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Simultaneous micro-PIV measurements and real-time control trapping in a cross-slot channel

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Abstract

Here we report novel micro-PIV measurements around micron-sized objects that are trapped at the centre of a stagnation point flow generated in a cross-slow microchannel using real-time control. The method enables one to obtain accurate velocity and strain rate fields around the trapped objects under straining flows. In previous works, it has been assumed that the flow field measured in the absence of the object is the one experienced by the object in the stagnation point flow. However, the results reveal that this need not be the case and typically the strain rates experienced by the objects are higher. Therefore, simultaneously measuring the flow field around a trapped object is needed to accurately estimate the undisturbed strain rate (away from the trapped object). By combining the micro-PIV measurements with an analytical solution by Jeffery (Proc R Soc Lond A 102(715):161–179, 1922), we are able to estimate the velocity and strain rate around the trapped object, thus providing a potential fluidic method for characterising mechanical properties of micron-sized materials, which are important in biological and other applications.

Graphical abstract

A novel combination of classical micro-PIV and real-time flow control setups enabled us to measure the velocity field around a target trapped in the extensional flow, which opens up new vistas of characterisation of the mechanical properties of micron-sized objects.

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References

  • Akbaridoust F (2017) Characterisation of a microfluidic hydro-trap to study the effect of straining flow on waterborne microorganisms. Ph.D. thesis, University of Melbourne

  • Akbaridoust F, Philip J, Marusic I (2016) A miniature high strain rate device. In: Proceedings of 20th AFMC conference

  • Akbaridoust F, Philip J, Marusic I (2018) Assessment of a miniature four-roll mill and a cross-slot microchannel for high-strain-rate stagnation point flows. Meas Sci Technol 29(4):045302

    Article  Google Scholar 

  • Alicia TGG, Yang C, Wang Z, Nguyen N-T (2016) Combinational concentration gradient confinement through stagnation flow. Lab Chip 16(2):368–376

    Article  Google Scholar 

  • Amsler CD (2008) Algal chemical ecology, vol 468. Springer, Berlin

    Book  Google Scholar 

  • Ashkin A, Dziedzic JM, Bjorkholm JE, Chu S (1986) Observation of a single-beam gradient force optical trap for dielectric particles. Opt Lett 11(5):288–290

    Article  Google Scholar 

  • Barnkob R, Kähler CJ, Rossi M (2015) General defocusing particle tracking. Lab Chip 15(17):3556–3560

    Article  Google Scholar 

  • Bernassau AL, Glynne-Jones P, Gesellchen F, Riehle M, Hill M, Cumming DRS (2014) Controlling acoustic streaming in an ultrasonic heptagonal tweezers with application to cell manipulation. Ultrasonics 54(1):268–274

    Article  Google Scholar 

  • Cha S, Shin T, Lee SS, Shim W, Lee G, Lee SJ, Kim Y, Kim JM (2012) Cell stretching measurement utilizing viscoelastic particle focusing. Anal Chem 84(23):10471–10477

    Article  Google Scholar 

  • Cook PLM, Holland DP, Longmore AR (2010) Effect of a flood event on the dynamics of phytoplankton and biogeochemistry in a large temperate Australian lagoon. Limnol Oceanogr 55(3):1123–1133

    Article  Google Scholar 

  • Curtis MD, Sheard GJ, Fouras A (2011) Feedback control system simulator for the control of biological cells in microfluidic cross slots and integrated microfluidic systems. Lab Chip 11(14):2343–2351

    Article  Google Scholar 

  • De Loubens C, Deschamps J, Boedec G, Leonetti M (2015) Stretching of capsules in an elongation flow, a route to constitutive law. J Fluid Mech 767:R3

    Article  Google Scholar 

  • Dylla-Spears R, Townsend JE, Jen-Jacobson L, Sohn LL, Muller SJ (2010) Single-molecule sequence detection via microfluidic planar extensional flow at a stagnation point. Lab Chip 10(12):1543–1549

    Article  Google Scholar 

  • Gosse C, Croquette V (2002) Magnetic tweezers: micromanipulation and force measurement at the molecular level. Biophys J 82(6):3314–3329

    Article  Google Scholar 

  • Gossett DR, Henry TK, Lee SA, Ying Y, Lindgren Anne G, Yang OO, Rao J, Clark AT, Di Carlo D (2012) Hydrodynamic stretching of single cells for large population mechanical phenotyping. PNAS 109(20):7630–7635

