arxiv: 2604.19228 · v1 · submitted 2026-04-21 · ❄️ cond-mat.mtrl-sci

Recognition: unknown

Rippled graphene pores as fluidic memristive devices with synaptic and neuromorphic functionalities

Wenzhe Zhou , Dongjiao Ge , Ao Zhang , Jincheng Xu , Yu Ji , Yiran Gong , Wenchang Zhang , Jidong Li

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Li Lin Zhiping Xu Pengzhan Sun

Authors on Pith no claims yet

Pith reviewed 2026-05-10 02:40 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci

keywords graphene poresfluidic memristorsionic memoryneuromorphic devicesnanofluidicssynaptic plasticityrippled grapheneion transport

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The pith

A micrometer-sized pore wrapped by rippled graphene exhibits memory effects in ion transport.

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

The paper shows that a micrometer size pore, normally expected to show only linear ion transport, can produce a pronounced memory effect when its rim consists of strongly curved and tightly stacked graphene. The effect arises from slow ion dynamics confined in those rippled edges. This approach removes the need to restrict pores to nanometer sizes for memory behavior, allowing easier scaling and integration into fluidic circuits. The resulting devices display ion-selective memory with long endurance and can be programmed by voltage spikes to change conductance states reversibly, supporting synaptic-like plasticity for information storage and processing.

Core claim

The central claim is that nanoscale morphology at the pore wall, specifically the rippled and stacked graphene rim, regulates ion transport to create memory even in micrometer-scale pores. Slow ion dynamics in the curved edges produce history-dependent conductance that persists over time, enabling neuromorphic functionalities such as reversible state modification and reliable performance in image identification and neural signal analysis when devices are integrated into circuits.

What carries the argument

Rippled and tightly stacked graphene edges wrapping the pore rim, which confine ions to produce slow dynamics and memory in conductance.

If this is right

The memory is ion-selective and shows endurance comparable to synaptic protein lifetimes.
Programmable voltage spikes can reversibly modify conductance states across various electrolytes.
Integrated circuits can store and process information with high reliability, including identification of greyscale and color images.
Real-time analysis of emulated neural signals becomes possible through the synaptic plasticity.
The nanoconfinement requirement for ionic memory shifts from overall pore size to design of the rim structure.

Where Pith is reading between the lines

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

If the rim-structure approach works, similar memory could be created in other curved two-dimensional material pores without shrinking the entire channel to nanoscale.
Fabrication of large arrays becomes simpler because micrometer pores avoid the precision challenges of nanometer drilling.
These devices might interface more readily with biological ionic environments since they use water-dissolved ions as carriers.
Varying the degree of rippling or number of stacked layers could be tested to tune retention times for specific applications.

Load-bearing premise

The memory effect is caused specifically by slow ion dynamics in the rippled graphene edges rather than surface chemistry changes, contamination, or measurement artifacts.

What would settle it

If devices fabricated with rippled graphene rims show no hysteresis or memory when the graphene is smoothed to remove ripples while keeping all other conditions identical, the attribution to confined edge dynamics would be falsified.

Figures

Figures reproduced from arXiv: 2604.19228 by Ao Zhang, Dongjiao Ge, Jidong Li, Jincheng Xu, Li Lin, Pengzhan Sun, Wenchang Zhang, Wenzhe Zhou, Yiran Gong, Yu Ji, Zhiping Xu.

**Figure 1.** Figure 1: Experimental device and ionic memory effect. a, Schematics for our micropores with rippled graphene edges (top left) and the measurement setup (bottom left). Electron micrographs for one of our devices (top right) and zoom-in showing the pore edges (bottom right). b, Representative I-V curves 0 100 200 0.1 0.2 0 Fitting Loop area (W) Frequency (mHz) KCl -0.6 0 0.6 1 2 0 Voltage (V) Current (A) 1 mM-1 M 1… view at source ↗

**Figure 2.** Figure 2: Robustness, integration, and quantification of the memory effect. a, Full set of cyclic I-V measurements (f ≈ 0.4 mHz) for a representative device over a few days. Main figure, I-V curves after reaching stabilization. The compilation constitutes 142 curves in total with the red one highlighting the characteristic memristive behavior. Inset, before stabilization (color coded). b, Histogram of the working du… view at source ↗

