ArticleAdvanced materials (Deerfield Beach, Fla.)2021
Force-Based Wetting Characterization of Stochastic Superhydrophobic Coatings at Nanonewton Sensitivity.
Article in Advanced materials (Deerfield Beach, Fla.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Unlocking the Full Potential of Jumping Condensation on Microstructured Surfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Review
- Droplet Friction on Superhydrophobic Surfaces Scales With Liquid-Solid Contact Fraction.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Determination of solid-liquid adhesion work on flat surfaces in a direct and absolute manner.Scientific reports · 2024Article
- Toward vanishing droplet friction on repellent surfaces.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Droplet slipperiness despite surface heterogeneity at molecular scale.Nature chemistry · 2024Article
- Imparting scalephobicity with rational microtexturing of soft materials.Science advances · 2023Article
- Fluorine-Free Super-Liquid-Repellent Surfaces: Pushing the Limits of PDMS.Nano letters · 2023Article
- A Thermoregulatory Flexible Phase Change Nonwoven for All-Season High-Efficiency Wearable Thermal Management.Nano-micro letters · 2023Article
- Advances in the Fabrication and Characterization of Superhydrophobic Surfaces Inspired by the Lotus Leaf.Biomimetics (Basel, Switzerland) · 2022Review
- Force-Based Wetting Characterization of Stochastic Superhydrophobic Coatings at Nanonewton Sensitivity.Advanced materials (Deerfield Beach, Fla.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.