This work was supported by the Research Executive Agency, through the 7th Framework Programme of the European Union, Support for Training and Career Development of Researchers (Marie Curie - FP7-PEOPLE-2012-ITN), which funded the Initial Training Network (ITN) HYTECH &#34;Hydrodynamic Transport in Ecologically Critical Heterogeneous Interfaces&#34; (number 316546). It was also supported by the Polo Territoriale di Lecco of the Politecnico di Milano. The experiments were performed during a visit by S. S. to the Politecnico di Milano as a visiting scientist. The authors thank Tarcisio Fazzini, Stefania Gherbi, Francesco Mottini (B.Sc. students at the Politecnico di Milano) and Seyed Abbas Hosseini-Sadabadi (fellow of the HYTECH project and Ph.D. student at the Politecnico di Milano) for supporting the experimental activity and the data analysis. The authors gratefully thank Prof. Roger Nokes (University of Canterbury, Christchurch, New Zealand) for providing the Streams software and constant advice. Finally, the authors thank the JoVE managing editor and three anonymous reviewers for their thought-provoking comments and suggestions, thanks to which the manuscript could be significantly improved.

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The authors declare that they have no competing financial interests.

Designing a bed-load transport experiment with particle visualization involves several steps, including the choice of an experimental configuration and hardware tools, flow measurement, particle seeding and visualization, and image analysis. Variations at each step have advantages and disadvantages. The key characteristics of the protocol presented in this manuscript are: (i) using a pressurized flow and a fixed rough bed, (ii) seeding a low number of bed-load particles having a contrasting color to the fixed bed color, (iii) using natural light and, (iv) using multiple cameras to obtain independent track sets to be joined to each other.
The experimental method and the data processing enable the bed-load particles to be reliably tracked for the final measurement. The covered flow guarantees an optimal vision of the moving particles. The fixed bed however, prevents observation of some processes (e.g., those linked with vertical displacements of sediment particles within the active bed-load layer), and thus limits the applicability of the technique to weak bed-loads.
The size of the data samples obtained using only 100 s of movie was relatively small. However, the sample size can easily be increased by lengthening the experimental duration of image acquisition and processing. Feeding a limited number of particles requires a longer experimental time than feeding at a substantially higher rate; but it is well worth the effort because of a relatively straightforward particle tracking due to the small concentration of particles in motion and the use of different colors, both of which reduce the probability of tracking mistakes. The use of natural light in the experiment avoids the need for lighting devices; however, a downside is that good lighting depends on the weather conditions.
The CFDs of particle hop length and duration depicted in Figure 4 show the lowest values as the most frequent ones. The largest measured values of hop length and duration were around 600 mm and 7 s, respectively. This was significantly larger compared to analogous values from the literature4,16,30, since measuring longer tracks runs the risk of long particle hops. The benefit of using two cameras is evident considering that a single camera had a focus area length of around 850 mm, which would not be much larger than hop length values to be measured. The measuring protocol using two cameras instead, ensured a satisfactory separation between the length scales of the process and those of the measuring field, thus reducing the risk of biasing the phenomenological results due to experimental limitations. Also, the focus area can be additionally lengthened by increasing the number of cameras placed along the flume.
An alternative procedure compared to the protocol described here is to create overlapped images before the particle identification and tracking. Our protocol (of performing the tracking twice and linking particle tracks) was preferred as the image merging method would have doubled the size of the data files, requiring a memory consumption that was not affordable.
With the processing algorithms described here, several particle tracks that were shorter than the length of the overlapping area were discarded because they prevented a full reconstruction of the sediment tracks. However, the threshold length of 120 mm was one order of magnitude shorter than the track lengths that could be obtained, and the loss of these data was therefore acceptable. Moreover, the track joining seen in the lower 8 cases of Figure 1 would not enable a significant increase in the track length to be obtained. On the other hand, these situations might help in retrieval of long tracks, such as the situation in Figure 5 that could be due to track interruptions. In a similar case, a long track could be reconstructed by iterative joining operations. It is however important to keep in mind that track interruptions like those in Figure 5 are clearly related to the tracking process rather than to the joining process.
