Underwater Curvature-Driven Transport between Oil Droplets on

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Underwater Curvature-Driven Transport between Oil Droplets on Patterned Substrates Xiaolong Yang, Victor Breedveld, Won Tae Choi, Xin Liu, Jinlong Song, and Dennis W. Hess ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b02413 • Publication Date (Web): 09 Apr 2018 Downloaded from http://pubs.acs.org on April 9, 2018

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ACS Applied Materials & Interfaces

Underwater Curvature-Driven Transport between Oil Droplets on Patterned Substrates Xiaolong Yang,†,‡ Victor Breedveld,‡ Won Tae Choi,§ Xin Liu,† Jinlong Song,*,† and Dennis W. Hess*,‡ †

Key Laboratory for Precision and Non-Traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian 116023, People’s Republic of China

‡

School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA 30332, USA

§

School of Materials Science and Engineering, Georgia Institute of Technology, 500 10th Street, Northwest, Atlanta GA 30332, USA

KEYWORDS: Underwater superoleophobicity, patterned surface, Laplace pressure, droplet transport, lab-on-a-chip devices

ABSTRACT: Roughness contrast patterns were generated on copper surfaces by a simple onestep site-selective oxidation process using a felt-tipped ink pen masking method. The patterned surface exhibited strong underwater oil wettability contrast which allows oil droplet confinement. Oil droplets placed on two patterned smooth dots (reservoirs) connected by a patterned smooth channel will spontaneously exchange liquid as a result of Laplace pressure

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differences until their shapes have reached equilibrium. In our experiments, residual solubility of the oil in water was overcome by using saturated oil-in-water solutions as the aqueous medium. In the saturated solution, the dependence of pattern geometry and oil viscosity on transported volume and flow rate in the underwater oil transport process were investigated for dichloromethane and hexadecane. Experimental results were in good agreement with a simple model for Laplace pressure-driven flow. Depending on droplet curvatures, oil can be transported from large to small reservoirs or vice versa. The model predictions enable design of reservoir and channel dimensions to control liquid transport in the water-solid surface-oil system. The patterning technique was extended to more complex patterns with multiple reservoirs for smart oil separation and mixing processes. The concepts demonstrated in this study can be employed to seed droplet arrays with specific initial drop volumes and achieve subsequent droplet mixing at controlled flow rates for potential lab-on-a-chip applications ranging from oil-droplet-based miniature reactors and sensors to high-throughput assays.

1. INTRODUCTION Liquid-in-air wettability of bio-inspired nano/micro structured surfaces has been investigated intensively for applications such as anti-icing,1-5 drag reduction,6 self-cleaning7, 8, waterharvesting,9 and lab-on-a-chip devices.10-16 Considerably less information has been generated on surface wettability by liquid droplets submersed in another immiscible liquid. Jiang et al.17 studied the underwater wetting of oil on fish scales and reported that their surfaces are covered by micro-scale oriented papillae structures with nano-scale roughness. This hierarchical structure imparts superhydrophilic properties to the fish scale surface, so that underwater it is wetted completely and all crevices are filled with water. As a result, oil droplets that come into contact with the wetted water-solid interface bead up, displaying a high oil contact angle (OCA) and


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ultra-low oil adhesion. Based on observations on fish scales, Jiang’s group studied the effect of surface topography and chemical composition on the underwater wettability.18, 19 Inspired by Jiang’s work, numerous techniques, including lithography,20 anodization,21 laser irradiation,22 dip coating,23 and self-assembly24 have been proposed to fabricate underwater superoleophobic surfaces for self-cleaning25, 26, oil/water separation,27-30 and micro-oil-droplet manipulation.31-33 In addition to uniform surface modifications, patterned superhydrophobic surfaces have been studied because of their ability to manipulate droplets, which is of particular value for energy and biomedical applications.34-39 In recent years, research on underwater superoleophobic surfaces has also been extended to patterned substrates that display spatial variations in wetting properties.40-42 Such substrates can provide controlled oil droplet manipulation for applications that involve sampling, sensing, or chemical reactions. Various techniques, including direct laser writing,41,

43,

44

wire electrical discharge machining (WEDM),42 and masked surface

modification40 have been developed to fabricate such functional patterned surfaces. For instance, Wu et al.41 used femtosecond laser irradiation to prepare self-organized, hierarchical microcone arrays on nickel with smooth patterned regions. The smooth areas exhibited high-adhesion, “sticky” oleophobic properties underwater, while the laser irradiated area showed low-adhesion, “roll-off” underwater superoleophobicity. Underwater oil droplet manipulations including droplet storage, guiding and mixing were achieved on patterned surfaces by taking advantage of this contrast in wettability. Yu et al.42 reported that WEDM produces rough oxidized layers in the machining process and therefore can be employed to machine 3D structures with underwater superoleophobicity on diverse alloy substrates. In their work, a tunnel-like structure was fabricated on an aluminum surface to enable underwater oil droplet guidance: submerged oil drops could be moved along the tunnel under the influence of gravity by tilting the surface by


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only 3°. These researchers also highlighted the potential use of underwater oil droplet manipulation for applications such as droplet-based miniaturized reactors for organic reactions. Huang et al.40 fabricated wedge-shaped underwater superoleophilic patterns using nitrogen cold plasma treatment through openings in a mask. Nonpolar organic oil droplets dispensed on these patterns could be spontaneously transported underwater, similar to what had been achieved in air previously.12 Limitations of these methods to fabricate functional surfaces with controllable underwater oil wettability include the requirement of complex multistep processing and expensive equipment, and the fact that the fabricated macroscopic underwater structures cannot control oil transport with desired precision.40, 41 For example, Wu’s method41 involved expensive femtosecond lasers and is not suitable for large-area applications; furthermore, to achieve droplet transport and mixing, vibrations must be applied to the substrate to overcome adhesion and maintain droplet mobility. The fabrication process of Huang40 is fairly complex, requiring a plasma generator, and the use of hard masks limits process versatility. The spatial resolution of this method is also limited: the millimeter-scale wedge-shaped patterns cannot transport small amounts of liquid in a controlled, quantitative manner. Therefore, considerable interest remains in alternative, novel approaches to achieve precise, quantitatively controlled oil transport on patterned substrates. Passive pumping systems for water in air have been reported previously. For instance, Beebe and Williams developed 3D microfluidic devices comprising two differentsized wells and a connecting closed channel. Aqueous liquid injected into the smaller well can be pumped into the larger one due to a Laplace pressure differential.45,

