- Email: [email protected]
Uniform droplet splitting and detection using Lab-on-Chip flow cytometry on a microfluidic PDMS device
Accepted Manuscript Title: Uniform droplet splitting and detection using Lab-on-Chip Flow Cytometry on a microfluidic PDMS device Author: Casper Kunstmann-Olsen Martin M. Hanczyc James Hoyland Steen Rasmussen Horst-G¨unter Rubahn PII: DOI: Reference:
S0925-4005(16)30120-4 http://dx.doi.org/doi:10.1016/j.snb.2016.01.120 SNB 19631
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
6-10-2015 21-1-2016 25-1-2016
Please cite this article as: Casper Kunstmann-Olsen, Martin M. Hanczyc, James Hoyland, Steen Rasmussen, Horst-G¨unter Rubahn, Uniform droplet splitting and detection using Lab-on-Chip Flow Cytometry on a microfluidic PDMS device, (2016), http://dx.doi.org/10.1016/j.snb.2016.01.120 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Casper Kunstmann-Olsen*
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Uniform droplet splitting and detection using Lab-on-Chip Flow Cytometry on a microfluidic PDMS device
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University of Southern Denmark, Mads Clausen Institute, Alsion 2, DK-6400 Sønderborg - Denmark
Present address: University of Liverpool, Department of Chemistry, Crown Street, Liverpool L69 7ZD, United Kingdom
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Martin M. Hanczyc
University of Southern Denmark, Center for Fundamental Living Technology, Campusvej 55, DK-5230 Odense M - Denmark
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Present address: Laboratory for Artificial Biology, Centre for Integrative Biology (CIBIO), University of Trento, Via Sommarive 9, I-38123 Povo (TN) - Italy
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James Hoyland
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University of Southern Denmark, Mads Clausen Institute, Alsion 2, DK-6400 Sønderborg - Denmark
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Present address: Kwantlen Polytechnic University, 12666 72 Avenue, Surrey, B.C. V3W 2M8, Canada
Steen Rasmussen
University of Southern Denmark, Center for Fundamental Living Technology, Campusvej 55, DK-5230 Odense M - Denmark Present address: Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, New Mexico 87501, USA
Horst-G¨ unter Rubahn
University of Southern Denmark, Mads Clausen Institute, Alsion 2, DK-6400 Sønderborg - Denmark
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Corresponding author Email address: [email protected] (Casper Kunstmann-Olsen*)
Preprint submitted to Sensors and Actuators B: Chemical
January 20, 2016
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Abstract
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A PDMS chip is fabricated using soft lithography and applied to investigate the formation and division of nitrobenzene (NB) droplets in a two-phase system stabilized by oleic acid. Using an integrated on-chip flow cytometer setup, effected with optical fibers, droplet size distributions are analyzed in situ based on optical signal intensities. By controlling the hydrodynamic flow focusing, uniform droplets of sizes between 100 µm and 300 µm are created with precise size control. Cross-flow shearing allows one to divide these droplets into anything from 2 to 9 individual droplets, depending on flow parameters.
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Keywords: Microfluidic, Droplets, Flow Cytometry, Optical Detection, PDMS Device 1. Introduction
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Droplet based microfluidics offers unique possibilities in a range of application fields where precise volume and concentration control is required. This includes chemical synthesis [1], biological and chemical microreactors [2], and protocells [3]. Controlled droplet formation, -growth and -division are of central importance to these applications. Droplets have for instance been used as microscopic biochemical reaction vessels in on-chip computer controlled microfluidics reaction- and synthesis networks [2, 1, 4, 5]. Of particular interest to the authors is the use of droplets to form and investigate the evolution of self-replicating chemical containers or protocells [6]. There are several protocell models where the container is based on vesicles, droplets, micelles, coacercates and vesicle-droplet hybrids [3]. Previously we have shown that droplets of nitrobenzene with embedded chemical potential can both be self-motile [7] and also self-dividing [3]. We have demonstrated this principle with a rudimentary fission and fusion cycle for nitrobenzene droplets. The droplets contain chemical potential and are initially far from equilibrium and the observed dynamics occur while the system approaches equilibrium. In these investigations the experimenter imposes the formation and manipulation of the droplets manually, with limited control over volumetric, spatial and temporal dynamics that affect the state of the protocell system. 2
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It is therefore desirable to integrate droplet formation and manipulation functions directly on the microfluidic chip, allowing formation of controlled droplet size distributions or sub-populations. Continuous monitoring of the process is essential and can be used to form feedback-controlled loops essential for system refinement and development. Monitoring can be done using integrated optical systems as demonstrated in this paper. In-vitro droplet splitting may be driven by a competition between gravity and surface tension although there is a lower droplet size limit for gravity driven droplet divisions [6]. In this paper we investigate an alternative approach capable of handling much smaller droplet sizes using microfluidic induced shear forces as a step towards microfluidic-based protocell production and evolution.
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The interface between continuous and dispersed phases is essential when considering droplet formation and splitting. In systems with small Reynolds number Re, the main effects acting on an interface are viscosity µ and surface tension γ. The relative strength of these two parameters is given by the Capillary number Ca = µV0 /γ, where V0 is a characteristic velocity of the flows. A low value of Ca indicates that the stresses due to interfacial tension are strong compared to viscous stresses. Droplets flowing under such a condition minimize their surface area by producing spherical ends. In the opposite situation of high Ca, viscous effects dominate and large deformations of droplets can be observed [8]. When designing microfluidic systems for droplet formation, three main approaches exist based on different physical mechanisms. These are best described by the flow topology in the vicinity of the droplet formation zone: • (I) breakup in co-flowing streams (nozzle inside capillary) [9] • (II) breakup in cross-flowing streams (T-junction) [10, 11] • (III) breakup in elongated strained flows (hydrodynamic flow focusing) [12, 13]
In all three cases, the dispersed phase is driven into a microchannel, where it encounters the immiscible carrier fluid that is driven independently. The junction where the two fluids meet must be designed to optimize the reproducibility of droplet formation. The geometry of the junction, together with the flow rates and the physical properties of the fluids (γ and µ) determine the local flow, which in turn deforms the interface and eventually leads to 3
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droplet pinch off if the conditions are right. The size of the droplet is controlled by a competition between the external pressures and the resulting viscous shear stresses, on the one hand, and the capillary pressure (from surface tension) resisting deformation on the other. The capillary number Ca is therefore crucial when trying to predict droplet formation [9]. For droplet splitting many methods are available. As shown earlier [6], chemically driven splitting, which relies on gravity as an external force, has limitations with respect to size. In order to create a fully integrated microfluidic device, alternative methods are necessary. These usually rely on either complex external manipulation e.g. electrowetting [14] or optical methods [15] or are an inflexible inherent function of channel geometry [5]. Seki and coworkers have produced simple devices in which daughter droplets can be split off from larger droplets with varying sizes [16, 17]. In this work we aim to utilize the same methods as traditionally used for droplet formation and demonstrate complete flow driven droplet splitting. We investigate controlled and variable splitting of oil-in-water droplets in a confined system using cross flow streams. The presented method allows for complete splitting of one size-population into another. Using simple microfluidics, our design can generate droplets with a wide range of sizes, while also offering a flexible method of division using shearing forces in a 2D planar cross flow geometry. A simple flow cytometer (FCM) using fiber optics allows optical detection and size characterization, which offers faster and more versatile detection than traditional CCD-imaging tracking. 2. Materials and Methods
2.1. Droplet Chemistry Nitrobenzene (NB) and oleic acid were purchased from Sigma-Aldrich. Solutions of oleate were prepared by manually agitating the oleic acid in water with additional sodium hydroxide added until a clear solution at pH 12 was achieved. A surfactant solution of 40 mM oleate at pH 12 was used as the continuous phase in all experiments [6]. This concentration is experimentally found to be the minimum required to ensure a stable droplet suspension without any fusion of droplets. This is higher than in external conditions (outside microfluidic devices), where 5 mM oleate is sufficient [6]. The necessity of this increase, is attributed to the increased surface energy of the droplet as it is compressed in the microfluidic channel geometry.
