Pith. sign in

REVIEW 4 major objections 6 minor 54 references

Dynamics of Irreversible Particle Adsorption to Fluid Interfaces

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Coupling bulk diffusion with a random-sequential-adsorption blocking boundary condition quantitatively reproduces irreversible particle adsorption to toluene/water droplets across two drop sizes and identifies a coverage-dependent…

desk verdict Solid experiments and a plausible diffusion-RSA model, but the Thiele modulus derivation has real algebraic errors that undermine the quantitative crossover claims. read the letter →

arxiv 2507.21026 v1 pith:OCZG2JSV submitted 2025-07-28 cond-mat.soft cond-mat.mtrl-sci

Marina Pasquet , Yu Fu , Joelle Frechette This is my paper · ORCID
classification cond-mat.softcond-mat.mtrl-sci
keywords irreversibleparticleadsorptionRandomSequentialblockingfunctiondynamicinterfacialtensionThielemodulusdiffusion-limitedkinetically-limitedcolloidalparticlesatfluidinterfaces
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

This paper proposes that dynamic adsorption of colloidal particles onto a droplet surface is governed by two competing steps: Fickian diffusion through the bulk liquid and irreversible attachment at the interface whose available area shrinks as coverage grows. The authors encode the second step with a Random Sequential Adsorption (RSA) blocking function and couple it to diffusion in spherical coordinates. They show the resulting model quantitatively matches interfacial-tension measurements for TPM particles at toluene/water interfaces for 80-micron microtensiometer droplets and 1-mm pendant drops over a range of bulk concentrations. If correct, the standard diffusion-limited Ward–Tordai picture holds only at early times and low coverage, while the long-time slowdown is kinetic and driven by crowding. The paper also derives a coverage-dependent Thiele modulus that locates the crossover from diffusion control to kinetic control during a single experiment.

What carries the argument

The load-bearing object is the Robin boundary condition $-D \partial c/\partial r|_{r=r_d} = k_a c(r_d,t) B(\theta)$ with $B(\theta) = (1+0.812\theta+0.426\theta^2+0.072\theta^3)(1-\theta)^{-3}$, which couples bulk diffusion to irreversible, crowding-limited adsorption. A second object, the dynamic Thiele modulus $\phi(\theta) = 4.6 k_a B(\theta)\sqrt{h_s^3 h_p}/D$, with $h_p=\Gamma_m/c_b$ and $h_s=r_d[(1+3h_p/r_d)^{1/3}-1]$, converts the same physics into a diagnostic of which step limits adsorption at each coverage. The boundary condition is what makes the model predictive; the modulus is what locates the diffusion-to-kinetic crossover.

What would settle it

Measure, for the same TPM/toluene/water system, the surface coverage independently (for example by confocal fluorescence imaging calibrated to particle counts) while simultaneously recording interfacial tension; if $\gamma_0-\gamma$ stops tracking linearly with $\Gamma$ above the coverages reached in these experiments, then the coverage axis, fitted $k_a$, and Thiele-modulus crossover are all miscalibrated. A second, complementary check: in a purely kinetics-limited configuration, such as a sub-10 $\mu$m droplet, the adsorption flux should scale as the square of bulk concentration; a linear flux would falsify the cooperative second-order kinetics claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that irreversible particle adsorption to a droplet interface is quantitatively described by a single mixed boundary condition, Eq. (7), which balances the spherical Fickian diffusion flux at the interface with a Random Sequential Adsorption (RSA) rate law $k_a c(r_d,t) B(\theta)$, where $B(\theta)$ is the coverage-dependent blocking function. Fitting this model to dynamic interfacial tension data for TPM particles at the toluene/water interface — droplet radii of roughly 80 $\mu$m and 1 mm, several bulk concentrations, and two particle coatings — reproduces the measured trajectories across the adsorption process, apart from a residual mismatch at high concentration in the microtensiometer that the authors attribute to unregulated droplet expansion. The fits yield an intrinsic adsorption rate constant $k_a \sim 10^{-7}$ m/s that is independent of droplet geometry and grows linearly with bulk concentration, which the authors read as evidence of cooperative, second-order attachment kinetics. From the same framework they construct a dynamic Thiele modulus $\phi(\theta)$ that decreases as the interface jams; every measured system starts diffusion-limited and crosses to kinetically limited adsorption above a critical coverage.

