Peter J Vach1, Peter Fratzl1, Stefan Klumpp1, Damien Faivre1. 1. Department of Biomaterials and ‡Department of Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces , Science Park Golm, 14424 Potsdam, Germany.
Abstract
Studying propulsion mechanisms in low Reynolds number fluid has implications for many fields, ranging from the biology of motile microorganisms and the physics of active matter to micromixing in catalysis and micro- and nanorobotics. The propulsion of magnetic micropropellers can be characterized by a dimensionless speed, which solely depends on the propeller geometry for a given axis of rotation. However, this dependence has so far been only investigated for helical propeller shapes, which were assumed to be optimal. In order to explore a larger variety of shapes, we experimentally studied the propulsion properties of randomly shaped magnetic micropropellers. Surprisingly, we found that their dimensionless speeds are high on average, comparable to previously reported nanofabricated helical micropropellers. The highest dimensionless speed we observed is higher than that of any previously reported propeller moving in a low Reynolds number fluid, proving that physical random shape generation can be a viable optimization strategy.
Studying propulsion mechanisms in low Reynolds number fluid has implications for many fields, ranging from the biology of motile microorganisms and the physics of active matter to micromixing in catalysis and micro- and nanorobotics. The propulsion of magnetic micropropellers can be characterized by a dimensionless speed, which solely depends on the propeller geometry for a given axis of rotation. However, this dependence has so far been only investigated for helical propeller shapes, which were assumed to be optimal. In order to explore a larger variety of shapes, we experimentally studied the propulsion properties of randomly shaped magnetic micropropellers. Surprisingly, we found that their dimensionless speeds are high on average, comparable to previously reported nanofabricated helical micropropellers. The highest dimensionless speed we observed is higher than that of any previously reported propeller moving in a low Reynolds number fluid, proving that physical random shape generation can be a viable optimization strategy.
Entities:
Keywords:
Nanomachines; magnetic actuation; micropropellers; microswimmers; nanomotors; nanopropellers
The discovery
of the rotation of bacterial flagella nearly 50 years ago[1] seems to suggest that a rotating helical structure
could be an optimum shape for propulsion in low Reynolds number fluids.
Consequently, attempts to create magnetically driven propellers have
focused in particular on helical shapes. Magnetic propellers are a
particularly simple and comparatively well understood[2−6] actuation mechanism for artificial microstructures. We understand
a magnetic propeller to be a rigid structure, rotated by an external
rotating magnetic field.[7,8] Propulsion is achieved
if the structure possesses finite rotation-translation coupling. However,
which is the optimum shape for magnetic propellers remains unknown
and, in addition, there is no estimate for the maximum speed that
a propeller of given length could reach if it had an optimal shape.
Given the wide range of possible shapes, experimental approaches are
difficult. But even modeling approaches were only able to explore
possible shape within a narrow range.[2] Here
we devise a combinatorial approach, synthesizing a large number of
propellers with random shapes and investigating their dimensionless
speeds (that is, speed normalized by length and frequency of rotation).
Investigating the histogram of dimensionless speeds in a large population,
we propose a lower bound for the highest dimensionless speed that
is physically possible. In addition, analyzing the relationship between
propeller geometry and dimensionless speed, we aim at determining
design principles that make such micropropellers efficient.Many different approaches have been used to fabricate such magnetic
micropropellers. One of the first micropropellers was created by Zhang
et al. using self-scrolling thin films.[9,10] These bioinspired
“artificial bacterial flagella” are between 30 and 50
μm in size and have been used for micromanipulation tasks.[11−16] Smaller propellers were fabricated by Ghosh and Fischer (about 0.25
× 1.5 μm)[17] and recently by
Schamel et al. (about 0.1 × 0.4 μm)[18] using glancing angle vapor deposition (GLAD). These have
been used to demonstrate the separation of chiral species by magnetic
fields[4] and after applying a cytocompatible
coating were able to move through human blood.[19] Because of their small size, special attention has been
paid to the effect of thermal noise on these propellers.[6,20,21] Recently Walker et al. used GLAD
to produce length optimized helical propellers.[22] Using direct laser writing (DLW), helical micropropellers
with precisely controlled geometric parameters were fabricated and
after coating them with a magnetic material were used for cargo transport
and micromanipulation tasks.[23−31] It has also been shown that the magnetic material can be internalized,
