Bioinspired flexible sensing-processing-visualizing integrated system towards tactile-visual signal recognition

Domain: neuroscience
Published: 2026-01-19
Editorial outcome: major_revision

Abstract
The rapid growth of flexible Internet of Things devices and multisensory human-computer interaction necessitates advanced systems capable of multimodal data sensing, processing, and feedback in an intelligent and energy-efficient manner. However, conventional architectures face limitations in energy inefficiency and interface mismatch due to the fragmented architecture of sensing, processing, and feedback units. Inspired by biological sensory systems, we introduce an MXene-based flexible dual-mode sensing-processing-visualizing integrated system into a single wearable device through a hierarchical MXene platform combining piezoelectric nanogenerators (mechanosensation), optoelectronic synapses (visual processing), and color-shifting quantum dot light-emitting diodes (optical feedback) with optimized interfaces. The wearable integrated system demonstrates tactile-visual signal recognition capabilities, biological self-protection behavior to adapt to environmental stimuli, dynamic trajectory recognition, and spatial positioning for motion recognition, which are highly desired in multisensory interaction. The bio-inspired material-architecture-function co-design strategy and integrated system promote the development of wearable neuromorphic hardware, edge computing, and intelligent human-machine interaction.

Introduction
The proliferation of flexible Internet of Things (IoT) devices and the emergence of multisensory interaction applications have heightened the demand for optical and mechanical data processing to meet the diverse needs across various user scenarios1,2. The acquisition of large volumes of data has further fueled the growing need for integrated edge computing solutions that can facilitate information perception, processing, visualization, and human-computer interaction. However, in traditional von Neumann architecture, the physical separation of multimodal sensing units and computing units presents significant challenges in data transmission efficiency, energy consumption, communication bandwidth, and flexibility3–5.

Highly evolved biological sensory systems enable humans to sense multimodal information and then process and respond to information efficiently and intelligently, which can be attributed to their highly-physical-integrated one-body architecture. Inspired by the biological sensory system, researchers have focused on emulating its functions to enhance the efficiency of information perception and processing. Notable advancements have been achieved in mimicking the fundamental nervous system functions by functionally integrating sensors6–8, neuromorphic devices9–12, actuators13–15, and peripheral circuits16,17. Integration of sensory memory processing devices has been partially achieved to reaching in-sensory/memory computing system relying on the serial connection of peripheral circuits7,18–22. Most existing approaches mainly rely on serial connections between distinct components for multi-mode sensory system, which is limited in complicated system design and flexibility of wearable devices due to device interface mismatch at both mechanical and electrical levels18,23.

Integration of tactile and visual sensory modalities and the corresponding processing and feedback systems—with matched mechanical and electrical interfaces between different functional units—remains a key challenge.

Living organisms have effectively addressed these challenges by utilizing shared flexible quasi-two dimensional (2D) cell membranes that not only allow for outstanding mechanical interface matching but also enable efficient ionic-electronic signal transfer and conduction, essential for coordinated sensory processing and feedback. In detail, the cell membranes—as a component-shared universal platform that enhances nutrient utilization and interface compatibility for both mechanical and electrical interfaces—serve multiple roles including tactile sensation via the inserted Piezo2, vision sensation through the inserted rhodopsin, conversion of the sensory stimuli into ion-electron signals, and efficient transportation of these signals for interacting and processing, thereby facilitating diverse physiological processes (Fig. 1a).

The working principle and structure of a quasi-2D multi-functional cell membrane-based multisensory perception-processing-feedback system provide valuable inspiration for designing fused material and architecture in the field of bio-inspired electronics to achieve multi-mode, wearable and interface-matching functions. Advances in 2D materials, particularly MXenes, may open new avenues for overcoming these challenges. Among them, Ti3C2Tx MXene—known for its high electrical conductivity (>25000 S cm-1), excellent mechanical flexibility (single-layer thickness of approximately 1 nm), excellent bendability (by sliding the MXene layers to disperse stress), rich and tunable physicochemical properties (by tailoring sizes and surface terminations), solution processability, and high transparency at thin film24–27—outperforms conventional materials (more detailed comparisons can be seen in Supplementary Table 1) and have attracted intensive interests and demonstrated varied applications28–32. Due to these merits, MXene is promising in constructing the bio-inspired integrated multisensory perception-processing-feedback system, yet unexplored.

Here, we present a bioinspired MXene-based flexible sensing-processing-visualizing integrated system (FSPVS) featuring mechanically and electronically matched interfaces towards wearable intelligent sensory systems (Fig. 1b). By tailoring the size and surface states, MXene acts multiple roles including a shared high-efficiency electrical conduction platform, tactile-sensation-enhanced material, visual sensation and synaptic material, and preferable charge-injection layer with minimized energy barrier, thereby achieving the conversion of the sensory stimuli into electron signals and their efficient transportation (Fig. 1b). Large-sized MXene (L-MXene) serves as a shared platform encompassing the following integrated unites: mechanosensation via medium-sized MXene (M-MXene) interfacial polarization-locking regulated piezoelectric nanogenerator (PENG); visual sensation and synaptic functionalities through oxidized small-sized MXene (OSMX)-based artificial optoelectronic synapse (AOS); optical feedback by integrated color-shifting quantum dot light-emitting diode (CS-QLED). Through the integration of these functions, the FSPVS demonstrates significant advancements in tactile-visual signal intelligence recognition and its applications in (1) bioinspired self-protection behaviors for environmental adaptation; (2) multimode image recognition task; (3) dynamic strike type and trajectory recognition via dual-mode input reservoir computing (RC) and high-precision spatial positioning for motion recognition—functionalities that are highly desired in the multisensory interaction scenarios. This work shows the potential of MXene in bioinspired flexible electronics and offers a promising solution for next-generation wearable intelligent devices, human-computer interaction, and multisensory interaction applications.

Results
Design of MXene-based flexible sensing-processing-visualizing integrated system

After millions of years of natural selection, biological organisms have developed highly matching interfaces between various tissues, optimizing compatibility in terms of material properties, and mechanical and electrical characteristics (Fig. 1a). (1) The cell membrane of biological cells is mainly composed of a quasi-2D phospholipid bilayer (thickness: 5-10 nm) and various transmembrane proteins inserted on it, which is a key structure for maintaining important functions such as cell morphology (mechanical stability) and signal transduction (neural electrical signals). (2) The mechanical properties (e.g., elastic modulus and tensile strength) of cell membranes in different tissues are similar, maintaining tissue structural and functional stability. (3) Neural conduction characteristics are consistent across different sensory perceptions. For example, tactile perception relies on Piezo2 channels that let cations flow into the cell upon pressure, triggering an action potential (Fig. 1a left)33. Visual perception uses rhodopsin; light-induced changes in retinaldehyde conformation alter membrane potential (Fig. 1a center)34. Neural transmission between synapses occurs when neurotransmitters bind to the postsynaptic membrane, causing a potential change (Fig. 1a, center and right). The consistency in material composition, mechanical properties, and ionic transport characteristics allows organisms to synthesize the necessary substances using minimal diversity during physiological processes, thereby significantly enhancing inter-tissue transport efficiency and information transmission.

Inspired by the cell membrane-based biological organisms, a material-architecture-function co-design strategy based on Ti3C2Tx MXene (2D material with a thickness of only a few atomic layers) is proposed for constructing a flexible sensing-processing-visualizing integrated system (Fig. 1b) with mechanical and electrical interface compatibility. By tailoring the size and surface states, MXene serves multiple functional roles: (1) a tactile-sensation-enhanced material in PENG through M-MXene with abundant surface −OH terminations that can form hydrogen bonds with the C−F bonds in poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) to improve piezoelectric phase (Fig. 1b left); (2) a visual sensation and synaptic material in AOS via OSMX photoresponse and defect-mediated charge trapping for synaptic conductance modulation (Fig. 1b center); (3) a preferable charge-injection layer in CS-QLED through L-MXene with engineered energy barriers (Fig. 1b right); and (4) a shared flexible high-efficiency electrical conduction platform in the FSPVS (preparation process and optical images are shown in Supplementary Fig. 1). During the system operation, the piezoelectric potential formed by polarized charges is transmitted through L-MXene to the AOS during the PENG compression process, thereby modulating its conductive state. Meanwhile, light stimulation generates a photocurrent in the AOS. The EPSCs triggered jointly by pressure and light stimulation are subsequently transmitted to the CS-QLED through L-MXene, inducing its luminescence. The use of homogeneous MXene-based materials in system integration not only simplifies the fabrication process but also enhances the mechanical flexibility and electrical contact of individual components (Supplementary Note 1 for a more detailed analysis of interface matching)35.

The morphology and phase characterization of these MXene are shown in Fig. 1c top from left to right (corresponding to Fig. 1b) and Supplementary Fig. 2. Fortunately, the above-mentioned MXene can be obtained during our designed single synthesis and post-processing flow (Fig. 1c bottom). Firstly, the Al-rich Ti3AlC2 MAX (MXene precursor) was etched using the “MILD” method to obtain multilayer-MXene with minimal defects. Subsequently, the multilayer-MXene was exfoliated using the mechanical force generated by vortex oscillation to obtain L-MXene and M-MXene, thereby maintaining the lateral size and minimizing in-plane defects. M-MXene rich in -OH terminations and large-lateral-sized L-MXene with minimal defects were further separated by centrifugal force to respectively match the device applications of PENG, AOS, and CS-QLED. In addition, to address both material utilization and AOS application requirements, the mechanically unexfoliable multilayer-MXene was first ultrasonically fragmented into S-MXene (Supplementary Fig. 3), then subsequently oxidized and surface-functionalized to create photoresponsive OSMX containing abundant defect sites (Supplementary Fig. 4). All the reaction products of MXene precursor can be efficiently utilized in the system, preferable for low-cost environmentally-friendly production.

MXene-dual-functional PENG

Self-powered MXene-dual-functional PENGs, similar to the tactile sensing unit of Piezo2, are fabricated by depositing M-MXene/P(VDF-TrFE) (MXP) on shared L-MXene conductive layer (Fig. 2a) with exceptional electrical conductivity and mechanical strength. Additionally, MXene, with abundant and tunable surface terminations, facilitates solution-processability, enabling uniform dispersion in various solvents, including water, polar and non-polar organic solvents (N,N-dimethylformamide, dimethyl sulfoxide, chloroform, etc.)36. These characteristics of MXene enable dual-functionalization in PVDF-based ferroelectrics: (1) flexible mechanically-interface-matched conductive channels via solution-processable fabrication, in contrast to conventional Cu/Al tapes (where interfacial adhesion deterioration upon repeated bending cycles causes performance degradation, Supplementary Figs. 5 and 6); (2) enhanced tactile sensing through electroactive phase engineering enabled by interfacial polarization locking, achieved via hydrogen-bond interactions between rich −OH in MXene surfaces (Supplementary Fig. 7) and the C−F dipoles in P(VDF-TrFE) polymer matrices.

Figures 2b and c illustrate the working mechanism of MXene-dual-functional PENG in initial and pressurized states, respectively. The interfacial polarization between the M-MXene and the P(VDF-TrFE) matrix results in a relatively uniform orientation of electric dipoles within the composite material (Fig. 2b). Upon compression, the self-polarization effect, coupled with the formation of deformed dipoles, induces the generation of polarized charges on the surface of the film. These charges are subsequently attracted to and accumulate on the charged surfaces, thereby establishing a piezoelectric potential (Fig. 2c). Consequently, external free charges migrate to the electrodes and accumulate to counterbalance the piezoelectric potential. The impact of MXene with varying flake sizes on the piezoelectric performance of PENG was initially examined (Fig. 2d). When M-MXene with a suitable flake size was used as an additive, the resulting MXene-dual-functional PENG demonstrated the highest output, which can be attributed to efficiently enhanced interface polarization and minimized leak current. Subsequently, the effect of M-MXene content on P(VDF-TrFE) was further investigated (Supplementary Fig. 8 and Supplementary Note 2). X-ray diffraction (XRD) and Fourier transform infrared (FT-IR) spectra analyses revealed that at an M-MXene loading of 0.5 wt% (the optimal sample is labeled MXP-0.5), the β-phase content reached its highest level, coinciding with the maximum dielectric constant. The piezoelectric output performance was further evaluated, including measurements of open-circuit voltage and short-circuit current (Supplementary Fig. 9 and Fig. 2e). Both metrics indicated that the output performance of MXene-dual-functional PENG peaked at an MXene content of 0.5 wt%, consistent with the aforementioned characterization results. The enhancement in piezoelectric output can be attributed to the increased β-phase content due to interface polarization locking. Fig. 2f and Supplementary Fig. 10 show that the short-circuit current of MXP-0.5 increases linearly with applied pressure ranging from 1.5 to 13 N, demonstrating a linear relationship between output current and pressure (piezoelectric sensitivity line slope: 0.419 ± 0.015 μA N-1), which allows for precise signal calibration and accurate pressure estimation.

The dual-functional roles of M-MXene as an additive and L-MXene as an electrode in the PENG improved interface contact, significantly enhancing charge collection efficiency (Fig. 2g and Supplementary Table 2). This led to a substantial increase in current density under pressure stimulation, which was notably higher than that observed in piezoelectric films using other electrodes. The prepared MXene-dual-functional PENG exhibits excellent mechanical interface matching and stable output voltage over more than 9900 cycles (as confirmed by the long-term fatigue test in Supplementary Fig. 11), showcasing superior mechanical durability and reliability in practical applications. The results of the COMSOL Multiphysics finite element analysis simulation further supported the experimental data (Fig. 2h).

Synaptic properties of OSMX-based AOS

Visual sensation and synaptic functionalities are obtained by integrating OSMX-based AOS on a shared flexible and transparent L-MXene thin conductive layer (Fig. 3a). To achieve the light perception capability and the charge trapping ability required for AOS while improving the efficiency of the MXene synthesis process, we implemented a two-step modification procedure: (1) S-MXene obtained via ultrasonic fragmentation of waste multilayer-MXene was controllably oxidized into TiO2-based OSMX (Supplementary Fig. 12), simultaneously enhancing light responsivity and defect density for charge trapping towards synaptic property (Supplementary Fig. 13); (2) Surface functionalization with n-dodecylphosphonic acid (DDPA) improved colloidal stability in organic solvents (verified DDPA successful grafting via the emergence of P 2p peaks in Fig. 3b and Supplementary Fig. 14). Transmission electron microscope (TEM) analysis of solution-processed ZnO nanocrystals (NCs) in Supplementary Fig. 15 confirms their uniform NCs morphology with a narrow size distribution, and atomic force microscopy (AFM) characterization (Supplementary Fig. 16) reveals a smooth surface morphology (RMS = 2.57 nm), ensuring uniform layer formation in the AOS device architecture.

Furthermore, the exceptional electrical conductivity, robust mechanical strength, and superior flexibility of L-MXene render it a promising candidate to replace commercial indium tin oxide (ITO). This substitution could potentially address several critical limitations of ITO, including its inherent brittleness, high manufacturing costs, and insufficient electrical conductivity on flexible substrates such as polyethylene terephthalate (PET)37. We investigated the effects of L-MXene concentration on transparency and conductivity by spin-coating solutions with varying concentrations onto PET substrates. In Supplementary Fig. 17, at a concentration of 7 mg mL-1, the film exhibited 80.4% transparency and a sheet resistance of ~64 Ω □-1. Meanwhile, we evaluated the bending resistance performance of the L-MXene/PET and ITO/PET electrodes. The results show that L-MXene/PET demonstrates stable electrical resistance (Supplementary Fig. 18) and superior surface integrity (Supplementary Fig. 19) after 5000 bending cycles compared to ITO/PET, thereby enhancing its potential in flexible electronic devices.

To evaluate the synaptic plasticity of the device, we conducted electrical tests on the AOS. The input voltage pulse, device conductance, and response current were used as analogs for the synaptic pre-spike, synaptic strength, and synaptic post-current, respectively. The analog resistance switching behavior was characterized by measuring the I-V curve, which demonstrated good resistance switching capability (Supplementary Fig. 20). A continuous voltage scanning mode was employed to apply a positive voltage (0 V → +1.2 V → 0 V) to the L-MXene electrode, resulting in a gradual increase in current (Fig. 3c). Conversely, applying a negative voltage (0 V → −1.2 V → 0 V) led to a continuous decrease in current (Supplementary Fig. 21). The progressive change in current induced by electrical signals mimic the alteration of synaptic weight (detailed calculation formula is provided in Supplementary Note 3) triggered by neural impulses. Supplementary Fig. 22 illustrates that 16 electrical pulse patterns, ranging from (0000) to (1111), correspond to 16 distinct response states, highlighting the potential of this approach in RC applications.

To elucidate the electrical mechanism of AOS, we performed a detailed fitting analysis of the I-V curve (Supplementary Fig. 23). The results demonstrated that the behavior was consistent with the space charge limited current (SCLC) theory38. Figure 3d illustrates the modulation process of device conductance (synaptic weight) via voltage pulse stimulation. By applying fixed-width voltage pulses with varying amplitudes, the device conductance increased with the amplitude of positive pulses. Supplementary Figure 24 further demonstrates that both increasing pulse amplitude and stimulation number lead to an enhancement in current. Precise regulation of the device conductivity can be achieved by deliberately tuning the pulse amplitude and number. As a result, the piezoelectric pulse signal generated by PENG can be utilized to modulate the conductive state of the AOS, thereby enhancing its conductance level. During the testing process, the PENG was directly connected to the AOS. PENG can directly generate electrical pulses with varying amplitudes and pulses through its piezoelectric effect, without using an external pulse power source. When both the number and frequency of the generated electrical pulses increase (Fig. 3e and f, detailed EPSC gain analysis results are presented in Supplementary Note 4), the AOS demonstrates an enhanced current response, mimicking the heightened reactivity of biological systems under external stimulation. From the perspective of the resistance switching mechanism, an increase in the number of pulses leads to further accumulation of charges captured at defect sites, thereby enhancing the electrical conductivity of the device. Moreover, when the pulse frequency increases, charge trapped during the previous pulse cannot be fully released before subsequent pulses are applied, resulting in a progressive charge accumulation over time. We also evaluated the energy consumption of the OSMX-based AOS under single-pulse stimulation. The results reveal a low energy consumption of 1.094 pJ (Supplementary Fig. 25), indicating high energy efficiency among the existing two-terminal MXene-based neuromorphic devices (Supplementary Table 3).

In the human visual system, the intensity and duration of optical signal irradiation are crucial parameters for image information. The detection and recognition of invisible ultraviolet (UV) light can effectively avoid visible light interference, which is beneficial in artificial visual systems39. The photocurrent response behavior of the AOS under different UV (365 nm) light durations, pulse numbers, and light intensities is presented in Fig. 3g and h, and Supplementary Fig. 26a, respectively, demonstrating the regulatory effect of optical stimulation parameters on EPSC. The alteration of illumination conditions not only directly determines the response amplitude but also effectively modulates the subsequent relaxation process. When a negative bias is applied, AOS shows a similar trend to that under a positive bias at different illumination times (Supplementary Fig. 26b). Figure 3i further illustrates the EPSC when the AOS is subjected to continuous light pulse sequences at varying frequencies. As the spike frequency increases from 0.2 Hz to 0.67 Hz, the cumulative effect becomes increasingly significant, leading to a pronounced EPSC response. To investigate the potential of photo-induced paired-pulse facilitation (PPF) behavior, two consecutive light pulses with a density of 13.2 mW cm-2 and a duration of 1 s each were applied to the AOS (Fig. 3j). As shown in the inset of Fig. 3j, the response current to the second light pulse was significantly greater than that of the first pulse. The time constants τ1 and τ2 for the photo-induced PPF were fitted to 0.241 s and 3.059 s, respectively (R2 = 0.998, calculation and fitting methods are described in Supplementary Note 5). Notably, τ2 is approximately one order of magnitude larger than τ1, which aligns well with the characteristics observed in biological synapses40. Furthermore, we fabricated AOS devices with varying OSMX-to-PMMA mass ratios to modulate their photoelectric properties (Supplementary Fig. 27). We also conducted systematic tests under different bending radii to investigate the mechanical flexibility of the AOS. When the bending radius ranged from 10 to 3 mm, the electrical pulse conductance modulation performance (Supplementary Fig. 28a) and the optical pulse response current (Supplementary Fig. 28b) of the AOS did not show significant changes. In addition, at the bending radius of 3 mm, the electrical conductance modulation performance and the optical pulse response maintain stability after 1000 bending cycles (Supplementary Fig. 29), further demonstrating the bending resistance of the device. We further analyzed the overall energy consumption of the PENG and AOS system (Supplementary Fig. 30). The result demonstrated that the PENG and AOS system can operate in a self-powered mode (0 J) under pressure stimulation via PENG, and achieve a single-pulse energy consumption as low as 1.437 fJ under optical stimulation. Compared to existing PENG and memristor integrated systems, our PENG and AOS integrated system exhibits distinct advantages in terms of device integration, multimodal sensing capabilities, and versatility in functional applications (Supplementary Table 4).

