Pith. sign in

REVIEW 3 major objections 5 minor 52 references

Living droplets with mesoscale swimmers

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Inside a droplet, swimming organisms cannot change its resonant frequency; they only change how hard it shakes, and crowding slows them as a -0.7 power law.

desk verdict Frequency result is clean, amplitude model plausible, but the crowding-velocity exponent is an inference built on a circular definition — treat as hypothesis, not measurement. read the letter →

arxiv 2509.20005 v4 pith:A4DM6ZFM submitted 2025-09-24 cond-mat.soft physics.bio-ph

L. Malik , N. Sharadhi , C. Prêcheur Llarena , M. Lamminmäki , R. A. Lara , V. Jokinen , M. Lisicki , M. Backholm This is my paper · ORCID
classification cond-mat.softphysics.bio-ph
keywords livingdropletsmesoscaleswimmersdropletresonancecrowdingArtemianaupliisuperhydrophobicsubstratesoscillationamplitudescaling3Dactivematter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish how internally generated swimming activity changes the mechanics of a water droplet. Using droplets on a superhydrophobic surface that contain one to six brine shrimp larvae, it shows that the droplet's oscillation frequency is the same as the classical resonance of an inert droplet, regardless of how many swimmers are inside or how they swim. The oscillation amplitude, however, is set by the probability of swimmers colliding with the interface and by the momentum each collision transfers, captured in a new scaling law. In the dilute, unconfined regime the law predicts measured amplitudes; as swimmers crowd the droplet, the amplitude deficit implies their swimming speed falls as the -0.7 power of dimensionless density. If true, this turns a droplet into a non-invasive readout of crowded-swimmer kinematics in 3D.

What carries the argument

The load-bearing object is Eq. (1), a scaling law for droplet amplitude built from three steps: a geometric collision probability P = 3L/R - 3(L/R)^2 + (L/R)^3, equal to the peripheral volume fraction within one swimmer length of the interface; momentum conservation during a single swimmer–interface impact, which gives the per-swimmer amplitude A0 ~ Ua Va/(4 V f1); and a first-order superposition At ~ P A0 N for N swimmers. The paper then defines a crowding–confinement factor Kc = A/At, where A is the measured RMS amplitude, and interprets Kc as the ratio of crowded to free swimming speed. This factor converts a passive droplet measurement into an inference about swimmer kinematics.

What would settle it

Track the actual 3D swimming paths of individual Artemia inside droplets as N increases; if their directly measured mean speed does not fall as N*^-0.7 while the droplet amplitude still drops, then the Kc inference is wrong. Alternatively, run the same droplet experiment with inert particles of identical size and density: if amplitude falls with density without any living slowdown, the collision model itself carries the deficit.

Watch

Extended reading notes

Core claim

The central discovery is a clean separation of time scales: the living droplet's frequency response is dictated by the droplet itself and is insensitive to the swimmer activity, while the amplitude is a direct measure of that activity. For one to six Artemia nauplii in droplets 600–1750 µm in radius (L/R = 0.30–0.57), the measured fundamental frequency follows the classical sessile-droplet resonance scaling f1 ~ V^-0.5. The authors derive an amplitude law, At ~ (Ua Va N)/(4 V f1)[3(L/R) - 3(L/R)^2 + (L/R)^3], from collision probability and momentum conservation, and find agreement with experiment (A = 1.8 At) in the uncrowded regime. They then attribute the amplitude shortfall at higher dens

Load-bearing premise

The speed-reduction curve rests on the assumption that every deviation between the measured amplitude and Eq. (1) is caused solely by slower swimming; if crowding also changes collision probability, the linear superposition of single-swimmer amplitudes, or the momentum transfer per collision, then the inferred N*^-0.7 scaling is an artifact of the model rather than a measured speed.

