Pith. sign in

REVIEW 5 major objections 5 minor 21 references

This paper argues that extremely low-frequency electric fields, around 1–5 Hz and 200–2000 V/m, can shift and rotate the probability cloud of extracellular vesicles, enabling electrophoretic beamforming for targeted drug delivery.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-04 19:16 UTC pith:RD5NMCLF

load-bearing objection Standard FPE with oscillating drift applied to EVs; math is fine but the 'substantial directional control' claim overstates a zero-net-displacement wobble, and several internal inconsistencies need fixing. the 5 major comments →

arxiv 2509.09339 v1 pith:RD5NMCLF submitted 2025-09-11 physics.bio-ph

Electrophoretic Beamforming in Molecular Communication: Toward Targeted Extracellular Vesicle Delivery

Mohammad Zoofaghari , Liv Cornelia Middelthon , Mladen Veleti\'c , Ilangko Balasingham This is my paper
classification physics.bio-ph
keywords extracellular vesiclesmolecular beamformingFokker-Planck equationelectrophoresisextremely low-frequency electromagnetic fieldstargeted drug deliverymolecular communicationzeta potential
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Extracellular vesicles are natural drug carriers, but once released they mostly diffuse randomly. This paper tries to show that an extremely low-frequency, oscillating electric field can impose a directional drift on the charged vesicles, turning their spreading cloud into a steerable beam. The core result is an exact solution to the Fokker-Planck equation: the center of the vesicle distribution oscillates at the field frequency with amplitude v0/ω, where v0 follows from the Smoluchowski electrophoretic velocity. Because the displacement grows as the frequency falls, 1–5 Hz fields of a few hundred to a few thousand volts per meter are enough to shift the beam across a micrometer-scale domain, and phase differences between field components rotate it. If right, this gives molecular communication and nanomedicine a non-invasive handle for pointing vesicle traffic at chosen cells.

Core claim

The paper's central claim is that extremely low-frequency electromagnetic fields can perform molecular beamforming on extracellular vesicles. It models EVs as diffusing particles with sinusoidal drift ve,i(t) = v0,i cos(ωt + φi), solves the Fokker-Planck equation via a co-moving coordinate transformation, and obtains a Gaussian cloud whose center is displaced by (v0,i/ω) sin(ωt + φi). At any observation time the peak of the vesicle distribution sits at this displaced position, so the field amplitude sets the radial distance, the frequency sets the magnitude of the displacement, and the phase between field components sets the rotation angle. Simulations with EV biophysical parameters show mea

What carries the argument

The central mechanism is the co-moving frame transformation ξi = ri − (v0,i/ω) sin(ωt + φi). Substituting this into the Fokker-Planck equation removes the sinusoidal drift term, leaving a pure diffusion equation whose Green's function is a spreading Gaussian. Transforming back to the laboratory frame gives the beam-like solution whose center oscillates with the field at amplitude v0/ω. The drift amplitude itself is set by the Smoluchowski electrophoretic velocity v0 = εζE0/η, so the beam position is controlled directly by field amplitude, frequency, and phase.

Load-bearing premise

The argument hinges on treating the vesicle's response to the oscillating field as the simple electrophoretic rule—drift speed proportional to field strength through a fixed surface charge and water-like viscosity—holding at every instant in real tissue, with no extra flows, electrode effects, field distortion, or screening; if those complications dominate, the predicted beam shift disappears.

