Wet Self-Cleaning of Superhydrophobic Microfiber Adhesives Formed from High Density Polyethylene

Biologically inspired adhesives developed for switchable and controllable adhesion often require repetitive uses in general, dirty, environments. Superhydrophobic microstructures on the lotus leaf lead to exceptional self-cleaning of dirt particles on nonadhesive surfaces with water droplets. This paper describes the self-cleaning properties of a hard-polymer-based adhesive formed with high-aspect-ratio microfibers from high-density polyethylene (HDPE). The microfiber adhesive shows almost complete wet self-cleaning of dirt particles with water droplets, recovering 98% of the adhesion of the pristine microfiber adhesives. The low contact angle hysteresis indicates that the surface of microfiber adhesives is superhydrophobic. Theoretical and experimental studies reveal a design parameter, length, which can control the adhesion without affecting the superhydrophobicity. The results suggest some properties of biologically inspired adhesives can be controlled independently by adjusting design parameters

Negligible contribution of the acceleration term in the wrist joint model (1) for the two tasks.

<p>(A) <i>CC</i>’s for both the <i>step-tracking</i> task and the <i>pursuit</i> task were almost identical regardless of with or without the acceleration term in the wrist joint model (1) for both <i>controls</i> (<i>green dots</i>) and <i>patients</i> (<i>red dots</i>). Note the high values of <i>R</i>^<i>2</i> between the <i>CC’</i>s with and without the acceleration term. (B) <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios for both the <i>step-tracking</i> task and the <i>pursuit</i> task were almost identical regardless of with or without the acceleration term in the wrist joint model (1) for both <i>controls</i> (<i>green dots</i>) and <i>patients</i> (<i>red dots</i>). Note the high values of <i>R</i>^<i>2</i>. Also note that the average ratio of <i>M</i>_<i>r</i> and <i>K</i>_<i>r</i> (<i>M</i>_<i>r</i>/<i>K</i>_<i>r</i>) obtained with <i>CCA</i> for the controls and the patients (n = 29) was 0.0048 ± 0.0036 (range: 0.0019–0.0189) for the <i>step-tracking</i> task, and 0.012 ± 0.0088 (range: 0.0032–0.0345) for the <i>pursuit</i> task, explaining why the acceleration term is negligible in the regression.</p

Different <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios for the two movement tasks.

<p>Contour plots of the correlation coefficient <i>R</i> between the <i>muscle torque</i> and <i>kinematic torque</i> for the step-tracking task (A) and the pursuit task (B) obtained with the <i>searching method</i>. For each combination of parameters <i>B</i>_<i>r</i> and <i>K</i>_<i>r</i> (both within the physiological ranges), the correlation coefficient <i>R</i> between the muscle torque and the kinematic torque was calculated to generate a contour plot of <i>R</i> for each task. The color codes on the right side of (A) and (B) indicate the mean value of <i>R</i> for the X and Y components. The white dashed lines indicate the combination of <i>B</i>_<i>r</i> and <i>K</i>_<i>r</i> for the best <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios. (C) and (D) Relationship between <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios obtained with CCA (<i>absciss</i>a) and the <i>searching method</i> (<i>ordinate</i>) for the step-tracking task (C) and the pursuit task (D) for the controls (<i>green dots</i>) and the patients (<i>red dots</i>).</p

Omniphobic Polyvinylidene Fluoride (PVDF) Membrane for Desalination of Shale Gas Produced Water by Membrane Distillation

Microporous membranes fabricated from hydrophobic polymers such as polyvinylidene fluoride (PVDF) have been widely used for membrane distillation (MD). However, hydrophobic MD membranes are prone to wetting by low surface tension substances, thereby limiting their use in treating challenging industrial wastewaters, such as shale gas produced water. In this study, we present a facile and scalable approach for the fabrication of omniphobic polyvinylidene fluoride (PVDF) membranes that repel both water and oil. Positive surface charge was imparted to an alkaline-treated PVDF membrane by aminosilane functionalization, which enabled irreversible binding of negatively charged silica nanoparticles (SiNPs) to the membrane through electrostatic attraction. The membrane with grafted SiNPs was then coated with fluoroalkylsilane (perfluorodecyltrichlorosilane) to lower the membrane surface energy. Results from contact angle measurements with mineral oil and surfactant solution demonstrated that overlaying SiNPs with ultralow surface energy significantly enhanced the wetting resistance of the membrane against low surface tension liquids. We also evaluated desalination performance of the modified membrane in direct contact membrane distillation with a synthetic wastewater containing surfactant (sodium dodecyl sulfate) and mineral oil, as well as with shale gas produced water. The omniphobic membrane exhibited a stable MD performance, demonstrating its potential application for desalination of challenging industrial wastewaters containing diverse low surface tension contaminants

Similarities between the agonist muscle activity and wrist position in the step-tracking task.

