Robust micropatterned superhydrophobic/superhydrophilic polymer surfaces for inkjet printing and lab-on-a-chips

The ability for water to wet a surface and/or for a surface to repel water has important technological implications, ranging from microfluidics to cell microarrays.  A subfield of applied research has focused on the creation of stable patterns of superhydrophobic and superhydrophilic areas.  To be clear, hydrophobic is the technical term for "water-repelling"; phobic is in the name after all!  Of course, hydrophilic means attracted to water.  When scientists add the "super" to the name, you know these properties are in the upper echelons of the scale.

Microfluidics is important in inkjet printing, DNA chips, and lab-on-a-chip devices.  These technologies play very important roles in biotechnology, especially concerning clinical pathology, where immediate diagnosis of disease is critical.  Microfluidics is divided in many subcategories, so let's focus on one: droplet-based microfluidics–the generation of micro droplets in more ways than one would normally care to remember.

Microdroplets can be used as incubators for single cells.  Cell behavior is typically observed as populations in bulk assays.  However, medical fields such as immunology are described at the single-cell level, which means a technique that can enable one to observe one cell would yield important insights for that field.  Recently, a team in Holland devised a method that uses microdroplets of agrose to encapsulate T-cells.  These cells secrete cytokine, which then binds to beads already present in the microdroplets.  This method could be automated and performed multiple times simultaneously. This can detect differences or variations among individual cells, and map subsets within cell populations with specific functions.

Micro-patterns of superhydrophobic and superhydrophilic areas are created by modifying the surface of a superhydrophilic substrate through a mask to reverse the hydrophilicity  of the exposed areas.  However, this usually necessitates harsh conditions, risks irreversible modifications, and requires the entire substrate to perform the modification.

Here, researchers in Germany report an easy method for printing superhydrophilic patterns on a superhydrophobic substrate.  The "ink" is an ethanol solution containing phospholipid and is deposited onto a superhydrophobic porous polymer surface.  Lipids may sound familiar from high school biology because you may remember it's a fancy word for fats.  The key property about these molecules is their amphiphilic nature, meaning they have hydrophobic and hydrophilic parts.  Like attracts like, so the hydrophobic segment of the phospholipid should attach to the superhydrophobic substrate.  This leaves a free hydrophilic phosphate in that spot.  To put it another way, the amphiphilic lipid is the ink that creates superhydrophilic patterns on the superhydrophobic surface.

Figure 1. (A) Schematic representation of switching from superhydrophobicity to superhydrophilicity by applying an “ink” containing a phospholipid. SEM images of the microporous structure of the superhydrophobic (B) and superhydrophilic (C) polymer film. (D) SEM images and images of water droplets on the BMA-EDMA surfaces with different morphologies (scale bars 1 μm; average sizes of polymer globules are indicated under SEM images). (E) Static water contact angles on BMA-EDMA surfaces with different morphologies before and after modification with the POPG lipid. Average sizes of polymer globules are indicated.

The figure above is taken from the paper itself.  1(A) is the schematic of how the normally superhydrophobic surface becomes superhydrophilic, and vice-versa.  The best part about these materials is how easy it is to switch between the two states of water interaction.  You add an ethanol solution with phospholipid to the surface, and the hydrophilic ends of the phospholipid molecules stick up.  You then just add methanol to the surface, and the lipid is washed away, rendering the substrate superhydrophobic again.  This switch was done 30 times by the researchers without performance decay.

Key to this performance is the porosity of the polymer surface.  1(B) and 1(C) are scanning electron micrographs (SEMs) of the cross-sectioned polymer film (left panels) and closeup of polymer surface.  Porosity is indicate by average polymer particle in 1(E), meaning that it's harder for larger particles to close holes.  Thus, polymer surfaces with the largest average particles performed best.  Best performance means ease of switching between superhydrophobic and superhydrophilic without decrease in performance.  Smaller particles just didn't respond as well to the repeated washing.  A nonporous did the worst.  When water is applied to a porous substrate with superhydrophilic regions, the pores might protect the lipids from the mechanical action of the applied water flow.  Without pores, there is little for the lipids to hang on to, and are easily washed away by water.

Robust, easily fabricated micro-patterned polymer substrates could open the floodgates to new applications.  Easy switching between superhydrophobicity and superhydrophilicity makes for easy incorporation into well-established techniques for printing, including microcontact printing, dip-pen nano lithography, or inkjet printers.  Naturally, the question now is it possible to fabricate a superhydrophilic substrate that is available for printing a micro-pattern consisting of ordered superhydrophobic regions.  Time to dive deeper into the Obscura.

Source:

1. Printable Superhydrophilic–Superhydrophobic Micropatterns Based on Supported Lipid Layers

   Junsheng S. Li, Erica Ueda, Asritha Nallapaneni, Linxian X. Li, and Pavel A. Levkin
   Langmuir 2012 28 (22), 8286-8291
   DOI: 10.1021/la3010932

Smart Manufacturing is smart politics.

