P. Wojtaszczyk et al., STATISTICAL PROPERTIES OF SURFACES COVERED BY DEPOSITED PARTICLES, The Journal of chemical physics, 103(18), 1995, pp. 8285-8295
The statistical properties of surfaces covered by irreversibly adsorbe
d colloidal particles are studied as a function of the Peclet number (
or equivalently as a function of their rescaled radius R). More preci
sely, the radial distribution function g(r) is determined as a functio
n of the coverage theta for five systems corresponding to different va
lues of R. Also measured is the reduced variance sigma(2)/[n] of the
number n of adsorbed particles on surfaces of given area out of the ad
sorption plane. Finally, the evolution of [n] with the concentration o
f particles in solution during the deposition process is determined fo
r the different systems. This allows us to obtain information on the a
vailable surface function Phi. All these parameters are compared to th
eir expected behavior according to the random sequential adsorption (R
SA) model and to the ballistic model (BM). It is found that the radial
distribution function of the system of particles characterized by R<
1 is well predicted by the RSA model whereas for R>3 the BM can serve
as a good first approximation. On the other hand, one finds surprisin
gly that the available surface function Phi and the reduced variance s
igma(2)/[n] vary with the coverage theta in a similar way for all the
systems over the range of value of R investigated. Their behavior cor
responds, in first approximation, to the expectations from the BM. In
particular, the reduced variance sigma(2)/[n] exhibits a horizontal ta
ngent at low coverage whereas the RSA model predicts an initial slope
of -4. This result is the more intriguing that sigma(2)/[n] is directl
y related to the radial distribution function g(r), which does vary wi
th R. Finally, higher order moments of the distribution of the number
of particles n adsorbed on our surfaces are also determined as a func
tion of the coverage. They behave, within experimental errors, like th
ose of a Gaussian distribution as predicted by the central limit theor
em. (C) 1995 American Institute of Physics.