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Figure 1: Chip design used to create (a) and split (b) oil droplets of various size, equipped with optical fiber inlets that allows flow cytometry measurements (c). Flow direction leftto-right. The large oval pads are flow connectors (inlets, outlets and two connectors to expel air when fibers are inserted). Microscope images showing flow focusing junction used for formation of oil droplets (which contain red dye for illustrative purposes) (a) and cross flow junction used for droplet splitting (b). Also shown, oil droplets passing the optical fibers and their interaction (fluorescence) with the laser beam in two consecutive images (c). 10 × magnification, black scalebars 500 µm.
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2.2. Chip Fabrication The fabrication of the PDMS microfluidic devices used in this study is essentially a three step process, including SU8 master production, PDMS replication and PDMS sealing. The pattern of the microfluidic channels is transferred into hard baked SU8 photoresist using simple photolithography (100 µm high structures) [18]. The design consists of a flow focusing junction for forming droplets, a cross flow junction for breaking these into smaller droplets and optical fiber inlets which allow for on-chip flow cytometry (see schematic in figure 1). The main channels are 200 µm wide, while the flow focusing and tapered cross-flow junctions are only 100 µm wide. Thus the main channels are 100 µm × 200 µm and the cross flow junctions are 100 µm × 100 µm. By molding PDMS onto the SU8 structured Si wafer master, the microfluidic channel pattern can be replicated repeatedly, producing many chips from a single master [19]. In the same production step, both optical and fluid connectors are molded into the chip [20]. Liquid PDMS (Sylgard 184, Dow Coring) is poured into a custom-made mold and cured at 100◦ C for 30 min. Finally the chip is sealed with a 3 mm thin PDMS sheet using plasma activation [21, 22].
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Figure 2: Left: Schematic showing the setup used for on-chip flow cytometry. Setup includes sample stage with chip-chuck (1), CCD camera and microscope setup (2), 3× syringe drivers (3), laser and fiber-coupler (4), optical detection block including beamsplitter (5), photodiode for laser light detection (6) and PMT for fluorescence detection (7). These are connected to an oscilloscope (8) and eventually a PC (9) for data collection. Right: Image showing the PDMS chip with optical fibers and fluid connectors.
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2.3. Flow Cytometry Setup The custom-built flow cytometer setup is shown schematically in figure 2 left. It consists of a Ar-ion laser (488 nm) which is guided into the device using a single-mode fiber. The laser light scattered by the droplets is collected using a multimode fiber connected to a simple photodiode (PDA36A-EC, Thorlabs). This fiber also collects the fluorescence signal, which is detected using a photomultiplier (PMT) (R928, Hamamatsu). The signals are digitized using an ADC card (PCI-17-14U, Advantech) and a PC. Up to 60 events are recorded per second. The setup is also equipped with a CCDcamera setup (Nikon 1 V2). This allows for image tracking (using LabView ), where the droplets can be counted and measured. Inside the PDMS device the two optical fibers are positioned on either side of the fluid channel (see figure 1 (c)). They are positioned at an angle (18◦ off-axis) to prevent direct coupling of laser light into the output fiber. The images in figure 1 (c) shows uniform droplets (top) pass by the laser light (coming from the top fiber) which induces fluorescence (bottom). The NB droplets are colored using Nile Red (Sigma Aldrich). 2.4. Automated Droplet Tracking Automatic droplet tracking was implemented to enable large-scale data gathering for comparative studies. Video-segments are obtained using the maximum frame rate of the camera (50 fps) and after recording, the video 6
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segments are cut, edited and compressed using Virtual Dub, which is also used to extract every frame of the segment. Using Vision Assistant 2011 (part of National Instruments’ LabView ) each frame is analyzed according to a custom made image-treatment sequence. After adjusting color planes and setting intensity thresholds, a built-in routine tracks and measures circular objects. All frames are then batch-processed and droplet sizes are stored for further processing.
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3. Results and Discussion
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3.1. Droplet Formation Surfactant concentration (and general chemical compatibility) controls the stability of the formed droplets, while physical flow parameters control the size distribution and formation frequency [23]. As discussed earlier, flow focusing is used to form droplets, and therefore the same parameters (the total volume flow through the junction (Vtotal ), and the relative flow ratio between the phases (r) which control hydrodynamic focusing, also controls droplet formation. The flow ratio r is given by the ratio between the sample and continuous flow (r = Vcont /VN B ). The total flow Vtotal is given by the sum of the NB flow rate (VN B ) and that of the continuous phase (Vcont ), so that Vtotal = VN B + 2 × r × VN B . To investigate the influence of flow ratio r and total volume flow Vtotal , NB droplets are generated using 40 mM oleate as surfactant in the flow focusing junction. Video segments are recorded for a wide range of Vtotal and r and analyzed to get droplet size distributions. The NB flow is kept constant (at either 1, 3 or 5 µL/min) and the continuous flow speed is varied to obtain different flow ratios (from r=1 to r=30). Averaged droplet sizes are calculated based on obtained video-segments. The results are displayed in figure 3, displayed both for each constant NB flow (left) and total volume flow (right). Error bars indicate the size distribution of each data set. The results indicate that droplet size is strongly dependent on both Vtotal and r. This relation is also indicative for the behavior of flow focusing on continuous flows [20], which is expected as the forces involved are similar. As with flow focusing, the biggest changes are achieved at low r while the dependency levels out for higher values. By changing the oil-phase flow some size control is possible, while the minimum droplet size seems constant for all flow parameters (in this case around 96 µm). This is believed to be a geometrical limit set by the dimensions of the device (which corresponds 7
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Figure 3: Plot showing droplet size (diameter) as function of flow ratio r, for three NB flow rates (left-hand side) and as a function of total volume flow (Vtotal ) (right-hand side), calculated from r and the sample flows.
to the 100 µm width of the flow focusing junction). This observation is supported by similar studies [23].
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3.2. Droplet Splitting in Fluid Flow Simple thermodynamic calculations considering the Gibbs free energy indicate that small NB droplets (radius <2.25 mm) are too stable to split under current equilibrium conditions [6]. However, a non-equilibrium cross flow junction geometry can be used to break off significantly smaller droplets if channel dimensions and flow parameters are correct [16]. In a region where the channel width ratio is smaller than or close to 1 (i.e. square channel cross-section), the droplet size is expected to decrease linearly with increasing cross-flow speeds. These considerations form the basis of the microfluidic device design (figure 1), which facilitates droplet splitting. As the cross flow velocity (Vc ) is increased, droplets of a certain size are split into smaller and smaller droplets, as they behave like a localized continuous phase. Figure 4 illustrates the effect and show six consecutive images of a single droplet being stretched in the thin channel and split into three daughter droplets by the cross-flow. Droplets were created with a fixed size in the flow focusing junction and channeled to the tapered cross flow junction where Vc is slowly increased. Video-segments are obtained for a range of Vc and analyzed using the LabView routine. At least a few thousand droplets are measured for each value. 8
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Figure 4: Six consecutive frames showing one droplet splitting into 3 daughter droplets at Vc = 20 µl/min. Scalebar 200 µm.
