Please help! Will rate! Thanks! Only image found A Young\'s Interferometer biose
ID: 2109811 • Letter: P
Question
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Only image found A Young's Interferometer biosensor is a micro-optical platform in which light in a single waveguide is split into different arms, which are exposed to an aqueous environment. The outputs from these waveguide arms are allowed to project out to a CCD camera and interfere. In the simplest form the waveguide is split into two channels, a sample channel and a reference channel. Antibodies are attached to the sample arm so that when the target analyte is captured it results in a slight change in the refractive index on top of the waveguide. This change in refractive index alters the phase of the light wave propagating through that arm of the interferometer. As a result of this optical phase shift, the interference pattern recorded by the CCD camera shifts. Treat the two channel Young's Interferometer Biosensor as a two-channel device and determine an expression for the phase shift as a function of the change in refractive index on the sample arm of the interferometer. Suppose then that the interference pattern recorded by the CCD camera shifts by two pixels determine the required phase shift and the refractive index change that leads to that shift. Finally, if the change in refractive index is due to the adsorption of biological particles, compute the fraction of the sense region that is covered by the target analyte Useful information: CCD element is 8.8 mm wide and is comprised of 767 Times 575 pixels (W Times H). Each pixel is square and is 11.5 on a side. The wavelength in waveguide is 650 nm and use 1.33 for the refracive index of water and 1.41 for the refractive index of the analyte. The sample region is 20 mm long, the output ends of the waveguide are 100 mu m apart and the distance from the output to the CCD is 65 mm.Explanation / Answer
The interferometric biosensor, which is a single-sensor, real-time optical measurement
device targeted for label-free protein detection and analysis, has been developed.
This is the compound result of various improvements to the optical construction, such
as the introduction of the beam-forming optics and the adjustment of the double-slit,
as well as its secondary use as a spatial filter at the input coupling of the optical grating
for the successful reduction of scattering.
The optical biochips supplied by Unaxis optics have undergone measurement and examination
in order to determine their operation characteristics. It is concluded that the
addition of protective layers above the grating couplers is an acceptable solution for
the prevention of the unwanted measurement of the silicon flow cells’ refractive index.
Even with the protective layers, the optical coupling efficiency is more than sufficient
for measurements. Furthermore, the scattering phenomena resulting from foreign material
on the waveguide surface and their disruptive effects have been acceptably
compensated by the aforementioned double-slit solution.
The software of the interferometric biosensor has been enhanced and expanded into a
stable software-measurement system. There are, in fact, two available LabVIEW-based
measurement programs, which each use different fundamental signal analysis algorithms:
FFT-based and Fourier-based correlation. Each of these programs contains a full
compliment of signal processing methods (i.e. window functions and zero padding)
and noise averaging algorithms (i.e. block averaging and moving-average filtering).
As for the signal analysis algorithms themselves, there is no conclusive evidence to the
support the superiority of either algorithm. This is mainly based on a lack of comparative
data and the existence of yet unexplained phenomena (e.g. oscillations in the
phase trend and a large drift) for the Fourier-based correlation algorithm.
Measurements and simulations have shown that the implementation of specialized window
functions offers no justifiable improvement to the quality of the final measurement
signal. Furthermore, windowing is a method for reducing the random noise component
of the overall noise if and only if its implementation increases the SNR enough to
compensate for its own contribution to the random noise component, or uncertainty.
Zero padding as a linear interpolation method also offers no justifiable improvement
to the quality of the final measurement signal, since the interferogram period precision
without spectral linear interpolation is sufficient for the analysis of the interferogram’s
phase. Thus, the use of other, possibly more sophisticated interpolation methods is also
unjustified. Therefore, zero padding should remain a method for the fulfillment of the
requirements, such that the signal analysis can be performed with the more efficient
FFT algorithm.
The exact nature and components of the noise in the measurement signal has not
been conclusively determined, but rather significantly narrowed down to a few probabilities.
The nature of the noise has been shown to be statistical in nature, without the
presence of periodicity and with the small presence of a random noise component.
Theoretically, the major contributer to the noise and fluctuations of the measurement
signal is the fluctuation in the external propagation path difference. Calculations, simulations,
and observations support this, but the findings are not yet conclusive.
With noise averaging it has been shown to be possible to reduce the noise present in
the measurement signal, but averaging is not an elimination method. Measurements
and simulations with block averaging suggest a noise minimum at a CCD camera exposure
time of nearly 50 ms. This may perhaps be uncharacteristic of other CCD camera
applications due to the interim FFT analysis needed to arrive at the measurement
signal and due to the presence of operating system conflicts at exposure times lower
than 40-60 ms. Based on this and the need for an acceptable resolution of the measurement
signal, a system time constant has been recommended in the range between
200 ms and 600 ms, assuming the use of block averaging factors between 5
and 10. Similar measurements and simulations justify the additional use of smoothing
algorithms based on a moving-average filter. Due to the subjectivity of their usage a
conclusive smoothing factor has not been suggested, however a factor range from
16-64 will result in noise reductions around a factor of 5 or greater.
The improvement of the interferometric biosensor and its final operation have been
tracked and recorded through the execution of numerous glycerin-based tests. Several
remaining areas of improvement would be the signal drift and the continual elimination
of noise sources and reduction of noise in the measurement signal. Again, despite
exhaustive measurements and simulations, it has not yet been possible to correlate the
drift to any single source or any combination of sources. The current drift factor remains
near 5.0 ×10−6 ne f f /hour, which is larger than the interim goal of 1.0 ×10−6 ne f f /hour.
The layering of the test sample on the surface of the waveguide has proved to be a
promising hypothesis for the drift, but only based on observation. Therefore, the cause
of the drift remains inconclusive.