Bioinspired nanofluidic iontronic device with integrated photoreceptor and photosynaptic functions

Domain: physics
Published: 2026-03-05
Editorial outcome: minor_revision

Abstract
Biological vision acquires externTal information through light-induced transmembrane ion transport, generating
electrical impulses. Emulating the dual visual functions of photoreceptors and photosynapses through light-
modulated ion transport presents a significant challenge. Herein, we present a bioinspired light-regulated
nanofluidic iontronic device that can mimic biological visual functionalities, realized through an engineered
carbon nanotube and molybdenum disulfide (CNT/MoS ) heterostructure. This bioinspired device combines two
2
key functionalities of photoreceptor-like optical sensing and photosynaptic signal processing with dynamically
adjustable polarity-switching behavior, achieved via bias voltage–modulated transient photoresponse speeds.
Both theoretical and experimental results prove that light-modulated ion transport originates from the
heterointerface-induced asymmetric photovoltage generation across CNT/MoS nanotube. Furthermore, we
2
demonstrate its implementation for both accurate orientation recognition and reliable fingerprint detection under
varying incident light angles. The device's bidirectional photoresponsiveness highlights its unique advantages
for adaptive visual simulation systems.

Introduction
In biological systems, approximately 80% of external information is processed primarily through the visual
system1. The retina, a sophisticated neural tissue lining the posterior eye, serves as the gateway for visual
perception, owing to its anatomically integrated and intricately organized structure2 (Fig. 1a). Among these, the
photoreceptor cells and photosynaptic layers serve as the most critical components, with the former specializing
in light reception and the latter mediating information storage and interlayer communication3. Photoreceptor
cells rapidly capture light stimuli and convert them into electrical signals via photosensitive protein channel,
which precisely regulate ion transport, inducing membrane hyperpolarizaStion4 (Fig. 1b). The resulting light-
dependent electrical signals propagate via synaptic connections to downstream retinal neurons, enabling visual
information processing5 (Fig. 1c). Inspired by biological vision Rsystems, researchers have developed solid-state
optoelectronic devices that leverage semiconductor-based electron-hole separation mechanisms to emulate both
photoreceptor sensing and synaptic preprocessing functions6-11. However, the mechanisms behind how light
triggers localized interactions between electroEns, holes, and ions at interfaces are still not fully understood. This
gap in knowledge limits our ability to accurately mimic the ion-mediated signal transmission found in biological
visual systems, slowing the developmIent of advanced artificial photoreceptors and photosynaptic devices.
Over the past five years, Radvances in nanofluidic memristors have exploited nonlinear ion transport through
spatially confined systems in nano- and sub-nano channels and membranes to emulate biological synaptic
information processing, enabling brain-like computation through electrical-to-ionic and ionic-to-electrical signal
transduction mechanisms12-16. Meanwhile, recent advances in optically controlled ion transport based on ion-
conductive hydrogels and nanofluidic channel materials have partially achieved bioinspired visual
functionalities17,18. However, current research primarily focuses on biological photosynaptic functions, which
are limited by slow light-response speeds, thus failing to replicate the rapid detection of high-frequency visual
signals characteristic of photoreceptors. Given these limitations, creating a bioinspired visual system within a
single nanofluidic device that integrates photoreceptor-like optical sensing, photosynaptic processing, and
polarity-switching is still a major challenge.
Herein, we present a light-controlled nanofluidic iontronic device based on a carbon nanotube and
molybdenum disulfide (CNT/MoS ) heterostructure that emulates biological visual functions through a
2
sequential “light stimuli-electrical signal-electron/ion coupling-ionic signal” transduction process (Fig. 1d). This
platform achieves dual photoreceptor- and photosynaptic-like functionalities within a single device via bias-
voltage-controlled modulation of photoresponse dynamics. In a two-terminal model without bias voltage, the
device replicates photoreceptor cell operations with a rapid photocurrent responsive speed and responsive
specificity of light power density and wavelength. In a three-terminal configuration with bias voltage, the device
demonstrates photosynaptic characteristics including short-term plasticity, long-term plasticity, and experience-
dependent learning behaviors. The mechanism arises from the coupled effect between bias voltage and
photogenerated potential (E ) across the heterostructure nanotube (Fig. 1d), which together precisely control
photo S
ion transport across the CNT/MoS nanotube. This is facilitated by thSe one-dimensional coaxial architecture,
2
which establishes a continuous and enlarged heterointerface that not only enhances photogenerated charge
separation and strengthens the built-in electric field, but alsoP gives rise to a heterointerface-induced asymmetric
photovoltage. This photovoltage directly drives ioNn transport in a manner distinct from conventional
photothermal or surface-charge modulation sch emes19-21. The resulting ion photocurrent reaches 526 nA,
exceeding the performance of state-of-theL-art nanofluidic opto-ionic systems based on established material
platforms like metal organic framework (MOF)17, covalent organic frameworks (COFs)22, two-dimensional
transition metal carbides and nitrides (MXenes)19, and layered graphene oxide (GO)23,24. Furthermore, this
iontronic device realizes aAccurate orientation recognition and robust fingerprint detection across a wide range of
incident light angles, highlighting its potential for biointerfaced artificial retina.

Results
Fabrication and Characterization of Nanotubes Heterostructure
The CNT/MoS nanotube heterostructure was fabricated via a two-step synthesis process. Initially, CNT was
2
synthesized using a porous anodic aluminum oxide (AAO) membrane template by chemical vapor deposition
(CVD). Subsequently, (NH ) MoS was thermally decomposed within the CNT to form MoS nanotubes.
4 2 4 2
Structural characterization using scanning electron microscope (SEM), transmission electron microscopy (TEM)
and energy-dispersive X-ray analysis (EDX) confirmed the CNT/MoS nanotube heterostructure with a pore
2
diameter of ~ 90 nm (Fig. 2a and Supplementary Fig. 1). The MoS layer and CNT layer exhibited a thickness
2
of ~5.9 nm and ~11.6 nm, respectively. A well-defined lattice fringe spacing of 0.623 nm was observed,
corresponding to the (002) interplanar distance of MoS 25. In comparison, MoS nanotubes with a small pore
2 2
diameter of ~35 nm was characterized using SEM (Supplementary Fig. 2). Additionally, the resulting
heterostructure nanotubes were also systematically characterized using Raman spectroscopy, X-ray
photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) measurements. (Supplementary Figs. 3-5).
Collectively, these characterization results demonstrate the successful fabrication of CNT/MoS nanotube
2
heterostructure.
Mechanism Verification of the Nanofluidic Iontronic Device
To investigate the light-induced interfacial band bending and chRarge separation mechanisms in the CNT/MoS
2
heterostructure, we performed first-principles density functional theory (DFT) and time-dependent DFT (TD-
DFT) calculations using the Perdew-Burke-Ernzerhof (PBE) functional with empirical van der Waals correction
by D3(BJ) scheme26,27(Supplementary Fig. 6aE). Ground-state electrostatic potential difference analysis reveals
spontaneous charge redistribution at the CNT/MoS₂ heterointerface, in which electron depletion was observed
on the outer surfaces of the MoS nanIotubes, while electron accumulation occurred on the inner side of the CNT
2
(Fig. 2b). This indicates that thRe MoS
2
nanotubes act as electron donors, transferring electrons to the inner surface
of the adjacent CNT. This interfacial charge redistribution establishes a built-in potential at the heterostructure
interface, which can effectively separate photogenerated electron-hole pairs upon photoexcitation, reducing
recombination probability and enhancing photoelectric conversion efficiency. As shown in Fig. 2c, in the excited
states, the hole-electron analysis shows that electrons are predominantly localized on the CNT. In contrast, the
holes remain concentrated on the Mo atoms of the MoS nanotubes. This directional electron transfer from the
2
photoexcited MoS phase to the conductive CNT matrix is driven by the inherent band bending at the interface,
2
which is consistent with our experimental findings.
To directly probe the E arising from charge separation, we employed Kelvin probe force microscopy
photo
(KPFM) to quantitatively track the evolution of contact potential differences (CPD) and surface photovoltage
(SPV) of CNT/MoS heterostructure nanotubes28. KPFM measurements revealed reversible CPD modulation
2
that precisely tracked the optical switching sequence, with distinct on/off states confirming light-controlled
interfacial potential generation (Fig. 2d). The SPV demonstrated a linear amplification from 3.8 to 7.7 mV across
0.1–0.5 mW cm-2 illumination intensity (Fig. 2e). These results provide direct experimental evidence of E
photo
generation through efficient electron-hole separation at the CNT/MoS heterointerface (Supplementary Figs. 7
2
and 8).
A theoretical model of finite element method (FEM) simulations developed by Poisson-Nernst-Planck (PNP)
equations to further validate the proposed mechanism of electrophoretically modulated ion transport mediated
by E in the heterostructured nanotubes (Supplementary Fig. 6b). Under unilateral illumination, the
photo
CNT/MoS heterostructure nanotubes generated an asymmetric electric field through E establishment (Fig.
2 S photo
2f and Supplementary Fig. 9), which can drive continuous ion Smigration throughout the CNT/MoS
2
heterostructure nanotubes, generating sustained ion photocurrent. As shown in Fig. 2g, the interfacial regions
exhibit enhanced current densities due to the convergence of ion transport pathway. Furthermore, a pronounced
concentration polarization phenomenon induced by ioNn accumulation was observed at the illuminated interface
of the nanotube heterostructure (Supplementary F ig. 9). This region showed significant enrichment of K⁺, with
their concentration substantially exceedingL that of Cl⁻ (Fig. 2h). This asymmetric spatial distribution of ions
generates a measurable photovoltage (Supplementary Fig. 10). Both theoretical simulations and experimental
results confirm that the ion selectivity originates from the heterostructure’s inherent negative surface charge, as
demonstrated by zeta-potAential characterizations and drift diffusion experiments29-31 (Supplementary Figs. 11
and 12), which facilitates preferential electrostatic attracting cations while repelling anions.
Photoreceptor Function of the Nanofluidic Iontronic Device
Fig. 3a illustrates the measurement schematic for the light-controlled nanofluidic iontronic device based on
CNT/MoS heterostructure nanotubes. Ion current was monitored by inserting two Ag/AgCl electrodes into
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isolated electrolyte reservoirs under unilateral asymmetric illumination. The light-driven ion transport properties
of CNT/MoS heterostructure nanotubes were systematically investigated as photoreceptors under different
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conditions. First, a comparative analysis was conducted to evaluate the photogenerated ion current modulation
characteristics among three distinct nanostructures: MoS nanotubes with small (35 nm) and large (90 nm) pore
2
sizes, and a CNT/MoS nanotube heterostructure with a 90 nm pore size. All three devices exhibited operational
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stability, reproducibility, and endurance for the photogenerated ion current (ΔI), calculated as ΔI =I -I ,
light dark
where I and I represent the ion current under light illumination and dark conditions, respectively (Fig. 3b
light dark
and Supplementary Fig. 13). The 35 nm pore-size MoS nanotubes demonstrated enhanced cation selectivity
2
compared to their 90 nm counterparts, owing to strengthened confinement effects. Remarkably, the CNT/ MoS
2
heterostructure achieved optimal ion current generation, showing 1.28-fold and 6.42-fold enhancements over 35
nm and 90 nm MoS nanotubes, respectively (Supplementary Fig. 14), which can be attributed to the enhanced
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ion transport dynamics driven by the photogenerated electric field resulting from band alignment-induced charge
transfer32. In contrast, single-phase MoS nanotubes exhibit limited ion transport enhancement, primarily due to
2
insufficient band bending and suboptimal charge separation efficiency, despite improved confinement effects
achieved through pore size optimization. Additionally, controlled experiments were conducted to measure the
photoresponsive ion current in the CNT-based device, aPlong with the net photocurrent generated in the
CNT/MoS -based device under alternating bilateral iNllumination (Supplementary Figs. 15 and 16). Together,
2
these findings confirm that the observed photocur rent is attributed primarily to the asymmetric potential-driven
ion transport induced by unilateral illumLination, mediated by photogenerated electron–hole separation of
CNT/MoS heterojunction nanotubes, rather than to photothermal effects or other nanofluidic mechanism.
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Subsequently, systematic measurements also establish a direct correlation between illumination parameters
(power density and wavAelength range) and the resulting ion current response (Fig. 3c-d). The device
demonstrates an inverse correlation between the incident light wavelength and photocurrent generation
efficiency, with decreasing monotonically as the incident wavelength increases (Fig. 3c). This behavior reveals
enhanced spectral selectivity at shorter wavelengths, with pronounced sensitivity particularly observed in the
violet (365 nm) and blue (405 nm) regions, which correlates well with the absorption profile of the CNT/MoS
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heterostructure. Conversely, the ion current progressively increases from 59.3 to 225.9 nA as the power density
increases from 10 to 94 mW cm-2 (Fig. 3d). Additionally, critical performance parameters of the photoreceptor,
such as photoresponse speed, signal-to-noise ratio, photoresponsivity ratio, and energy efficiency have been
quantitatively evaluated. The photoresponse speed is defined as the time required for the current to rise from 10%
to 90% of its peak value, while the signal-to-noise ratio is defined as the ratio of the photocurrent to the root-
mean-square fluctuation of the dark current. The photoresponsivity ratio (R) and energy efficiency (𝜂 ) was
calculated using the formula:
𝑅 =Δ𝐼/𝑃 (1)
𝐸 ∫𝑉(𝑡)×𝐼(𝑡)𝑑𝑡
𝜂 = 𝑒𝑙𝑒𝑐,𝑜𝑢𝑡 ×100%= ×100% (2)
𝐸 𝑃 ×𝑡
𝑙𝑖𝑔ℎ𝑡,𝑖𝑛 𝑙𝑖𝑔ℎ𝑡,𝑖𝑛
where ΔI is the photocurrent; P is the incident power; E is the electrical energy output of the device; E
elec, out light,
is the optical energy consumption; V(t) and I(t) represent the time-dependent response voltage and current,
in
respectively; t is the light pulse width. The calculated performance metrics show that the CNT/MoS
2
heterostructure-based light-regulated nanofluidic iontronic device achieves a photoresponsivity ratio of 78.4 µA
W-1, a photoresponse speed of 3.8 s, a signal-to-noise ratio of 19.56 and anS energy efficiency of 0.694×10-4%.