**Figure 3.** Figure 3: Modulating the conductance states using voltage spikes. Current ratio I2/I1 in response to a pair of positive (blue) or negative (red) spikes versus the time interval Δt for a, KCl and b, MgCl2 solutions, respectively. Symbols, measurement data. Error bars on the grey symbols in (a, b) indicate the accuracy. Solid curves, best exponential fits. Black dashed lines mark the position of I1/I2 = 1. Insets in (… view at source ↗

read the original abstract

Nanofluidic memristive devices work with nanoscale pores and ions dissolved in water, which harness the ionic memory effect aiming to store and process information. These devices share the same charge carriers as biological systems and bring hope for better emulating the neural functions and developing ionic circuits for neuromorphic applications. Specially, theory and experiments suggest that nanoconfinement is essential for inducing a memory effect, which places limit on the pore size to nm-scale or smaller. Such devices are difficult to scale up with precision and operate with long-term stability. Here, we show that a micrometer size pore, generally expected to exhibit a linear ion transport, can display a pronounced memory effect, if its rim is wrapped by strongly curved and tightly stacked graphene. We attribute the observation to slow ion dynamics confined in the rippled graphene edges. The devices are easy to scale up and integrate into fluidic circuits. The memory effect is ion-selective and exhibits long endurance comparable to the lifetime of synaptic proteins, which enables reversible modification of the conductance states using programmable voltage spikes and various electrolytes over a long time, akin to biological synaptic plasticity. Thanks to this plasticity, our devices and their integrated circuits enable storing, transmitting and processing information with high reliability, fidelity and accuracy, as evidenced in the identification of both greyscale and color images, and in the real-time analysis of emulated neural signals. Our results highlight nanoscale morphology of the pore wall as an important parameter regulating ion transport and indicate that the stringent nanoconfinement for ionic memory can be lifted from restricting the pore size to designing its rim structure. The devices and their integrated circuits may find use in ionic neuromorphic applications.

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

Micrometer pores show memristive behavior with rippled graphene rims, but the ion dynamics attribution needs better isolation from other effects.

read the letter

Hi, the main thing to know is that this work gets ionic memory and synaptic plasticity out of micrometer-scale graphene pores by wrapping the rim with strongly curved and stacked layers. That moves away from the usual requirement for nanoscale confinement and could make these devices easier to fabricate and integrate into fluidic circuits. They report the effect is ion-selective, shows long endurance, and responds to voltage spikes in ways that mimic biological plasticity. They then use the devices and circuits for greyscale and color image identification plus real-time analysis of emulated neural signals, which gives a concrete sense of what the plasticity can do across electrolytes. That practical demonstration is the strongest part and shows the approach has some engineering legs. The softer spot is the mechanism. The claim pins the hysteresis and memory squarely on slow ion dynamics inside the rippled edges, yet the description does not lay out controls that would separate this from surface chemistry shifts, adsorbed contaminants, electrode polarization, or measurement artifacts. Side-by-side comparisons with flat-rim pores or post-fabrication cleaning checks would help, but those are not referenced. The abstract also omits quantitative details like effect sizes, error bars, or variability across devices, so it is hard to judge how reliable or reproducible the observations are. If the full paper supplies those data and rules out alternatives, the causal story strengthens; otherwise it stays tentative. This is aimed at people working on nanofluidic memristors, neuromorphic ionics, or bio-compatible hardware who want larger, more scalable pores. It deserves a serious referee because the size-relaxation idea is useful if the results hold, even though the mechanism section will need tightening and more evidence.

Referee Report

2 major / 2 minor

Summary. The manuscript experimentally demonstrates that micrometer-scale graphene pores with strongly curved and tightly stacked rippled rims exhibit ionic memory effects, ion selectivity, and long-term endurance, which the authors attribute to slow ion dynamics confined within the rippled edges; this enables synaptic plasticity, neuromorphic functionalities including image identification, and suggests that rim morphology can relax the conventional nanoconfinement requirement for fluidic memristors.

