Degree Type

Dissertation

Date of Award

2007

Degree Name

Doctor of Philosophy

Department

Mechanical Engineering

First Advisor

Sriram Sundararajan

Second Advisor

Gary Tuttle

Third Advisor

Pranav Shrotriya

Abstract

Surfaces of materials strongly affect functional properties such as mechanical, biological, optical, acoustic and electronic properties of materials, particularly at the micro/nano scale. Surface effects stem from the interplay of surface morphology and surface chemical properties. This dissertation focuses on (1) modeling the effect of surface roughness parameters on solid-solid contact and solid-liquid interaction as well as; (2) developing a surface engineering method that can generate random surfaces with desired amplitude and spatial roughness parameters for tribological and biomimetic applications.;Autocorrelation length (ACL) is a surface roughness parameter that provides spatial information of surface topography that is not included in amplitude parameters such as root-mean-square roughness. A relationship between ACL and the friction behavior of a rough surface was developed. The probability density function of peaks and the mean peak height of a profile were given as functions of its ACL. These results were used to estimate the number of contact points when a rough surface comes into contact with a flat surface, and it was shown that the larger the ACL of the rough surface, the less the number of contact points. Based on Hertzian contact mechanics, it was shown that the real area of contact increases with increasing of number of contact points. Results from microscale friction experiments (where friction force is proportional to real area of contact) on polished and etched silicon surfaces are presented to verify the analysis.;A versatile surface processing method based on electrostatic deposition of particles and subsequent dry etching was shown to be able to independently tailor the amplitude and spatial roughness parameters of the resulting surfaces. Statistical models were developed to connect process variables to the amplitude roughness parameters center line average, root mean square and the spatial parameter, autocorrelation length of the final surfaces. Process variables include particle coverage, which affected both amplitude and spatial roughness parameters, particle size, which affected only spatial parameters and etch depth, which affects only amplitude parameters. The autocorrelation length of the final surface closely followed a power law decay with particle coverage, the most significant processing parameter. Center line average, root mean square followed a nonlinear relation with particle coverage and particle size. Experimental results on silicon substrates agreed reasonably well with model predictions.;This same hybrid surface engineering process was used to demonstrate adhesion and friction reduction. Microscale adhesion and friction tests were conducted on flat (smooth) and processed silicon surfaces with a low elastic modulus thermoplastic rubber (Santoprene) probe that allowed a large enough contact area to observe the feature size effect. Both adhesion and friction force of the processed surfaces were reduced comparing to that of the flat surfaces.;The process is also used to generate superhydrophobic engineering surfaces by mimicking the structure of lotus leaves. Tunable bimodal roughness (in both micro and nano scale) and a thin hydrophobic fluorocarbon film were generated on an engineering material surface by the hybrid process. These surfaces exhibit contact angles with water of more than 160°. A geometric model was developed to related air-trapping ability of hydrophobic surfaces with hillock features to process variables (hillock diameter, etching depth and coverage) and contact angle. The model is shown to be able to predict minimum coverage of hillocks required for air-trapping on hydrophobic rough surfaces. The model predictions agree with experimental observations reasonably well. This model can particularly be extended to utilizing statistical roughness parameters to predict air-trapping for rough hydrophobic surfaces.

DOI

https://doi.org/10.31274/rtd-180813-17133

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Yilei Zhang

Language

en

Proquest ID

AAI3274876

OCLC Number

191805380

ISBN

9780549154679

File Format

application/pdf

File Size

126 pages

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