Condensation on Surface Energy Gradient Shifts Drop Size Distribution toward Small Drops

During dropwise condensation from vapor onto a cooled surface, distributions of drops evolve by nucleation, growth, and coalescence. Drop surface coverage dictates the heat transfer characteristics and depends on both drop size and number of drops present on the surface at any given time. Thus, manipulating drop distributions is crucial to maximizing heat transfer. On earth, manipulation is achieved with gravity. However, in applications with small length scales or in low gravity environments, other methods of removal, such as a surface energy gradient, are required. This study examines how chemical modification of a cooled surface affects drop growth and coalescence, which in turn influences how a population of drops evolves. Steam is condensed onto a horizontally oriented surface that has been treated by silanization to deliver either a spatially uniform contact angle (hydrophilic, hydrophobic) or a continuous radial gradient of contact angles (hydrophobic to hydrophilic). The time evolution of number density and associated drop size distributions are measured. For a uniform surface, the shape of the drop size distribution is unique and can be used to identify the progress of condensation. In contrast, the drop size distribution for a gradient surface, relative to a uniform surface, shifts toward a population of small drops. The frequent sweeping of drops truncates maturation of the first generation of large drops and locks the distribution shape at the initial distribution. The absence of a shape change indicates that dropwise condensation has reached a steady state. Previous reports of heat transfer enhancement on chemical gradient surfaces can be explained by this shift toward smaller drops, from which the high heat transfer coefficients in dropwise condensation are attributed to. Terrestrial applications using gravity as the primary removal mechanism also stand to benefit from inclusion of gradient surfaces because the critical threshold size required for drop movement is reduced

Effect of Roughness Geometry on Wetting and Dewetting of Rough PDMS Surfaces

Rough PDMS surfaces comprising 3 μm hemispherical bumps and cavities with pitches ranging from 4.5 to 96 μm have been fabricated by photolithographic and molding techniques. Their wetting and dewetting behavior with water was studied as model for print surfaces used in additive manufacturing and printed electronics. A smooth PDMS surface was studied as control. For a given pitch, both bumpy and cavity surfaces exhibit similar static contact angles, which increase as the roughness ratio increases. Notably, the observed water contact angles are shown to be consistently larger than the calculated Wenzel angles, attributable to the pinning of the water droplets into the metastable wetting states. Optical microscopy reveals that the contact lines on both the bumpy and cavity surfaces are distorted by the microtextures, pinning at the lead edges of the bumps and cavities. Vibration of the sessile droplets on the smooth, bumpy, and cavity PDMS surfaces results in the same contact angle, from 110°–124° to ∼91°. The results suggest that all three surfaces have the same stable wetting states after vibration and that water droplets pin in the smooth area of the rough PDMS surfaces. This conclusion is supported by visual inspection of the contact lines before and after vibration. The importance of pinning location rather than surface energy on the contact angle is discussed. The dewetting of the water droplet was studied by examining the receding motion of the contact line by evaporating the sessile droplets of a very dilute rhodamine dye solution on these surfaces. The results reveal that the contact line is dragged by the bumps as it recedes, whereas dragging is not visible on the smooth and the cavity surfaces. The drag created by the bumps toward the wetting and dewetting process is also visible in the velocity-dependent advancing and receding contact angle experiments

Two-Dimensional Continuous Extraction in Multiphase Lipid Bilayers To Separate, Enrich, and Sort Membrane-Bound Species