Significance. If the attribution to rim-confined ion dynamics is substantiated, the result would be significant for relaxing the pore-size limit in ionic neuromorphic devices, enabling easier fabrication and integration. The reported endurance comparable to synaptic proteins and demonstrations of programmable conductance states with various electrolytes and real-time neural signal analysis represent concrete strengths in experimental scope.

major comments (2)

[Results (I-V characteristics and device performance)] Results section on I-V hysteresis and memory effect: The central attribution of the observed memory effect specifically to slow ion dynamics in the rippled graphene edges is not supported by comparative controls (e.g., flat-rim pores, cleaned surfaces, or electrolyte-specific tests) that would exclude alternatives such as surface chemistry variations or artifacts; this is load-bearing for the claim that rim structure lifts the nanoconfinement requirement.
[Methods] Methods and data presentation: The reporting of quantitative metrics, error bars, statistical controls, and detailed fabrication/characterization protocols for the memory effect, endurance, and ion selectivity is insufficient to assess robustness, as the abstract provides no such data and the full methods do not appear to include them.

minor comments (2)

[Figures] Figure captions and legends should explicitly label conditions (e.g., different electrolytes, spike protocols) and include scale bars or error indicators where data are plotted.
[Abstract] The abstract could more precisely separate observed phenomena from mechanistic attribution to avoid implying the mechanism is directly measured.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive evaluation of our work's significance and for the constructive major comments, which help clarify the presentation and strengthen the attribution of the observed effects. We address each point below and have revised the manuscript accordingly where appropriate.

read point-by-point responses

Referee: Results section on I-V hysteresis and memory effect: The central attribution of the observed memory effect specifically to slow ion dynamics in the rippled graphene edges is not supported by comparative controls (e.g., flat-rim pores, cleaned surfaces, or electrolyte-specific tests) that would exclude alternatives such as surface chemistry variations or artifacts; this is load-bearing for the claim that rim structure lifts the nanoconfinement requirement.

Authors: We acknowledge that direct side-by-side controls with flat-rim pores are not included in the current manuscript, which limits the strength of the attribution. The paper does report ion selectivity across multiple electrolytes and long-term endurance data that are consistent with confinement in the rippled rims rather than generic surface effects. To address this concern rigorously, we will add a new discussion subsection that explicitly considers alternative explanations (surface chemistry, contamination, or bulk electrolyte effects) and explains why the rim morphology is the most parsimonious interpretation based on the pore-size dependence and the stacked-ripple geometry. If additional control data become available from ongoing experiments, they will be incorporated; otherwise the discussion will note this limitation transparently. revision: partial
Referee: Methods and data presentation: The reporting of quantitative metrics, error bars, statistical controls, and detailed fabrication/characterization protocols for the memory effect, endurance, and ion selectivity is insufficient to assess robustness, as the abstract provides no such data and the full methods do not appear to include them.

Authors: We agree that the methods and results sections require expanded quantitative detail for reproducibility. In the revised manuscript we will (i) add error bars and statistical measures (standard deviation or SEM across multiple devices) to all I-V, endurance, and selectivity plots; (ii) include a dedicated methods subsection with step-by-step fabrication protocols, cleaning procedures, and characterization techniques (SEM, Raman, ionic current measurements); and (iii) report numerical values for key metrics such as on/off ratios, retention times, and endurance cycle counts. These additions will appear both in the main text and in an expanded supplementary information file. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental observations independent of self-referential modeling or fitted predictions

full rationale

The manuscript is an experimental study reporting fabrication of micrometer-scale graphene pores with rippled rims, direct I-V measurements showing hysteresis and synaptic-like plasticity, and attribution of the effect to confined ion dynamics based on observed morphology. No derivation chain, equations, or first-principles predictions appear that reduce to inputs by construction. The central claim rests on comparative device behavior and endurance tests rather than any self-citation load-bearing uniqueness theorem, ansatz smuggling, or renaming of known results. Self-citations, if present in the full text, are not required to justify the core experimental attribution, satisfying the criteria for a self-contained observational result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is experimental and relies on established nanofluidic principles and graphene fabrication methods from prior literature; no new free parameters, ad-hoc axioms, or invented entities are introduced in the abstract.

axioms (1)

domain assumption Standard principles of ion transport under nanoconfinement apply to rippled graphene edges
Invoked to attribute memory effect to slow ion dynamics without new derivation.

pith-pipeline@v0.9.0 · 5644 in / 1166 out tokens · 26100 ms · 2026-05-10T02:40:19.205756+00:00 · methodology

discussion (0)

Reference graph

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2023