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Figure 5 (left) show a sample histogram of collected data, before and after splitting, for Vc = 75 µl/min. Note that the spread in size before splitting corresponds to the error-bars in figure 3, while the variation after splitting is a combination of uneven splitting (see droplet ”3” in figure 3) and the automated tracking method which might struggle to fit correctly sized circular shapes to very fast moving objects (in the high end of Vc ). Based on a measured average size (diameter D) of a droplet, the average volume can be calculated (droplets are assumed to be oblate spheroids, all 100 µm high). By comparing this to the calculated volume of the split droplets, an average splitting ratio (SR = Vbef ore /Vaf ter ) can be estimated for each cross flow velocity. This can only be done up to a flow of 75 µl/min as at higher speeds, even the slow motion videos can not resolve the fast moving droplets. Table 1 shows calculated volumes and splitting ratios for all measurements. Figure 5 (right) shows the splitting ratios plotted against the cross flow speed and a fitted sigmoidal curve. The measured data show excellent correspondence with the fit. This is expected as the droplets do not start to split until a certain threshold flow speed is reached (in this case between 10 µl/min and 15 µl/min), then follow a linear dependency and finally reach a maximum value once the split droplets reach a size similar to the splitting channel width (100 µm).
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Figure 5: Left: Histograms showing measured droplet sizes for a cross flow velocity of Vc = 75 µl/min, before (top) and after (bottom) splitting. Sizes are obtained by automatic droplet tracking using CCD imaging. The result clearly illustrates a significant change in size-distribution of the droplets. Right: Measured and calculated droplet splitting ratios SR as a function of cross flow speeds Vc following a sigmoidal fit.
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Vc (µl/min) Dbef ore (µm) Vbef ore (nL) Daf ter (µm) Vaf ter (nL) 3 293 4.495 286 4.291 293 4.495 282 4.162 5 10 295 4.567 279 4.077 15 295 4.567 198 2.048 20 294 4.525 172 1.549 25 296 4.587 160 1.341 30 296 4.587 152 1.209 35 297 4.619 142 1.055 40 299 4.681 127 0.842 50 299 4.681 103 0.556 60 298 4.650 108 0.609 75 301 4.744 105 0.557
SR 1.05 1.08 1.12 2.23 2.92 3.42 3.87 4.38 5.56 6.68 7.64 8.52
Table 1: Average droplet size (and volume) before and after splitting. Splitting ratios (SR) calculated based on volume ratio.
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Figure 6: Left: Microscope images showing NB droplets generated with three different sample flow rates (20, 30 and 40 µl/min, from top to bottom) resulting in different sizes (volumes). Right: Scatterplots obtained individually for the three different populations (legend denotes average droplet size).
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3.3. Flow cytometry (FCM) on droplet splitting The flow parameters used in this section are exactly the same as in the previous section, only now the flow cytometer setup is used to collect optical data on the split droplets. To relate the optical data to the microscopic CCD measurements a simple calibration is performed using both the CCD and the FCM setup. By changing the flow ratio r, droplets of different sizes are created (see figure 3). Using the automated droplet tracking the sizes are calculated. Simultaneously, the FCM setup is used to obtain scatterplots for each population. In figure 6 selected CCD images and corresponding scatterplots for each size are shown. The primary optical signal (scattered laser light), is known to be directly related to particle (or in this case) droplet size [24], which these results also clearly show. Electric amplification is here optimized for maximum spread and illustrates very nicely how even small size differences can easily be distinguished using the optical detection system in the FCM setup. The measurements also reveal very similar spread in the secondary optical channel (fluorescence). This is somewhat expected as the droplet contain a highly fluorescent dye (Nile Red), which to some extent saturates the PMT. 11
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Figure 7: Left: Selected FCM scatterplots for droplets split increasingly smaller units as the cross flow speed increases (smaller values). Right: Mean values for all measured crossflow velocities Vc obtained by fitting a Gaussian function to the corresponding intensity histogram for the primary optical channel. Secondary y-axis shows calibrated droplet size as a function of Vc .
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Accordingly, the sensitivity is reduced drastically to prevent this, at the cost of resolution in the data. However, as the PMT is highly sensitive there are many instances where the secondary signal can be used to differentiate between different populations of species containing various amounts of fluorescence (e.g. biological samples with DNA-specific fluorescent markers). To investigate droplet splitting using FCM, the system is now set to generate droplets of a constant size (300 µm) which are subsequently split using different cross flow rates (as in the previous experiment). Figure 7 shows selected FCM scatterplots of scatter- versus fluorescence-signal intensities for a range of cross flow rates. For all obtained plots, a histogram is collected for the scattered laser light and the maximum value is found by fitting a Gaussian function. The insert in figure 7 shows the central position of this peak as a function of Vc . These results can be directly related to the splitting ratios calculated in the previous section (figure 5), which also indicated a sigmoidal relation between splitting ratio and cross-flow velocity. Furthermore, using the results from section 3.2 and figure 6, we can directly correlate the signal intensity to the droplet size after splitting (also on figure 7 insert).
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4. Conclusion
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In this work a microfluidic PDMS chip has been used to create droplets of controllable sizes via hydrodynamic focusing and subsequently split these using cross flow streams. CCD imaging tracing and FCM has been used to characterize both size and splitting ratios based on external inputs. Both measured size distribution (droplet video tracking) and the FCM plots reveal a sigmoidal relation between splitting ratio and cross flow velocity. We can therefore conclude that this device is both able to create droplets with precise sizes and afterward split these accordingly into programmed ratios. Furthermore, the in-line FCM allows for fast and easy size estimation without use of CCD image tracking. This enables one to create devices with very small footprints that integrate organic droplet creation, in-line analysis and manipulation. This microfluidic platform could also incorporate drop to drop variation, testing and selection. By making the generation and handling of protocells faster and more autonomous, a fully contained on-chip evolution machine could be realized.
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acknowledgments
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CKO, JH and HGR acknowledge financial support provided by the European Commissions (EC) Fund for Regional Development (EFRU) through the InterReg 4A project ”Lab-on-a-chip technique for quality-control in foodand bio-industry”. Further CKO acknowledge an EC sponsorship through the FET proactive FP7 MATCHIT project. MMH was supported in part by the Emerging Technologies Proactive 611640 (EVOBLISS). SR and MMH acknowledge financial support from the EC FET proactive MATCHIT project as well as the Danish National Research Foundation (Grundforskningsfonden). [1] M. Wayland, H. Fellerman, M. Hadorn, D. Sorek, D. Lacent, S. Rasmussen, R. Fuchslin, The matchit automaton: Exploiting compartmentalization for the synthesis of branched polymers, Computational and Mathematical Methods in Medicine (2013). [2] H. Song, D. L. Chen, R. F. Ismagilov, Reactions in droplets in microfluidic channels, 2006. doi:10.1002/anie.200601554. [3] S. Rasmussen, M. Bedau, L. Chen, D. Deamer, D. Rakauer, N. Packard, P. Stadler (Eds.), Protocells: Bridging Nonliving and Living Matter, MIT Press, 2008. 13
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[4] M. Amos, P. Dittrich, J. McCaskill, S. Rasmussen, Biological and chemical information technologies, Procedia Computer Science 7 (2011) 56–60. [5] T. Ichii, H. Suzuki, T. Yomo, Micro-droplet model for recursive growth and division dynamics of the cell, Europhysics Letters 96 (2011) 48006.