This manuscript presented proof-of-concept results for a single experiment in order to demonstrate the capabilities of the adopted protocol. In future experiments, the protocol will be applied to a series of different hydro-dynamic conditions to achieve a detailed analysis of the bed-load sediment transport process.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55874/55874fig5.jpg" /> 
Figure 5: A Situation of Track Joining in the Presence of Interruptions. The joining of these tracks into a single track is not possible with the protocol described here. As mentioned in the caption of Figure 1 and in step 8.4 of the Protocol, the tracks shorter than the length of the overlapping region are excluded. This eliminates the short red and green tracks; therefore, the remaining long ones cannot be joined because they have no common point. <a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55874/55874fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Since pioneering works appeared some decades ago1,2, the use of image analysis for the study of river sediment transport has been constantly increasing. This technique indeed proved its capability to provide relatively high-resolution and low-cost data for detailed analyses of physical phenomena3,4,5. With time, significant improvements have been obtained for both hardware and software tools.
The measurement of sediment transport can be performed using a Eulerian approach that targets measurement of sediment fluxes, or a Lagrangian one that aims at measuring trajectories of individual grains as they move. Image processing offers unique possibilities for particle tracking in comparison to other Eulerian methods6,7. However, despite these potentialities, the application of image analysis to bed-load sediment transport suffers from some critical experimental limitations, in terms of spatial/temporal support scales for the measurement and size of data samples. For example, it is difficult to achieve simultaneously an appropriate combination of a large spatial area, long duration of an experiment, and high measuring frequency3,4,8, without compromising the quality and quantity of data. In addition, the particle tracking can be performed manually2,4, which requires a great human effort, or automatically3,8, with the possibility of tracking errors made by the software used for the analysis.
This paper presents a protocol for the experimental investigation of bed-load sediment transport, where long duration was achieved by the type of camera used, large field of view was ensured by simultaneous use of two cameras at different locations, and reliable automatic processing was made possible by ad hoc experimental conditions. The experimental operation was designed and the processing tools were selected based on experience acquired by the authors in several research works dealing with the detailed investigation of sediment transport by image methods3,9,10,11,12,13,14,15,16,17,18.
A sediment transport experiment is described, that was performed releasing particles over a fixed, rough bed. The particle feeding was much less than the transport capacity of the flow to maintain a low concentration of moving grains, thus avoiding the congestion of particles to be tracked. Furthermore, the transported particles were not moving continuously, but intermittent motion was observed. The use of a fixed bed rather than a movable one represents a loss of similarity to natural conditions. However, a fixed bed was frequently used in sediment transport experiments19,20,21 under the assumption that the results are more simple and explanatory than those from complicated scenarios with a variety of acting processes. The use of a fixed bed obviously prevents processes of sediment burial and reappearance from being observed. On the other hand, in the presence of a weak bed load, the transport of sediment takes place in a superficial layer of a loose bed, and in this case, the use of a fixed bed may be adequate. In fact, specific comparisons between the properties of particle motion in experiments run with the two conditions did not present any significant differences3,14. Finally, the experiment presented here was performed with a pressurized flow to ensure an optimal condition for particle visualization through a transparent cover. Sediment transport with a pressurized flow has been experimentally studied in research prototyping ice-covered rivers, showing that the interaction between the near-bed boundary layer and the sediment is analogous to that of open-channel flow22,23. In the following sections, all methods are outlined and some representative results are provided.

<table><tbody><tr><td>Laser distance sensor</td><td>METRICA</td><td>PREXISOX2</td><td>Used to measure the flume slope</td></tr><tr><td>Two-component polyester resin</td><td>Gelson</td><td>MS 65213</td><td>Used to glue sediment particles onto steel plates</td></tr><tr><td>Water-resistant spray paint</td><td>Any</td><td></td><td>Used to paint the fixed bed</td></tr><tr><td>Ultrasonic Velocity Profiler</td><td>Signal Processing</td><td>DOP 2000</td><td>Used to measure the water velocity profiles</td></tr><tr><td>Camera</td><td>Go-Pro</td><td>Hero 4 Black</td><td>Used to acquire movies of bed-load particle motion</td></tr><tr><td>Streams</td><td>University of Canterbury</td><td>2.01</td><td>Used for particle identification and tracking</td></tr><tr><td>MatLab</td><td>MathWorks</td><td>R14</td><td>Used to develop ad hoc codes for a variety of operations</td></tr><tr><td>Plexiglas</td><td></td><td></td><td>Transparent acrylic material</td></tr></tbody></table>