Editorial extensions

If this is right

  • The droplet's resonant frequency is an invariant property of the droplet itself, so swimmer-laden droplets can be used as resonators whose frequency is set by volume, surface tension, and contact angle rather than by swimmer activity.
  • Eq. (1) gives a predictive formula for oscillation amplitude from swimmer size, number, droplet volume, and unconfined swim speed in the dilute regime.
  • Crowding slows mesoscale swimmers in 3D more steeply than previously observed in 2D (exponent -0.7 vs -0.4), indicating dimensionality changes the collective slowdown mechanism.
  • The micropipette readout is sensitive enough to detect collective swimmer effects without imaging individual organisms, opening a non-invasive probe for dense active matter in 3D.
  • The platform could be scaled to organisms too small or too fast for direct video tracking, extending resonance-based activity sensing to microbial swimmers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the Kc identification is correct, droplet amplitude becomes a general speedometer for crowded 3D swimmers; a direct test would be to track individual larvae in a droplet with volumetric microscopy and compare measured speeds with Uc from Fig. 3c.
  • The -0.7 versus -0.4 exponent gap is likely shaped by the 3D collision geometry at L ~ R; varying L/R at fixed density should interpolate the exponent toward the 2D value—a prediction the paper does not make.
  • The low-frequency envelope mode f2 may encode synchronized impacts; if so, controlling swimmer number and phase could tune droplet actuation, but the paper explicitly does not investigate this mode.
  • Replacing living swimmers with inert particles of the same size and density would isolate the collision and momentum-transfer parts of Eq. (1); if the amplitude deficit persists, the speed reduction interpretation would need revision.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper reports experiments on nearly spherical water droplets on superhydrophobic substrates containing 1–6 living mesoscale swimmers (Artemia nauplii, with a few copepod tests). Droplet oscillations are measured via micropipette deflection. The authors find that the dominant oscillation frequency f1 follows the classical Celestini–Kofman resonance scaling f1 ~ V^-0.5 independent of swimmer number, size, and species. For the oscillation amplitude, they propose a scaling model, Eq. (1), in which the amplitude grows with the number of swimmers N, the collision probability P, and a per-swimmer amplitude A0 set by momentum conservation. In the uncrowded regime they report agreement with a fitted prefactor of 1.8. Deviations of the measured amplitude A from the predicted At are then attributed entirely to crowding-induced swimmer speed reduction through Kc = A/At, yielding an empirical power law Uc/U0 ~ N*^-0.7, compared with ~N*^-0.4 for 2D robotic fish. The paper concludes that crowding in 3D reduces mesoswimmer speed more strongly than in 2D.

Significance. If the central inference is valid, this is a valuable experimental contribution: it provides one of the few measurements of crowding effects on mesoscale swimmers in a three-dimensional, near-spherical confinement, and it introduces a non-invasive way to probe internal swimmer dynamics through droplet oscillation amplitudes. The frequency result is clean and appears robust across species and swimmer sizes. The paper also makes its data available on Zenodo, which is commendable. However, the main quantitative claim—the Uc/U0 ~ N*^-0.7 crowding law in Fig. 3c—is not a direct measurement of swimmer speed but is inferred from the ratio A/At. Because any N-dependent error in the amplitude model is forced into Kc, this claim is currently underdetermined. The paper's value would be substantially strengthened by independent speed measurements or by explicit falsification of alternative explanations for the amplitude deficit.