46

However, these 3D

structures are relatively complex, and the closed channels are sensitive to bubble trapping and limit access to the surface. Also, extending the in-air passive water pumping to oil transport


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raises issues such as evaporation of volatile oil and the need to use oil-compatible materials for device fabrication. In previous work, we developed an ink-masked oxidation method to fabricate nano-structured metal surfaces with patterned smooth areas.47 In this work, that concept is applied to create twodimensional patterns with high underwater oil wettability contrast, which enables oil droplet confinement and manipulation. In particular, oil on two smooth dot patterns (reservoirs) connected by a smooth channel pattern can be transported spontaneously as a result of Laplace pressure gradients that depend on droplet shape. The effects of pattern dimensions and liquid viscosity on the transported volume and flow rate in this underwater oil transport process were investigated experimentally and compared with theoretical models. Flow rate and total transported volumes can be controlled over wide ranges by regulating the pattern dimensions and the viscosity of the transported oil, thus enabling precise oil transport. These concepts can be extended to complex patterns with multiple reservoirs for smart droplet separation and mixing processes. The current research facilitates the design of oil-droplet-based miniature reactors for organic droplet-based reactions and other microfluidic devices for high-throughput assays. 2. MATERIALS AND METHODS 2.1. Materials. Copper foils (0.127 mm thick, annealed, 99.9%) were purchased from Alfa Aesar (Haverhill, MA, United States). Water-proof alcohol-based ink pens (Sharpie ultrafine point permanent marker, tip diameter 200 µm, black) were obtained from a local Office Depot. Ammonium persulfate ((NH4)2S2O8) and sodium bicarbonate (NaHCO3) were purchased from Amresco (Solon, OH, United States). Dichloromethane (99.5%), diiodomethane (99%) and hexadecane (99.5%) used for the underwater wettability measurements were purchased from


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Sigma-Aldrich (St. Louis, MO, United States). All chemicals are ACS grade and were used as received. 2.2. Fabrication of the Patterned Surface. The copper foils were immersed in acetic acid for 1 min and subsequently dried by nitrogen to remove surface contamination and oxide layers.48 Then the ink pen was used to directly draw mask patterns on the pretreated copper surface. The masked substrate was immersed in an aqueous solution of NaHCO3 (0.1 M) and (NH4)2S2O8 (0.02 M) for 24 h to site-selectively modify the unmasked areas.49 Finally, the sample was cleaned using acetone to remove the ink tracks and air-dried (Fig. 1). 2.3. Characterization. All static-contact-angle (SCA) measurements were performed by placing 5 µL droplets of selected fluid onto the samples. The underwater oil SCAs were measured in an acrylic box (102 × 102 × 102 mm, 3.2 mm thick walls). Advancing and receding angles were also measured via the tilting plate method in order to quantify contact angle hysteresis.50 All reported contact angle (CA) values are the average of five measurements. Droplet images and videos were captured and analyzed with a ramé-hart CA goniometer (model 290). Digital photos were captured by a camera (Canon 700d, Japan) equipped with an EFS 18-135 mm lens.

Figure 1. Schematic of the fabrication process for patterned surfaces with wettability contrast. 3. RESULTS AND DISCUSSION 3.1 Wettability Contrast of Patterned Surface in Air and Underwater. The area without ink patterns on the copper surface contacts the solution and the reaction proceeds according to:49


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2Cu ( s ) + 4HCO3− ( aq ) + 2S2 O82 − ( aq ) → Cu 2 ( OH ) 2 CO3 ( s ) + 4SO 24− ( aq ) + 3CO 2 ( g ) + H 2 O (l )

(1)

The oxidation process produces superhydrophilic Cu2(OH)2CO3 rod-like nanostructures on the copper surface (Fig. 2a).49 The ink-masked pattern on the copper surface displays substantial water-resistance, which protects the surface beneath the ink from contacting the solution (Fig. 2b), so that the ink-covered copper surface area is not oxidized and retains its original smoothness after ink removal (Figs. 2c, d). Previous studies have shown that micro- and nanostructured metal oxides with high surface energy are superhydrophilic, which means that water spreads completely and rapidly when brought into contact with the structures (Fig. 2e, left inset in Fig. 2d).51 In contrast, the original smooth copper surface exhibits moderate hydrophilicity, with a water contact angle (WCA) of 54.0 ± 6.0° (Fig. 2e, right inset in Fig. 2d). The patterned substrates thus exhibit high wettability contrast in air; as a result, water can be easily trapped on an oxidized superhydrophilic area that is surrounded by closed-loop smooth, hydrophilic boundaries (inset in Fig. 2f). As shown in Fig. 2f, water droplets with different volumes were trapped in a circular area with a diameter of 4.5 mm. When the droplet volume is increased, the triple-phase line (TPL) does not move until the water contact angle (WCA) of the trapped water exceeds 75° at the contact line with the smooth track. This wettability contrast in air can have application in the design of water storage platforms. Previous studies have reported that in-air superhydrophilicity is crucial for underwater superoleophobicity.17, 52 The droplet images in the insets of Fig. 2g show high underwater oil contact angles (OCAs) for diiodomethane and dichloromethane droplets on the oxidized areas of 167.1 ± 1.7° and 159.5 ± 2.7°, resp., indicating excellent superoleophobicity. When oil droplets were placed on the patterned smooth area instead, the OCA values for diiodomethane and dichloromethane were 123.4 ± 7.1° and 124.6 ± 6.9°, resp., as shown in the insets in Fig. 2g,