To evaluate its capability to incorporates sequential optical inputs, we subjected the memristor to sequences of optical pulses. Each sequence comprised four pulses, with “1” and “0” denoting light pulses of 13.2 and 0 mW cm-2 intensity, respectively, and each pulse lasting for 1 s. In Fig. 3k, single-mode 4-bit optical pulse input varying from (0000) to (1111) yielded 16 distinct states. Moreover, the AOS exhibits multimodal perception capabilities, as demonstrated by processing a 4-bit binary input stream under the “EEOO” stimulation mode, in which the first two pulses were designated as electrical stimuli and the following two as optical stimuli (Supplementary Fig. 31). This dual-mode response facilitates the implementation of a mixed-input RC system. The findings indicate that OSMX-based AOS exhibits rich biomimetic dynamics in response to diverse electrical and optical stimuli, positioning them as a promising component for neuromorphic computing systems.

The nonlinear behavior of the AOS under light can be attributed to the following mechanism: light illumination generates a large number of electron-hole pairs in ZnO NCs and OSMX, thereby enhancing the conductivity of the device. Under an applied voltage, these photogenerated carriers migrate toward the electrodes and may become trapped at the interface between ZnO NCs and OSMX. Upon removal of the light source, the trapped carriers are gradually released, leading to short-term memory effects. This is consistent with the results characterized by Kelvin probe force microscopy (KPFM) (Fig. 3l). Under illumination, the surface potential of the film is higher than in its initial state, indicating charge accumulation at the interface. Meanwhile, the role of OSMX in AOS was investigated through in-situ KPFM characterization (Supplementary Fig. 32). The results demonstrated that OSMX not only significantly enhanced the response to UV light but also facilitated the charge trapping processes at the ZnO NCs/OSMX interface.

Visualized dual-mode sensory for environment-adaptive application

Sensory feedback functionality is obtained by integrating CS-QLED on the shared L-MXene conductive layer (Fig. 4a). Utilizing CS-QLED as feedback units leverages their high color purity and tunability in the visible spectrum, along with their solution-processability on diverse substrates, thereby facilitating the development of flexible electronic skin41,42. Different wavelengths of light emission can be achieved by mixing red and green quantum dots (R/G QDs). In addition to its excellent electrical conductivity, good transparency, and outstanding bending resistance, L-MXene exhibits a work function of approximately 4.9 eV, which closely matches that of the PEDOT:PSS charge injection layer (approximately 5.1 eV). This alignment facilitates highly efficient charge injection and enables optimal electrical interface matching (Fig. 4b). As the voltage of the CS-QLED increases (2.8~7.4 V, Fig. 4c), the green emission intensity at ~543 nm gradually increases, while the red emission intensity at ~632 nm gradually decreases. Supplementary Fig. 33 presents the current density–voltage–luminance characteristic curves of the CS-QLED. To investigate the mechanical flexibility of the CS-QLED, we performed bending tests under varying radii and bending cycles (Supplementary Fig. 34). The results demonstrate that the current density–voltage characteristics remain nearly constant as the bending radius decreases from 10 mm to 3 mm (Supplementary Fig. 34a). Furthermore, after 1000 cycles of dynamic bending at a radius of 3 mm (Supplementary Fig. 34b), the current density–voltage characteristics curve does not show marked degradation, indicating preferable mechanical flexibility and bending tolerance.

Inspired by the unique sensory and neural characteristics of octopuses, we developed a flexible bionic tactile-visual-optical feedback neural network system to harness the potential of PENG, AOS, and CS-QLED (Supplementary Fig. 35). The signal acquisition and processing workflow of the system is illustrated in Fig. 4d. Since CS-QLED requires a substantial driving voltage to induce color changes from red to green, a current amplification module was incorporated to fulfill its operational demands. The designed circuit can function adequately when receiving only visual signal input without tactile input. The decision logic of the FSPVS operates as follows: the PENG detects pressure signals, while the AOS receives optical inputs. The combined pressure and light stimuli acts synergistically on the AOS to trigger the generation of EPSCs. These EPSCs are then captured by a conversion unit in the peripheral circuit and transformed into voltage with varying amplitudes, which effectively drive the CS-QLED to produce visual output and enable color switching. Furthermore, the light emitted by the CS-QLED is detected by an avalanche photodiode connected to an oscilloscope.

The skin of some organisms possesses unique additional functions that enable them to perform physiological activities efficiently. For example, the skin of an octopus can rapidly change color in response to external stimuli (such as pressure or light stimuli), allowing efficient disguise (Fig. 4e)43. Figure 4f illustrates the schematic of our system, which is designed to mimic the environment-adaptive dual-mode perception and visualization capabilities of the octopus. When light and piezoelectric stimuli are simultaneously applied, photon excitation and piezoelectric regulation synergistically enhance conductivity. Consequently, as the resistance of AOS decreases, an enlarged current is generated and subsequently converted into an enlarged driving voltage, which effectively induces pronounced color changes in the CS-QLED. As shown in Fig. 4g, in the absence of additional light stimulation, increasing pressure can induce a transition (threshold 1 of light emitting intensity) in the emission of the CS-QLED from red light (state 1: non-defense mode) to yellow light (state 2: environmental monitoring mode). Similarly, without external pressure stimulation, an increase in light intensity can also trigger the same emission transition. When both light and pressure stimuli are applied simultaneously, with the light input being dominant (20 mW·cm-2 light stimulus and 1.5 N pressure stimulus), the CS-QLED emits yellow light that exceeds threshold 1 (state 2). Conversely, when the pressure stimulus dominates (5 N pressure stimulus and 6 mW·cm-2 light stimulus), the CS-QLED initially emits yellow light above threshold 1 and gradually transitions to green light surpassing threshold 2 (state 3: alarm mode) as the number of stimuli increases. When moderate pressure and light intensity are concurrently applied, the CS-QLED emits yellow light exceeding threshold 1 (state 2). When both high pressure and high light intensity are simultaneously applied, the CS-QLED exceeds threshold 2 (state 3) and emits green light. The luminescence color of the CS-QLED changes in response to the intensity of stimuli, reflecting the severity of the harmful stimulus, and this variation can be intuitively displayed through visible light-emitting signals (Fig. 4h).

Multi-mode information perception and recognition

To exhibit the advantages of our integrated system, we implemented multimodal image recognition tasks. When identifying contaminated images (Fig. 5a), the left side—being covered by opaque materials—can only be perceived through tactile signals (electrical signals), while the right side remains perceivable via visual signals (optical signals). Therefore, a multimodal mixed-input “EEOO” mode (comprising two electrical pulses followed by two light pulses) is employed. This mode demonstrates distinct functional spatial partitioning: the “EE” segment selectively responds to the left half of the image (with the “OO” segment inactive), whereas the “OO” segments specifically respond to the right half (with the “EE” segment inactive). The original 28×28 pixel images were restructured into a 196×4 pixel format. Subsequently, the processed images were read using a mixed-input “EEOO” mode. A total of 196 input channels were fed into corresponding memory units for processing. Supplementary Fig. 36a illustrates the distribution of weights in the fully connected layer. After the training process, the synaptic weights change from a random to a normal distribution, indicating that the neural network has been trained effectively. The confusion matrix (Fig. 5b) and the achieved accuracy of 91% (Supplementary Fig. 36b) show the effectiveness of the mixed-input strategy in multimodal recognition tasks.

In addition to static image recognition, we further evaluated potential of the system in dynamic information perception and recognition by using a simplified badminton scenario as a example (Fig. 5c). In this application scenario where the CS-QLED is only required to emitting light rather than achieving color-changing function (that is, the driving voltage is small), we have designed a highly integrated FSVPS (based on L-MXene-integrated PENG, AOS, and CS-QLED); it can operate effectively without an additional amplification module, merely by providing a turn-on voltage (Supplementary Fig. 37). This wearable system can achieve several key functions including multi-modal perception, integrated perception-processing architecture, trajectory recognition, and accurate spatial positioning (Supplementary Fig. 38), which is highly desired for multisensory interaction applications. Specifically, User A and User B each wore an FSPVS on their wrists. Even when located in different spaces, these two users were able to engage in online interaction (Fig. 5c). For instance, when User A swung the racket to strike the virtual badminton shuttlecock, the FSPVS could accurately detect four fundamental strike types: forehand heavy shot (F-HS), forehand light shot (F-LS), backhand heavy shot (B-HS), and backhand light shot (B-LS). Simultaneously, the FSPVS identified the racket swing trajectories in eight fundamentally different directions. In addition, the induced CS-QLED light-emitting signal was captured by two photodetectors (PD) to determine the positional data. These strike types, trajectory recognition, and spatial positioning enabled User B to respond appropriately and promptly.

During the badminton scenario, joint movements (representing piezoelectric signals) and racket swings (representing optical signals) occur simultaneously and are separately input into the AOS working in electrical and optical modes (serving as RC units), respectively (Fig. 5d and Supplementary Fig. 39). For strike types recognition, one-dimensional piezoelectric data (2000) was converted into a four-row pixel format (4×500) through cropping and binarization. Subsequently, the strike type data are fed into the AOS reservoir in electrical mode for computation. Finally, a single-layer fully connected neural network is employed to perform the strike type recognition task. For racket swing trajectory recognition, optical pulses are essential for information processing. To integrate the spatial and temporal information from consecutive frames into a compact representation, we divide each frame image into four consecutive subframes (t0, t1, t2, and t3), representing four distinct moments of the racket swing. When the racket (yellow pixels) is detected, an optical pulse is applied to the AOS corresponding to each pixel. When no racket movement (blue pixels) is detected, no optical pulse is applied. For each pixel, its temporal evolution is compressed into a 4-bit sequences (t), composed of a 16-bit binary vector. The sequence of light pulses (from (0000) to (1111)) is fed as input to the physical reservoir of the memristor, causing changes in the EPSC value of the device. This process embeds the temporal evolution of each pixel into the conductance of individual memristors, thereby characterizing the temporal features of object motion.

The confusion matrix of the target labels and output labels in the test set was verified (dataset can be seen in Supplementary Fig. 40), and the results indicated that the system could effectively identify different strike types (Fig. 5e). In addition, the internal repository of the sensor using custom datasets (Supplementary Fig. 41) accurately recognized racket motion including eight different directions (Fig. 5f and g, and Supplementary Fig. 42). During training, the readout function (F) value for target inputs continuously increases, while the F values for non-target inputs decrease. Fig. 5h shows the initial and final weight distributions before/after training. Cross-entropy loss and accuracy for racket swing trajectory recognition are shown in Fig. 5i.

The generated CS-QLED light-emitting signal can be used for position detection. Two PDs are placed at appropriate positions to ensure they can receive the light information emitted from any point within the plane (Fig. 5j). The light intensity data acquired from two PDs are utilized as feature vectors. These vectors are subsequently processed into input features (R1, θ1, R2, θ2) and output label coordinates (X, Y). Figure 5k illustrates the comparison between the estimated position coordinates and the actual positions. The results demonstrate that the average positioning error for each point in the validation set is 0.0048, indicating the high precision of the proposed method (Fig. 5l). These results underscore the significant potential of the FSPVS with multi-parameter information parallel output in multimodal recognition for human-computer interaction.

Discussion

In summary, a bioinspired flexible sensing-processing-visualizing integrated system has been introduced via a material-architecture-function co-design strategy. Through functionally tailored MXene, the flexible system unifies mechanosensation through a piezoelectric nanogenerator, visual sensation and processing through an artificial optoelectronic synapse, and optical feedback via color-shifting quantum dot light-emitting diodes in a shared flexible platform. Relying on this system, the intelligent integration of tactile and visual signal processing demonstrates its capability in environmental adaptation by emulating bioinspired self-protection behaviors that enable responsive actions to environmental stimuli. This system achieves a classification accuracy of 91% in multimodal image recognition tasks based on the Fashion-MNIST dataset; furthermore, it is capable of identifying dynamic strike types and trajectories, as well as providing high-precision spatial positioning—functionalities that are desired in the multisensory interaction scenarios. The ability to process tactile and visual data efficiently highlight the potential of MXenes in bioinspired function-integrated flexible electronics. Our work demonstrates a promising pathway toward intelligent wearable devices and multisensory human-computer interaction.

Methods
Materials
Poly(3,4-ethylenedioxythiophene)/poly(styr-enesulfonate) (PEDOT:PSS, Clevios AI4083) and poly(9,9-dioctylfluorene-alt-N-(4-sec-butylphenyl)-diphenylamine) (TFB) was purchased from Xi’an Polymer Light Technology Co., Ltd. Red and green CdSe/ZnSe QDs were procured from Xingshuo Nano Technology Co., Ltd. N, N-dimethylformamide (DMF, ACS), KOH (AR), methanol (AR), Poly(methyl methacrylate) (PMMA, Mw ∼ 350000), n-butanol (AR) and LiF (AR) were purchased from Aladdin. Chloroform (AR), HCl acid (AR, 36-38%), acetone (AR) and absolute ethanol (99.9%) were purchased from Sinopharm Chemical Reagent Co., Ltd. Al-rich Ti3AlC2 MAX (400 mesh, Foshan Xinxi Technology Co., Ltd.), zinc acetate dehydrated (99%, Sigma-Aldrich), n-dodecylphosphonic acid (DDPA, 98.5%, Macklin), P(VDF-TrFE) (80:20, Solvay). All materials were used as received without any purification.

Preparation of S/M/L-Ti3C2Tx MXene aqueous suspension
The MXene suspension is prepared using a minimally intensive layer delamination (MILD) method44. To obtain L-MXene and M-MXene, we centrifuged the MXene dispersion at 240g for 15 min to remove unetched MAX. The liquid was subsequently centrifuged at 3210g for 30 min. The resulting supernatant was identified as M-MXene, while the precipitate was designated as L-MXene. To prepare defect-rich S-MXene for subsequent oxidation treatment, multi-layered MXene was ultrasonicated in a water bath for 2 h. The resulting dispersion was then centrifuged at 1300g for 1 h to isolate the supernatant.

Preparation of Ti3C2Tx MXene DMF suspension
To replace the solvent in the MXene aqueous dispersion, a solvent substitution method was used. The MXene aqueous dispersion was centrifuged at 10610g for 30 min to obtain the MXene precipitate. After that, the precipitate was dispersed into 20 mL of DMF. The operation was repeated 3 times to prepare MXene DMF dispersion.

Preparation of OSMX chloroform suspension
To make OSMX well dispersed in organic solutions, we grafted it with organofunctional groups. First, DDPA was added to chloroform and stirred until completely dissolved. At the same time, the pH of the OSMX aqueous solution (1 mg mL-1) was adjusted to below 2.5 with HCl. Afterward, the aqueous OSMX solution was added to the DDPA chloroform solution and stirred vigorously for 3 h. At the end of the reaction, the stirring was stopped, and the reaction was allowed to stand and wait for stratification, and the lower organic phase solution was taken. To remove the unreacted DDPA, the solution was centrifuged at 6790g and washed 3 times with chloroform. Finally, the precipitate was dispersed in an appropriate amount of chloroform to obtain the OSMX dispersion. To enhance the smoothness of the spin-coated film, a small amount of PMMA was added to the OSMX dispersion.

Preparation of ZnO NCs suspension
The synthesis of ZnO NCs was optimized based on previous reports and dispersed in n-butanol45. Typically, 0.59 g of zinc acetate dehydrated was dissolved in 25 mL of methanol and heated at 65 °C in a water bath. Then, 13 mL of 4.6 mmol KOH methanol solution was dropped into the zinc acetate solution with continuous stirring for 2.5 h. The precipitated ZnO NCs were centrifuged, washed with methanol three times, and then dispersed in n-butanol solution.

Device fabrication
Fabrication of MXene-dual-functional PENG. Piezoelectric films were prepared using a drop-casting method. Firstly, P(VDF-TrFE) powder was added to MXene DMF dispersion containing different mass fractions of MXene and stirred for 12 h to ensure its complete dissolution to obtain the MXene/P(VDF-TrFE)-n (MXP-n, n represents the mass fraction of MXene relative to P(VDF-TrFE)) solution. Afterward, the MXP-n solution was dropped on a clean and flat glass plate placed in a blast oven at 70 °C and heated for 7 h to allow complete drying. Then, the MXP-n films were annealed in a vacuum oven at 130 °C for 1 h to improve the crystallinity. The MXene electrode was spin-coated to the surface of the MXP-n films on both sides. The electrodes were used to lead the wires using silver adhesive and then covered with Kapton tape to complete the encapsulation.

Fabrication of AOS
The PET substrate was sequentially cleaned in deionized water, acetone, and absolute ethanol for 10 min each, followed by drying with flowing nitrogen. To enhance surface wettability, the PET substrate underwent plasma treatment. L-MXene was initially deposited onto the PET substrate and allowed to rest for 3 mins. It was then subjected to spin-coating at 2000 rpm for 60 s, followed by an additional spin-coating process at 6000 rpm for 90 s. Subsequently, the L-MXene/PET was annealed at 100 °C in a tube furnace for 12 h to eliminate adsorbed impurities and improve electrical conductivity. The ZnO NCs solution was spin-coated at 2000 rpm for 40 s and heated at 80 °C for 10 min. The OSMX/PMMA solution (mass ratio of 4:6) was spin-coated at 4000 rpm for 40 s and heated at 80 °C for 10 min. After that, another layer of ZnO NCs was spin-coated with the same parameters as before. Finally, the Al electrode with a thickness of 50 nm was thermally evaporated through a mask plate.

Fabrication of CS-QLED
The preparation of L-MXene/PET was carried out as described above. PEDOT:PSS and TFB layers were deposited onto the substrate via spin-coating at 3000 rpm for 40 s, after which the samples were annealed at 100 °C for 20 min. The red and green QDs are mixed at a mass ratio of 1:15. For the QDs and ZnO layers, the spin-coating parameters were 2000 rpm for 60 s, followed by annealing at 80 °C for 10 min.

Fabrication of FSPVS
To fabricate the FSPVS, a thin patterned PET mask was first attached to a PET substrate (3×4.5 cm2), and its hydrophilicity was enhanced via oxygen plasma treatment. Subsequently, an L-MXene film was spin-coated on the substrate and annealed at 100 °C for 12 h. Following this, the MXP solution was drop-cast onto the PENG region of the PET substrate, dried at 70 °C for 7 h, and vacuum-annealed at 100 °C for 2 h to improve crystallinity. Then, L-MXene was deposited onto the upper surface of MXP film using a similar procedure. Next, the PENG and AOS regions were masked, and PEDOT:PSS, TFB, R/G QDs, and ZnO layers were sequentially spin-coated onto the CS-QLED region. Afterward, the CS-QLED region was re-masked, and ZnO, OSMX/PMMA, and ZnO layers were sequentially deposited. Finally, the top electrode was thermally evaporated, and the entire device was encapsulated. To ensure an efficient electrical conduction connection between the devices, MXene can be further sprayed in the conduction pathway.

Device characterization
The sample morphologies were characterized by SEM (Hitachi SU-8600), TEM (JEOL JEM-2100F), AFM, and KPFM (Bruker Multimode 8). Physical phases of samples were characterized by XRD (Bruker D8 advance, Cu Kα radiation, λ = 1.5406 Å), FT-IR (Thermo Fisher Nicolet IS50), and ultraviolet-visible-near infrared absorption spectroscopy (Hitachi UH5700). The surface elemental distribution of the samples was characterized using XPS (Thermo Scientific ESCALAB Xi+) instrumentation provided by eceshi (www.eceshi.com). The output signals of the PENG were measured by an oscilloscope (Siglent SDS1202X-C) and an electrometer (Keithley 6514). The optical and electrical properties of the AOS were characterized using a source meter (Keithley 2400 and Tonghui 1992) by connecting to both ends. The AOS is stimulated by 365 nm UV light sources, and the distance of the UV light source was fixed to ensure consistent light intensity across all generated pulses. The transient electroluminescence characteristics of the CS-QLED were measured using an avalanche photodiode and an oscilloscope (Rigol DS1202Z-E). The luminance and chromaticity of the integrated FSPVS were measured using a specialized radiance colorimeter (Everfine, SRC-600). The overall FSPVS relies on an external power source to ensure the reliable operation of all components. Informed consent was obtained from the human research participant (Zhen Wang). The sheet resistance was measured by the digital four-probe tester (Suzhou Jingge Electronic Co., Ltd., ST-2258C).

Network training
For multimodal Fashion-MNIST recognition, the original 28×28 pixel images were binarized and restructured into a 196×4 pixel format. Subsequently, the processed images were read by using a mixed-input “EEOO” mode. The covered yellow pixels on the left half could only be sensed via electrical pulses from tactile signals, whereas the purple pixels represent their absence; all pixels on the right half indicate the absence of an electrical pulse. The yellow pixels on the right half correspond to light pulse inputs. A total of 196 input channels were fed into corresponding memory units for processing. Then, a single-layer fully connected neural network is employed to perform the multimodal Fashion-MNIST recognition task.

