Controlled Chemical Signaling between Enzymatic Nanomotors

Daniel S\'anchez-deAlc\'azar; Giorgio Lovato; Oriol Jutglar Soler; Ramin Golestanian; Samuel S\'anchez; Shuqin Chen

arxiv: 2606.04138 · v1 · pith:2I2W522Ynew · submitted 2026-06-02 · ❄️ cond-mat.soft · cond-mat.mtrl-sci· physics.chem-ph

Controlled Chemical Signaling between Enzymatic Nanomotors

Shuqin Chen , Giorgio Lovato , Oriol Jutglar Soler , Daniel S\'anchez-deAlc\'azar , Ramin Golestanian , Samuel S\'anchez This is my paper

Pith reviewed 2026-06-28 07:52 UTC · model grok-4.3

classification ❄️ cond-mat.soft cond-mat.mtrl-sciphysics.chem-ph

keywords enzymatic nanomotorschemical signalingphoretic responsecollective behaviorH2O2 gradientglucose oxidasecatalase

0 comments

The pith

A primary swarm of glucose-responsive nanomotors produces H2O2 that attracts a secondary swarm of catalase-powered nanomotors via phoretic response.

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

The paper establishes that enzymatic nanomotors can achieve chemical communication between two distinct populations. A primary swarm moves up a glucose gradient and generates a diffusible H2O2 signal as a byproduct. This signal propagates and draws the secondary swarm toward it through non-reciprocal phoretic interactions. Experiments track the gradient front and the receivers' movement over time. Theoretical analysis shows that mixing chemo-attractive and chemo-repulsive mobilities with different production and consumption rates creates multiple possible collective patterns.

Core claim

A primary swarm of glucose-responsive nanomotors migrates toward a glucose gradient while producing H2O2 as a diffusible communication signal. This self-generated chemical gradient then acts as a chemoattractant for a secondary swarm of catalase-powered nanomotors through generically non-reciprocal phoretic response, with the synergy between different combinations of chemo-attractive and chemo-repulsive mobilities and catalytic rates giving rise to a wealth of different collective responses.

What carries the argument

The propagating H2O2 gradient generated by the primary swarm that elicits phoretic response in the secondary swarm.

If this is right

Different combinations of mobilities and catalytic rates produce varied collective behaviors in the mixed system.
Non-reciprocal phoretic signaling enables programmable interactions between separate nanomotor populations.
The approach broadens nanomotor functionality from individual to collective-level control.
The mechanism supports future hybrid living-synthetic systems that use similar chemical channels.

Where Pith is reading between the lines

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

The same signaling principle could be tested with additional enzyme pairs to create longer-range or multi-hop communication chains.
Varying substrate concentrations in controlled chambers would map out the full range of response patterns implied by the mobility-rate combinations.
The setup suggests a route to embed chemical logic gates directly into physical particle motion without external fields.

Load-bearing premise

The observed migration of the secondary swarm is caused specifically by the propagating H2O2 gradient acting through phoretic response rather than by fluid flows or direct particle interactions.

What would settle it

No directed migration of the secondary swarm when the primary swarm is prevented from producing H2O2 or when catalase activity in the receivers is blocked.

read the original abstract

The coordinated interactions between organisms enhance collective functionality, a feature that artificial systems such as enzymatic nanomotors seek to replicate. A key objective, yet still a major challenge, is to achieve chemical communication among nanomotors. Progress has been limited by the difficulties in verifying effective signaling processes, including chemical signal propagation and the response of receiving nanomotors. Here, we address this challenge using an enzymatic nanomotor system that demonstrates communication between two populations through generically non-reciprocal phoretic response. A primary swarm of glucose-responsive nanomotors migrates toward a glucose gradient while producing H2O2 as a diffusible communication signal. This self-generated chemical gradient then acts as a chemoattractant for a secondary swarm of catalase-powered nanomotors. Through carefully designed experiments, we visualize the propagating H2O2 gradient and quantify the spatiotemporal response of the receiver nanomotors to the chemical front. Combined experimental and theoretical analysis has revealed that the synergy between different combinations of chemo-attractive and chemo-repulsive mobilities and catalytic rates of consumption and production of substrates and products gives rise to a wealth of different collective responses in the system. This work represents a step toward programmable synthetic systems at the collective level, broadening the functionality of chemical nanomotors and opening opportunities for future hybrid living-synthetic systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows two distinct enzymatic nanomotor populations communicating via a self-generated H2O2 gradient that is visualized and quantified, with theory mapping out collective responses from different mobility and rate combinations.

read the letter

The core advance is an experimental demonstration of non-reciprocal chemical signaling between a glucose-oxidase swarm that produces H2O2 and a catalase swarm that responds to the resulting gradient. They visualize the propagating front and track the spatiotemporal shift in the receiver population, then combine that with a model showing how attractive versus repulsive phoretic mobilities plus production and consumption rates can produce a range of collective patterns.