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[6] F. Cashera, S. Rasmussen, M. Hanczyc, An oil droplet division-fusion cycle, ChemPlusChem Communications 78 (2013) 52–54.
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[7] M. M. Hanczyc, T. Taro, I. Takashi, N. Packard, T. Sugawara, Fatty acid chemistry at the oil-water interface: Self-propelled oil droplets, Journal of the American Chemical Society 129 (2007) 9386–9391.
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[8] H. Bruus, Theoretical Microfluidics, Oxford University Press, 2008.
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[9] C. Baroud, F. Gallaireb, R. Danglaa, Dynamics of microfluidic droplets, Lab Chip 10 (2010) 2032–2045.
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[10] T. Thorsen, R. Roberts, F. Arnold, S. Quake, Dynamic pattern formation in a vesicle-generating microfluidic device, Physical Review Letters 86 (2001) 4163–4166.
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[11] P. Garstecki, M. Fuerstmann, H. Stone, G. Whitesides, Formation of droplets and bubbles in a microfluidc t-junction - scaling and mechanism of break-up, Lab Chip 6 (2006) 437–446.
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[12] S. L. Anna, N. Bontoux, H. A. Stone, Formation of dispersions using flow-focusing in microchannels, Applied Physics Letters 82 (2003) 364– 366. [13] R. Dreyfus, P. Tabeling, H. Willaime, Ordered and discordered patterns in two phase flows in microchannels, Physical Review Letters 90 (2003) 144505. [14] S. K. Cho, H. J. Moon, C. J. Kim, Creating, transporting, cutting, and merging liquid droplets by electrowetting-based actuation for digital microfluidic circuits, Journal of Microelectromechanical Systems 12 (2003) 70–80. [15] C. N. Baroud, M. R. de Saint Vincent, J.-P. Delville, An optical toolbox for total control of droplet microfluidics, Lab on a Chip 7 (2007) 1029– 1033. 14
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[16] T. Moritani, M. Yamada, M. Seki, Generation of uniform-size droplets by multistep hydrodynamic droplet division in microfluidic circuits, Microfluidics and Nanofluidics 11 (2011) 601–610.
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[17] M. Yamada, S. Doi, H. Maenaka, M. Yasuda, M. Seki, Hydrodynamic control of droplet division in bifurcating microchannel and its application to particle synthesis, Journal of Colloid and Interface Science 331 (2008) 401–407.
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[18] J. Hoyland, C. Kunstmann-Olsen, H. Rubahn, Simple photolithographic rapid prototyping of microfluidic chips, Microelectronic Engineering 98 (2012) 689–692.
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[19] J. McDonald, G. Whitesides, Poly(dimethylsiloxane) as a material for fabricating microfluidic devices, Accounts of Chemical Research 35 (2002) 491–499.
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[20] C. K.-O. . J. H. . H. Rubahn, Influence of geometry on hydrodynamic focusing and long-range fluid behavior in pdms microfluidic chips, Microfluidics and Nanofluidics 12 (2012) 795–803.
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[21] B. Jo, L. van Lerberghe, K. Motsegood, D. Beebe, Three-dimensional micro-channel fabrication un pdms elastomer, Journal of Microelectrochemical Systems 9 (2000) 76–81.
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[22] D. Duffy, J.C.McDonald, O. A. Schueller, G. Whitesides, Rapid prototyping of microfluidic systems in poly(dimethylsiloxane), Analytical Chemistry 70 (1998) 4974–4984. [23] J. Wehking, M. Gabany, L. Chew., R. Kumar, Effects of viscosity, interfacial tension, and flow geometry on droplet formation in a microfluidic t-junction, Microfluidics and Nanofluidics 16 (2014) 441–453. [24] H. M. Shapiro, Practical Flow Cytometry, 4. ed., Wiley-Liss, 2003.
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Casper Kunstmann-Olsen Casper Kunstmann-Olsen obtained his M.Sc.Eng degree in Nanotechnology (2008) from Aalborg University and his Ph.D in Nanotechnology and Microfluidics (2012) from University of Southern Denmark. From 2014 to present he is a post-doc research associate at the University of Liverpool, UK. His research interest includes microfluidics, nanoparticles and electron microscopy and his current work is focused on creating and manipulating chemical non-equilibrium systems to mimic processes found in nature.
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Martin M. Hanczyc
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Martin Hanczyc is a Principal Investigator at the University of Trento, Italy. He formally was an Honorary Senior Lecturer at the Bartlett School of Architecture, University College London, Chief Chemist at ProtoLife and Associate Professor at the University of Southern Denmark. He received a bachelor’s degree in Biology from Pennsylvania State University, a doctorate in Genetics from Yale University and was a postdoctorate fellow under Jack Szostak at Harvard University. He has published in the area of droplets, complex systems, evolution and the origin of life in specialized journals including JACS and Langmuir as well as PNAS and Science. Currently he heads the Laboratory for Artificial Biology, developing novel synthetic chemical systems based on the properties of living systems. James Hoyland
Steen Rasmussen
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James Hoyland got his PhD in laser applications in materials science in 2001 from the University of Hull, UK. He worked at BC Cancer Agency in British Columbia, Canada on flow cytometer development and has since conducted applied research in the field of microfluidics and optofluidics. He was an Assistant Professor at the University of Southern Denmark until 2014 and now works in the Physics department at Kwantlen Polytechnic University in British Columbia, Canada where he is pursuing research on microfluidic sensor networks for precision agriculture.
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Steen Rasmussen got his M.Sc. (Physics & Physical Chemistry) at the Technical University of Denmark (DTU) in 1982 and a Ph.D. (Physics), also from Technical University of Denmark (DTU), in 1985. He was one of the founders of the Artificial Life movement in the late 1980s and in 2004 he co-founded the European Center for Living Technology in Venice, Italy. Since 2007 he has been a Professor and center-leader at the Fundamental Living Technology Center (FLinT) at University of Southern Denmark. He is also an External Research Professor at the Santa Fe Institute, USA.
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His scientific activities have mostly focused on pioneering and implementing new approaches, methods, and applications for self-organizing processes in natural and human made systems, such as: Abstract self-programmable matter, which allows computer assembly code environments to program themselves and generate self-replicators from noise. Molecular dynamics (MD) lattice gases, mesoscale simulation tools developed for addressing molecular self-assembly questions. Rational and evolutionary protocell designs to identify minimal physicochemical implementation routes for self-replicating nanomachines. Prof. Rasmussen has published more than 90 peer reviewed papers and many internal technical reports, given more than 170 invited presentations outside of home institutions, and he has coorganized eight international and several national conferences.
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Horst-Günter Rubahn
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HG Rubahn has obtained his PhD in physics from University of Göttingen 1988. After working as postdoctoral associate in Stanford and Kaiserslautern, he worked at the MPI für Strömungsforschung and obtained his venia legendi 1999 at University of Göttingen. He has been visiting professor at Toulouse University and associate professor at University of Southern Denmark. Since 2005 he is professor at University of Southern Denmark, since 2012 director of the Mads Clausen institute and head of campus Sonderborg.