For strike types recognition: the experimental participants swung the racket with varying types, and raw data was collected. The one-dimensional strike-type data (2000) was converted into a four-row pixel format (4×500) through cropping and binarization. Subsequently, the strike type data are fed into the AOS reservoir in electrical mode for computation. The AOS reservoir in the electrical mode consists of 500 storage devices that serve as reservoir nodes. Then, a single-layer fully connected neural network with 500 input nodes was designed to perform the strike types recognition task. For racket swing trajectory recognition: image sequences of each racket trajectory were captured, and the frame difference method combined with binarization processing was applied to generate image sequences. Each input image (56×56) was divided into four consecutive pixel rows (4×3136), spanning from the leftmost to the rightmost column in a top-down manner. Subsequently, the swing trajectory information was fed into the AOS reservoir in optical mode for computation. The AOS reservoir in the optical mode consists of 3136 storage devices that serve as reservoir nodes. Their conductance values are programmed and calibrated based on experimentally measured data from individual devices. Then, a single-layer fully connected neural network with 3136 input nodes was designed to perform the racket swing trajectory recognition task. The readout layer was implemented in software, and the model was trained using the Adam optimizer’s gradient descent algorithm (with an initial learning rate of 0.001) to minimize classification cross-entropy loss. Finally, the data were divided into training and test sets to evaluate the performance of the model.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper presents a bioinspired, MXene-based flexible sensing-processing-visualizing integrated system (FSPVS). By tailoring the size and surface states of MXene, it exhibits various mechanical and electrical properties, enabling its use in multiple functions. For example, a piezoelectric generator with mechanosensation was developed using medium-sized MXene (M-MXene) to interface with P(VDF-TrFE). An artificial optical synaptic (AOS) device with visual sensation and synaptic functionalities was fabricated using oxidized small-sized MXene (OSMX). Additionally, large-sized MXene (L-MXene) serves as electrodes in a nanogenerator, AOS device, and as a charge-injection layer in a quantum dot light-emitting diode (CS-QLED) with an optical feedback function. The FSPVS demonstrates significant advancements in tactile-visual signal intelligence recognition and its applications in environmental adaptation and interaction, such as in metaverse scenarios. For reconsideration for publication, some improvements are recommended.\n1. As argued in the Introduction, the motivation of this paper is the integration of tactile and visual sensory modalities, along with the corresponding processing and feedback systems, through seamless mechanical and electrical interfaces between different functional units. However, this paper presents no results demonstrating the seamless mechanical and electrical interfaces between these functional units.\n2. The paper develops a sensing-processing-visualizing integrated system. However, it does not present any real optical images, schematic diagrams, or electronic circuit diagrams of the integrated system to demonstrate that the different functional units were fabricated on the same substrate . Based on the description in the main text and the device fabrication process in the Methods section, it seems to be that individual devices were fabricated on separate substrates. In my opinion, a fully integrated system should have the tactile and visual sensory units, processing unit, and feedback unit fabricated on the same platform (same substrate), with complete circuit lines connecting each unit.\n3. Large-sized MXene (L-MXene) serves as electrodes in a nanogenerator, an AOS device, and as a charge-injection layer in a quantum dot light-emitting diode (CS-QLED). Therefore, the conductivity (or sheet resistance), and transparency of MXene at various thicknesses should be provided.\n4. The TEM image in Supplementary Fig. 13 only show the lattice spacing. It does not present the surface morphology of ZnO nanocrystals. Therefore, this result cannot be used to support arguments about the surface morphology or the uniformity of ZnO nanocrystals, as discussed in lines 234–236.\n5. In Fig. 3d, how can you calculate the synaptic weight? The pulse width and duration should also be specified. Additionally, the output current of the device under electrical stimulation with various amplitudes and numbers of pulses should be included.\n6. Lines 258–259 state, “Furthermore, we utilized the piezoelectric signals generated by PENG to enhance the performance of AOS”. Can you explain how the piezoelectric signals generated by PENG enhance the performance of AOS?\n7. How does the synaptic weight of AOS change when stimulated by various pulse frequencies and different numbers of piezoelectric signals generated by PENG? An optical image of the integrated PENG with AOS should be provided.\n8. In the case of the AOS device, the wavelength of the light pulse used for stimulation should be specified. Additionally, the sensing mechanism and charge trapping mechanism of the OSMX/PMMA layer in AOS should be discussed in more detail to clarify the role of OSMX in the AOS device. The mass ratio of OSMX:PMMA can affect to output current and synaptic weight of AOS device under electrical and optical stimulation, so the various mass ratio of OSMX:PMMA should be studied.\n9. Fig. 4d is a schematic of the signal acquisition, processing, and feedback units in an FSPVS. However, this schematic only presents three individual devices (PENG, AOS, and CS-QLEDs) without any connecting lines for signal acquisition, processing, or feedback. To demonstrate that the FSPVS is a fully integrated system, an optical image, schematic diagram, and circuit diagram of the FSPVS should be provided.\n10. To my knowledge, chameleon skin can rapidly change color in response to temperature or light stimulation, but not due to pressure or light stimulation, as mentioned by the authors in lines 324–325. Therefore, using the chameleon as an example to demonstrate biological self-protection behavior (example chameleon) in response to pressure and light stimuli does not make sense. A different biological system should be provided for this demonstration.\n11. The sensing mechanism of AOS under simultaneous pressure stimulation from PENG and direct light stimulation should be provided to clarify the results in Fig. 4g.\n12. For dynamic information perception and recognition, the experimental setup and measurement process should be clearly presented. Honestly, I found this section difficult to follow, so I am providing my comments based on my understanding.\n- Based on my understanding of this section, three device systems were attached to the wrist to conduct this experiment. Is that correct?\n- For classification and recognition of hitting types: The PENG was used to generate signals during the hitting process, and these signals were processed through a memristor array. Was the PENG directly integrated with the memristor array? How did you read the output signal during measurement—was it done wirelessly or through a connection line? If a connection line was used, how did you mitigate noise signals generated by human motion?\n- For recognizing racket swing trajectories: The AOS device was used in combination with an RC system. How was the light pulse stimulated in the AOS device during human movement? Since the distance between the AOS device and the light source can vary during movement, this could cause output current variations. How did you address this issue? Additionally, as mentioned earlier, you did not specify the wavelength of the light source, but based on the materials used in the AOS, I assume UV light was used for stimulation. If this is the case, UV light can be harmful to human eyes. How did you conduct the experiment with UV light stimulation? Also, how did you read the output signal during measurement?", "reviewer_2": "Wang et al. utilized MXene two-dimensional materials as substrates to fabricate a system seamlessly integrating piezoelectric nanogenerators for pressure sensing, artificial optoelectronic synapses for visual sensation and processing, and color-shifting quantum dot light-emitting diodes for optical feedback. The tactile-visual signal recognition capabilities of the system were applied for environmental adaptation and dynamic trajectory identification.\nOverall, the manuscript proposes an innovative concept of a tactile-visual multimodal sensing and recognition system based on MXene two-dimensional materials to mimic biological multi-sensory mechanisms. However, the current state of the manuscript does not adequately support the proposed multimodal integration concept. Moreover, there are several critical issues regarding device fabrication and system presentation that need to be addressed thoroughly. Specific comments are as follows:\n1. The authors think MXene films to cell membranes for multimodal information transmission. However, the schematic of the sensing-feedback system indicates that the MXene films connecting individual sensing modules are not clearly depicted. Authors should clarify the signal transmission mechanism between modules and explicitly describe the role of MXene films. If multimodal sensing and processing are achievable, detailed experimental process diagrams and images of integrated multimodal devices or arrays in hardware must be provided.\n2. The manuscript lacks a clear discussion of the advantages of MXene films compared to other conductive films. A comparative analysis highlighting the specific benefits of MXene materials is required.\n3. The presented multimodal sensing measurements appear limited to data shown in Figure 4g. Additional data under more finely tuned and continuously varying pressure and optical intensity stimuli are necessary to thoroughly demonstrate the multimodal sensing capability of the system.\n4. Several questions regarding the multimodal sensing claims remain unclear:\no Does the system function adequately when only visual signals are present without tactile inputs?\no How is multimodal sensory information encoded and decoded within the system?\no How is the sensing-feedback system powered? Is it directly powered by the PENG module, or is an additional driving circuit required? A clear explanation should be provided.\no What are the specific operational ranges of the PENG and AOS modules?\n5. In the dynamic trajectory and motion recognition application (badminton recognition system), the claimed multimodal sensory functionality is not clearly demonstrated. The visual and pressure signals appear completely isolated, with each modality individually implemented. This configuration fails to clearly exhibit the advantages of multimodal integration. Furthermore, the authors should clarify how pressure information is integrated into the reservoir computing task.\n6. Although the authors state that “the system demonstrated its tactile-visual signal recognition applications for environment adaption and interaction in metaverse scenarios.” the manuscript does not describe how metaverse scenarios are realized. Simple dynamic trajectory recognition alone is insufficient to justify such claims.\n7. Simulation experiments lack essential details. Specifically, the datasets and neural network architecture used for classification and racket trajectory prediction tasks are not provided."}, "author_response": "Point-by-point responses to the reviewers’ comments\n(Comments are in italics, responses in blue, and manuscript corrections in yellow.)\nThe authors would like to thank the reviewers for their helpful comments to improve\nthe article’s quality. The manuscript has been corrected accordingly, and point-by-point\nresponses to the reviewers’ comments are as follows:\nReviewer #1\nComments:\nThe paper presents a bioinspired, MXene-based flexible sensing-processing-visualizing\nintegrated system (FSPVS). By tailoring the size and surface states of MXene, it exhibits\nvarious mechanical and electrical properties, enabling its use in multiple functions. For\nexample, a piezoelectric generator with mechanosensation was developed using\nmedium-sized MXene (M-MXene) to interface with P(VDF-TrFE). An artificial optical\nsynaptic (AOS) device with visual sensation and synaptic functionalities was fabricated\nusing oxidized small-sized MXene (OSMX). Additionally, large-sized MXene (L-MXene)\nserves as electrodes in a nanogenerator, AOS device, and as a charge-injection layer\nin a quantum dot light-emitting diode (CS-QLED) with an optical feedback function.\nThe FSPVS demonstrates significant advancements in tactile-visual signal intelligence\nrecognition and its applications in environmental adaptation and interaction, such as\nin metaverse scenarios. For reconsideration for publication, some improvements are\nrecommended.\nResponse: We sincerely appreciate your recognition of the innovation in our work and\nyour constructive feedback. As you highlighted, this biomimetic MXene-based FSPVS\nsystem exhibits remarkable capabilities in tactile-visual signal recognition and\nenvironmental adaptation, particularly in metaverse applications. We highly value your\ninsightful comments and have thoroughly revised the manuscript based on your\nsuggestions. The key improvements are as follows: (1) detailed supplementation of the\nsystem’s manufacturing process, physical images, and circuit diagrams; (2) analysis of\nthe variation trends of sheet resistance and transmittance of L-MXene films with respect\nto thickness; (3) additional TEM images of ZnO NCs and AFM images of the ZnO NCs\nfilms; (4) additional testing data for AOS devices; (5) clarification of the role of OSMX\nin AOS devices; (6) refinement of the biomimetic model.\nOur point-by-point responses to the reviewer’s comments are as follows:\n1. As argued in the Introduction, the motivation of this paper is the integration of tactile\nand visual sensory modalities, along with the corresponding processing and feedback\nsystems, through seamless mechanical and electrical interfaces between different\nfunctional units. However, this paper presents no results demonstrating the seamless\nmechanical and electrical interfaces between these functional units.\nResponse: We greatly appreciate your valuable comments. By regulating the size and\nsurface state of MXene materials, they can meet the diverse requirements of electrode\nmaterials and functional layers in multifunction-integrated devices with homogeneous\nmaterials/interfaces, thereby achieving coordinated matching of the entire system at\nboth mechanical and electrical properties.\nAs a response, we have (1) added Supplementary Fig. S1 in the Supplementary\nInformation and corresponding description: “By tailoring the size and surface states,\nMXene serves multiple functional roles: (1) a tactile-sensation-enhanced material in\nPENG through M-MXene with abundant surface −OH terminations that can form\nhydrogen bonds with the C−F bonds in poly(vinylidene fluoride-co-trifluoroethylene)\n(P(VDF-TrFE)) to improve piezoelectric phase (Fig. 1b left); (2) a visual sensation and\nsynaptic material in AOS via OSMX photoresponse and defect-mediated charge\ntrapping for synaptic conductance modulation (Fig. 1b center); (3) a preferable charge-\ninjection layer in CS-QLED through L-MXene with engineered energy barriers (Fig.\n1b right); and (4) a shared flexible high-efficiency electrical conduction platform in the\nFSPVS (preparation process and optical images are shown in Supplementary Fig. S1).”\nin the manuscript.\nSupplementary Fig. S1. b Optical image of FSPVS (scale bar: 1 cm).\n(2) added Supplementary Fig. S6 in the Supplementary Information and\ncorresponding description: “These characteristics of MXene enable dual-\nfunctionalization in PVDF-based ferroelectrics: (1) flexible mechanically-interface-\nmatched conductive channels via solution-processable fabrication, in contrast to\nconventional Cu/Al tapes (where interfacial adhesion deterioration upon repeated\nbending cycles causes performance degradation, Supplementary Figs. S5 and S6); (2)\nenhanced tactile sensing through electroactive phase engineering enabled by interfacial\npolarization locking, achieved via hydrogen-bond interactions between rich −OH in\nMXene surfaces (Supplementary Fig. S7) and the C−F dipoles in P(VDF-TrFE)\npolymer matrices.” in the manuscript.\nSupplementary Fig. S6. SEM images of L-MXene on P(VDF-TrFE) films: a pristine\nand b after 5000 bending cycles at a bending radius of 3 mm (scale bars: 20 μm).\nSupplementary Fig. S6 presents SEM images of L-MXene on P(VDF-TrFE) films\nbefore and after 5000 bending cycles. Compared with the Cu tape that deforms during\nthe bending process (Supplementary Fig. S5), the nearly unchanged morphology of L-\nMXene after 5000 bending cycles indicates a stable interfacial contact between L-\nMXene and the P(VDF-TrFE) film. This enduring interface is attributed to hydrogen\nbonding formed between the –OH terminations on the L-MXene surface and P(VDF-\nTrFE) molecules, further confirming their superior mechanical compatibility. Such a\ndurable interfacial connection facilitates enhanced stability and reliability in\npiezoelectric sensing performance and output.\n(3) added mechanical interface match corresponding description: “The results show\nthat L-MXene/PET demonstrates stable electrical resistance (Supplementary Fig. S18)\nand superior surface integrity (Supplementary Fig. S19) after 5000 bending cycles\ncompared to ITO/PET, thereby enhancing its potential in flexible electronic devices.”\nin the manuscript.\n(4) added electrical interface match corresponding description: “In addition to its\nexcellent electrical conductivity, good transparency, and outstanding bending resistance,\nL-MXene exhibits a work function of approximately 4.9 eV, which closely matches that\nof the PEDOT:PSS charge injection layer (approximately 5.1 eV). This alignment\nfacilitates highly efficient charge injection and enables optimal electrical interface\nmatching (Fig. 4b)” in the manuscript.\n(5) added Supplementary Table S1 (along with detailed description) in the\nSupplementary Information and corresponding description: “Advances in 2D materials,\nparticularly MXenes, may open new avenues for overcoming these challenges. Among\nthem, Ti C T MXene—known for its high electrical conductivity (>25000 S cm-1),\n3 2 x\nexcellent mechanical flexibility (single-layer thickness of approximately 1 nm),\nexcellent bendability (by sliding the MXene layers to disperse stress), rich and tunable\nphysicochemical properties (by tailoring sizes and surface terminations), solution\nprocessability, and high transparency at thin film—outperforms conventional materials\n(more detailed comparisons can be seen in Supplementary Table S1) and have attracted\nintensive interests and demonstrated varied applications.” in the manuscript.\nSupplementary Table S1. Comparison of various transparent conductive films for\nflexible electronics.\nElectronic Work\nConductive Mechanical Solution Multifuncti\nconductivity function Ref.\nfilms properties processability onalization\n(S cm−1) (eV)\nITO 103–104 4.7–4.9 Poor None No 1-3\nPEDOT:PSS 10–1000 5.0–5.2 Excellent Excellent No 4-6\nAg NWs 103–104 4.3-4.8 Excellent Moderate No 7-9\nSWCNTs 104–105 4.5–4.8 Excellent Moderate No 10-12\nGraphene 104–105 4.3–4.9 Excellent Moderate No 13-15\nTi C T\n3 2 x >2.5104 1.6–6.2 Excellent Excellent Yes 16-25\nMXene\nAmong flexible electronics, Ti C T MXene offers numerous distinct advantages\n3 2 x\nover other conductive films. (i) MXene nanosheets exhibit excellent electrical\nconductivity, with a single-layer conductivity exceeding 25000 S cm–1 (ACS Nano 2021,\n15, 6420-6429; Nanoscale 2021, 13, 3572-3580). Additionally, MXene demonstrates\noutstanding mechanical flexibility, attributed to its ultrathin nanosheet structure with a\nsingle-layer thickness of approximately 1 nm, which substantially reduces bending\nstiffness. Concurrently, the existence of surface functional groups improves tolerance\nto lattice distortion, while the robust covalent or metallic bonding between transition\nmetals and carbon atoms within the layers imparts sufficient mechanical strength to\neffectively prevent fracture during deformation. Moreover, its surface terminations (–\nOH/–O) enhance chemical bonding with flexible substrates such as PET, effectively\npreventing interfacial delamination and improving mechanical interface matching. (ii)\nThe highly tunable physical and chemical properties of MXene enable precise\nmodulation of its work function within a range of 1.6 to 6.2 eV through the modification\nof surface terminal groups (Light Sci. Appl. 2021, 10, 177; Matter 2023, 6, 800-837).\nThis tunability facilitates efficient carrier injection and transport, thereby enhancing\nelectrical interface matching. (iii) MXene demonstrates excellent solution-\nprocessability, enabling stable dispersion in both water and organic solvents (Sci. Adv.\n2022, 8, eabl5299; Nat. Chem. 2023, 15, 1722-1729). It can be directly fabricated into\nhighly conductive films using techniques such as spray coating, spin coating, dip\ncoating, doctor blading, inkjet printing, and screen printing—without requiring high-\ntemperature annealing (processing temperature < 120 °C), suitable for use with flexible\nsubstrates. (iv) Owing to the abundant and tunable surface states and composition of\nMXene, surface modification holds promise for enabling multifunctional applications,\nthereby fulfilling the diverse requirements of various optoelectronic devices.\nIn summary, by regulating the size and surface state of MXene materials, they can\nmeet the diverse requirements of electrode materials and functional layers in\nmultifunction-integrated devices with homologous materials/interfaces, thereby\nachieving coordinated matching of the entire system at both mechanical and electrical\nproperties.\nMechanical interface matching at both the inter-device and intra-device levels: (i)\nMXene enables inter-device integration through interconnected nanosheets, which not\nonly simplifies the fabrication process but also facilitates the formation of more uniform\nand coherent interfaces. Furthermore, homogeneous materials minimize mechanical\nproperty mismatches, thereby enhancing interfacial compatibility. (ii) For intra-device\nlevel, ultrathin MXene nanosheets exhibit exceptional mechanical flexibility, electrical\nconductivity, and optical transparency. These properties allow them to replace Cu tape\nelectrodes prone to interfacial contact failure in PENG, as well as brittle ITO electrodes\nin flexible optoelectronic devices, significantly improving mechanical durability during\nbending operations. Specifically, (i) In the integrated system, L-MXene was employed\nas the electrode material to connect PENG, AOS, and CS-QLED, thereby minimizing\nmechanical interfacial mismatch caused by the use of different fabrication processes.\n(ii) For PENG, we use L-MXene to replace the traditional Cu tape electrode. Compared\nwith the Cu tape that deforms during the bending process (Supplementary Fig. S5), the\n–OH terminal groups on the surface of ultrathin L-MXene are capable of forming\nhydrogen bonds with the P(VDF-TrFE) film, thereby establishing a tighter and more\nstable interface connection (Supplementary Fig. S6). This demonstrates that L-MXene\nexhibits excellent mechanical compatibility with the P(VDF-TrFE) film, which in turn\nensures superior cycling stability (Supplementary Fig. S11). Moreover, introducing M-\nMXene, which has the same composition as L-MXene, into PENG helps enhance the\ntensile strength of the P(VDF-TrFE) film. (iii) For AOS and CS-QLED, L-MXene is\nalso used as the electrode material. Compared with ITO, L-MXene has higher electrical\nconductivity and mechanical strength, and can maintain good electrical conductivity\neven after 5000 bending cycles (Supplementary Fig. S18). Additionally, the OSMX\nmaterial derived from S-MXene is utilized as a functional layer in AOS, where the use\nof homologous materials helps mitigate mechanical mismatch effects.