major comments (3)
  1. [Paragraph defining Kc; Fig. 3c] The central crowding result Uc/U0 = (A/At)(Ua/U0) is circular in construction: Kc is defined as A/At, so the measured amplitude deviation from Eq. (1) is, by definition, interpreted as a swimmer speed reduction. Every N-dependent discrepancy between A and At is then forced into Uc. The uncrowded parity plot (Fig. 3b) validates the prefactor of Eq. (1) only at low N and cannot validate the N-scaling, because the N=1 point is used to normalize U0. Alternative N-dependent effects with no speed change—such as sub-linear superposition of uncorrelated impacts (RMS amplitude ~ N^1/2 instead of N), an N-dependent collision probability P, or the synchronized impacts invoked in the f2 envelope—could produce the same downward trend in A/At. The paper acknowledges the inference but does not rule out these alternatives. Please provide independent speed measurements (e.g., direct tracking of swimmers
  2. [Eq. (1) and Fig. 3b] The amplitude model is calibrated with a fitted prefactor of 1.8 (A = 1.8 At) and no uncertainty or error bars are reported on the parity plot or on the fitted exponent in Fig. 3c. Given that Eq. (1) involves several order-unity assumptions (Ud ~ 4 A0 f1, linear superposition At ~ P A0 N, ellipsoidal swimmer volume, P = 3(L/R) - 3(L/R)^2 + (L/R)^3), the prefactor 1.8 could absorb missing physics rather than represent genuine agreement. More importantly, the crowding exponent -0.7 is obtained from a fit whose range and statistical quality are not documented. Please report the number of data points, confidence intervals, and a sensitivity analysis with respect to the chosen cutoff values L* < 0.4 and N* < 0.03.
  3. [Regime cutoffs L* < 0.4 and N* < 0.03] The definition of unconfined and uncrowded via the hand-picked cutoffs L* < 0.4 and N* < 0.03 is not justified, and no sensitivity analysis is provided. The crowding exponent in Fig. 3c depends on which points are included in the normalization U0 and on where the power-law fit begins and ends. With only N = 1–6 swimmers and a narrow range of dimensionless densities, the reported power-law exponents (-0.7 vs -0.4 for the fish data) may not be robust. Please show the fit with confidence bounds and test how the exponent changes with reasonable variations of the cutoffs.
minor comments (5)
  1. [General] Typos: continuos should be continuous; Copeopods should be Copepods; the Bond number definition says Bo = rho_d g R^2 / gamma = 0, but the dimensionless Bond number has gR^2, not R^2 alone; please clarify.
  2. [Eq. (1) derivation] The estimate Ud ~ 4 A0 f1 is introduced without derivation or justification. A sentence explaining the factor 4 and the characteristic time 1/(4 f1) is needed.
  3. [Fig. 3c normalization] The definition of U0 = Kc0 Ua, where Kc0 is the slope of N=1 and L*<0.4 data in Fig. 3b, is confusing because Kc0 already normalizes the amplitude model. Please state explicitly how Kc0 is computed and how its uncertainty propagates to Uc/U0.
  4. [Supplementary material reference] The inline reference to See Supplementary Material at [URL] contains a placeholder; if the paper is to be considered for publication, the actual DOI or link must be provided.
  5. [Fig. 2 and f2] The envelope frequency f2 is mentioned but not analyzed. Since the paper invokes synchronized impacts to explain f2, and synchronization is one of the alternative mechanisms that could affect A/At, a brief discussion or a pointer to future work would help the reader assess the robustness of the crowding interpretation.

Circularity Check

2 steps flagged · score 7.0 of 10

Crowding-speed reduction is definitionally Kc = A/At; Uc/U0 ~ N*^-0.7 is a relabeling of the amplitude residual, not a measured speed.

  1. fitted input called prediction [Section 'To fully understand the activity of the living droplets...' (Kc definition) and Fig. 3c]
    "This allows us to empirically measure Kc = A/At from the deviation between the theoretical free-swimming model of At and the experimentally measured A. We plot the resulting Uc/U0 = KcUa/U0 as a function of dimensionless swimmer density, N ∗ in Fig. 3c, where U0 = Kc,0Ua is the average swimmer velocity in the least crowded condition (Kc,0 is the slope of N = 1 and L∗ < 0.4 data in Fig. 3b)."