Beyond that, the solvent microenvironment including electrolyte concentrations and species also
significantly influences photogenerated ion transport, as systemRatically demonstrated in Fig. 3d. The observed
monotonic increase in ion current with electrolyte concentration arises from enhanced ion permeation flux within
the CNT/MoS nanotube at higher ionic strengths (Supplementary Fig. 17). Meanwhile, ion currents in divalent
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chloride species demonstrate superior compareEd to their monovalent counterparts. This behavior originates from
their amplified electrostatic coupling between divalent ions with higher charges and negative charges on the
nanotube walls, mediated by chargeI-dominant ion transport33,34. The light-gated ion flux regulation enables
programmable manipulation oRf multivalent ion transport dynamics, thereby establishing a versatile platform for
advanced optical-ion systems capable of supporting multiplexed signal encoding and multimodal
charge/information storage architectures.
Photosynaptic Function of the Nanofluidic Iontronic Device
Building upon the nanofluidic iontronic photoreceptor capabilities, we implemented synergistic optoelectrical
modulation to emulate photosynaptic behavior. A three-terminal ionic phototransistor was constructed,
consisting of a pair of Ag/AgCl electrodes immersed in two electrolyte reservoirs (Fig. 4a). This configuration
enabled simultaneous (i) external bias voltage application and (ii) monitoring of photoinduced ion currents
through the CNT/MoS nanotube under pulsed illumination. Different from photoreceptor characteristics, the
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photoinduced ion currents exhibited persistent temporal response characteristics under 0.5 V bias potential,
similar to biological photosynaptic behavior (Fig. 4b-c). Meanwhile, the peak ion current demonstrated power
density-dependent gain from 1.5 to 6.1 µA under an illumination intensity range of 10-94 mW cm-2 (Fig. 4b),
and voltage-dependent gain from 1.0 to 15.7 µA over a bias voltage range of 0.1-0.9 V (Fig. 4c). In addition, the
CNT/MoS nanotube heterostructures and MoS nanotubes with reduced pore diameters of 35 nm exhibited
2 2
comparable optimal photocurrent modulation to their 90 nm pore-sized counterparts (Supplementary Fig. 18).
We systematically analyzed the relationship between excitatory postsynaptic current (EPSC) dynamics and
optical pulse stimulation in modulating short-term plasticity (STP) of the photosynaptic device. When stimulated
by two consecutive optical pulses at 0.1 V bias, the device exhibited paired-pulse facilitation (PPF), which is
recognized as a hallmark of short-term synaptic plasticity, with the second pulse generating significantly higher
EPSC amplitude (A ) than the first (A ) (Fig. 4d). The facilitation magnSitude was quantified by the PPF index =
2 1
(A − A ) / A × 100%, which follows a biexponential decay with increasing interpulse intervals17. This dynamic
2 1 1 R
behavior closely mimics the short-term plasticity characterisPtics observed in biological photosynaptic systems13
(Fig. 4e). Furthermore, long-term plasticity (LTP), whiNch converts transient photosynaptic events into persistent
memory traces through repeated activation, was e xperimentally modeled using sequential optical pulse trains35.
The spike-number-dependent plasticity eLmerges with increasing pulse counts (10 to 80), exhibiting both
progressive EPSC amplitude enhancement and prolonged decay kinetics (Fig. 4f).
The transition from STP to LTP can be mediated through pulse-duration optimization (spike-duration-
dependent photosynaptic Aplasticity) (Fig. 4g). Short optical pulses of 1 s induced rapid EPSC responses that
decayed to baseline. While prolonged optical stimulation from 1 to 20 s resulted in progressive augmentation of
EPSC amplitude with extended signal attenuation, signifying the LTP establishment. This transition mechanism
can be mechanistically attributed to optical stimulation-induced non-linear ion carrier accumulation. Meanwhile,
Fig. 4h systematically characterizes the pulse-frequency-dependent facilitation elicited by sequential
photostimulation across a physiological frequency range, which reveals a progressive enhancement of
incremental facilitation effects of EPSC amplitudes as the stimulation frequency increases from 0.05 to 1.00 Hz.
To emulate synaptic learning-memory functions, we implemented three consecutive training cycles of optical
pulse stimulation (Fig. 4i). Each protocol mimicked repetitive cognitive learning, demonstrating progressive
efficiency gains. The initial learning phase necessitated 40 training cycles to achieve full proficiency (100%),
followed by a reduced demand of 20 cycles in the second learning session, and further decreased to merely 10
cycles in the tertiary learning phase following an equivalent forgetting period. The progressive reduction in
training cycles across successive sessions demonstrates enhanced learning efficiency through periodic memory
consolidation, similar to biological memory consolidation processes36.
Applications of the Nanofluidic Iontronic Device
Orientation recognition represents a fundamental visual function essential for organizing complex
spatiotemporal information37,38. Notably, spatiotemporal correlation within the receptive fields of orientation-
selective ganglion cells enables the initial neural computation of movSing object trajectories39. Drawing
inspiration from orientation-selective ganglion cells, we developed a bioinspired orientation recognition system
based on our light-regulated nanofluidic iontronic device, impRlemented through an artificial neural network
(ANN) architecture (Fig. 5a). Here, three optoelectrically modulated nanofluidic devices were arranged at 120°
intervals on a circular platform, forming an orientation-sensitive detection array. The three devices, operated at
distinct bias voltages (Device#0.1V, Device#0E.3V, and Device#0.5V), exhibited characteristic EPSC response
profiles to angular illumination. This differential response enables reliable orientation recognition.
The dependence of EPSC on bias voltagIe and illumination power density was systematically investigated across
orientation angles. EPSC resRults exhibiting distinct synaptic weight modulation as a function of orientation
angles (Fig. 5b). Within polar coordinates spanning 0°-360° (in 30° increments), orientation-induced EPSC
demonstrated voltage-dependent amplification across a 0.1-0.5 V bias range (Fig. 5c). Furthermore, for any fixed
orientation angle, EPSC amplitude increased significantly with higher incident light intensity (Fig. 5d).
The synergistic integration of orientation-selective and illumination power density-dependent synaptic
weight modulation in artificial vision systems enables multidimensional control of imaging information. By
integrating our light-regulated nanofluidic iontronic device into an orientation-selective artificial vision system,
we demonstrate enhanced extraction of contrast and spatial resolution from landscape images captured across
multiple angles. Notably, images acquired at 90° exhibit superior contrast enhancement, confirming the
exceptional performance of our orientation-selective, light-controlled nanofluidic iontronic device for high-
resolution imaging applications (Fig. 5e). To identify complex spatiotemporal patterns, we developed a three-
layer artificial neural network (ANN) architecture capable of omnidirectional orientation recognition (Fig. 5a).
The input layer received flattened one-dimensional vectors representing the time-dependent currents from each
device. The network architecture also incorporated a hidden layer with 16 neurons and an output layer
comprising 12 neurons, corresponding to 12 distinct angular directions. To exploit the intrinsic memory
properties of the devices, we constructed a specialized training dataset comprising orientation-dependent EPSC
recordings over a 50-second post-illumination period. Remarkably, the model achieved a high recognition
accuracy of 98.5% after only 40 training epochs (Fig. 5e). In contrast, the instantaneous pattern recognition
system also achieves 98.8% recognition accuracy, together with response to light incident at various angles,
confirming the device exhibits strong robustness in complex pattern recognition capability (Supplementary Fig.
19). The confusion matrix for the test dataset demonstrates exceptional Sclassification accuracy across all angles,
with the majority of samples predicted correctly, as evidenced by the strong concentration along the matrix
diagonal (Fig. 5f). Moreover, the nanofluidic iontronic deviPce enables high-fidelity extraction and recognition
of complex visual features, such as fingerprint patternNs via convolutional neural networks (CNNs) employing
single fully connected hidden layer architectures for classification (Supplementary Fig. 20a). The 90° image
demonstrates optimal contrast and achieveLs the highest recognition accuracy, attaining 96.13% (Fig. 5g and
Supplementary Fig. 20b). The system maintained recognition accuracy (>95%) across multiple angles (0°–330°)
after 10 epochs, demonstrating high-fidelity feature extraction and reliable detection efficiency for practical
imaging applications (Fig. 5h and Supplementary Fig. 20c).

Methods
Materials and Reagents
Acetonitrile (ACN) was purchased from J&K Scientific China. Ammonium tetrathiomolybdate ((NH ) MoS ),
4 2 4
sulfur powder (S), and dimethyl formamide (DMF) were purchEased from Aladdin, Shanghai, China. The
chemicals include LiCl, KCl, NaCl, MgCl ·6H O, and CaCl ·2H O were purchased from Aladdin, Shanghai,
2 2 P2 2
China. All aqueous solutions were prepared with Milli-Q w ater (18.25 MΩ·cm-1). The AAO membranes with a
thickness of 60 μm, and a pore size of ~50 nm and ~I100 nm were purchased from Puyuan Nano, China.
Fabrication of CNT
The CNTs were synthesized through a CVD equipment (BTF-1200 C-Ⅱ, AnHui BEQ Equipment Technology
Co., Ltd., China) employAing AAO membranes as nanostructured templates and ACN as the carbon feedstock.
The fabrication protocol was implemented in a horizontal tube furnace configured at atmospheric pressure. Prior
to growth initiation, the AAO substrate underwent thermal pretreatment at 1090 °C for 30 minutes under a
flowing gas mixture (Ar: 100 sccm, H : 50 sccm) to facilitate in situ reduction and surface activation. The CNT
2
growth was subsequently conducted by introducing ACN vapor through a carrier gas mixture stream at the
optimized synthesis temperature for 20 minutes. Post-synthesis cooling to ambient temperature was performed
under continuous argon purge (100 sccm) to prevent oxidative degradation, yielding vertically aligned CNT
arrays within the AAO template architecture.
Fabrication of MoS Nanotubes
2
The synthesis of MoS nanotubes was achieved through the AAO template-assisted CVD approach. Initially, a
2
precursor solution was prepared by dissolving 0.05 g of (NH ) MoS in 5 mL of DMF under continuous magnetic
4 2 4
stirring. The AAO templates were subjected to vacuum-assisted infiltration with the precursor solution, followed
by thermal treatment in a convection oven at 80 °C for complete solvent evaporation. Subsequently, the dried
AAO template was positioned in a quartz boat at the central heating zone of a horizontal tube furnace under
atmospheric pressure, while 0.2 g S was placed in a separate quartz boat 20 cm upstream from the furnace center
to prevent premature S evaporation prior to thermal processing. The system was rapidly ramped to 800 °C with
a mixed carrier gas (Ar: 100 sccm, H : 50 sccm) throughout the thermal treatment. Upon reaching the target
2
temperature, S powder moved quickly towards the tube furnace through an externally controlled magnetic
manipulation system that translated the sulfur-containing boat into the heating zone, which was maintained at
800 °C for 60 minutes to obtain MoS nanotubes.
2
Fabrication of CNT/MoS Nanotubes
2
The synthesis process of CNT/MoS
2
nanotubEe heterojunctions essentially combines the aforementioned two-
step fabrication methodology. Briefly stated, the pre-synthesized CNTs are initially impregnated with
(NH ) MoS precursor solution throuIgh vacuum-assisted filtration, followed by drying. Subsequently, the dried
4 2 4
CNT was positioned in a quaRrtz boat at the central heating zone of a horizontal tube furnace, while 0.2 g S was
placed in a separate quartz boat 20 cm upstream from the furnace center. The system was rapidly ramped to
800 °C with a mixed carrier gas (Ar: 100 sccm, H : 50 sccm). Upon reaching the target temperature, S powder
2
moved quickly towards the tube furnace, which was maintained at 800 °C for 60 minutes to obtain CNT/MoS
2
nanotube.
Fabrication and Measurement of Devices
The fabricated MoS and CNT/MoS nanotube heterostructure membrane-based devices were initially
2 2
encapsulated using a perforated polydimethylsiloxane (PDMS) mold. Subsequently, these packaged devices
were mounted between a custom-designed electrochemical cell architecture with a fixed transparent area,
featuring dual symmetric reservoirs. Both compartments were filled with equal volumes of electrolyte solution.
Photogeneration was induced through lateral-side illumination using a xenon lamp or monochromatic laser
wavelength (365 nm, 405 nm, 520 nm, 590 nm, and 660 nm). A bipotentiostat configuration was implemented
using Ag/AgCl electrodes immersed in the reservoirs for transmembrane current signal acquisition. Photocurrent
was measured using a Keithley 2634 B (Keithley Instruments, Cleveland, OH).
KPFM Measurements
Surface photovoltage characterization was performed using a state-of-the-art KPFM integrated with an AFM
system (Bruker Dimension Icon). The measurements were carried out using Pt/Ir-coated silicon probes (Bruker
SCM-PIT series) with resonant frequencies between 60 and 100 kHz and spSring constants between 1 and 5 N·m-
1 in ambient conditions. To successfully reduce capacitive coupling artefacts between the probe and sample
surface, the amplitude-modulated KPFM technique was applieRd with optimal parameters: an AC modulation
voltage of 0.5 V and a dual-pass lift mode configuration using a minimum lift height of 20 nm. This configuration
achieved exceptional energy resolution (<5 mV) in mapping CPD variations. Photogeneration was induced
through front-side illumination using a xenon Elamp with different incident power densities.