\nElectrical interface matching at both the inter-device and intra-device levels: (i)\nMXene enables inter-device integration through interconnected nanosheets, which not\nonly simplifies the fabrication process but also promotes the formation of more uniform\nand coherent interfaces, thereby helping to reduce interface mismatch during the\nconnection of electrodes fabricated via different processes. (ii) At the intra-device level,\nthe use of homogeneous materials contributes to minimizing work function differences.\nSpecifically, (i) In the integrated system, L-MXene is used to connect PENG, AOS, and\nCS-QLED devices. Compared with traditional metal electrodes (such as ITO or other\nmetals), the interface barrier between L-MXene nanosheets layers is lower, effectively\nreducing energy loss during charge transport (Science 2020, 369, 979-983). (ii) In\nPENG, the tight hydrogen bond interface connection formed between L-MXene and\nthe P(VDF-TrFE) film significantly improves the charge collection efficiency and\ngenerates a current output higher than that reported in the literature. (iii) In AOS, the\nabundant hydrophilic groups on the surface of L-MXene allow ZnO nanocrystals (NCs)\nto be directly deposited through various methods such as spin coating and atomic layer\ndeposition without additional surface treatment steps (Adv. Mater. 2018, 30, 1706656).\n(iv) In CS-QLED, the tunable work function of L-MXene enables it to form a good\ninterface contact with the hole transport layer PEDOT:PSS, significantly reducing the\nhole injection barrier (Supplementary Table S1).\n2. The paper develops a sensing-processing-visualizing integrated system. However, it\ndoes not present any real optical images, schematic diagrams, or electronic circuit\ndiagrams of the integrated system to demonstrate that the different functional units were\nfabricated on the same substrate. Based on the description in the main text and the\ndevice fabrication process in the Methods section, it seems to be that individual devices\nwere fabricated on separate substrates. In my opinion, a fully integrated system should\nhave the tactile and visual sensory units, processing unit, and feedback unit fabricated\non the same platform (same substrate), with complete circuit lines connecting each unit.\nResponse: Thanks for the reviewer’s valuable comments. We could not agree more with\nyour perspective that a fully integrated system should have the tactile and visual sensory\nunits, processing unit, and feedback unit fabricated on the same platform (same\nsubstrate), with complete circuit lines connecting each unit. To address the requirements\nof diverse application scenarios, we have developed two circuits for implementing the\ntactile-visual-optical feedback system. For applications mimicking the adaptive color\nchanges of octopuses in response to external stimuli, the designed circuit operates via\na signal acquisition and processing workflow (Supplementary Fig. S29). Specifically,\nthe integrated dual-modal sensing device enables the PENG to detect pressure signals\nand the AOS to sense light signals, while simultaneously processing the combined\ninformation of pressure and light stimuli to generate EPSCs. Subsequently, these EPSCs\nare captured by the neuromorphic conversion unit in the peripheral circuit and\nconverted into voltage pulses with varying amplitudes, thereby driving the CS-QLED\nto produce visual output. This circuit requires an external power supply to support the\ncolor-changing functionality of the CS-QLED. Although we employed external\namplification circuits, L-MXene still serves as a crucial component connecting PENG\nand AOS, as illustrated in the Supplementary Fig. S29a, holding promise for realizing\nfully MXene-integrated flexible circuits in the future.\nAs a response, we have (1) added the preparation process in the Methods section:\n“Fabrication of FSPVS\nTo fabricate the FSPVS, a thin patterned PET mask was first attached to a PET substrate\n(3×4.5 cm2), and its hydrophilicity was enhanced via oxygen plasma treatment.\nSubsequently, an L-MXene film was spin-coated on the substrate and annealed at\n100 °C for 12 h. Following this, the MXP solution was drop-cast onto the PENG region\nof the PET substrate, dried at 70 °C for 7 h, and vacuum-annealed at 100 °C for 2 h to\nimprove crystallinity. Then, L-MXene was deposited onto the upper surface of MXP\nfilm using a similar procedure. Next, the PENG and AOS regions were masked, and\nPEDOT:PSS, TFB, R/G QDs, and ZnO layers were sequentially spin-coated onto the\nCS-QLED region. Afterward, the CS-QLED region was re-masked, and ZnO,\nOSMX/PMMA, and ZnO layers were sequentially deposited. Finally, the top electrode\nwas thermally evaporated, and the entire device was encapsulated. To ensure an\nefficient electrical conduction connection between the devices, MXene can be further\nsprayed in the conduction pathway.” in the manuscript.\n(2) added Supplementary Fig. S1 in the Supplementary Information and\ncorresponding description: “and (4) a shared flexible high-efficiency electrical\nconduction platform in the FSPVS (preparation process and optical images are shown\nin Supplementary Fig. S1).” in the manuscript.\nSupplementary Fig. S1. a Schematic diagram of the FSPVS preparation process. b\nOptical image of the FSPVS (scale bar: 1 cm).\n(3) added Supplementary Fig. S29 in the Supplementary Information and added\ncorresponding description: “Inspired by the unique sensory and neural characteristics\nof octopuses, we developed a flexible bionic tactile-visual-optical feedback neural\nnetwork system to harness the potential of PENG, AOS, and CS-QLED (Supplementary\nFig. S29). The signal acquisition and processing workflow of the system is illustrated\nin Fig. 4d. Since CS-QLED requires a substantial driving voltage to induce color\nchanges from red to green, a current amplification module was incorporated to fulfill\nits operational demands. The designed circuit can function adequately when receiving\nonly visual signal input without tactile input. Specifically, PENG detects pressure\nsignals, and AOS senses light signals and processes the combined information of\npressure and light stimuli to generate EPSCs. Subsequently, these EPSCs are captured\nby the conversion unit in the peripheral circuit and transformed into voltage pulses of\nvarying amplitudes, which drive the CS-QLED for visual output with shifting colors\nfrom red to green. Furthermore, the light emitted by the CS-QLED is detected by an\navalanche photodiode connected to an oscilloscope.” in the manuscript.\nSupplementary Fig. S29. a Schematic diagram of the circuit inspired by the octopus.\nb Optical image of the circuit inspired by octopus (scale bar: 2 cm). c Optical images\nof the flexible PCB during the bending process (right), and a magnified view of the\nflexible PCB (left, scale bar: 0.5 cm).\n(4) added Supplementary Fig. S31 in the Supplementary Information and added\ncorresponding description: “In this application scenario where the CS-QLED is only\nrequired to emitting light rather than achieving color-changing function (that is, the\ndriving voltage is small), we have designed a highly integrated FSVPS (based on L-\nMXene-integrated PENG, AOS, and CS-QLED); it can operate effectively without an\nadditional amplification module, merely by providing a turn-on voltage\n(Supplementary Fig. S31).” in the manuscript.\nSupplementary Fig. S31. a Circuit diagram of FSPVS. b Optical images of the circuit\nof FSPVS attached to the wrist. Although the PENG can directly drive the CS-QLED\nto emit light, its range of brightness variation is relatively limited. To enhance the\nbrightness modulation of the CS-QLED, this system only requires a power supply to\nprovide a bias close to the turn-on voltage for the CS-QLED, enabling the piezoelectric\npulses from the PENG and light stimuli to cooperatively modulate the resistance value\nof the AOS, thus guiding more current to the CS-QLED and effectively regulating its\nbrightness for detection by the avalanche diode.\n3. Large-sized MXene (L-MXene) serves as electrodes in a nanogenerator, an AOS\ndevice, and as a charge-injection layer in a quantum dot light-emitting diode (CS-\nQLED). Therefore, the conductivity (or sheet resistance), and transparency of MXene\nat various thicknesses should be provided.\nResponse: Thank you very much for your important comment. We have systematically\ncharacterized the thickness-dependent conductivity (sheet resistance) and transparency\nof L-MXene films by controlling coating thickness through spin-coating dispersion\nconcentrations. Specifically, as demonstrated in Supplementary Fig. S17, the film spin-\ncoated with 7 mg mL-1 L-MXene dispersion exhibits a sheet resistance of 64.4 Ω sq-1\nand 80.4% transparency (at 550 nm). Although the transparency of the L-MXene film\nwas slightly inferior to that of commercial ITO, it exhibited significantly superior\nflexibility, maintaining approximately 6% resistance change after 5000 bending cycles\nat a bending radius of 3 mm (Supplementary Fig. S18), thereby meeting the essential\nrequirements for flexible optoelectronic devices as outlined in our application scenarios.\nAs a response, we have added Supplementary Fig. S17 in the Supplementary\nInformation and corresponding description: “We investigated the effects of L-MXene\nconcentration on transparency and conductivity by spin-coating solutions with varying\nconcentrations onto PET substrates. In Supplementary Fig. S17, at a concentration of 7\nmg mL-1, the film exhibited 80.4% transparency and a sheet resistance of ~64 Ω □-1.\nMeanwhile, we evaluated the bending resistance performance of the L-MXene/PET and\nITO/PET electrodes. The results show that L-MXene/PET demonstrates stable\nelectrical resistance (Supplementary Fig. S18) and superior surface integrity\n(Supplementary Fig. S19) after 5000 bending cycles compared to ITO/PET, thereby\nenhancing its potential in flexible electronic devices.” in the manuscript.\nSupplementary Fig. S17. a Sheet resistance and b transmittance at 550 nm of L-\nMXene films as a function of concentration (ranging from 4 to 10 mg mL-1).\n4. The TEM image in Supplementary Fig. 13 only show the lattice spacing. It does not\npresent the surface morphology of ZnO nanocrystals. Therefore, this result cannot be\nused to support arguments about the surface morphology or the uniformity of ZnO\nnanocrystals, as discussed in lines 234–236.\nResponse: We greatly appreciate your valuable comments. To comprehensively address\nthe concerns regarding surface morphology characterization, we conducted AFM\nanalysis on the ZnO NCs films (Supplementary Fig. S16). The measured surface\nroughness (RMS = 2.57 nm) quantitatively demonstrates the excellent uniformity and\nsmoothness of the deposited films. Furthermore, we have supplemented the TEM\ncharacterization in Supplementary Fig. S15 with lower magnification to specifically\nresolve the uniformity of nanocrystals, confirming their consistent size distribution that\ncollectively contributes to the uniform film formation. This multi-technique approach\n(AFM for film morphology and TEM for nanocrystal structure) provides\ncomplementary evidence supporting our conclusions in lines 234-236.\nAs a response, we have added Supplementary Fig. S15 and S16 in the Supplementary\nInformation, and added the following sentence: “Transmission electron microscope\n(TEM) analysis of solution-processed ZnO nanocrystals (NCs) in Supplementary Fig.\nS15 confirms their uniform NC morphology with a narrow size distribution, and atomic\nforce microscopy (AFM) characterization (Supplementary Fig. S16) reveals a smooth\nsurface morphology (RMS=2.57 nm), ensuring uniform layer formation in the AOS\ndevice architecture.” in the manuscript.\nSupplementary Fig. S15. TEM and HRTEM image of ZnO NCs.\nSupplementary Fig. S16. AFM image of ZnO NCs film (scale bar: 500 nm).\n5. In Fig. 3d, how can you calculate the synaptic weight? The pulse width and duration\nshould also be specified. Additionally, the output current of the device under electrical\nstimulation with various amplitudes and numbers of pulses should be included.\nResponse: We greatly appreciate your valuable comments. In our study, the synaptic\nweight (conductance, G) was calculated as G = I /V (Nat. Electron. 2023, 6, 949-\nread read\n962), where I is the immediate current response measured at 0.5 V read voltage after\nread\neach programming pulse (pulse width: 0.37s, interval: 0.37s). As shown in the updated\nFig. 3d (inset), we now explicitly provide the temporal parameters: positive pulses\n(1.60~1.90V), negative pulses (–0.30 V), all with fixed 0.37 s duration. The output\ncurrent of the device under electrical stimulation with various amplitudes (1.60~1.90V)\nand numbers of pulses (5~20) has been included as Supplementary Fig. S23,\ndemonstrating nonlinear conductance accumulation matching biological synaptic\nintegration.\nAs a response, we have (1) revised Fig. 3d and corresponding description: “Fig. 3d\nillustrates the modulation process of device conductance (synaptic weight) via voltage\npulse stimulation. By applying fixed-width voltage pulses with varying amplitudes, the\ndevice conductance increased with the amplitude of positive pulses. Supplementary Fig.\nS24 further demonstrates that both increasing pulse amplitude and stimulation number\nlead to an enhancement in current. Precise regulation of the device conductivity can be\nachieved by deliberately tuning the pulse amplitude and number.” in the manuscript.\nFig. 3. Characterization of synaptic properties of OSMX-based AOS. d Continuous\nconductance modulation by 15 positive electrical pulses with gradually increasing\namplitudes (1.60~1.90 V) and 15 negative electrical pulses at a fixed amplitude (–0.30\nV), with both pulse width and interval set to 0.37 s.\n(2) added Supplementary Fig. S24 and Note S2 in the Supplementary Information,\nas follows:\nSupplementary Fig. S24. a Continuous current modulation by 15 positive electrical\npulses with gradually increasing amplitudes (1.60~1.90 V) and 15 negative electrical\npulses at a fixed amplitude (–0.30 V), with both pulse width and interval set to 0.37 s.\nb Continuous current modulation by various positive electrical pulses at a fixed\namplitude (1.20 V), with both pulse width and interval set to 0.37 s.\nSupplementary Note S2. The calculation methods of the synaptic weight.\nThe synaptic weight (conductance, G) was calculated as follow:\nI\nread\nG =\nV\nread\nwhere I is the immediate current response measured at 0.5 V read voltage (V )\nread read\nafter each programming pulse (pulse width: 0.37s, interval: 0.37s).\n6. Lines 258–259 state, “Furthermore, we utilized the piezoelectric signals generated\nby PENG to enhance the performance of AOS”. Can you explain how the piezoelectric\nsignals generated by PENG enhance the performance of AOS?\nResponse: We sincerely appreciate your valuable comments and reminders. In fact, we\nutilized the piezoelectric signal generated by PENG to modulate the conductive state of\nAOS (rather than enhancing the performance of AOS), thereby improving its\nconductance level.\nAs a response, we have revised the corresponding expression as follows: “As a result,\nthe piezoelectric pulse signal generated by PENG can be utilized to modulate the\nconductive state of the AOS device, thereby enhancing its conductance level.” in the\nmanuscript.\n7. How does the synaptic weight of AOS change when stimulated by various pulse\nfrequencies and different numbers of piezoelectric signals generated by PENG? An\noptical image of the integrated PENG with AOS should be provided.\nResponse: We greatly appreciate your valuable comments. In biological neural\nnetworks, synapses are the structures that connect two neurons and mediate signal\ntransmission between them. As illustrated in Fig. 1a, a synapse comprises three main\ncomponents: the presynaptic membrane, synaptic cleft, and postsynaptic membrane.\nWhen an excitation signal reaches the presynaptic membrane from the presynaptic\nneuron, synaptic vesicles move toward and fuse with the presynaptic membrane,\nreleasing neurotransmitters into the synaptic cleft. These neurotransmitters then diffuse\nto the postsynaptic membrane, where they bind to specific receptors, altering its ion\npermeability and thereby transmitting the signal to the postsynaptic neuron. According\nto Hebbian learning theory, co-activated neurons strengthen their synaptic\nconnections—a phenomenon known as synaptic plasticity, which serves as the\nbiological foundation for learning and memory. The structure and function of synapses\ncan dynamically adapt through experience and learning, enabling neural networks to\nrespond effectively to novel tasks and environments.\nSimilarly, artificial synaptic devices play a pivotal role in artificial neural networks.\nAnalog memristors such as our AOS, due to their two-terminal architecture and\nnonlinear transmission characteristics resembling those of biological synapses, have\nemerged as promising candidates for emulating synaptic behavior. In biological\nsynapses, external stimuli trigger action potentials at the presynaptic terminal, inducing\nthe release and migration of neurotransmitters from the presynaptic to the postsynaptic\nmembrane. This process generates an excitatory postsynaptic current (EPSC), which\nforms the basis of neuronal signal transmission. The amplitude and duration of the\nEPSC determine its influence on the activity of the postsynaptic neuron. Under\nappropriate conditions, these transient changes can accumulate and reach the threshold\nrequired to initiate an action potential in the postsynaptic neuron, thus propagating the\nsignal to downstream neurons. Once the stimulus is removed, neurotransmitters in the\npostsynaptic membrane are gradually cleared, causing the EPSC to return to baseline.\nThis dynamic recovery process is essential for synaptic information storage, processing,\nand transmission.\nAs shown in Supplementary Fig. S24a, electrical pulses with varying amplitudes\nbut a fixed pulse width of 0.37 s were applied to the AOS device, while a readout\nvoltage of 0.5 V was used to monitor the resulting current changes. The experimental\nresults demonstrated that increasing the pulse amplitude led to a gradual rise in the peak\nvalue of the device’s response current. Furthermore, when the pulse amplitude was held\nconstant, applying a greater number of pulses also resulted in a progressive increase in\nthe response current peak, indicating that the current enhancement is dependent on both\npulse amplitude and pulse count. From the perspective of resistive switching\nmechanisms, when the interval between pulses is short, charges trapped at defect sites\nduring the first pulse are not fully released before subsequent pulses, resulting in charge\naccumulation over time.\nPENG can directly generate such electrical pulses with varying amplitudes and\npulses through its piezoelectric effect, without using an external pulse power source.\nWhen PENG is employed to stimulate AOS, the generated electrical pulses can induce\nan EPSC response and tune the synaptic weight of AOS, which is similar to the way\ninduced by using an external pulse power source. This self-driven mechanism\neliminates the requirement for an external read voltage and thereby significantly\nreduces the system’s overall energy consumption. Furthermore, the conductive state of\nthe device can be reliably assessed based on the peak amplitude of the EPSC. This\nmethod was also applied to the performance evaluation of other self-powered tactile\nsensors and neuromorphic devices connected in series (Sci. Adv. 2025, 11, eadr5337;\nNat. Commun. 2023, 14, 2648). Similarly, piezoelectric signals can be treated as\nelectrical pulses. When the number of pulses was increased from 5 to 15 (Fig. 3e), the\nEPSC gain (defined as the ratio of the EPSC amplitude of the nth pulse to that of the\nfirst pulse, A /A ) increased from 141.3% to 166.2%. When the pulse frequency was\nn 1\nincreased from 8.6 Hz to 14.1 Hz (Fig. 3f), the EPSC gain (defined as the ratio of the\nEPSC amplitude of the 10th pulse to that of the 1st pulse, A /A ) increased from 111.2%\n10 1\nto 152.8%. This enhancement arises from increased trapping of charges under higher\npulse frequencies or greater pulse numbers, ultimately boosting conductance of the\ndevice. From the perspective of the resistance switching mechanism, an increase in the\nnumber of pulses leads to further accumulation of charges captured at defect sites,\nthereby enhancing the electrical conductivity of the device. Moreover, when the pulse\nfrequency increases, charges trapped during the previous pulse cannot be fully released\nbefore subsequent pulses are applied, resulting in a progressive charge accumulation\nover time.\nAs a response, we have (1) added Supplementary Note S3 in the Supplementary\nInformation and added the following sentence: “PENG can directly generate electrical\npulses with varying amplitudes and pulses through its piezoelectric effect, without\nusing an external pulse power source. When both the number and frequency of the\ngenerated electrical pulses increase (Figs. 3e and 3f, detailed EPSC gain analysis results\nare presented in Supplementary Note S3), the AOS device demonstrates an enhanced\ncurrent response, mimicking the heightened reactivity of biological systems under\nexternal stimulation. From the perspective of the resistance switching mechanism, an\nincrease in the number of pulses leads to further accumulation of charges captured at\ndefect sites, thereby enhancing the electrical conductivity of the device. Moreover,\nwhen the pulse frequency increases, charges trapped during the previous pulse cannot\nbe fully released before subsequent pulses are applied, resulting in a progressive charge\naccumulation over time.” in the manuscript.\nFig. 3. Characterization of synaptic properties of OSMX-based AOS. EPSC of the\nAOS device triggered by PENG at different e pulse numbers and f frequencies.\nSupplementary Note S3\nThe analysis methods for the EPSC gain stimulated by PENG (corresponding to Fig.\n3e and 3f). When the number of pulses was increased from 5 to 15 (Fig. 3e), the EPSC\ngain (defined as the ratio of the EPSC amplitude of the nth pulse to that of the 1st pulse,\nA /A ) increased from 141.3% to 166.2%. When the pulse frequency was increased\nn 1\nfrom 8.6 Hz to 14.1 Hz (Fig. 3f), the EPSC gain (defined as the ratio of the EPSC\namplitude of the 10th pulse to that of the 1st pulse, A /A ) increased from 111.2% to\n10 1\n152.8%.\n(2) added Supplementary Fig. S1 in the Supplementary Information and\ncorresponding description: “and (4) a shared flexible high-efficiency electrical\nconduction platform in the FSPVS (preparation process and optical images are shown\nin Supplementary Fig. S1).” in the manuscript.\nSupplementary Fig. S1. b Optical image of FSPVS (scale bar: 1 cm).\n8. In the case of the AOS device, the wavelength of the light pulse used for stimulation\nshould be specified. Additionally, the sensing mechanism and charge trapping\nmechanism of the OSMX/PMMA layer in AOS should be discussed in more detail to\nclarify the role of OSMX in the AOS device. The mass ratio of OSMX:PMMA can affect\nto output current and synaptic weight of AOS device under electrical and optical\nstimulation, so the various mass ratio of OSMX:PMMA should be studied.