image-based lagrangian particle tracking in bed-load experiments

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a relatively low cost. This manuscript describes an application of particle tracking velocimetry (PTV) to a bed-load experiment with lightweight sediment. The key characteristics of the investigated sediment transport conditions were the presence of a covered flow and of a fixed rough bed above which particles were released in limited number at the flume inlet. Under the applied flow conditions, the motion of the individual bed-load particles was intermittent, with alternating movement and stillness terms. The flow pattern was preliminarily characterized by acoustic measurements of vertical profiles of the stream-wise velocity. During process visualization, a large field of view was obtained using two action-cameras placed at different locations along the flume. The experimental protocol is described in terms of channel calibration, experiment realization, image pre-processing, automatic particle tracking, and post-processing of particle track data from the two cameras. The presented proof-of-concept results include probability distributions of the particle hop length and duration. The achievements of this work are compared to those of existing literature to demonstrate the validity of the protocol.

Results presented in this section are for an experiment where the flume slope was set to zero (slope values were computed with &#177; 0.05% accuracy). The used sediment was made of PBT particles that were quasi-spherical, with a size d = 3 mm and a density &#961;p = 1,270 kg/m3. The experiment was run with a flow rate Q = 9.7 &#215; 10-3 m3/s resulting in a bulk velocity U = 0.31 m/s.
For the velocity measurements with the UVP, a 2 MHz probe was used at 81&#176; inclination. Velocity data were acquired at 20 Hz for 250 s. A representative velocity profile is depicted in Figure 2. It was taken at the channel axis and at 4.5 m from the flume inlet, where the flow was fully developed. Some values related to invalid elevations measurements were removed. An asymmetric profile resulted from the different roughness of the plastic lid and sediment bed. The plots also show the portion of the profile used for the estimation of the shear velocity, obtaining us = 25.9 &#177; 1.3 mm/s. The particle Reynolds number (Rep = us&#215;d/&#957;, with &#957; as the kinematic viscosity of water) was therefore equal to 78, indicating a transitionally rough regime.
The visualization of sediment transport was performed with two cameras placed at 3.5 m and 4.3 m from the flume inlet. The cameras operated at a frequency of 30 fps and with a resolution of 1,920 x 1,080 pixel. The factor for correction of image distortion was k = 0.6. After removal of distortion, the image calibration was 1 pixel = 0.5 mm. The length of overlap was from 760.15 to 880.11 mm (where the latter was the length of the focus area of the first camera from its upstream edge). The threshold intensity for particle identification was set to 80 and the expected blob size ranged from 0.5 to 8 mm. The search window for particle tracking was as follows: 1 mm upstream and 7 mm downstream, 4 mm laterally. The search window for reconnection of interrupted tracks was as follows: 1 mm upstream and 31 mm downstream, 16 mm laterally along 4 following frames. The threshold value of the square root of the mean squared difference of x and y values between two tracks to be joined was set to 10 mm.
The particle tracks measured using a subset of 3,000 images from each camera (corresponding to 100 s duration) are depicted in Figure 3. The database comprehended 37 and 34 tracks from the upstream and downstream camera, respectively. An overlap of the tracks obtained by the two cameras is first proposed and then the resulting full set of tracks is displayed. It is evident that the overlap in the central portion of the measuring area was satisfactory. 12 links were obtained at the end 59 tracks. The longest track spanned the entire observation window with a total length of approximately 1.6 m (more than 530 particle sizes, 15.2 flow depths or 5.3 flume widths), which is very large in comparison to other literature studies where similar analyses were performed3,4,5,8.
By taking a Lagrangian framework, the key indicators of particle kinematics are here applied in terms of properties of particle hops. Under an intermittent bed-load transport like the one in this experiment, these hops are motions separated by periods of rest. To detect hops within a full track for a single particle, the identification of particle motion and stillness is a necessary preliminary step. In this work, we applied a criterion30 that considers a particle in motion at a certain instant if its x position at that instant is larger than all the previous ones and lower than all the following ones. A total number of 98 hops was obtained from the 59 measured particle tracks. Figure 4 depicts the obtained Cumulative Frequency Distribution (CFD) for the hop length and duration.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55874/55874fig2.jpg" /> 
Figure 2: Measured Velocity Profile. (Top) The time-averaged vertical profile of the stream-wise velocity component. (Bottom) The estimation of the shear velocity by fitting a logarithmic equation to the lower portion of the profile. Note that a vertical axis starting from the top of the channel and oriented downwards is used in the first plot, representing the result from the measurement with the UVP. An axis from the channel bottom and directed upwards is instead used in the second plot, as needed to estimate the shear velocity by equation fitting. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55874/55874fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55874/55874fig3.jpg" /> 
Figure 3: Plan View of Measured Particle Tracks. (Top) The tracks from the two cameras (upstream camera in red and downstream in black). (Bottom) The sample of joined tracks (changing color for clarity and some tracks highlighted by a thicker line). <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55874/55874fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55874/55874fig4.jpg" /> 
Figure 4: Cumulative Frequency Distribution (CFD) of Hop Length (Top) and Duration (Bottom). Within each track of Figure 3, the particle was labeled at each time instant to represent if the particle was in motion or at rest at that instant. Particle hops were then extracted from the tracks as portions between particle entrainment (transition from stillness to motion) and disentrainment (transition from motion to stillness). The samples obtained for hop lengths and durations were used to create the distributions depicted here. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55874/55874fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Code Files: join_cameras.m <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55874/join_cameras.m">Please click here to download this file.</a>