    Article  Google Scholar 

  • Grier DG (2003) A revolution in optical manipulation. Nature 424(6950):810–816

    Article  Google Scholar 

  • Hall DO, Scurlock JMO, Bolhar-Nordenkampf HR, Leegood RC, Long SP (1993) Photosynthesis and production in a changing environment: a field and laboratory manual. Chapman & Hall, London

    Google Scholar 

  • Henon Y, Sheard GJ, Fouras A (2014) Erythrocyte deformation in a microfluidic cross-slot channel. RSC Adv 4(68):36079–36088

    Article  Google Scholar 

  • Henry TK, Gossett DR, Moon YS, Masaeli M, Sohsman M, Ying Y, Mislick K, Adams RP, Rao J, Carlo DD (2013) Quantitative diagnosis of malignant pleural effusions by single-cell mechanophenotyping. Sci Transl Med 5(212):212ra163–212ra163

    Article  Google Scholar 

  • Hertz HM (1995) Standing-wave acoustic trap for nonintrusive positioning of microparticles. J Appl Phys 78(8):4845–4849

    Article  Google Scholar 

  • Jeffery GB (1922) The motion of ellipsoidal particles immersed in a viscous fluid. Proc R Soc Lond A 102(715):161–179

    Article  Google Scholar 

  • Johnson-Chavarria EM, Tanyeri M, Schroeder CM (2011) A microfluidic-based hydrodynamic trap for single particles. J Vis Exp 47:e2517–e2517

    Google Scholar 

  • Johnson-Chavarria EM, Agrawal U, Tanyeri M, Kuhlman TE, Schroeder CM (2014) Automated single cell microbioreactor for monitoring intracellular dynamics and cell growth in free solution. Lab Chip 14(15):2688–2697

    Article  Google Scholar 

  • Latinwo F, Hsiao K-W, Schroeder CM (2014) Nonequilibrium thermodynamics of dilute polymer solutions in flow. J Chem Phys 141(17):174903

    Article  Google Scholar 

  • Lee H, Purdon AM, Westervelt RM (2004) Manipulation of biological cells using a microelectromagnet matrix. Appl Phys Lett 85(6):1063–1065

    Article  Google Scholar 

  • Li Y, Hsiao K-W, Brockman CA, Yates DY, Robertson-Anderson RM, Kornfield JA, San Francisco MJ, Schroeder CM, McKenna GB (2015) When ends meet: circular DNA stretches differently in elongational flows. Macromolecules 48(16):5997–6001

    Article  Google Scholar 

  • Pajdak-Stós A, Fiakowska E, Fyda J (2001) Phormidium autumnale (cyanobacteria) defense against three ciliate grazer species. Aquat Microb Ecol 23(3):237–244

    Article  Google Scholar 

  • Pathak JA, Hudson SD (2006) Rheo-optics of equilibrium polymer solutions: wormlike micelles in elongational flow in a microfluidic cross-slot. Macromolecules 39(25):8782–8792

    Article  Google Scholar 

  • Perkins TT, Smith DE, Chu S (1997) Single polymer dynamics in an elongational flow. Science 276(5321):2016–2021

    Article  Google Scholar 

  • Qiu Y, Wang H, Demore CEM, Hughes DA, Glynne-Jones P, Gebhardt S, Bolhovitins A, Poltarjonoks R, Weijer K, Schönecker A et al (2014) Acoustic devices for particle and cell manipulation and sensing. Sensors 14(8):14806–14838

    Article  Google Scholar 

  • Rossi M, Kähler CJ (2014) Optimization of astigmatic particle tracking velocimeters. Exp Fluids 55(9):1809

    Article  Google Scholar 

  • Santiago JG, Wereley ST, Meinhart CD, Beebe DJ, Adrian RJ (1998) A particle image velocimetry system for microfluidics. Exp Fluids 25(4):316–319

    Article  Google Scholar 

  • Schroeder CM, Babcock HP, Shaqfeh ESG, Chu S (2003) Observation of polymer conformation hysteresis in extensional flow. Science 301(5639):1515–1519

    Article  Google Scholar 

  • Schroeder CM, Shaqfeh ESG, Chu S (2004) Effect of hydrodynamic interactions on DNA dynamics in extensional flow: simulation and single molecule experiment. Macromolecules 37(24):9242–9256

    Article  Google Scholar 

  • Shenoy A, Tanyeri M, Schroeder CM (2015) Characterizing the performance of the hydrodynamic trap using a control-based approach. Microfluid Nanofluid 18(5–6):1055–1066