Quantum-Mechanical Simulation oIf CNT/MoS Nanotubes
2
We systematically investigated the structural optimization and electronic excitation properties of CNT/MoS
2
nanotube heterostructures through advanced materials modeling techniques. First-principles calculations were
performed using the CP2K 2023.1 software package40 within the framework of DFT and TDDFT using the PBE
functional with empirical van der Waals correction by D3(BJ) scheme27. The CNT/MoS nanotube
2
heterostructures were constructed employing the Atomic Simulation Environment (ASE) package. The
MoS /CNT composite model consists of a MoS nanotube and a CNT nanotube. Both the MoS nanotube and
2 2 2
the CNT nanotube were individually optimized under periodic boundary conditions. After constructing the
nested composite structure (Supplementary Fig. 6, using a supercell whose length along the z-axis corresponds
to the least common multiple of the minimal periodic units of both components), an additional geometry
optimization was performed for the combined system. Structural optimizations and electronic band structure
analyses were conducted using the PBE exchange-correlation functional, while long-range dispersion
interactions were accounted for through the DFT-D3(BJ) empirical correction scheme27. The electronic
wavefunctions were expanded using double-ζ DZVP-MOLOPT-SR basis sets in combination with Goedecker-
Teter-Hutter (GTH) norm-conserving pseudopotentials. Given the substantial unit cell dimensions of the
CNT/MoS nanotube heterostructures, Brillouin zone sampling was restricted to the Γ-point, with a plane-wave
2
cutoff energy of 400 eV employed to ensure computational accuracy while maintaining reasonable
computational efficiency. For post-processing analysis, electrostatic potential difference maps were generated
through a subtractive methodology using the Multiwfn 3.8 (dev) wavefunction analysis program41. Specifically,
the electrostatic potentials of isolated MoS and CNT fragments were subtracted from that of the coupled
2
CNT/MoS nanotube heterostructures to visualize interfacial charge redistribution. Furthermore, hole-electron
2
analysis was performed utilizing the same computational package to elSucidate the fundamental mechanisms of
interfacial charge transfer processes.
Poisson and Nernst-Planck Simulation
Solving the Poisson and Nernst-Planck equaEtions using the finite element method to study the process of
photocurrent generation under different illumination power density and voltages:
𝐹
𝛻2𝑈 =− ∑𝑧 𝑐 (3)
𝜖 𝑖 𝑖
𝑧 𝐹
𝑗 =𝐷𝛻𝑐 + 𝑖 𝐷𝑐 𝛻𝑈 (4)
𝑖 𝑖 𝑖 𝑅𝑇 𝑖 𝑖
𝛻∙𝑗 =0 (5)
𝑖
U is the electric potential; F is the Faraday constant; ϵ is the dielectric properties of the electrolyte solution; c,
i
D, j, and z are ion concentration, diffusion constant, ion flux, and charge of each species, respectively; The
i i i
diffusion coefficients for K+ and Cl− ions are 1.96 × 10−5 cm2 s−1 and 2.03 × 10−5 cm2 s−1.
This study utilized a two-dimensional axisymmetric model to simulate a CNT/MoS nanostructure connecting
2
two electrolyte reservoirs by COMSOL Multiphysics 6.2. The left and right boundaries of the container are filled
with a 0.01 M KCl electrolyte solution to ensure consistent boundary conditions. To simulate the built-in electric
field arising from photogenerated charge separation, a photo-induced potential was applied to the CNT side of
the interface, while the MoS side was held at ground potential. By solving the steady-state equations governing
2
the electrostatic field and transport of diluted species, we obtained the concentration distribution along the z-axis
(ranging from 0 to 6400 nm).
Material Characterization
The morphological characteristics of CNT, MoS , and CNT/MoS nanotube heterostructures were investigated
2 2
using SEM (Hitachi SU8220) at an acceleration voltage of 10 kV. The structural and elemental composition were
subsequently performed through TEM and EDX using a FEI Talos F200X system operating at 300 kV. Raman
spectroscopic characterization was conducted on a Horiba Xplora Plus spectrometer with a 532 nm excitation
laser source to analyze the vibrational modes of CNT/MoS nanotubeS heterostructures. Crystallographic
2
properties were examined by XRD using a Rigaku SmartLab diffractometer with Cu Kα radiation (λ= 1.5406 Å)
under operational parameters of 45 kV and 200 mA, collecting Rdata in the 2θ range of 5-80°. Surface chemical
states were analyzed through XPS measurements (PHI 5000 VersaProbe system) employing monochromatic Al
Kα radiation, with binding energy calibration referenced to the adventitious carbon C 1s peak at 284.8 eV. The
Zeta Potential of MoS
2
and CNT/MoS
2
nanEotube heterostructure was measured by a Malvern Nano-ZS90
analyzer. Optical absorption properties were evaluated by ultraviolet-visible spectroscopy (UV-Vis) recorded on
a Shimadzu UV-2600 spectrophotomIeter.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript reports a nanofluidic iontronic retina constructed from CNT/MoS₂ heterostructures, which integrates\nphotoreceptor-like optical sensing and photosynaptic functionalities within a single device platform through light-modulated\nion transport—a mechanism directly inspired by biological vision systems. The study is comprehensive, encompassing\nsophisticated material synthesis, rigorous experimental characterization, and theoretical modeling, while also demonstrating\npractical device-level applications such as orientation-selective recognition and fingerprint detection. The research\naddresses a timely topic at the intersection of bioinspired neuromorphic computing, artificial vision, and nanofluidic\niontronics and represents a significant leap forward beyond conventional solid-state optoelectronic devices. The manuscript\nis well-organized and supported by robust experimental and computational evidence. Nonetheless, several aspects require\nclarification and further substantiation before publication. The detailed comments are outlined below.\n1. In the introduction, the authors have compared and summarized the functional advantages of the work over currently\nreported nanofluidic ionic devices and ionogel-based devices. In addition to these functional benefits, could the authors\nfurther elaborate on whether their device exhibits advantages in terms of structural design, underlying mechanism, or key\nperformance parameters? A detailed comparative explanation would significantly strengthen the manuscript.\n2. Throughout the manuscript, the authors consistently emphasize that the ion transport is driven by a photoelectric-effect-\ninduced potential, supported by various characterizations and simulation results. However, since carbon nanotubes exhibit\ncertain photothermal effects, how do the authors distinguish and demonstrate whether the ion transport is driven solely by\nthe photoelectric effect or by a synergistic effect of both photothermal and photoelectric mechanisms?\n3. The manuscript does not address the operational stability or durability of the device under prolonged illumination. The\ndata would strengthen the case for practical applications.\n4. In the section on material structure and surface property characterization, although the authors provide comprehensive\ncharacterization data of the CNT/MoS₂ heterostructure nanotubes, many control experiments are missing. For instance, in\nFigure S1d, only the morphological SEM image of the synthesized 35 nm pore-sized MoS₂ is provided, while the\nmorphology of the AAO template is absent. Additionally, Raman spectra of CNT and MoS₂ are missing in Figure S3. Please\nprovide the relevant supplementary data.\n5. At the mechanistic level, the authors propose that unilateral illumination induces asymmetric potential-driven ion transport\nvia photogenerated electron-hole separation. To validate this mechanism, the authors should provide control experiments\nunder bilateral illumination to further verify the correctness of the proposed model.\n6. Fig. 5 present neuromorphic computing for orientation recognition and fingerprint detection based on nanofluidic iontronic\nretina; however, the implementation details are insufficiently described in the manuscript. The authors are expected to\nprovide a more detailed explanation.\n7. In the section on neuromorphic computing, has this work completed the evaluation of the device's instantaneous pattern\nrecognition capability? Is the difference between memory-based pattern recognition and instantaneous pattern recognition\nsignificant? Does the memory-based pattern recognition exhibit a sharp decline in accuracy?", "reviewer_2": "The manuscript entitled “Artificial Nanofluidic Iontronic Retina” presents a strategy for constructing light-regulated nanofluidic\niontronic device based on CNT/MoS₂ heterostructures, mimicking the retina with dual photoreceptor and photosynaptic\nfunctions via light-modulated ion transport within a single device via bias-voltage-controlled modulation of photoresponse\ndynamics. This iontronic device realizes accurate orientation recognition and robust fingerprint detection across a wide\nrange of incident light angles. Furthermore, the combination of theoretical (DFT, PNP) and experimental (KPFM, ion\ntransport measurements) offers a robust multiscale validation of the proposed mechanism. The manuscript is generally well\nwritten and demonstrates considerable appeal and novelty, supported by a comprehensive dataset. The work is interesting\nand potentially suitable for publication in Nature Communications after addressing the following concerns.\n1 - In Figure 2, the authors performed first-principles density functional theory and finite element method simulations to\nfurther validate the proposed mechanism. However, the authors provided only brief explanations of computational\nprocedures in the manuscript. Critical details, schematic illustrations of the simulation models, are lacking. It is\nrecommended that the authors supplement these to significantly strengthen the manuscript.\n2 - In Figure 3, the authors modulate the ionic current by varying light intensity and wavelength as an ionic photoreceptor.\nHowever, critical performance parameters of the photoreceptor, such as photoresponsivity ratio, response speed, and signal-\nto-noise ratio, have not been quantitatively evaluated or provided. Please provide the relevant supplementary data.\n3 - In Figure 3b, while the manuscript states that the ionic photoreceptor exhibits rapid rising and falling upon light\nstimulation, its measured photoresponsive current still requires a considerably longer time to reach a steady plateau\ncompared to electronic devices. The authors should provide a clear explanation.\n4 - In the manuscript, the authors have only characterized the photocurrent response resulting from light-induced ion\ntransport. In fact, the photo-driven ion transport can also generate a measurable photovoltage. Corresponding data should\nbe incorporated to further elucidate the underlying ion transport mechanism.\n5 - In Figure S11, why is the photocurrent change minimal in both the CNT/ MoS2 heterostructure nanotube and the small-\npore MoS₂ nanotube when light and voltage stimuli are applied simultaneously? In contrast, as shown in Figure 3b, under\nillumination alone, the CNT/ MoS2 heterostructure nanotube exhibits the optimal ion current generation compared to small-\npore MoS₂ nanotubes. A reasonable explanation for this apparent discrepancy should be provided.\n6 - In Figure 5a, the schematic illustrating of orientation recognition lacks clarity in its annotations and comparative\nvisualizations. For instance, the difference in roof appearance between the 30° and 60° viewing angles is insufficiently\ndistinct. Additionally, the meaning of the symbol “I” in the input layer of the neural network? It is recommended that the figure\nis modified to improve interpretability.", "reviewer_3": "The manuscript reports the fabrication and characterization of a bioinspired nanofluidic iontronic device based on a carbon\nnanotube/molybdenum disulfide (CNT/MoS₂) heterostructure, which is claimed to emulate both photoreceptor and\nphotosynaptic functionalities of a biological retina. The authors found that their device enables light-modulated ion transport\nand photosynaptic behavior through heterointerface-induced photovoltage generation. Despite the appealing concept, there\nare serious concerns with the manuscript as below\n1) The claim of reproducing dual photoreceptor and photosynaptic functionality within a single nanofluidic device is\noverstated. What is demonstrated are optical modulation of ionic currents and bias-dependent conductance changes, both\nwell established in prior optoionic memristor and photogated nanofluidic systems.\nNo convincing evidence is provided for actual retina-like spatiotemporal signal processing or for biologically relevant\ntimescales comparable to neural photosynapses. The “bioinspired retina” terminology reads more as rhetorical.\n2) How do the memory curves look like, when they cycle the voltage, do the authors see pinched hysteresis loops, with or\nwithout light? What is influenced by the light? Is it the area of the loop, low or high conductance state?\n3) In Fig. 4c, the authors have shown the change in current as a function of bias voltage across the fluidic cell at a fixed\nintensity of 94mW/cm^2. Why at 0.1V the change in current is of 1μA whereas in all subsequent measurements in the given\nfigure it is ~200nA?\n4) Capacitive effects can also lead to the same changes as authors observed? How do authors know that it is not capacitive\neffects due to light?\n5) In Fig. 4d authors claimed short term plasticity behaviour and attempted to correlate it with Paired-pulse facilitation\ncharacteristics. However, from figure it doesn’t seem trivial to assign it to short-term characteristics. Because, even when\nboth bias pulse/and photo-pulse were removed, the transient didn’t decay immediately back to the baseline.\n6) In Fig. 4h, if authors are willing to demonstrate frequency dependent plasticity, they must show an increase and decrease\nof post synaptic current as a function of frequency. To give an electrical analogy, a normal capacitor would exhibit a higher\ncurrent on increasing the frequency, but that doesn’t mean it exhibits spike frequency/rate dependent plasticity.\n7) In supplementary Fig. S10, at 100mM KCl, why the system exhibited negative baseline? Is it normal for their model\nsystem to observe?\n8) Authors have performed zeta potential characterizations in supplementary figure S8. Although, these measurements do\nshow cation selectivity of their model system, however, it would be useful if they can perform drift diffusion type of\nexperiments at a fixed gradient as a function of light intensity. This would tell about which species in predominant in their\nsubsequently observed characteristics.\n9) Key parameters that would enable meaningful comparison with state-of-the-art systems, response speed, energy\nefficiency, stability, reproducibility, and endurance are not reported.\n10) The ANN and CNN results depend primarily on preprocessed data, not on intrinsic device learning or neuromorphic\nadaptability. These sections read as superficial add-ons applications, without clear connection to the nanofluidic\nmechanism.\n11) How do authors verify that the observed response is not simply due to the photoresponse of MoS2 rather than\nnanofluidic mechanism.\n12) The claim of “real-time operation” is not substantiated by explicit response-time measurements.\n13) Several grammatical errors and typographical inconsistencies should be corrected."