How sticky droplets behave when there's a lot of them

I've been writing my dissertation, so updates have been scarce, but I can report some interesting papers I've read along the way.

There is a great paper by Ryotaro Shimizu and Hajime Tanaka that explains a new mechanism for how immiscible droplets within a fluid merge into bigger droplets, and has universal applications from emulsions to cosmetics and foods.  Immiscible means two fluids cannot mix.  The models described here work are applicable for low viscosities.  This work is based on computer simulations, but the authors claim there are experiments to back up their claims.  The paper has been recently published in Nature Communications, so time will tell on what further peer review will reveal. 

Shimizu and Tanaka focus on phase separation, which is a fundamental physical phenomenon of multiple bodies collectively interacting.  Most familiar encounter I can think in everyday life is olive oil droplets merging into a single mass in a cooking pot.  The process is called coarsening, and the oil droplets can be thought of as domains in a continuous medium called water.  The way by which droplets coalesce is dependent on their initial concentration.  The concentration changes the mechanism by which they interact.  

When the concentration is low, droplets merge via a mechanism called Ostwald ripening.  This is as old as the hills (actually since the 1890s), and works by smaller droplets steadily diffusing into large droplets at the expense of the smaller droplets.  

Figure 1.  Small droplets spontaneously dissolve due to their instability.  The material then diffuses into larger droplets, which are more energetically stable.    Source: Wikipedia

Another mechanism is works when there are enough droplets to occupy about 50% of the total volume in a given object.  There, hydrodynamic forces coarsen the droplets into a so-called bicontinuous pattern, as seen in Figure 2.  Bicontinuous refers to two continuous phases.   You can see the black and white patterns continuing beyond the box in the moments of the animation.  The merging is so rapid that normal diffusion is dwarfed by the swift currents that result in the final structure of the animation. 

Figure 2.  Emergence of bicontinuous structure.  Note the rapid emergence of two continuous phases; that is indicative of rapid flow.

The third mechanism accounts for an intermediate droplet concentration, somewhere between 21 and 35% of the volume occupied by the droplets.  This is the most difficult mechanism and has been the subject of various researchers for decades.  That is because neither diffusion nor hydrodyanmcs can be neglected and thus makes it difficult to make approximations.  Hydrodynamic forces stem from thermally-induced Brownian motion, the technical term for the random collisions between particles in a fluid, as seen in Figure 3.    Diffusion occurs during the collision, and thus the hydrodynamics and diffusion are coupled.

Figure 3.  Animation of Brownian motion without coalescence.   The blue path traces the trajectory taken by the yellow particle from  incessant collisions.

However, what Shimizu and Tanaka claim is that the collisions are not random, but are driven by a so-called composition Marangoni force, which is induced by long-range interfacial tension gradients on the droplets.  In other words, the interfacial energy on a droplet varies because of other droplets near it.   This is illustrated from the paper in Figure 4.

Figure 4.  Snapshot of droplets with interfacial energy gradients, with blue color being the minimum, and red as the maximum.  Velocity arrows are drawn on and in the droplets of b to indicate preferred velocities on a given droplet region.  Thus, the droplet in the low left is drawn toward the small droplet next to it due to the largest arrows pointing toward it from the further end of this droplet.

A droplet much smaller than its neighbors draws neighboring droplets toward it and eventually induce them to collide.  In short, a larger droplet tends to eat a smaller droplet by direct collision.   The attraction from droplets stems from the different gradients of surface tension between the droplets.  The merging then lowers the overall energy of the system, since equilibrium just means the system has fallen to its lowest energy state.  

Source:  R. Shimizu, H. Tanaka, A novel coarsening mechanism of droplets in immiscible fluid mixtures, Nature Communications, 6 (2015).

Probing C and N doped titanium dioxide with hard x-ray photoelectron spectroscopy

Titanium dioxide (TiO₂) is a widely used band-gap semiconductor with vast applications in photocatalysis, photovoltaic, and spintronics.    Recent advances by Japanese researchers has sparked interest in TiO₂ for electro-photo-catalytic splitting of water, since this mineral is a widely used white pigment with UV absorbance characteristics.  The Achilles heal of this application is its large band-gap (3.20 and 3.00 eV for anatase and rutile crystal phases, respectively).  Hence the push toward doping for lower band-gap.  Among the several anionic dopants attempted, N was the most effective for lower band-gap and thus enhance photocatalysis under visible light.  This is made possible by the overlap of the N 2p orbitals with the O 2p orbitals with respect to the energy it would take for a photon to knock an electron out of those orbitals and into the so-called conduction band, where current can be generated.  This makes intuitive sense since N and O are neighbors on the periodic table.  Doping with N can be further enhanced with C.  Doping with only carbon is bad because its 2p orbitals do not overlap with the O 2p orbitals, which would create separate quantum levels for conducting electrons to occupy and not do anything.  However, the C 2p orbital overlaps with the N 2p orbital.  This means we'd have 2p orbitals from two dopant atoms overlap with the oxygen 2p, which gives engineers more flexibility in lowering the TiO₂ band-gap.

