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Related Concept Videos

Contact Angle01:13

Contact Angle

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When a solid is dipped inside a liquid, the liquid surface becomes curved near the contact. For some solid–liquid interfaces, the liquid is pulled up along the solid, while for others, the liquid surface is convex or depressed near the solid surface. This phenomenon can be explained using the concept of cohesive and adhesive forces.
The adhesive force is the molecular force between molecules of different materials, that is, between the molecules of the solid and the liquid. The cohesive...
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Neural Network Prediction of Micrometer-Scale Equivalent Contact Angle Mapping: From Microforce Measurements to Local

Shiyu Zhang1, Lingzhe Zhao1, Lingkun Han1

  • 1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

ACS Applied Materials & Interfaces
|October 20, 2025
PubMed
Summary

Researchers developed a new method combining adhesion force measurements and neural networks to map microscopic wettability variations on superhydrophobic surfaces. This technique provides a detailed understanding of surface properties at the micrometer scale.

Keywords:
equivalent contact anglemicrometer scaleneural networkssuperhydrophobic surfaceswettability

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Area of Science:

  • Materials Science
  • Surface Science
  • Nanotechnology

Background:

  • Superhydrophobic surfaces exhibit microscopic structural heterogeneity.
  • This heterogeneity can cause variations in wettability at the micrometer scale.
  • Traditional methods lack the resolution to characterize these microscopic variations.

Purpose of the Study:

  • To develop a high-resolution method for assessing wettability variations on superhydrophobic surfaces.
  • To create a micrometer-scale equivalent contact angle distribution map.
  • To validate the universality of the proposed method across different microstructures.

Main Methods:

  • Utilized the droplet cantilever probe technique to measure interaction forces between droplets and superhydrophobic surfaces.
  • Developed and trained a neural network model using experimental adhesion force data.
  • Employed a force-neural network fusion method (F-NNFM) for quantitative wettability assessment.

Main Results:

  • Generated micrometer-scale equivalent contact angle distribution maps with a spatial resolution of 5 μm.
  • Achieved accurate nonlinear mapping between adhesion force and macroscopic contact angle (R > 0.9, errors <5°).
  • Demonstrated the method's effectiveness on surfaces with varying microstructures.

Conclusions:

  • The F-NNFM accurately reveals microscopic wettability variations and states on superhydrophobic surfaces.
  • This approach extends macroscopic wetting state characterization to the microscopic scale.
  • The developed method offers a significant advancement over traditional wettability characterization techniques.