\nResponse: We greatly appreciate your valuable comments. The wavelength of the light\npulses used to stimulate the AOS device is fixed at 365 nm, and this parameter has been\nadded to the manuscript. To elucidate the role of OSMX in AOS devices more\ncomprehensively, we conducted in-situ KPFM characterization on various thin films\n(Supplementary Fig. S28). Upon UV illumination, the ZnO NCs film exhibited no\nsustained potential change, and its potential rapidly decreased once the irradiation\nceased, a behavior analogous to the photoconductive response of a photodetector\n(Supplementary Fig. S28a and d). Similarly, the ZnO NCs/PMMA film demonstrated\ncomparable potential variations (Supplementary Fig. S28b and e). In contrast, the ZnO\nNCs/OSMX-PMMA film showed a persistent potential change under illumination, with\na significantly enhanced amplitude of 381 mV compared to 210 mV for ZnO NCs and\n186 mV for ZnO NCs/PMMA (Supplementary Fig. S28c and f). Furthermore, after the\ncessation of UV light, the ZnO NCs/OSMX-PMMA film exhibited pronounced short-\nterm memory effects, suggesting that OSMX not only markedly improves the UV-light\nresponse but also facilitates charge trapping and release processes at the ZnO\nNCs/OSMX interface. In addition, AOS devices with different mass ratios of OSMX to\nPMMA were also investigated to explore the changes in output current and synaptic\nweights of AOS devices under electrical and optical stimulation (Supplementary Fig.\nS26 and Figs. 3d and 3h). With the increasing proportion of PMMA, the conductance\nof the device gradually decreases after 15 positive pulse stimulations, primarily due to\nthe insulating nature of PMMA. Moreover, an increase in PMMA content results in a\ndecrease in photoresponse current. Consequently, the ratio of PMMA can be\nappropriately adjusted to control the output current.\nAs a response, we have (1) added the following description: “The photocurrent\nresponse behavior of the device under different ultraviolet (UV, 365 nm) light durations,\npulse numbers, and light intensities is presented in Figs. 3g and 3h, and Supplementary\nFig. S25a, respectively, demonstrating the regulatory effect of optical stimulation\nparameters on EPSC.” in the manuscript.\n(2) added Supplementary Fig. S28 in the Supplementary Information and\ncorresponding description to clarify the role of OSMX in the AOS device: “Meanwhile,\nthe role of OSMX in AOS devices was investigated through in-situ KPFM\ncharacterization (Supplementary Fig. S28). The results demonstrated that OSMX not\nonly significantly enhanced the response to UV light but also facilitated the charge\ntrapping processes at the ZnO NCs/OSMX interface.” in the manuscript.\nSupplementary Fig. S28. a-c Surface potential of ZnO NCs thin film, ZnO\nNCs/PMMA, and ZnO NCs/OSMX-PMMA thin film characteristic by in-situ KPFM\n(scale bars: 100 nm). d-f Surface potential curve of ZnO NCs film, ZnO NCs/PMMA\nfilm, and ZnO NCs/OSMX-PMMA film before and after UV light illumination (light\nduration is 2 min). To elucidate the role of OSMX in AOS devices more\ncomprehensively, we conducted in-situ KPFM characterization on various thin films\n(Supplementary Fig. S28). Upon UV illumination, the ZnO NCs film exhibited no\nsustained potential change, and its potential rapidly decreased once the irradiation\nceased (Supplementary Fig. S28a and d), a behavior analogous to the photoconductive\nresponse of a photodetector. Similarly, the ZnO NCs/PMMA film demonstrated\ncomparable potential variations (Supplementary Fig. S28b and e). In contrast, the ZnO\nNCs/OSMX-PMMA film showed a persistent potential change under illumination, with\na significantly enhanced amplitude of 381 mV compared to 210 mV for ZnO NCs and\n186 mV for ZnO NCs/PMMA (Supplementary Fig. S28c and f). Furthermore, after the\ncessation of UV light, the ZnO NCs/OSMX-PMMA film exhibited pronounced short-\nterm memory effects, suggesting that OSMX not only markedly improves the UV-light\nresponse but also facilitates charge trapping processes at the ZnO NCs/OSMX interface.\n(3) added AOS properties of OSMX:PMMA with various mass ratios in\nSupplementary Fig. S26 in the Supplementary Information (details about the mass ratio\nof OSMX:PMMA=4:6 were offered in Fig.3) and corresponding description:\n“Furthermore, we investigated AOS devices with varying OSMX-to-PMMA mass\nratios to achieve more effective modulation of their photoelectric properties\n(Supplementary Fig. S26).” in the manuscript.\nSupplementary Fig. S26. a-b Continuous conductance modulation by 15 positive\nelectrical pulses with gradually increasing amplitudes (1.60~1.90 V) and 15 negative\nelectrical pulses at a fixed amplitude (–0.30 V), with both pulse width and interval set\nto 0.37 s. c-d Photoresponse of the AOS device under illuminating with varied pulses\nat a fixed wavelength of 365 nm. With the increasing proportion of PMMA, the\nconductance of the device gradually decreases after 15 positive pulse stimulations,\nprimarily due to the insulating nature of PMMA. Moreover, an increase in PMMA\ncontent results in a decrease in photoresponse current. Consequently, the ratio of\nPMMA can be appropriately adjusted to control the output current.\n9. Fig. 4d is a schematic of the signal acquisition, processing, and feedback units in an\nFSPVS. However, this schematic only presents three individual devices (PENG, AOS,\nand CS-QLEDs) without any connecting lines for signal acquisition, processing, or\nfeedback. To demonstrate that the FSPVS is a fully integrated system, an optical image,\nschematic diagram, and circuit diagram of the FSPVS should be provided.\nResponse: We greatly appreciate your valuable comments. Figure 4d illustrates the\napplication of the FSPVS system in mimicking the adaptive color-shifting behavior of\noctopuses. The system modulates the EPSC of the AOS through combined pressure and\nlight stimulation. These EPSC signals are subsequently captured by the conversion unit\nin the peripheral circuit and transformed into voltage pulses with varying amplitudes,\nwhich drive the CS-QLED to generate visual output (Supplementary Fig. S29). Given\nthat the CS-QLED requires a substantial driving current to induce color changes, a\ncurrent amplification module was incorporated to fulfill its operational demands. In this\napplication scenario, only the optical information from the CS-QLED—specifically its\nemission wavelength and light intensity—needs to be collected via an avalanche diode\nand oscilloscope to determine its current state, eliminating the need for additional\nexternal signal acquisition lines.\nIn addition, for application scenarios where the color-shifting capability of the CS-\nQLED is not required, we have developed a simplified circuit. This design eliminates\nthe need for an external amplification module and can function effectively with only\nthe activation voltage applied, as illustrated in Supplementary Fig. S31.\nAs a response, we have (1) added Supplementary Fig. S29 in the Supplementary\nInformation and added corresponding description: “Inspired by the unique sensory and\nneural characteristics of octopuses, we developed a flexible bionic tactile-visual-optical\nfeedback neural network system to harness the potential of PENG, AOS, and CS-QLED\n(Supplementary Fig. S29). The signal acquisition and processing workflow of the\nsystem is illustrated in Fig. 4d. Since CS-QLED requires a substantial driving voltage\nto induce color changes from red to green, a current amplification module was\nincorporated to fulfill its operational demands. The designed circuit can function\nadequately when receiving only visual signal input without tactile input. Specifically,\nPENG detects pressure signals, and AOS senses light signals and processes the\ncombined information of pressure and light stimuli to generate EPSCs. Subsequently,\nthese EPSCs are captured by the conversion unit in the peripheral circuit and\ntransformed into voltage pulses of varying amplitudes, which drive the CS-QLED for\nvisual output with shifting colors from red to green. Furthermore, the light emitted by\nthe CS-QLED is detected by an avalanche photodiode connected to an oscilloscope.”\nin the manuscript.\nSupplementary Fig. S29. a Schematic diagram of the circuit inspired by the octopus.\nb Optical image of the circuit inspired by octopus (scale bar: 2 cm). c Optical images\nof the flexible PCB during the bending process (right), and a magnified view of the\nflexible PCB (left, scale bar: 0.5 cm).\n(2) added Supplementary Fig. S31 in the Supplementary Information and added\ncorresponding description: “In this application scenario where the CS-QLED is only\nrequired to emitting light rather than achieving color-changing function (that is, the\ndriving voltage is small), we have designed a highly integrated FSVPS (based on L-\nMXene-integrated PENG, AOS, and CS-QLED); it can operate effectively without an\nadditional amplification module, merely by providing a turn-on voltage\n(Supplementary Fig. S31).” in the manuscript.\nSupplementary Fig. S31. a Circuit diagram of FSPVS. b Optical images of the circuit\nof FSPVS attached to the wrist. Although the PENG can directly drive the CS-QLED\nto emit light, its range of brightness variation is relatively limited. To enhance the\nbrightness modulation of the CS-QLED, this system only requires a power supply to\nprovide a bias close to the turn-on voltage for the CS-QLED, enabling the piezoelectric\npulses from the PENG and light stimuli to cooperatively modulate the resistance value\nof the AOS, thus guiding more current to the CS-QLED and effectively regulating its\nbrightness for detection by the avalanche diode.\n10. To my knowledge, chameleon skin can rapidly change color in response to\ntemperature or light stimulation, but not due to pressure or light stimulation, as\nmentioned by the authors in lines 324–325. Therefore, using the chameleon as an\nexample to demonstrate biological self-protection behavior (example chameleon) in\nresponse to pressure and light stimuli does not make sense. A different biological system\nshould be provided for this demonstration.\nResponse: We sincerely appreciate your insightful comments. After carefully re-\nevaluating the biological models, we agree that chameleons do not possess pressure-\nresponsive color-changing mechanisms. As suggested, we have revised the example\nwith octopuses, which exhibit dual-responsive (mechanical/optical) chromatophore\ncontrol, aligning more closely with our FSPVS’s pressure-light dual-mode sensing\ncapability.\nAs a response, we have revised Fig. 4e and its corresponding descriptions: “The skin\nof some organisms possesses unique additional functions that enable them to perform\nphysiological activities efficiently. For example, the skin of an octopus can rapidly\nchange color in response to external stimuli (such as pressure or light stimuli), allowing\nefficient disguise (Fig. 4e).” in the manuscript.\nFig. 4. Visualized dual-mode sensory for environment-adaptive application based\non FSPVS. e Schematic of the octopus’s color change upon light and tactile pressure\nstimulation.\n11. The sensing mechanism of AOS under simultaneous pressure should be provided to\nclarify the results in Fig. 4g.\nResponse: We greatly appreciate your valuable comments.\nUnder ultraviolet light stimulation, AOS excites the generation of photogenerated\ncarriers (electron-hole pairs), whose migration behavior significantly modulates\nconductivity of the device. As the light intensity increases, the concentration of these\ncarriers rises, leading to a higher EPSC amplitude and triggering a transition from STM\nto LTM (Supplementary Fig. S25a), thereby demonstrating the regulatory effect of light\nintensity on synaptic plasticity.\nUnder electric pulse stimulation, charges captured by defect states in the AOS device\nenhance carrier injection efficiency, thus increasing conductivity. With increasing\nmechanical pressure, the built-in electric field generated by piezoelectric polarization\nfurther elevates the density of trapped charges at defect sites, resulting in additional\nconductivity enhancement.\nWhen light and piezoelectric stimuli are simultaneously applied, photon excitation\nand piezoelectric regulation synergistically enhance conductivity. Consequently, as the\nresistance of AOS decreases, a larger current is generated and subsequently converted\ninto a higher driving voltage, which effectively induces more pronounced color changes\nin the CS-QLED. Furthermore, to thoroughly demonstrate the multimodal sensing\ncapability of the system, we acquired additional data under finely tuned and\ncontinuously varying pressure and optical intensity stimuli (Fig. 4g).\nAs a response, we have (1) added the sensing mechanism of AOS under simultaneous\npressure as follows: “When light and piezoelectric stimuli are simultaneously applied,\nphoton excitation and piezoelectric regulation synergistically enhance conductivity.\nConsequently, as the resistance of AOS decreases, an enlarged current is generated and\nsubsequently converted into an enlarged driving voltage, which effectively induces\npronounced color changes in the CS-QLED.” in the manuscript.\n(2) added Supplementary Fig. S25a in the Supplementary Information and added\nfollowing sentence: “The photocurrent response behavior of the device under different\nultraviolet (UV, 365 nm) light durations, pulse numbers, and light intensities is\npresented in Figs. 3g and 3h, and Supplementary Fig. S25a, respectively, demonstrating\nthe regulatory effect of optical stimulation parameters on EPSC.” in the manuscript.\nSupplementary Fig. S25. a Photoresponse of the AOS device under illumination with\nvaried light intensity at a fixed wavelength of 365 nm and a fixed exposure time of 1 s.\nUnder ultraviolet light stimulation, AOS excites the generation of photogenerated\ncarriers (electron-hole pairs), whose migration behavior significantly modulates\nconductivity of the device. As the light intensity increases, the concentration of these\ncarriers rises, leading to a higher EPSC amplitude and triggering a transition from STM\nto LTM (Supplementary Fig. S25a), thereby demonstrating the regulatory effect of light\nintensity on synaptic plasticity. Under electric pulse stimulation, charges captured by\ndefect states in the AOS device enhance carrier injection efficiency, thus increasing\nconductivity. With increasing mechanical pressure, the built-in electric field generated\nby piezoelectric polarization further elevates the density of trapped charges at defect\nsites, resulting in additional conductivity enhancement. When light and piezoelectric\nstimuli are simultaneously applied, photon excitation and piezoelectric regulation\nsynergistically enhance conductivity. Consequently, as the resistance of AOS decreases,\na larger current is generated and subsequently converted into a higher driving voltage,\nwhich effectively induces more pronounced color changes in the CS-QLED.\nFurthermore, to thoroughly demonstrate the multimodal sensing capability of the\nsystem, we acquired additional data under finely tuned and continuously varying\npressure and optical intensity stimuli (Fig. 4g).\n(3) updated Fig. 4g and corresponding description: “As shown in Fig. 4g, in the\nabsence of additional light stimulation, increasing pressure can induce a transition\n(threshold 1 of light emitting intensity) in the emission of the CS-QLED from red light\n(state 1: non-defense mode) to yellow light (state 2: environmental monitoring mode).\nSimilarly, without external pressure stimulation, an increase in light intensity can also\ntrigger the same emission transition. When moderate pressure and light intensity are\nconcurrently applied, the CS-QLED emits yellow light exceeding threshold 1 (state 2).\nWhen both high pressure and high light intensity are simultaneously applied, the\nCS-QLED exceeds threshold 2 (state 3: alarm mode) and emits green light.” in the\nmanuscript.\nFig. 4. Visualized dual-mode sensory for environment-adaptive application based\non FSPVS. g Light-emitting intensity of the CS-QLED varies in response to different\npressure and optical intensity stimuli.\n12. For dynamic information perception and recognition, the experimental setup and\nmeasurement process should be clearly presented. Honestly, I found this section\ndifficult to follow, so I am providing my comments based on my understanding.\n- Based on my understanding of this section, three device systems were attached to the\nwrist to conduct this experiment. Is that correct?\nResponse: We greatly appreciate your valuable comments. These three devices were\nintegrated into the FSPVS and attached to the wrist by medical PU tape for\nexperimental operation (Supplementary Fig. S31). The PENG module was positioned\nnear the wrist joint to harvest the mechanical energy generated during wrist flexion.\nAs a response, we have added Supplementary Fig. S31 in the Supplementary\nInformation and added corresponding description: “In this application scenario where\nthe CS-QLED is only required to emitting light rather than achieving color-changing\nfunction (that is, the driving voltage is small), we have designed a highly integrated\nFSVPS (based on L-MXene-integrated PENG, AOS, and CS-QLED); it can operate\neffectively without an additional amplification module, merely by providing a turn-on\nvoltage (Supplementary Fig. S31).” in the manuscript.\nSupplementary Fig. S31. a Circuit diagram of FSPVS. b Optical images of the circuit\nof FSPVS attached to the wrist. The FSPVS circuit (Supplementary Fig. S31) does not\nfunction adequately when only visual signals are present without tactile inputs. During\nthis badminton scenario, joint motion (generating piezoelectric signals) and racket\nswinging (receiving light signals) occur simultaneously, indicating that independent\nvisual signal modulation is not needed in this case. As a result, the simultaneous\napplication of tactile and visual signals to the AOS triggers the CS-QLED to emit light.\n- For classification and recognition of hitting types: The PENG was used to generate\nsignals during the hitting process, and these signals were processed through a\nmemristor array. Was the PENG directly integrated with the memristor array? How did\nyou read the output signal during measurement—was it done wirelessly or through a\nconnection line? If a connection line was used, how did you mitigate noise signals\ngenerated by human motion?\nResponse: We greatly appreciate your valuable comments. In this study, PENG was\ndirectly connected to the AOS (Supplementary Fig. S1). The strike-type task was\nperformed using piezoelectric sensors. Experimental participants swung the racket with\nvarying types, and raw data were collected. By screening and segmenting the collected\ndata, a dataset suitable for classification tasks was constructed. During the testing\nprocess, the output signal of the PENG was directly acquired via a wired connection to\nan oscilloscope. As shown in Supplementary Fig. S34, the signal exhibited almost no\nnoise interference. We highlight that our work is focused on bioinspired MXene-based\nFSPVS featuring mechanically and electronically matched interfaces towards wearable\nintelligent sensory systems, and a simple experiment is used here to demonstrate our\nconcept. For future application scenarios, a wearable electrochemical workstation could\nbe employed to acquire the output signals and facilitate wireless information\ntransmission.\nAs a response, we have (1) added the measurement details as follows: “During the\ntesting process, the PENG was directly connected to the AOS device.” in the manuscript.\n(2) added Supplementary Fig. S1 and S34 in the Supplementary Information, and\nadded classification and recognition of strike types details to the Methods section: “For\nstrike types recognition: the experimental participant swung the racket with varying\ntypes, and raw data was collected. The one-dimensional strike-type data (2000) was\nconverted into a four-row pixel format (4×500) through cropping and binarization.\nSubsequently, the strike type data are fed into the AOS reservoir in electrical mode for\ncomputation. The AOS reservoir in the electrical mode consists of 500 storage devices\nthat serve as reservoir nodes. Then, a single-layer fully connected neural network with\n500 input nodes was designed to perform the strike types recognition task.” in the\nmanuscript.\nSupplementary Fig. S1. b Optical image of the FSPVS (scale bar: 1 cm).\nSupplementary Fig. S34. Dataset (part) of the strike types recognition. During the\ntesting process, the output signal of the PENG was directly acquired via a wired\nconnection to an oscilloscope.\n- For recognizing racket swing trajectories: The AOS device was used in combination\nwith an RC system. How was the light pulse stimulated in the AOS device during human\nmovement? Since the distance between the AOS device and the light source can vary\nduring movement, this could cause output current variations. How did you address this\nissue? Additionally, as mentioned earlier, you did not specify the wavelength of the light\nsource, but based on the materials used in the AOS, I assume UV light was used for\nstimulation. If this is the case, UV light can be harmful to human eyes. How did you\nconduct the experiment with UV light stimulation? Also, how did you read the output\nsignal during measurement?\nResponse: We greatly appreciate your valuable comments. In the detection of racket\nswing trajectories, a video sequence was captured for each racket trajectory, and the\nframe difference method was applied to process each video, generating an image\nsequence. The input image (56×56) was then divided into four vertically consecutive\npixels (top to bottom) from the leftmost to the rightmost column, forming a 4 × 3136\ninput matrix. Each row was converted into four pulses and applied to the optical\nreservoir (Fig. 5d). When a racket is detected at a specific pixel location (yellow pixel),\na light pulse is applied to the corresponding AOS; conversely, if no racket movement is\ndetected (blue pixel), no light pulse is delivered.\nA schematic of the reservoir implementation is provided in Supplementary Fig. S33. In\nthis study, the reservoir can be physically implemented using AOS devices. However,\ndue to the challenges in hardware implementation for high-density arrays, the devices\nare not connected randomly as recurrent connections. Instead, they are arranged in\nparallel to process the input voltage signals and generate dynamic output current\nresponses. The generated current value was collected after every four pulses and fed\ninto a software readout layer containing eight output neurons to compute the probability\nof different outputs (Supplementary Fig. S33). This processing method has been\nemployed in computational applications involving optoelectronic memristors (Nat.\nCommun. 2024, 15, 740; Nat. Electron. 2022, 5, 761-773). Therefore, the distance of\nthe UV light source was fixed to ensure consistent light intensity across all generated\npulses. In future applications, the light intensity within the motion area throughout the\nracket-swinging process can be rendered relatively uniform through the appropriate\nconfiguration of the light field, such as the location and number of light sources. This\nstudy focuses on a flexible MXene-integrated multimodal perception-processing-\nfeedback system to achieve effective interface matching, with the feasibility of the\nproposed scheme demonstrated through a simplified application example and\ncorresponding simulations.\nThe AOS device is stimulated by 365 nm UV light sources. Sensing and recognizing\ninvisible UV light is vital for exploiting an advanced artificial visual perception system.\nUV excitation not only effectively eliminates interference from visible light but also\nefficiently promotes electron transitions in semiconductor materials due to its high\nphoton energy. Such a UV sensing application has been used in other memristors (Nat.\nNanotechnol. 2024, 20, 93–103). During the experiment, to ensure the safety of the\noperators and prevent potential damage to the eyes from ultraviolet light, all personnel\nwore protective glasses.\nAdditionally, to accurately read the current signal of the memristor, a source meter\nwas connected to both ends of the memristor in the experiment to collect the output\nsignal. The obtained result has verified the feasibility of this method.\nAs a response, we have (1) added Supplementary Figs. S33 and corresponding\ndescription in the Supplementary Information, and added the way of the light pulse\nstimulating the AOS device in the Methods section: “The input image (56×56) was then\ndivided into four vertically consecutive pixels (top to bottom) from the leftmost to the\nrightmost column, forming a 4×3136 input matrix. Each row was converted into four\npulses and applied to the reservoir. When a racket is detected at a specific pixel location\n(yellow pixel), a light pulse is applied to the corresponding AOS; if no racket movement is\ndetected (blue pixel), no light pulse is delivered. Each row of the matrix corresponds\nto one of 16 optical input responses (encoded from “0000” to “1111”).” in the\nmanuscript.