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He has published 190 publications, 8 patents and 10 monographs and is among others in the editorial board of Reports on Progress in Physics.
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S0925-4005(16)30120-4 http://dx.doi.org/doi:10.1016/j.snb.2016.01.120 SNB 19631
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
6-10-2015 21-1-2016 25-1-2016
Please cite this article as: Casper Kunstmann-Olsen, Martin M. Hanczyc, James Hoyland, Steen Rasmussen, Horst-G¨unter Rubahn, Uniform droplet splitting and detection using Lab-on-Chip Flow Cytometry on a microfluidic PDMS device, (2016), http://dx.doi.org/10.1016/j.snb.2016.01.120 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Casper Kunstmann-Olsen*
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Uniform droplet splitting and detection using Lab-on-Chip Flow Cytometry on a microfluidic PDMS device
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University of Southern Denmark, Mads Clausen Institute, Alsion 2, DK-6400 Sønderborg - Denmark
Present address: University of Liverpool, Department of Chemistry, Crown Street, Liverpool L69 7ZD, United Kingdom
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Martin M. Hanczyc
University of Southern Denmark, Center for Fundamental Living Technology, Campusvej 55, DK-5230 Odense M - Denmark
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Present address: Laboratory for Artificial Biology, Centre for Integrative Biology (CIBIO), University of Trento, Via Sommarive 9, I-38123 Povo (TN) - Italy
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James Hoyland
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University of Southern Denmark, Mads Clausen Institute, Alsion 2, DK-6400 Sønderborg - Denmark
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Present address: Kwantlen Polytechnic University, 12666 72 Avenue, Surrey, B.C. V3W 2M8, Canada
Steen Rasmussen
University of Southern Denmark, Center for Fundamental Living Technology, Campusvej 55, DK-5230 Odense M - Denmark Present address: Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, New Mexico 87501, USA
Horst-G¨ unter Rubahn
University of Southern Denmark, Mads Clausen Institute, Alsion 2, DK-6400 Sønderborg - Denmark
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Corresponding author Email address: [email protected] (Casper Kunstmann-Olsen*)
Preprint submitted to Sensors and Actuators B: Chemical
January 20, 2016
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Abstract
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A PDMS chip is fabricated using soft lithography and applied to investigate the formation and division of nitrobenzene (NB) droplets in a two-phase system stabilized by oleic acid. Using an integrated on-chip flow cytometer setup, effected with optical fibers, droplet size distributions are analyzed in situ based on optical signal intensities. By controlling the hydrodynamic flow focusing, uniform droplets of sizes between 100 µm and 300 µm are created with precise size control. Cross-flow shearing allows one to divide these droplets into anything from 2 to 9 individual droplets, depending on flow parameters.
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Keywords: Microfluidic, Droplets, Flow Cytometry, Optical Detection, PDMS Device 1. Introduction
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Droplet based microfluidics offers unique possibilities in a range of application fields where precise volume and concentration control is required. This includes chemical synthesis [1], biological and chemical microreactors [2], and protocells [3]. Controlled droplet formation, -growth and -division are of central importance to these applications. Droplets have for instance been used as microscopic biochemical reaction vessels in on-chip computer controlled microfluidics reaction- and synthesis networks [2, 1, 4, 5]. Of particular interest to the authors is the use of droplets to form and investigate the evolution of self-replicating chemical containers or protocells [6]. There are several protocell models where the container is based on vesicles, droplets, micelles, coacercates and vesicle-droplet hybrids [3]. Previously we have shown that droplets of nitrobenzene with embedded chemical potential can both be self-motile [7] and also self-dividing [3]. We have demonstrated this principle with a rudimentary fission and fusion cycle for nitrobenzene droplets. The droplets contain chemical potential and are initially far from equilibrium and the observed dynamics occur while the system approaches equilibrium. In these investigations the experimenter imposes the formation and manipulation of the droplets manually, with limited control over volumetric, spatial and temporal dynamics that affect the state of the protocell system. 2
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It is therefore desirable to integrate droplet formation and manipulation functions directly on the microfluidic chip, allowing formation of controlled droplet size distributions or sub-populations. Continuous monitoring of the process is essential and can be used to form feedback-controlled loops essential for system refinement and development. Monitoring can be done using integrated optical systems as demonstrated in this paper. In-vitro droplet splitting may be driven by a competition between gravity and surface tension although there is a lower droplet size limit for gravity driven droplet divisions [6]. In this paper we investigate an alternative approach capable of handling much smaller droplet sizes using microfluidic induced shear forces as a step towards microfluidic-based protocell production and evolution.
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The interface between continuous and dispersed phases is essential when considering droplet formation and splitting. In systems with small Reynolds number Re, the main effects acting on an interface are viscosity µ and surface tension γ. The relative strength of these two parameters is given by the Capillary number Ca = µV0 /γ, where V0 is a characteristic velocity of the flows. A low value of Ca indicates that the stresses due to interfacial tension are strong compared to viscous stresses. Droplets flowing under such a condition minimize their surface area by producing spherical ends. In the opposite situation of high Ca, viscous effects dominate and large deformations of droplets can be observed [8]. When designing microfluidic systems for droplet formation, three main approaches exist based on different physical mechanisms. These are best described by the flow topology in the vicinity of the droplet formation zone: • (I) breakup in co-flowing streams (nozzle inside capillary) [9] • (II) breakup in cross-flowing streams (T-junction) [10, 11] • (III) breakup in elongated strained flows (hydrodynamic flow focusing) [12, 13]
In all three cases, the dispersed phase is driven into a microchannel, where it encounters the immiscible carrier fluid that is driven independently. The junction where the two fluids meet must be designed to optimize the reproducibility of droplet formation. The geometry of the junction, together with the flow rates and the physical properties of the fluids (γ and µ) determine the local flow, which in turn deforms the interface and eventually leads to 3
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droplet pinch off if the conditions are right. The size of the droplet is controlled by a competition between the external pressures and the resulting viscous shear stresses, on the one hand, and the capillary pressure (from surface tension) resisting deformation on the other. The capillary number Ca is therefore crucial when trying to predict droplet formation [9]. For droplet splitting many methods are available. As shown earlier [6], chemically driven splitting, which relies on gravity as an external force, has limitations with respect to size. In order to create a fully integrated microfluidic device, alternative methods are necessary. These usually rely on either complex external manipulation e.g. electrowetting [14] or optical methods [15] or are an inflexible inherent function of channel geometry [5]. Seki and coworkers have produced simple devices in which daughter droplets can be split off from larger droplets with varying sizes [16, 17]. In this work we aim to utilize the same methods as traditionally used for droplet formation and demonstrate complete flow driven droplet splitting. We investigate controlled and variable splitting of oil-in-water droplets in a confined system using cross flow streams. The presented method allows for complete splitting of one size-population into another. Using simple microfluidics, our design can generate droplets with a wide range of sizes, while also offering a flexible method of division using shearing forces in a 2D planar cross flow geometry. A simple flow cytometer (FCM) using fiber optics allows optical detection and size characterization, which offers faster and more versatile detection than traditional CCD-imaging tracking. 2. Materials and Methods
2.1. Droplet Chemistry Nitrobenzene (NB) and oleic acid were purchased from Sigma-Aldrich. Solutions of oleate were prepared by manually agitating the oleic acid in water with additional sodium hydroxide added until a clear solution at pH 12 was achieved. A surfactant solution of 40 mM oleate at pH 12 was used as the continuous phase in all experiments [6]. This concentration is experimentally found to be the minimum required to ensure a stable droplet suspension without any fusion of droplets. This is higher than in external conditions (outside microfluidic devices), where 5 mM oleate is sufficient [6]. The necessity of this increase, is attributed to the increased surface energy of the droplet as it is compressed in the microfluidic channel geometry.