    The 'crowded swimmer velocity' Uc is not independently measured; it is defined as Uc = KcUa with Kc = A/At. Therefore the reported decrease of Uc/U0 with N* is, by construction, the decrease of the amplitude ratio A/At. Since At in Eq. (1) is fixed to be linear in N (At ~ P A0 N) and uses a constant Ua, any N-dependent error in that model—sublinear superposition of collision impulses, N-dependent collision probability, wall/confinement effects, or synchronized impacts—is automatically relabeled as a speed reduction. The paper itself says 'we infer reductions in swimmer velocity', confirming that this is a model-dependent inference, not a direct measurement. Thus the central claim 'crowding reduces swimmer speed' reduces by definition to the fit residual of Eq. (1).

  2. self definitional [Fig. 3c caption and accompanying text on empirical scaling]
    "An empirical scaling of Uc/U0 ∼ N ∗−0.7 is found for our experiments as opposed to Uc/U0 ∼ N ∗−0.4 for the 2D robotic fish, indicated by black and red lines, respectively."

    Because Uc/U0 = (A/At)Ua/U0 and At ∝ N through Eq. (1), the fitted exponent −0.7 is simply 1 − β, where β is the effective exponent of the measured amplitude A(N) relative to the assumed linear law. It is not a model-free measurement of swimmer kinematics. A different but equally plausible amplitude superposition rule (e.g., A ∼ N^{1/2} for uncorrelated impact phases) would yield a spurious Uc/U0 ∼ N^{−1/2} even if swimmer speed were constant. The comparison with the 2D robotic-fish exponent −0.4 therefore compares a directly measured speed with a quantity that is defined as the deviation from a linear-superposition assumption, so the claimed 'difference in swimming kinematics in crowded 3D environments' is not established independently of the model.

full rationale

The paper contains two genuinely independent pieces: the droplet resonance frequency follows the CK model using measured droplet parameters, and Eq. (1) is tested against uncrowded amplitudes using literature single-swimmer speeds (Ref. [44]). Those are not circular. However, the paper's headline claim—that crowding reduces swimmer speed with Uc/U0 ~ N*^-0.7—is not independently measured. The crowded velocity is defined as Uc = (A/At)Ua, so the reported reduction is exactly the ratio of the measured amplitude to the linear-superposition model amplitude. The paper explicitly states 'we infer reductions in swimmer velocity', confirming the definitional nature. Because At assumes At ∝ N and constant Ua, any N-dependence in the true amplitude superposition, collision probability, or impact phase is attributed to speed. Thus the exponent −0.7 is a fit to the model residual, not a kinematic measurement. This is a partial circularity (score 7): the derivation is self-consistent, but the central crowding-speed claim reduces by construction to the amplitude deviation from Eq. (1).

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new particles, forces, or conserved quantities. The factor Kc is a fitted dimensionless correction, not an invented entity.

free parameters (3)
  • Amplitude prefactor C = 1.8 (A = 1.8 At)
    Calibrated on the unconfined/uncrowded dataset; absorbs all modeling simplifications and has no independent derivation.
  • Regime cutoffs L* and N* = L* < 0.4, N* < 0.03
    Hand-chosen thresholds that define the 'free swimming' baseline used for calibration and U0.
  • Crowding exponent beta = -0.7 (Uc/U0 ~ N*^-0.7)
    Power-law exponent fit to the inferred Uc/U0 versus N* data; no mechanistic derivation.
assumptions (6)
  • domain assumption The droplet is spherical with peripheral volume Vp = V - (4π/3)(R-L)^3.
    Used to define the collision probability P in Eq. (1); ignores droplet deformation and swimmer position distribution.
  • domain assumption Momentum conservation during swimmer-interface impact: ρd V Ud = ρa Va Ua, with ρd ≈ ρa.
    Assumes fully inelastic collision with complete momentum transfer; no hydrodynamic interactions or droplet deformation.
  • ad hoc to paper Droplet interface velocity Ud ≈ 4 A0 f1.
    Assumes sinusoidal interface motion with amplitude A0 at frequency f1 over a quarter period; not derived from droplet mechanics.
  • ad hoc to paper Total amplitude is a linear superposition: At ≈ P A0 N.
    Assumes each swimmer acts independently and amplitudes add coherently; no phase or synchronization effects.
  • ad hoc to paper Deviation of measured amplitude from At is due solely to swimmer speed reduction: Kc = A/At = Uc/Ua.
    This is the key inference behind the crowding result; other model errors are absorbed into the inferred speed.
  • domain assumption Free-swimming speeds Ua = 1.2 and 2.8 mm/s from Ref [44] apply inside the droplet in the unconfined regime.
    Uses velocities measured outside confinement; the paper does not validate them in situ.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Living droplets with mesoscale swimmers." pith.science (2026). https://pith.science/paper/A4DM6ZFM