A new method is presented to separate, enrich, and sort membrane-bound biomolecules based on their affinity for different coexisting lipid phases in a supported lipid bilayer using a two-dimensional, continuous extraction procedure. Analogous to classic liquid–liquid phase extraction, we created two distinct lipid phases in our planar membrane system: a liquid-ordered (<i>l</i>_o) phase and a liquid-disordered (<i>l</i>_d) phase arranged in parallel stripes inside a microfluidic device. Membrane-bound biomolecules in an adjacent supported lipid bilayer are convected in plane along the microfluidic channel and brought into contact with a different lipid phase using hydrodynamic force. A mixture of two lipid species, a glycolipid and a phospholipid, with known affinities for the two lipid phases employed here are used to demonstrate continuous extraction of the lipid-microdomain preferring glycolipid to the <i>l</i>_o phase, while the phospholipid remains primarily in the <i>l</i>_d phase. In this demonstration, we characterize the performance of this affinity-based separation device by building models to describe the velocity profile and transport in the two-phase coexistent membrane. We then characterize the impact of residence time on the extraction yield of each species. This new procedure sorts membrane species on the basis of chemical properties and affinities for specific lipid phases within a membrane environment near physiological conditions, critical for extending this method to the separation of lipid-linked proteins and transmembrane proteins while minimizing denaturation. This platform could facilitate the separation and identification of lipid membrane domain residents, or the characterization of changes in membrane affinity due to post-translational modifications or environmental conditions

Two-Dimensional Continuous Extraction in Multiphase Lipid Bilayers To Separate, Enrich, and Sort Membrane-Bound Species

A new method is presented to separate, enrich, and sort membrane-bound biomolecules based on their affinity for different coexisting lipid phases in a supported lipid bilayer using a two-dimensional, continuous extraction procedure. Analogous to classic liquid–liquid phase extraction, we created two distinct lipid phases in our planar membrane system: a liquid-ordered (<i>l</i>_o) phase and a liquid-disordered (<i>l</i>_d) phase arranged in parallel stripes inside a microfluidic device. Membrane-bound biomolecules in an adjacent supported lipid bilayer are convected in plane along the microfluidic channel and brought into contact with a different lipid phase using hydrodynamic force. A mixture of two lipid species, a glycolipid and a phospholipid, with known affinities for the two lipid phases employed here are used to demonstrate continuous extraction of the lipid-microdomain preferring glycolipid to the <i>l</i>_o phase, while the phospholipid remains primarily in the <i>l</i>_d phase. In this demonstration, we characterize the performance of this affinity-based separation device by building models to describe the velocity profile and transport in the two-phase coexistent membrane. We then characterize the impact of residence time on the extraction yield of each species. This new procedure sorts membrane species on the basis of chemical properties and affinities for specific lipid phases within a membrane environment near physiological conditions, critical for extending this method to the separation of lipid-linked proteins and transmembrane proteins while minimizing denaturation. This platform could facilitate the separation and identification of lipid membrane domain residents, or the characterization of changes in membrane affinity due to post-translational modifications or environmental conditions

Sample images of viruses binding to glycolipids with and without image restoration.

<p>Few examples of restored particles are shown by the colored triangles. The white number shows the particle count, <i>P</i>_<i>count</i>, for the right half of each image, which represents a physical size of 82 μm high x 41 μm wide using 512 x 256 pixels. Note that <i>P</i>_<i>count</i> is not the same as <i>N</i>. We show <i>P</i>_<i>count</i> for qualitative comparisons only, since quantitative comparisons must be done using <i>N</i> instead, which is determined after the particle linking step. The time on left is the video recording time, which starts ~60 s after the virus is loaded, and therefore some virus exists at time 0. We show images starting at 10 s merely because the performance of STAWASP is optimal after 10 frames. We provide original movies without any image restoration for the first 300 s as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.s004" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.s007" target="_blank">S4</a> Videos, played at 10x real time where 1 frame = 1 second. STAWASP-enhanced movies are provided for X31 binding to G_M3 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.s008" target="_blank">S5 Video</a>) and G_D1a (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.s009" target="_blank">S6 Video</a>). Variations in particle intensities are caused by several factors, such as variable levels of dye that incorporated into the viral membrane, different degrees of photobleaching, and uneven microscope illumination (viruses in the center are generally brighter than those near the edges). [Image Processing Note: All images, including left and right halves, have undergone background subtraction as explained in Part D in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.s003" target="_blank">S1 Text</a>, and intensities were linearly scaled. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.s004" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.s007" target="_blank">S4</a> Videos for original images].</p

Observing how binding kinetics changes with choice of <i>t</i>_{<i>cutoff</i>} for a 1% G_D1a trial.

