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Particle capture via discrete binding elements: systematic variations in binding energy for randomly distributed

Surachate Kalasin1, Surangkhana Martwiset, E Bryan Coughlin

  • 1Department of Polymer Science and Engineering, University of Massachusetts, 120 Governor's Drive, Amherst, Massachusetts 01003, United States.

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Stronger surface "stickers" (adhesive functionalities) significantly increase particle adhesion, even when total adhesive energy per area is fixed. Discretized functionalities are more adhesive than uniformly distributed ones.

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Area of Science:

  • Surface science
  • Adhesion science
  • Colloid science

Background:

  • Surface interactions govern adhesion between materials.
  • Uniformly distributed adhesive forces are often modeled using mean-field approaches.
  • Heterogeneous surfaces present complex adhesion behaviors.

Purpose of the Study:

  • To investigate how binding strength of surface-immobilized "stickers" affects adhesion between repulsive surfaces.
  • To quantify the impact of redistributing adhesive functionality into larger clusters on adhesion probability.
  • To explore the transition from non-adhesive to adhesive states by increasing surface heterogeneity.

Main Methods:

  • Utilizing an electrostatic model system with negatively charged silica spheres and flats.
  • Introducing nanoscale cationic patches on the silica flats to create local attractions.
  • Analyzing particle capture curves and comparing experimental data with a simple adhesion model.

Main Results:

  • Increasing sticker strength, while keeping average adhesive energy per area constant, enhances adhesion.
  • Adhesion signatures characteristic of a renormalized random distribution were observed.
  • Experimental results showed good quantitative agreement with a model assuming a fixed adhesion energy for capture.

Conclusions:

  • Heterogeneous surface functionalities can dramatically increase adhesion, transitioning systems from non-adhesive to strongly adhesive.
  • Discretized adhesive functionalities are more effective for adhesion than uniformly distributed ones.
  • A simple formalism for predicting particle adhesion based on sticker strength and distribution was developed.