by applying DLW to photoresist mixed with superparamagnetic nanoparticles,
which were aligned by an external magnetic field during the writing
process.[32]The propellers listed
above required expensive equipment for their production. Different
attempts have been made to reduce the production costs of propellers,
by, for example, using self-assembly or templates. Curved nickel nanowires
have been assembled into helix-like structures and were used as propellers,
actuated by a rotating magnetic field.[16] The metallization of helical liposome scaffolds was also used to
create magnetic propellers.[33] Plant-based
magnetic helical propellers have been created by coating the spiral
xylem vessels of vascular plants with titanium and nickel layers.[34] Nickel-coated helical palladium nanosprings
were created by a template electrosynthesis method and used as propellers.[35] All of these techniques were developed in order
to produce helix-shaped propellers, similar to the previously reported
nanofabricated micropropeller designs.Previous investigations
of the relationship between propeller shape and dimensionless speed
could only explore narrowly limited shape spaces. Several experimental
studies investigated variations of helical propellers[23,32,31] and also a theoretical study
compared different types of helices.[3] In
another theoretical study, Keaveny et al. were able to investigate
more general shapes and even performed numerical optimization in shape
space but remained limited to slender shapes with a single centerline.[2] Apart from two recent studies on achiral propellers,[36,37] the propulsion properties of alternative morphologies remained unexplored.Here, we therefore experimentally explore a large shape space by
studying the actuation of randomly shaped structures. We recently
demonstrated an extremely simple approach to the production of very
large quantities of magnetic micro- and nanopropellers, by selecting
propellers from a pool of randomly shaped carbon-coated magnetic nanoparticle
aggregates.[5] The reason why this approach
worked was already stated by Purcell in his seminal 1976 talk “Life
at low Reynolds number”:[39] “Turn
anything – if it isn’t perfectly symmetrical you’ll
swim.” However, how well “anything” swims, remained
an open question. Additionally, how big is the subset of randomly
shaped structures that can be efficiently actuated by rotating magnetic
fields? Surprisingly, we observed that most of the randomly shaped
structures can indeed be efficiently actuated. About one in two random
shapes we subjected to a rotating magnetic field moved indeed faster
than the “artificial bacterial flagella” discussed above,[11] when using a dimensionless speed that depends
only on the geometry of the actuated structure as a basis for comparison.[39]
Theoretical Background
We consider
a propeller to be a rigid structure rotating with frequency f around a fixed axis of rotation. The propeller is actuated
by an external force F and an external torque τ.
Because the Reynolds number is low (see the Supporting
Information for an estimate), the hydrodynamics of the propeller
motion is described by the Stokes equationsandwhere p is
the pressure, η is the dynamic viscosity, and u is the speed of the moving fluid. If the propeller is rotating in
bulk fluid, the effect of gravity is neglected and we are interested
in time scales ≫(1/f), the hydrodynamic problem
is on average rotationally symmetric around the axis of rotation of
the propeller. Therefore, the translatory propeller movement will
be parallel to the axis of rotation and the propeller speed can be
described by the scalar quantity v. Assuming a no-slip
boundary condition,[40] it follows from the
Stokes equations that the speed v and rotation frequency f of the propeller motion must be linearly related to the
applied external force and torque[41]L is a size parameter for the propeller
and A, C, and D are parameters that depend only on the shape of the propeller for
a fixed axis of rotation. The symmetric and positive definite matrix P is called the resistance matrix.[42] In the case of magnetic propellers, the external force is zero (F = 0). Therefore, the speed of the propeller is related
to the frequency of rotation in the following wayIn order to
compare the propulsion properties of propellers of different shapes,
it is therefore useful to define a dimensionless speed[39] that depends only on the shape of the propeller
and the axis of rotationwhere v is the component of the propeller speed parallel to the
vector of rotation of the rotating magnetic field that actuates the
propeller. This dimensionless speed corresponds to the number of body
lengths a propeller moves during one rotation and can be thought of
as an effective screw pitch (see Figure a,b). In our experiments, we took care to
ensure that the magnetic torque was always sufficient to rotate the
propellers with the frequency of the rotating magnetic field. Thus, U characterizes the hydrodynamic
coupling between rotation and translation for a particular propeller
shape and axis of rotation.