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which indicates that smooth copper is oleophobic underwater. Young’s equation, which is typically applied for the liquid on a solid substrate in air, can be extended to the underwater OCA on a smooth surface to describe the relations between these contact angles:17, 22, 29, 41

cos θ ow =

γ oa cos θ oa - γ wa cos θ wa γ ow

(2)

where γoa, γwa and γow are oil-in-air surface tension, water-in-air surface tension and oil-in-water surface tension, resp. Similarly, θow, θoa and θwa represent the CAs of oil in water, oil in air and water in air. It can be inferred from Equation 2 that a hydrophilic surface in air should become oleophobic underwater. For example, the surface tensions of dichloromethane in air and in water are 27.8 and 27.93 mN·m-1,53 while the water surface tension is 72.0 mN·m-1. In air, the OCA of dichloromethane on smooth copper is 14.6 ± 1.2° while the WCA on that surface is 54.0 ± 6.0°. The underwater OCA of dichloromethane on smooth copper can therefore be calculated to be 123.5°, which is in excellent agreement with the experimentally measured value of 124.6 ± 6.9°. The high static underwater OCA predicted by the extended Young’s equation and observed experimentally suggests that oil droplets should not spread well on smooth copper patterns. On the other hand, the in-air data shows that smooth copper has a lower OCA than WCA, indicating that the surface actually prefers contact with oil over water. As a result, the underwater copper/oil/water TPL does not easily move and high underwater OCA hysteresis is observed, with advancing and receding underwater OCAs of 149.2 ± 7.4° and 36.2 ± 3.3°, resp., for dichloromethane (Fig. 2h). The high adhesion is further enhanced by contact line pinning at the borders of the wetting contrast patterns. The high underwater OCA hysteresis and pinning thus lead to an apparent underwater oleophilicity in spite of the high static underwater OCA; oil films stick readily to the patterned areas and are therefore difficult to remove. The picture in Fig. 2g


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shows that smooth tracks with different shapes were covered with oil films due to the high underwater OCA hysteresis. When micro/nano hierarchical structures are introduced to the surface, an underwater CassieBaxter model can be used to describe wetting of the water-solid-oil system.17 When such a structured surface is immersed in water, water easily spreads and fills the microscopic interstitial spaces on the substrate. The oil droplet beads up with a high OCA (Fig. 2i), similar to water droplets on a superhydrophobic surface in air. In this water-solid-oil Cassie-Baxter system, water serves the role of air in the air-solid-water system. The high OCA of oil droplets on a rough surface can be described using the following generalized Cassie's equation:22, 54, 55 * cosθow = f ⋅ cosθow + f −1

(3)

where f is the area ratio of oil-solid to oil-water interface, and θow is the underwater OCA on a flat substrate. Taking dichloromethane as an example, using the measured θ*ow of 159.5 ± 2.7° and θow of 124.6 ± 6.9°, f =0.1465, which means that the solid-oil contact area is small, providing little resistance to movement of the TPL. Therefore, oil droplets on the surfaces showed a low OCA hysteresis (Fig. 2h); the sliding angle (SA) of a 5 µL dichloromethane droplet on the textured copper is indeed only 1°. As discussed above, the smooth patterned area is technically underwater oleophobic with static OCA >120°, the smooth patterned area can easily be wetted by oil due to the high underwater OCA hysteresis (Fig. 2g). In conclusion, the oxidized, nanostructured copper exhibits underwater superoleophobicity, while the patterned smooth area can become underwater oleophilic after wetted by oil; the patterned substrate possesses strong wettability contrast underwater as well as in air.


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Figure 2. (a) SEM images of the oxidized rod-like nanostructures; inset is the magnified SEM image of the nanostructures. (b) Schematic showing how the ink mask prevents the patterned area from oxidation. (c) SEM images of the smooth area beneath the ink mask after oxidation process; inset is the magnified SEM image of this area. (d) Digital photo of the patterned surface in air; insets are images of water droplets in air on the textured area (left) and smooth area (right). (e) Schematic of water droplet on the patterned surface in air. (f) Droplet WCA as a function of volume on superhydrophilic area that is confined by a closed-loop boundary (diameter 4.5 mm); insets are droplet images at different volumes. (g) Digital photo of the patterned surface underwater (oil dyed red for visualization); insets are underwater images of 5 µL diiodomethane droplets (left two) and dichloromethane droplets (right two) on the textured area and smooth area, resp. (h) Underwater OCA hysteresis of dichloromethane and


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diiodomethane on the smooth patterned area and oxidized textured area; RCA and ACA in the legend represent the receding and advancing contact angles. (i) Schematic of oil droplets on the patterned surface underwater.

3.2 Effects of Oil-in-Water Solubility on Quantifying Droplet Dynamics. Many commonly used oils for studying underwater wetting behavior have non-negligible solubility in water. For example, dichloromethane has a solubility in water of 17.5 g·L-1 at 25 °C and 1,2-dichloroethane is soluble in water at 8.7 g·L-1 at 20 °C. In particular for time-dependent studies of small oil droplets like the ones performed in this study, this level of solubility could greatly affect data interpretation, because oil droplets will gradually shrink when oil dissolves in the surrounding medium. Fig. 3a and 3b show that the dimensions of 10 µL dichloromethane droplets (heights and widths) decrease with increased exposure time in water. Since the objective of this study is to perform detailed analysis of oil transport processes between droplets, to minimize droplet dissolution into the surrounding water we used saturated aqueous solutions of oil. It is clear that changes in both dimensions and OCAs of the dichloromethane droplets in water are greatly suppressed by using saturated water (Fig. 3). It should be mentioned that the evaporation of dichloromethane from the saturated solution causes the height and width of the dichloromethane droplet to decrease slowly after 20 minutes, albeit at a much lower speed than in pure water (Fig. 3a). Since the underwater oil droplet manipulations generally take less than 20 min, the saturated surrounding medium is still suitable for quantitative studies of underwater droplet manipulations of partially soluble oils. In contrast, in the case of poorly soluble oils such as hexadecane, pure water can be used without sacrificing accuracy as shown in Fig. 3. Results indicate that underwater hexadecane droplets are stable when submerged in water for 50 min. Based on these


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observations all underwater wettability studies for dichloromethane were carried out in saturated water.