    Article  Google Scholar 

  • Shenoy A, Rao CV, Schroeder CM (2016) Stokes trap for multiplexed particle manipulation and assembly using fluidics. PNAS 113(15):3976–3981

    Article  Google Scholar 

  • Smith SW et al (1997) The scientist and engineer’s guide to digital signal processing. California Technical Publications, San Diego

    Google Scholar 

  • Tanyeri M, Schroeder CM (2013) Manipulation and confinement of single particles using fluid flow. Nano Lett 13(6):2357–2346

    Article  Google Scholar 

  • Tanyeri M, Johnson-Chavarria EM, Schroeder CM (2010) Hydrodynamic trap for single particles and cells. Appl Phys Lett 96(22):224101

    Article  Google Scholar 

  • Tanyeri M, Ranka M, Sittipolkul N, Schroeder CM (2011) A microfluidic-based hydrodynamic trap: design and implementation. Lab Chip 11(10):1786–1794

    Article  Google Scholar 

  • Taylor GI (1934) The formation of emulsions in definable fields of flow. Proc R Soc Lond A 146(858):501–523

    Article  Google Scholar 

  • Ulloa C, Ahumada A, Cordero M (2014) Effect of confinement on the deformation of microfluidic drops. Phys Rev E 89(3):033004

    Article  Google Scholar 

  • Wacklin P, Hoffmann L, Komárek J et al (2009) Nomenclatural validation of the genetically revised cyanobacterial genus. Dolichospermum (Ralfs ex Bornet et Flahault) comb. nova. Fottea 9(1):59–64

    Google Scholar 

  • Weilin X, Muller SJ (2011) Exploring both sequence detection and restriction endonuclease cleavage kinetics by recognition site via single-molecule microfluidic trapping. Lab Chip 11(3):435–442

    Article  Google Scholar 

  • Yang AHJ, Moore SD, Schmidt BS, Klug M, Lipson M, Erickson D (2009) Optical manipulation of nanoparticles and biomolecules in sub-wavelength slot waveguides. Nature 457(7225):71–75

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the Australian Research Council for the financial support of this work. This work was performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF).

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Correspondence to Farzan Akbaridoust.

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Appendices

Appendix A: Mechanism of the microfluidic hydrodynamic trap

1.1 A.1: Continuous stagnation point repositioning

Fig. 13
figure 13

Different views of the cross-slot junction with the two fixed and variable constraints on the outlets and the effect of the constraint on the stagnation point position and streamlines

Fig. 14
figure 14

The effect of displacing the stagnation point on the flow streamlines and the trajectory of an object in cross-junction. The red ellipse, solid black circle, hollow circle and the hollow cross represent the target object, its centroid, the stagnation point and the centre of the channel, respectively

If the two outlets of the cross channel have the same flow resistance, the inlet streams are equally bifurcated to the two outlets. If there is a constraint on one of the outlets (e.g. the constraint shown at the bottom outlet in Fig. 13a), the outlet flow rates will no longer be the same because of the inequality in the flow resistance of the outlets. Moreover, the stagnation point is displaced towards the outlet with higher flow resistance. Therefore, if the cross-sectional area of the outlets constantly varies, the stagnation point will be continuously repositioned along the extensional axis.

In this work, repositioning the stagnation point (shown in Fig. 13b) is conducted by implementing a fixed width constriction in one of the outlets, and a variable height constriction on the other (Tanyeri et al. 2010). The former is shown in the bottom outlet and the latter is shown in red on the top outlet in Fig. 13a. Figure 13c shows the section A–A of the variable height constriction, known as an on-chip membrane valve (in Fig. 13a), and how its deformation constricts the height of the fluidic channel. The control channel is filled with pressurised gas, which results in the deflection of the thin membrane, thereby constricting the height of the fluidic channel. It is called the variable constriction because changes in the gas pressure leads to changes in the deflection of the membrane, and consequently constrict the channel at different heights.

1.2 A.2: Automation of the stagnation point repositioning to confine a target object at the channel centre

In the microfluidic hydrodynamic trap, when a target object enters the cross-slot region (shown in Fig. 14a), a camera captures and streams the image of the cross-junction to a computer. Using image-processing methods the shape of the object is determined and the position of the object centroid is computed. Based on the location of the object in extensional direction, the ratio of the outlet flow rates is changed and consequently the stagnation point is repositioned. The repositioning is carried out using the on-chip membrane valve, which allows the flow streamlines to be manipulated. This manipulation places the target object between the stagnation point and the centre of the channel (shown in Fig. 14b), thereby exerting a hydrodynamic force on the target object towards the trap centre. The manipulation results in placing the object on a new streamline and moving the object towards the channel centre. All of these steps are repeated until both the target and the stagnation point converge in the centre of the trap/channel (shown in Fig. 14c), and at this stage the pressure stays unchanged. However, if due to an external or internal disturbance the target object is displaced from the centre, the same procedure is re-applied forcing the object to return to the centre.