Load-bearing premise

The argument converts every measured interfacial tension value into a surface coverage using the linear equation of state $\Pi(t)=\Delta E\,\Gamma(t)$; if interfacial pressure does not stay linear in coverage at the dense packings reached in these experiments, the inferred coverage, fitted rate constant, and Thiele-modulus trajectories are all biased.

Editorial extensions

If this is right

  • The Ward–Tordai diffusion-limited model is adequate only before substantial crowding; at higher coverage, descriptions of particle adsorption to droplets must include a coverage-dependent blocking term to avoid overestimating adsorbed amounts.
  • Deviation from the short-time $t^{1/2}$ diffusion law is not by itself a sign that kinetic control has begun; a system can remain diffusion-limited with $\phi>1$ well after the $t^{1/2}$ regime ends.
  • The approximately linear increase of $k_a$ with bulk concentration makes the kinetic flux quadratic in concentration, implying that particle attachment involves cooperative events such as subsurface clustering rather than independent single-particle adsorption.
  • A truly kinetics-limited experiment from the outset would require droplets with radius below roughly 10 $\mu$m for these particles, while larger $k_a$ values shift that threshold.
  • Because the model collapses fits from two geometries and two coatings onto the same $k_a(c_b)$ line, the same equations can be used as a forward predictor for other droplet radii and concentrations in this particle chemistry.

Reading between the lines

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

  • Beyond the paper: the same diffusion-plus-RSA coupling could be applied to protein or polymer adsorption, where irreversibility and interfacial crowding are also debated; a testable signature would be whether the fitted $k_a$ remains linear in bulk concentration or flattens.
  • Beyond the paper: the prediction that sub-10 $\mu$m droplets are needed to observe kinetics-limited adsorption from the start could be tested directly by measuring the initial Thiele modulus on droplets of decreasing radius; if the crossover radius does not scale as predicted by Eq. (18), the assumed blocking function or rate law would need revision.
  • Beyond the paper: the linear equation of state $\Pi(t)=\Delta E\,\Gamma(t)$ is not independently checked at dense coverages; a Langmuir-trough measurement of surface pressure against area fraction for the same TPM particles would show whether the inferred coverage trajectories and Thiele modulus are biased at high $\theta$.
  • Beyond the paper: the quadratic-kinetics interpretation could be sharpened by a control experiment with non-interacting particles or particles whose surface chemistry suppresses clustering; if $k_a$ becomes concentration-independent, the cooperative-attachment explanation would be confirmed.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript proposes a unified diffusion-Random Sequential Adsorption (RSA) model for irreversible nanoparticle adsorption to spherical fluid interfaces, couples Fickian diffusion in spherical coordinates with an RSA blocking boundary condition, and compares it to dynamic interfacial tension measurements of TPM particles at toluene/water interfaces using microtensiometry (80 µm drops) and pendant drop tensiometry (1 mm drops). The authors report good agreement between the model and experiments, extract an intrinsic adsorption rate constant ka that depends linearly on bulk concentration, and introduce a dynamic Thiele modulus to argue that adsorption crosses from diffusion-limited to kinetically limited control above a critical surface coverage. The central quantitative claim therefore rests on the correctness of the Thiele-modulus derivation in Section 5.

Significance. The experimental dataset is substantial: two complementary tensiometric platforms, two particle surface chemistries, and a range of bulk concentrations, and the Diffusion-RSA fits in Figure 4 appear to capture the data well. The collapse of fitted ka values across different droplet geometries in Figure 5 is a useful consistency check. If the Section 5 derivations are corrected and the linear equation-of-state assumption is validated, the proposed dynamic Thiele modulus could be a valuable diagnostic for adsorption regime transitions. As written, however, the quantitative transition claim is not supported because of algebraic and dimensional errors in Section 5 and an unvalidated equation of state used for all coverage conversions.