Figure 3. (a) Dimensions, (b) optical images and (c) OCAs of 10 µL dichloromethane and hexadecane droplets versus submersion time in 100 ml pure water and 100 ml oil-saturated water; DCM represents dichloromethane.

3.3 Volume-dependent Laplace Pressure of Underwater Oil Droplets on Patterned Surfaces. Oil droplets dispensed underwater on a smooth circular dot patterned on the oxidized copper surface adhere firmly to the dot because of the high OCA hysteresis. When the droplet volume is increased, the TPL of the droplet remains pinned at the boundary between the pattern and the surrounding superoleophobic area until its CA exceeds the advancing CA of the superoleophobic area (droplet sizes e1, e2 and e3 in Fig. 4a).36, 56, 57 Fig. 4b and 4c show the OCAs of dichloromethane droplets with different volumes on smooth dots with different diameters. The OCA increases with droplet volumes until the droplet volumes reaches 35 µL for the 2.2 mm diameter dot and 50 µL for the 3.3 mm dot (Fig. 4b and 4c). In other words, shapes of the underwater oil droplets vary with the droplet volume and dot size. Hence, the internal Laplace pressure of oil droplets, which is proportional to the droplet curvature (C), can be controlled easily by regulating the droplet volume and dot size. Assuming


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that underwater oil droplets are perfectly spherical, curvature and volume of the droplet can be calculated using the following geometrical equations:58 C=

V=

2H r + H2

(4)

2

π 6

( H 3 + 3Hr 2 )

(5)

where r is the radius of the dot and H is the height of the underwater oil droplet. Using Equations 4 and 5, the curvature of underwater oil droplets can be mapped as a function of droplet volume for each dot size, as shown in Fig. 4d, where theoretical predictions are compared directly with experimental data that were obtained by measuring the curvature of the upper half of the droplet using AutoCAD. Results show that for volumes below 20 µL, the maximum relative error between the calculated curvature and the measurements is 10.2 %, and the minimum error is 0.4 %; above 20 µL, clear discrepancies can be noted due to gravity-induced droplet deformation. Generally, however, the curvature of underwater oil droplets with different volumes on the circular patterns can be described accurately by Equations 4 and 5, and Fig. 4d shows that droplet curvatures vary significantly with droplet volume and dot size. Because the internal Laplace pressure of the oil droplets is proportional to the droplet curvature, this quantity can also be controlled over a wide range of values by regulating dot size and droplet volume:

∆P = 2γ ow C =

4 H γ ow r2 + H 2

(6)


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Figure 4. (a) Schematic of oil droplets with different volumes on a smooth dot surrounded by textured structures in water. (b, c) Underwater OCA of dichloromethane droplets as a function of droplet volume on dots with diameters of 2.2 mm (b) and 3.3 mm (c). (d) Curvature of dichloromethane droplets on dots with different diameters as function of droplet volume; lines indicate model predictions for spherical droplets.

3.4 Spontaneous Underwater Oil Transport. Two dots that serve as droplet reservoirs are connected by a smooth channel. After the patterned reservoirs and channels were wetted by oils to form a stable oil film, droplets dispensed on the wetted reservoirs can spontaneously be transported due to internal Laplace pressure differences between the reservoirs. Fluid flow stops after dynamic equilibrium is achieved wherein the Laplace pressure (and droplet curvature) are


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the same for both reservoirs (Fig. 5a). In dichloromethane-saturated water, spontaneous pressuredriven transport of oil due to Laplace pressure gradient was studied on superoleophobic surfaces with smooth patterns composed of two circular reservoirs connected by a straight open channel (Fig. 5). Both large-to-small (Figs. 5c and 5f) and small-to-large fluid movement (Figs. 5d and 5g) was observed. For instance, a 10 µL dichloromethane droplet was dispensed on reservoir 2 of the pattern as illustrated in Fig. 5b. As discussed above, reservoir 1 was pre-wetted by oil and thus covered by a thin oil film with very low curvature. The resulting Laplace pressure differential drives oil transport from reservoir 2 to reservoir 1; a droplet then forms on reservoir 1, but flow is maintained until eventually the curvature of the oil droplet in reservoir 1 matches reservoir 2 and the system reaches equilibrium, which occurs after ~100 s (Fig. 5c). Meanwhile, if 5.2 µL and 10 µL oil droplets were dispensed on reservoirs 1 and 2, respectively, oil from the smaller reservoir 1 was transported to the larger reservoir 2, with equilibrium reached after ~240 s (Fig. 5d). Similar transport processes were observed on the pattern shown in Fig. 5e, which has two reservoirs with equal diameters (Fig. 5f and 5g). Based on previous studies in air, the Laplace pressure difference ∆P between droplets on the two reservoirs drives flow at a volumetric rate Q that also depends on the hydrodynamic resistance Rc of the connecting channel: Q=∆P/Rc. In a closed system, the flow rate can directly be connected to changes in volume of both droplets (Q=dV/dt) and the resistance in the connecting channel can be approximated as Rc = 3µl/wh3.59 This leads to the following set of differential equations to describe the evolution of both reservoir droplet sizes:58  4γ ow H 2 4γ ow H1 3µ l H12 + r12 dH1  r 2 + H 2 − r 2 + H 2 = wh3 π ( 2 ) dt  2 2 1 1  2 2  4γ ow H1 − 4γ ow H 2 = 3µ l π ( H 2 + r2 ) dH 2  r12 + H12 r22 + H 22 wh 3 2 dt

(7)