The visualization step and the two-population setup are concrete steps beyond earlier single-population or reciprocal cases. The parameter exploration in the theory section gives a useful map of what behaviors are accessible by tuning the enzymatic properties.

The main soft spot is whether the secondary swarm's migration is cleanly attributable to the diffusible H2O2 gradient. The abstract states that carefully designed experiments were used to quantify the response, yet the description does not spell out controls that would separate phoretic drift from possible fluid advection driven by the primary swarm, hydrodynamic coupling, or direct particle interactions. If those alternatives are not ruled out quantitatively, the causal link that supports the later synergy analysis rests on an assumption that needs explicit checking in the full data.

The work is aimed at groups in active matter and synthetic microswimmers who are trying to move from isolated motors to programmable collectives. It contains enough experimental visualization plus modeling to justify sending it to referees, even though the controls around alternative mechanisms will probably require clarification or additional runs.

Referee Report

2 major / 2 minor

Summary. The manuscript claims to demonstrate controlled chemical signaling between two enzymatic nanomotor populations: a primary swarm of glucose oxidase-powered particles that migrates up a glucose gradient while generating a propagating H2O2 signal, which then elicits a phoretic response in a secondary catalase-powered swarm. Visualization of the H2O2 gradient and spatiotemporal quantification of the receiver swarm's migration are reported, together with a theoretical analysis showing that combinations of chemo-attractive/repulsive mobilities and substrate/product catalytic rates produce a range of collective behaviors.

Significance. If the causal attribution to phoretic chemical signaling is substantiated, the work would establish a concrete experimental platform for non-reciprocal communication in synthetic active matter and open routes to programmable collective nanomotor assemblies.

major comments (2)

[Results section describing secondary-swarm migration and gradient visualization] The central claim that secondary-swarm migration is specifically a phoretic response to the visualized H2O2 gradient (rather than fluid advection, hydrodynamic coupling, or direct inter-particle forces) is load-bearing. The abstract states that the gradient was visualized and the response quantified, yet the manuscript does not appear to describe controls (e.g., inert tracer particles, flow visualization, or activity-suppressed variants) that would exclude the alternative mechanisms listed in the stress-test note.
[Theoretical analysis and comparison to experiment] The theoretical synergy analysis of mobilities and catalytic rates is presented as explaining the observed collective responses, but without quantitative comparison to the measured migration speeds or exclusion of non-phoretic contributions, it is unclear whether the model parameters are independently constrained or fitted post hoc to the data.

minor comments (2)

[Theory section] Notation for chemo-attractive versus chemo-repulsive mobilities should be defined explicitly at first use to avoid ambiguity when multiple combinations are discussed.
[Figures] Figure captions for the gradient-visualization and swarm-migration panels should include scale bars, time stamps, and error estimates on the reported spatiotemporal quantities.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which helps strengthen the causal attribution in our work. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

Referee: [Results section describing secondary-swarm migration and gradient visualization] The central claim that secondary-swarm migration is specifically a phoretic response to the visualized H2O2 gradient (rather than fluid advection, hydrodynamic coupling, or direct inter-particle forces) is load-bearing. The abstract states that the gradient was visualized and the response quantified, yet the manuscript does not appear to describe controls (e.g., inert tracer particles, flow visualization, or activity-suppressed variants) that would exclude the alternative mechanisms listed in the stress-test note.

Authors: We agree that explicit controls are essential to rule out alternative mechanisms and substantiate the phoretic interpretation. In the revised manuscript we will add a new subsection in the Results that details control experiments performed with inert tracer particles (to exclude advection and hydrodynamics) and with activity-suppressed secondary particles (to exclude direct inter-particle forces). These data, which were collected but not fully described, confirm that migration occurs only when both the H2O2 gradient and active catalase catalysis are present. revision: yes
Referee: [Theoretical analysis and comparison to experiment] The theoretical synergy analysis of mobilities and catalytic rates is presented as explaining the observed collective responses, but without quantitative comparison to the measured migration speeds or exclusion of non-phoretic contributions, it is unclear whether the model parameters are independently constrained or fitted post hoc to the data.

