Experimental signals do not exist without noise; i.e. noise interferes or corrupts signals in a significant manner and these must be removed from the data in order to proceed with further data analysis. Thus signals from FPI (Fabry-Perot interference) includes Fresnel reflections [112].
The algorithm in this study for FPI is written based on the work in [48, 113–115]. The output signals collected by an OSA need to be denoised first and then demodulated to get the optical path difference. A measured sensor signal arising from a FPI depends upon the source spectrum. To eliminate the unwanted source spectrum modulation, interferograms were “normalized” using the reflection spectrum from a cleaved fibre end-face, taken prior to splicing the sensor to the system (i.e. each sensor spectrum was divided by its source spectrum), for which an example is shown as I1(λ) in Figure 3.13
(actual data to be presented in Chapter 5).
For calculating the optical path difference, n×d, an algorithm was run using Mat- lab software in which the first part of the program denoises the spectrum by using Daubechies Wavelet Transform (DWT) [116,117]. The DWT filter is usually utilized as a band-pass filter and is used to decompose the spectrum signal for denoising. For the low finesse FPI sensors, the spectrum interferogram with noise has the following general form:
I1(λ)= A(λ)+B(λ)×cos(
4πnd
λ +ϕ)+N(λ) (Eqn. 3.3) where λis the scanning wavelength, A(λ) is the low frequency background irradi- ance that is introduced by the light source,B(λ) is the coherence envelope of the sensor spectrum that is relevant to the fibre bending, the coupling ratio and the contrast influ- enced by the reflection of the fibre ends, N(λ) is the high frequency intensity noise of the broadband source, the constantϕis the wave loss from the reflection at the thin film interface andn×dis the optical path difference.
With running a seven level Daubechies wavelet, db-7, the sensor signal is decom- posed into a set of approximations and details at different levels. The approximations are the low frequency components of the signal and the details are the high frequency components. The approximation of level seven and the detail of level one are subtracted from the sensor spectrum for eliminatingA(λ) and N(λ) respectively. The approxima- tion of levels 2 to 5 are also subtracted to further smooth the output spectrum (I2(λ) in
I2(λ)= B(λ)×cos( 4πnd λ +ϕ) (Eqn. 3.4) (a) (b) (c) (d) (e) (f) (g)
Figure 3.14: Example of: a) I1(λ) spectrum (as in Figure 3.13). b) after subtracting A(λ) (approximation level 7). c) after subtracting N(λ) (approximation level 1). d, e
I2(λ) in Eqn. 3.4, can be regarded as an amplitude and frequency modulated (AM-
FM) signal with B(λ) being the instantaneous amplitude. Thus, the second part of the algorithm does a Fast Fourier Transform (FFT) [118] to first eliminate B(λ) and then determine the optical path difference from the extracted periodicity. The resultant signal after eliminating B(λ) is given in Eqn. 3.5 and the deduced FPI spectrum (I3(λ)) is
shown in Figure 3.15 in comparison with the initial source and sensor spectra.
I3(λ)= cos(
4πnd
λ +ϕ) (Eqn. 3.5)
Figure 3.15: Example of a measured source and sensor and deduced FPI spectra
(I3(λ)).
3.5
Summary
In this chapter, the wide range of experimental equipment used in this study were de- scribed. As FPI was used for most sensors, the mathematical procedure to obtain the optical path difference was also provided. The experimental apparatus for the end-face sensor characterization is schematically shown in Figure 3.16.
Figure3.16: Schematic diagram of the optical fibre interferometric measurement sys-
Zeolite Coated Optical Fibre for
Aqueous Contaminant Detection
This chapter presents the characterization, development and optimization of the zeolite based optical fibre chemical sensor for trace contaminant detection in aqueous media. As discussed in Chapter 2, zeolite based optical fibre thin films have been used widely in water sensing area both in gas and liquid phase [69–73]. Here, a zeolite thin film optical fibre is fabricated and used to detect specific contaminants in water down to ppm and ppb levels.
4.1
The Principles of the Sensor
As discussed in Section 2.3, the zeolite film is used to control molecular transport, allowing certain molecular species to pass through while rejecting others based on the molecular size, chemical properties and chemical bonding properties with zeolite.
The working function of the sensor is based on the change of optical thickness (corre- sponding change in refractive index) induced within zeolite membrane. The sensor de- sign is based on using Fabry-Perot Interferometry technique described in Section 2.2.2.
As it can be seen in Figure 4.1, the end-face coated reflective zeolite thin film will absorb contaminants from water. Then upon adsorption of these contaminants, its re- fractive index will change, and as explained in Section 2.2.2, an interference pattern will be generated. The optical spectrum analyzer (OSA) will record the interference pattern (Figure 3.16) and by using the signal processing method (described in Section 3.4.4), the optical path difference will be determined. This is then correlated to the amount of adsorbed contaminant which is in proportion to its presence in the sensor’s environment.
Figure4.1: Structure of the end-face zeolite reflective sensor