Biomedical Engineering Reference
In-Depth Information
Other modeling attempts also need to be mentioned. One might justifiably argue that
appropriate modeling may be achieved by using a Langmuirian or other approach. The
Langmuirian approach may be used to model the data presented if one assumes the presence
of discrete classes of sites, for example double exponential analysis as compared with the
single-fractal analysis.
Lee and Lee (1995)
report that the fractal approach has been applied
to surface science, for example, adsorption and reaction processes. These authors point out
that the fractal approach provides a convenient means to represent the different structures
and the morphology at the reaction surface. They also draw attention to using the fractal
approach to develop optimal structures and as a predictive approach. Another advantage of
the fractal technique is that the analyte-receptor association is a complex reaction, and the
fractal analysis via the fractal dimension and the rate coefficient provide a useful lumped
parameter analysis of the diffusion-limited reaction occurring on a heterogeneous surface.
In a classical situation, to demonstrate fractality, one should make a log-log plot, and one
should definitely have a large amount of data. It may be useful to compare the fit to some
other forms, such as the exponential form, or one involving saturation, etc. At present, no
independent proof or physical evidence of fractals in the examples is presented. Nevertheless,
we still use fractals and the degree of heterogeneity on the biosensor surface to gain insights
into enhancing the different biosensor performance parameters. The fractal approach is a con-
venient means (since it is a lumped parameter) to make the degree of heterogeneity that exists
on the surface more quantitative. Thus, there is some arbitrariness in the fractal approach to
be presented. The fractal approach provides additional information about interactions that
may not be obtained by a conventional analysis of biosensor data. In this chapter as men-
tioned above, an attempt is made to relate the fractal dimension,
D
f
, or the degree of hetero-
geneity on the biosensor surface with different biosensor performance parameters. More
specifically, the interest is in finding out how changes in the fractal dimension or the degree
of heterogeneity on the biosensor chip surface affect the different biosensor parameters of
interest.
Unless specifically mentioned there is no nonselective adsorption of the analyte. In other
words, nonspecific binding is ignored. Nonselective adsorption would skew the results
obtained very significantly. In these types of systems, it is imperative to minimize this non-
selective adsorption. It is also recognized that, in some cases, this nonselective adsorption
may not be a significant component of the adsorbed material and that the rate of association,
which is of a temporal nature would depend on surface availability.
Wang et al. (2006)
have recently developed a sensitive immunoassay for the biomarker,
TNF-
a
based on a poly(guanine)-functionalized silica NP label. These authors report that
TNF-
a
is an extremely potent peptide cytokine which serves as an endogeneous mediator
of inflammatory, immunodefense, and host defense function (
Old, 1985, 1987; Jones et al.,
1989
).
Wang et al. (2006)
point out that TNF-
a
is involved in a wide variety of pathological