Up to then, little data has been collected on these C- and N-doped TiO₂. The paper by Ruzybayev et al attempts to fill those gaps.   Experimental data was acquired via hard x-rays from the National Synchrotron Light Source (NSLS)  in Brookhaven National Laboratory (now closed because NSLS II is opening now).  Hard x-rays have an energy range of 5-15 keV (about the same as medical x-rays), and penetrate into the sample bulk; soft x-rays range 1-5 keV and only penetrate the surface of a sample .  Both types of x-rays are useful, but it depends on the application.  This makes for an interesting paper because my master's thesis was a theoretical study of hard x-rays inducing photoemission from a magnetic multilayer structure.  The technique is known as hard x-ray photoelectron spectroscopy (HXPS).

The experimental section will be skipped for brevity, but the specimens were TiO₂ films that were only 500 nm thick.  That's close to the wavelength of blue light.  The optical band-gap of pure TiO₂ and co-doped TiO₂ is shown in UV-Vis diffuse reflectance spectroscopy data on Figure 1.

Figure 1.  Approximated band-gaps for pure TiO2 and C and N doped TiO2.

Pure TiO₂ has a band-gap of 3.30 eV, as evidenced by the upward slope of the solid curve in Figure 1.  Codoping lowered the band-gap to 2.39 eV, (deeply slanted slope of the dashed curve).  The analysis from HXPS data is displayed in Figure 2.  This can be considered the "meat" of the paper because it explicitly shows how the C and N dopants affect the electronic structure of TiO₂.  "Electronic structure" has many contexts, but the important one here is the oxidation state of titanium.

Figure 2.  HXPS of titanium 2p orbital in pure form and doped with C and N.

The two peaks in the pure TiO₂ spectrum show the characteristic 2p_3/2 and 2p_1/2 spin-orbit split of Ti⁴⁺ (marked A and B in the upper curve, respectively).  The superscript 4+ represents its oxidation state, i.e., the charge experienced by the titanium atom after giving away four electrons to two oxygen atoms (each oxygen atom having two additional electrons).  Doping induced two additional peaks adjacent to the the orbitals.  The 'C' and 'D' peaks represent Ti³⁺ due to the extra net electron contributed by the dopants, which then produces oxygen vacancies.  The vacancies produce an occupied Ti 3d orbital just above the valance band maximum, which is the last quantum state for an electron to occupy before surpassing the band-gap to reach the conduction band.  The jump to the conduction band is the key to generating electricity from light.  The smaller the jump for the electron, the easier it is to generate electricity from a hypothetical TiO₂ cell.

Photoelectron spectroscopy can be measured at ultra-violet wavelengths.  That is useful for measuring the valance band maximum, which the authors have done here.  More specifically, they measured the change in photoelectron kinetic energy relative to the O 2p orbital.

Figure 3.  Valence orbital data of pure, C doped, N doped, and both C and N co-doped TiO₂.  These spectra are measured relative to the O 2p orbital.

Spectra from TiO₂ doped only with N or only with C are included to ascertain the contributions to the altered band-gap by the individual dopants. The key feature is the tailing of the large peak at 5 eV.  The curve for pure TiO₂ is flat at this kinetic energy, but doped TiO₂ curves are still still slanting downward here, with the C and N co-doped curve being the highest one; in jargon, the C and N co-doped curve has the highest valence band maximum.

Experimental data was compared with data calculated from computational models that varied the locations of dopant atoms in a TiO₂ unit cell.  According to Figure 4, what matters is which atoms are the dopant atoms bonded to.  That in turn affects the photoelectron spectra due to the so-called density of states (DOS), which I won't go into because that is too advanced for this blog.  What I can say is the DOS allows you to calculate photoelectron spectra that you then compare to experiment.  If theory  and experiment don't match, try a different model.

Figure 4.  Density of states for co-doped TiO₂ unit cells.  In the insets, blue, light blue, red, and orange spheres represent Ti, O, C, and N atoms, respectively.

Trial-and-error led the authors to conclude that the model in the lower left of Figure 4 was the closest match to the spectra from the experiment, as seen in Figure 5.

Figure 5.  DOS and experimental photoelectron valence band for C and N co-doped TiO₂.  The experimental curve is the green curve from Figure 3.  The red curves are the theoretical photoelectron spectra, and the dark curves (excluding experiment) are the DOS.

That unit cell is represented in the middle spectrum of Figure 5.  It is a fairly good match despite the sloping background of the experimental spectrum.  This has led the authors to conclude that carbon preferentially sticks to titanium, while nitrogen prefers oxygen.  The results show that electronic structure of TiO₂ can be manipulated to decrease the band-gap for photocatalysis.