What would settle it

An experiment with video microscopy of EVs in a shallow buffer chamber between parallel electrodes, at 5 Hz and 1000 V/m, measuring the cloud center displacement over one cycle: the model predicts a shift of roughly v0/ω ≈ 0.45 µm for ζ = −20 mV, ε = 7.08×10−10 F/m, and η = 10−3 Pa·s. If the observed displacement is much smaller, or is dominated by AC electro-osmosis or electrode polarization rather than Smoluchowski drift, the central claim is refuted. A second check is measuring zeta potential at 1–5 Hz; strong frequency dispersion would break the constant-ζ premise.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • Beam position can be tuned continuously: field intensity sets the radial distance (with a plateau near 1800 V/m in the modeled domain), phase shifts rotate the beam, and frequency modulates displacement with a sinc-like response.
  • A practical operating window exists around 200–2000 V/m and 1–5 Hz, with 2000 V/m below the 5 kV/m lethal-effect threshold cited by the paper.
  • The probability peak decays after roughly 50 ms in the simulation because diffusion continues to spread the cloud, so beam delivery is time-sensitive and should be timed to the concentration maximum.
  • Larger vesicles (for example 2000 nm) form narrower beams with much lower peak concentrations, while smaller vesicles give broader but stronger beams.
  • Multi-component fields with a 90-degree phase difference produce sustained beam rotation with little radial motion, which could be used to sweep a target region.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Not in the paper: the v0/ω dependence implies a low-frequency trade-off—displacement grows as frequency shrinks, but the field direction changes so slowly that the motion approaches a quasi-DC push; pulsed or chirped fields may be needed to exploit the scaling without holding a static field.
  • Not in the paper: in real extracellular space, frequency-dependent zeta potential, electrode polarization, electroosmotic flow, and tissue screening would alter the effective v0, so measuring ζ(ω) and the actual cloud displacement at 5 Hz would directly test whether the predicted operating window survives.
  • Not in the paper: the same Gaussian-beam solution could serve as a spatial multiplexing scheme in molecular communication, where different receivers are addressed by phase-coded field components rather than by releasing different molecule types.
  • Not in the paper: since displacement scales linearly with zeta potential, engineered EVs with higher surface charge (already mentioned as future work) would proportionally increase the achievable beam shift or lower the required field strength.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper proposes using extremely low-frequency electric fields to steer extracellular vesicles (EVs) by electrophoretic drift. It models EV transport with a Fokker-Planck equation containing a time-harmonic drift term, solves it via a co-moving coordinate transform, and obtains a Gaussian solution whose center oscillates as v0/ω sin(ωt+φ). The authors then evaluate this solution for a 2 μm × 2 μm domain, varying field amplitude (200–2000 V/m), frequency (1–10 Hz), phase, axial ratio, and EV size, and report beam displacement, rotation, and peak concentration changes. The abstract claims that frequencies below 5 Hz with field strengths of 200–2000 V/m can induce substantial directional control of EV motion, and the conclusion extends this to targeted drug delivery in the extracellular space (ECS).

Significance. If the idealizations are accepted, the paper provides a clean, explicit closed-form solution for the FPE with harmonic drift and illustrates how phase and amplitude can position the peak of a diffusing Gaussian packet at a chosen observation time. This is a useful proof-of-concept for molecular beamforming and is clearly relevant to the molecular communication community. The parametric sensitivity analysis is systematic and the figures are easy to interpret. However, the central quantitative claims depend entirely on the unvalidated Eq. (1) (Smoluchowski mobility with constant ζ, water permittivity, and E=E0 inside the ECS) and on interpreting an oscillating, zero-mean displacement as directional control. The paper does not provide experimental calibration, error estimates for the homogeneous-medium assumption, or a clear treatment of the phase offset in the analytical solution. These issues make the paper defensible as an idealized feasibility study, but not yet as a basis for in vivo targeted-delivery claims.