major comments (4)
  1. [Section 5.1, Eq. (13)] Equation (13) is not the solution of the stated initial-value problem. With B(θ)=2.32(1−θ)^3, Eq. (4) integrates for constant subsurface concentration cb to Γ(t)=Γm[1−(1+4.64 ka cb t/Γm)^{−1/2}], which satisfies Γ(0)=0 and increases monotonically to Γm. The printed Eq. (13) instead gives Γ(0)=Γm and decays to zero as t→∞, and it uses ka,eff even though the blocking function has already been inserted. The subsequent kinetic time scale τ_k in Eq. (14) and the Thiele modulus built on it are therefore not derived from a correct solution. Please correct Eq. (13) and re-derive Eqs. (14)–(15) from the proper solution.
  2. [Section 5.1, Eq. (15)] As printed, Eq. (15) is dimensionally inconsistent. Using τ_D = sqrt(h_s^3 h_p)/D and τ_k = Γm/(4.6 ka cb B), one obtains φ = 4.6 ka B sqrt(h_s^3/h_p)/D, not 4.6 ka B sqrt(h_s^3 h_p)/D; the printed expression has units of length. Since Figure 6 and the universal map in Eq. (18) depend on this quantity, the manuscript must state which formula was used to generate Figure 6 and must correct the printed formula. If the figure was generated with the printed dimensional formula, the φ=1 crossover is not a dimensionless criterion; if a corrected formula was used, that correction must be documented.
  3. [Section 4.1, Eq. (2)] The conversion of every measured interfacial tension into surface coverage relies on the linear wetting equation of state Π(t)=ΔE Γ(t)=γ0 η(t). This relation is not validated for the dense coverages reached in these experiments (ηmax≈0.65 for F108 and ≈0.45 for Tween), and a nonlinear equation of state at high coverage would bias the inferred θ(t), the fitted ka values, and all Thiele-modulus trajectories. The authors should validate Eq. (2) against an independent coverage measurement, for example by image analysis of confocal micrographs such as those in Figure 2, or provide quantitative evidence for its validity over the full coverage range used.
  4. [Section 5.2] The statement that the system crosses from diffusion-limited to kinetically limited control is, in this model, a built-in consequence of the definition φ(θ) ∝ B(θ), since the blocking function B(θ) decreases monotonically with θ. The data-dependent content is whether φ starts above 1 and reaches 1 within the experimentally accessible coverage, not the existence of the downward trend. The manuscript should present Figure 6 as a model-based diagnostic and should provide uncertainty estimates for φ(θ) obtained by propagating the fitted-parameter uncertainties; otherwise the quantitative claim of a critical surface coverage is overstated.
minor comments (6)
  1. [Section 2.1, Eq. (1)] The Ward-Tordai equation as printed is missing the factor 1/√(t−τ) in the integral; the displayed expression is dimensionally inconsistent and should be corrected to Γ(t)=2c0√(Dt/π) − √(D/π)∫_0^t cs(τ)/√(t−τ) dτ.
  2. [Section 4.1] The notation for the normalized coverage is inconsistent: Eq. (4) defines θ=Γ/Γm, while Section 4.1 defines θ=η/ηmax, and the caption of Figure 4 states that Γm corresponds to the RSA jamming limit θ=0.547. Please reconcile these definitions and clarify the relationship between ηmax, Γm, and the RSA jamming limit.
  3. [Figures 4 and 5] No error bars or replicate counts are reported for the experimental data or for the fitted ka values. Given that the Thiele-modulus trajectories in Figure 6 depend directly on fitted parameters, the authors should report uncertainties, at least for the global linear fit in Figure 5.
  4. [Section 4.1] The area-dilation caveat for the high-concentration microtensiometer data is acknowledged, but it is not stated whether those data points were included in the model fits and in the global ka versus χ fit in Figure 5; please clarify this.
  5. [Section 5.1] The numerical factor in Eqs. (13)–(15) is printed as 4.6, but the exact factor arising from 2×2.32 in the integration is 4.64; this should be harmonized after correcting the derivation.
  6. [References] Reference [18] gives a DOI (10.1016/0021-9797(80)90358-X) that appears to belong to the Journal of Colloid and Interface Science, while the cited article is listed as J. Theor. Biol.; please check and correct the reference metadata.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Diffusion–RSA model is calibrated by fitting ka, and the Thiele-modulus crossover is an in-model consequence of the assumed decreasing blocking function, not an independent empirical prediction.

