Effects of Varying Surface Film Thickness on Particle Adhesion

Tuesday, November 9, 2010: 12:30 PM
250 F Room (Salt Palace Convention Center)
Katie M. Smith, Chemical Engineering, Purdue University, West Lafayette, IN, Ravi P. Jaiswal, Chemical Engineering, Purdue University, W. Lafayette, IN and Stephen Beaudoin, Chemical Engineering, Purdue Univesity, West Lafayette, IN

The effects of surface film (coating) thickness, d, on the adhesion of a particle to a surface are investigated.  The model system in this work includes a pre-manufactured silicon nitride probe (initial radius of curvature ~15 nm) and a silicon substrate onto which gallium nitride films have been deposited via atomic layer deposition.  The gallium nitride films have varying thicknesses (0, 5, 11, 12, 16, and 80 nm).  This system is well approximated by the idealized sphere-flat plate geometry.

We develop theory supporting the hypothesis that the coating material will appear transparent to the particle if d is small in comparison to the particle-substrate separation distance, D However, as d increases a transition in the particle-substrate interaction will take place.  This implies the existence of a critical coating thickness, d*, beyond which the adhesion behavior of the coating material will be equivalent to bulk.  We show that d* is a simple function of D and a parameter termed the permissible fractional error, ε.  Assuming D = 0.4 nm at contact, for ε = 0.05 (i.e. 5% permissible fractional error), d* ≈ 28 nm.

The adhesion between the model system particle and substrate is measured using contact mode atomic force microscopy.  Experimental results confirm that for small (i.e. 5, 11, and 12 nm) d, the interaction between the silicon nitride probe and underlying silicon is relatively unaffected by the presence of the gallium nitride film; for large (i.e. 80 nm) d, the interaction parallels that between silicon nitride and bulk (≥ 5 Ám) gallium nitride.  The d* for this model system is approximately 30 nm, which is in good agreement with our theoretical predictions.

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