\n(2) added wavelength of the light source corresponding expression: “The\nphotocurrent response behavior of the device under different ultraviolet (UV, 365 nm)\nlight durations, pulse numbers, and light intensities is presented in Figs. 3g and 3h, and\nSupplementary Fig. S25a, respectively, demonstrating the regulatory effect of optical\nstimulation parameters on EPSC.” and “The AOS device is stimulated by 365 nm UV\nlight sources, and the distance of the UV light source was fixed to ensure consistent\nlight intensity across all generated pulses.” in the manuscript.\n(3) added device characterization corresponding expression to the Methods section\n“The optical and electrical properties of the AOS were characterized using a source\nmeter (Keithley 2400) by connecting to both ends.” in the manuscript and offered the\nobtained results in Supplementary Figs. S33:\nSupplementary Fig. S33. Framework diagram of recognition processes for the strike\ntypes and racket swing trajectories. The recognition processes are realized through the\nAOS reservoir in different modes. In the badminton scenario, the identification of strike\ntypes can be performed in an electrical mode using the AOS reservoir, while dynamic\nracket trajectory recognition is processed in an optical mode through the same AOS\nreservoir. In contrast to other multimodal information processing approaches—that\nintegrate two modalities to produce a single output—this system enables the\nindependent and effective processing of multiple modalities, resulting in two distinct\nand reliable classification outputs. Further multimodal fusion can be achieved in a\ndecision-level way where all results processed within the reservoir are transmitted to\nthe subsequent neural network for integration, thereby enabling the output of more\ncomprehensive information. The detailed description is offered in the Methods.\nReviewer #2\nComments:\nWang et al. utilized MXene two-dimensional materials as substrates to fabricate a\nsystem seamlessly integrating piezoelectric nanogenerators for pressure sensing,\nartificial optoelectronic synapses for visual sensation and processing, and color-\nshifting quantum dot light-emitting diodes for optical feedback. The tactile-visual\nsignal recognition capabilities of the system were applied for environmental adaptation\nand dynamic trajectory identification.\nOverall, the manuscript proposes an innovative concept of a tactile-visual multimodal\nsensing and recognition system based on MXene two-dimensional materials to mimic\nbiological multi-sensory mechanisms. However, the current state of the manuscript\ndoes not adequately support the proposed multimodal integration concept. Moreover,\nthere are several critical issues regarding device fabrication and system presentation\nthat need to be addressed thoroughly. Specific comments are as follows:\nResponse: We sincerely thank you for recognizing the innovation of this research and\nproviding valuable suggestions. As you pointed out, this study utilizes MXene two-\ndimensional materials as a foundation to construct a tactile-visual multimodal sensing-\nprocessing-visualizing system, aiming to simulate biological multisensory coordination\nmechanisms. We deeply appreciate your profound insights review. In response to your\nsuggestions, we have made comprehensive revisions to the manuscript, with specific\nimprovements outlined below: (1) the preparation process of the multimodal system\nintegration has been detailed, and physical images have been included to enhance\nintuitiveness; (2) a thorough analysis of the unique advantages of MXene materials in\nterms of performance and functionality has been conducted; (3) systematic testing\nunder more precise and continuous variations of pressure and light intensity stimuli has\nbeen performed to further validate the system’s responsiveness; (4) mixed input of\npressure and light information has been successfully achieved through the photoelectric\nfusion reservoir; (5) detailed descriptions of the algorithm design and its\nimplementation have been provided to improve the reproducibility of the research. We\nbelieve that our revised version can adequately support the proposed multimodal\nintegration concept and deal with the critical issues of the reviewer.\nOur point-by-point responses to the reviewer’s comments are as follows:\n1. The authors think MXene films to cell membranes for multimodal information\ntransmission. However, the schematic of the sensing-feedback system indicates that the\nMXene films connecting individual sensing modules are not clearly depicted. Authors\nshould clarify the signal transmission mechanism between modules and explicitly\ndescribe the role of MXene films. If multimodal sensing and processing are achievable,\ndetailed experimental process diagrams and images of integrated multimodal devices\nor arrays in hardware must be provided.\nResponse: We greatly appreciate your valuable comment. In our work, MXene serves\nmultiple functional roles in the system: (1) a tactile-sensation-enhanced material in\nPENG through M-MXene with abundant surface −OH terminations that can form\nhydrogen bonds with the C−F bonds in poly(vinylidene fluoride-co-trifluoroethylene)\n(P(VDF-TrFE)) to improve piezoelectric phase (Fig. 1b left); (2) a visual sensation and\nsynaptic material in AOS via OSMX photoresponse and defect-mediated charge\ntrapping for synaptic conductance modulation (Fig. 1b center); (3) a preferable charge-\ninjection layer in CS-QLED through L-MXene with engineered energy barriers (Fig.\n1b right); and (4) a shared flexible high-efficiency electrical conduction platform\nthrough L-MXene in the FSPVS, thereby achieving the conversion of the sensory\nstimuli into electron signals and their efficient transportation. During the system\noperation, the piezoelectric potential formed by polarized charges is transmitted\nthrough L-MXene to the AOS during the PENG compression process, thereby\nmodulating its conductive state. Meanwhile, light stimulation generates a photocurrent\nin the AOS. The EPSCs triggered jointly by pressure and light stimulation are\nsubsequently transmitted to the CS-QLED, inducing its luminescence. As shown in Figs.\n4 and 5, our system demonstrates the perception-processing-optical feedback function\nof multimodal information. For this purpose, we have supplemented the detailed\nexperimental process diagrams and optical images of integrated multimodal devices.\nTo further describe the role of MXene films, we systematically characterized the\nthickness-dependent conductivity (sheet resistance) and transparency of L-MXene\nfilms by controlling coating thickness through spin-coating dispersion concentrations.\nSpecifically, as demonstrated in Supplementary Fig. S17, the film spin-coated with 7\nmg mL-1 L-MXene dispersion exhibits a sheet resistance of 64.4 Ω sq-1 and 80.4%\ntransparency (at 550 nm). Although the transparency of the L-MXene film was slightly\ninferior to that of commercial ITO, it exhibited significantly superior flexibility,\nmaintaining approximately 6% resistance change after 5000 bending cycles at a\nbending radius of 3 mm (Supplementary Fig. S18), thereby meeting the essential\nrequirements for flexible optoelectronic devices as outlined in our application scenarios.\nIn addition, to elucidate the role of OSMX (oxidized S-MXene) in AOS devices more\ncomprehensively, we conducted in-situ KPFM characterization on various thin films\n(Supplementary Fig. S27). Upon UV illumination, the ZnO NCs film exhibited no\nsustained potential change, and its potential rapidly decreased once the irradiation\nceased, a behavior analogous to the photoconductive response of a photodetector\n(Supplementary Fig. S26a and d). Similarly, the ZnO NCs/PMMA film demonstrated\ncomparable potential variations (Supplementary Fig. S27b and e). In contrast, the ZnO\nNCs/OSMX-PMMA film showed a persistent potential change under illumination, with\na significantly enhanced amplitude of 381 mV compared to 210 mV for ZnO NCs and\n186 mV for ZnO NCs/PMMA (Supplementary Fig. S27c and f). Furthermore, after the\ncessation of UV light, the ZnO NCs/OSMX-PMMA film exhibited pronounced short-\nterm memory effects, suggesting that OSMX not only markedly improves the UV-light\nresponse but also facilitates charge trapping and release processes at the ZnO\nNCs/OSMX interface.\nAs a response, we have (1) added the following description about the signal\ntransmission mechanism between modules: “During the system operation, the\npiezoelectric potential formed by polarized charges is transmitted through L-MXene to\nthe AOS during the PENG compression process, thereby modulating its conductive\nstate. Meanwhile, light stimulation generates a photocurrent in the AOS. The EPSCs\ntriggered jointly by pressure and light stimulation are subsequently transmitted to the\nCS-QLED through L-MXene, inducing its luminescence.” in the manuscript.\n(2) added the preparation process in the Methods section:\n“Fabrication of FSPVS\nTo fabricate the FSPVS, a thin patterned PET mask was first attached to a PET substrate\n(3×4.5 cm2), and its hydrophilicity was enhanced via oxygen plasma treatment.\nSubsequently, an L-MXene film was spin-coated on the substrate and annealed at\n100 °C for 12 h. Following this, the MXP solution was drop-cast onto the PENG region\nof the PET substrate, dried at 70 °C for 7 h, and vacuum-annealed at 100 °C for 2 h to\nimprove crystallinity. Then, L-MXene was deposited onto the upper surface of MXP\nfilm using a similar procedure. Next, the PENG and AOS regions were masked, and\nPEDOT:PSS, TFB, R/G QDs, and ZnO layers were sequentially spin-coated onto the\nCS-QLED region. Afterward, the CS-QLED region was re-masked, and ZnO,\nOSMX/PMMA, and ZnO layers were sequentially deposited. Finally, the top electrode\nwas thermally evaporated, and the entire device was encapsulated. To ensure an\nefficient electrical conduction connection between the devices, MXene can be further\nsprayed in the conduction pathway.” in the manuscript.\n(3) added Supplementary Fig. S17 in the Supplementary Information and\ncorresponding description about the electrical conductivity of L-MXene: “We\ninvestigated the effects of L-MXene concentration on transparency and conductivity by\nspin-coating solutions with varying concentrations onto PET substrates. In\nSupplementary Fig. S17, at a concentration of 7 mg mL-1, the film exhibited 80.4%\ntransparency and a sheet resistance of ~64 Ω □-1. Meanwhile, we evaluated the bending\nresistance performance of the L-MXene/PET and ITO/PET electrodes. The results show\nthat L-MXene/PET demonstrates stable electrical resistance (Supplementary Fig. S18)\nand superior surface integrity (Supplementary Fig. S19) after 5000 bending cycles\ncompared to ITO/PET, thereby enhancing its potential in flexible electronic devices.”\nin the manuscript.\nSupplementary Fig. S17. a Sheet resistance and b transmittance at 550 nm of L-\nMXene films as a function of concentration (ranging from 4 to 10 mg mL-1).\n(4) added Supplementary Fig. S28 and corresponding description to further elucidate\nthe role of OSMX (oxidized S-MXene) in AOS devices: “Meanwhile, the role of\nOSMX in AOS devices was investigated through in-situ KPFM characterization\n(Supplementary Fig. S28). The results demonstrated that OSMX not only significantly\nenhanced the response to UV light but also facilitated the charge trapping processes at\nthe ZnO NCs/OSMX interface.” in the manuscript.\nSupplementary Fig. S28. a-c Surface potential of ZnO NCs thin film, ZnO\nNCs/PMMA, and ZnO NCs/OSMX-PMMA thin film characterized by in-situ KPFM\n(scale bars: 100 nm). d-f Surface potential curve of ZnO NCs film, ZnO NCs/PMMA\nfilm, and ZnO NCs/OSMX-PMMA film before and after UV light illumination (light\nduration is 2 min). To elucidate the role of OSMX in AOS devices more\ncomprehensively, we conducted in-situ KPFM characterization on various thin films\n(Supplementary Fig. S28). Upon UV illumination, the ZnO NCs film exhibited no\nsustained potential change, and its potential rapidly decreased once the irradiation\nceased (Supplementary Fig. S28a and d), a behavior analogous to the photoconductive\nresponse of a photodetector. Similarly, the ZnO NCs/PMMA film demonstrated\ncomparable potential variations (Supplementary Fig. S28b and e). In contrast, the ZnO\nNCs/OSMX-PMMA film showed a persistent potential change under illumination, with\na significantly enhanced amplitude of 381 mV compared to 210 mV for ZnO NCs and\n186 mV for ZnO NCs/PMMA (Supplementary Fig. S28c and f). Furthermore, after the\ncessation of UV light, the ZnO NCs/OSMX-PMMA film exhibited pronounced short-\nterm memory effects, suggesting that OSMX not only markedly improves the UV-light\nresponse but also facilitates charge trapping processes at the ZnO NCs/OSMX interface.\n(5) added the schematic of the preparation process, physical images, and circuit\ndiagram in the Supplementary Fig. S1 and S31, as follows:\nSupplementary Fig. S1. a Schematic of the FSPVS preparation process. b Optical\nimage of FSPVS (scale bar: 1 cm).\nSupplementary Fig. S31. a Circuit diagram of FSPVS. b Optical images of the circuit\nof FSPVS attached to the wrist. The FSPVS circuit (Supplementary Fig. S31) does not\nfunction adequately when only visual signals are present without tactile inputs. During\nthis badminton scenario, joint motion (generating piezoelectric signals) and racket\nswinging (receiving light signals) occur simultaneously, indicating that independent\nvisual signal modulation is not needed in this case. As a result, the simultaneous\napplication of tactile and visual signals to the AOS triggers the CS-QLED to emit light.\n2. The manuscript lacks a clear discussion of the advantages of MXene films compared\nto other conductive films. A comparative analysis highlighting the specific benefits of\nMXene materials is required.\nResponse: Thank you very much for your important comment. This research focuses\non highly biomimetic characteristics of biological cell membranes to achieve tactile-\nvisual-optical feedback functions. During physiological processes, organisms leverage\nthe high consistency in material composition, mechanical properties, and ion transport\ncharacteristics to synthesize necessary substances with minimal diversity, significantly\nimproving inter-organ transport efficiency and information transmission (Fig. 1a).\nConsequently, in material selection, priority should be given to using the same material\nor its derivatives, which simplifies process flow and enhances compatibility among\ncomponents, thus enabling more precise simulation of organismal behavior and\nfunctions. Leveraging the diverse and tunable excellent properties of MXene materials,\nwe have successfully developed a flexible sensing-processing-visualization integrated\nsystem. By tailoring the size and surface states, MXene serves multiple functional roles\n(that is challenging for other conductive films): (1) a tactile-sensation-enhanced\nmaterial in PENG through M-MXene with abundant surface −OH terminations that can\nform hydrogen bonds with the C−F bonds in poly(vinylidene fluoride-co-\ntrifluoroethylene) (P(VDF-TrFE)) to improve piezoelectric phase (Fig. 1b left); (2) a\nvisual sensation and synaptic material in AOS via OSMX photoresponse and defect-\nmediated charge trapping for synaptic conductance modulation (Fig. 1b center); (3) a\npreferable charge-injection layer in CS-QLED through L-MXene with engineered\nenergy barriers (Fig. 1b right); and (4) a shared flexible high-efficiency electrical\nconduction platform in the FSPVS, thereby achieving the conversion of the sensory\nstimuli into electron signals and their efficient transportation.\nAs a response, to facilitate clear discussion of the advantages of MXene films\ncompared to other conductive films, we have (1) added the following sentence:\n“Advances in 2D materials, particularly MXenes, may open new avenues for\novercoming these challenges. Among them, Ti C T MXene—known for its high\n3 2 x\nelectrical conductivity (>25000 S cm-1), excellent mechanical flexibility (single-layer\nthickness of approximately 1 nm), excellent bendability (by sliding the MXene layers\nto disperse stress), rich and tunable physicochemical properties (by tailoring sizes and\nsurface terminations), solution processability, and high transparency at thin film—\noutperforms conventional materials (more detailed comparisons can be seen in\nSupplementary Table S1) and have attracted intensive interests and demonstrated varied\napplications.” in the manuscript.\n(2) added Supplementary Table S1 in the Supplementary Information, offering a\ncomparative analysis highlighting the specific benefits of MXene materials:\nSupplementary Table S1. Comparison of various transparent conductive films for\nflexible electronics.\nElectronic Work\nConductive Mechanical Solution Multifuncti\nconductivity function Ref.\nfilms properties processability onalization\n(S cm−1) (eV)\nITO 103–104 4.7–4.9 Poor None No 1-3\nPEDOT:PSS 10–1000 5.0–5.2 Excellent Excellent No 4-6\nAg NWs 103–104 4.3-4.8 Excellent Moderate No 7-9\nSWCNTs 104–105 4.5–4.8 Excellent Moderate No 10-12\nGraphene 104–105 4.3–4.9 Excellent Moderate No 13-15\nTi C T\n3 2 x >2.5104 1.6–6.2 Excellent Excellent Yes 16-25\nMXene\nAmong flexible electronics, Ti C T MXene offers numerous distinct advantages\n3 2 x\nover other conductive films:\n(i) MXene nanosheets exhibit excellent electrical conductivity, with a single-layer\nconductivity exceeding 25000 S cm–1 (ACS Nano 2021, 15, 6420-6429; Nanoscale\n2021, 13, 3572-3580). Additionally, MXene demonstrates outstanding mechanical\nflexibility, attributed to its ultrathin nanosheet structure with a single-layer thickness of\napproximately 1 nm, which substantially reduces bending stiffness. Concurrently, the\nexistence of surface functional groups improves tolerance to lattice distortion, while the\nrobust covalent or metallic bonding between transition metals and carbon atoms within\nthe layers imparts sufficient mechanical strength to effectively prevent fracture during\ndeformation. Moreover, its surface terminations (–OH/–O) enhance chemical bonding\nwith flexible substrates such as PET, effectively preventing interfacial delamination and\nimproving mechanical interface matching.\n(ii) The highly tunable physical and chemical properties of MXene enable precise\nmodulation of its work function within a range of 1.6 to 6.2 eV through the modification\nof surface terminal groups (Light Sci. Appl. 2021, 10, 177; Matter 2023, 6, 800-837).\nThis tunability facilitates efficient carrier injection and transport, thereby enhancing\nelectrical interface matching.\n(iii) MXene demonstrates excellent solution-processability, enabling stable\ndispersion in both water and organic solvents (Sci. Adv. 2022, 8, eabl5299; Nat. Chem.\n2023, 15, 1722-1729). It can be directly fabricated into highly conductive films using\ntechniques such as spray coating, spin coating, dip coating, doctor blading, inkjet\nprinting, and screen printing—without requiring high-temperature annealing\n(processing temperature < 120 °C), suitable for use with flexible substrates.\n(iv) Owing to the abundant and tunable surface states and composition of MXene,\nsurface modification holds promise for enabling multifunctional applications, thereby\nfulfilling the diverse requirements of various optoelectronic devices.\nIn summary, by regulating the size and surface state of MXene materials, they can\nmeet the diverse requirements of electrode materials and functional layers in\nmultifunction-integrated devices with homologous materials/interfaces, thereby\nachieving coordinated matching of the entire system at both mechanical and electrical\nproperties.\n3. The presented multimodal sensing measurements appear limited to data shown in\nFigure 4g. Additional data under more finely tuned and continuously varying pressure\nand optical intensity stimuli are necessary to thoroughly demonstrate the multimodal\nsensing capability of the system.\nResponse: We greatly appreciate your valuable comments. To thoroughly demonstrate\nthe multimodal sensing capability of the system, we acquired additional data under\nfinely tuned and continuously varying pressure and optical intensity stimuli.\nAs a response, we have offered additional data in Fig. 4g and added corresponding\ndescription: “As shown in Fig. 4g, in the absence of additional light stimulation,\nincreasing pressure can induce a transition (threshold 1 of light emitting intensity) in\nthe emission of the CS-QLED from red light (state 1: non-defense mode) to yellow light\n(state 2: environmental monitoring mode). Similarly, without external pressure\nstimulation, an increase in light intensity can also trigger the same emission transition.\nWhen moderate pressure and light intensity are concurrently applied, the CS-QLED\nemits yellow light exceeding threshold 1 (state 2). When both high pressure and high\nlight intensity are simultaneously applied, the CS-QLED exceeds threshold 2 (state 3:\nalarm mode) and emits green light.” in the manuscript.\nFig. 4. Visualized dual-mode sensory for environment-adaptive application based\non FSPVS. g Light-emitting intensity of the CS-QLED varies in response to different\npressure and optical intensity stimuli.\n4. Several questions regarding the multimodal sensing claims remain unclear:\n- Does the system function adequately when only visual signals are present without\ntactile inputs?\nResponse: We appreciate the reviewer’s helpful comments.\nFor applications that mimic the adaptive color changes of octopuses in response to\nexternal stimuli, the designed circuit can still function adequately when receiving only\nvisual signal input without tactile input. This functionality is achieved through a signal\nacquisition and processing workflow (Supplementary Fig. S28). Under the condition of\nonly visual signals input, the luminous intensity of CS-QLED increases with the\nincrease of light intensity and the number of light pulses (Fig. 4g).\nFig. 4. Visualized dual-mode sensory for environment-adaptive application based\non FSPVS. g Light-emitting intensity of the CS-QLED varies in response to different\npressure and optical intensity stimuli.\nIn the badminton scenario illustrated in Fig. 5, we implemented a simplified FSPVS\ncircuit (Supplementary Fig. S31) that does not function adequately when only visual\nsignals are present without tactile inputs. During this badminton scenario, joint motion\n(generating piezoelectric signals) and racket swinging (receiving light signals) occur\nsimultaneously, indicating that independent visual signal modulation is not needed in\nthis case. As a result, the simultaneous application of tactile and visual signals to the\nAOS triggers the CS-QLED to emit light. If an isolated visual signal input is required,\nwe have added the circuit (Supplementary Fig. S29) that can be employed to fulfill this\npurpose.\nAs a response and to avoid misleading readers, we have added corresponding\ndescription: “The designed circuit can function adequately when receiving only visual\nsignal input without tactile input.” and “During the badminton scenario, joint\nmovements (representing piezoelectric signals) and racket swings (representing optical\nsignals) occur simultaneously and are separately input into the AOSs working in\nelectrical and optical modes (serving as RC units), respectively (Fig. 5d and\nSupplementary Fig. S33).” in the manuscript.\nIn addition, we have added the circuit diagrams of the system in Supplementary Fig.\nS29 and S31 in the Supplementary Information:\nSupplementary Fig. S29. a Schematic diagram of the circuit inspired by the octopus.\nb Optical image of the circuit inspired by octopus (scale bar: 2 cm). c Optical images\nof the flexible PCB during the bending process (right), and a magnified view of the\nflexible PCB (left, scale bar: 0.5 cm).