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Figure 1: Chip design used to create (a) and split (b) oil droplets of various size, equipped with optical fiber inlets that allows flow cytometry measurements (c). Flow direction leftto-right. The large oval pads are flow connectors (inlets, outlets and two connectors to expel air when fibers are inserted). Microscope images showing flow focusing junction used for formation of oil droplets (which contain red dye for illustrative purposes) (a) and cross flow junction used for droplet splitting (b). Also shown, oil droplets passing the optical fibers and their interaction (fluorescence) with the laser beam in two consecutive images (c). 10 × magnification, black scalebars 500 µm.
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2.2. Chip Fabrication The fabrication of the PDMS microfluidic devices used in this study is essentially a three step process, including SU8 master production, PDMS replication and PDMS sealing. The pattern of the microfluidic channels is transferred into hard baked SU8 photoresist using simple photolithography (100 µm high structures) [18]. The design consists of a flow focusing junction for forming droplets, a cross flow junction for breaking these into smaller droplets and optical fiber inlets which allow for on-chip flow cytometry (see schematic in figure 1). The main channels are 200 µm wide, while the flow focusing and tapered cross-flow junctions are only 100 µm wide. Thus the main channels are 100 µm × 200 µm and the cross flow junctions are 100 µm × 100 µm. By molding PDMS onto the SU8 structured Si wafer master, the microfluidic channel pattern can be replicated repeatedly, producing many chips from a single master [19]. In the same production step, both optical and fluid connectors are molded into the chip [20]. Liquid PDMS (Sylgard 184, Dow Coring) is poured into a custom-made mold and cured at 100◦ C for 30 min. Finally the chip is sealed with a 3 mm thin PDMS sheet using plasma activation [21, 22].
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Figure 2: Left: Schematic showing the setup used for on-chip flow cytometry. Setup includes sample stage with chip-chuck (1), CCD camera and microscope setup (2), 3× syringe drivers (3), laser and fiber-coupler (4), optical detection block including beamsplitter (5), photodiode for laser light detection (6) and PMT for fluorescence detection (7). These are connected to an oscilloscope (8) and eventually a PC (9) for data collection. Right: Image showing the PDMS chip with optical fibers and fluid connectors.
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2.3. Flow Cytometry Setup The custom-built flow cytometer setup is shown schematically in figure 2 left. It consists of a Ar-ion laser (488 nm) which is guided into the device using a single-mode fiber. The laser light scattered by the droplets is collected using a multimode fiber connected to a simple photodiode (PDA36A-EC, Thorlabs). This fiber also collects the fluorescence signal, which is detected using a photomultiplier (PMT) (R928, Hamamatsu). The signals are digitized using an ADC card (PCI-17-14U, Advantech) and a PC. Up to 60 events are recorded per second. The setup is also equipped with a CCDcamera setup (Nikon 1 V2). This allows for image tracking (using LabView ), where the droplets can be counted and measured. Inside the PDMS device the two optical fibers are positioned on either side of the fluid channel (see figure 1 (c)). They are positioned at an angle (18◦ off-axis) to prevent direct coupling of laser light into the output fiber. The images in figure 1 (c) shows uniform droplets (top) pass by the laser light (coming from the top fiber) which induces fluorescence (bottom). The NB droplets are colored using Nile Red (Sigma Aldrich). 2.4. Automated Droplet Tracking Automatic droplet tracking was implemented to enable large-scale data gathering for comparative studies. Video-segments are obtained using the maximum frame rate of the camera (50 fps) and after recording, the video 6
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segments are cut, edited and compressed using Virtual Dub, which is also used to extract every frame of the segment. Using Vision Assistant 2011 (part of National Instruments’ LabView ) each frame is analyzed according to a custom made image-treatment sequence. After adjusting color planes and setting intensity thresholds, a built-in routine tracks and measures circular objects. All frames are then batch-processed and droplet sizes are stored for further processing.
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3. Results and Discussion
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3.1. Droplet Formation Surfactant concentration (and general chemical compatibility) controls the stability of the formed droplets, while physical flow parameters control the size distribution and formation frequency [23]. As discussed earlier, flow focusing is used to form droplets, and therefore the same parameters (the total volume flow through the junction (Vtotal ), and the relative flow ratio between the phases (r) which control hydrodynamic focusing, also controls droplet formation. The flow ratio r is given by the ratio between the sample and continuous flow (r = Vcont /VN B ). The total flow Vtotal is given by the sum of the NB flow rate (VN B ) and that of the continuous phase (Vcont ), so that Vtotal = VN B + 2 × r × VN B . To investigate the influence of flow ratio r and total volume flow Vtotal , NB droplets are generated using 40 mM oleate as surfactant in the flow focusing junction. Video segments are recorded for a wide range of Vtotal and r and analyzed to get droplet size distributions. The NB flow is kept constant (at either 1, 3 or 5 µL/min) and the continuous flow speed is varied to obtain different flow ratios (from r=1 to r=30). Averaged droplet sizes are calculated based on obtained video-segments. The results are displayed in figure 3, displayed both for each constant NB flow (left) and total volume flow (right). Error bars indicate the size distribution of each data set. The results indicate that droplet size is strongly dependent on both Vtotal and r. This relation is also indicative for the behavior of flow focusing on continuous flows [20], which is expected as the forces involved are similar. As with flow focusing, the biggest changes are achieved at low r while the dependency levels out for higher values. By changing the oil-phase flow some size control is possible, while the minimum droplet size seems constant for all flow parameters (in this case around 96 µm). This is believed to be a geometrical limit set by the dimensions of the device (which corresponds 7
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Figure 3: Plot showing droplet size (diameter) as function of flow ratio r, for three NB flow rates (left-hand side) and as a function of total volume flow (Vtotal ) (right-hand side), calculated from r and the sample flows.
to the 100 µm width of the flow focusing junction). This observation is supported by similar studies [23].
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3.2. Droplet Splitting in Fluid Flow Simple thermodynamic calculations considering the Gibbs free energy indicate that small NB droplets (radius <2.25 mm) are too stable to split under current equilibrium conditions [6]. However, a non-equilibrium cross flow junction geometry can be used to break off significantly smaller droplets if channel dimensions and flow parameters are correct [16]. In a region where the channel width ratio is smaller than or close to 1 (i.e. square channel cross-section), the droplet size is expected to decrease linearly with increasing cross-flow speeds. These considerations form the basis of the microfluidic device design (figure 1), which facilitates droplet splitting. As the cross flow velocity (Vc ) is increased, droplets of a certain size are split into smaller and smaller droplets, as they behave like a localized continuous phase. Figure 4 illustrates the effect and show six consecutive images of a single droplet being stretched in the thin channel and split into three daughter droplets by the cross-flow. Droplets were created with a fixed size in the flow focusing junction and channeled to the tapered cross flow junction where Vc is slowly increased. Video-segments are obtained for a range of Vc and analyzed using the LabView routine. At least a few thousand droplets are measured for each value. 8
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Figure 4: Six consecutive frames showing one droplet splitting into 3 daughter droplets at Vc = 20 µl/min. Scalebar 200 µm.