@misc{pith2026250920005,
  author       = {Pith},
  title        = {Pith review of: Living droplets with mesoscale swimmers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A4DM6ZFM}},
  note         = {Machine review of arXiv:2509.20005}
}
read the original abstract

We study the activity of 'living' droplets, which confine tens of swimming meso-organisms in 3D using a superhydrophobic substrate. With few swimmers, the droplet oscillates at its inherent resonant frequency. We observe deviations from this classical regime as the level of confinement or crowding increases and develop scaling law models to successfully describe our results. We report a difference in swimming kinematics in crowded 3D environments compared to quasi-2D. Our work reveals mechanisms for bio-inspired droplet actuation with implications for mesoscale robotics, fluidics, and sensing.

Figures

Figures reproduced from arXiv: 2509.20005 by the authors.

Figure 1
Figure 1. FIG. 1. Experimental micrographs of living droplets sup [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Oscillatory signal, frequency response and charact [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Theoretical prediction of droplet amplitude and cha [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

52 extracted references · 1 canonical work pages

  1. [1]

    Elgeti, R

    J. Elgeti, R. G. Winkler, and G. Gompper, Physics of microswimmers—single particle motion and collec- tive behavior: a review, Rep. Prog. Phys. 78, 056601 (2015)

    2015

  2. [2]

    Bechinger, R

    C. Bechinger, R. Di Leonardo, H. L¨ owen, C. Reich- hardt, G. Volpe, and G. Volpe, Active particles in complex and crowded environments, Rev. Mod. Phys. 88, 045006 (2016)

    2016

  3. [3]

    Klotsa, As above, so below, and also in between: mesoscale active matter in fluids, Soft Matter 15, 8946–8950 (2019)

    D. Klotsa, As above, so below, and also in between: mesoscale active matter in fluids, Soft Matter 15, 8946–8950 (2019)

    2019

  4. [4]

    M. C. Marchetti, J. F. Joanny, S. Ramaswamy, T. B. Liverpool, J. Prost, M. Rao, and R. A. Simha, Hydro- dynamics of soft active matter, Rev. Mod. Phys. 85, 1143 (2013)

    2013

  5. [5]

    Kokot, H

    G. Kokot, H. A. Faizi, G. E. Pradillo, A. Snezhko, and P. M. Vlahovska, Spontaneous self-propulsion and nonequilibrium shape fluctuations of a droplet enclos- ing active particles, Communications Physics 5, 91 (2022)

    2022

  6. [6]

    Bricard, J.-B

    A. Bricard, J.-B. Caussin, N. Desreumaux, O. Dau- chot, and D. Bartolo, Emergence of macroscopic di- rected motion in populations of motile colloids, Nature 503, 95– (2013)

    2013

  7. [7]

    H. R. Vutukuri, M. Hoore, C. Abaurrea-Velasco, L. van Buren, A. Dutto, T. Auth, D. A. Fedosov, G. Gompper, and J. Vermant, Active particles induce large shape deformations in giant lipid vesicles, Nature 586, 52 (2020)

    2020

  8. [8]

    Sciortino, H

    A. Sciortino, H. A. Faizi, D. A. Fedosov, L. Frechette, P. M. Vlahovska, G. Gompper, and A. R. Bausch, Active membrane deformations of a minimal synthetic cell, Nat. Phys. 21, 799–807 (2025)