Watch this Scientific Journal Video about Image-based Lagrangian Particle Tracking in Bed-load Experiments at JoVE.com

Image-based Lagrangian Particle Tracking in Bed-load Experiments

The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a ...

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8.9K Views.  Politecnico di Milano. The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

Video: Image-based Lagrangian Particle Tracking in Bed-load Experiments

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

The behavior of confined colloidal suspensions with attractive interparticle interactions is critical to the rational design of materials for directed assembly1-3, drug delivery4, improved hydrocarbon recovery5-7, and flowable electrodes for energy storage8. Suspensions containing fluorescent colloids and non-adsorbing polymers are appealing model systems, as the ratio of the polymer radius of gyration to the particle radius&#160;and concentration of polymer control the range and strength of the interparticle attraction, respectively. By tuning the polymer properties and the volume fraction of the colloids, colloid fluids, fluids of clusters, gels, crystals, and glasses can be obtained9.&#160;Confocal microscopy, a variant of fluorescence microscopy, allows an optically transparent and fluorescent sample to be imaged with high spatial and temporal resolution in three dimensions. In this technique, a small pinhole or slit blocks the emitted fluorescent light from regions of the sample that are outside the focal volume of the microscope optical system. As a result, only a thin section of the sample in the focal plane is imaged. This technique is particularly well suited to probe the structure and dynamics in dense colloidal suspensions at the single-particle scale: the particles are large enough to be resolved using visible light and diffuse slowly enough to be captured at typical scan speeds of commercial confocal systems10. Improvements in scan speeds and analysis algorithms have also enabled quantitative confocal imaging of flowing suspensions11-16,37.&#160;In this paper, we demonstrate confocal microscopy experiments to probe the confined phase behavior and flow properties of colloid-polymer mixtures. We first prepare colloid-polymer mixtures that are density- and refractive-index matched. Next, we report a standard protocol for imaging quiescent dense colloid-polymer mixtures under varying confinement in thin wedge-shaped cells. Finally, we demonstrate a protocol for imaging colloid-polymer mixtures during microchannel flow.

Confocal microscopy is used to image quiescent and flowing colloid-polymer mixtures, which are studied as model systems for attractive suspensions. Image analysis algorithms are used to calculate structural and dynamic metrics for the colloidal particles that measure changes due to geometric confinement.

The behavior of confined colloidal suspensions with attractive interparticle interactions is critical to the rational design of materials for directed ...

