The RIfS
principle detects changes of the optical thickness nd of the sensitive layer whereby in most
cases the changes of the thickness d
are dominant [260]. As Makrolon is a hard polycarbonate with
a high glass transition temperature, mainly the refractive index n
and not the thickness d changes
during exposure to analyte resulting in bad signal to noise ratios for the measurements
using the 2 RIfS setups (see figure 69). Although
the signal to noise ratio can be improved by the use of thicker sensitive layers
and longer times of exposure to analyte (for the bigger molecules), both approaches
drastically increase the time needed for measurements and between measurements
(see figure 25), which is not desired
for sensor applications. Additionally, the thickness of the sensitive layer
of the 4l setup can be varied
only within certain limits [261].
Therefore, it is investigated if the reduction of noise by the use of a smoothing
technique is beneficial for the calibration. In figure
69, the sensor signals of the 80 nm Makrolon layer are shown for 1-propanol
and for methanol before and after the application of an FFT filter for smoothing.
It is visible that 1-propanol has the poorest signal to noise ratio, as not
all micropores are occupied by the analyte in contrast to methanol. Thus, the
sensor signals for propanol benefit most from smoothing. On the other hand,
the sensor response of methanol shows a counterproductive effect of smoothing.
The rectangular sensor signal of methanol before smoothing changes into a rounder
sensor profile after smoothing whereas the shape of the wave-like sensor signal
of 1-propanol is practically not affected by smoothing. This means that the
shapes of the sensor responses of the different analytes are made more similar
by smoothing. Thus, the quantification of the analytes is rendered more difficult
as the quantification is based on the differences of the shapes. To investigate
the effects of smoothing the data are evaluated separately with and without
smoothing. Additionally, the effect of smoothing for sensitive layers with a
different thickness is investigated as the thickness influences the signal to
noise ratio.
figure 69: Sensor responses before
and after filtering with an FFT-Filter for the 80 nm layer.
Ph. D. Thesis