Figure 1
Schematic explanation of the dimensionless
speed. (a) The red shape is an example of a 3D reconstruction of a
propeller. The dashed black line indicates the axis of rotation. (b)
After n (here n = 79 to suit the
illustration) rotations, the propeller has moved a certain distance
due to rolling (Δx) and a certain distance
due to propulsion (Δy). The propeller speed v is the speed parallel to the rotation axis. The length
scale L is chosen to be the size of the propeller
in the projection in which it appears largest (see methods in Supporting Information). The dimensionless speed
is thus the number of body lengths moved per rotation (times 1000)
and can be thought of as an effective screw pitch. (c) The net magnetic
moment (yellow arrow) of the propeller is likely rotating in the same
plane (indicated by the blue shape) as the external magnetic field,
perpendicular to the axis of rotation. This corresponds to a precession
angle of 90°. (d) The precession angle does not necessarily have
to be equal to 90°. Because of the interplay between magnetic
and hydrodynamic forces, other precession angles are possible as well.
In our experiments, the axis of
rotation is set by the magnetization state of the propeller and the
hydrodynamic forces acting on the propeller. Typically, the rotation
axis will be perpendicular to the net magnetic moment of the propeller
and perpendicular to the plane in which the external magnetic field
is rotating. This leaves two degrees of freedom for the position of
the propeller relative to the axis of rotation. We observe that the
axis of rotation passes through a point in the propeller that seems
to be typically close to its center of mass (see Figure c). In principle, the net magnetic
moment of the propeller does not need to rotate in the plane in which
the external magnetic field is rotating. One could imagine that the
net magnetic moment rotates (precesses) on a cone around the axis
of rotation (see Figure d). Therefore, we take the axis of rotation as given and do not make
connections to the likely magnetization state of the propeller.The rotational friction constant cF of
the propeller, defined by the relationship τ = cFf, is given by cF = ηL3(D – C2/A). In
the Supporting Information, we show that cF is always positive, based on the fact that
the resistance matrix P is positive definite. Interestingly,
the rotational friction constant cF is
reduced when the coupling between rotation and translation is strong
(large C). This suggests that arbitrarily shaped
structures might have a tendency to rotate around an axis with strong
rotation translation coupling.Schematic explanation of the dimensionless
speed. (a) The red shape is an example of a 3D reconstruction of a
propeller. The dashed black line indicates the axis of rotation. (b)
After n (here n = 79 to suit the
illustration) rotations, the propeller has moved a certain distance
due to rolling (Δx) and a certain distance
due to propulsion (Δy). The propeller speed v is the speed parallel to the rotation axis. The length
scale L is chosen to be the size of the propeller
in the projection in which it appears largest (see methods in Supporting Information). The dimensionless speed
is thus the number of body lengths moved per rotation (times 1000)
and can be thought of as an effective screw pitch. (c) The net magnetic
moment (yellow arrow) of the propeller is likely rotating in the same
plane (indicated by the blue shape) as the external magnetic field,
perpendicular to the axis of rotation. This corresponds to a precession
angle of 90°. (d) The precession angle does not necessarily have
to be equal to 90°. Because of the interplay between magnetic
and hydrodynamic forces, other precession angles are possible as well.
Synthesized Structures Have Random Shapes
Magnetic microstructures were obtained from a high-throughput synthesis
described earlier.[5] Their dimensionless
speeds were measured (512 structures) and their shapes were reconstructed
(47 structures, see methods in the Supporting
Information). The reconstructed shapes were compared to simulated
shapes generated in silico by a random process (spheres are joined
in random directions without overlap, see Supporting
Information for details) to assess whether the synthesized
structures are similarly random. In order to allow a quantitative
comparison, several geometric parameters were extracted from the reconstructed,
as well as the simulated shapes (see Supporting
Information). The general accuracy of the reconstruction method
was assessed by comparing a size measure of the reconstructed shape
(lmax), to the size of the microstructures
as measured in the original video frames (Figure a). lmax is the
largest distance (in μm) between a pair of voxels belonging
to (and forming) the reconstructed shape. The two measures are generally
in agreement, although the maximum voxel to voxel distance tends to
be a little larger. This was expected, because only five different
2D projections were recorded and the 2D projection in which the propeller
would appear largest might not have been one of them. Also, the maximum
voxel to voxel distance in the reconstruction might be larger than
the true size of the propeller due to overshadowing. The compactness
of the reconstructed shapes as well as the artificially generated
shapes is characterized by plotting the surface area of the volume
equivalent sphere against the actual surface area of the shape (Figure c). Similarly, the
sphericity is defined as the ratio between the surface area of the
volume equivalent sphere to the actual surface area of a shape. The
distribution of this dimensionless geometric parameter is broad for
both reconstructed as well as generated shapes, indicating that the
reconstructed shapes are similarly random (Figure b). We also introduce a geometric parameter,
which we call pseudochirality. A shape is said to be chiral if it
cannot be superposed on its mirror image by rotations and translations.