full rationale

The derivation chain is not circular. The kinetic rate equation Eq. (4), the RSA blocking functions Eqs. (6) and (12), and the diffusion boundary condition Eq. (7) are independent, externally sourced ingredients (Adamczyk, Feder, Talbot, Bizmark), not outputs of the present paper. Eq. (2) converts measured interfacial tension to surface coverage via a stated linear wetting equation of state; whether that relation is accurate at high coverage is an experimental and modeling accuracy risk, not a circular reduction. The intrinsic rate constant ka is fit to the data in Section 4.2, so the model-data agreement in Figure 4 is a fit-quality statement; the paper does not explicitly mislabel it as a parameter-free prediction. The dynamic Thiele modulus in Eq. (15) is constructed from the same fitted ka and from the assumed blocking function, and the statement that phi decreases as B(theta) decreases is an algebraic consequence of Eq. (14). Thus the 'transition to kinetic control' is a model-internal diagnostic rather than an independent, falsifiable empirical finding. That framing is worth flagging as an interpretive limitation, but it does not make the derivation circular: the model could have failed to fit the two droplet geometries or the two particle functionalizations, and the comparison to those independent experiments gives the framework real empirical content. The self-citations [26, 27, 33] concern particle synthesis and the comparison Ward-Tordai model; they are not load-bearing for the core Diffusion-RSA result. For completeness, Eqs. (13) and (15) appear mathematically questionable as printed (Eq. (13) gives Gamma(0)=Gamma_m rather than 0, and Eq. (15) has units of length); these are correctness concerns outside the circularity definition and do not raise the circularity score.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new entities are postulated. The model relies on literature-derived blocking functions, a linear coverage-pressure relation, and kinetic parameters fitted to the authors' own data, so the ledger is dominated by domain assumptions and fitted parameters.

free parameters (4)
  • ka (intrinsic adsorption rate constant) = ~10^-7 m/s, varies with particle volume fraction
    Fitted per adsorption curve from the diffusion-RSA model (Section 4.2); central to the Thiele modulus.
  • ηmax per particle system = 0.65 (TPM-F108), 0.45 (TPM-Tween20)
    Experimental maximum area fraction used to define normalized coverage θ; affects B(θ) and all fits.
  • k0 and α (linear ka vs χ) = global linear fit to ka data (Figure 5)
    Parameters of the empirical linear relation ka = k0 + αχ; presented as evidence for cooperative adsorption.
  • Γm (maximum surface excess) = not explicitly reported; inferred from ηmax and particle geometry
    Used in Eq. 4 and in hp; tied to the same experimental normalization as ηmax.
assumptions (5)
  • domain assumption RSA blocking function Eq. 6 describes the available-area fraction for irreversible adsorption.
    Used as the kinetic boundary condition; its form sets the coverage dependence of the whole model.
  • domain assumption High-coverage blocking approximation B(θ) ≈ 2.32(1-θ)^3 (Eq. 12).
    Basis for the analytical Γ(t), τk, and Thiele modulus expressions in Section 5.1.
  • domain assumption Linear wetting equation of state Π = ΔE Γ (Eq. 2).
    Converts measured interfacial tension into surface coverage; assumed valid at all coverages.
  • domain assumption Adsorption is irreversible; desorption is negligible.
    Core premise justifying the RSA boundary condition; consistent with high adsorption energies.
  • standard math Stokes-Einstein relation provides particle diffusion coefficient D.
    Used to set D in Fick's equation; reasonable for dilute dispersions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Dynamics of Irreversible Particle Adsorption to Fluid Interfaces." pith.science (2026). https://pith.science/paper/OCZG2JSV

@misc{pith2026250721026,
  author       = {Pith},
  title        = {Pith review of: Dynamics of Irreversible Particle Adsorption to Fluid Interfaces},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OCZG2JSV}},
  note         = {Machine review of arXiv:2507.21026}
}
read the original abstract

Understanding the dynamic adsorption of colloidal particles at fluid interfaces is essential for applications ranging from emulsion stabilization to interfacial assembly of functional materials. Adsorption dynamics is often described through diffusion-limited models (such as the Ward-Tordai framework) along with assuming dynamic equilibrium between the adsorbed and dispersed particles. However, most experiments show that particle adsorption is irreversible, and diffusion-limited models fail as the surface coverage goes beyond the dilute limit where particle crowding limits further adsorption. Here, we present a unified model that captures the transition from diffusion-limited to kinetic-limited regimes by coupling diffusion with a Random Sequential Adsorption (RSA)-based boundary condition that accounts for irreversible adsorption and particle blocking for a spherical droplet. Using both a microtensiometer and pendant drop tensiometry, we measure dynamic interfacial tension changes for 3-(Trimethoxysilyl)propyl methacrylate (TPM) particles at the toluene/water interface across a range of bulk concentrations, drop sizes, and particle functionalization. Our analysis shows that the adsorption flux becomes increasingly hindered as the surface area fills, in agreement with RSA predictions. Furthermore, we calculate the Thiele modulus as a dimensionless number that quantifies the relative importance of adsorption kinetics to diffusion. We find that above a critical surface coverage, adsorption becomes reaction-limited, marking a transition to kinetically controlled dynamics. This approach provides a predictive framework for particle adsorption at fluid interfaces and highlights the necessity of moving beyond equilibrium diffusion-limited models.

