Surfaces with extreme wettability
(too low, superhydrophobic; too
high, superhydrophilic) have attracted considerable attention over
the past two decades. Titanium dioxide (TiO2) has been
one of the most popular components for generating superhydrophobic/hydrophilic
coatings. Combining TiO2 with ethanol and a commercial
fluoroacrylic copolymer dispersion, known as PMC, can produce coatings
with water contact angles approaching 170°. Another property
of interest for this specific TiO2 formulation is its photocatalytic
behavior, which causes the contact angle of water to be gradually
reduced with rising timed exposure to UV light. While this formulation
has been employed in many studies, there exists no quantitative guidance
to determine or tune the contact angle (and thus wettability) with
the composition of the coating and UV exposure time. In this article,
machine learning models are employed to predict the required UV exposure
time for any specified TiO2/PMC coating composition to
attain a certain wettability (UV-reduced contact angle). For that
purpose, eight different coating compositions were applied to glass
slides and exposed to UV light for different time intervals. The collected
contact-angle data was supplied to different regression models to
designate the best method to predict the required UV exposure time
for a prespecified wettability. Two types of machine learning models
were used: (1) parametric and (2) nonparametric. The results showed
a nonlinear behavior between the coating formulation and its contact
angle attained after timed UV exposure. Nonparametric methods showed
high accuracy and stability with general regression neural network
(GRNN) performing best with an accuracy of 0.971, 0.977, and 0.933
on the test, train, and unseen data set, respectively. The present
study not only provides quantitative guidance for producing coatings
of specified wettability, but also presents a generalized methodology
that could be employed for other functional coatings in technological
applications requiring precise fluid/surface interactions.
Spreading of liquid droplets on wettability-confined
paths has
attracted considerable attention in the past decade. On the other
hand, the inverse scenario of a gas bubble spreading on a submerged,
wettability-confined track has rarely been studied. In the present
work, an experimental investigation of the spreading of millimetric
gas bubbles on horizontally submerged, textured, wettability-confined
tracks is carried out. The width of the track is kept fixed along
its entire length, and the spreading behavior of a gas bubble, dispensed
at one end of the track, is studied. The effects of varying track
width, bubble diameter, and ambient liquid are investigated. Post-contact,
the gas bubble spreads along the track at a linear rate with time,
while remaining pinned at its back end; the recorded spreading speed
is O(0.5 m/s). An inertio-capillary force balance
describes the experimentally observed spreading dynamics with excellent
agreement.
The
interaction of rising gas bubbles with submerged air-repelling or
air-attracting surfaces is relevant to various technological applications
that rely on gas-microvolume handling or removal. This work demonstrates
how submerged metal meshes with super air-attracting/repelling properties
can be employed to manipulate microvolumes of air, rising buoyantly
in the form of bubbles in water. Superaerophobic meshes are observed
to selectively allow the passage of air bubbles depending on the mesh
pore size, the bubble volume-equivalent diameter, and the bubble impact
velocity on the mesh. On the other hand, superaerophilic meshes reduce
or amplify the volume captured from a train of incoming bubbles. Finally,
a spatial wettability pattern on the mesh is used to control the size
of the outgoing bubble, and an empirical relation is formulated to
predict the released gas volume. The study demonstrates how porous
materials with controlled wettability can be used to precisely modulate
and control the outcome of bubble/mesh interactions.
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