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L. Acosta-Soto and S. Hosseini
layers that were electrochemically deposited onto the surface of the devices. Subsequently, a single Pt layer was galvanostatically deposited, to enhance the mechanical
performance, followed by subsequent depositions of Pt-Ni, solely Ni, and finally Pt,
in the same fashion (Fig. 3.8a). The template membrane was washed and sputter
coated with gold. As a consequence, this fabrication strategy, the micro-motor was
susceptible to magnetic fields, particularly when in accordance to its momentum.
It was shown the micro-motor could be manipulated and guided towards specific
areas when using sufficiently strong magnets. It is important to note, however, that
control over micro-motor’s motion depended on the geometry of the cone, as more
asymmetrical structures will tend towards erratic spiraling paths given an uneven
propulsion. The devices exhibited a targetable movement to target the phycocyanin.
The earlier exposures of the cones left available binding sites on the outside surfaces
of the cones (imprinted sites) that allowed a relatively quick adsorption of phycocyanin to the surface. Later evaluation in actual seawater showed that the presence
of different molecules and compounds did not significantly interfere with the rate of
phycocyanin adsorption or the movement and direction control of the micro-motors
in the medium.
The detection of cyanobacteria (Spirulina) can be done using phycocyanin.
In specific cases, a need arises to simultaneously monitor the presence and ratio of
several species within the same space. Such a scenario happens in biofuel production,
where a specific ratio of green algae (Chlorella vulgaris) to cyanobacteria is desirable.
Shin et al. (2018) took advantage of the fact that green algae produce chlorophyll a and
chlorophyll b, both of which can produce a fluorescent response to the right stimuli.
The proposed device is a ready-to-use biosensor that integrates a microcontroller,
corresponding circuitry to control a LED used as excitation sources, along with
an amplification circuit for the signal readout. The device is also equipped with
a temperature compensating mechanism for the photodetector with an inlet for a
vial with the sample to be analyzed and a screen that outputs the data from the test
(Fig. 3.9). Unlike most fluorescent devices, this strategy heavily depended on the code
that reads the sensors input. The code was responsible for the control and operation
of the 3 LEDs that were used to obtain different responses from the three different
fluorescent substances within the samples. Through the use of an amber LED, the
response of phycocyanin was maximized, while for the two chlorophylls the signal
was amplified. In the case of the blue LED, an UV source was used to measure total
phytoplankton population, as it elicits similar responses from both Spirulina and
Chlorella vulgaris. The algorithms used to process the information applied concepts
of linear algebra analysis to correlate readouts of the three excitation sources and find
relative concentrations of the different organisms. The partial least square regression
(PLSR) method was used to best estimate the biomass of the different phytoplankton
species. The device was calibrated by reading different samples with predetermined
concentrations, and manually inserted into the microcontroller, before it can begin
to quantitatively estimate unknown values. The device’s predictions were within 2–
16% of the real values of the respective biomasses. Reducing the background noise
can further enhance the accuracy of prediction by this device.
L. Acosta-Soto and S. Hosseini
layers that were electrochemically deposited onto the surface of the devices. Subsequently, a single Pt layer was galvanostatically deposited, to enhance the mechanical
performance, followed by subsequent depositions of Pt-Ni, solely Ni, and finally Pt,
in the same fashion (Fig. 3.8a). The template membrane was washed and sputter
coated with gold. As a consequence, this fabrication strategy, the micro-motor was
susceptible to magnetic fields, particularly when in accordance to its momentum.
It was shown the micro-motor could be manipulated and guided towards specific
areas when using sufficiently strong magnets. It is important to note, however, that
control over micro-motor’s motion depended on the geometry of the cone, as more
asymmetrical structures will tend towards erratic spiraling paths given an uneven
propulsion. The devices exhibited a targetable movement to target the phycocyanin.
The earlier exposures of the cones left available binding sites on the outside surfaces
of the cones (imprinted sites) that allowed a relatively quick adsorption of phycocyanin to the surface. Later evaluation in actual seawater showed that the presence
of different molecules and compounds did not significantly interfere with the rate of
phycocyanin adsorption or the movement and direction control of the micro-motors
in the medium.
The detection of cyanobacteria (Spirulina) can be done using phycocyanin.
In specific cases, a need arises to simultaneously monitor the presence and ratio of
several species within the same space. Such a scenario happens in biofuel production,
where a specific ratio of green algae (Chlorella vulgaris) to cyanobacteria is desirable.
Shin et al. (2018) took advantage of the fact that green algae produce chlorophyll a and
chlorophyll b, both of which can produce a fluorescent response to the right stimuli.
The proposed device is a ready-to-use biosensor that integrates a microcontroller,
corresponding circuitry to control a LED used as excitation sources, along with
an amplification circuit for the signal readout. The device is also equipped with
a temperature compensating mechanism for the photodetector with an inlet for a
vial with the sample to be analyzed and a screen that outputs the data from the test
(Fig. 3.9). Unlike most fluorescent devices, this strategy heavily depended on the code
that reads the sensors input. The code was responsible for the control and operation
of the 3 LEDs that were used to obtain different responses from the three different
fluorescent substances within the samples. Through the use of an amber LED, the
response of phycocyanin was maximized, while for the two chlorophylls the signal
was amplified. In the case of the blue LED, an UV source was used to measure total
phytoplankton population, as it elicits similar responses from both Spirulina and
Chlorella vulgaris. The algorithms used to process the information applied concepts
of linear algebra analysis to correlate readouts of the three excitation sources and find
relative concentrations of the different organisms. The partial least square regression
(PLSR) method was used to best estimate the biomass of the different phytoplankton
species. The device was calibrated by reading different samples with predetermined
concentrations, and manually inserted into the microcontroller, before it can begin
to quantitatively estimate unknown values. The device’s predictions were within 2–
16% of the real values of the respective biomasses. Reducing the background noise
can further enhance the accuracy of prediction by this device.