major comments (5)
  1. [§2.3, Eqs. (6)–(11)] The phase-offset term is dropped incorrectly. Eq. (6) gives ∫v dτ = (v0/ω)(sin(ωt+φ) − sinφ), but Eq. (10)–(11) omit the −(v0/ω)sinφ term. Consequently Eq. (11) does not satisfy the initial condition P(r,0)=δ(r) for φ≠0; at t=0 the center is displaced by (v0/ω)sinφ. This affects the phase-sensitivity results in Figs. 6 and 10. The solution should read r_i = ξ_i + (v0,i/ω)(sin(ωt+φ_i) − sinφ_i), or the coordinate shift must be explicitly tracked in the source location.
  2. [§3.2, Eq. (11), Fig. 11] The central claim of 'directional control' overstates what the model predicts. Averaged over any integer number of cycles, the beam center has zero net displacement; the apparent positioning is a transient phase-dependent offset v0/ω sin(ωt+φ). At the headline point (5 Hz, 1000 V/m) the one-time displacement amplitude is about 0.45 μm, which is comparable to the diffusion length √(2Dt) ≈ 0.55 μm at t=150 ms. The abstract and §3.3 should explicitly state that steering is a transient synchronization effect, not net transport, and should discuss whether this suffices for the targeted-delivery scenarios envisioned.
  3. [§2.1, Eq. (1); §3, Table 1] The load-bearing physical premise is the Smoluchowski relation v_e = εζE/η applied at every instant with a single zeta potential ζ=−20 mV, water permittivity, and the externally imposed field E0. The manuscript itself concedes in §3 that the ECS is treated as homogeneous and isotropic, but it provides no estimate or bound for the error introduced by tissue heterogeneity, field screening, electroosmotic flow, or dielectrophoresis. Because the predicted displacement is linear in εζE/(ηω), even a factor-of-2–10 reduction in effective mobility or local field would erase the claimed 200–2000 V/m operating window. This should be addressed quantitatively or the claims should be restricted to idealized in vitro conditions.
  4. [§2.3.2, Eqs. (12)–(13)] The source representation is internally inconsistent. Eq. (12) defines S(r,t) as a time-independent Gaussian with no normalization, while Eq. (13) says the solution is obtained by spatiotemporal convolution. If S is literally a persistent source, the convolution with the 3D Green's function has a singular/divergent time integral; if it is meant to describe a burst at t=0, S should include a factor δ(t) (or be reformulated as an initial condition). The text in §3.0 says 'a burst occurring at time t=0,' which suggests an initial-value problem. Please specify the exact convolution performed and give the resulting closed form for the finite-width source.
  5. [Table 1 and Eq. (3)] The listed diffusion coefficient D=1×10⁻¹² m²/s is inconsistent with the Stokes-Einstein formula Eq. (3) at the listed radius a=50 nm and water viscosity η=1×10⁻³ Pa·s; at T≈300–310 K one obtains D≈4.4–4.5×10⁻¹² m²/s. Since D controls the beamwidth and peak decay in all simulations, the quantitative figures depend on a factor-4.4 discrepancy. Either the D value should be recomputed/justified (e.g., as an effective tortuosity-reduced value) or the parameters should be made mutually consistent.
minor comments (5)
  1. [Fig. 11 and §3.2] The physical explanation for the sinc-shaped frequency response ('EVs can build momentum and maintain a net drift') is inconsistent with the overdamped, zero-inertia model of Eq. (1). The sinc dependence follows directly from Eq. (11) at fixed observation time; the text should use kinematic language rather than momentum language.
  2. [Eq. (11)] The symbol G is reused for the Green's function in Eq. (9) and for the probability density in Eq. (11), which is confusing. Use P or a distinct notation for the density.
  3. [Eq. (12)] The notation r_{i,L} and σ_i is not defined consistently with the vector notation used elsewhere. Also 'repectively' should be 'respectively'.
  4. [Abstract and §4] The abstract says the framework was 'numerically solved,' but the paper derives an analytical solution and evaluates it. This wording should be corrected.
  5. [Fig. 7 caption] There is a missing comma before 'a=2000nm'; the values should be listed as a=25 nm, 100 nm, 200 nm, 2000 nm for readability.

Circularity Check

0 steps flagged

No significant circularity: the derivation is a self-contained mathematical consequence of the stated electrokinetic assumptions, with no fitted target or load-bearing self-citation.

full rationale

The paper's central claim—that frequencies below 5 Hz and field strengths of 200–2000 V/m can produce substantial directional EV displacement—is obtained by solving the Fokker–Planck equation with a time-harmonic Smoluchowski drift. Equation (11) gives the Gaussian center as v0,i/ω · sin(ωt + φi) = (εζE0,i/(ηω)) sin(ωt + φi), which follows from integrating the assumed drift velocity (4); the displacement is a mathematical consequence of the input, not an additional hidden assumption. No parameter is fitted to any target result; all constants come from literature (Table 1), and the sensitivity analyses simply evaluate the closed-form solution. The homogeneous/isotropic medium and thin-EDL assumptions are explicitly stated simplifications that affect real-world validity, but they are not circular. Self-citations (e.g., [3], [6], [11]) provide biological or communication-theory background and are not load-bearing for the derivation; no uniqueness theorem or ansatz is imported from the authors' prior work to force the conclusion. The only plausible concern—whether Eq. (1) accurately describes EVs in a real extracellular space—is a physical-fidelity limitation, not a circularity of the derivation chain.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