Figures

Figures reproduced from arXiv: 2507.21026 by the authors.

Figure 1
Figure 1. (a) Particle adsorption occurs in two primary steps: diffusion from the bulk water phase to the subsurface, [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Blocking function and representative images of the interface at selected coverages. (Top) The RSA blocking [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. (a) In the microtensiometer, a toluene drop (radius [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Time-evolution of (a,b) interfacial tension ( [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: plots the fitted values of ka versus the particle volume fraction χ (proportional to cb). A linear dependence, ka = k0 + αχ, emerges, robustly consistent across both microtensiometry and pendant drop geometries. This collapse confirms that ka is an intrinsic, geometry-…
Figure 6
Figure 6. Figure 6: The Dynamic Thiele Modulus as a Function of Surface Coverage. The evolution of [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Universal Map of Adsorption Regimes. The transition between diffusion-limited and kinetic-limited regimes [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 53 canonical work pages

  1. [1]

    Adamczyk

    Z. Adamczyk. Kinetics of diffusion-controlled adsorption of colloid particles and proteins. Journal of Colloid and Interface Science, 229(2):477–489, 2000. ISSN 0021-9797. doi:https://doi.org/10.1006/jcis.2000.6993

    work page arXiv

  2. [2]

    Adamczyk, K

    Z. Adamczyk, K. Jaszczolt, A. Michna, B. Siwek, L. Szyk-Warszynska, and M. Zembala. Irreversible adsorption of particles on heterogeneous surfaces. Adv Colloid Interface Sci, 118(1-3):25–42, 2005. doi:10.1016/j.cis.2005.03.003

    work page doi:10.1016/j.cis.2005.03.003 2005

  3. [3]

    Alvarez, L

    N. Alvarez, L. Walker, and S. Anna. A microtensiometer to probe the effect of radius of curvature on surfactant transport to a spherical interface. Langmuir, 26(16):13310–13319, 2010. doi:10.1021/la101870m

    work page doi:10.1021/la101870m 2010

  4. [4]

    Alvarez, L

    N. Alvarez, L. Walker, and S. Anna. Diffusion-limited adsorption to a spherical geometry: the impact of curvature and competitive time scales. Phys. Rev. E Stat. Nonlin. Soft Matter Phys, 82(1 Pt 1):011604, 2010. doi:10.1103/PhysRevE.82.011604

    work page doi:10.1103/physreve.82.011604 2010

  5. [5]

    Aussillous and D

    P. Aussillous and D. Quere. Liquid marbles. Nature, 411(6840):924–927, 2001. doi:10.1038/35082026

    work page doi:10.1038/35082026 2001

  6. [6]

    J. Berg. An Introduction to Interfaces and Colloids: The Bridge to Nanoscience. World Scientific Publishing Co. Pte. Ltd, 2009. doi:10.1142/7579

    work page doi:10.1142/7579 2009

  7. [7]

    B. Binks. Particles as surfactants—similarities and differences. Curr. Opin. Colloid Interface Sci, 7(1-2):21–41,

    work page

  8. [8]

    Bizmark, M

    N. Bizmark, M. Ioannidis, and D. Henneke. Irreversible adsorption-driven assembly of nanoparticles at fluid interfaces revealed by a dynamic surface tension probe. Langmuir, 30(3):710–717, 2014. doi:10.1021/la404357j

    work page doi:10.1021/la404357j 2014

Show all 54 references
  1. [9]

    Bleys and P

    G. Bleys and P. Joos. Adsorption kinetics of bolaform surfactants at the air/water interface. J. Phys. Chem, 89(6): 1027–1032, 1985

    1985

  2. [10]

    M. E. Cates and P. S. Clegg. Bijels: a new class of soft materials. Soft Matter, 4:2132–2138, 2008. doi:10.1039/B807312K

    2008 doi

  3. [11]

    Y . Chai, J. Hasnain, K. Bahl, M. Wong, D. Li, P. Geissler, P. Y . Kim, Y . Jiang, P. Gu, S. Li, D. Lei, B. A. Helms, T. P. Russell, and P. D. Ashby. Direct observation of nanoparticle-surfactant assembly and jamming at the water-oil interface. Science Advances, 6(48):eabb8675...