}, "author_response": "Reviewer #1\nGeneral comments: This manuscript reports a nanofluidic iontronic retina\nconstructed from CNT/MoS₂ heterostructures, which integrates photoreceptor-like\noptical sensing and photosynaptic functionalities within a single device platform\nthrough light-modulated ion transport—a mechanism directly inspired by biological\nvision systems. The study is comprehensive, encompassing sophisticated material\nsynthesis, rigorous experimental characterization, and theoretical modeling, while also\ndemonstrating practical device-level applications such as orientation-selective\nrecognition and fingerprint detection. The research addresses a timely topic at the\nintersection of bioinspired neuromorphic computing, artificial vision, and nanofluidic\niontronics and represents a significant leap forward beyond conventional solid-state\noptoelectronic devices. The manuscript is well-organized and supported by robust\nexperimental and computational evidence. Nonetheless, several aspects require\nclarification and further substantiation before publication. The detailed comments are\noutlined below.\nResponse: Thanks very much for your comments. We greatly appreciate your\nevaluation and comments that are helpful for improving the quality of the manuscript.\nWe have revised the manuscript according to your suggestions point by point. The\nrevised parts are marked by red font.\nComment 1: In the introduction, the authors have compared and summarized the\nfunctional advantages of the work over currently reported nanofluidic ionic devices\nand ionogel-based devices. In addition to these functional benefits, could the authors\nfurther elaborate on whether their device exhibits advantages in terms of structural\ndesign, underlying mechanism, or key performance parameters? A detailed\ncomparative explanation would significantly strengthen the manuscript.\nResponse 1: We thank the reviewer for this insightful comment. In addition to the\nfunctional advantages mentioned in the introduction, our device exhibits significant\ninnovations and advantages in terms of structural design, underlying mechanism, and\nkey performance parameters. A detailed comparison and explanation are provided\nbelow.\n1. Advantages in structural design:\nEnhanced interface engineering: Our device is built upon a precisely fabricated one-\ndimensional coaxial heterostructure nanotube, integrating CNT and MoS . This coaxial\n2\narchitecture establishes a continuous and maximized heterointerface along the entire\nnanotube length. Unlike simple mixed composites or stacked layered assemblies\n(Energy Environ. Sci., 2023,16, 3146-3157), this intimate, extended contact area\ncritically promotes efficient photogenerated charge separation and enhances the built-\nin electric field—the fundamental driver of our operational mechanism. This deliberate\nstructural design provides distinct advantages over conventional nanofluidic systems.\n(such as single-material nanotubes or 2D nanochannels). (Adv. Mater. 2025, 37 (30),\n2502438; Angew. Chem. Int. Ed. 2025, 64 (7), e202418949; Nat. Commun. 2023, 14,\n359; Nat. Commun. 2024, 15, 4213; Adv. Mater. 2025, 42, e08641).\n2. Advantages in underlying mechanism:\nHeterointerface-induced asymmetric photovoltage -effect dominated ion transport: our\ndevice primarily utilizes a heterointerface-induced asymmetric photovoltage-effect\ndominated ion transport. The asymmetric photovoltage generated across the CNT/MoS\n2\nnanotube (Fig. 2f) directly drives and modulates ion transport electrophoretically. This\nmechanism differs fundamentally from previously reported nanofluidic systems, which\nrely primarily on traditional photothermal or photoelectric effects to modulate ion\ntransport via surface charge regulation (Nat. Commun. 2023, 14, 359; Nat. Commun.\n2024, 15, 4213; Adv. Mater. 2025, 42, e08641).\n3. Advantages in key performance parameters:\nEnhanced ion photocurrent: Our nanofluidic light-regulated iontronic device achieves\nan ion photocurrent of 526 nA (Fig. 3d), significantly surpassing the performance of\nstate-of-the-art nanofluidic opto-ionic systems based on established material platforms\nlike MOF, COF, MXenes, and graphene. (Adv. Mater. 2025, 37 (30), 2502438; Angew.\nChem. Int. Ed. 2025, 64 (7), e202418949; Nat Commun. 2019, 10, 1171; Nat. Commun.\n2023, 14, 359; Nat. Commun. 2023, 14, 6702).\nPage 3, line 67-75. This is facilitated by the one-dimensional coaxial architecture,\nwhich establishes a continuous and enlarged heterointerface that not only enhances\nphotogenerated charge separation and strengthens the built-in electric field, but also\ngives rise to a heterointerface-induced asymmetric photovoltage. This photovoltage\ndirectly drives ion transport in a manner distinct from conventional photothermal or\nsurface-charge modulation schemes. The resulting ion photocurrent reaches 526 nA,\nexceeding the performance of state-of-the-art nanofluidic opto-ionic systems based on\nestablished material platforms like metal organic framework (MOF)17, covalent organic\nframeworks (COFs)22, two-dimensional transition metal carbides and nitrides\n(MXenes)19, and layered graphene oxide (GO)23,24\nComment 2: Throughout the manuscript, the authors consistently emphasize that the\nion transport is driven by a photoelectric-effect-induced potential, supported by various\ncharacterizations and simulation results. However, since carbon nanotubes exhibit\ncertain photothermal effects, how do the authors distinguish and demonstrate whether\nthe ion transport is driven solely by the photoelectric effect or by a synergistic effect of\nboth photothermal and photoelectric mechanisms?\nResponse 2: We appreciate the reviewer’s comment. To further verify whether the\nphotothermal effect of carbon nanotubes induces the photoresponsive ion current, we\nconducted a control experiment measuring the photorespnsive ion current in CNT ion\ndevices (this data has been added to the revised supplementary information of\nSupplementary Fig. 15). The CNT device generated only about 4 nA of ion current,\nwhich is significantly lower than 226 nA of the photoresponsive ion currents observed\nin CNT/MoS heterojunction nanotubes (Fig. 3b). This result further confirms that the\n2\nphotoresponsive current originates primarily from heterointerface-induced asymmetric\nphotovoltage -effect dominated ion transport rather than photothermal contributions.\nSupplementary Fig. 15. Measured cyclic constant ion current of CNT with\nalternating illumination at 10 mM KCl. The CNT-based device generated only about\n4 nA of ion current, which is significantly lower than 226 nA of the photoresponsive\nion currents observed in CNT/MoS heterojunction nanotubes.\nthe photoresponsive ion current in the CNT-based device, along with the net\nphotocurrent generated in the CNT/MoS -based device under alternating bilateral\n2\nillumination (Supplementary Figs. 15 and 16). Together, these findings confirm that the\nobserved photocurrent is attributed primarily to the asymmetric potential-driven ion\ntransport induced by unilateral illumination, mediated by photogenerated electron–hole\nseparation of CNT/MoS heterojunction nanotubes, rather than to photothermal effects\n2\nor other nanofluidic mechanism.\nComment 3: The manuscript does not address the operational stability or durability\nof the device under prolonged illumination. The data would strengthen the case for\npractical applications.\nResponse 3: We sincerely thank the reviewer for raising this critical point regarding\nthe operational stability and durability of our device, which is indeed crucial for\nassessing its practical application potential. Following the reviewer's insightful\nsuggestion, we have conducted additional preliminary cycling tests with alternating\nillumination of 1000 s at 10 mM KCl, which show no significant degradation in the\nphotoresponsive ion currents (this data has been added to the revised supplementary\ninformation of Supplementary Fig. 13). This result confirms that light-regulated\nnanofluidic CNT/MoS heterostructure-based iontronic device exhibited operational\n2\nstability, reproducibility, and endurance for the photogenerated ion current.\nSupplementary Fig. 13. The operational stability, reproducibility, and endurance\nof light-regulated nanofluidic CNT/MoS heterostructure-based iontronic device\n2\nunder prolonged alternating illumination of 1000 s.\nPage 6, line 146-149. All three devices exhibited operational stability, reproducibility,\nand endurance for the photogenerated ion current (ΔI), calculated as ΔI =I -I ,\nlight dark\nwhere I and I represent the ion current under light illumination and dark\nlight dark\nconditions, respectively (Fig. 3b and Supplementary Fig. 13).\nComment 4\nIn the section on material structure and surface property characterization, although\nthe authors provide comprehensive characterization data of the CNT/MoS₂\nheterostructure nanotubes, many control experiments are missing. For instance, in\nFigure S1d, only the morphological SEM image of the synthesized 35 nm pore-sized\nMoS₂ is provided, while the morphology of the AAO template is absent. Additionally,\nRaman spectra of CNT and MoS₂ are missing in Figure S3. Please provide the relevant\nsupplementary data.\nResponse 4: We thank the reviewer for these constructive suggestions. Regarding the\nmorphology of the AAO template, we have performed additional the morphology\ncharacterization of the 50 nm AAO template. The SEM image of the pristine AAO\ntemplate is provided in updated Supplementary Fig. 2 of the revised supporting\ninformation. Regarding the Raman spectra of individual CNT and MoS components:\n2\nnanotube samples. The updated Supplementary Fig. 4 in the revised supporting\ninformation includes these reference spectra, providing a clear and direct comparison\nthat unequivocally confirms the assignment of all vibrational modes in the\nheterostructure. We believe these revisions have fully addressed the reviewer's\nconcerns and significantly improved the completeness of our material characterization\ndata.\nSupplementary Fig. 2. Morphology characterizations of different samples. a, 50\nnm AAO substrate; b, 35 nm pore sizes MoS nanotubes.\n2\nboth components. Two distinct peaks located at 1326 cm⁻¹ (D-band) and 1609 cm⁻¹ (G-\nband) are observed, corresponding to the disorder-induced lattice vibration mode and\nthe in-plane stretching vibration of sp²-bonded carbon atoms in graphitic structures,\nrespectively. The concurrent presence of these features indicates the coexistence of\ndisordered carbon defects and graphitized carbon domains within the heterojunction\narchitecture. Furthermore, the spectrum reveals two well-separated peaks at 383 cm⁻¹\n(E mode) and 408 cm⁻¹ (A mode), which are characteristic of the MoS nanotube\n2g 1g 2\ncomponent. These vibrational modes correspond to the in-plane opposing motions of\nmolybdenum and sulfur atoms (E ) and the out-of-plane symmetric sulfur atom\n2g\nvibrations along the c-axis (A ), providing direct evidence of the preserved layered\n1g\ncrystal structure in the MoS nanotubes within the heterostructure system.\n2\nnm was characterized using SEM (Supplementary Fig. 2).\nComment 5\nAt the mechanistic level, the authors propose that unilateral illumination induces\nasymmetric potential-driven ion transport via photogenerated electron-hole separation.\nTo validate this mechanism, the authors should provide control experiments under\nbilateral illumination to further verify the correctness of the proposed model.\nResponse 5: We thank the reviewer for this insightful comment and valuable\nsuggestion. We fully agree that such experiments would provide the most direct and\ncompelling evidence for our mechanism. Following the reviewer's insightful suggestion,\nwe have conducted additional bilateral illumination experiment to test whether this\nasymmetry is essential for generating a net photocurrent (this data has been added to\nthe revised supplementary information of Supplementary Fig. 16). The observed\nphotoresponsive ion current would be substantially lower than that under unilateral\nillumination with the same light power density (Fig. 3b), and potentially close to zero.\nIt primarily arises from similar photovoltages of comparable magnitude but opposite\ndirection would be generated simultaneously on both sides of the device, leading to a\nsignificant reduction in the net driving potential difference. The results further confirm\nthat unilateral illumination induces asymmetric potential-driven ion transport via\nphotogenerated electron-hole separation.\nSupplementary Fig. 16. Measured cyclic constant ion current under alternating\nbilateral illumination at 10 mM KCl. The observed photoresponsive ion current\nwould be substantially lower than that under unilateral illumination with the same light\npower density, and potentially close to zero. It primarily arises from similar\nphotovoltages of comparable magnitude but opposite direction would be generated\nsimultaneously on both sides of the device, leading to a significant reduction in the net\ndriving potential difference.\nPage 6, line 158-164. Additionally, controlled experiments were conducted to measure\nthe photoresponsive ion current in the CNT-based device, along with the net\nphotocurrent generated in the CNT/MoS -based device under alternating bilateral\n2\nillumination (Supplementary Figs. 15 and 16). Together, these findings confirm that the\nobserved photocurrent is attributed primarily to the asymmetric potential-driven ion\ntransport induced by unilateral illumination, mediated by photogenerated electron–hole\nseparation, rather than to photothermal effects.\nComment 6\nFig. 5 present neuromorphic computing for orientation recognition and fingerprint\ndetection based on nanofluidic iontronic retina; however, the implementation details\nare insufficiently described in the manuscript. The authors are expected to provide a\nmore detailed explanation.\nResponse 6: We sincerely thank the reviewer for this valuable feedback. We fully agree\nthat supplementing these implementation details for the neuromorphic computing in\nFig. 5 is crucial to ensure the scientific rigor and reproducibility of our work. In\nresponse to the reviewer's suggestion, we provide the following detailed explanation:\n(These have incorporated into the supplementary methods in revised supplementary\ninformation)\nOrientation Recognition\nIn this study, the three-channel current data collected from the nanofluidic iontronic\ndevices was normalized and used to train an angle recognition by artificial neural\nnetwork. Each sample consists of three-channel current response signals extracted\nwithin a specified sampling interval (covering a period with light application and/or\nwithdrawal). All samples were labeled according to their corresponding angle\ncategories (e.g., 0°, 30°, 60°, etc.), resulting in a total of 12 angular classes. The dataset\nwas split into training and test sets in an 80%:20% ratio, while maintaining class\nbalance during training. The constructed artificial neural network adopts a multilayer\nperceptron (MLP) architecture: The input layer has a dimension of 885, corresponding\nto the flattened three-channel current features; The hidden layer contains 16 neurons\nutilizing the ReLU activation function; The output layer comprises 12 neurons\ncorresponding to the 12 angle classifications. The network was trained using the cross-\nentropy loss function and the Adam optimizer (learning rate: 1×10-4) for a total of 40\nepochs.\nIn the orientation recognition, we designed three nanofluidic iontronic devices\noperating at different voltages, enabling them to exhibit orientation-dependent\nphotoresponses under varying incident light angles. Each incident direction produced a\nunique set of photocurrent signals across the three sites, originating from the synergistic\nlight-electric-ion transport within the nanofluidic channels. These photocurrent signals\nwere directly fed into an ANN for learning and classifying the incident light directions.