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where µ is the viscosity of the transported oil; l, w and h are length, width and fluid height in the open channel, respectively, and the subscripts 1 and 2 represent parameters of reservoir droplets 1 and 2. Equation 7 was solved with MATLAB software. The unknown parameter h for each pattern is calculated from the first experimental datum point and considered constant when solving the equations. Linking the computed results for H to droplet volumes V using Equation 5, both the droplet volume and flow rate can be predicted as a function of time for the oil transport processes depicted in Fig. 5. As can be seen in Fig. 6, the model values agree well with the experimental measurements, except that the final droplet volume on reservoir 1 was underestimated by 12.2 % in the model (Fig. 6a). This deviation can be attributed to gravityinduced droplet deformation in the initial stage (droplet in reservoir 2 in Fig. 5c), which results in underestimated droplet curvature and Laplace pressure, thus reducing the calculated initial flow rates (Fig. 6b). As the oil transport proceeds, the droplet volume decreases and shapes become more spherical, which leads to improved accuracy in the calculated flow rate. An interesting phenomenon in the process shown in Fig. 5d is that the flow rate initially increases until a maximum value is reached at ~60 s, and then decreases gradually to 0 at the equilibrium state (Fig. 6d). This behavior occurs because the curvature (or internal Laplace pressure) of the droplet on reservoir 1 initially experiences a gradual increase with decreased droplet volume. The increase continues until the maximum value is reached at the volume where the droplet becomes a perfect hemi-sphere (Fig. 4d, stage 1). Subsequently, the curvature (or internal Laplace pressure) decreases rapidly until the droplet shrinks to the final state (stage 2). During stage 1, the pressure difference between droplets on the two reservoirs increases, resulting in an increasing flow rate; in stage 2, the pressure difference decreases which leads to a reduction in flow rate.


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Figure 5. (a) Schematic illustration of underwater oil transport driven by an internal Laplace pressure gradient. (b) Schematic of pattern composed of two reservoirs with different diameters. (c) Large-to-small and (d) small-to-large fluid movement on the pattern shown in (b). (e) Schematic of pattern composed of two reservoirs with same diameters. (f) Large-to-small and (g) small-to-large fluid movement on the pattern as shown in (e).


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Figure 6. Experimental results and model predictions for droplet volume and flow rates of reservoir 1 as a function of time for processes depicted in Fig. 5c (a, b) and Fig. 5d (c, d).

3.5 Effect of Pattern Geometry and Dimensions on Oil Transport. Equation 7 shows that the overall changes in droplet volume and flow rates during the transport process can be controlled by manipulating the pattern designs: reservoir diameters, channel width and channel lengths. Figure 7 presents experimental data and the corresponding model predictions for several pattern variations. Fig. 7a shows the droplet volume as a function of time for different channel


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widths (reservoir diameters 3.4 mm; channel length 6.0 mm; initial droplet volumes dispensed on reservoirs 1 and 2 were 0 and 13.3 µL, resp.). The process on the pattern with channel width of 0.9 mm proceeded much faster, taking only 70 s to reach equilibrium, while the process with a narrow channel (0.6 mm wide) failed to equilibrate even after 420 s. It can be seen from Fig. 7b that the initial flow rate for the channel width of 0.9 mm was 12 times larger than for 0.6 mm channel width. The theoretical results computed using Equation 7 agree well with the experimental data. A slight increase in channel width clearly leads to a dramatic increase in flow rate. These observations can be ascribed to the fact that the liquid height h in the channel is also strongly affected by channel width (Fig. 7a); h and w are both in the denominator of the hydrodynamic resistance coefficient (Rc=3µl/wh3) (Equation 7), causing a very strong effect of channel width on oil transport. Figures 7c and 7d display the results for patterns with different channel lengths (reservoir 1 and 2 diameters are 2.3 and 3.3 mm, respectively; channel width is 0.55 mm; initial droplet volumes dispensed on reservoirs 1 and 2 were 3.3 µL and 16.2 µL). Times to reach equilibrium with channel lengths of 3.0 and 12.0 mm were 40 and 200 s indicating that the average flow rate on the shorter channel length of 3.0 mm is 5 times larger than on the longer channel length of 12.0 mm. A 4 times shorter channel length resulted in a 5 times larger flow rate, which agrees quite well with predictions from Equation 7. The relation between flow rate and channel length is not perfectly linear, as one may expect, because meniscus deformation at the channel entrance and exit causes slight variation in h: for the 3.0 mm and 12.0 mm channel lengths, fitted values for h were 0.041 and 0.036 mm, resp. Finally, Figures 7e and 7f demonstrate the effect of reservoir diameters. Droplet volume and flow rate are presented as a function of time for patterns with different reservoir 1 diameters


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(reservoir 2 diameter 3.4 mm; channel width 0.60 mm; channel length 6.0 mm; initial droplet volumes dispensed on reservoir 1 and reservoir 2 were 0 µL and 13.3 µL, resp.). Droplets with larger diameter possess smaller curvature and lower internal Laplace pressure (Fig. 4d). Therefore, when comparing fluid movement from a 3.4 mm diameter reservoir 2 to a 3.4 mm diameter reservoir 1 (process P1) with the process from 3.4 mm diameter reservoir 2 to a 2.3 mm diameter reservoir 1 (process P2), the initial flow rate in P1 is greater because there is lower opposing pressure in the larger reservoir, while overall transported volumes that lead to balanced Laplace pressures also differ. Both the transported volume and flow rate can thus be controlled simply by modulating the reservoir 1 size. The volume transported into reservoir 1 in the P2 was 1.1 µL after 240 s pumping while that value for the P1 was 5.1 µL at 420 s. The predictions from Equation 7 are consistent with experimental measurements. The calculated h for symmetric and asymmetric reservoirs are 0.024 and 0.022 mm respectively., which means that changes in reservoir size have little impact on the liquid height in the connecting channel, as one would expect. It should be noted again that in the large-to-small fluid movement (Figs. 7b and 7f), the driving pressure generated by the large droplet is underestimated due to the gravity-induced droplet deformation in the initial stage. Hence, experimental flow rates at the beginning were slightly larger than the computed values while in the small-to-large fluid movement (Fig. 7d), the opposing pressure generated by the large droplet is underestimated, therefore resulting in lower initial experimental flow rates than the computed values.