    2020 doi

  4. [12]

    Z. Chen, L. Zhou, W. Bing, Z. Zhang, Z. Li, J. Ren, and X. Qu. Light controlled reversible inversion of nanophosphor-stabilized pickering emulsions for biphasic enantioselective biocatalysis. J. Am. Chem. Soc, 136 (20):7498–7504, 2014. doi:10.1021/ja503123m

    2014 doi

  5. [13]

    M. Cui, C. Miesch, I. Kosif, H. Nie, P. Kim, H. Kim, T. Emrick, and T. Russell. Transition in dynamics as nanopar- ticles jam at the liquid/liquid interface. Nano Lett, 17(11):6855–6862, 2017. doi:10.1021/acs.nanolett.7b03159

    2017 doi

  6. [14]

    K. Du, E. Glogowski, T. Emrick, T. Russell, and A. Dinsmore. Adsorption energy of nano- and microparticles at liquid-liquid interfaces. Langmuir, 26(15):12518–12522, 2010. doi:10.1021/la100497h. 13 Dynamics of Irreversible Particle Adsorption to Fluid Interfaces A PREPRINT

    2010 doi

  7. [15]

    Eastoe and J

    J. Eastoe and J. Dalton. Dynamic surface tension and adsorption mechanisms of surfactants at the air–water interface. Adv. Colloid Interface Sci, 85(2):103–144, 2000. doi:10.1016/S0001-8686(99)00017-2

    doi

  8. [16]

    Elimelech, J

    M. Elimelech, J. Gregory, X. Jia, and R. A. Williams. Particle deposition and aggregation: measurement, modelling and simulation. 1998

    1998

  9. [17]

    Fainerman, R

    V . Fainerman, R. Miller, J. Ferri, H. Watzke, M. Leser, and M. Michel. Reversibility and irreversibility of adsorption of surfactants and proteins at liquid interfaces. Adv Colloid Interface Sci, 123-126:163–171, 2006. doi:10.1016/j.cis.2006.05.023

    2006 doi

  10. [18]

    J. Feder. Random sequential adsorption. J. Theor. Biol., 63(1):51–89, 1980. doi:10.1016/0021-9797(80)90358-X

    1980 doi

  11. [19]

    Ferri and K

    J. Ferri and K. Stebe. A structure-property study of the dynamic surface tension of three acetylenic diol surfactants. Colloids Surf., A, 156(1–3):567–577, 1999. doi:10.1016/S0927-7757(99)00121-1

    1999 doi

  12. [20]

    Z. Fink, P. Y . Kim, J. Han, S.-Y . Kim, N. To, S.-M. Bang, S.-H. Park, T. P. Russell, B. A. Helms, and P. D. Ashby. Repairable and Reconfigurable Structured Liquid Circuits. Advanced Functional Materials, 34(15):2311406, 2024

    2024

  13. [21]

    Y . Fu, S. Zhao, W. Chen, Q. Zhang, and Y . Chai. Self-assembly of nanoparticles with stimulated responses at liquid interfaces. Nano Today, 54:102073, 2024. doi:10.1016/j.nantod.2023.102073

    2024

  14. [22]

    Garbin, J

    V . Garbin, J. Crocker, and K. Stebe. Forced desorption of nanoparticles from an oil-water interface.Langmuir, 28 (3):1663–1667, 2012. doi:10.1021/la202954c

    2012 doi

  15. [23]

    Guzman, F

    E. Guzman, F. Martinez-Pedrero, C. Calero, A. Maestro, F. Ortega, and R. Rubio. A broad perspective to particle-laden fluid interfaces systems: from chemically homogeneous particles to active colloids. Adv Colloid Interface Sci, 302:102620, 2022. doi:10.1016/j.cis.2022.102620

    2022

  16. [24]

