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The various IMFs between identical molecules of a substance are examples of cohesive forces. The molecules within a liquid are surrounded by other molecules and are attracted equally in all directions by the cohesive forces within the liquid. However, the molecules on the surface of a liquid are attracted only by about one-half as many molecules. Because of the unbalanced molecular attractions on the surface molecules, liquids contract to form a shape that minimizes the number...
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Quantitative surface free energy with micro-colloid probe pairs.

Ehtsham-Ul Haq1, Yongliang Zhang1, Noel O'Dowd2

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This study presents a novel method using atomic force microscopy (AFM) to accurately measure surface free energy (SFE) on microscale surfaces. The technique correlates AFM force measurements with contact angle data, enabling reliable SFE prediction for materials.

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Area of Science:

  • Materials Science
  • Surface Science
  • Nanotechnology

Background:

  • Surface free energy (SFE) is crucial for predicting material adhesion properties.
  • Characterizing SFE at micro/sub-micro scales is challenging due to measurement complexities and varied techniques.
  • Inaccurate adhesion characterization leads to product defects and costly process optimization.

Purpose of the Study:

  • To develop and validate a method for quantitative SFE measurement at the micron scale using atomic force microscopy (AFM).
  • To correlate AFM force-distance measurements with contact angle (CA) data for SFE determination.
  • To assess the effectiveness of silica and polystyrene (PS) colloidal probes for SFE analysis.

Main Methods:

  • Utilized AFM to collect force-distance curves with silica and PS colloidal probes on various surfaces (mica, graphite, silica, silicon, super-hydrophobic silicon).
  • Correlated AFM data with quantitative contact angle measurements and derived SFE values.
  • Employed principal component analysis (PCA) to classify AFM measurements and build a regression model.

Main Results:

  • AFM measurements showed excellent classification when analyzed using principal components (PCs) with both silica and PS probes.
  • A regression model successfully predicted SFE at the micron scale (approx. 1 micron) based on AFM data.
  • As few as ten AFM force-distance curves were sufficient to predict local SFE after model calibration.

Conclusions:

  • The developed AFM-based method provides a reliable approach for quantitative SFE characterization at the microscale.
  • This technique offers a more accessible and accurate alternative to traditional methods for complex surfaces.
  • The findings have significant implications for improving material design and reducing industrial process optimization costs.