Mehrabani H et al. (2014), Bio-inspired design of ice-retardant devices ba...

ECB-ART-43647

PeerJ 2014 Jan 01;2:e588. doi: 10.7717/peerj.588.

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Bio-inspired design of ice-retardant devices based on benthic marine invertebrates: the effect of surface texture.

Mehrabani H , Ray N , Tse K , Evangelista D .

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Growth of ice on surfaces poses a challenge for both organisms and for devices that come into contact with liquids below the freezing point. Resistance of some organisms to ice formation and growth, either in subtidal environments (e.g., Antarctic anchor ice), or in environments with moisture and cold air (e.g., plants, intertidal) begs examination of how this is accomplished. Several factors may be important in promoting or mitigating ice formation. As a start, here we examine the effect of surface texture alone. We tested four candidate surfaces, inspired by hard-shelled marine invertebrates and constructed using a three-dimensional printing process. We examined sub-polar marine organisms to develop sample textures and screened them for ice formation and accretion in submerged conditions using previous methods for comparison to data for Antarctic organisms. The sub-polar organisms tested were all found to form ice readily. We also screened artificial 3-D printed samples using the same previous methods, and developed a new test to examine ice formation from surface droplets as might be encountered in environments with moist, cold air. Despite limitations inherent to our techniques, it appears surface texture plays only a small role in delaying the onset of ice formation: a stripe feature (corresponding to patterning found on valves of blue mussels, Mytilus edulis, or on the spines of the Antarctic sea urchin Sterechinus neumayeri) slowed ice formation an average of 25% compared to a grid feature (corresponding to patterning found on sub-polar butterclams, Saxidomas nuttalli). The geometric dimensions of the features have only a small (∼6%) effect on ice formation. Surface texture affects ice formation, but does not explain by itself the large variation in ice formation and species-specific ice resistance observed in other work. This suggests future examination of other factors, such as material elastic properties and surface coatings, and their interaction with surface pattern.

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Genes referenced: LOC100887844 LOC100893907 LOC115925415

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	Figure 1. Biological inspiration and surface fabrication.(A)–(B) Saxidomas nuttalli and grid texture; (C)–(D) Crassostra gigas and valley texture, similar to the Antarctic scallop Adamussium colbecki; (E)–(F) Pisaster ochraceus (Pavlov, 2011) and cone texture, similar to the Antarctic starfish Odontaster validus; (G)–(H) Mytilus edulis and striped texture, similar to the patterning on spines of the Antarctic urchin Sterechinus neumayeri. Photos scaled to approximately 3 cm width.
	Figure 2. Ice formation test and droplet test.(A) Submerged ice formation test from Denny et al. (2011). Plates were placed in 250 ml beakers and watched for ice formation in a −20 °C walk-in freezer. (B) Droplet test to test for ice formation in cold air (intertidal or terrestrial case). Plates and controls were randomly arranged on a tray in the same −20 °C freezer. Droplet freezing was identified by color shift from red (middle arrow; contrast agent red food coloring added) to white (upper arrow). Plates 0.03 m square. (C) Textures and control used during droplet test. Feature width and height varied between 0.5–4 mm and 0.25–1 mm respectively.
	Figure 3. Ice formation test of sub-polar organisms (Saxidomas nuttalli, Crassostrea gigas, Pisaster ochraceus, and Mytilus edulis, dark gray) and artificial seawater control compared to Antarctic data and McMurdo seawater control reproduced from Denny et al., 2011, figure 3, light gray).All sub-polar samples tested here initiated ice formation prior to completion of the ice formation test (n = 3 replicates, with 5 beakers in each replicate). Bars indicate mean ± 2 s.e.
	Figure 4. Ice formation test (see Fig. 2A) of sample plates, time to initial ice formation on samples (mean ± 2 s.e.), n = 13 sample plates for each texture.Differences between textures are significant (ANOVA, P = 0.019); light grey lines indicate groups from post-hoc Tukey analysis. Artificial seawater controls in empty beakers did not exhibit submerged ice formation.
	Figure 5. Droplet test (see Figs. 2B–2C), freeze time normalized to mean of flat plate controls versus (A) feature spacing (n = 45 droplets) and (B) feature height (n = 40 droplets for grid, n = 45 for stripes), examining sensitivity to surface parameters for two different textures.Grid texture in dark grey; stripes texture in light grey as in Fig. 4. Maxima indicated by * for stripes and ** for grid; normalized freeze time does depend on surface parameters (ANOVA, P = 0.0004 for spacing, P = 0.0389 for height), but increases in freeze time are small relative to the noise in the measurement. Bars indicate mean ± 2 s.e.; for flat plate controls, the mean freeze time was 834 s.

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