1.3 A.3: Feedback control algorithm

A linear feedback control algorithm was implemented to update the pressure in the control layer and displace the stagnation point using (Tanyeri et al. 2010):

$$\begin{aligned} P_{\text {val}} = P_{\text {tc}} + K_{\text {P}} ~ K_{\text {C}} ~ e_{\text {tt}}, \end{aligned}$$
(1)

where \(P_{\text {val}}\) is the pressure in the control channel (on-chip valve), \(P_{\text {tc}}\) is the required pressure to keep the stagnation point at the trap centre. The proportional gain is presented by \(K_{\text {P}}\) and it can be considered as a constant (\(K_{\text {P}} = -\ 1.5\)) and \(K_{\text {C}}\) is a factor to convert pressure to distance. If there is a linear relation between the pressure and the stagnation point position, \(K_{\text {C}}\) will be a constant. The offset error (\(e_{\text {tt}}\)) in Eq. 1 is defined as

$$\begin{aligned} e_{\text {tt}} = Y_{\text {tp}}-Y_{\text {tc}}, \end{aligned}$$
(2)

which is the distance between the position of target (\(Y_{\text {tp}}\)) and trap centre (\(Y_{\text {tc}}\)) along the extensional axis (shown in Fig. 14a). Figure 15 depicts the flowchart of the steps employed for confining a target object in the centre of the cross-slot junction.

Fig. 15
figure 15

Algorithm for confining a target object in the cross-slot region of the microchannel, which is schematically shown in Fig. 14

Fig. 16
figure 16

Implementation of the image-processing steps that were used for foreground detection (detecting the A. circinalis filament in the microchannel)

Fig. 17
figure 17

An example image of 150 overlaid recordings (at the flow rate of \(50\, \upmu {\text {L/h}}\) and control channel pressure of about 8 psi) that were used to determine the calibration curve of the microfluidic trap

Fig. 18
figure 18

Experimentally determined calibration curve of the microfluidic trap. The green line is the trend line fitted to the linear region, and the slope of this line represents the conversion factor (\(K_{\text {C}}\))

1.4 A.4: Inline image processing

In this work, the foreground detection was carried out using the “Computer Vision System” toolbox in MATLAB. This includes acquiring the cross-region as the background and comparing it with the image of the target in the cross-region. Image comparison was followed by determining whether each pixel belongs to the background (channel) or the foreground (target). Before and after the foreground detection, image contrast enhancement and some morphological operations were carried out to improve in locating the accuracy the target’s centroid.

Figure 16 depicts the implementation of the image-processing steps used in the present work to locate the centroid of the A. circinalis filament. First, the background image of the region of interest (ROI) was acquired. This was conducted prior to the filament entering ROI (partially shown in Fig. 16a). Figure 16b shows a filament of the cyanobacteria in the ROI. Upon acquiring the image of the target (filament), the image contrast was enhanced to increase pixel intensity of the filament (shown in Fig. 16c). Afterwards, the foreground was detected. Foreground detection requires defining a threshold parameter that depends on the light condition to determine whether each pixel belongs to the channel or the target. The background was then subtracted from the foreground, thereby achieving the target object mask (the binary image shown in Fig. 16d). This step was followed by removing small objects and noises, by defining a certain threshold and removing the blobs (connected white regions) smaller than the threshold (shown in Fig. 16e). Due to the low resolution of camera and the shape of the objects, detecting the connections of the filaments is challenging. As can be seen, the connections are neither observable in Fig. 16b, nor detectable in Fig. 16e. To alleviate this issue, we used morphologically close operation by a \(7\times 7\)-pixel square-shaped structural element to increase the connectivity of the blobs without over-fattening it. Stated in Smith et al. (1997), morphologically close operation in processing of a binary image includes a morphologically erosive operation (making the blob smaller) followed by a morphologically dilative operation (making the blob larger). Figure 16f shows the post-processed mask of the filament after applying the morphologically close operation. Eventually, the centroid of the filament was determined using the blob analysis of the MATLAB computer vision system toolbox.