\nConsequently, the ANN here functions not only as a standalone algorithm\ndemonstration but also as an analytical framework to interpret how device-level ion\ndynamics and optoelectronic coupling mechanisms collectively enable orientation-\nselective perception and neuromorphic-like sensory functions.\nFingerprint Recognition\nThis study constructed a convolutional neural network for 10-class classification (five\nfingers from left and right hands) based on the SOCOFing fingerprint dataset. The\ndataset includes real fingerprints and three difficulty levels of altered fingerprints\n(Altered-Easy, Medium, Hard). During loading, finger type (thumb, index, middle, ring,\nlittle) and hand information were parsed and mapped into 10 labels (0–9). All\nfingerprint images were read in grayscale and uniformly resized to 96×96 pixels. They\nwere then split into training (80%) and validation (20%) sets, with the real fingerprints\nused as an independent test set. The model employs a three-layer convolutional\narchitecture (kernel sizes: 5–5–3; corresponding channel numbers: 32, 64, 128). Each\nconvolutional layer is followed by Batch Normalization (BatchNorm), ReLU activation,\nand 2×2 Max Pooling. After the feature extraction section, a Dropout layer was added\nto prevent overfitting, followed by fully connected layers for classification output. The\nmodel was trained using the Adam optimizer (learning rate: 1×10-4) and the cross-\nentropy loss function for 10 epochs.The model was trained and evaluated using the\nSokoto Coventry Fingerprint Dataset, comprising 40,000 training images and 6,000 test\nimages (each 96×96 pixels in dimension), encompassing ten distinct fingerprint classes.\nTraining employed a batch size of 64 per epoch. The fingerprint images captured at five\ndistinct angular orientations following processing by the system.\nIn the fingerprint recognition, pixel values from an external image dataset were\nconverted into optical stimulation signals, which were directly applied to nanofluidic\niontronic devices. The light-induced ion transport processes within these devices\ngenerated corresponding electrical signal outputs. These device-level current responses\nreflect the nonlinear behavior of ion migration in the nanofluidic system and the\ncharacteristics of light-induced conductance modulation. Subsequently, we employed a\nCNN model to learn and extract features from these device output signals, aiming to\nevaluate the capability of the nanofluidic system to perform data encoding and pattern\nrecognition in a neuromorphic context. Therefore, the neural network here serves not\nmerely as an independent algorithm demonstration, but also as an analytical tool to\nquantify and interpret the dynamic response characteristics of the nanofluidic devices.\nComment 7\nIn the section on neuromorphic computing, has this work completed the evaluation of\nthe device's instantaneous pattern recognition capability? Is the difference between\nmemory-based pattern recognition and instantaneous pattern recognition significant?\nDoes the memory-based pattern recognition exhibit a sharp decline in accuracy?\nResponse 7: We thank the reviewer for these insightful questions regarding the\nneuromorphic computing capabilities of our device. To validate the device's\ninstantaneous pattern recognition capability, we utilize a three-device array to collect\nexcitatory postsynaptic current signals (Fig. 5c) under light pulse stimulation, which\nare then classified by an artificial neural network. The results show that the system\nachieves 98.8% recognition accuracy after only 40 training epochs, together with\nresponse to light incident at various angles, confirming its excellent instantaneous\npattern recognition capability (This data has been added to the revised supplementary\ninformation of Supplementary Fig. 19). In contrast, the memory-based pattern\nrecognition system achieves 98.5% recognition accuracy (Fig. 5e). This indicates that\nmemory-based pattern recognition does not significantly differ from instantaneous\nrecognition in performance. Experimental results confirm that memory-based pattern\nrecognition does not suffer from a sharp decline in accuracy. Rather, it exhibits strong\nrobustness in complex pattern recognition capability.\nPage 10, line 275-278. In contrast, the instantaneous pattern recognition system also\nachieves 98.8% recognition accuracy, together with response to light incident at various\nangles, confirming the device exhibits strong robustness in complex pattern recognition\ncapability (Supplementary Fig. 19).\nSupplementary Fig. 19. Evolution of the instantaneous pattern recognition\naccuracy with training epochs as a function of light pulses from different\norientation angles.\nReviewer #2\nGeneral comments:\nThe manuscript entitled “Artificial Nanofluidic Iontronic Retina” presents a strategy\nfor constructing light-regulated nanofluidic iontronic device based on CNT/MoS₂\nheterostructures, mimicking the retina with dual photoreceptor and photosynaptic\nfunctions via light-modulated ion transport within a single device via bias-voltage-\ncontrolled modulation of photoresponse dynamics. This iontronic device realizes\naccurate orientation recognition and robust fingerprint detection across a wide range\nof incident light angles. Furthermore, the combination of theoretical (DFT, PNP) and\nexperimental (KPFM, ion transport measurements) offers a robust multiscale\nvalidation of the proposed mechanism. The manuscript is generally well written and\ndemonstrates considerable appeal and novelty, supported by a comprehensive dataset.\nThe work is interesting and potentially suitable for publication in Nature\nCommunications after addressing the following concerns.\nResponse: Thanks very much for your comments. We greatly appreciate your\nevaluation and comments that are helpful for improving the quality of the manuscript.\nWe have revised the manuscript according to your suggestions point by point. The\nrevised parts are marked by red font.\nComment 1\nIn Figure 2, the authors performed first-principles density functional theory and finite\nelement method simulations to further validate the proposed mechanism. However, the\nauthors provided only brief explanations of computational procedures in the\nmanuscript. Critical details, schematic illustrations of the simulation models, are\nlacking. It is recommended that the authors supplement these to significantly strengthen\nthe manuscript.\nResponse 1: We sincerely thank the reviewer for this valuable suggestion. We agree\nthat a more detailed description of the computational methods and schematic\nillustrations of the simulation models would enhance the clarity and reproducibility of\nour work. In the revised manuscript and revised supplementary information, we have\nadded the following enhancements:\n1. Detailed computational procedures:\nWe have expanded the “Quantum-Mechanical Simulation of CNT/MoS Nanotubes”\n2\nand “Poisson and Nernst–Planck (PNP) Simulation” sections in the Methods in the\nrevised manuscript to include more comprehensive descriptions of the model setup used\nin both the first-principles density functional theory (DFT) and finite element method\n(FEM) simulations.\nPage 14, line 379-384. The MoS /CNT composite model consists of a MoS nanotube\n2 2\nand a CNT nanotube. Both the MoS nanotube and the CNT nanotube were individually\n2\noptimized under periodic boundary conditions. After constructing the nested composite\nstructure (Supplementary Fig. 6, using a supercell whose length along the z-axis\ncorresponds to the least common multiple of the minimal periodic units of both\ncomponents), an additional geometry optimization was performed for the combined\nsystem.\nPage 15, line 412-414. To simulate the built-in electric field arising from\nphotogenerated charge separation, a photo-induced potential was applied to the CNT\nside of the interface, while the MoS side was held at ground potential.\n2\n2. Schematic illustrations of simulation models:\nWe have included new schematic diagrams in the revised supplementary information\n(Supplementary Fig. 6) that visually depict the simulation geometries for both the DFT\nheterostructure model and the nanofluidic PNP simulation domain. These schematics\nclearly illustrate the structural configuration, illumination direction, ion transport\npathways, and key dimensions used in our simulations. We believe these additions will\nsignificantly improve the transparency and rigor of our computational analysis and help\nreaders better understand the theoretical foundation of our proposed mechanism.\nSupplementary Fig. 6. Schematic illustrations of simulation models. a, Quantum-\nmechanical simulation of CNT/MoS nanotubes based on first-principles density\n2\nfunctional theory. b, Poisson and Nernst-Planck simulation of CNT/MoS nanotubes\n2\nusing the finite element method.\nComment 2\nIn Figure 3, the authors modulate the ionic current by varying light intensity and\nwavelength as an ionic photoreceptor. However, critical performance parameters of\nthe photoreceptor, such as photoresponsivity ratio, response speed, and signal-to-noise\nratio, have not been quantitatively evaluated or provided. Please provide the relevant\nsupplementary data.\nResponse 2: We thank the reviewer for this valuable suggestion. As suggested, we have\nprovided relevant data and analysis for critical performance parameters, including\nphotoresponsivity ratio, photoresponse speed, and signal-to-noise ratio in the revised\nmanuscript. The details are summarized below:\n1.The photoresponsivity was calculated using the formula:\n𝑅 = ∆I/𝑃\nwhere ΔI is the photocurrent; P is the incident power. Under illumination at 94 mW cm-\n2, the CNT/MoS heterostructure-based light-regulated nanofluidic iontronic device\n2\nexhibits a photoresponsivity of approximately 78.4 µA W-1.\n2. The photoresponse speed of the device, defined as the time for the current to rise\nfrom 10% to 90% of the peak value. Under zero bias, the CNT/MoS heterostructure-\n2\nbased light-regulated nanofluidic iontronic device achieves photoresponse speed of 3.8\ns.\n3. The signal-to-noise ratio of the device, defined as the ratio of the photocurrent to the\nfluctuation of the dark current. The CNT/MoS heterostructure-based light-regulated\n2\niontronic device achieves a signal-to-noise ratio of 19.56.\nPage 7, line 173-186. Additionally, critical performance parameters of the\nphotoreceptor, such as photoresponse speed, signal-to-noise ratio, photoresponsivity\nratio, and energy efficiency have been quantitatively evaluated. The photoresponse\nspeed is defined as the time required for the current to rise from 10% to 90% of its peak\nvalue, while the signal-to-noise ratio is defined as the ratio of the photocurrent to the\nroot-mean-square fluctuation of the dark current. The photoresponsivity ratio (R) and\nenergy efficiency (𝜂) was calculated using the formula:\nR (0)\nE\nVttdt\n elec,out 100 100 (2)\nE Pt\nlight,out\nwhere ΔI is the photocurrent; P is the incident power; E is the electrical energy\nelec, out\noutput of the device; E is the optical energy consumption; V(t) and I(t) represent\nlight, in\nthe time-dependent response voltage and current, respectively; t is the light pulse width.\nThe calculated performance metrics show that the CNT/MoS heterostructure-based\n2\nlight-regulated nanofluidic iontronic device achieves a photoresponsivity ratio of ~78.4\nµA W-1, a photoresponse speed of 3.8 s, a signal-to-noise ratio of 19.56 and an energy\nefficiency of 0.694×10-4%.\nComment 3\nIn Figure 3b, while the manuscript states that the ionic photoreceptor exhibits rapid\nrising and falling upon light stimulation, its measured photoresponsive current still\nrequires a considerably longer time to reach a steady plateau compared to electronic\ndevices. The authors should provide a clear explanation.\nResponse 3: We thank the reviewer for this insightful comment regarding the\nphotoresponse time of our ionic photoreceptor. The relatively slower response kinetics\nare not a performance shortcoming but rather an intrinsic feature arising from the\ndifferent charge carriers involved. Our device operates on the principle of light-\nmodulated ion transport, in contrast to electronic devices that rely on the ultrafast\nmovement of electrons and holes. The explanation is as follows:\n(1) Mass and mobility of charge carriers: The mass of ions (K⁺, Cl⁻) is several orders\nof magnitude greater than that of electrons. Consequently, the mobility of ions in\naqueous solutions or nanochannels is drastically lower than electron mobility in\nsemiconductors. The finite time required for ions to physically migrate and redistribute\nunder the photogenerated electric field inherently leads to a slower current response.\n(2) Interface-induced ion accumulation and concentration polarization: As detailed in\nour manuscript (Fig. 2h and Supplementary Fig. 9), the operation of our CNT/MoS\n2\nheterostructure relies on the establishment of a photogenerated potential, which drives\nions and leads to significant concentration polarization at the heterointerface. This\nprocess of building up a stable ionic concentration gradient is inherently slower than\nthe almost instantaneous separation of electron-hole pairs in a p-n junction.\nComment 4\nIn the manuscript, the authors have only characterized the photocurrent response\nresulting from light-induced ion transport. In fact, the photo-driven ion transport can\nalso generate a measurable photovoltage. Corresponding data should be incorporated\nto further elucidate the underlying ion transport mechanism.\nResponse 4: We sincerely thank the reviewer for this insightful suggestion. We agree\nthat measuring the photovoltage is crucial for comprehensively understanding the\nmechanism of light-driven ion transport. In the revised manuscript and revised\nsupplementary information, we have incorporated additional photovoltage\nmeasurements and elucidated the underlying ion transport mechanism. The device\nexhibited a stable and reproducible photovoltage of 10.4 mV under alternating\nillumination (The new data now included as Supplementary Fig. 10). The measurable\nphotovoltage originates from asymmetric spatial distribution of K⁺ and Cl⁻. As\nindicated by the Zeta potential measurements in Supplementary Fig. 11, the CNT/MoS\n2\nheterostructure surface is typically negatively charged. Under unilateral illumination,\nthe CNT/MoS heterostructure nanotubes generated an asymmetric electric field\n2\nthrough E establishment (Fig. 2f and Supplementary Fig. 9), which can drive\nphoto\ncontinuous ion migration. This creates a natural electrostatic attraction for cations (K⁺)\nand repulsion for anions (Cl⁻), generating ion concentration polarization Supplementary\nFig. 9). As shown in the simulation in Fig. 2h, the concentration of K⁺ at the illuminated\ninterface is significantly higher than that of Cl⁻. This asymmetric spatial distribution of\nions generates a measurable photovoltage, also known as concentration potential.\nSupplementary Fig. 10. Measured cyclic constant ion voltage under alternating\nillumination at 10 mM KCl. The device exhibited a stable and reproducible\nphotovoltage of 10.4 mV under alternating illumination.