The model's output is essentially a function of Table 1 inputs plus a hidden Gaussian source width. D is set by hand and contradicts the paper's own Stokes-Einstein formula; sigma_L and the overall normalization are unreported although they set the temporal peak and all figure scales; zeta potential is a literature average treated as uniform, and displacement is linear in it. Axioms are standard FPE math plus strong domain assumptions: uniform medium, instantaneous Smoluchowski response, zero-mean drift as the only deterministic force, and a Gaussian burst release. No invented entities.

free parameters (3)
  • Diffusion coefficient D = 1e-12 m²/s (Table 1)
    Set by hand in Table 1 but inconsistent with Eq. (3) evaluated at the same table's a = 50 nm, which gives about 4.5e-12 m²/s. The smaller D suppresses diffusion in every figure and inflates the apparent beam sharpness.
  • Gaussian source width sigma_L = unreported, implied near 0.32 µm by the t = 50 ms peak claim
    Never given a value, yet it controls the temporal evolution of the peak value (Fig. 12); the claimed 50 ms maximum cannot come out of Eqs. (9)-(13) as written without an appropriate chosen sigma_L.
  • PDF normalization / released EV count = unknown; figure scales in arbitrary 1e-5 to 1e-10 units
    No normalization is specified. The closed-form unit-mass PDF (11) has a peak near 1e17 at t = 150 ms, so the plotted values imply an unexplained scale factor that affects every figure.
axioms (5)
  • standard math Fokker-Planck equation with linear drift and its Green's function solution via co-moving transform is valid for the stated process
    Used in Secs. 2.2-2.3. The transform (5)-(8) is standard, but the source term S(r,t) is dropped in the transformed equation (8) and never properly handled in the stated convolution (13).
  • domain assumption Smoluchowski electrophoretic mobility v_e = εζE/η with constant ζ applies instantaneously at each time instant
    Eq. (1), Sec. 2.1. Assumes a thin EDL, low Reynolds number, and neglects frequency dispersion of ζ and ε, electroosmotic flow, dielectrophoresis, and tissue-induced field distortion.
  • domain assumption The extracellular space is homogeneous, isotropic, and unbounded; the electric field is spatially uniform
    Sec. 3, opening paragraph. The true tortuous and heterogeneous ECS and field screening are explicitly set aside.
  • domain assumption A zero-mean sinusoidal drift is the only deterministic dynamics, with no rectification or time-averaged force
    Eq. (4). This is why the beam center oscillates with zero net displacement per cycle, a fact the 'directional control' claim does not qualify.
  • domain assumption EV release is a Gaussian burst source (Eq. 12) whose width sigma_L is left unspecified
    Sec. 2.3.1. sigma_L is never assigned a value although it controls the temporal peak position claimed in Fig. 12.

pith-pipeline@v1.3.0-alltime-deepseek · 9540 in / 25394 out tokens · 239404 ms · 2026-08-04T19:16:09.130727+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of Electrophoretic Beamforming in Molecular Communication: Toward Targeted Extracellular Vesicle Delivery." pith.science (2026). https://pith.science/paper/RD5NMCLF

@misc{pith2026250909339,
  author       = {Pith},
  title        = {Pith review of: Electrophoretic Beamforming in Molecular Communication: Toward Targeted Extracellular Vesicle Delivery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RD5NMCLF}},
  note         = {Machine review of arXiv:2509.09339}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Directing extracellular vesicles (EVs), such as exosomes and microvesicles, toward specific cells is an emerging focus in nanomedicine, owing to their natural role as carriers of proteins, RNAs, and drugs. EVs can be manipulated by external electric fields due to their intrinsic surface charge and biophysical properties. This study investigates the feasibility of using extremely low-frequency electromagnetic fields to guide EV transport. A theoretical framework based on the Fokker-Planck equation was developed and numerically solved to model vesicle trajectories under time-harmonic drift. Computational simulations were conducted to systematically assess the influence of key electric field parameters, including phase, frequency, and intensity, on vesicle displacement and trajectory. The findings demonstrate that frequencies below 5 Hz combined with field strengths of 200-2000 V/m can induce substantial directional control of EV motion. Moreover, enhanced directivity was achieved through the application of multi-component electric fields. Overall, this work establishes a theoretical foundation for the external-field-based beamforming of nanoparticles within the framework of molecular communication.