Chirality can be quantified bywhere A defines the reconstructed shape and A′ its mirror image, V(...) is the
volume and the minimization is performed over all rotations and translations.[43] The pseudochirality is defined similarly; however,
the minimization is done only for one axis of rotation after moving
the center of mass (assuming constant density) of the shape and its
mirror image to the origin of the coordinate system. For the reconstructed
shapes, the rotation is around an axis parallel to the actual axis
of rotation of the experimental microstructure. This pseudochirality
is easier to calculate and seemingly more useful for the characterization
of the propulsion properties of magnetic microstructures. Chirality
is independent of the axis of rotation of a shape, whereas pseudochirality
is not. It has recently been observed that also achiral shapes can
propel, depending on their axis of rotation.[36,37] The distributions of pseudochirality are broad for both reconstructed
as well as generated shapes (Figure d). We thus conclude that the synthesized structures
are indeed random,in the sense that their distributions of geometric
parameters are similar to those of shapes generated by a random process.
Figure 2
(a) Plot
of the microstructure size measured in video frames (L) against the maximum voxel to voxel distance in the reconstructed
shapes (lmax). The reconstructed shape
consists of 3D voxels, thus lmax is a
measure of the microstructure size. The green line is a line through
the origin with slope one. The good correspondence between the two
measures of propeller size is an indication of the quality of the
3D reconstruction. (b) Normalized histograms of the geometric parameter
sphericity extracted from reconstructed (blue) as well as artificially
generated (red) shapes. (c) The surface area of spheres, equivalent
in volume to the reconstructed (blue) or artificially generated (red)
shapes, is plotted against the measured surface area of the shapes.
This plot is used to characterize the compactness of shapes and again
indicates the similarity of reconstructed and generated shapes. (d)
Same as (b) but for pseudochirality instead of sphericity.
(a) Plot
of the microstructure size measured in video frames (L) against the maximum voxel to voxel distance in the reconstructed
shapes (lmax). The reconstructed shape
consists of 3D voxels, thus lmax is a
measure of the microstructure size. The green line is a line through
the origin with slope one. The good correspondence between the two
measures of propeller size is an indication of the quality of the
3D reconstruction. (b) Normalized histograms of the geometric parameter
sphericity extracted from reconstructed (blue) as well as artificially
generated (red) shapes. (c) The surface area of spheres, equivalent
in volume to the reconstructed (blue) or artificially generated (red)
shapes, is plotted against the measured surface area of the shapes.
This plot is used to characterize the compactness of shapes and again
indicates the similarity of reconstructed and generated shapes. (d)
Same as (b) but for pseudochirality instead of sphericity.
Random Shapes Have High Dimensionless Speeds
The distribution of the measured dimensionless speed values is
shown in Figure a.
We arbitrarily set the dimensionless speed to be negative for propellers
moving away from the observer when rotating clockwise, being viewed
along the axis of rotation. A Gaussian fit to the data yields a width
of 46.5, which coincides with the standard deviation of the sample.
The mean is close to zero as expected for symmetry reasons. The measured
distribution of propeller speeds is lower than expected for a Gaussian
at |U| ≈ 25 and
higher than expected at U ≈ 0. Whether these deviations from a Gaussian distribution
are statistically significant (at the 5% level) depends on the statistical
test that is used. While the Lilliefors test does reject the null
hypothesis that the measured distribution stems from the normal family,
the Anderson-Darling and the Jarque-Bera test cannot reject this null
hypothesis. Thus, we do not rule out the possibility of deviations
from a Gaussian distribution but use the Gaussian as a phenomenological
description.