\nPage 5, line 124-136. Under unilateral illumination, the CNT/MoS heterostructure\n2\nnanotubes generated an asymmetric electric field through E establishment (Fig. 2f\nphoto\nand Supplementary Fig. 9), which can drive continuous ion migration throughout the\nCNT/MoS heterostructure nanotubes, generating sustained ion photocurrent. As\n2\nshown in Fig. 2g, the interfacial regions exhibit enhanced current densities due to the\nconvergence of ion transport pathway. Furthermore, a pronounced concentration\npolarization phenomenon induced by ion accumulation was observed at the illuminated\ninterface of the nanotube heterostructure (Supplementary Fig. 9). This region showed\nsignificant enrichment of K⁺, with their concentration substantially exceeding that of\nCl⁻ (Fig. 2h). This asymmetric spatial distribution of ions generates a measurable\nphotovoltage (Supplementary Fig. 10). Both theoretical simulations and experimental\nresults confirm that the ion selectivity originates from the heterostructure’s inherent\nnegative surface charge, as demonstrated by zeta-potential characterizations and drift\ndiffusion experiments (Supplementary Figs. 11 and 12), which facilitates preferential\nelectrostatic attracting cations while repelling anions.\nComment 5\nIn Figure S11, why is the photocurrent change minimal in both the CNT/ MoS\n2\nheterostructure nanotube and the small-pore MoS₂ nanotube when light and voltage\nstimuli are applied simultaneously? In contrast, as shown in Figure 3b, under\nillumination alone, the CNT/ MoS heterostructure nanotube exhibits the optimal ion\n2\ncurrent generation compared to small-pore MoS₂ nanotubes. A reasonable explanation\nfor this apparent discrepancy should be provided.\nResponse 5: We thank the reviewer for this insightful question. The apparent\ndiscrepancy between the photocurrent changes in updated Supplementary Fig. 18 and\nin Fig. 3b is not due to a contradiction but rather stems from a fundamental shift in the\ndominant physical mechanism when the device operates in two-terminal (zero-bias)\nversus three-terminal (applied-bias) configurations. The explanation is as follows:\nIn this two-terminal configuration (Fig. 3b), the ion current is driven solely by the\nphotogenerated potential arising from efficient band alignment-induced charge transfer\nat the CNT/MoS heterointerface. This interfacial engineering strategy effectively\n2\npromotes spatial separation of photogenerated carriers through built-in potential\nmodulation. In contrast, single-phase MoS nanotubes exhibit suboptimal charge\n2\nseparation efficiency and limited ion transport enhancement, primarily due to\ninsufficient band bending and homogeneous surface characteristics, despite improved\nconfinement effects achieved through pore size optimization. Overall, in this two-\nterminal configuration, the photocurrent contribution originating from the\nphotogenerated potential-driven ion transport in the biphasic CNT/MoS heterojunction\n2\nnanotubes significantly outweighs that induced purely by nanoconfinement effects.\nIn this three-terminal configuration (updated Supplementary Fig. 18), the ion transport\nis governed by the coupled effect of two electric fields: the external bias electric field\nand the photogenerated potential. Although the coupled dual electric fields can\naccelerate ion transport to generate a higher ionic current, the CNT/MoS\n2\nheterostructure nanotube, due to their larger pore size, exhibit a relatively smaller\nproportional increase in ion current induced by externally applied bias-driven ion\nselectivity compared to nanotubes with small-pore (35 nm) MoS . Consequently, the\n2\nrelative change in net photocurrent (ΔI) induced by the two electric fields appears\nminimal. In contrast, for the small-pore MoS nanotube, its primary advantage is the\n2\nextremely strong spatial confinement effect. When both light and an external bias are\napplied simultaneously, the external bias may accelerate selective ion transport within\nthe confined nanotube. This results in a diminished difference in the measured ion\nphotocurrent compared to that of the CNT/MoS heterostructure nanotube. Overall, in\n2\nthis three-terminal configuration, the photocurrent contribution generated by bias-\nmodulated ion transport in confined nanotube exceeds that arising from photogenerated\npotential-driven ion transport in the biphasic CNT/MoS heterojunction nanotubes.\nIn Figure 5a, the schematic illustrating of orientation recognition lacks clarity in its\nannotations and comparative visualizations. For instance, the difference in roof\nappearance between the 30° and 60° viewing angles is insufficiently distinct.\nAdditionally, the meaning of the symbol “I” in the input layer of the neural network?\nIt is recommended that the figure is modified to improve interpretability.\nResponse 6: We thank the reviewer for the valuable suggestion. The following\nrevisions have been made to updated Fig. 5a: (1) the portrait image has been replaced;\n(2) the color contrast of the roof appearance at 30° and 60° visual angles has been\nenhanced; (3) the symbol \"I\" representing the ion current has been explicitly labeled.\nFig. 5. Orientation recognition applications of the artificial nanofluidic iontronic\nretina. a, Schematic illustration of bioinspired vision system with ANN based on light-\nregulated nanofluidic iontronic retina for orientation recognition. b, The responsive\nEPSC versus time by stimulating the device as a function of light pulses from different\norientation angles. c, The polar coordinate plots of responsive EPSC variations as a\nfunction of light pulses from different orientation angles. d, The responsive EPSC\nversus time by stimulating the device as a function of light pulses from different\norientation angles and illumination power density. e, Evolution of the recognition\naccuracy with training epochs and the extract contrast and spatial resolution from\nlandscape images captured as a function of light pulses from different orientation angles.\nf, Confusion matrix between the predicted angle direction and true angle direction.\ng, The fingerprint images feature extraction and recognition captured at five distinct\nangular orientations following processing by the system. h, Evolution of the fingerprint\nimages recognition accuracy with training epochs.\nReviewer #3\nGeneral comments:\nThe manuscript reports the fabrication and characterization of a bioinspired\nnanofluidic iontronic device based on a carbon nanotube/molybdenum disulfide\n(CNT/MoS₂) heterostructure, which is claimed to emulate both photoreceptor and\nphotosynaptic functionalities of a biological retina. The authors found that their device\nenables light-modulated ion transport and photosynaptic behavior through\nheterointerface-induced photovoltage generation. Despite the appealing concept, there\nare serious concerns with the manuscript as below.\nResponse: Thanks very much for your comments. We greatly appreciate your\nevaluation and comments that are helpful for improving the quality of the manuscript.\nWe have revised the manuscript according to your suggestions point by point. The\nrevised parts are marked by red font.\nComment 1\nThe claim of reproducing dual photoreceptor and photosynaptic functionality within a\nsingle nanofluidic device is overstated. What is demonstrated are optical modulation of\nionic currents and bias-dependent conductance changes, both well established in\nprior optoionic memristor and photogated nanofluidic systems.\nNo convincing evidence is provided for actual retina-like spatiotemporal signal\nprocessing or for biologically relevant timescales comparable to neural photosynapses.\nThe “bioinspired retina” terminology reads more as rhetorical.\nResponse 1: We thank the reviewer for this insightful comment. We agree that optical\nmodulation of ionic currents and bias-dependent conductance changes have been\nindividually demonstrated in prior optoionic systems, such as photogated nanochannels\nand ionic memristors (Adv. Mater. 2025, 37 (30), 2502438; Angew. Chem. Int. Ed. 2025,\n64 (7), e202418949; Nat. Commun. 2023, 14, 359; Nat. Commun. 2024, 15, 4213; Adv.\nMater. 2025, 42, e08641). Here, we further demonstrate the coordinated regulation of\ntransient photoresponse speeds in ionic current (or conductance) within heterojunction\nnanotubes at the single-device level through synergistic optoelectrical modulation.\nIn addition, the key advancement of our manuscript lies in the integrated and switchable\nemulation of photoreceptors and photosynaptic dual functionalities within a single\ndevice. As detailed in Figs. 3 & 4, in a two-terminal, zero-bias configuration, our device\nexhibits a rapid photocurrent response, mimicking the fast transduction of\nphotoreceptor cells. Conversely, under a three-terminal configuration with an applied\nbias, it demonstrates persistent photoconductivity with short/long-term plasticity,\nemulating the slow, history-dependent processing of biological synapses. This voltage-\ngated transition in photoresponse kinetics is a distinctive feature not reported in\nprevious systems. Previous works typically require separate device architectures or\nmaterial systems to achieve isolated photoreceptor or synaptic functions (Adv. Mater.\n2025, 37 (30), 2502438; Angew. Chem. Int. Ed. 2025, 64 (7), e202418949; Angew.\nChem. Int. Ed. 2019, 58, 12574–12579).\nWe acknowledge the reviewer's concern regarding terminology. It is true that we only\nemulate partial functions of visual perception and signal processing, specifically those\nof photoreceptors and photosynaptic functionality by modulating the fast and slow\ncomponents of ion currents, without fully replicating the complex functionalities of\nretina. To prevent potential misunderstanding, in the revised manuscript, we have\nreplaced strong terms like \"reproduce\" with more accurate descriptors such as\n\"emulate\" or \"mimic.\" We have also toned down the claims surrounding \"bioinspired\nretina\" and refocused the narrative on the demonstrated bioinspired functionalities\n(i.e., co-mimicking photoreceptor and photosynaptic behaviors) rather than implying a full\nreplication of the entire retinal structure. The title of this manuscript “Artificial\nNanofluidic Iontronic Retina” has been corrected to “Bioinspired Nanofluidic Iontronic\nDevice with Integrated Photoreceptor and Photosynaptic Functions”\nComment 2\nHow do the memory curves look like, when they cycle the voltage, do the authors see\npinched hysteresis loops, with or without light? What is influenced by the light? Is it\nthe area of the loop, low or high conductance state?\nResponse 2: We thank the reviewer for this insightful question, which has prompted us\nto further clarify the key memory characteristics and photomodulation mechanisms in\nour nanofluidic iontronic device. In response, we performed voltage‑cycling\nmeasurements using triangular wave sweeps to obtain the corresponding current–\nvoltage (I–V) characteristics. As illustrated in the accompanying figure, a well‑defined\npinched hysteresis loop, characterized by its “figure‑eight” shape that converges near\nthe origin, is clearly observed under both dark and illuminated conditions. Importantly,\nillumination substantially enhances the overall ion current and device conductance.\nQuantitatively, in the dark the area enclosed by the hysteresis loop is 1.36 mW, with\ncorresponding low‑ and high‑conductance states of 23.5 μS and 43.8 μS, respectively.\nUnder illumination, the loop area increases to 3.14 mW, while the conductance states\nshift to 21.5 μS (low) and 51.2 μS (high). These changes indicate that light not only\nenlarges the hysteretic memory window but also selectively elevates the\nhigh‑conductance state, whereas the low‑conductance state undergoes a slight reduction.\nSuch behavior suggests that photoexcitation promotes ion migration and accumulation\nwithin the CNT/MoS nanotube heterostructure, thereby enhancing the conductance\n2\nwhen the device is switched into the “on” state.\nThe observed light‑tunable pinched hysteresis represents a hallmark of ion‑based\nmemristive systems, which are capable of emulating synaptic plasticity. This\nphotomodulated memory effect aligns closely with photosynaptic functionality (Fig. 4),\nwherein optical pulses can induce persistent conductance changes analogous to\nspike‑timing‑dependent plasticity (STDP) in biological synapses. Consequently, this\ndual electrical and optical tunability renders the device highly suitable for applications\nin vision sensors and neuromorphic computing of orientation recognition and\nfingerprint recognition (Fig. 5).\nIn summary, the clear emergence of a pinched hysteresis loop under voltage cycling,\nand its pronounced amplification under illumination, underscores the role of\nphotogenerated potentials in governing ion transport and memory behavior. This\nsynergy between optical stimulation and ionic memristance is central to the device’s\nability to co‑emulate photoreceptor‑like sensing and photosynaptic processing within a\nunified nanofluidic platform.\nFig. R1. The current–voltage hysteresis characteristics of the nanofluidic iontronic\ndevice with or without light illumination under periodic voltage excitation.\nComment 3\nIn Fig. 4c, the authors have shown the change in current as a function of bias voltage\nacross the fluidic cell at a fixed intensity of 94mW/cm^2. Why at 0.1V the change in\ncurrent is of 1μA whereas in all subsequent measurements in the given figure it is\n~200nA?\nResponse 3: We thank the reviewer for this thoughtful question. In fact, the apparent\ndiscrepancy between the ~1 μA current observed at 0.1 V bias in Fig. 4c and the ~200\nnA currents in subsequent figures (Fig. 4d-i) stems primarily from different testing\nconditions. As stated in the manuscript, under identical applied bias voltage and\nillumination power density, the generated photocurrent of the device is positively\ncorrelated with the illumination duration of the optical pulse. The purpose of Fig. 4c\nwas conducted under 10-second optical pulse duration to establish a stable current-\nvoltage relationship. Under these conditions, charge carriers have sufficient time to\ngenerate and accumulate, resulting in the larger measured current (~1 μA). In contrast,\nfor the subsequent photosynaptic characterizations (Fig. 4d-i), brief optical pulse\nduration (2 seconds) was used to mimic biological synaptic responses to transient\nstimuli. The shorter pulse duration limits the total number of photogenerated carriers;\nhence the resulting transient current is smaller (~200 nA).\nComment 4\nCapacitive effects can also lead to the same changes as authors observed? How do\nauthors know that it is not capacitive effects due to light?\nResponse 4: We thank the reviewer for raising this important point regarding the\npotential contribution of capacitive effects. We understand that light-induced charge\nrearrangement at the device interface could potentially trigger transient capacitive\neffects. We have carefully considered this possibility and would like to clarify that the\nobserved phenomena are primarily attributed to the photoelectric effects at the\nCNT/MoS₂ heterointerface, rather than purely capacitive effects. The key evidence is\noutlined below:\n(1) Distinct characteristics of surface photovoltage: Our KPFM measurements (Fig. 2d,\ne) reveal a continuous and linear amplification of the contact potential difference and\nsurface photovoltage with increasing light intensity (from 3.8 mV at 0.1 mW cm-2 to\n7.7 mV at 0.5 mW cm-2). This linear relationship is a hallmark of the photoelectric\neffect, stemming from the continuous generation and separation of photo-induced\nelectron-hole pairs. In contrast, capacitive effects typically manifest as transient\ncharging/discharging current spikes and do not exhibit this sustained, linear correlation\nwith light power density.\n(2) Theoretical evidence from first-principles calculations: Our DFT calculations (Fig.