Chemistry

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute transport in rivers and many important biogeochemical processes. To improve understanding of these processes through visual demonstration, we created a hyporheic flow simulation in the multi-agent computer modeling platform NetLogo. The simulation shows virtual tracer flowing through a streambed covered with two-dimensional bedforms. Sediment, flow, and bedform characteristics are used as input variables for the model. We illustrate how these simulations match experimental observations from laboratory flume experiments based on measured input parameters. Dye is injected into the flume sediments to visualize the porewater flow. For comparison virtual tracer particles are placed at the same locations in the simulation. This coupled simulation and lab experiment has been used successfully in undergraduate and graduate laboratories to directly visualize river-porewater interactions and show how physically-based flow simulations can reproduce environmental phenomena. Students took photographs of the bed through the transparent flume walls and compared them to shapes of the dye at the same times in the simulation. This resulted in very similar trends, which allowed the students to better understand both the flow patterns and the mathematical model. The simulations also allow the user to quickly visualize the impact of each input parameter by running multiple simulations. This process can also be used in research applications to illustrate basic processes, relate interfacial fluxes and porewater transport, and support quantitative process-based modeling.

This manuscript describes how to create regular bedforms in a flume, visualize flow through the bedforms, and use computer simulations to simulate the hyporheic flow. The computer simulations compare well with the experimental observations. This coupled simulation and experiment is well-suited for both research and educational purposes.

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute ...

Engineering

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

3D-PTV is a quantitative flow measurement technique that aims to track the Lagrangian paths of a set of particles in three dimensions using stereoscopic recording of image sequences. The basic components, features, constraints and optimization tips of a 3D-PTV topology consisting of a high-speed camera with a four-view splitter are described and discussed in this article. The technique is applied to the intermediate flow field (5 &#60;x/d &#60;25) of a circular jet at Re &#8776; 7,000. Lagrangian flow features and turbulence quantities in an Eulerian frame are estimated around ten diameters downstream of the jet origin and at various radial distances from the jet core. Lagrangian properties include trajectory, velocity and acceleration of selected particles as well as curvature of the flow path, which are obtained from the Frenet-Serret equation. Estimation of the 3D velocity and turbulence fields around the jet core axis at a cross-plane located at ten diameters downstream of the jet is compared with literature, and the power spectrum of the large-scale streamwise velocity motions is obtained at various radial distances from the jet core.

A three-dimensional particle tracking velocimetry (3D-PTV) system based on a high-speed camera with a four-view splitter is described here. The technique is applied to a jet flow from a circular pipe in the vicinity of ten diameters downstream at Reynolds number Re &#8776; 7,000.

3D-PTV is a quantitative flow measurement technique that aims to track the Lagrangian paths of a set of particles in three dimensions using stereoscopic ...

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Two different experimental methods for determining the threshold of particle motion as a function of geometrical properties of the bed from laminar to turbulent flow conditions are presented. For that purpose, the incipient motion of a single bead is studied on regular substrates that consist of a monolayer of fixed spheres of uniform size that are regularly arranged in triangular and quadratic symmetries. The threshold is characterized by the critical Shields number. The criterion for the onset of motion is defined as the displacement from the original equilibrium position to the neighboring one. The displacement and the mode of motion are identified with an imaging system. The laminar flow is induced using a rotational rheometer with a parallel disk configuration. The shear Reynolds number remains below 1. The turbulent flow is induced in a low-speed wind tunnel with open jet test section. The air velocity is regulated with a frequency converter on the blower fan. The velocity profile is measured with a hot wire probe connected to a hot film anemometer. The shear Reynolds number ranges between 40 and 150. The logarithmic velocity law and the modified wall law presented by Rotta are used to infer the shear velocity from the experimental data. The latter is of special interest when the mobile bead is partially exposed to the turbulent flow in the so-called hydraulically transitional flow regime. The shear stress is estimated at onset of motion. Some illustrative results showing the strong impact of the angle of repose, and the exposure of the bead to shear flow are represented in both regimes.

Two different methods for characterizing the incipient particle motion of a single bead as a function of the sediment bed geometry from laminar to turbulent flow are presented.

Two different experimental methods for determining the threshold of particle motion as a function of geometrical properties of the bed from laminar to ...