Figure 3
Distribution of dimensionless speed for random shapes
has a remarkably high standard deviation. (a) The measured values
for the dimensionless propulsion speed U are displayed as a histogram. The red line is a
Gaussian fit to the data. However, Lilliefors test rejects the null
hypothesis that the data comes from a normal distribution at the 5%
significance level. The pronounced maximum at 0 and the minima at
about ±25 are thus potentially no statistical flukes and in fact
the distribution might be bimodal. Although a model with one central
Gaussian and two symmetrically shifted Gaussians is (trivially) better,
we do not include it because we lack a physical argument motivating
such a bimodal distribution. The standard deviation of the data and
the width of the Gaussian fit is 46.5. The mean of the distribution
is −0.1 and thus close to zero as expected for symmetry reasons.
(b–e) From all 47 propellers for which the 3D shape was successfully
reconstructed, 4 examples were selected according to the following
criteria: The absolute value of the dimensionless speed was lowest
in (b) (U ≈ 1),
highest in (c) (U ≈
−190), and close to the standard deviation in (d) (U ≈ 47) and (e) (U ≈ −50). The
dashed black lines in (b–e) indicate the approximate position
of the rotation axis. The propellers are rotating clockwise when viewed
from below. The movement is upward if the dimensionless speed is negative
and vice versa (see Supporting Information Figure S1). Scale bars are 1 μm.
As discussed in the introduction, arbitrarily shaped
propellers might have a tendency to rotate around an axis for which
the rotation translation coupling is strong. Thus, the distribution
of dimensionless speeds we measure might not be identical to the dimensionless
speed distribution of randomly shaped structures rotating around random
axes of rotation in bulk fluid. The dimensionless speeds we measure
might be increased by the dependence of the rotational friction constant
(cF) on the rotation translation (C) coupling discussed above, and surface effects might have
an influence as well. Measuring the speed close to a surface probably
decreases the speed with respect to the bulk propulsion speed (see Supporting Information). However, the propulsion
speeds of previously published designs for magnetically actuated propellers
have almost always also been measured near a solid surface.[6,9,11,12,15−17,19,20,22,23,26,28,31,32] Thus, although the dimensionless speeds reported here might not
correspond with an idealized theoretical situation, they lend themselves
perfectly to comparisons with published propeller designs.Figure b–e shows
four examples of reconstructed shapes and their measured dimensionless
speeds. The structure in Figure c has a dimensionless speed of 190, which is faster
than all previously reported magnetic micropropellers moving in low
Reynolds number liquid. Interestingly, the shape of this propeller
is similar to optimized shapes reported in a theoretical study by
Keaveny et al.[2] The propeller is approximately
helical with less than one full helical turn. The optimization performed
by Walker et al. arrives at a similar shape as well and they also
experimentally verify that this shape moves particularly fast.[22] However, the dimensionless speed of their optimal
propeller (92) is significantly below the highest dimensionless speed
we find here (190).Distribution of dimensionless speed for random shapes
has a remarkably high standard deviation. (a) The measured values
for the dimensionless propulsion speed U are displayed as a histogram. The red line is a
Gaussian fit to the data. However, Lilliefors test rejects the null
hypothesis that the data comes from a normal distribution at the 5%
significance level. The pronounced maximum at 0 and the minima at
about ±25 are thus potentially no statistical flukes and in fact
the distribution might be bimodal. Although a model with one central
Gaussian and two symmetrically shifted Gaussians is (trivially) better,
we do not include it because we lack a physical argument motivating
such a bimodal distribution. The standard deviation of the data and
the width of the Gaussian fit is 46.5. The mean of the distribution
is −0.1 and thus close to zero as expected for symmetry reasons.
(b–e) From all 47 propellers for which the 3D shape was successfully
reconstructed, 4 examples were selected according to the following
criteria: The absolute value of the dimensionless speed was lowest
in (b) (U ≈ 1),
highest in (c) (U ≈
−190), and close to the standard deviation in (d) (U ≈ 47) and (e) (U ≈ −50). The
dashed black lines in (b–e) indicate the approximate position
of the rotation axis. The propellers are rotating clockwise when viewed
from below. The movement is upward if the dimensionless speed is negative
and vice versa (see Supporting Information Figure S1). Scale bars are 1 μm.In contrast, most previous designs for magnetic micropropellers
have used screw-like shapes, consisting of many helical turns, a design
originally inspired by the shape of bacterial flagella.[11]Figure compares the dimensionless speeds of all previously reported
rigid magnetic micropropeller designs, now with the absolute value
of the dimensionless speeds of the random shapes reported here. This
comparison shows that the observed standard deviation of σ =
46.5 in the dimensionless speed of random shapes is unexpectedly high.