\n2b, c) directly demonstrate spontaneous charge redistribution and the formation of a\nbuilt-in electric field at the CNT/MoS heterointerface. Furthermore, it shows clear\n2\nspatial separation of photogenerated electrons (localized on CNT) and holes (localized\non Mo atoms of MoS ) upon excitation. This provides a solid theoretical foundation for\n2\nthe origin of the photogenerated potential, which is intrinsic to the photoelectric\nmechanism and cannot be explained by capacitive effects.\n(3) Ion transport behavior inconsistent with capacitive charging: The finite element\nmethod simulations based on the Poisson-Nernst-Planck equations (Fig. 2f-h) show the\ngeneration of an asymmetric electric field and pronounced ion concentration\npolarization (significant K⁺ enrichment at the illuminated interface) under unilateral\nillumination. This spatially asymmetric and sustained redistribution of ions and\nphotocurrent is consistent with ion migration driven by a persistent photogenerated\npotential, and is fundamentally different from the symmetric and transient characteristic\nof capacitive double-layer charging/discharging.\n(4) Experimental observation of long-timescale responses inconsistent with capacitive\ncharging: The characteristic timescale of capacitive responses is typically on the order\nof milliseconds, with current decaying rapidly (Fig. R2b). In contrast, the photocurrent\nwe observed remains stable during illumination (Fig. 3b and R2a) and decays slowly\nafter light cessation, following ion diffusion and recombination kinetics, particularly\nshowing excitatory postsynaptic current relaxation under applied bias (Fig. 4). These\nresponses, lasting from seconds to tens of seconds, align with the migration,\naccumulation, and release dynamics of ions in a nano-confined environment and cannot\nbe explained by conventional electric double-layer or interfacial capacitance models.\nFig. R2. Our Device-dominated vs. capacitance-dominated modulation of\nphotocurrent response kinetics. a, Our device-governed photoresponse speed\nvariation in current; b, Interfacial-capacitance-governed photoresponse speed variation\nin current.\n(5) Electrochemical impedance spectroscopy ruling out dominant capacitive\ncontribution: We performed electrochemical impedance spectroscopy measurements to\nob measurements to\nobtain the Nyquist plot and Bode phase angle plot characteristics. If the photo-\nresponsive ion transport in our device were dominated by capacitive effects, the Nyquist\nplot shows a straight line lies in y-axis (the real impedance is zero). Values close to\nzero corresponds to high frequencies, while at lower frequencies the impedance values\nare higher. The Bode phase angle plot shows a straight line that crosses the right axis\nat φ = −90° based on the previous reported work (ACS Meas. Sci. Au 2023, 3, 162−193).\nIn contrast, the Nyquist plot we tested shows the majority of point lying in the real axis.\nThat is, the impedance values at all the excitation frequencies does not change\nsignificantly in the dark or light illumination condition. The Bode phase angle plot\nshows a straight line that crosses the right axis at φ = 0-5°, which equal to the resistance\ncharacteristics. The electrochemical impedance spectroscopy measurements were\nconducted to further rule out a capacitance-dominated mechanism in the photo-\nresponsive ion transport.\nFig. R3. The electrochemical impedance spectroscopy measurements of the\nnanofluidic iontronic device with or without light illumination. a, The Nyquist plot\ncharacteristics of the nanofluidic iontronic device; b, Bode phase angle plot\ncharacteristics of the nanofluidic iontronic device.\nIn summary, the combination of SPV measurement, theoretical modeling, and control\nexperiments collectively confirms that the modulation of photoresponsive ion current\nis predominantly governed by the photoelectric effects at the CNT/MoS\n2\nheterointerface, not by capacitive effects.\nComment 5\nIn Fig. 4d authors claimed short term plasticity behaviour and attempted to correlate\nit with Paired-pulse facilitation characteristics. However, from figure it doesn’t seem\ntrivial to assign it to short-term characteristics. Because, even when both bias\npulse/and photo-pulse were removed, the transient didn’t decay immediately back to\nthe baseline.\nResponse 5: We sincerely thank the reviewer for this insightful observation. We agree\nthat in the original Fig. 4d, the transient current does not immediately decay back to the\nbaseline after the cessation of the second optical pulse and bias voltage within the\ndisplayed timeframe. We attribute this behavior to the characteristic ion relaxation\ndynamics inherent in our nanofluidic ionic system, which differ from purely electronic\ndevices. Upon removal of the optical pulse stimuli, the accumulated ions (K⁺, as\nindicated in Fig. 2h) at the heterojunction interface require a finite time to diffuse and\nredistribute, leading to the observed current \"tail.\" This gradual decay reflects a short-\nlived \"memory\" effect related to ion mobility within the nanotube. Importantly, the\ndefining feature of short-term plasticity is not necessarily an instantaneous return to\nbaseline. The key evidence for short-term plasticity here is the clear paired-pulse\nfacilitation, where the amplitude of the second excitatory postsynaptic current (A ) is\n2\nsignificantly larger than the first (A ). Fig. 4d and 4e robustly demonstrate A > A and\n1 2 1\na biexponential decay of the PPF index with increasing pulse interval, aligning perfectly\nwith established short-term plasticity characteristics in biological and artificial\nsynapses.\nTo directly address the concern about baseline recovery, we performed additional\nexperiments with extended observation times. The updated Fig. 4d in the revised\nmanuscript confirm that when a sufficiently long relaxation period is allowed (e.g., 150\ns), the excitatory postsynaptic current does fully recover to the original baseline,\nunequivocally demonstrating the short-term nature of the response.\nFig. 4. Photosynaptic function of the artificial nanofluidic iontronic retina. a, The\nmeasurement schematic of the nanofluidic opto-electrical device for ion transport\nmodulation based on CNT/MoS heterostructure nanotubes. b, Ion current variations as\n2\na function of light density of 10 mW cm-2, 36 mW cm-2, 55 mW cm-2, 74 mW cm-2, and\n94 mW cm-2 under a fixed bias voltage of 0.5 V. c, Ion current variations as a function\nof bias voltage of 0.1 V, 0.3 V, 0.5 V, 0.7 V, and 0.9 V under a fixed light density of 94\nmW cm-2. d, The PPF effect of the nanofluidic photosynapses. e, The PPF ratio of the\nnanofluidic photosynapses as a function of the pulse interval. f, The responseivee EPSC\nversus time by stimulating the device as a function of consecutive light pulses number\nof 10, 20, 50, and 80, respectively. g, The responseivee EPSC versus time by\nstimulating the device as a function of light pulses duration of 1, 2, 5, 10, and 20 s,\nrespectively. h, The responseivee EPSC versus time by stimulating the device as a\nfunction of light pulses frequencies of 0.05, 0.1, 0.2, 0.5, and 1 Hz, respectively. i,\nLearning experience behavior of nanofluidic photosynapses.\nComment 6\nIn Fig. 4h, if authors are willing to demonstrate frequency dependent plasticity, they\nmust show an increase and decrease of post synaptic current as a function of frequency.\nTo give an electrical analogy, a normal capacitor would exhibit a higher current on\nincreasing the frequency, but that doesn’t mean it exhibits spike frequency/rate\ndependent plasticity.\nResponse 6: We sincerely thank the reviewer for this insightful and important comment\nregarding the interpretation of frequency-dependent plasticity. We fully acknowledge\nthe reviewer’s point that a purely capacitive system would exhibit a linear increase in\ncurrent with frequency. This stands in fundamental contrast to biological spike-\nfrequency-dependent plasticity, which is characterized by nonlinear, history-dependent\nmodulation of synaptic efficacy.\n(1) In fact, as the frequency increases, particularly at the high frequency of 1 Hz, the\namplification of the observed postsynaptic current exhibits a distinct saturation trend.\nThis behavior clearly differentiates it from the linear current increase characteristic of\na capacitive system. Moreover, as demonstrated in Response 4, the photoresponsive\ncurrent in our nanofluidic iontronic system is attributed to light-induced asymmetric\npotential-driven ion transport, rather than capacitive effects.\n(2) In our experiments, the observed increase in EPSC amplitude with increasing\nstimulation frequency (0.05–1 Hz) is not simply due to capacitive charging, but reflects\na cumulative facilitation of ion transport that is sustained beyond the stimulus period.\nThis behavior arises from the interplay between light-induced ion accumulation and the\nfinite relaxation time of ions within the confined nanotube heterostructure. At higher\nfrequencies, the inter-pulse interval is shorter than the ion redistribution time, leading\nto progressive accumulation of ions at the illuminated heterointerface, thereby\nenhancing the EPSC amplitude in a manner analogous to short-term facilitation in\nbiological synapses. To further distinguish our device’s response from that of a\ncapacitor, we performed additional analysis of the EPSC decay kinetics after each light\npulse train. As shown in the Fig. 4h, after high-frequency stimulation, the current does\nnot immediately return to baseline but decays with a time constant that depends on the\nprior stimulation history, indicating a “memory” of the preceding activity. This post-\ntetanic potentiation-like behavior is a hallmark of frequency-dependent plasticity and\ncannot be explained by a simple capacitance circuit model.\nTo make this distinction explicit, we have revised the manuscript in Section\n“Photosynaptic Characteristics” to explicitly describe the observed behavior as “pulse-\nfrequency-dependent facilitation”. We appreciate the reviewer’s critical comment,\nwhich has helped us to more precisely characterize and explain the synaptic-like\ndynamics of our device.\nPage 8, line 231-234. Meanwhile, Fig. 4h systematically characterizes the pulse-\nfrequency-dependent facilitation elicited by sequential photostimulation across a\nphysiological frequency range, which reveals a progressive enhancement of\nincremental facilitation effects of EPSC amplitudes as the stimulation frequency\nincreases from 0.05 to 1.00 Hz.\nComment 7\nIn supplementary Fig. S10, at 100mM KCl, why the system exhibited negative baseline?\nIs it normal for their model system to observe?\nResponse 7: We sincerely thank the reviewer for this insightful observation. In updated\nSupplementary Fig. 17, the photoresponse ion current variations were measured as a\nfunction of KCl electrolyte with concentrations from 0.1mM to 100 mM. A significant\nnegative baseline drift was observed exclusively in the 100 mM KCl solution. We\nattribute this phenomenon to the following potential causes:\n(1) Interfacial polarization effects: In high-concentration KCl solutions, the elevated\nionic strength may induce concentration polarization of ions at the heterojunction\nnanotube interface. This alters the interfacial charge distribution, leading to the\nobserved negative baseline drift.\n(2) Ion adsorption and electric double layer perturbation: Both K⁺ and Cl⁻ ions in\nconcentrated KCl solutions may strongly adsorb onto the surface of the heterostructured\nnanotube, particularly given the high surface area and active sites of carbon nanotubes\nCNT and MoS . Such adsorption disrupts the electric double layer structure, resulting\n2\nin a negative shift of the measurement baseline.\nAlthough ion concentration influences the initial current baseline drift, the current\nstabilizes at a baseline level after a sufficient equilibration period. To clearly illustrate\nthe effect of ion concentration on the change in photocurrent, specifically, the absolute\ndifference between illuminated and dark conditions, in the revised supplementary\ninformation of updated Supplementary Fig. 17, the data were zero-referenced (baseline-\nsubtracted) for enhanced comparability.\nSupplementary Fig. 17. Ion current variations as a function of KCl electrolyte with\nconcentrations from 0.1 mM to 100 mM.\nComment 8\nAuthors have performed zeta potential characterizations in supplementary figure S8.\nAlthough, these measurements do show cation selectivity of their model system,\nhowever, it would be useful if they can perform drift diffusion type of experiments at a\nfixed gradient as a function of light intensity. This would tell about which species in\npredominant in their subsequently observed characteristics.\nResponse 8: We sincerely thank the reviewer for this insightful suggestion regarding\nthe characterization of ion selectivity. Following the suggestion, we performed an\nadditional drift-diffusion experiment to directly quantifying the transference numbers\nof specific ions (e.g., K⁺ vs. Cl⁻) based on the methods widely used in nanofluidics\n(Science 2017, 358, 511–513; Sci. Adv. 2025, 11, eadw7882; Nat. Commun. 2024,\n15:5876). The detailed testing, calculation, and analysis process have incorporated into\nthe supplementary methods and Supplementary Fig. 12 in the revised supplementary\ninformation and the revised manuscript.\nSupplementary Fig. 12. The drift-diffusion experiment at a fixed gradient of 1.0\nmM and 10 mM KCl solution.\nPage 5, line 133-136. Both theoretical simulations and experimental results confirm\nthat the ion selectivity originates from the heterostructure’s inherent negative surface\ncharge, as demonstrated by zeta-potential characterizations and drift diffusion\nexperiments29-31 (Supplementary Figs. 11 and 12), which facilitates preferential\nelectrostatic attracting cations while repelling anions.\nSupplementary Methods\nDrift-Diffusion Experiment\nThe fabricated CNT/MoS nanotube heterostructure membrane-based devices were\n2\nmounted between a custom-designed electrochemical cell architecture with a fixed\ntransparent area, featuring dual symmetric reservoirs. Both compartments were filled\nwith equal volumes of different concentrations of 1.0 mM and 10 mM KCl solution,\nrespectively. The Ag/AgCl electrodes immersed in the reservoirs for transmembrane\ncurrent signal acquisition using a Keithley 6487 B picoammeter.\nFrom the acquired current-voltage (I-V) characteristics, the short-circuit current (I )\nSC\nand open-circuit potential (V ) was directly determined from the absolute intercept of\nOC\nthe curve with the current and voltage axes, respectively. The measured V comprises\nOC\ntwo components: the diffusion potential (E ) and the redox potential (E ). The E\ndiff redox redox\nwas generated at the Ag/AgCl electrodes as a result of the established concentration\ngradient. Consequently, the net E was calculated by subtracting this E from the\ndiff redox\nmeasured V . The E was calculated using the Nernst equation as follows:\nOC redox\n𝐸 = \n𝑅𝑇\nln\n𝑐𝐻𝛾𝐻\n（1）\n𝑟𝑒𝑑𝑜𝑥\n𝑧𝐹 𝑐𝐿𝛾𝐿\nThe activity coefficient of KCl solutions was calculated using the Nernst equation as\nfollows:\nlog (𝛾 )=−𝐴|𝑧 𝑧 |√𝐼 （2）\n10 ± + −\nThe transference numbers for cations (𝑡 ) were calculated using the Goldman\n+𝑚\nHodgkins-Katz (GHK) equation as follows:\n𝐸 = (2𝑡 −1)\n𝑅𝑇\nln\n𝑐𝐻𝛾𝐻\n（3）\n𝑑𝑖𝑓𝑓 +𝑚\n𝑧𝐹 𝑐𝐿𝛾𝐿\nwhere R, T, z, and F are the universal gas constant, temperature, ion valence and the\nFaraday constant, respectively. The γ with subscript of H or L represent the activity\ncoefficient of KCl solutions at high concentration c (10 mM) and low concentration\nH\nc (1 mM), respectively. I is the ion strength and equals the KCl solution concentration.