Methods for Characterizing the Co-development of Biofilm and Habitat Heterogeneity

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is strongly regulated by the surrounding flow and nutritional environment. Biofilm growth also increases the heterogeneity of the local microenvironment by generating complex flow fields and solute transport patterns. To investigate the development of heterogeneity in biofilms and interactions between biofilms and their local micro-habitat, we grew mono-species biofilms of Pseudomonas aeruginosa and dual-species biofilms of P. aeruginosa and Escherichia coli under nutritional gradients in a microfluidic flow cell. We provide detailed protocols for creating nutrient gradients within the flow cell and for growing and visualizing biofilm development under these conditions. We also present protocols for a series of optical methods to quantify spatial patterns in biofilm structure, flow distributions over biofilms, and mass transport around and within biofilm colonies. These methods support comprehensive investigations of the co-development of biofilm and habitat heterogeneity.

Biofilms have complex interactions with their surrounding environment. To comprehensively investigate biofilm-environment interactions, we present here a series of methods to create heterogeneous chemical environment for biofilm development, to quantify local flow velocity, and to analyze mass transport in and around biofilm colonies.

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is ...

Bioengineering

Generation and Control of Electrohydrodynamic Flows in Aqueous Electrolyte Solutions

To drive electrohydrodynamic (EHD) flows in aqueous solutions, the separation of cation and anion transport pathways is essential because a directed electric body force has to be induced by ionic motions in liquid. On the other hand, positive and negative charges attract each other, and electroneutrality is maintained everywhere in equilibrium conditions. Furthermore, an increase in an applied voltage has to be suppressed to avoid water electrolysis, which causes the solutions to become unstable. Usually, EHD flows can be induced in non-aqueous solutions by applying extremely high voltages, such as tens of kV, to inject electrical charges. In this study, two methods are introduced to generate EHD flows induced by electrical charge separations in aqueous solutions, where two liquid phases are separated by an ion-exchange membrane. Due to a difference in the ionic mobility in the membrane, ion concentration polarization is induced between both sides of the membrane. In this study, we demonstrate two methods. (i) The relaxation of ion concentration gradients occurs via a flow channel that penetrates an ion-exchange membrane, where the transport of the slower species in the membrane selectively becomes dominant in the flow channel. This is a driving force to generate an EHD flow in the liquid. (ii) A long waiting time for the diffusion of ions passing through the ion-exchange membrane enables the generation of an ion-dragged flow by externally applying an electric field. Ions concentrated in a flow channel of a 1 x 1 mm2 cross-section determine the direction of the liquid flow, corresponding to the electrophoretic transport pathways. In both methods, the electric voltage difference required for an EHD flow generation is drastically reduced to near 2 V by rectifying the ion transport pathways.

The rectification of ion transport pathways is an effective method to generate one-directional ion-dragged electrohydrodynamic flows. By setting an ion-exchange membrane in a flow channel, an electrically polarized condition is generated and causes a liquid flow to be driven when an electric field is externally applied.

To drive electrohydrodynamic (EHD) flows in aqueous solutions, the separation of cation and anion transport pathways is essential because a directed ...

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine micro-organisms, and/or sediments in the ocean, or fluidized bed reactors and combustion processes in engineered systems. In order to study the effect of turbulence on the kinematics of particles in such flows, simultaneous measurements of both the flow and particle kinematics are required. Non-intrusive, optical flow measurement techniques for measuring turbulence, or for tracking particles, exist but measuring both simultaneously can be challenging due to interference between the techniques. The method presented herein provides a low cost and relatively simple method to make simultaneous measurements of the flow and particle kinematics. A cross section of the flow is measured using a particle image velocimetry (PIV) technique, which provides two components of velocity in the measurement plane. This technique utilizes a pulsed-laser for illumination of the seeded flow field that is imaged by a digital camera. The particle kinematics are simultaneously imaged using a light emitting diode (LED) line light that illuminates a planar cross section of the flow that overlaps with the PIV field-of-view (FOV). The line light is of low enough power that it does not affect the PIV measurements, but powerful enough to illuminate the larger particles of interest imaged using the high-speed camera. High-speed images that contain the laser pulses from the PIV technique are easily filtered by examining the summed intensity level of each high-speed image. By making the frame rate of the high-speed camera incommensurate with that of the PIV camera frame rate, the number of contaminated frames in the high-speed time series can be minimized. The technique is suitable for mean flows that are predominantly two-dimensional, contain particles that are at least 5 times the mean diameter of the PIV seeding tracers, and are low in concentration.