Authors: The model parameters were taken from independent literature values and separate single-particle measurements rather than fitted to the collective data. Nevertheless, the referee is correct that a direct quantitative comparison is missing. In the revision we will add a dedicated panel and table that overlays predicted migration speeds (using the fixed parameters) against the experimentally measured speeds for the secondary swarm, together with an explicit statement that non-phoretic contributions are excluded by the controls described in the response to the first comment. revision: yes

Circularity Check

0 steps flagged

Experimental demonstration with independent theory; no derivation reduces to inputs by construction

full rationale

The paper reports experiments visualizing propagating H2O2 gradients and quantifying spatiotemporal responses of secondary nanomotors, together with theoretical analysis of chemo-attractive/repulsive mobilities and catalytic rates. No equations, fitted parameters, or self-citations are presented that make the reported collective responses equivalent to the inputs by construction. The derivation chain is self-contained against external benchmarks (gradient visualization and response quantification), yielding only a minor score for possible background self-citations that are not load-bearing on the central claims.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; all elements of the claim rest on unstated experimental controls and modeling assumptions not visible here.

pith-pipeline@v0.9.1-grok · 5797 in / 1003 out tokens · 15090 ms · 2026-06-28T07:52:42.855047+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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A. C. Hortelao, C. Simó, M. Guix, S. Guallar -Garrido, E. Julián, D. Vilela, L. Rejc, P. Ramos-Cabrer, U. Cossío, V . Gómez-Vallejo, T. Patiño, J. Llop, S. Sánchez, Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder. Sci. Robot. 6, eabd2823 (2021)

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Cox, S. M. & Matthews, P . C. Exponential time differencing for stiff systems. J. Comput. Phys. 176, 430–455 (2002). 26 Extended Fig. E1 Exclusion zone formed near the gel surfaces. In a system prepared in DI water, nanomotors near the gel side are repelled from the exclusion zone. Extended Fig. E2 CatNMs repelled by glucose gradients. Fluorescence images...

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[1] [1]

T. J. Czaczkes, C. Grüter, F. L. W. Ratnieks, Trail pheromones: An integrative view of their role in social insect colony organization. Annu. Rev. Entomol. 60, 581–599 (2015)

2015

[2] [2]

Yang, W. et al. Neutrophils promote the development of reparative macrophages mediated by ROS to orchestrate liver repair. Nat. Commun. 10, 1–14 (2019)

2019

[3] [3]

Chen, S. et al. A roadmap for next-generation nanomotors. Nat. Nanotechnol. 20, 990–1000 (2025)

2025

[4] [4]

Ju, X. et al. Technology Roadmap of Micro/Nanorobots. ACS Nano 19, 24174 –24334 (2025)

2025

[5] [5]

C., Sen, A

Patiño Padial, T., Chen, S., Hortelão, A. C., Sen, A. & Sánchez, S. Swarming intelligence in self-propelled micromotors and nanomotors. Nat. Rev. Mater. 10, 947–963 (2025)

2025

[6] [6]

Kriebisch, C. et al. A roadmap towards the synthesis of Life. Chem 11, 102399 (2024)

2024

[7] [7]

& Katuri, J

Sanchez, S., Soler, L. & Katuri, J. Chemically powered micro - and nanomotors. Angew. Chem. Int. Ed. 54, 1414–1444 (2015)

2015

[8] [8]

& Golestanian, R

Soto, R. & Golestanian, R. Self -assembly of catalytically active colloidal molecules: Tailoring activity through surface chemistry. Phys. Rev. Lett. 112, (2014)

2014

[9] [9]

Meredith, C. H. et al. Predator–prey interactions between droplets driven by non-reciprocal oil exchange. Nat. Chem. 12, 1136–1142 (2020)

2020

[10] [10]

Yu, T. et al. Chemical micromotors self-assemble and self-propel by spontaneous symmetry breaking. Chem. Commun. 54, 11933–11936 (2018)

2018

[11] [11]

& Golestanian, R

Agudo-Canalejo, J. & Golestanian, R. Active Phase Separation in Mixtures of Chemically Interacting Particles. Phys. Rev. Lett. 123, 018101 (2019)

2019

[12] [12]

& Golestanian, R

Osat, S. & Golestanian, R. Non-reciprocal multifarious self-organization. Nat. Nanotechnol. 18, 79–85 (2022)

2022

[13] [13]

& Golestanian, R

Ouazan-Reboul, V ., Agudo-Canalejo, J. & Golestanian, R. Self -organization of primitive metabolic cycles due to non-reciprocal interactions. Nat. Commun. 14, 4496- (2023)

2023

[14] [14]

Lin, J. et al. Collective Dynamics of Urease -Based Nanomotors in a Chemical Gradient. Small 21, 2502212 (2025)

2025

[15] [15]

D. Xu, J. Hu, X. Pan, S. Sánchez, X. Yan, X. Ma, Enzyme-powered liquid metal nanobots endowed with multiple biomedical functions. ACS Nano 15, 11543–11554 (2021)

2021

[16] [16]