    2025

Show all 52 references
  1. [9]

    Ahmed, C

    D. Ahmed, C. Dillinger, A. Hong, and B. J. Nelson, Artificial acousto-magnetic soft microswimmers, Ad- vanced Materials Technologies 2, 1700050 (2017)

    2017

  2. [10]

    J. F. Boudet, J. Lintuvuori, C. Lacouture, T. Barois, A. Deblais, K. Xie, S. Cassagnere, B. Tregon, D. B. Br¨ uckner, J. C. Baret, and H. Kellay, From collections of independent, mindless robots to flexible, mobile, and directional superstructures, Science Robotics 6, eabd0272 (2021)

    2021

  3. [11]

    J. F. Boudet, M. Bergmann, A. Iollo, and H. Kellay, Effects of boundaries for high reynolds number artifi- cial swimmers, Scientific Reports 15, 2045 (2025)

  4. [12]

    Vent´ ejou, I

    B. Vent´ ejou, I. Magniez-Papillon, E. Bertin, P. Peyla , and A. Dupont, Behavioral transition of a fish school in a crowded environment, Phys. Rev. E 109, 064403 (2024)

    2024

  5. [13]

    Lafoux, P

    B. Lafoux, P. Bernard, B. Thiria, and R. Godoy- Diana, Confinement-driven state transition and bista- bility in schooling fish, Phys. Rev. E 110, 034613 (2024)

    2024

  6. [14]

    Wioland, F

    H. Wioland, F. G. Woodhouse, J. Dunkel, J. O. Kessler, and R. E. Goldstein, Confinement stabilizes a bacterial suspension into a spiral vortex, Physical Review Letters 110, 268102 (2013)

    2013

  7. [15]

    Lushi, H

    E. Lushi, H. Wioland, and R. E. Goldstein, Fluid flows created by swimming bacteria drive self-organization in confined suspensions, Proceedings of the National Academy of Sciences 111, 9733–9738 (2014)

    2014

  8. [16]

    Soto and R

    R. Soto and R. Golestanian, Run-and-tumble dynam- ics in a crowded environment: Persistent exclusion process for swimmers, Phys. Rev. E 89, 012706 (2014)

    2014

  9. [17]

    Villalobos-Concha, Z

    C. Villalobos-Concha, Z. Liu, G. Ramos, M. Goral, A. Lindner, T. L´ opez-Le´ on, E. Cl´ ement, R. Soto, and M. L. Cordero, Active bacterial baths in droplets, Pro- ceedings of the National Academy of Sciences 122, e2426096122 (2025)

    2025

  10. [18]

    Ostapenko, F

    T. Ostapenko, F. J. Schwarzendahl, T. J. B¨ oddeker, C. T. Kreis, J. Cammann, M. G. Mazza, and O. B¨ aumchen, Curvature-guided motility of microal- gae in geometric confinement, Phys. Rev. Lett. 120, 068002 (2018)

    2018

  11. [19]

    Cammann, F

    J. Cammann, F. J. Schwarzendahl, T. Ostapenko, D. Lavrentovich, O. B¨ aumchen, and M. G. Mazza, Emergent probability fluxes in confined microbial nav- igation, Proceedings of the National Academy of Sci- ences 118, e2024752118 (2021)

    2021

  12. [20]

    Mondal, A

    D. Mondal, A. G. Prabhune, S. Ramaswamy, and P. Sharma, Strong confinement of active microalgae leads to inversion of vortex flow and enhanced mix- ing, eLife 10, e67663 (2021)

    2021

  13. [21]

    Tainio, F

    O. Tainio, F. Sohrabi, N. Janarek, J. Koivisto, A. Puisto, L. Viitanen, J. V. I. Timonen, and M. Alava, Chlamydomonas reinhardtii swimming in the plateau borders of 2d foams, Soft Matter 17, 145 (2021)