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Figure 7. Droplet volume and flow rate as function of time for fluid movement on patterns with different: (a, b) Channel widths; diameters of the two reservoirs are 3.4 mm; channel length is 6.0 mm; initial droplet volumes on reservoir 1 and reservoir 2 were 0 and 13.3 µL, resp. (c, d) Channel lengths; diameters of the reservoir 1 and 2 are 2.3 and 3.3 mm, resp.; channel width is 0.55 mm; initial droplet volumes on reservoir 1 and reservoir 2 were 3.3 µL and 16.2 µL, resp. (e, f) Reservoir diameters; diameter of the reservoir 2 is 3.4 mm; channel width is 0.60 mm; channel length is 6.0 mm; initial droplet volumes on reservoir 1 and reservoir 2 were 0 µL and 13.3 µL, resp.

3.6 Effect of Oil Properties on Transport Processes. The fabricated patterns exhibit strong oil wettability contrasts underwater for a wide range of oils, including heavy (ρoil > ρwater) and light oils (ρoil < ρwater). For instance, hexadecane (ρoil = 0.77 g·cm-3) can also easily wet the smooth pattern and be transported by Laplace pressure gradients. Fig. 8a is an image sequence showing the small-to-large fluid movement of hexadecane on a pattern (diameters of reservoirs 1 and 2


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are 2.3 and 3.3 mm, resp.; channel width 0.55 mm; channel length 3.0 mm). A 3.5 µL hexadecane droplet dispensed on reservoir 1 was gradually transported into a larger 16.0 µL droplet on reservoir 2. The viscosity of hexadecane at 20 °C is 3.5 mPa⋅s, which is about 8.5 times larger than dichloromethane (0.41 mPa⋅s). Based on Equation 7, the higher viscosity of the transported oil (µ in Equation 7) should result in a higher flow resistance and much slower flow rate than for dichloromethane transport on the same pattern (Fig. 8b and 8c). Additionally, the viscosity µ and channel length l both appear in the numerator of Equation 7, which means that changes in the viscosity of transported liquid have the same effect on transport flow rate as changing the channel length. This explains why both volume and flow rate data as a function of time for hexadecane along a short channel were analogous to the data for transporting dichloromethane along a longer channel of 12.0 mm.

Figure 8. (a) Time lapse images showing the small-to-large fluid movement of hexadecane on a pattern. (b) Droplet volume and (c) flow rate of hexadecane and dichloromethane movement on the same pattern; diameters of reservoirs 1 and 2 are 2.3 and 3.3 mm, resp.; channel width is 0.55 mm; channel length is 3.0 mm.

3.7 Oil Transport Processes on Patterns with Multiple Reservoirs. Superhydrophobic surfaces with superhydrophilic patterns have been used as a miniaturized platform for high-


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throughput assays. Aqueous solutions applied to such surfaces spontaneously form an array of separated microdroplets due to the extreme wettability contrast between superhydrophobic and superhydrophilic areas and discontinuous dewetting. The resulting microdroplet array can then be used as a microreservoir for culturing cells or other processes.37, 60 This method to generate droplets suffers from lack of control over droplet sizes during initial generation and the lack of ability to add fluid to or remove it from the droplet reservoirs in a controlled manner, for example to exchange reagents. In this section, we extend the work on oil transport between two connected droplet reservoirs to more complex patterns with multiple reservoirs to illustrate potential use of the concept as a smart platform to generate oil droplet arrays with precisely controlled inter-droplet transport. Fig. 9a shows the schematic of a pattern composed of three reservoirs with nearly the same diameters (M1). When a 10 µL oil droplet was dispensed on reservoir 3, the oil was transported spontaneously to reservoirs 2 and 1 until equilibrium was reached after 170 s with equal droplet volumes (Fig. 9b and c). If oil is subsequently removed from one reservoir, liquid from other reservoirs is transported due to the Laplace pressure differential until the oil drop in the depleted reservoir becomes of sufficient size to balance the Laplace pressures, reaching a new equilibrium with equal, smaller droplets (Fig. 9d, see supplementary Movie S1). The droplet distribution process can be extended to complex patterns with more reservoirs. Fig. 9e displays the schematic of a pattern which is composed of five reservoirs with nominally the same diameters; the simple patterning technique leads to minor variations. A 20 µL droplet dispensed in the center reservoir was separated into five 3.6 ± 0.3 µL droplets (Fig. 9f, see supplementary Fig. S1a and Movie S1). When the complexity of the patterns is increased further by using different dot sizes, droplet volumes can be manipulated with even greater degree of sophistication. As shown in Fig. 10a and b, a 22 µL droplet on a


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pattern (M3) composed of a small reservoir and two larger satellite reservoirs was divided into three individual droplets with different equilibrium volumes after 120 s (see supplementary Movie S2). This functionality can also be extended to a complex pattern with a small center reservoir and four larger satellite reservoirs (M4, Fig. 10c). A 30 µL droplet dispensed on the center reservoir is transported outward to create four 6.8 ± 0.4 µL satellite droplets, leaving a 1.1 µL center droplet at equilibrium after 150 s (Fig. 10d, see supplementary Fig. S1b). Similarly, if the droplet is then removed from one of the satellite reservoirs, oil is transported between droplets to re-establish equilibrium (Fig. 10d, see supplementary Movie S2).

Figure 9. (a) Schematic of pattern M1 composed of three reservoirs with nominally the same diameters. (b) Time lapse images of oil transport on the pattern M1. (c) Digital photo of the oil uniformly distributed on the pattern M1. (d) Time lapse images of oil transport on pattern M1 when oil was removed from the left reservoir at equilibrium state in (b). (e) Schematic of pattern M2 composed of five reservoirs with nearly the same diameters. (f) Time lapse images of oil transport on pattern M2. Imperfect camera alignment and depth perspective can cause the droplet volumes to appear unequal in some cases (e.g. bottom frames in Figs. 9b and 9d), but droplet volume measurements indicate that the volumes are equal.


