Force‐Based Wetting Characterization of Stochastic Superhydrophobic Coatings at Nanonewton Sensitivity
Source abstract
Abstract
Abstract Superhydrophobic coatings have extraordinary properties like self‐cleaning and staying dry, and have recently appeared on industrial and consumer markets. The stochastic nature of the coating components and coating processes (e.g., spraying, painting) affects the uniformity of the water repellency across the coated substrate. The wetting properties of those coatings are typically quantified on macroscale using contact angle goniometry (CAG). Here, highly sensitive force‐based methods, scanning droplet adhesion microscopy (SDAM), and micropipette force sensor (MFS), are used, to quantify the microscale heterogeneity in the wetting properties of stochastic superhydrophobic coatings with irregular surface topography that cannot be investigated by CAG. By mapping the wetting adhesion forces with SDAM and friction forces with MFS, it is demonstrated that even the best coatings on the market are prone to heterogeneities that induce stick–slip motion of droplets. Thus, owing to their high spatial and force resolution, the advantages of these techniques over CAG are demonstrated.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Surface Modification and Superhydrophobicity · Adhesion, Friction, and Surface Interactions · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Applied Physics Aalto University School of Science Puumiehenkuja 2, 02150 Espoo, P.O. Box 15100 Aalto FI‐00076 Finland
Department of Electrical Engineering and Automation Aalto University School of Electrical Engineering P.O. Box 15500 Aalto FI‐00076 Finland
Department of Bioproducts and Biosystems Aalto University School of Chemical Engineering P.O. Box 16000 Aalto FI‐00076 Finland
Funding
European Research Council · Academy of Finland
References (34)
Show source-provided references
1. 10.1021/la803860d10.1021/la803860d ↗
2. 10.1039/C6TA04420D10.1039/C6TA04420D ↗
3. 10.1016/j.coldregions.2011.02.00610.1016/j.coldregions.2011.02.006 ↗
4. 10.1002/adma.20110441210.1002/adma.201104412 ↗
5. 10.1039/C3NR06644D10.1039/C3NR06644D ↗
6. 10.1002/adma.20060194610.1002/adma.200601946 ↗
7. 10.1002/smll.20140051610.1002/smll.201400516 ↗
8. 10.1038/nature1274010.1038/nature12740 ↗
9. 10.1126/science.123377510.1126/science.1233775 ↗
10. 10.1051/epn/201750510.1051/epn/2017505 ↗
11. 10.1073/pnas.181997911610.1073/pnas.1819979116 ↗
12. 10.1126/sciadv.aba519710.1126/sciadv.aba5197 ↗
13. 10.1021/la000598o10.1021/la000598o ↗
14. 10.1039/B712575P10.1039/B712575P ↗
15. 10.1088/0034-4885/78/8/08650110.1088/0034-4885/78/8/086501 ↗
16. 10.1016/j.cis.2011.08.00510.1016/j.cis.2011.08.005 ↗
17. 10.1007/s11998-017-0011-x10.1007/s11998-017-0011-x ↗
18. 10.3390/coatings904027010.3390/coatings9040270 ↗
19. 10.1038/s41596-018-0003-z10.1038/s41596-018-0003-z ↗
20. 10.1021/la203120810.1021/la2031208 ↗
21. 10.1016/j.cocis.2014.04.00910.1016/j.cocis.2014.04.009 ↗
22. 10.1126/science.aav538810.1126/science.aav5388 ↗
23. 10.1038/s41467-017-01510-710.1038/s41467-017-01510-7 ↗
24. 10.1038/s41596-018-0110-x10.1038/s41596-018-0110-x ↗
25. 10.1038/s43246-020-00065-310.1038/s43246-020-00065-3 ↗
26. 10.1002/adma.20180389010.1002/adma.201803890 ↗
27. 10.1021/la026416310.1021/la0264163 ↗
28. 10.1021/la304106710.1021/la3041067 ↗
29. 10.1038/nphys430510.1038/nphys4305 ↗
30. 10.1103/PhysRevLett.120.24450310.1103/PhysRevLett.120.244503 ↗
31. 10.1039/C9SM01221D10.1039/C9SM01221D ↗
32. 10.1006/jcis.1995.113010.1006/jcis.1995.1130 ↗
33. 10.1016/j.jcis.2006.02.01810.1016/j.jcis.2006.02.018 ↗
34. 10.1021/jp203246610.1021/jp2032466 ↗