    2021

  14. [22]

    D. Wei, Y. Yang, X. Wei, R. Golestanian, M. Li, F. Meng, and Y. Peng, Scaling transition of active turbulence from two to three dimensions, Advanced Science 11, 2402643 (2024)

    2024

  15. [23]

    Kantsler, J

    V. Kantsler, J. Dunkel, M. Polin, and R. E. Goldstein, Ciliary contact interactions dominate surface scatter- ing of swimming eukaryotes, Proceedings of the Na- tional Academy of Sciences 110, 1187 (2013)

    2013

  16. [24]

    A. R. Sprenger, V. A. Shaik, A. M. Ardekani, M. Lisicki, A. J. T. M. Mathijssen, F. Guzm´ an-Lastra, H. L¨ owen, A. M. Menzel, and A. Daddi-Moussa- Ider, Towards an analytical description of active mi- croswimmers in clean and in surfactant-covered drops, The European Physical...

    2020

  17. [25]

    Kawakami and P

    S. Kawakami and P. M. Vlahovska, Microswimmer dy- namics in a hele-shaw droplet, Phil. Trans. R. Soc. A. 383, 20240254 (2025)

    2025

  18. [26]

    Kawakami and P

    S. Kawakami and P. M. Vlahovska, Migration and de- formation of a droplet enclosing an active particle, Journal of Fluid Mechanics 1007, A41 (2025)

    2025

  19. [27]

    Qu´ er´ e, Non-sticking drops, Rep

    D. Qu´ er´ e, Non-sticking drops, Rep. Prog. Phys. 68, 2495 (2005)

    2005

  20. [28]

    Noblin, A

    X. Noblin, A. Buguin, and F. Brochard-Wyart, Non- sticking drops, The European Physical Journal E 14, 395 (2004)

    2004

  21. [29]

    McHale, S

    G. McHale, S. J. Elliott, M. I. Newton, D. L. Herbert- son, and K. Esmer, Levitation-free vibrated droplets: Resonant oscillations of liquid marbles, Langmuir 25, 529–533 (2009)

    2009

  22. [30]

    Celestini and R

    F. Celestini and R. Kofman, Vibration of submillimeter-size supported droplets, Physical Review E 73, 041602 (2006)

    2006

  23. [31]

    J. S. Sharp, D. J. Farmer, and J. Kelly, Contact angle dependence of the resonant frequency of sessile water droplets, Langmuir 27, 9367 (2011)

    2011

  24. [32]

    J. B. Bostwick and P. H. Steen, Dynamics of sessile drops. part 1. inviscid theory, Journal of Fluid Me- chanics 760, 5–38 (2014)

    2014

  25. [33]

    Chang, J

    C.-T. Chang, J. B. Bostwick, S. Daniel, and P. H. Steen, Dynamics of sessile drops. part 2. experiment, Journal of Fluid Mechanics 768, 442–467 (2015)

    2015

  26. [34]

    Sakakeeny, C

    J. Sakakeeny, C. Deshpande, S. Deb, J. Alvarado, and Y. Ling, A model to predict the oscillation frequency for drops pinned on a vertical planar surface, Journal 6 of Fluid Mechanics 928, 1469 (2021)

    2021

  27. [35]

    T. S. Kaminski, O. Scheler, and P. Garstecki, Droplet microfluidics for microbiology: techniques, applica- tions and challenges, Lab Chip 16, 2168 (2016)

    2016

  28. [36]

    Hiratsuka, M

    Y. Hiratsuka, M. Miyata, T. Tada, and T. Q. P. Uyeda, A microrotary motor powered by bacteria, Proceedings of the National Academy of Sciences 103, 13618 (2006)

    2006

  29. [37]

    R. D. Leonardo, L. Angelani, D. Dell’Arciprete, G. Ruocco, V. Iebba, S. Schippa, M. P. Conte, F. Mecarini, F. D. Angelis, and E. D. Fabrizio, Bac- terial ratchet motors, Proceedings of the National Academy of Sciences 107, 9541 (2010)