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Figure 5 (left) show a sample histogram of collected data, before and after splitting, for Vc = 75 µl/min. Note that the spread in size before splitting corresponds to the error-bars in figure 3, while the variation after splitting is a combination of uneven splitting (see droplet ”3” in figure 3) and the automated tracking method which might struggle to fit correctly sized circular shapes to very fast moving objects (in the high end of Vc ). Based on a measured average size (diameter D) of a droplet, the average volume can be calculated (droplets are assumed to be oblate spheroids, all 100 µm high). By comparing this to the calculated volume of the split droplets, an average splitting ratio (SR = Vbef ore /Vaf ter ) can be estimated for each cross flow velocity. This can only be done up to a flow of 75 µl/min as at higher speeds, even the slow motion videos can not resolve the fast moving droplets. Table 1 shows calculated volumes and splitting ratios for all measurements. Figure 5 (right) shows the splitting ratios plotted against the cross flow speed and a fitted sigmoidal curve. The measured data show excellent correspondence with the fit. This is expected as the droplets do not start to split until a certain threshold flow speed is reached (in this case between 10 µl/min and 15 µl/min), then follow a linear dependency and finally reach a maximum value once the split droplets reach a size similar to the splitting channel width (100 µm).
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Figure 5: Left: Histograms showing measured droplet sizes for a cross flow velocity of Vc = 75 µl/min, before (top) and after (bottom) splitting. Sizes are obtained by automatic droplet tracking using CCD imaging. The result clearly illustrates a significant change in size-distribution of the droplets. Right: Measured and calculated droplet splitting ratios SR as a function of cross flow speeds Vc following a sigmoidal fit.
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Vc (µl/min) Dbef ore (µm) Vbef ore (nL) Daf ter (µm) Vaf ter (nL) 3 293 4.495 286 4.291 293 4.495 282 4.162 5 10 295 4.567 279 4.077 15 295 4.567 198 2.048 20 294 4.525 172 1.549 25 296 4.587 160 1.341 30 296 4.587 152 1.209 35 297 4.619 142 1.055 40 299 4.681 127 0.842 50 299 4.681 103 0.556 60 298 4.650 108 0.609 75 301 4.744 105 0.557
SR 1.05 1.08 1.12 2.23 2.92 3.42 3.87 4.38 5.56 6.68 7.64 8.52
Table 1: Average droplet size (and volume) before and after splitting. Splitting ratios (SR) calculated based on volume ratio.
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Figure 6: Left: Microscope images showing NB droplets generated with three different sample flow rates (20, 30 and 40 µl/min, from top to bottom) resulting in different sizes (volumes). Right: Scatterplots obtained individually for the three different populations (legend denotes average droplet size).
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3.3. Flow cytometry (FCM) on droplet splitting The flow parameters used in this section are exactly the same as in the previous section, only now the flow cytometer setup is used to collect optical data on the split droplets. To relate the optical data to the microscopic CCD measurements a simple calibration is performed using both the CCD and the FCM setup. By changing the flow ratio r, droplets of different sizes are created (see figure 3). Using the automated droplet tracking the sizes are calculated. Simultaneously, the FCM setup is used to obtain scatterplots for each population. In figure 6 selected CCD images and corresponding scatterplots for each size are shown. The primary optical signal (scattered laser light), is known to be directly related to particle (or in this case) droplet size [24], which these results also clearly show. Electric amplification is here optimized for maximum spread and illustrates very nicely how even small size differences can easily be distinguished using the optical detection system in the FCM setup. The measurements also reveal very similar spread in the secondary optical channel (fluorescence). This is somewhat expected as the droplet contain a highly fluorescent dye (Nile Red), which to some extent saturates the PMT. 11
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Figure 7: Left: Selected FCM scatterplots for droplets split increasingly smaller units as the cross flow speed increases (smaller values). Right: Mean values for all measured crossflow velocities Vc obtained by fitting a Gaussian function to the corresponding intensity histogram for the primary optical channel. Secondary y-axis shows calibrated droplet size as a function of Vc .
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Accordingly, the sensitivity is reduced drastically to prevent this, at the cost of resolution in the data. However, as the PMT is highly sensitive there are many instances where the secondary signal can be used to differentiate between different populations of species containing various amounts of fluorescence (e.g. biological samples with DNA-specific fluorescent markers). To investigate droplet splitting using FCM, the system is now set to generate droplets of a constant size (300 µm) which are subsequently split using different cross flow rates (as in the previous experiment). Figure 7 shows selected FCM scatterplots of scatter- versus fluorescence-signal intensities for a range of cross flow rates. For all obtained plots, a histogram is collected for the scattered laser light and the maximum value is found by fitting a Gaussian function. The insert in figure 7 shows the central position of this peak as a function of Vc . These results can be directly related to the splitting ratios calculated in the previous section (figure 5), which also indicated a sigmoidal relation between splitting ratio and cross-flow velocity. Furthermore, using the results from section 3.2 and figure 6, we can directly correlate the signal intensity to the droplet size after splitting (also on figure 7 insert).
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4. Conclusion
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In this work a microfluidic PDMS chip has been used to create droplets of controllable sizes via hydrodynamic focusing and subsequently split these using cross flow streams. CCD imaging tracing and FCM has been used to characterize both size and splitting ratios based on external inputs. Both measured size distribution (droplet video tracking) and the FCM plots reveal a sigmoidal relation between splitting ratio and cross flow velocity. We can therefore conclude that this device is both able to create droplets with precise sizes and afterward split these accordingly into programmed ratios. Furthermore, the in-line FCM allows for fast and easy size estimation without use of CCD image tracking. This enables one to create devices with very small footprints that integrate organic droplet creation, in-line analysis and manipulation. This microfluidic platform could also incorporate drop to drop variation, testing and selection. By making the generation and handling of protocells faster and more autonomous, a fully contained on-chip evolution machine could be realized.
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acknowledgments
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CKO, JH and HGR acknowledge financial support provided by the European Commissions (EC) Fund for Regional Development (EFRU) through the InterReg 4A project ”Lab-on-a-chip technique for quality-control in foodand bio-industry”. Further CKO acknowledge an EC sponsorship through the FET proactive FP7 MATCHIT project. MMH was supported in part by the Emerging Technologies Proactive 611640 (EVOBLISS). SR and MMH acknowledge financial support from the EC FET proactive MATCHIT project as well as the Danish National Research Foundation (Grundforskningsfonden). [1] M. Wayland, H. Fellerman, M. Hadorn, D. Sorek, D. Lacent, S. Rasmussen, R. Fuchslin, The matchit automaton: Exploiting compartmentalization for the synthesis of branched polymers, Computational and Mathematical Methods in Medicine (2013). [2] H. Song, D. L. Chen, R. F. Ismagilov, Reactions in droplets in microfluidic channels, 2006. doi:10.1002/anie.200601554. [3] S. Rasmussen, M. Bedau, L. Chen, D. Deamer, D. Rakauer, N. Packard, P. Stadler (Eds.), Protocells: Bridging Nonliving and Living Matter, MIT Press, 2008. 13
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[4] M. Amos, P. Dittrich, J. McCaskill, S. Rasmussen, Biological and chemical information technologies, Procedia Computer Science 7 (2011) 56–60. [5] T. Ichii, H. Suzuki, T. Yomo, Micro-droplet model for recursive growth and division dynamics of the cell, Europhysics Letters 96 (2011) 48006.