<p>a) <i>N</i>_<i>+</i> vs <i>t</i> at varying <i>t</i>_{<i>cutoff</i>} settings shown in the legend. b) <i>R</i>_<i>on</i> vs <i>t</i>_{<i>cutoff</i>} plot showing how <i>R</i>_<i>on</i> is affected with <i>t</i>_{<i>cutoff</i>} choice.</p

Testing various image restorations on a simulated movie.

<p>A simulated movie with noise was generated to compare image restoration performance (see Supporting Materials 1.3 for simulation details). The pure video shows particles with varying intensities appearing at frame 20 and disappearing after frame 33. Noise is added according to the function N = -0.2ln(R), where R is a uniform random number from 0 to 1. The SNRs of the 9 particles, from top left to bottom right are as follows: 5.0, 4.4, 3.9, 3.3, 2.8, 2.2, 1.6, 1.1, 0.5. The LoG (Laplacian of Gaussian) spatial filtering method is described by others [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.ref034" target="_blank">34</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.ref063" target="_blank">63</a>].</p

Using structural arguments to understand binding results.

<p>Left) View of HA protein head group in relation to G_D1a. Red regions show the binding pockets and yellow circles show where sialic acid are located. Right) Side view of HA protein in relation to G_D1a. Teal molecules are sialic acids at potential secondary binding sites. The SLB would be on the bottom side of the protein, while the viral membrane would be on the top side. The hemagglutinin structure and sialic acid positions were obtained by Sauter et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.ref065" target="_blank">65</a>] PDB ID: 1HGG.</p

Biologically Complex Planar Cell Plasma Membranes Supported on Polyelectrolyte Cushions Enhance Transmembrane Protein Mobility and Retain Native Orientation

Reconstituted supported lipid bilayers (SLB) are widely used as <i>in vitro</i> cell-surface models because they are compatible with a variety of surface-based analytical techniques. However, one of the challenges of using SLBs as a model of the cell surface is the limited complexity in membrane composition, including the incorporation of transmembrane proteins and lipid diversity that may impact the activity of those proteins. Additionally, it is challenging to preserve the transmembrane protein native orientation, function, and mobility in SLBs. Here, we leverage the interaction between cell plasma membrane vesicles and polyelectrolyte brushes to create planar bilayers from cell plasma membrane vesicles that have budded from the cell surface. This approach promotes the direct incorporation of membrane proteins and other species into the planar bilayer without using detergent or reconstitution and preserves membrane constituents. Furthermore, the structure of the polyelectrolyte brush serves as a cushion between the planar bilayer and rigid supporting surface, limiting the interaction of the cytosolic domains of membrane proteins with this surface. Single particle tracking was used to analyze the motion of GPI-linked yellow fluorescent proteins (GPI-YFP) and neon-green fused transmembrane P2X2 receptors (P2X2-neon) and shows that this platform retains over 75% mobility of multipass transmembrane proteins in its native membrane environment. An enzyme accessibility assay confirmed that the protein orientation is preserved and results in the extracellular domain facing toward the bulk phase and the cytosolic side facing the support. Because the platform presented here retains the complexity of the cell plasma membrane and preserves protein orientation and mobility, it is a better representative mimic of native cell surfaces, which may find many applications in biological assays aimed at understanding cell membrane phenomena

Representative X31 binding survival curves and empirical fits.

<p>a) The number of virus bound, N, is plotted against the residence time, <i>t</i>_<i>res</i>, to yield a survival curve for binding. The Eq 3 fit parameter for G_M3 is [A = 0.87, B = 0.19] and G_D1a is [A = 2.56, B = 1.38]. The Eq 4 fit parameters for G_M3 is [A = 2.14, B = 1.63] and G_D1a is [A = 2.06, B = 1.72]. Note that the Eq 4 fit parameters differ from those found from the related log plots in panel c. The binning of the binding data in the log plots causes an approximation error. b) Log plots used to derive Eqn 3. c) Log plots used to derive Eqn 4 as used Bally et al.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163437#pone.0163437.ref001" target="_blank">1</a>] (the data has been binned).</p