\nSupplementary Fig. S31. a Circuit diagram of FSPVS. b Optical images of the circuit\nof FSPVS attached to the wrist. The FSPVS circuit (Supplementary Fig. S31) does not\nfunction adequately when only visual signals are present without tactile inputs. During\nthis badminton scenario, joint motion (generating piezoelectric signals) and racket\nswinging (receiving light signals) occur simultaneously, indicating that independent\nvisual signal modulation is not needed in this case. As a result, the simultaneous\napplication of tactile and visual signals to the AOS triggers the CS-QLED to emit light.\n- How is multimodal sensory information encoded and decoded within the system?\nResponse: We would like to thank the reviewer for the constructive comments.\nAs a response and to illustrate the multimodal sensory information encoding and\ndecoding, we have (1) added the corresponding description in the Methods part: “For\nstrike types recognition: the experimental participant swung the racket with varying\ntypes, and raw data was collected. The one-dimensional strike-type data (2000) was\nconverted into a four-row pixel format (4×500) through cropping and binarization.\nSubsequently, the strike type data are fed into the AOS reservoir in electrical mode for\ncomputation. The AOS reservoir in the electrical mode consists of 500 storage devices\nthat serve as reservoir nodes. Then, a single-layer fully connected neural network with\n500 input nodes was designed to perform the strike types recognition task. For racket\nswing trajectory recognition: image sequences of each racket trajectory were captured,\nand the frame difference method combined with binarization processing was applied to\ngenerate image sequences. Each input image (56×56) was divided into four consecutive\npixel rows (4×3136), spanning from the leftmost to the rightmost column in a top-down\nmanner. Subsequently, the swing trajectory information was fed into the AOS reservoir\nin optical mode for computation. The AOS reservoir in the optical mode consists of\n3136 storage devices that serve as reservoir nodes. Their conductance values are\nprogrammed and calibrated based on experimentally measured data from individual\ndevices. Then, a single-layer fully connected neural network with 3136 input nodes was\ndesigned to perform the racket swing trajectory recognition task. The readout layer was\nimplemented in software, and the model was trained using the Adam optimizer’s\ngradient descent algorithm (with an initial learning rate of 0.001) to minimize\nclassification cross-entropy loss. Finally, the data were divided into training and test\nsets to evaluate the performance of the model.” in the manuscript.\n(2) added Supplementary Fig. S33 and corresponding description: “During the\nbadminton scenario, joint movements (representing piezoelectric signals) and racket\nswings (representing optical signals) occur simultaneously and are separately input into\nthe AOSs working in electrical and optical modes (serving as RC units), respectively\n(Fig. 5d and Supplementary Fig. S33). For strike types recognition, one-dimensional\npiezoelectric data (2000) was converted into a four-row pixel format (4×500) through\ncropping and binarization. Subsequently, the strike type data are fed into the AOS\nreservoir in electrical mode for computation. Finally, a single-layer fully connected\nneural network is employed to perform the strike type recognition task. For racket\nswing trajectory recognition, to integrate the spatial and temporal information from\nconsecutive frames into a compact representation, we divide each frame image into four\nconsecutive subframes (t , t , t , and t ), representing four distinct moments of the racket\n0 1 2 3\nswing.” in the manuscript.\nSupplementary Fig. S33. Framework diagram of recognition processes for the strike\ntypes and racket swing trajectories. The recognition processes are realized through the\nAOS reservoir in different modes. In the badminton scenario, the identification of strike\ntypes can be performed in an electrical mode using the AOS reservoir, while dynamic\nracket trajectory recognition is processed in an optical mode through the same AOS\nreservoir. In contrast to other multimodal information processing approaches—that\nintegrate two modalities to produce a single output—this system enables the\nindependent and effective processing of multiple modalities, resulting in two distinct\nand reliable classification outputs. Further multimodal fusion can be achieved in a\ndecision-level way where all results processed within the reservoir are transmitted to\nthe subsequent neural network for integration, thereby enabling the output of more\ncomprehensive information. The detailed description is offered in the Methods.\n- How is the sensing-feedback system powered? Is it directly powered by the PENG\nmodule, or is an additional driving circuit required? A clear explanation should be\nprovided.\nResponse: We greatly appreciate your valuable comments. During the operational cycle\nof the system, the sensing and processing module can achieve self-power. Specifically,\nthe PENG generates piezoelectric potential in response to external mechanical pressure\nthrough the piezoelectric effect, which modulates the electrical conductivity of AOS.\nFurthermore, AOS exhibits photoconductive behavior when exposed to ultraviolet light\npulses, resulting in varied EPSCs. However, the CS-QLED necessitates an independent\npower supply unit to regulate its luminescent properties. Consequently, the overall\nsystem remains dependent on an external power source to ensure reliable operation\nacross all components.\nAs a response, we have (1) added the following description to the Method section:\n“The overall FSPVS relies on an external power source to ensure reliable operation of\nall components.” in the manuscript.\n(2) added Supplementary Fig. S29 in the Supplementary Information to show the\ndriving circuit and added corresponding explanation: “Inspired by the unique sensory\nand neural characteristics of octopuses, we developed a flexible bionic tactile-visual-\noptical feedback neural network system to harness the potential of PENG, AOS, and\nCS-QLED (Supplementary Fig. S29). The signal acquisition and processing workflow of\nthe system is illustrated in Fig. 4d. Since CS-QLED requires a substantial driving\nvoltage to induce color changes from red to green, a current amplification module was\nincorporated to fulfill its operational demands. The designed circuit can function\nadequately when receiving only visual signal input without tactile input. Specifically,\nPENG detects pressure signals, and AOS senses light signals and processes the\ncombined information of pressure and light stimuli to generate EPSCs. Subsequently,\nthese EPSCs are captured by the conversion unit in the peripheral circuit and\ntransformed into voltage pulses of varying amplitudes, which drive the CS-QLED for\nvisual output with shifting colors from red to green. Furthermore, the light emitted by\nthe CS-QLED is detected by an avalanche photodiode connected to an oscilloscope.”\nin the manuscript.\nSupplementary Fig. S29. a Schematic diagram of the circuit inspired by the octopus.\nb Optical image of the circuit inspired by octopus (scale bar: 2 cm). c Optical images\nof the flexible PCB during the bending process (right), and a magnified view of the\nflexible PCB (left, scale bar: 0.5 cm).\n(3) added Supplementary Fig. S31 in the Supplementary Information to show the\ndriving circuit and added corresponding description: “In this application scenario where\nthe CS-QLED is only required to emitting light rather than achieving color-changing\nfunction (that is, the driving voltage is small), we have designed a highly integrated\nFSVPS (based on L-MXene-integrated PENG, AOS, and CS-QLED); it can operate\neffectively without an additional amplification module, merely by providing a turn-on\nvoltage (Supplementary Fig. S31).” in the manuscript.\nSupplementary Fig. S31. a Circuit diagram of FSPVS. b Optical images of the circuit\nof FSPVS attached to the wrist. Although the PENG can directly drive the CS-QLED\nto emit light, its range of brightness variation is relatively limited. To enhance the\nbrightness modulation of the CS-QLED, this system only requires a power supply to\nprovide a bias close to the turn-on voltage for the CS-QLED, enabling the piezoelectric\npulses from the PENG and light stimuli to cooperatively modulate the resistance value\nof the AOS, thus guiding more current to the CS-QLED and effectively regulating its\nbrightness for detection by the avalanche diode.\n- What are the specific operational ranges of the PENG and AOS modules?\nResponse: We greatly appreciate your valuable comments. To further explore the\nspecific operational ranges of the PENG, we have conducted additional tests on the\nPENG output under higher stimulation of 13 N. Within the pressure range of 1.5–13 N,\nthe amplitude of the piezoelectric output signal increased significantly with the applied\npressure. Supplementary Fig. S10 illustrates the variation of the composite film’s\noutput current as a function of pressure. These results further confirm that the composite\nfilm exhibits excellent linear response characteristics within the tested pressure range.\nThis linear relationship facilitates the processing and calibration of sensing signals,\nthereby enabling more accurate pressure measurements. The pressure sensitivity,\ndefined as the slope of the line in Fig. 2f, was measured to be 0.419 ± 0.015 μA N-1,\nwith a corresponding coefficient of determination (R2) of 0.993. Although PENGs have\nbeen widely reported to respond to pressures from 0.05 kPa to 100 kPa (Nat. Commun.\n2023, 14, 4000; Adv. Mater. 2024, 36, 2307896), due to limitations in our experimental\nsetup, this study was only able to test pressures from 1.5 N to 13 N (approximately\n2.12–18.4 kPa). Nevertheless, the device still demonstrated outstanding linear response\ncharacteristics, highlighting its strong sensing performance.\nTo further explore the specific operational ranges of the AOS, we also conducted\nsupplementary tests on the photoelectric response characteristics of the AOS device\nunder different light intensities (Supplementary Fig. S25a, 6 to 20 mW cm-2). As the\nintensity of the light pulse increases, the amplitude and retention time of the EPSC also\ngradually increase, a phenomenon similar to the transformation process of biological\nsynapses from short-term plasticity (STP) to long-term potentiation (LTP). Under our\ntest conditions, the linear response range of PENG is 1.5–13 N, and the optical response\nrange of AOS is 6–20 mW cm-2.\nAs a response, we have (1) revised Fig. 2f and corresponding description as follows:\n“Fig. 2f and Supplementary Fig. S10 show that the short-circuit current of MXP-0.5\nincreases linearly with applied pressure ranging from 1.5 to 13 N, demonstrating a\nlinear relationship between output current and pressure (piezoelectric sensitivity line\nslope: 0.419 ± 0.015 μA N-1), which allows for precise signal calibration and accurate\npressure estimation.” in the manuscript.\nFig. 2. Characterization of MXene-dual-functional PENG. f Short-circuit of MXP-\n0.5 at different compression forces.\n(2) revised Supplementary Fig. S10 in the Supplementary Information:\nSupplementary Fig. S10. Output current of the device under various forces and the fit\nlines of the relationship between current and force.\nSupplementary Fig. S10 illustrates the relationship between the output current of the\ncomposite film and the applied pressure. These results confirm a linear relationship\nbetween the two variables in the composite film. This linear relationship facilitates the\nprocessing and calibration of sensing signals, thereby enabling more accurate pressure\nestimation. The slope of the piezoelectric sensitivity line is 0.419 ± 0.015 μA N-1, with\na coefficient of determination of 0.993.\n(3) added Supplementary Fig. S25a in the Supplementary Information and added the\nfollowing sentence: “The photocurrent response behavior of the device under different\nultraviolet (UV, 365 nm) light durations, pulse numbers, and light intensities is\npresented in Figs. 3g, 3h, and Supplementary Fig. S25a, respectively, demonstrating\nthe regulatory effect of optical stimulation parameters on EPSC.” in the manuscript.\nSupplementary Fig. S24. a Photoresponse of the AOS device under illumination with\nvaried light intensity at a fixed wavelength of 365 nm and a fixed exposure time of 1 s.\n5. In the dynamic trajectory and motion recognition application (badminton recognition\nsystem), the claimed multimodal sensory functionality is not clearly demonstrated. The\nvisual and pressure signals appear completely isolated, with each modality individually\nimplemented. This configuration fails to clearly exhibit the advantages of multimodal\nintegration. Furthermore, the authors should clarify how pressure information is\nintegrated into the reservoir computing task.\nResponse: Thanks for the reviewer’s careful comments and suggestions.\nIn our system, the multimodal sensory functionality can be demonstrated through the\nsimultaneous capture of tactile and visual signals, resulting in clearly distinguishable\nluminescent color and intensity variations under different stimulus intensities (newly\nadded Fig. 4g), which effectively validates the system’s ability to perform multimodal\nsensing and information processing. In the badminton scenario, the identification of\nstrike types can be performed in an electrical mode using the AOS reservoir, while\ndynamic racket trajectory recognition is processed in an optical mode through the same\nAOS reservoir. In contrast to other multimodal information processing approaches—\nthat integrate two modalities to produce a single output—this system enables the\nindependent and effective processing of multiple modalities, resulting in two distinct\nand reliable classification outputs. Further multimodal fusion can be achieved in a\ndecision-level way where all results processed within the reservoir are transmitted to\nthe subsequent neural network for integration, thereby enabling the output of more\ncomprehensive information. This multimodal processing is consistent with the reported\nwork (Nat. Commun. 2024, 15, 3257, without achieving device integration).\nWe highlight that our work focuses on MXene-based flexible dual-mode sensing-\nprocessing-visualizing integrated system that unifies these functions into a single\nwearable device with optimized mechanical and electrical interfaces. To further clearly\nexhibit the advantages of multimodal integration in our device in multimodal\ninformation processing, we have introduced a new application (newly added Figure 5a).\nAs a response, we have (1) added Supplementary Fig. S27 and S30 in the\nSupplementary Information, and modified Fig. 5 (added and reconstructed the\nexperiments and neural network architecture) and corresponding description as follows:\n“To exhibit the advantages of our integrated system, we implemented multimodal\nimage recognition tasks. When identifying contaminated images (Fig. 5a), the left\nside—being covered by opaque materials—can only be perceived through tactile\nsignals (electrical signals), while the right side remains perceivable via visual signals\n(optical signals). Therefore, a multimodal mixed-input “EEOO” mode (comprising two\nelectrical pulses followed by two light pulses) is employed. This mode demonstrates\ndistinct functional spatial partitioning: the “EE” segment selectively responds to the left\nhalf of the image (with the “OO” segment inactive), whereas the “OO” segment\nspecifically responds to the right half (with the “EE” segment inactive). The original\n28×28 pixel images were restructured into a 196×4 pixel format. Subsequently, the\nprocessed images were read using a mixed-input “EEOO” mode. A total of 196 input\nchannels were fed into corresponding memory units for processing. Supplementary Fig.\nS30a illustrates the distribution of weights in the fully connected layer. After the\ntraining process, the synaptic weights change from a random to a normal distribution,\nindicating that the neural network has been trained effectively. The confusion matrix\n(Fig. 5b) and the achieved accuracy of 91% (Supplementary Fig. S30b) show the\neffectiveness of the mixed-input strategy in multimodal recognition tasks.” in the\nmanuscript.\n(2) added Network training to the Methods section to clarify how pressure\ninformation is integrated into the reservoir computing task as follows: “For strike types\nrecognition: the experimental participant swung the racket with varying types, and raw\ndata was collected. The one-dimensional strike-type data (2000) was converted into a\nfour-row pixel format (4×500) through cropping and binarization. Subsequently, the\nstrike type data are fed into the AOS reservoir in electrical mode for computation. The\nAOS reservoir in the electrical mode consists of 500 storage devices that serve as\nreservoir nodes. Then, a single-layer fully connected neural network with 500 input\nnodes was designed to perform the strike types recognition task.” in the manuscript.\nFig. 5. Multi-mode information perception and recognition. a Schematic of\nrecognizing the multimodal Fashion-MNIST in complex environments. b Confusion\nmatrix for classifying seven categories of images. c Schematic of strike type recognition,\ndynamic racket swing trajectory recognition, and position recognition implemented by\nFSPVS in a simple badminton sports scene towards metaverse (R denotes the distance\nbetween the light-emitting point and the receiver detector). d Schematic of the in-sensor\nRC system for dynamically recognizing the strike types and racket swing trajectories.\ne Confusion matrix for classifying distinct striking types. f and g Readout function (F)\nvalue of the unit of the read-out layer, representing the probability of the corresponding\ntrajectory. h Initial and final weight distributions before/after training. i Cross-entropy\nloss and accuracy for strike type and racket swing trajectory recognition.\nSupplementary Fig. S27. Experimental outputs of 16 different 4-bit “EEOO” pulse\ntrains, ranging from (0000) to (1111). Under the mixed-input mode “EEOO”, the system\ndemonstrates distinguishable final states even when starting from identical initial\nconditions. The capability to reliably differentiate among 16 output states highlights the\npromising applicability of our device in mixed-input RC systems.\nSupplementary Fig. S30. Mixed-input reservoir for recognition of multimodal\nFashion-MNIST datasets. a Initial and final weight distributions before/after training.\nb Image recognition accuracy rate.\n6. Although the authors state that “the system demonstrated its tactile-visual signal\nrecognition applications for environment adaption and interaction in metaverse\nscenarios.” the manuscript does not describe how metaverse scenarios are realized.\nSimple dynamic trajectory recognition alone is insufficient to justify such claims.\nResponse: We greatly appreciate your valuable comments. The metaverse is a digital\nthree-dimensional space that integrates virtual reality (VR), augmented reality (AR),\nartificial intelligence (AI), and Internet of Things (IoT) technologies. It expands human\nperception capabilities through multi-sensory real-time perception and processing\ntechnologies as well as high-precision spatial positioning technologies, thereby\nachieving an immersive experience of the deep integration of virtual and real worlds.\nMetaverse technology needs to have functions such as multi-modal perception,\nwearable integrated perception-processing system, trajectory recognition, and precise\nspatial positioning (Supplementary Fig. S32). Our work designed and constructed a\nflexible perception-processing-visualization integrated system (FSPVS) based on the\nmultifunctional properties of MXene and its derivatives, which has the potential to\npartially realize certain functionalities (by taking the badminton sports scene as a\nconceptual example) that are highly desired in the metaverse.\nAs a response and to avoid misleading readers, we have modified the corresponding\ndescription of “the system demonstrated its tactile-visual signal recognition\napplications for environment adaption and interaction in metaverse scenarios” as\nfollows: “The system demonstrates its tactile-visual signal recognition capabilities for\nenvironmental adaptation and functionalities that are highly desired in the metaverse.”\nand added Supplementary Fig. S32 and corresponding description: “This wearable\nsystem can achieve several key functions including multi-modal perception, integrated\nperception-processing architecture, trajectory recognition, and accurate spatial\npositioning (Supplementary Fig. S32), which is highly desired for metaverse\napplications.” in the manuscript.\nSupplementary Fig. S32. Schematic showing the illustration of our simple badminton\nexample satisfying key requirements that are highly desired in the metaverse in the\nfuture. The metaverse is a digital three-dimensional space that integrates virtual reality\n(VR), augmented reality (AR), artificial intelligence (AI), and Internet of Things (IoT)\ntechnologies. It expands human perception capabilities through multi-sensory real-time\nperception and processing technologies as well as high-precision spatial positioning\ntechnologies, thereby achieving an immersive experience of the deep integration of\nvirtual and real worlds. Metaverse technology needs to have functions such as multi-\nmodal perception, wearability, and integrated perception-processing system, trajectory\nrecognition, and precise spatial positioning (Supplementary Fig. S32). However,\ncurrent technologies are limited by the dispersion and low efficiency of modular design\nand information processing architecture, resulting in complex system structures, high\nenergy consumption, and significant impacts on immersive experience and\ninterconnection performance. Our work designed and constructed a FSPVS based on\nthe multifunctional properties of MXene and its derivatives, which has the potential to\npartially realize certain functionalities associated with the metaverse. Taking a simple\nbadminton sports scene as a conceptual example, we proposed a motion scheme of dual-\nmodal perception-processing-light feedback of touch and vision, fully leveraging the\nunique advantages of this system. This system can dynamically recognize the strike\ntype and racket swing trajectory, and achieve high-precision spatial positioning at the\nsame time, providing an important reference for building a deeply integrated metaverse\nthat connects humans, physical environments, and virtual spaces.\n7. Simulation experiments lack essential details. Specifically, the datasets and neural\nnetwork architecture used for classification and racket trajectory prediction tasks are\nnot provided.\nResponse: We greatly appreciate your valuable comments. In the Methods section, we\nhave supplemented detailed simulation content, including the neural network\narchitecture and parameter settings. Additionally, in the Supplementary information,\nwe have offered the dataset construction process for classification tasks and racket\ntrajectory prediction (Supplementary Figs. S33−S35).\nAs a response, we have added Supplementary Figs. S33−S35, and added neural\nnetwork architecture details to the Methods section:\n“Network training\nFor multimodal Fashion-MNIST recognition: the original 28×28 pixel images were\nbinarized and restructured into a 196×4 pixel format. Subsequently, the processed\nimages were read by using a mixed-input “EEOO” mode. The covered yellow pixels\non the left half could only be sensed via electrical pulses from tactile signals, whereas\nthe purple pixels represent their absence; all pixels on the right half indicate the absence\nof an electrical pulse. The yellow pixels on the right half correspond to light pulse inputs.\nA total of 196 input channels were fed into corresponding memory units for processing.\nThen, a single-layer fully connected neural network is employed to perform the\nmultimodal Fashion-MNIST recognition task.