1.5 A.5: Calibrating the microfluidic trap

The calibration curve of the microfluidic trap is the variation of the stagnation point position at different control channel pressures. The linear region of this curve is used to determine the conversion factor (\(K_{\text {C}}\) in Eq. 1). To experimentally determine this curve, a fluid visualisation experiment was conducted, which delivered the fluid seeded with tracer particles to the cross-junction. By capturing the images of the cross-region, the flow is visualised. In this experiment, the flow was illuminated using the Nikon microscope Epi-fl illuminator (Mercury lamp). The epifluorescence imaging setup configuration that was used in micro-PIV experiments Akbaridoust et al. (2018), except the PIV camera that was replaced by the control camera was employed to acquire the images. The backgrounds (out-of-focus particles) of the acquired images were then removed Akbaridoust et al. (2018) and then 150 images were overlaid. This procedure was repeated at the different pressures in the control channel, and for each set of images the stagnation point was determined manually/visually with the accuracy of one pixel. One pixel corresponds to one micron for the control camera used in this experiment.

Figure 17 shows an example of 150 overlaid images at the flow rate of \(50~\upmu {\text {L/h}}\) and control channel pressure of about 8 psi. The distance of the stagnation point position from the centre line of the channel along the extensional and compressional axes are represented by \(Y_o\) and \(X_o\), respectively. As can be seen, the stagnation point is not located on the vertical centre line, due to the flow added to one of the inlets where the sample injection port is located. However, \(X_o\ne 0\) only occurs when the injection port is open. In trapping experiments once a target is trapped, the injection port is manually closed and the stagnation point gradually moves on the centre line along the compressional axis. Figure 18 shows the calibration curve that was experimentally determined (at \(50~\upmu {\text {L/h}}\)) at different pressures in the control channel. The slope of the fitted trend line represents the conversion factor (\(K_{\text {C}}\)).

Appendix B: Methodology of simultaneous micro-PIV measurements and trapping

In principle, the use of volume illumination in PIV measurements results in bright background in the images that drastically reduce the signal-to-noise ratio and the correlation peak detectability. Therefore, in micro-PIV, the epifluorescence imaging technique (i.e. illuminating the fluorescent particles by green pulsed light and capturing the red light from them) is implemented to alleviate this issue (Santiago et al. 1998). In hydrodynamic microfluidic trap experiments (in this work and also Schroeder and co-workers (Johnson-Chavarria et al. 2011, 2014; Latinwo et al. 2014; Li et al. 2015; Shenoy et al. 2015; Tanyeri and Schroeder; Tanyeri et al. 2010, 2011), a constant white light source (here ambient room lights) was used for imaging. Hence, simultaneous trapping of objects and micro-PIV measurements is challenging because both require their own illumination, optics and camera focused on the micron-sized objects. Simply combining the two experimental setups (micro-PIV and the microfluidic trap setups) to conduct simultaneous micro-PIV measurements and trapping in the microfluidic device leads to two major problems. The combination of the two setups is shown in Fig. 19. The first issue is the disruption of the control camera by the laser pulses. The second issue is the production of an extremely bright background in the images acquired by the PIV camera sensor due to the constant white light source. The former results in preventing the detection of a target object in the cross-slot channel. The top inset image in Fig. 19a shows an example of the image captured by the control camera when the laser was pulsing. As can be seen, the laser pulse interferes with the image-processing techniques for object detection. While, the latter is the production of extremely bright background that precludes correlating the recorded images. The bottom inset image in Fig. 19b shows an image of the particles captured by the PIV camera, while the constant white light was illuminating the flow.

Fig. 19
figure 19

Schematic of the combination of micro-PIV and active control setups and the resulting imaging problems in the images acquired by the PIV and the control camera

To tackle the first issue we replaced the continuous white light with a continuous blue light source (i.e. filtering all the wavelengths except blue). Whilst, to address the second issue, we filtered all the wavelengths (except blue) incident on the control camera sensor. Using this arrangement, the control camera sensor does not capture the laser pulses, and the PIV camera does not acquire the bright background caused by the constant light source. Figure 4 shows the schematic of the combined micro-PIV and active control system setups, where the two problems that were pointed out are solved using two single-band (blue) bandpass filters. The bandpass filters block all the wavelengths out of the range 420–480 nm.

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Akbaridoust, F., Philip, J., Hill, D.R.A. et al. Simultaneous micro-PIV measurements and real-time control trapping in a cross-slot channel. Exp Fluids 59, 183 (2018). https://doi.org/10.1007/s00348-018-2637-6

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