    Y . He, P. Yazhgur, A. Salonen, and D. Langevin. Adsorption-desorption kinetics of surfactants at liquid surfaces. Adv Colloid Interface Sci, 222:377–384, 2015. doi:10.1016/j.cis.2014.09.002

    2015 doi

  17. [25]

    Hill and J

    C. Hill and J. Eastoe. Foams: From nature to industry. Advances in Colloid and Interface Science, 247:496–513,

  18. [26]

    X. Hua, M. Bevan, and J. Frechette. Reversible partitioning of nanoparticles at an oil-water interface. Langmuir, 32(44):11341–11352, 2016. doi:10.1021/acs.langmuir.6b02255

    2016 doi

  19. [27]

    X. Hua, J. Frechette, and M. Bevan. Nanoparticle adsorption dynamics at fluid interfaces. Soft Matter, 14(19): 3818–3828„ 2018. doi:10.1039/c8sm00273h

    2018 doi

  20. [28]

    Iasella, S

    S. Iasella, S. Barman, C. Ciutara, B. Huang, M. Davidson, and J. Zasadzinski. Microtensiometer for confocal microscopy visualization of dynamic interfaces. J Vis Exp, 187:64110, 2022. doi:10.3791/64110

    2022 doi

  21. [29]

    D. Kaz, R. McGorty, M. Mani, M. Brenner, and V . Manoharan. Physical ageing of the contact line on colloidal particles at liquid interfaces. Nat. Mater, 11(2):138–142, 2012. doi:10.1038/nmat3190

    2012 doi

  22. [30]

    S. Lin, K. McKeigue, and C. Maldarelli. Diffusion-controlled surfactant adsorption studied by pendant drop digitization. AIChE journal, 36(12):1785–1795, 1990

    1990

  23. [31]

    Manga, T

    M. Manga, T. Hunter, O. Cayre, D. York, M. Reichert, S. Anna, L. Walker, R. Williams, and S. Biggs. Mea- surements of submicron particle adsorption and particle film elasticity at oil-water interfaces. Langmuir, 32(17): 4125–4133, 2016. doi:10.1021/acs.langmuir.5b04586

    2016 doi

  24. [32]

    Moghimikheirabadi, P

    A. Moghimikheirabadi, P. Fischer, M. Kroger, and L. Sagis. Relaxation behavior and nonlinear surface rhe- ology of peo-ppo-peo triblock copolymers at the air-water interface. Langmuir, 35(44):14388–14396, 2019. doi:10.1021/acs.langmuir.9b02540

    2019 doi

  25. [33]

    Neibloom, M

    D. Neibloom, M. Bevan, and J. Frechette. Surfactant-stabilized spontaneous 3-(trimethoxysilyl) propyl methacry- late nanoemulsions. Langmuir, 36(1):284–292, 2020. doi:10.1021/acs.langmuir.9b03412

    2020 doi

  26. [34]

    Y . Pan, S. Gao, C. Ge, Q. Gao, S. Huang, Y . Kang, G. Luo, Z. Zhang, L. Fan, Y . Zhu, and A. Wang. Re- moving microplastics from aquatic environments: A critical review. Environ Sci Ecotechnol, 13:100222, 2023. doi:10.1016/j.ese.2022.100222

    2023

  27. [35]

    Park and D

    B. Park and D. Lee. Particles at fluid–fluid interfaces: From single-particle behavior to hierarchical assembly of materials. MRS Bull, 39(12):1089–1098, 2014. doi:10.1557/mrs.2014.253

    2014 doi

  28. [36]

    Peito, D

    S. Peito, D. Peixoto, I. Ferreira-Faria, A. Margarida Martins, H. Margarida Ribeiro, F. Veiga, J. Marto, and A. Claudia Paiva-Santos. Nano- and microparticle-stabilized pickering emulsions designed for topical therapeutics and cosmetic applications. Int J Pharm, 615:121455, 20...