Most previously published propellers have dimensionless speeds below
140 (3 σ). The only propeller reported to have a significantly
higher dimensionless speed, moved in a high density solution of microbeads,
instead of pure water.[16] This structure
might thus have moved more like a screw turning in a solid medium,
rather than a propeller moving in low Reynolds number liquid.[16] Some nanofabricated propellers have dimensionless
speeds, which are smaller than 46.5 (1 σ). The dimensionless
speed of the artificial bacterial flagella, used successfully in many
studies, is 33[11] and thus 49% of the observed
random shapes have a higher dimensionless speed.
Figure 4
Comparison of randomly
shaped propellers from solution synthesis with previously published
nanofabricated propellers. The dimensionless speeds of nanofabricated
propellers are shown in increasing order as in Supporting Information Figure S3 (Zhang et al.,[11] Li et al.,[35] Schamel
et al.,[18] Schuerle et al.,[33] Gao et al.,[34] Walker et al.,[22] Tottori et al.,[23] Peters et al.,[32] Ghosh et al.,[17] Zhang et al.[16]).
(a) The absolute value of dimensionless speeds of the random shapes
and the dimensionless speed values from the literature are assigned
a propeller number, by sorting the values in ascending order. The
dimensionless speed of both random shapes and nanofabricated propellers
is then plotted against the propeller number. The propeller number
of a nanofabricated propeller is thus an indication of how many random
shapes (from the set of the 512) have a lower dimensionless speed.
(b) The distribution of absolute dimensionless speed values is displayed
as a histogram. The scale of |U| is the same as in (a). The multiples of the standard deviation
of U are displayed as
horizontal green lines. Assuming a Gaussian distribution, 68% of random
shapes have dimensionless speeds lower than the one standard deviation,
95% lower than two, and 99.7% lower than three standard deviations.
Comparison of randomly
shaped propellers from solution synthesis with previously published
nanofabricated propellers. The dimensionless speeds of nanofabricated
propellers are shown in increasing order as in Supporting Information Figure S3 (Zhang et al.,[11] Li et al.,[35] Schamel
et al.,[18] Schuerle et al.,[33] Gao et al.,[34] Walker et al.,[22] Tottori et al.,[23] Peters et al.,[32] Ghosh et al.,[17] Zhang et al.[16]).
(a) The absolute value of dimensionless speeds of the random shapes
and the dimensionless speed values from the literature are assigned
a propeller number, by sorting the values in ascending order. The
dimensionless speed of both random shapes and nanofabricated propellers
is then plotted against the propeller number. The propeller number
of a nanofabricated propeller is thus an indication of how many random
shapes (from the set of the 512) have a lower dimensionless speed.
(b) The distribution of absolute dimensionless speed values is displayed
as a histogram. The scale of |U| is the same as in (a). The multiples of the standard deviation
of U are displayed as
horizontal green lines. Assuming a Gaussian distribution, 68% of random
shapes have dimensionless speeds lower than the one standard deviation,
95% lower than two, and 99.7% lower than three standard deviations.In the literature, there are also
flexible magnetic structures that can be actuated by rotating magnetic
fields (for example, ref (39)). We exclude these from our comparison, as their swimming
mechanism is different and does not allow the definition of a dimensionless
speed. An exception could be the micropropellers reported by Cheang
and Kim,[44] which consist of chains of flexibly
linked magnetic nanoparticles. At least for a fixed actuation frequency,
the shape of the structures is constant and a dimensionless speed
can be calculated. They report swimming speeds for two micrometer-sized
structures moving in salt solution far from a solid interface. The
corresponding dimensionless speeds are about 43 and 18 for the bigger
and smaller structure, respectively, both below the standard deviation
of random shapes we report here.