<p>(A1) Comparison of agonist muscle activity (<i>Agonist activity</i>), displacement (<i>Displ</i>), and tangential velocity (<i>Vel</i>) of the wrist joint for the eight directions for the control subject shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g004" target="_blank">Fig 4A</a>. Note the similarity of <i>Agonist</i> to <i>Displ</i>, regardless of the movement direction. (A2) Correlation coefficient (<i>R</i> values) for <i>Agonist</i> (<i>Ago</i>) and <i>Displ</i> or <i>Ago</i> and <i>Vel</i> for all of the control subjects. For the directions with two agonists, we calculated the mean <i>R</i> for the two muscles. The data from 10 control subjects were averaged. For the abbreviations of direction and muscle name, see Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g002" target="_blank">2</a>, respectively. (B1) The same convention as in (A1) for the data from the patient shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g005" target="_blank">Fig 5A</a>. (B2) The same convention as in (A2) for the data from the 19 patients with cerebellar ataxia.</p

Identification of the relationship between muscle activities and movement kinematics for a patient with cerebellar ataxia.

<p>(A) An example of the step-tracking task. (B) An example of the pursuit task. For both (A) and (B), the conventions are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g004" target="_blank">Fig 4</a>. The bottom panels demonstrate the kinematic torques (blue) and the muscle torques (red). The upper panel (<i>Acceleration term</i> (+)) shows the estimated torques that were calculated with Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.e001" target="_blank">1</a>) that includes the acceleration term. The lower panel (<i>Acceleration term</i> (-)) shows the estimated torque that was calculated with Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.e008" target="_blank">2</a>) that excludes the acceleration term. The high <i>CC’s</i> for the two estimated torques in both (A) and (B) indicate the similarities of the two estimates in the patient. Note that the estimated torques without the acceleration term (blue lines in the lower panel) are almost identical to the estimated torques with the acceleration term (blue lines in the upper panel), suggesting a minor contribution of the acceleration term in the identification in the patient. The parameters <i>M</i>_<i>r</i>, <i>B</i>_<i>r</i>, and <i>K</i>_<i>r</i> for the fit in (A) were set as 0.0014, 0.023, and 0.082, respectively, while the same parameters for the fit in (B) were set as 0.0014, 0.030, and 0.114, respectively. The damping ratio ζ for the step-tracking task (A) was 1.054, while the ζ for the pursuit task (B) was 1.198.</p

Experimental tasks.

<p>(A) Arrangement of targets and required movement of a cursor for the step-tracking task. The target was displayed as an open circle with an internal diameter of 4.5° of wrist movement. The cursor was a black point (ϕ = 0.9°) that moved in proportion to the subject's wrist movements. The position of the cursor in the center target corresponded to the center of the screen, and the cursor moved left for flexion movements, right for extension movements, up for radial deviation, and down for ulnar deviations of the wrist. The subjects were required to move the cursor from the center target to the new target as rapidly and accurately as possible. UP, up; UR, up and right; RT, right; DR, down and right; DN, down; DL, down and left; LF, left; UL, up and left; Rad, radial deviation; Ext, extension; Uln, ulnar deviation; Flx, flexion. (B) The pursuit wrist task. The subject was instructed to maintain the position of the cursor within the target moving along the path of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g002" target="_blank">Fig 2</a> at a constant speed (6.2°/s).</p

An alternative way to estimate the optimal combination of fitting parameters <i>B</i>_<i>r</i> and <i>K</i>_<i>r</i> for the wrist joint model (1): <i>searching method</i>.

<p>First, we calculated the inertia parameter <i>M</i> for each subject using the Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.e008" target="_blank">2</a>) and chose a combination of <i>B</i>_<i>r</i> and <i>K</i>_<i>r</i> within the physiological ranges (<i>upper right inset</i>) to obtain <i>kinematic torque</i> (<i>lower right</i>). The average inertia parameter (<i>M</i>) for the controls and the patients (n = 29) was 0.0017 ± 0.00036 kgm² (range: 0.0012–0.0023 kgm²). Note that contribution of the acceleration term to the <i>kinematic torque</i> was almost negligible (see <i>flat line</i>). Then we searched for the optimal combination of <i>a</i>_<i>1</i><i>-a</i>_<i>4</i> that maximized the <i>R-</i>value between the <i>muscle torque</i> and the <i>kinematic torque</i> obtained above (<i>lower right</i>). For the regression, we set signs (+ or-) of <i>a</i>_<i>1</i>-<i>a</i>_<i>4</i> (<i>lower left</i>) in order to limit pulling directions of the four muscles [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.ref015" target="_blank">15</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.ref024" target="_blank">24</a>]. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g007" target="_blank">Fig 7A or 7B</a> for examples of this method. Note the correlation <i>R</i> (= 0.93) is almost identical to <i>CC</i> (= 0.94) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.g003" target="_blank">Fig 3</a>.</p

Comparison of the <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios for the two tasks between the controls (A) and the patients (B).

<p>(A) <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios of the control subjects for the step-tracking task (top) and the pursuit task (bottom) (n = 10). Note the highly significant difference in the <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios for the two tasks. (B) The <i>B</i>_<i>r</i><i>/K</i>_<i>r</i> ratios of the patients (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132983#pone.0132983.t002" target="_blank">Table 2</a>, n = 19) for the two tasks (the same convention as in (A)).</p

Characteristics of the control subjects.

<p>Characteristics of the control subjects.</p