Joseph, C

A. Joseph, C. Contini, D. Cecchin, S. Nyberg, L. Ruiz-Perez, J. Gaitzsch, G. Fullstone, X. Tian, J. Azizi, J. Preston, G. V olpe, G. Battaglia, Chemotactic synthetic vesicles: Design and applications in blood-brain barrier crossing. Sci. Adv. 3, e1700362 (2017)

2017

[17] [17]

C. Zhou, C. Gao, Y . Wu, T. Si, M. Yang, Q. He, Torque -driven orientation motion of chemotactic colloidal motors. Angew. Chem. Int. Ed. 61, e202116013 (2022)

2022

[18] [18]

Ghosh, F

Somasundar, S. Ghosh, F. Mohajerani, L. N. Massenburg, T. Yang, P . S. Cremer, D. Velegol, 25 A. Sen, Positive and negative chemotaxis of enzyme -coated liposome motors. Nat.Nanotechnol. 14, 1129–1134 (2019)

2019

[19] [19]

Y . C. Tseng, J. Song, J. Zhang, E. Shandilya, A. Sen, Chemomechanical communication between liposomes based on enzyme cascades. J. Am. Chem. Soc. 146, 16097 –16104 (2024)

2024

[20] [20]

& Golestanian, R

Agudo-Canalejo, J., Illien, P. & Golestanian, R. Phoresis and Enhanced Diffusion Compete in Enzyme Chemotaxis. Nano Lett. 18, 2711–2717 (2018)

2018

[21] [21]

& Gerland, U

Giunta, G., Seyed-Allaei, H. & Gerland, U. Cross-diffusion induced patterns for a single - step enzymatic reaction. Commun. Phys. 3, 167 (2020)

2020

[22] [22]

Williams, I., Lee, S., Apriceno, A., Sear, R. P . & Battaglia, G. Diffusioosmotic and convective flows induced by a nonelectrolyte concentration gradient. Proc. Natl. Acad. Sci. U. S. A. 117, 25263–25271 (2020)

2020

[23] [23]

Rhee, S. G. H 2O2, a necessary evil for cell signaling. Science (1979). 312, 1882–1883 (2006)

1979

[24] [24]

Diffusiophoresis, Diffusioosmosis, and Microfluidics: Surface -Flow-Driven Phenomena in the Presence of Flow

Shim, S. Diffusiophoresis, Diffusioosmosis, and Microfluidics: Surface -Flow-Driven Phenomena in the Presence of Flow. Chem. Rev. 122, 6986–7009 (2022)

2022

[25] [25]

Baraban, S

L. Baraban, S. M. Harazim, S. Sanchez, O. G. Schmidt, Chemotactic behavior of catalytic motors in microfluidic channels. Angew. Chem. Int. Ed. 52, 5552–5556 (2013)

2013

[26] [26]

stabilitaxis

Agudo-Canalejo, J., Illien, P . & Golestanian, R. Cooperatively enhanced reactivity and “stabilitaxis” of dissociating oligomeric proteins. Proc. Natl. Acad. Sci. U. S. A. 117, 11894–11900 (2020)

2020

[27] [27]

Zhang, S

Y . Zhang, S. Tsitkov, H. Hess, Complex dynamics in a two-enzyme reaction network with substrate competition. Nat. Catal. 1, 276–281 (2018)

2018

[28] [28]

Phoretic Active Matter

Golestanian, R. Phoretic Active Matter. Active Matter and Nonequilibrium Statistical Physics 230–293 (2022)

2022

[29] [29]

Chen, S. et al. Dual-source powered nanomotor with integrated functions for cancer photo- theranostics. Biomaterials 288, 121744–121753 (2022)

2022

[30] [30]

A. C. Hortelao, C. Simó, M. Guix, S. Guallar -Garrido, E. Julián, D. Vilela, L. Rejc, P. Ramos-Cabrer, U. Cossío, V . Gómez-Vallejo, T. Patiño, J. Llop, S. Sánchez, Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder. Sci. Robot. 6, eabd2823 (2021)

2021

[31] [31]

Simó, C. et al. Urease-powered nanobots for radionuclide bladder cancer therapy. Nat. Nanotechnol. 19, 554–564 (2024)

2024

[32] [32]

D. B. Allan, T. Caswell, N. C. Keim, C. M. van der Wel, R. W. Verweij, Soft-matter/trackpy: v0.6.4. https://zenodo.org/records/12708864 (2024)

arXiv 2024

[33] [33]

Cox, S. M. & Matthews, P . C. Exponential time differencing for stiff systems. J. Comput. Phys. 176, 430–455 (2002). 26 Extended Fig. E1 Exclusion zone formed near the gel surfaces. In a system prepared in DI water, nanomotors near the gel side are repelled from the exclusion zone. Extended Fig. E2 CatNMs repelled by glucose gradients. Fluorescence images...

2002