Chirality Parameter Correlates
with Dimensionless Speed
We extracted nine geometric parameters
from the reconstructed 3D shapes (see Supporting
Information for parameter definitions). The only geometric
parameter that correlated significantly with the dimensionless speed
is the dimensionless pseudochirality introduced above. The Pearson
correlation coefficient is 0.49, with a p-value of 4 × 10–4, meaning that the null-hypothesis of the pseudochirality
being uncorrelated to the dimensionless can be rejected with high
confidence. The predictive power of the pseudochirality is limited,
but it is already remarkable that a simple geometric parameter can
characterize the rotation translation coupling of a particular shape
to some extent, the theoretical calculation of which is challenging
and involves complex hydrodynamic simulations. If more such geometric
parameters could be found, the search for optimal designs of magnetically
actuated propellers could be greatly simplified.Correlation between the
dimensionless speed and the pseudochirality. The absolute value of
the dimensionless speed is plotted against the pseudochirality in
panel (blue and red circles) (c). The data points corresponding to
propellers displayed in Figure are marked in red. The green line is a linear fit to the
data. (a) This structure has a high dimensionless speed, despite having
a relatively low pseudochirality. This might be because some small
but important geometric features could not be resolved in optical
microscopy. (b) This structure has a low pseudochirality and also
a low dimensionless speed. (d) This structure has a high pseudochirality
and also a high dimensionless-speed. (e) This relatively large structure
has a very low dimensionless speed, despite having a high pseudochirality.
The rotation axes of the propellers are marked by dashed lines, scale
bars are 1 μm.
Implications for the Use and Design of Micropropellers
The
observation that helical propeller designs are mostly only average
when compared to randomly shaped propellers is very surprising. However,
randomness per se is not an advantage; we simply use random shape
generation to sample a space of possible shapes. The high speeds with
which some of our synthesized propellers move depends critically on
their specific geometry. The discovery that randomly shaped magnetic
micropropellers have high dimensionless speeds has two main implications.First, it shows that randomly shaped magnetic micropropellers could
be used in applications, which do not require particularly high dimensionless
speeds. Randomly shaped magnetic nanostructures can be produced cheaply
in very large quantities and we have shown previously that propellers
can be selected from such nanostructures.[5] The present work shows that such selection is probably not necessary
for certain applications, like micromixing,[45] environmental remediation tasks,[46] or
catalyst recovery.[47−49] The majority of the produced structures have dimensionless
speeds comparable to those of nanofabricated propellers and samples
of nanofabricated (designed) propellers always contain defective (malformed)
structures with reduced speeds as well.Second, new micropropeller
designs could accommodate other design objectives, like ease of fabrication
or the incorporation of additional functionalities, without compromising
their ability to be effectively actuated by rotating magnetic fields.
The range of shapes for which such effective actuation is possible
seems to be much larger than was previously assumed. The predominant
helical, flagella-like designs are certainly not the only option and
might be suboptimal for certain applications, such as particular microassembly
tasks or specific envisioned medical procedures.[50−53] Nonetheless, helical propellers
with many turns might have specific advantages for certain applications,
like pick and place devices.[22]In
addition, we identify a structure that has a higher dimensionless
speed than any propeller reported previously, showing that random
shape generation can be a viable optimization method. The shape of
this fastest propeller is similar to theoretically optimized propeller
shapes, considering slender bodies with a single centerline.[2] Future theoretical studies and simulations could
be tremendously helpful to better understand the relationship between
propeller shape and dimensionless speed. Which geometric shape has
the highest possible dimensionless speed remains an intriguing unsolved
problem in hydrodynamics, although in practice additional constraints
(minimum material thickness, magnetization state, Brownian motion,
and so forth) and trade-offs (for example, achievable magnetic moment
is also shape-dependent) would need to be considered as well. For
biological applications, non-Newtonian and heterogeneous biological
fluids pose entirely new challenges.[18] Nonetheless,
the fastest propeller we observe sets a lower bound for the highest
physically possible dimensionless speed.
Authors: Soichiro Tottori; Li Zhang; Famin Qiu; Krzysztof K Krawczyk; Alfredo Franco-Obregón; Bradley J Nelson Journal: Adv Mater Date: 2012-01-02 Impact factor: 30.849
Authors: Lamar O Mair; Sagar Chowdhury; Genaro A Paredes-Juarez; Maria Guix; Chenghao Bi; Benjamin Johnson; Bradley W English; Sahar Jafari; James Baker-McKee; Jamelle Watson-Daniels; Olivia Hale; Pavel Stepanov; Danica Sun; Zachary Baker; Chad Ropp; Shailesh B Raval; Dian R Arifin; Jeff W M Bulte; Irving N Weinberg; Benjamin A Evans; David J Cappelleri Journal: Micromachines (Basel) Date: 2019-03-31 Impact factor: 2.891