Evidence map›Paper›PMID 42525771›Full record

ArticleScience advances2026

Architecting mechanosensitive nanofluidic transport in graphite nanoslits.

Mathieu Lizée, Zhijia Zhang, Baptiste Coquinot, Qian Yang, Lydéric Bocquet

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mathieu LizéeLaboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, 24 rue Lhomond, 75005 Paris, France.ORCID 0000-0003-2497-3336
Zhijia ZhangDepartment of Physics and Astronomy, University of Manchester, Manchester M13 9PL, UK.ORCID 0000-0002-7545-5806
Baptiste CoquinotInstitute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria.ORCID 0000-0001-5524-596X
Qian YangDepartment of Physics and Astronomy, University of Manchester, Manchester M13 9PL, UK.ORCID 0000-0002-6203-7867
Lydéric BocquetLaboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, 24 rue Lhomond, 75005 Paris, France.ORCID 0000-0003-3577-5335

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mechanosensitive ion transport plays a central role in enabling living systems to perceive and adapt to their environment through the deformation of soft, embedded ion channels. In this work, we demonstrate that ion transport within a two-dimensional graphite nanoslit can be rationally engineered to achieve a bipolar, pressure-sensitive response without any structural deformation. The mechanosensitivity arises from the selective charging of one channel inlet, which acts as a reversible source of mobile charge carriers. These excess ions can then be advected in or out of the channel by the pressure-driven water flow, thereby modulating the ionic conductance. This mechanism is captured through a comprehensive electrohydrodynamic model that analytically accounts for coupled diffusion, convection, surface transport, diffusio-osmosis, and interfacial slippage, both inside and outside the nanoslit. The theoretical framework quantitatively reproduces the experimental data, showing that a simple surface charge pattern can give rise to complex, pressure-dependent conductance. These findings reveal how rich nonlinear couplings at the nanoscale can be harnessed to design adaptive, bioinspired nanofluidic systems, exemplified here by ionic pressure sensors.

Identifiers

PMID42525771
PMCPMC13418753

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.