Figures

Figures reproduced from arXiv: 2509.09339 by Ilangko Balasingham, Liv Cornelia Middelthon, Mladen Veleti\'c, Mohammad Zoofaghari.

Figure 1
Figure 1. Figure 1: Capability of electrophoretic beamforming in in vitro, in vivo, and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of chemotherapy delivery to the cancer cells, comparing [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The potential of layers of an extracellular vesicle. (Generated by [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: EV concentration distributions considering background noise ( [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Spatial variation of extracellular vesicles beam exposed to electric [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Radial and angular displacement and value of EVs’ PDF peak in [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Radial and angular displacement and value of EVs’ PDF peak in [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Radial and angular displacement and value of EVs’ PDF peak in [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
Figure 13
Figure 13. Figure 13: Radial and angular displacement and value of EVs’ PDF peak in [PITH_FULL_IMAGE:figures/full_fig_p008_13.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

21 extracted references · 1 canonical work pages

  1. [1]

    J. T. G ´omez, P. Hofmann, L. Y . Debus, O. T. Bas ¸aran, S. Lotter, R. Khanzadeh, S. Angerbauer, B. D. Unluturk, S. Abadal, W. Hasel- mayr, et al., Communicating smartly in molecular communication en- vironments: Neural networks in the internet of bio-nano things, arXiv preprint arXiv:2506.20589 (2025)

    Pith/arXiv arXiv 2025

  2. [2]

    Kianfar, Magnetic nanoparticles in targeted drug delivery: a review, Journal of Superconductivity and Novel Magnetism 34 (7) (2021) 1709– 1735

    E. Kianfar, Magnetic nanoparticles in targeted drug delivery: a review, Journal of Superconductivity and Novel Magnetism 34 (7) (2021) 1709– 1735

    2021

  3. [3]

    Zoofaghari, F

    M. Zoofaghari, F. Pappalardo, M. Damrath, I. Balasingham, Modeling extracellular vesicles-mediated interactions of cells in the tumor microen- vironment, IEEE Transactions on NanoBioscience 23 (1) (2024) 71–80. doi:10.1109/TNB.2023.3284090

    arXiv 2024

  4. [4]

    Zoofaghari, K

    M. Zoofaghari, K. Sagini, M. Damrath, A. Zargarnia, H. Flaten, M. Veleti´c, A. Llorente, I. Balasingham, In silico study of bloodstream penetrating extracellular vesicles, IEEE Transactions on Molecular, Bio- logical, and Multi-Scale Communications 11 (2) (2025) 166–175.doi: 10.1109/TMBMC.2025.3550323

    arXiv 2025

  5. [5]

    Arjmandi, H

    H. Arjmandi, H. K. Rudsari, J. Santos, M. Zoofaghari, O. Ievglevskyi, M. Kanada, A. Khaleghi, I. Balasingham, M. Veleti ´c, Extracellular vesicle-mediated communication nanonetworks: Opportunities and chal- lenges, IEEE Communications Magazine 59 (5) (2021) 68–73.doi: 10.1109/MCOM.001.2000994

    work page doi:10.1109/mcom.001.2000994 2021

  6. [6]

    H. K. Rudsari, M. Zoofaghari, M. Damrath, M. Veleti ´c, J. Bergsland, I. Balasingham, Anomalous diffusion of extracellular vesicles in an ex- tracellular matrix for molecular communication, IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 9 (1) (2023) 8– 12.doi:10.1109/TMBMC.2023.3240928

    arXiv 2023

  7. [7]

    E. H. Apu, et al., Biomedical applications of multifunctional magneto- electric nanoparticles, Materials Chemistry Frontiers 6 (11) (2022) 1368– 1390