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[6] F. Cashera, S. Rasmussen, M. Hanczyc, An oil droplet division-fusion cycle, ChemPlusChem Communications 78 (2013) 52–54.
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[7] M. M. Hanczyc, T. Taro, I. Takashi, N. Packard, T. Sugawara, Fatty acid chemistry at the oil-water interface: Self-propelled oil droplets, Journal of the American Chemical Society 129 (2007) 9386–9391.
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[8] H. Bruus, Theoretical Microfluidics, Oxford University Press, 2008.
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[9] C. Baroud, F. Gallaireb, R. Danglaa, Dynamics of microfluidic droplets, Lab Chip 10 (2010) 2032–2045.
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[10] T. Thorsen, R. Roberts, F. Arnold, S. Quake, Dynamic pattern formation in a vesicle-generating microfluidic device, Physical Review Letters 86 (2001) 4163–4166.
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[11] P. Garstecki, M. Fuerstmann, H. Stone, G. Whitesides, Formation of droplets and bubbles in a microfluidc t-junction - scaling and mechanism of break-up, Lab Chip 6 (2006) 437–446.
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[12] S. L. Anna, N. Bontoux, H. A. Stone, Formation of dispersions using flow-focusing in microchannels, Applied Physics Letters 82 (2003) 364– 366. [13] R. Dreyfus, P. Tabeling, H. Willaime, Ordered and discordered patterns in two phase flows in microchannels, Physical Review Letters 90 (2003) 144505. [14] S. K. Cho, H. J. Moon, C. J. Kim, Creating, transporting, cutting, and merging liquid droplets by electrowetting-based actuation for digital microfluidic circuits, Journal of Microelectromechanical Systems 12 (2003) 70–80. [15] C. N. Baroud, M. R. de Saint Vincent, J.-P. Delville, An optical toolbox for total control of droplet microfluidics, Lab on a Chip 7 (2007) 1029– 1033. 14
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[16] T. Moritani, M. Yamada, M. Seki, Generation of uniform-size droplets by multistep hydrodynamic droplet division in microfluidic circuits, Microfluidics and Nanofluidics 11 (2011) 601–610.
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[17] M. Yamada, S. Doi, H. Maenaka, M. Yasuda, M. Seki, Hydrodynamic control of droplet division in bifurcating microchannel and its application to particle synthesis, Journal of Colloid and Interface Science 331 (2008) 401–407.
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[18] J. Hoyland, C. Kunstmann-Olsen, H. Rubahn, Simple photolithographic rapid prototyping of microfluidic chips, Microelectronic Engineering 98 (2012) 689–692.
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[19] J. McDonald, G. Whitesides, Poly(dimethylsiloxane) as a material for fabricating microfluidic devices, Accounts of Chemical Research 35 (2002) 491–499.
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[20] C. K.-O. . J. H. . H. Rubahn, Influence of geometry on hydrodynamic focusing and long-range fluid behavior in pdms microfluidic chips, Microfluidics and Nanofluidics 12 (2012) 795–803.
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[21] B. Jo, L. van Lerberghe, K. Motsegood, D. Beebe, Three-dimensional micro-channel fabrication un pdms elastomer, Journal of Microelectrochemical Systems 9 (2000) 76–81.
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[22] D. Duffy, J.C.McDonald, O. A. Schueller, G. Whitesides, Rapid prototyping of microfluidic systems in poly(dimethylsiloxane), Analytical Chemistry 70 (1998) 4974–4984. [23] J. Wehking, M. Gabany, L. Chew., R. Kumar, Effects of viscosity, interfacial tension, and flow geometry on droplet formation in a microfluidic t-junction, Microfluidics and Nanofluidics 16 (2014) 441–453. [24] H. M. Shapiro, Practical Flow Cytometry, 4. ed., Wiley-Liss, 2003.
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Casper Kunstmann-Olsen Casper Kunstmann-Olsen obtained his M.Sc.Eng degree in Nanotechnology (2008) from Aalborg University and his Ph.D in Nanotechnology and Microfluidics (2012) from University of Southern Denmark. From 2014 to present he is a post-doc research associate at the University of Liverpool, UK. His research interest includes microfluidics, nanoparticles and electron microscopy and his current work is focused on creating and manipulating chemical non-equilibrium systems to mimic processes found in nature.
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Martin M. Hanczyc
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Martin Hanczyc is a Principal Investigator at the University of Trento, Italy. He formally was an Honorary Senior Lecturer at the Bartlett School of Architecture, University College London, Chief Chemist at ProtoLife and Associate Professor at the University of Southern Denmark. He received a bachelor’s degree in Biology from Pennsylvania State University, a doctorate in Genetics from Yale University and was a postdoctorate fellow under Jack Szostak at Harvard University. He has published in the area of droplets, complex systems, evolution and the origin of life in specialized journals including JACS and Langmuir as well as PNAS and Science. Currently he heads the Laboratory for Artificial Biology, developing novel synthetic chemical systems based on the properties of living systems. James Hoyland
Steen Rasmussen
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James Hoyland got his PhD in laser applications in materials science in 2001 from the University of Hull, UK. He worked at BC Cancer Agency in British Columbia, Canada on flow cytometer development and has since conducted applied research in the field of microfluidics and optofluidics. He was an Assistant Professor at the University of Southern Denmark until 2014 and now works in the Physics department at Kwantlen Polytechnic University in British Columbia, Canada where he is pursuing research on microfluidic sensor networks for precision agriculture.
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Steen Rasmussen got his M.Sc. (Physics & Physical Chemistry) at the Technical University of Denmark (DTU) in 1982 and a Ph.D. (Physics), also from Technical University of Denmark (DTU), in 1985. He was one of the founders of the Artificial Life movement in the late 1980s and in 2004 he co-founded the European Center for Living Technology in Venice, Italy. Since 2007 he has been a Professor and center-leader at the Fundamental Living Technology Center (FLinT) at University of Southern Denmark. He is also an External Research Professor at the Santa Fe Institute, USA.
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His scientific activities have mostly focused on pioneering and implementing new approaches, methods, and applications for self-organizing processes in natural and human made systems, such as: Abstract self-programmable matter, which allows computer assembly code environments to program themselves and generate self-replicators from noise. Molecular dynamics (MD) lattice gases, mesoscale simulation tools developed for addressing molecular self-assembly questions. Rational and evolutionary protocell designs to identify minimal physicochemical implementation routes for self-replicating nanomachines. Prof. Rasmussen has published more than 90 peer reviewed papers and many internal technical reports, given more than 170 invited presentations outside of home institutions, and he has coorganized eight international and several national conferences.
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Horst-Günter Rubahn
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HG Rubahn has obtained his PhD in physics from University of Göttingen 1988. After working as postdoctoral associate in Stanford and Kaiserslautern, he worked at the MPI für Strömungsforschung and obtained his venia legendi 1999 at University of Göttingen. He has been visiting professor at Toulouse University and associate professor at University of Southern Denmark. Since 2005 he is professor at University of Southern Denmark, since 2012 director of the Mads Clausen institute and head of campus Sonderborg.
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He has published 190 publications, 8 patents and 10 monographs and is among others in the editorial board of Reports on Progress in Physics.
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