are Chemotactic towards the Environmental PollutantsBenzene, Toluene, and Trichloroethylene. Appl. Environ. Mi-crobiol. 2000, 66, 4098–4104.

(30) Xia, Y. N.; Whitesides, G. M. Soft Lithography. Annu. Rev. Mater.Sci. 1998, 28, 153–184.

(31) Segel, L. A.; Chet, I.; Henis, Y. Simple Quantitative Assay forBacterial Motility. J. Gen. Microbiol. 1977, 98, 329–337.

(32) Duffy, K. J.; Cummings, P. T.; Ford, R. M. Random-WalkCalculations for Bacterial Migration in Porous-Media. Biophys.J. 1995, 68, 800–806.

(33) Olson, M. S.; Ford, R. M.; Smith, J. A.; Fernandez, E. J. Analysisof Column Tortuosity for MnCl2 and Bacterial Diffusion UsingMagnetic Resonance Imaging. Environ. Sci. Technol. 2005, 39,149–154.

(34) Acharya, R. C.; Valocchi, A. J.; Werth, C. J.; Willingham, T. W.Pore-Scale Simulation of Dispersion and Reaction along aTransverse Mixing Zone in Two-Dimensional Porous Media.Water Resour. Res. 2007, 43.

(35) Crank, J. The Mathematics of Diffusion, 2nd ed.;Clarendon Press:Oxford, 1975.

(36) Rani, S. A.; Pitts, B.; Stewart, P. S. Rapid Diffusion of FluorescentTracers into Staphylococcus epidermidis Biofilms Visualized byTime Lapse Microscopy. Antimicrob. Agents Chemother. 2005,49, 728–732.

(37) Lewus, P. University of Virginia, Charlottesville, VA, 2000.(38) Lewus, P.; Ford, R. M. Quantification of Random Motility and

Chemotaxis Bacterial Transport Coefficients Using Individual-Cell and Population-Scale Assays. Biotechnol. Bioeng. 2001, 75,292–304.

(39) Willingham, T. W.; Werth, C. J.; Valocchi, A. J. Evaluation of theEffects of Porous Media Structure on Mixing-Controlled Reac-tions Using Pore-Scale Modeling and Micromodel Experiments.Environ. Sci. Technol. 2008, 42, 3185–3193.

(40) Dong, H.; Rothmel, R.; Onstott, T. C.; Fuller, M. E.; DeFlaun,M. F.; Streger, S. H.; Dunlap, R.; Fletcher, M. SimultaneousTransport of Two Bacterial Strains in Intact Cores from Oyster,Virginia: Biological Effects and Numerical Modeling. Appl.Environ. Microbiol. 2002, 68, 2120–2132.

(41) Hornberger, G. M.; Mills, A. L.; Herman, J. S. Bacterial Transportin Porous-MediasEvaluation of a Model Using LaboratoryObservations. Water Resour. Res. 1992, 28, 915–923.

(42) Morley, L. M.; Hornberger, G. M.; Mills, A. L.; Herman, J. S.Effects of Transverse Mixing on Transport of Bacteria throughHeterogeneous Porous Media. Water Resour. Res. 1998, 34, 1901–1908.

(43) Mesibov, R.; Ordal, G. W.; Adler, J. The Range of AttractantConcentrations for Bacterial Chemotaxis and the Threshold andSize of Response over This Range: Weber Law and RelatedPhenomena. J. Gen. Physiol. 1973, 62, 203–223.

(44) Kusy, K.; Ford, R. M. Monte Carlo Simulations Derived fromDirect Observations of Individual Bacteria Inform MacroscopicMigration Models at Granular Porous Media Interfaces. Environ.Sci. Technol. 2007, 41, 6403–6409.

(45) Marcos; Stocker, R. Microorganisms in Vortices: A MicrofluidicSetup. Limnol. Oceanogr. Meth. 2006, 4, 392–398.

(46) DiLuzio, W. R.; Turner, L.; Mayer, M.; Garstecki, P.; Weibel,D. B.; Berg, H. C.; Whitesides, G. M. Escherichia coli Swim onthe Right-Hand Side. Nature 2005, 435, 1271–1274.

(47) Frymier, P. D.; Ford, R. M.; Berg, H. C.; Cummings, P. T.3-Dimensional Tracking of Motile Bacteria near a Solid PlanarSurface. Proc. Natl. Acad. Sci. U.S.A. 1995, 92, 6195–6199.

(48) Biondi, S. A.; Quinn, J. A.; Goldfine, H. Random Motility ofSwimming Bacteria in Restricted Geometries. AIChE J. 1998,44, 1923–1929.

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