Zwitterionic nanofibers of super-glue for transparent and biocompatible multi-purpose coatings

Here we show that macrozwitterions of poly(ethyl 2-cyanoacrylate), commonly called Super Glue, can easily assemble into long and well defined fibers by electrospinning. The resulting fibrous networks are thermally treated on glass in order to create transparent coatings whose superficial morphology recalls the organization of the initial electrospun mats. These textured coatings are characterized by low liquid adhesion and anti-staining performance. Furthermore, the low friction coefficient and excellent scratch resistance make them attractive as solid lubricants. The inherent texture of the coatings positively affects their biocompatibility. In fact, they are able to promote the proliferation and differentiation of myoblast stem cells. Optically-transparent and biocompatible coatings that simultaneously possess characteristics of low water contact angle hysteresis, low friction and mechanical robustness can find application in a wide range of technological sectors, from the construction and automotive industries to electronic and biomedical devices

Expert-guided optimization for 3D printing of soft and liquid materials

<div><p>Additive manufacturing (AM) has rapidly emerged as a disruptive technology to build mechanical parts, enabling increased design complexity, low-cost customization and an ever-increasing range of materials. Yet these capabilities have also created an immense challenge in optimizing the large number of process parameters in order achieve a high-performance part. This is especially true for AM of soft, deformable materials and for liquid-like resins that require experimental printing methods. Here, we developed an expert-guided optimization (EGO) strategy to provide structure in exploring and improving the 3D printing of liquid polydimethylsiloxane (PDMS) elastomer resin. EGO uses three steps, starting first with expert screening to select the parameter space, factors, and factor levels. Second is a hill-climbing algorithm to search the parameter space defined by the expert for the best set of parameters. Third is expert decision making to try new factors or a new parameter space to improve on the best current solution. We applied the algorithm to two calibration objects, a hollow cylinder and a five-sided hollow cube that were evaluated based on a multi-factor scoring system. The optimum print settings were then used to print complex PDMS and epoxy 3D objects, including a twisted vase, water drop, toe, and ear, at a level of detail and fidelity previously not obtained.</p></div

The expert-guided optimization (EGO) strategy.

<p>EGO has three steps: (i) Expert screening–identification by the expert of a parameter space (e.g., print parameter space), factors (e.g., A = print speed) and factor levels (e.g., A₁ = 5 mm/s, A₂ = 10 mm/s, A₃ = 15 mm/s, A₄ = 25 mm/s, A₅ = 30 mm/s) that matter for print optimization. (ii) Hill-climbing algorithm–includes a stochastic stage and a hill climb stage. The stochastic stage consists of 21 prints made by taking random combinations of factors-levels within the selected parameter space. The hill climb stage uses the highest scoring print (fittest) from the stochastic stage as lead run (parent) for iterative adjustments, seeking a stepwise climb toward the optimum. Hill climbs involve adjustments to one factor-level at a time, to a level above and in a subsequent run, to a level below the parent while the other factors are kept constant at the parent value. (iii) Expert decision-making–changes by the expert if the fittest run from the hill climb does not exceed the parent score. The changes can be to the parameter space (e.g., physical parameters) and/or factors (e.g., a = bath concentration) and/or factor levels (e.g., a₁ = 0.1 w/v%, A₂ = 0.4 w/v%, A₃ = 0.5 w/v%, A₄ = 1 w/v%, A₅ = 2 w/v%). The search stops after reaching a full score. The expert may also choose to terminate the optimization if the search is inconclusive after looping between EGO steps (i) and (ii) in attempts to escape local maxima or if the outcome is satisfactory. In the hypothetical example, the stochastic run is performed for four different print parameters (e.g., A, B, C, and D) followed by two generations of hill climb that end in the fittest factor levels for the print parameter space being from run 28 (A₃, B₁, C₃, D₂). These print parameters are then held constant and third and fourth generation hill climb are performed on the physical parameters (e.g., a, b, c, and d), resulting in the fittest factor levels for the physical parameter space from run 44 (a₅, b₃, c₂, d₃).</p

Characterization of the cylinder optimization conditions and resulting mechanical properties.

<p><b>(A)</b> Summary of the EGO strategy applied to optimize the cylinder showing the highest score from each generation and the target score of 30. The cylinder optimized after four generations of hill climb. <b>(B)</b> PDMS 3D printed using the EGO optimum scaled-up to five different sizes. The cylinder used throughout the EGO strategy is the second cylinder from the right. Scale bar is 1 cm. <b>(C)</b> Shear modulus as a function of frequency across the 1% w/v (starting) and 0.2% w/v (optimum) Carbopol 940 bath concentrations. The baths are dominantly viscoelastic solids with the storage (G’) modulus larger than the loss modulus (G”). <b>(D)</b> Shear stress versus shear rate at 1% w/v (starting) and 0.2% w/v (optimum) Carbopol 940 bath concentration with a yield stress of 140 Pa and 70 Pa yield stress, respectively. The yield stress is the shear stress at ~4.5 s^-1 shear rate (the y-intercept for the linear portion of the shear stress versus shear rate curve). <b>(E)</b> Representative stress-strain curve showing the 3D printed PDMS strip subject to uniaxial tensile tests with the inset displaying the region that is linearly elastic, y = 1.2x + 0.0042 (R² = 0.996), up to 10% strain. <b>(F)</b> The mean elastic modulus was 1.7 ± 0.1 MPa (SD) for monolithic PDMS (n = 6) made by casting in a previous study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194890#pone.0194890.ref017" target="_blank">17</a>], 1.2 ± 0.1 MPa (SD) for 3D printed using the EGO found optimum (n = 5), and 0.95 ± 0.2 MPa (SD) for 3D printed (n = 8) in a previous study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194890#pone.0194890.ref006" target="_blank">6</a>]. Both cast PDMS and 3D printed PDMS have the same base-to-catalyst ratio of 10:1. <b>(G)</b> Representative images of 3D printed PDMS across uniaxial tensile strains up to 130% elongation at break.</p

Parameter spaces for 3D printing soft materials using FRE.