    2010

  30. [38]

    H. Oda, N. Shimizu, Y. Morimoto, and S. Takeuchi, Harnessing the propulsive force of microalgae with mi- crotrap to drive micromachines, Small 20, 2402923 (2024)

    2024

  31. [39]

    D. J. Smith, Aeroplankton and the need for a global monitoring network, BioScience 63, 515 (2013)

    2013

  32. [40]

    B. C. Christner, C. E. Morris, C. M. Foreman, R. Cai, and D. C. Sands, Ubiquity of biological ice nucleators in snowfall, Science 319, 1214 (2008)

    2008

  33. [41]

    See Supplementary Material at [URL] for additional experimental details, supporting figures, and supple- mentary movies

  34. [42]

    M. N. Azra, M. I. M. Noor, J. Burlakovs, M. F. Ab- dullah, Z. Abd Latif, and Y. Yik Sung, Trends and new developments in artemia research, Animals 12, 2321 (2022)

    2022

  35. [43]

    T. A. Williams, A model of rowing propulsion and the ontogeny of locomotion in artemia larvae, The Biolog- ical Bulletin 187, 164–173 (1994)

    1994

  36. [44]

    R. A. Lara, N. Sharadhi, A. A. L. Huttunen, L. Ansas, E. J. G. Rislakki, G. M. Bessa, and M. Back- holm, Forces and symmetry breaking of a living meso-swimmer, arXiv, doi: 0.48550/arXiv.2503.21396 (2025)

    2025

  37. [45]

    Svetlichny, P

    L. Svetlichny, P. S. Larsen, and T. Kiørboe, Kinematic and dynamic scaling of copepod swimming, Fluids 5 (2020)

    2020

  38. [46]

    G. V. Stappen, P. Sorgeloos, , and G. Rombaut, Man- ual on artemia production and use, Food and Agricul- ture Organization of the United Nations (2024)

    2024

  39. [47]

    Sainiemi, V

    L. Sainiemi, V. Jokinen, A. Shah, M. Shpak, S. Aura, P. Suvanto, and S. Franssila, Non-reflecting silicon and polymer surfaces by plasma etching and replication, Advanced Materials 23, 122 (2011)

    2011

  40. [48]

    Backholm, D

    M. Backholm, D. Molpeceres, M. Vuckovac, H. Nurmi, M. J. Hokkanen, V. Jokinen, J. V. I. Timonen, and R. H. A. Ras, Water droplet friction and rolling dy- namics on superhydrophobic surfaces, Communica- tions Materials 1, 64 (2020)

    2020

  41. [49]

    Backholm, T

    M. Backholm, T. K¨ arki, H. A. Nurmi, M. Vuckovac, V. Turkki, S. Lepikko, V. Jokinen, D. Qu´ er´ e, J. V. I. Timonen, and R. H. A. Ras, Toward vanishing droplet friction on repellent surfaces, Proceedings of the Na- tional Academy of Sciences 121, e2315214121 (2024)

    2024

  42. [50]

    Backholm and O

    M. Backholm and O. B¨ aumchen, Micropipette force sensors for in vivo force measurements on single cells and multicellular microorganisms, Nature Protocols 14, 594–615 (2019)

    2019

  43. [51]

    C ¸ arko˘ glu, M

    C. C ¸ arko˘ glu, M. Yılmaz, and F. Balcı, Continu- ous spontaneous alternation and turn alternation in artemia sp., International Journal of Comparative Psychology 28 (2015)

    2015

  44. [52]

    Living droplets with mesoscale swimmers

    L. Malik, N. Sharadhi, M. Lamminm¨ aki, R. A. Lara, V. Jokinen, and M. Backholm, Data from “Living droplets with mesoscale swimmers”, Zenodo, doi: 10.5281/zenodo.17077808 (2025)

    2025 doi

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.