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Figure 10. (a) Schematic of pattern M3 composed of one small reservoir and two larger satellite reservoirs. (c) Schematic of pattern M4 composed of one small reservoir in the center and four larger satellite reservoirs. Time lapse images of oil transport on pattern (b) M3 and (d) M4. In addition to controlled distribution of a single droplet among patterns, it is also possible to achieve mixing of fluid from multiple reservoirs, which can be used to deliver reagents to a miniaturized reactor. Fig. 11a presents the schematic of such a reactor pattern (M5), which is composed of one center large reservoir and two smaller satellite reservoirs. When individual oil droplets are placed on the satellite reservoirs, oil is transported and mixed in the center reservoir (Fig. 11b and d, see supplementary Movie S3). When liquid is then removed from a satellite reservoir, liquid from the mixed droplet flows back towards the satellite reservoir due to the Laplace pressure difference. To better illustrate fluid transport during the mixing step, another mixing test was implemented on pattern M5 using two different colored oil droplets (See Fig. S2). On a pattern with five reservoirs, four droplets can also be mixed in the same manner. (See supplementary Fig. S3 and Movie S3). For the case of two connected droplet reservoirs, we successfully modeled the droplet dynamics using Equation 7. In principle, the same approach can be extended to multi-reservoir patterns; however, analogous to the case for piping networks with multiple connections, the flow dynamics is extremely sensitive to small variations in droplet shape (Laplace pressure) and line


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dimensions (hydrodynamic resistance). The equilibrium distribution of liquid across connected reservoirs can be predicted accurately, but the flow dynamics to reach equilibrium cannot be calculated as precisely for our simple patterning technique. Nevertheless, Figures 9-11 illustrate how Laplace pressure-driven flow can be applied to design a new category of underwater devices with predictable spontaneous inter-droplet liquid transport.

Figure 11. (a) Schematic of pattern M5 composed of one center large reservoir and two smaller satellite reservoirs. (b) Time lapse images of oil mixing on pattern M5. (c) Time lapse images of transporting the mixed oil to a satellite reservoir from which oil had been removed. (d) Digital photo of the mixed oil droplet on pattern M5 after reaching equilibrium.

4. CONCLUSIONS Selective oxidation was utilized to fabricate nano-structured copper oxide surfaces with patterned smooth copper areas. Ink tracks drawn with a felt-tipped ink pen can effectively prevent oxidation and thus act as a mask to control the size and shape of the smooth areas; these display high oil contact angle hysteresis and can be easily wetted by oil (oleophilic) while the surrounding nano-structured surface exhibits excellent underwater superoleophobicity. The oleophilic/superoleophobic wettability contrast enables pinning of the triple-phase-line (TPL) of


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oil droplets underwater; the pattern size and geometry can be used to determine shape-volume relations for oil droplets and thus design underwater microfluidic devices. Oil droplets placed on two patterned reservoirs connected by a patterned smooth channel can exhibit spontaneous oil transport as a result of Laplace pressure differences. When underwater oil transport processes were carried out between small droplets, it was important to prevent residual dissolution of oil in the surrounding aqueous medium by using a saturated aqueous solution of slightly soluble oils like dichloromethane. By designing channel dimensions, reservoirs sizes and varying the oil viscosity, the flow rate and the equilibrium droplet volumes on each reservoir could be controlled. Experimental results for the oil transport processes show that inter-droplet oil flow rate increases dramatically with an increase of channel width, similar to the strong dependence of flow rate on pipe diameter in Hagen-Poiseuille flow. In addition, the flow rate increased with a decrease in channel length and with an increase of inlet reservoir diameter, which agrees well with model predictions. In essence, droplet curvature- or Laplace pressure-driven oil transport between microliter droplets is the miniaturized version of macroscopic communicating vessels that exchange liquid until hydrostatic pressures are balanced. At the microscopic scale, liquid movement is sensitive to droplet curvature (or Laplace pressure) with negligible effects from hydrostatic pressure; at the macroscopic scale, liquid movement is predominantly controlled by hydrostatic forces with interfacial curvature being negligible. It should be noted that the curvature-driven transport between droplet reservoirs can occur from small to large and from large to small reservoirs, depending on pattern geometries and droplet volumes. The simple design of two connected reservoirs was suitable for model validation, but the methodology was also extended to more complex patterns with multiple interconnected


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reservoirs. The multi-reservoir patterns can automatically distribute droplets into arrays of smaller droplets or mix individual droplets in a larger reservoir. This approach provides the possibility of spontaneously seeding micro droplet arrays in a well-controlled way for highthroughput assays and creating new miniature reactors for transport rate-controlled reactions of multiple samples. Another advantage of such devices is the ability to re-distribute liquid by removing or adding a droplet at one of the reservoirs, which leads to re-equilibration. In this way, in-situ volume control of the droplet array and sample screening in the droplet mixing process can be achieved. This principle can be used to design more complex 2D/3D devices that can control the micro droplet curvature and therefore liquid movement for application to sensors and other microfluidic devices. Finally, the current method for underwater oil transport on patterned substrates is easily implemented, low-cost and does not require an active pump. From the principles established in this work, users can readily design and fabricate specific patterns on flat or curved substrates according to the desired application, providing a universal approach for the creation of novel droplet-based microfluidic devices. Although the patterning technique with a felt-tipped pen is suitable for simple, flexible fabrication of reservoirs with controlled sizes on the millimeter-scale and channels with widths of several hundred micrometers, it is not appropriate to fabricate complex large-area patterns or for applications that demand high dimensional accuracy; alternative patterning methods like inkjet printing and automated robotic pen control could overcome some of these limitations.

ASSOCIATED CONTENT


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Supporting Information. Video of uniform droplet separation on pattern M1 and M2, video of non-uniform droplet separation on pattern M3 and M4, video of droplet mixing on pattern M5 and M6. This material is available free of charge via the internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *Tel.: 1-404-8945922. E-mail: [email protected] *Tel.: 86-411-84708422. E-mail: [email protected]

Notes The authors declare no competing finical interest.