    2022

  29. [37]

    Prosser and E

    A. Prosser and E. Franses. Adsorption and surface tension of ionic surfactants at the air–water interface: review and evaluation of equilibrium models. Colloids Surf. Physicochem. Eng. Aspects, 178(1-3):1–40, 2001. doi:10.1016/S0927-7757(00)00552-5

    2001 doi

  30. [38]

    Riechers, A

    B. Riechers, A. M. Souza, M. Krüger, and M. Windbergs. ph-responsive surfactants for controlled drug delivery applications. Int J Pharm, 509:1–14, 2016. doi:10.1016/j.ijpharm.2016.05.020

    2016 doi

  31. [39]

    Schwenke, L

    K. Schwenke, L. Isa, and E. Del Gado. Assembly of nanoparticles at liquid interfaces: crowding and ordering. Langmuir, 30(11):3069–3074, 2014. doi:10.1021/la404254n

    2014 doi

  32. [40]

    Shi and T

    S. Shi and T. P. Russell. Nanoparticle assembly at liquid–liquid interfaces: From the nanoscale to mesoscale. Advanced Materials, 30(44):1800714, 2018. doi:https://doi.org/10.1002/adma.201800714

    2018 doi

  33. [41]

    D. Shin, T. Huang, D. Neibloom, M. Bevan, and J. Frechette. Multifunctional liquid marble compound lenses. ACS Appl Mater Interfaces, 11(37):34478–34486, 2019. doi:10.1021/acsami.9b12738

    2019 doi

  34. [42]

    Shipway, E

    A. Shipway, E. Katz, and I. Willner. Nanoparticle arrays on surfaces for electronic, optical, and sensor applications. Chemphyschem, 1(1):18–52, 2000

    2000

  35. [43]

    Slavchov and I

    R. Slavchov and I. Ivanov. Adsorption parameters and phase behaviour of non-ionic surfactants at liquid interfaces. Soft Matter, 13(46):8829–8848, 2017. doi:10.1039/c7sm01370a

    2017 doi

  36. [44]

    Smirnov, P

    E. Smirnov, P. Peljo, M. Scanlon, and H. Girault. Interfacial redox catalysis on gold nanofilms at soft interfaces. ACS Nano, 9(6):6565–6575, 2015. doi:10.1021/acsnano.5b02547

    2015 doi

  37. [45]

    Talbot, G

    J. Talbot, G. Tarjus, P. Tassel, and P. Viot. From car parking to protein adsorption: an overview of sequential adsorption processes. Colloids Surf. A Physicochem. Eng. Asp, 165(1-3):287–324, 2000. doi:10.1016/S0927- 7757(99)00409-4

    doi

  38. [46]

    Thompson, M

    K. Thompson, M. Williams, and S. Armes. Colloidosomes: synthesis, properties and applications. J Colloid Interface Sci, 447:217–228, 2015. doi:10.1016/j.jcis.2014.11.058

    2015 doi

  39. [47]

    C. Tian, J. Feng, H. Cho, S. Datta, and R. Prud’homme. Adsorption and denaturation of structured polymeric nanoparticles at an interface. Nano Lett, 18(8):4854–4860, 2018. doi:10.1021/acs.nanolett.8b01434

    2018 doi

  40. [48]

    Vethaak and J

    A. Vethaak and J. Legler. Microplastics and human health. Science, 371(6530):672–674, 2021. doi:10.1126/science.abe5041

    2021 doi

  41. [49]

    Vialetto, S

    J. Vialetto, S. Rudiuk, M. Morel, and D. Baigl. From bulk crystallization of inorganic nanoparticles at the air/water interface: tunable organization and intense structural colors. Nanoscale, 12(11):6279–6284, 2020. doi:10.1039/c9nr10965j

    2020 doi

  42. [50]

    Vialetto, S

    J. Vialetto, S. Rudiuk, M. Morel, and D. Baigl. Photothermally reconfigurable colloidal crystals at a fluid interface, a generic approach for optically tunable lattice properties. J. Am. Chem. Soc, 143(30):11535–11543, 2021. doi:10.1021/jacs.1c04220

    2021 doi

  43. [51]

    A. Wang, R. McGorty, D. Kaz, and V . Manoharan. Contact-line pinning controls how quickly colloidal particles equilibrate with liquid interfaces. Soft Matter, 12(43):8958–8967, 2016

    2016

  44. [52]

    Ward and L

    A. Ward and L. Tordai. Time-dependence of boundary tensions of solutions i. the role of diffusion in time-effects. J. Chem. Phys, 14(7):453–461, 1946. doi:10.1063/1.1724167. 15

    1946 doi

  45. [2002]

    doi:10.1016/s1359-0294(02)00008-0

    doi

  46. [2017]

    doi:10.1016/j.cis.2017.05.013

    2017 doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.