The technique described herein offers a low cost and relatively simple method to simultaneously measure particle kinematics and turbulence in flows with low particle concentrations. The turbulence is measured using particle image velocimetry (PIV), and particle kinematics are calculated from images obtained with a high-speed camera in an overlapping field-of-view.

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine ...

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Bacterial cells are able to form surface-attached biofilm communities known as biofilms by encasing themselves in extracellular polymeric substances (EPS). The EPS serves as a physical and protective scaffold that houses the bacterial cells and consists of a variety of materials that includes proteins, exopolysaccharides and DNA. The composition of the EPS may change, which remodels the mechanic properties of the biofilm to further develop or support alternative biofilm structures, such as streamers, as a response to environmental cues. Despite this, there are little quantitative descriptions on how EPS components contribute to the mechanical properties and function of biofilms. Rheology, the study of the flow of matter, is of particular relevance to biofilms as many biofilms grow in flow conditions and are constantly exposed to shear stress. It also provides measurement and insight on the spreading of the biofilm on a surface. Here, particle-tracking microrheology is used to examine the viscoelasticity and effective crosslinking roles of different matrix components in various parts of the biofilm during development. This approach allows researchers to measure mechanic properties of biofilms at the micro-scale, which might provide useful information for controlling and engineering biofilms.

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Particle-tracking microrheology investigates the viscoelasticity of materials. Here, the technique is used to determine the viscoelasticity, creep compliance and effective crosslinking roles of different matrix components of a bacterial biofilm. The matrix consists of polymeric substances secreted by the bacteria and its components determine biofilm structure and mechanical properties.

Bacterial cells are able to form surface-attached biofilm communities known as biofilms by encasing themselves in extracellular polymeric substances ...

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature

We present a method for using temperature to tune the flow speeds of kinesin-driven, microtubule-based three-dimensional (3D) active fluids. This method allows for tuning the speeds in situ without the need to manufacture new samples to reach different desired speeds. Moreover, this method enables the dynamic control of speed. Cycling the temperature leads the fluids to flow fast and slow, periodically. This controllability is based on the Arrhenius characteristic of the kinesin-microtubule reaction, demonstrating a controlled mean flow speed range of 4&#8211;8 &#181;m/s. The presented method will open the door to the design of microfluidic devices where the flow rates in the channel are locally tunable without the need for a valve.

The goal of this protocol is to use temperature to control the flow speeds of three-dimensional active fluids. The advantage of this method not only allows for regulating flow speeds in situ but also enables dynamic control, such as periodically tuning flow speeds up and down.

We present a method for using temperature to tune the flow speeds of kinesin-driven, microtubule-based three-dimensional (3D) active fluids. This method ...

A Protocol for Real-time 3D Single Particle Tracking

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although various RT-3D-SPT methods have been put forward in recent years, tracking high speed 3D diffusing particles at low photon count rates remains a challenge. Moreover, RT-3D-SPT setups are generally complex and difficult to implement, limiting their widespread application to biological problems. This protocol presents a RT-3D-SPT system named 3D Dynamic Photon Localization Tracking (3D-DyPLoT), which can track particles with high diffusive speed (up to 20 &#181;m2/s) at low photon count rates (down to 10 kHz). 3D-DyPLoT employs a 2D electro-optic deflector (2D-EOD) and a tunable acoustic gradient (TAG) lens to drive a single focused laser spot dynamically in 3D. Combined with an optimized position estimation algorithm, 3D-DyPLoT can lock onto single particles with high tracking speed and high localization precision. Owing to the single excitation and single detection path layout, 3D-DyPLoT is robust and easy to set up. This protocol discusses how to build 3D-DyPLoT step by step. First, the optical layout is described. Next, the system is calibrated and optimized by raster scanning a 190 nm fluorescent bead with the piezoelectric nanopositioner. Finally, to demonstrate real-time 3D tracking ability, 110 nm fluorescent beads are tracked in water.

This protocol details the construction and operation of a real-time 3D single particle tracking microscope capable of tracking nanoscale fluorescent probes at high diffusive speeds and low photon count rates.

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although ...

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Summary

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Chapters in this video

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Methods for Characterizing the Co-development of Biofilm and Habitat Heterogeneity

Generation and Control of Electrohydrodynamic Flows in Aqueous Electrolyte Solutions

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature

A Protocol for Real-time 3D Single Particle Tracking