ACKNOWLEDGEMENT This project was supported by National Natural Science Foundation of China (NSFC, 51605078), Science Fund for Creative Research Groups of NSFC (51621064) and the China Scholarship Council (201606060072). X.L.Y. thanks the China Scholarship Council for providing an opportunity to work at Georgia Tech as a joint PhD Student.

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Chang, B.; Zhou, Q.; Ras, R. H.; Shah, A.; Wu, Z.; Hjort, K. Sliding Droplets on Hydrophilic/Superhydrophobic Patterned Surfaces for Liquid Deposition. Appl. Phys. Lett. 2016, 108, 154102.


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Figure 1. Schematic of the fabrication process for patterned surfaces with wettability contrast. 41x9mm (600 x 600 DPI)


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Figure 2. (a) SEM images of the oxidized rod-like nanostructures; inset is the magnified SEM image of the nanostructures. (b) Schematic showing how the ink mask prevents the patterned area from oxidation. (c) SEM images of the smooth area beneath the ink mask after oxidation process; inset is the magnified SEM image of this area. (d) Digital photo of the patterned surface in air; insets are images of water droplets in air on the textured area (left) and smooth area (right). (e) Schematic of water droplet on the patterned surface in air. (f) Droplet WCA as a function of volume on superhydrophilic area that is confined by a closedloop boundary (diameter 4.5 mm); insets are droplet images at different volumes. (g) Digital photo of the patterned surface underwater (oil dyed red for visualization); insets are underwater images of 5 µL diiodomethane droplets (left two) and dichloromethane droplets (right two) on the textured area and smooth area, resp. (h) Underwater OCA hysteresis of dichloromethane and diiodomethane on the smooth patterned area and oxidized textured area; RCA and ACA in the legend represent the receding and advancing contact angles. (i) Schematic of oil droplets on the patterned surface underwater. 177x129mm (300 x 300 DPI)



Figure 3. (a) Dimensions, (b) optical images and (c) OCAs of 10 µL dichloromethane and hexadecane droplets versus submersion time in 100 ml pure water and 100 ml oil-saturated water; DCM represents dichloromethane. 48x13mm (300 x 300 DPI)


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Figure 4. (a) Schematic of oil droplets with different volumes on a smooth dot surrounded by textured structures in water. (b, c) Underwater OCA of dichloromethane droplets as a function of droplet volume on dots with diameters of 2.2 mm (b) and 3.3 mm (c). (d) Curvature of dichloromethane droplets on dots with different diameters as function of droplet volume; lines indicate model predictions for spherical droplets. 177x133mm (300 x 300 DPI)



Figure 5. (a) Schematic illustration of underwater oil transport driven by an internal Laplace pressure gradient. (b) Schematic of pattern composed of two reservoirs with different diameters. (c) Large-to-small and (d) small-to-large fluid movement on the pattern shown in (b). (e) Schematic of pattern composed of two reservoirs with same diameters. (f) Large-to-small and (g) small-to-large fluid movement on the pattern as shown in (e). 44x11mm (300 x 300 DPI)


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Figure 6. Experimental results and model predictions for droplet volume and flow rates of reservoir 1 as a function of time for processes depicted in Fig. 5c (a, b) and Fig. 5d (c, d). 178x155mm (300 x 300 DPI)



Figure 7. Droplet volume and flow rate as function of time for fluid movement on patterns with different: (a, b) Channel widths; diameters of the two reservoirs are 3.4 mm; channel length is 6.0 mm; initial droplet volumes on reservoir 1 and reservoir 2 were 0 and 13.3 µL, resp. (c, d) Channel lengths; diameters of the reservoir 1 and 2 are 2.3 and 3.3 mm, resp.; channel width is 0.55 mm; initial droplet volumes on reservoir 1 and reservoir 2 were 3.3 µL and 16.2 µL, resp. (e, f) Reservoir diameters; diameter of the reservoir 2 is 3.4 mm; channel width is 0.60 mm; channel length is 6.0 mm; initial droplet volumes on reservoir 1 and reservoir 2 were 0 µL and 13.3 µL, resp. 178x95mm (300 x 300 DPI)


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Figure 8. (a) Time lapse images showing the small-to-large fluid movement of hexadecane on a pattern. (b) Droplet volume and (c) flow rate of hexadecane and dichloromethane movement on the same pattern; diameters of reservoirs 1 and 2 are 2.3 and 3.3 mm, resp.; channel width is 0.55 mm; channel length is 3.0 mm. 53x16mm (300 x 300 DPI)



Figure 9. (a) Schematic of pattern M1 composed of three reservoirs with nominally the same diameters. (b) Time lapse images of oil transport on the pattern M1. (c) Digital photo of the oil uniformly distributed on the pattern M1. (d) Time lapse images of oil transport on pattern M1 when oil was removed from the left reservoir at equilibrium state in (b). (e) Schematic of pattern M2 composed of five reservoirs with nearly the same diameters. (f) Time lapse images of oil transport on pattern M2. Imperfect camera alignment and depth perspective can cause the droplet volumes to appear unequal in some cases (e.g. bottom frames in Figs. 9b and 9d), but droplet volume measurements indicate that the volumes are equal. 177x55mm (300 x 300 DPI)


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Figure 10. (a) Schematic of pattern M3 composed of one small reservoir and two larger satellite reservoirs. (c) Schematic of pattern M4 composed of one small reservoir in the center and four larger satellite reservoirs. Time lapse images of oil transport on pattern (b) M3 and (d) M4. 177x47mm (300 x 300 DPI)



Figure 11. (a) Schematic of pattern M5 composed of one center large reservoir and two smaller satellite reservoirs. (b) Time lapse images of oil mixing on pattern M5. (c) Time lapse images of transporting the mixed oil to a satellite reservoir from which oil had been removed. (d) Digital photo of the mixed oil droplet on pattern M5 after reaching equilibrium. 177x55mm (300 x 300 DPI)


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Underwater Curvature-Driven Transport between Oil Droplets on

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