<p>Five parameter spaces determine the 3D print outcome: physical parameters, printer hardware, model design, model geometry, and print parameters. The model design, model geometry, and print parameters are inputs used by the slicer algorithm to determine the extruder toolpath and material deposition rate. Two of the five parameter spaces (print parameters, physical parameters) are considered throughout the EGO strategy. The printer hardware and model properties (design and geometry) are relatively time intensive to alter quickly and are often preset as design criteria.</p

Applying the EGO strategy to 3D printing of calibration cylinders.

<p><b>(A)</b> Overview of the steps to optimize the cylinder (CAD model) that is imported in to the slicing software to determine the path of the extruder (slicer toolpath). The resulting product (initial 3D print) is assessed (score with rubric) and the fittest run is used as lead in the hill climb (use lead score and EGO) to reach full score (optimized 3D print). <b>(B)</b> Summary of the second step, hill climb, of the EGO strategy for calibration cylinders detailing the total number of runs and the lead run in each within the designated parameter space. <b>(C)</b> Representative images of PDMS 3D prints in each generation with the score for each response variables (layer fusion = F, infill = I, stringiness = S) and the total (lowest, highest, in between). Scale bars are 5 mm.</p

Using the EGO found optimum to 3D print epoxy and PDMS in complex geometries.

<p><b>(A)</b> The viscosity of both PDMS (~3 Pa·s) and epoxy (~5.3 Pa·s) is constant as a function of shear rate. <b>(B)</b> The linear increase of shear stress as a function of shear rate confirms that before the crosslinking step, both PDMS and epoxy inks are Newtonian fluids. <b>(C)</b> CAD models of the different geometries used for 3D printing with both experimental (PDMS, epoxy) and standard (PLA) materials. <b>(D)</b> Representative image of a twisted vase (15 mm × 33 mm, W×H) 3D prints with standard PLA (left), epoxy (middle), and PDMS (right). <b>(E)</b> Representative image of a water drop vase (17 mm × 52 mm, W×H) 3D prints with standard PLA (left), epoxy (middle), and PDMS (right). <b>(F)</b> Representative image of a life-size toe (25 mm × 44 mm, W×H) 3D prints with standard PLA (left), epoxy (middle), and PDMS (right). <b>(G)</b> Representative image of an ear (20 × 35 mm, W×H) 3D prints with standard PLA (left), epoxy (middle), and PDMS (right). <b>(H)</b> Representative color maps of the water drop vase highlighting the deviation of PLA, epoxy, and PDMS 3D prints from the CAD model. <b>(I)</b> Surface area of the CAD model versus the PLA control, epoxy and PDMS for each 3D printed structure (n = 3) shown in D–G.</p

Environmentally Benign Production of Stretchable and Robust Superhydrophobic Silicone Monoliths

Superhydrophobic materials hold an enormous potential in sectors as important as aerospace, food industries, or biomedicine. Despite this great promise, the lack of environmentally friendly production methods and limited robustness remain the two most pertinent barriers to the scalability, large-area production, and widespread use of superhydrophobic materials. In this work, highly robust superhydrophobic silicone monoliths are produced through a scalable and environmentally friendly emulsion technique. It is first found that stable and surfactantless water-in-polydimethylsiloxane (PDMS) emulsions can be formed through mechanical mixing. Increasing the internal phase fraction of the precursor emulsion is found to increase porosity and microtexture of the final monoliths, rendering them superhydrophobic. Silica nanoparticles can also be dispersed in the aqueous internal phase to create micro/nanotextured monoliths, giving further improvements in superhydrophobicity. Due to the elastomeric nature of PDMS, superhydrophobicity can be maintained even while the material is mechanically strained or compressed. In addition, because of their self-similarity, the monoliths show outstanding robustness to knife-scratch, tape-peel, and finger-wipe tests, as well as rigorous sandpaper abrasion. Superhydrophobicity was also unchanged when exposed to adverse environmental conditions including corrosive solutions, UV light, extreme temperatures, and high-energy droplet impact. Finally, important properties for eventual adoption in real-world applications including self-cleaning, stain-repellence, and blood-repellence are demonstrated

Superhydrophobic Nanocomposite Surface Topography and Ice Adhesion

A method to reduce the surface roughness of a spray-casted polyurethane/silica/fluoroacrylic superhydrophobic nanocomposite coating was demonstrated. By changing the main slurry carrier fluid, fluoropolymer medium, surface pretreatment, and spray parameters, we achieved arithmetic surface roughness values of 8.7, 2.7, and 1.6 μm on three test surfaces. The three surfaces displayed superhydrophobic performance with modest variations in skewness and kurtosis. The arithmetic roughness level of 1.6 μm is the smoothest superhydrophobic surface yet produced with these spray-based techniques. These three nanocomposite surfaces, along with a polished aluminum surface, were impacted with a supercooled water spray in icing conditions, and after ice accretion occurred, each was subjected to a pressurized tensile test to measure ice-adhesion. All three superhydrophobic surfaces showed lower ice adhesion than that of the polished aluminum surface. Interestingly, the intermediate roughness surface yielded the best performance, which suggests that high kurtosis and shorter autocorrelation lengths improve performance. The most ice-phobic nanocomposite showed a 60% reduction in ice-adhesion strength when compared to polished aluminum

Additional file 1: Table S1. of Exome sequencing reveals independent SGCD deletions causing limb girdle muscular dystrophy in Boston terriers

Primers used to define and genotype SGCD mutations. (DOCX 12 kb