\nL\nA is a constant related to temperature and pressure. The calculated transference\nnumbers for cations of 0.64 (>0.5), thereby directly proving that cations are the\npredominant species in the subsequently observed photoresponsive characteristics. In\naddition, the I and V change significantly increase under illuminated conditions,\nSC OC\ndue to the additional driving force for cation migration provided by the photogenerated\npotential.\nComment 9\nKey parameters that would enable meaningful comparison with state-of-the-art systems,\nresponse speed, energy efficiency, stability, reproducibility, and endurance are not\nreported.\nResponse 9: We thank the reviewer for this valuable suggestion. As suggested, we have\nprovided relevant data and analysis for key performance parameters, including response\nspeed, energy efficiency, stability, reproducibility, and endurance. The details are\nsummarized below:\n1. The photoresponse speed of the device, defined as the time for the current to rise\nfrom 10% to 90% of the peak value. Under zero bias, the light-regulated nanofluidic\niontronic device of CNT/MoS heterostructure achieves photoresponse speed of 3.8 s.\nreceived limited attention. Currently, it can only be preliminarily estimated by applying\nfixed-duration light pulses and observing the slope of the resulting photocurrent change.\nThe photoresponse speed of our device surpasses that of most previously reported\nnanofluidic ionic channel materials, such as graphene (Nat Commun. 2019, 10, 1171),\nMXenes (Nat. Commun. 2023, 14, 359), and MOF (Angew. Chem. Int. Ed. 2025, 64 (7),\ne202418949).\n2. The energy efficiency of the device, defined as the photon-to-electricity conversion\nefficiency, was primarily estimated using the following formula:\n𝐸 ∫𝑉(𝑡)×𝐼(𝑡)𝑑𝑡\nelec,𝑜𝑢𝑡\n𝜂 = ×100% = ×100%\n𝐸 𝑃 ×𝑡\n𝑙𝑖𝑔ℎ𝑡,𝑖𝑛 𝑙𝑖𝑔ℎ𝑡,𝑖𝑛\nwhere E is the electrical energy output of the device; E is the optical energy\nelec, out light, in\nconsumption incident on the active area of the device; V(t) is the response voltage as a\nfunction of time; t is the light pulse width; I(t) is the response current as a function of\ntime. the light-regulated nanofluidic iontronic device of CNT/MoS heterostructure\n2\nachieves energy efficiency of 6.5%, Indeed, quantitative metrics for the photon-to-\nelectricity conversion efficiency in previously reported light-driven nanofluidic ion\ntransport systems remain conspicuously lacking. The field has largely concentrated on\ndemonstrating the enhancement of osmotic power generation under salinity gradients\nin light-driven nanofluidic ion transport systems, with comparatively little emphasis\nplaced on rigorously quantifying the underlying energy conversion efficiency. (Nat\nCommun. 2023, 14, 6702; Nat. Commun. 2024, 15, 4213; Nat Commun. 2024, 15, 832;\nAngew. Chem. Int. Ed. 2022, 61, e202202698; Energy Environ. Sci., 2023,16, 3146-\n3157)\nPage 7, line 173-186. Additionally, critical performance parameters of the\nphotoreceptor, such as photoresponse speed, signal-to-noise ratio, photoresponsivity\nratio, and energy efficiency have been quantitatively evaluated. The photoresponse\nspeed is defined as the time required for the current to rise from 10% to 90% of its peak\nvalue, while the signal-to-noise ratio is defined as the ratio of the photocurrent to the\nroot-mean-square fluctuation of the dark current. The photoresponsivity ratio (R) and\nenergy efficiency (𝜂) was calculated using the formula:\nR (0)\nE\nVttdt\n elec,out 100 100 (4)\nE Pt\nlight,out\nwhere ΔI is the photocurrent; P is the incident power density; E is the electrical\nelec, out\nenergy output of the device; E is the optical energy consumption; V(t) and I(t)\nlight, in\nrepresent the time-dependent response voltage and current, respectively; t is the light\npulse width. The calculated performance metrics show that the CNT/MoS\n2\nheterostructure-based light-regulated nanofluidic iontronic device achieves a\nphotoresponsivity ratio of ~78.4 µA W-1, a photoresponse speed of ~3.8 s, a signal-to-\nnoise ratio of ~19.56 and an energy efficiency of 0.694×10-4%.\n3. We have conducted additional preliminary cycling tests (over 1000 s) which show\nno significant degradation in the photoresponse (this data has been added to the revised\nsupplementary information of Supplementary Fig. 13). The result show that nanofluidic\niontronic device exhibits excellent stability, reproducibility, and endurance.\nSupplementary Fig. 13. The operational stability, reproducibility, and endurance\nof light-regulated nanofluidic CNT/MoS heterostructure-based iontronic device\n2\nunder prolonged alternating illumination of 1000 s.\nPage 6, line 146-149. All three devices exhibited operational stability, reproducibility,\nand endurance for the photogenerated ion current (ΔI), calculated as ΔI =I -I ,\nlight dark\nwhere I and I represent the ion current under light illumination and dark\nlight dark\nconditions, respectively (Fig. 3b and Supplementary Fig. 13).\nComment 10\nThe ANN and CNN results depend primarily on preprocessed data, not on intrinsic\ndevice learning or neuromorphic adaptability. These sections read as superficial add-\nons applications, without clear connection to the nanofluidic mechanism.\nResponse 10: We appreciate the reviewer's insightful observation regarding the\nrelationship between the ANN/CNN implementations and the nanofluidic mechanism.\nWe acknowledge that the computational tasks were indeed performed on preprocessed\ndata, primarily through software-based algorithms. As an emerging field, nanofluidic\nneuromorphic devices face significant challenges in hardware-software integration, a\ndifficulty shared across neuromorphic electronics as a whole, and there remains\nconsiderable progress to be made. The key advancement of our manuscript lies in the\nintegrated and switchable emulation of photoreceptors and photosynaptic dual\nfunctionalities within a single device. As detailed in Figs. 3 & 4, in a two-terminal,\nzero-bias configuration, our device exhibits a rapid photocurrent response, mimicking\nthe fast transduction of photoreceptor cells. Conversely, under a three-terminal\nconfiguration with an applied bias, it demonstrates persistent photoconductivity with\nshort/long-term plasticity, emulating the slow, history-dependent processing of\nbiological synapses. The final demonstrations of orientation and fingerprint recognition\nserve to illustrate that such nanofluidic iontronic devices possess the potential for visual\nneuromorphic perception applications, achieving functionality comparable to that of\ntheir electronic counterparts. In addition, we acknowledge that the current presentation\nmay have created the impression of these being standalone computational components.\nIt is possible that this section was written with inadequate detail in our original\nmanuscript, leading to a lack of clarity in the explanation. To better and more clearly\ndemonstrate the connection between the ANN/CNN implementations and the\nnanofluidic mechanism, we provide the following detailed explanation: (These have\nincorporated into the supplementary methods in revised supplementary information)\nOrientation Recognition\nIn this study, the three-channel current data collected from the nanofluidic iontronic\ndevices was normalized and used to train an angle recognition by artificial neural\nnetwork. Each sample consists of three-channel current response signals extracted\nwithin a specified sampling interval (covering a period with light application and/or\nwithdrawal). All samples were labeled according to their corresponding angle\ncategories (e.g., 0°, 30°, 60°, etc.), resulting in a total of 12 angular classes. The dataset\nwas split into training and test sets in an 80%:20% ratio, while maintaining class\nbalance during training. The constructed artificial neural network adopts a multilayer\nperceptron (MLP) architecture: The input layer has a dimension of 885, corresponding\nto the flattened three-channel current features; The hidden layer contains 16 neurons\nutilizing the ReLU activation function; The output layer comprises 12 neurons\ncorresponding to the 12 angle classifications. The network was trained using the cross-\nentropy loss function and the Adam optimizer (learning rate: 1×10-4) for a total of 40\nepochs.\nIn the orientation recognition, we designed three nanofluidic iontronic devices\noperating at different voltages, enabling them to exhibit orientation-dependent\nphotoresponses under varying incident light angles. Each incident direction produced a\nunique set of photocurrent signals across the three sites, originating from the synergistic\nlight-electric-ion transport within the nanofluidic channels. These photocurrent signals\nwere directly fed into an ANN for learning and classifying the incident light directions.\nConsequently, the ANN here functions not only as a standalone algorithm\ndemonstration but also as an analytical framework to interpret how device-level ion\ndynamics and optoelectronic coupling mechanisms collectively enable orientation-\nselective perception and neuromorphic-like sensory functions.\nFingerprint Recognition\nThis study constructed a convolutional neural network for 10-class classification (five\nfingers from left and right hands) based on the SOCOFing fingerprint dataset. The\ndataset includes real fingerprints and three difficulty levels of altered fingerprints\n(Altered-Easy, Medium, Hard). During loading, finger type (thumb, index, middle, ring,\nlittle) and hand information were parsed and mapped into 10 labels (0–9). All\nfingerprint images were read in grayscale and uniformly resized to 96×96 pixels. They\nwere then split into training (80%) and validation (20%) sets, with the real fingerprints\nused as an independent test set. The model employs a three-layer convolutional\narchitecture (kernel sizes: 5–5–3; corresponding channel numbers: 32, 64, 128). Each\nconvolutional layer is followed by Batch Normalization (BatchNorm), ReLU activation,\nand 2×2 Max Pooling. After the feature extraction section, a Dropout layer was added\nto prevent overfitting, followed by fully connected layers for classification output. The\nmodel was trained using the Adam optimizer (learning rate: 1×10-4) and the cross-\nentropy loss function for 10 epochs.The model was trained and evaluated using the\nSokoto Coventry Fingerprint Dataset, comprising 40,000 training images and 6,000 test\nimages (each 96×96 pixels in dimension), encompassing ten distinct fingerprint classes.\nTraining employed a batch size of 64 per epoch. The fingerprint images captured at five\ndistinct angular orientations following processing by the system.\nIn the fingerprint recognition, pixel values from an external image dataset were\nconverted into optical stimulation signals, which were directly applied to nanofluidic\niontronic devices. The light-induced ion transport processes within these devices\ngenerated corresponding electrical signal outputs. These device-level current responses\nreflect the nonlinear behavior of ion migration in the nanofluidic system and the\ncharacteristics of light-induced conductance modulation. Subsequently, we employed a\nCNN model to learn and extract features from these device output signals, aiming to\nevaluate the capability of the nanofluidic system to perform data encoding and pattern\nrecognition in a neuromorphic context. Therefore, the neural network here serves not\nmerely as an independent algorithm demonstration, but also as an analytical tool to\nquantify and interpret the dynamic response characteristics of the nanofluidic devices.\nComment 11\nHow do authors verify that the observed response is not simply due to the\nphotoresponse of MoS rather than nanofluidic mechanism.\nnanofluidic mechanism induces the photoresponsive ion current, we conducted a\ncontrol experiment measuring the photorespnsive ion current in CNT ion devices (this\ndata has been added to the revised supplementary information of Supplementary Fig.\n15). As shown in the figure below, the CNT device generated only about 4 nA of ion\ncurrent, which is significantly lower than 226 nA of the photoresponsive ion currents\nobserved in CNT/MoS heterojunction nanotubes in the manuscript (Fig. 3b). This\n2\nresult further confirms that the photoresponsive current originates primarily from\nphotoresponse of MoS nanotubes or CNT/MoS heterojunction nanotubes rather than\n2 2\nnanofluidic mechanism.\nSupplementary Fig. 15. Measured cyclic constant ion current of CNT with\nalternating illumination at 10 mM KCl. The CNT device generated only about 4 nA\nof ion current, which is significantly lower than 226 nA of the photoresponsive ion\ncurrents observed in CNT/MoS heterojunction nanotubes.\nthe photoresponsive ion current in the CNT-based device, along with the net\nphotocurrent generated in the CNT/MoS -based device under alternating bilateral\n2\nillumination (Supplementary Figs. 15 and 16). Together, these findings confirm that the\nobserved photocurrent is attributed primarily to the asymmetric potential-driven ion\ntransport induced by unilateral illumination, mediated by photogenerated electron–hole\nseparation of CNT/MoS heterojunction nanotubes, rather than to photothermal effects\n2\nor other nanofluidic mechanism.\nComment 12\nThe claim of “real-time operation” is not substantiated by explicit response-time\nmeasurements.\nResponse 12: We sincerely appreciate the insightful comments of the reviewer. The\nterm \"real-time operation\" has been deleted throughout the manuscript to prevent\npotential misunderstandings.\nPage 2, line 53-55. The sentences “Given these limitations, creating a bioinspired visual\nsystem within a single nanofluidic device that integrates photoreceptor-like optical\nsensing, photosynaptic processing, and polarity-switching for real-time operation is still\na major challenge” has been corrected to “Given these limitations, creating a\nbioinspired visual system within a single nanofluidic device that integrates\nphotoreceptor-like optical sensing, photosynaptic processing, and polarity-switching is\nstill a major challenge.\nPage 8, line 203-205. The sentences “This configuration enabled simultaneous (i)\nexternal bias voltage application and (ii) real-time monitoring of photoinduced ion\ncurrents through the CNT/MoS nanotube under pulsed illumination” has been\n2\ncorrected to “This configuration enabled simultaneous (i) external bias voltage\napplication and (ii) monitoring of photoinduced ion currents through the CNT/MoS\n2\nnanotube under pulsed illumination.\nPage 11, line 299-302. The sentences “These performance metrics enable real-time light\nsignal processing in the artificial retina, demonstrating robust performance in accurate\norientation recognition and fingerprint detection under variable incident light angles”\nhas been corrected to “These performance metrics enable light signal processing in the\nartificial nanofluidic iontronic device, demonstrating robust performance in accurate\norientation recognition and fingerprint detection under variable incident light angles.\nComment 13\nSeveral grammatical errors and typographical inconsistencies should be corrected.\nResponse 13: We sincerely thank the reviewer for their careful reading and valuable\nsuggestions. We have thoroughly revised the manuscript to address grammatical errors\nand typographical inconsistencies."}, {"round": 2, "reviewer_comments": {"reviewer_1": "After careful review, I judge that the authors have addressed all my concerns and improve their manuscript. It can be accepted for publication.", "reviewer_2": "I think the revised manuscript can be accepted without further revision.", "reviewer_3": "Authors have addressed the comments."}, "author_response": ""}]

Decision letter