Machine Learning Prediction of TiO2-Coating Wettability Tuned via UV Exposure-Reference-Cited by-同舟云学术

Machine Learning Prediction of TiO2-Coating Wettability Tuned via UV Exposure

Published:2021-09-15 Issue:38 Volume:13 Page:46171-46179
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Jafari Gukeh Mohamad¹,Moitra Shashwata¹,Ibrahim Ali Noaman¹²,Derrible Sybil³,Megaridis Constantine M.¹^ORCID

Affiliation:

1. Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States

2. Mechanical Engineering College, University of Babylon, Hilla 51001, Iraq

3. Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.1c13262

Reference55 articles.

1. De Gennes, P.G.; Brochard-Wyart, F.; Quéré, D. Capillarity and Wetting Phenomena: Drops, Bubbles, Pearls, Waves; Springer Science & Business Media, 2013; pp 15–181.

2. Scaling Laws in Directional Spreading of Droplets on Wettability-Confined Diverging Tracks

3. Wettability patterning for high-rate, pumpless fluid transport on open, non-planar microfluidic platforms

4. Inkjet patterned superhydrophobic paper for open-air surface microfluidic devices

5. Experimental investigation of a vapor chamber featuring wettability-patterned surfaces

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