    2022

  8. [8]

    Scientific image and illustration software — biorender, Online, available: https://biorender.com/

  9. [9]

    Y . Wang, G. A. Worrell, H.-L. Wang, It is the frequency that matters: Ef- fects of electromagnetic fields on the release and content of extracellular vesicles, bioRxiv (2023)

    2023

  10. [10]

    C. J. K. Wong, Y . K. Tai, J. L. Y . Yap, C. H. H. Fong, L. S. W. Loo, M. Kukumberg, J. Fr ¨ohlich, S. Zhang, J. Z. Li, J.-W. Wang, et al., Brief exposure to directionally-specific pulsed electromagnetic fields stimulates extracellular vesicle release and is antagonized by streptomycin: A poten- tial regenerative medicine and food industry paradigm, Bioma...

    2022

  11. [11]

    H. K. Rudsari, M. Zoofaghari, M. Veleti ´c, J. Bergsland, I. Balasing- ham, The End-to-End molecular communication model of Extracellu- lar Vesicle-Based Drug Delivery, IEEE Transactions on NanoBioscience 22 (3) (2022) 498–510.doi:10.1109/tnb.2022.3206908. URLhttps://doi.org/10.1109/tnb.2022.3206908

    arXiv 2022

  12. [12]

    S. Wang, Y . Mao, S. Rong, G. Liu, Y . Cao, Z. Yang, H. Yu, X. Zhang, H. Fang, Z. Cai, et al., Engineering magnetic extracellular vesicles mimet- ics for enhanced targeting chemodynamic therapy to overcome ovary can- cer, ACS Applied Materials & Interfaces 16 (30) (2024) 39021–39034

    2024

  13. [13]

    Midekessa, et al., Zeta potential of extracellular vesicles: Toward un- derstanding the attributes that determine colloidal stability, ACS Omega 5 (27) (2020) 16701–16710

    G. Midekessa, et al., Zeta potential of extracellular vesicles: Toward un- derstanding the attributes that determine colloidal stability, ACS Omega 5 (27) (2020) 16701–16710

    2020

  14. [14]

    L. S. Cheung, S. Sahloul, A. Orozaliev, Y .-A. Song, Rapid detection and trapping of extracellular vesicles by electrokinetic concentration for liquid biopsy on chip, Micromachines 9 (6) (2018) 306

    2018

  15. [15]

    Lim, et al., Magnetophoresis of nanoparticles, ACS Nano 5 (1) (2010) 217–226

    J. Lim, et al., Magnetophoresis of nanoparticles, ACS Nano 5 (1) (2010) 217–226

    2010

  16. [16]

    S. Cho, T. C. Sykes, J. P. Coon, A. A. Castrej ´on-Pita, Electrophoretic molecular communication with time-varying electric fields, Nano Com- munication Networks 31 (2022) 100381

    2022

  17. [17]

    H. K. Rudsari, et al., The end-to-end molecular communication model of extracellular vesicle-based drug delivery, IEEE Transactions on NanoBio- science 22 (3) (2022) 498–510

    2022

  18. [18]

    Wilemski, On the derivation of smoluchowski equations with correc- tions in the classical theory of brownian motion, Journal of Statistical Physics 14 (2) (1976) 153–169

    G. Wilemski, On the derivation of smoluchowski equations with correc- tions in the classical theory of brownian motion, Journal of Statistical Physics 14 (2) (1976) 153–169

    1976

  19. [19]

    L. M. Doyle, M. Z. Wang, Overview of extracellular vesicles, their origin, composition, purpose, and methods for exosome isolation and analysis, Cells 8 (7) (2019) 727

    2019

  20. [20]

    S. Wang, J. Sun, R. M. Dastgheyb, Z. Li, Tumor-derived extracellular vesicles modulate innate immune responses to affect tumor progression, Frontiers in Immunology 13 (Nov. 2022)

    2022

  21. [21]

    Z.-C. Sun, et al., Extremely low frequency electromagnetic fields facil- itate vesicle endocytosis by increasing presynaptic calcium channel ex- pression at a central synapse, Scientific Reports 6 (1) (Feb. 2016). 8

    2016