\nFor strike types recognition: the experimental participant swung the racket with varying\ntypes, and raw data was collected. The one-dimensional strike-type data (2000) was\nconverted into a four-row pixel format (4×500) through cropping and binarization.\nSubsequently, the strike type data are fed into the AOS reservoir in electrical mode for\ncomputation. The AOS reservoir in the electrical mode consists of 500 storage devices\nthat serve as reservoir nodes. Then, a single-layer fully connected neural network with\n500 input nodes was designed to perform the strike types recognition task. For racket\nswing trajectory recognition: image sequences of each racket trajectory were captured,\nand the frame difference method combined with binarization processing was applied to\ngenerate image sequences. Each input image (56×56) was divided into four consecutive\npixel rows (4×3136), spanning from the leftmost to the rightmost column in a top-down\nmanner. Subsequently, the swing trajectory information was fed into the AOS reservoir\nin optical mode for computation. The AOS reservoir in the optical mode consists of\n3136 storage devices that serve as reservoir nodes. Their conductance values are\nprogrammed and calibrated based on experimentally measured data from individual\ndevices. Then, a single-layer fully connected neural network with 3136 input nodes was\ndesigned to perform the racket swing trajectory recognition task. The readout layer was\nimplemented in software, and the model was trained using the Adam optimizer’s\ngradient descent algorithm (with an initial learning rate of 0.001) to minimize\nclassification cross-entropy loss. Finally, the data were divided into training and test\nsets to evaluate the performance of the model.” in the manuscript.\nSupplementary Fig. S33. Framework diagram of recognition processes for the strike\ntypes and racket swing trajectories. The recognition processes are realized through the\nAOS reservoir in different modes. In the badminton scenario, the identification of strike\ntypes can be performed in an electrical mode using the AOS reservoir, while dynamic\nracket trajectory recognition is processed in an optical mode through the same AOS\nreservoir. In contrast to other multimodal information processing approaches—that\nintegrate two modalities to produce a single output—this system enables the\nindependent and effective processing of multiple modalities, resulting in two distinct\nand reliable classification outputs. Further multimodal fusion can be achieved in a\ndecision-level way where all results processed within the reservoir are transmitted to\nthe subsequent neural network for integration, thereby enabling the output of more\ncomprehensive information. The detailed description is offered in the Methods.\nSupplementary Fig. S34. Dataset (part) of the strike types recognition.\nSupplementary Fig. S35. Dataset (part) of badminton racket swing trajectories\n(direction 1) after data reduction and binarization processing.\nReferences\n1. Lu, X., Zhang, Y. & Zheng, Z. Metal-Based Flexible Transparent Electrodes:\nChallenges and Recent Advances. Adv. Electron. Mater. 7, 2001121 (2021).\n2. Macher, S., Rumpel, M., Schott, M., Posset, U., Giffin, G. A. & Löbmann, P.\nAvoiding Voltage-Induced Degradation in PET-ITO-Based Flexible\nElectrochromic Devices. ACS Appl. Mater. Interfaces 12, 36695-36705 (2020).\n3. Shankar, U., Oberoi, D. & Bandyopadhyay, A. A review on the alternative of\nindium tin oxide coated glass substrate in flexible and bendable organic\noptoelectronic device. Polym. Adv. Technol. 33, 3078-3111 (2022).\n4. Fan, X. et al. PEDOT:PSS for Flexible and Stretchable Electronics:\nModifications, Strategies, and Applications. Adv Sci 6, 1900813 (2019).\n5. Fan, X. et al. PEDOT:PSS materials for optoelectronics, thermoelectrics, and\nflexible and stretchable electronics. J. Mater. 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Commun. 15, 1566 (2024).\n18. Downes, M., Shuck, C. E., McBride, B., Busa, J. & Gogotsi, Y. Comprehensive\nsynthesis of Ti C T from MAX phase to MXene. Nat. Protoc. 19, 1807–1834\n3 2 x\n(2024).\n19. Luo, L. et al. MXene-GaN van der Waals metal-semiconductor junctions for\nhigh performance multiple quantum well photodetectors. Light Sci. Appl. 10,\n177 (2021).\n20. Xu, X., Guo, T., Lanza, M. & Alshareef, H. N. Status and prospects of MXene-\nbased nanoelectronic devices. Matter 6, 800-837 (2023).\n21. Mathis, T. S. et al. Modified MAX Phase Synthesis for Environmentally Stable\nand Highly Conductive Ti C MXene. ACS Nano 15, 6420-6429 (2021).\n3 2\n22. Shayesteh Zeraati, A., Mirkhani, S. A., Sun, P., Naguib, M., Braun, P. V. &\nSundararaj, U. Improved synthesis of Ti C T MXenes resulting in exceptional\n3 2 x\nelectrical conductivity, high synthesis yield, and enhanced capacitance.\nNanoscale 13, 3572-3580 (2021).\n23. Zhang, C. J. et al. Transparent, Flexible, and Conductive 2D Titanium Carbide\n(MXene) Films with High Volumetric Capacitance. Adv. Mater. 29, 1702678\n(2017).\n24. Park, G. S. et al. Comb-type polymer-hybridized MXene nanosheets dispersible\nin arbitrary polar, nonpolar, and ionic solvents. Sci. Adv. 8, eabl5299 (2022).\n25. Zhou, C. et al. Hybrid organic–inorganic two-dimensional metal carbide\nMXenes with amido- and imido-terminated surfaces. Nat. Chem. 15, 1722-1729\n(2023)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The paper has significantly improved after revision. However, I still have concerns regarding the configuration and the results obtained from the FSPVS. In the revised manuscript, the author presents an optical image and the fabrication process of a fully integrated FSPVS (revised FSPVS), and the additional results appear to have been obtained from this revised version.\n\nHowever, based on the description in the main text and the Methods section of the original manuscript, the initial FSPVS appears to have been assembled from individual devices placed on separate substrates. All results presented in the original manuscript were obtained from this initial version.\n\nThis raises concerns about the consistency of the reported data. If the initial FSPVS had been fabricated on a single substrate—as implied in the revised version—why were the optical image and detailed fabrication process of the fully integrated FSPVS not included in the original Methods section? This discrepancy suggests that the initial and revised FSPVS devices are not equivalent, which may affect the interpretation and reliability of the results.", "reviewer_3": "This manuscript presented a MXene-based bioinspired flexible dual-mode sensing–processing–visualizing integrated system for efficient tactile and visual signal perception, processing, and feedback. Although the authors have provided a comprehensive point-by-point response to the reviewers' comments, certain problems remain to be addressed and should be resolved prior to further consideration for publication.\n\n(1)The manuscript lacked sufficient data on the reproducibility and stability of the synaptic devices and light-emitting units under bending conditions. It would be beneficial to supplement the manuscript with electrical and optical performance evaluations under varying bending degrees—such as different radii of curvature or strain ratios—to comprehensively assess the flexibility of the integrated system.\n(2)The manuscript emphasizes the use of MXene as multiple roles to achieve compatibility in both mechanical and electronic interfaces. However, the energy consumption and efficiency of this integrated system is not mentioned. The authors should add them. Moreover, it would be valuable to provide a more detailed analysis of whether the excellent compatibility is attributed solely to the uniformity of the material.\n(3)For Figure 3i, it is recommended to add gridlines to both the horizontal and vertical axes to facilitate the reader's understanding of the specific numerical relationships across different frequencies. For Figure 3j, the number of data points appears limited. Additional data points should be included to enhance the reliability of the fitting and the clarity of the trend.\n(4)When optical and pressure stimuli are applied simultaneously and the optical input dominates, what will be the resulting output light signal? Or, the pressure input dominates, what will be the resulting output light signal? It is recommended to clearly explain the decision logic or output mapping relationship under such multimodal input conditions.\n(5)It is unclear whether the system requires an external 365 nm ultraviolet light source in the badminton application scenario, and if so, whether this may limit its practicality in real-world settings. Furthermore, the optical pulse appears to play a relatively limited role in the final application (e.g., motion recognition and positioning). It is recommended to highlight the specific contribution of optical signals within the processing pipeline. In addition, if a strong connection to the metaverse concept cannot be clearly established, it may be preferable to avoid mentioning the term altogether in the manuscript."}, "author_response": "Point-by-point responses to the reviewers’ comments\n(Comments are in italics, responses in blue, and manuscript corrections in yellow.)\nThe authors would like to thank the reviewers for their helpful comments to improve\nthe article’s quality. The manuscript has been corrected accordingly, and point-by-point\nresponses to the reviewers’ comments are as follows:\nReviewer #1\nComments:\nThe paper has significantly improved after revision. However, I still have concerns\nregarding the configuration and the results obtained from the FSPVS. In the revised\nmanuscript, the author presents an optical image and the fabrication process of a fully\nintegrated FSPVS (revised FSPVS), and the additional results appear to have been\nobtained from this revised version. However, based on the description in the main text\nand the Methods section of the original manuscript, the initial FSPVS appears to have\nbeen assembled from individual devices placed on separate substrates. All results\npresented in the original manuscript were obtained from this initial version. This raises\nconcerns about the consistency of the reported data. If the initial FSPVS had been\nfabricated on a single substrate—as implied in the revised version—why were the\noptical image and detailed fabrication process of the fully integrated FSPVS not\nincluded in the original Methods section? This discrepancy suggests that the initial and\nrevised FSPVS devices are not equivalent, which may affect the interpretation and\nreliability of the results.\nResponse: We sincerely appreciate your thorough review of our manuscript and your\nrecognition of the revisions we have made. In the original manuscript, the optical\nimages of the fully integrated FSPVS and the detailed fabrication process were not\nincluded for the following reasons:\n(1) The monolithic integration process closely mirrors that of discrete devices, and the\nseparated description is more detailed. To avoid redundant description, we omitted the\nrelevant statements in the original version and have added them in the revised version.\n(2) Although PENG and AOS had already been integrated at the time—demonstrating\nthe technical feasibility of integrating CS-QLED—the precise characterization of CS-\nQLED’s photometric properties (including luminance and chromaticity) required\nspecialized fixtures in the equipment of a fiber-coupled integrating sphere (XP-EQE-\nAdv, Fig. R1a); in this way, the luminance and chromaticity of the integrated FSPVS\nfail to be tested. For testing convenience, the CS-QLED was not monolithically\nintegrated with the PENG and AOS in the initial device configuration, so we did not\noffer the optical images of the fully integrated FSPVS in the original version. To deal\nwith the test challenge, we have refined the measurement approach in the previously\nrevised version by employing a specialized radiance colorimeter (Everfine, SRC-600,\nFig. R1b); the radiance colorimeter does not require specialized fixtures and can\nevaluate the optoelectronic performance of CS-QLED in the monolithically integrated\nFSPVS. Based on this, we supplemented the manuscript with the fabrication procedure\nand corresponding optical images of the monolithically integrated FSPVS. Using data\nacquired from this fully integrated device, we updated the results presented in Figs. 4g\nand 5e–i. We have added “The luminance and chromaticity of the integrated FSPVS\nwere measured using a specialized radiance colorimeter (Everfine, SRC-600).” in the\nDevice Characterization in this revision.\nFig. R1. Optical images of a fiber-coupled integrating sphere (XP-EQE-Adv) and b\nspecial radiance color meter (Everfine, SRC-600).\nWe further replace the data in Figs. 4c, 4h, and 5k–l with those obtained from the\nmonolithically integrated FSPVS in this revision. All system-level integration data and\napplication demonstrations reported in the manuscript are now based on the\nmonolithically integrated FSPVS. The consistency between the updated and previously\nreported data is attributed to the identical fabrication processes, material systems, and\nelectrode configurations used. Furthermore, we have added the current density–\nvoltage–luminance (J–V–L) characteristics of CS-QLED (Supplementary Fig. S31) to\nprovide a more comprehensive assessment of its light-emitting performance. We have\nadded Supplementary Fig. S31 in the Supplementary Information and corresponding\ndescription: “Supplementary Fig. S31 presents the current density–voltage–luminance\ncharacteristic curves of the CS-QLED.” in this revision.\nWe believe that the revisions presented have fully addressed the concerns raised\nregarding data consistency.\nFig. 4. Visualized dual-mode sensory for environment-adaptive application based\non FSPVS. a Schematic illustrating the structure of the CS-QLED. b Energy-level\ndiagram and charge carrier transfer process of the CS-QLED. c Normalized\nelectroluminescence (EL) spectrum at various voltages. d Schematic of signal\nacquisition and processing in an FSPVS. Pressure stimulation from PENG and optical\nstimulation from AOS can modulate the weighting of AOS, thereby altering the voltage\noutput of CS-QLED, which subsequently enables the avalanche photodiode to detect\nlight with varying intensities and wavelengths. e Schematic of the octopus’s color\nchange upon light and tactile pressure stimulation. f Schematic of visualized dual-mode\nsensory for environment-adaptive application. g Light-emitting intensity change of the\nCS-QLED in response to different pressure and optical intensity stimuli. h Commission\nInternationale de L’Eclairage (CIE) diagram of CS-QLED at various voltages.\nFig. 5. Multi-mode information perception and recognition. a Schematic of\nrecognizing the multimodal Fashion-MNIST in complex environments. b Confusion\nmatrix for classifying seven categories of images. c Schematic of strike type recognition,\ndynamic racket swing trajectory recognition, and position recognition implemented by\nFSPVS in a simple badminton sports scene towards multisensory interaction scenarios\n(R denotes the distance between the light-emitting point and the receiver detector). d\nSchematic of the in-sensor RC system for dynamically recognizing the strike types and\nracket swing trajectories. e Confusion matrix for classifying distinct striking types. f\nand g Readout function (F) value of the unit of the read-out layer, representing the\nprobability of the corresponding trajectory. h Initial and final weight distributions\nbefore/after training. i Cross-entropy loss and accuracy for racket swing trajectory\nrecognition. j Schematic of the principle of position detection. k Estimated point\ncoordinates and actual point coordinates. l Training loss and validation loss of position\ndetection.\nSupplementary Fig. S31. Current density–voltage–luminance curve of CS-QLED.\nReviewer #3\nComments:\nThis manuscript presented a MXene-based bioinspired flexible dual-mode sensing–\nprocessing–visualizing integrated system for efficient tactile and visual signal\nperception, processing, and feedback. Although the authors have provided a\ncomprehensive point-by-point response to the reviewers' comments, certain problems\nremain to be addressed and should be resolved prior to further consideration for\npublication.\nResponse: We sincerely thank you for the positive comments and valuable suggestions.\nAs a response, we have conducted the following additional work: (1) performed a more\ncomprehensive evaluation of the device’s flexible performance; (2) carried out\ncalculations and comparative analysis of energy consumption; (3) revised and refined\nFigs. 3i and 3j to improve clarity and accuracy; (4) provided a detailed explanation of\nthe emission logic of CS-QLED under concurrent photoelectric stimulation. We believe\nthat the revised manuscript can adequately address the reviewer’s concerns.\nOur point-by-point responses to the reviewer’s comments are as follows:\n1. The manuscript lacked sufficient data on the reproducibility and stability of the\nsynaptic devices and light-emitting units under bending conditions. It would be\nbeneficial to supplement the manuscript with electrical and optical performance\nevaluations under varying bending degrees—such as different radii of curvature or\nstrain ratios—to comprehensively assess the flexibility of the integrated system.\nResponse: We greatly appreciate your valuable comment. As a response, we have\n(1) added Supplementary Fig. S28 in the Supplementary Information and\ncorresponding description: “We also conducted systematic tests under different bending\nradii to investigate the mechanical flexibility of the AOS. The results demonstrated that\neven when the bending radius was reduced to 5 mm, the electrical pulse conductance\nmodulation performance (Supplementary Fig. S28a) and the optical pulse response\ncurrent (Supplementary Fig. S28b) of the AOS did not show significant changes.” in\nthe manuscript.\nSupplementary Fig. S28. a Continuous conductance modulation under different\nbending radii by 15 positive electrical pulses at a fixed amplitude (1.9 V) and 15\nnegative electrical pulses at a fixed amplitude (–0.3 V), with both pulse width and\ninterval set to 0.37 s. b Photoresponse of AOS under different bending radii at a fixed\nwavelength of 365 nm.\n(2) added Supplementary Fig. S32 in the Supplementary Information and\ncorresponding description: “To investigate the mechanical flexibility of the CS-QLED,\nwe performed bending tests under varying radii (Supplementary Fig. S32). The results\ndemonstrate that the current density–voltage characteristics remain nearly constant as\nthe bending radius decreases from 10 mm to 5 mm, indicating preferable bending\ntolerance.” in the manuscript.\nSupplementary Fig. S32. Current density–voltage characteristics of CS-QLED under\ndifferent bending radii.\n2. The manuscript emphasizes the use of MXene as multiple roles to achieve\ncompatibility in both mechanical and electronic interfaces. However, the energy\nconsumption and efficiency of this integrated system is not mentioned. The authors\nshould add them. Moreover, it would be valuable to provide a more detailed analysis of\nwhether the excellent compatibility is attributed solely to the uniformity of the material.\nResponse: Thank you very much for your important comment.\nAs a response, we have added Supplementary Fig. S25 and Table S3 in the\nSupplementary Information and corresponding description to analyze the energy\nconsumption: “We also evaluated the energy consumption of the OSMX-based AOS\nunder single-pulse stimulation. The results reveal a low energy consumption of 1.094\npJ (Supplementary Fig. S25), indicating high energy efficiency among the existing two-\nterminal MXene-based neuromorphic devices (Supplementary Table S3).” in the\nmanuscript.\nBased on our present study, excellent interface matching could be attributed to\nseveral key factors: (1) Process compatibility. The adoption of an all-solution spin-\ncoating approach minimizes interfacial mismatch arising from variations in equipment\nor processing conditions. (2) Use of homogeneous materials. The consistent\nincorporation of MXene and its derivatives across PENG, AOS, and CS-QLED units\ncontributes to mitigating interfacial mismatch due to material property differences. (3)\nRational material selection. Material screening should account for the functional\nrequirements of individual components, with material tuning enabling the coordinated\noptimization of functionality and performance across device subsystems; the rich and\ntunable physicochemical properties of MXene provide a robust foundation for such\nintegration.\nWe have added Supplementary Note S1 and the following description to analyze the\ninterface matching: “The use of homogeneous MXene-based materials in system\nintegration not only simplifies the fabrication process but also enhances the mechanical\nflexibility and electrical contact of individual components (see Supplementary Note S1\nfor a more detailed analysis of interface matching).1” in the manuscript.\nSupplementary Fig. S25. Energy consumption of the AOS device.\nEnergy consumption (E) can be calculated using the following formula:\nE=V ×I ×t\npulse pulse width\nwhere V represents the pulse amplitude, I denotes the pulse current, and t\npulse pulse pulse\nrefers to the duration of the pulse current.2\nWe integrated the instantaneous power over the entire time period, which\nencompassed the 20 ms pulse and the subsequent relaxation process under a read\nvoltage of 0.01 V. The resulting total energy consumption was approximately 1.094 pJ,\nrepresenting a conservative upper bound estimate. Despite this comprehensive\nevaluation, the energy consumption of the OSMX-based AOS remains considerably\nlower than that of previously reported MXene-based neuromorphic devices, as\nsummarized in Supplementary Table S3. Further reductions in energy consumption can\nbe achieved by minimizing the electrode area.\nSupplementary Table S3. Comparison of the state-of-the-art two-terminal MXene-\nbased neuromorphic devices.\nAvailability Functional Energy Electrode area\nDevice structure Flexibility Ref.\nof stimuli integration consumption (J) (mm2)\nSi/lithium-polymer-\nElectrical No No 25.910-12 - 3\nelectrolyte/MXene/Au\nAg/Ti3C2Tx−TiO2/Pt Electrical No No 18.8210-9 0.00785 4\nAg/Ti3C2Tx:TiO2/Pt Electrical No No 2710-9 0.00785 5\nAu/MXene/violet phosphorus/Au Optical No No 14.710-12 - 6\nElectrical/\nAu/CuSCN/MXene−TiOx/TiO2/Au No Yes 6.5110-12 - 7\noptical\nElectrical/\nITO/V2C/V2O5-x/Au No No 1.7510-9 0.00196 8\noptical\nL-MXene/ZnO/OSMX- Electrical/ This\nYes Yes 1.09410-12 4\nPMMA/ZnO/Al optical work\nSupplementary Note S1. Analysis of interface matching of MXene-based FSPVS.\nRegarding mechanical interface matching, the utilization of homogeneous MXene\nsimplifies fabrication processes and enhances interfacial adhesion through nanosheet\nintegration; its superior mechanical flexibility and electrical conductivity enable the\nreplacement of conventional electrodes, thereby significantly improving mechanical\ndurability. Mechanical interface matching at both the inter-device and intra-device\nlevels: (i) MXene enables inter-device integration through interconnected nanosheets,\nwhich not only simplifies the fabrication process but also facilitates the formation of\nmore uniform and coherent interfaces. Furthermore, homogeneous materials minimize\nmechanical property mismatches, thereby enhancing interfacial compatibility.1 (ii) For\nintra-device level, ultrathin MXene nanosheets exhibit exceptional mechanical\nflexibility, electrical conductivity, and optical transparency. These properties allow\nthem to replace Cu tape electrodes prone to interfacial contact failure in PENG, as well\nas brittle ITO electrodes in flexible optoelectronic devices, significantly improving\nmechanical durability during bending operations. Specifically, (i) In the integrated\nsystem, L-MXene was employed as the electrode material to connect PENG, AOS, and\nCS-QLED, thereby minimizing mechanical interfacial mismatch caused by the use of\ndifferent fabrication processes. (ii) For PENG, we use L-MXene to replace the\ntraditional Cu tape electrode. Compared with the Cu tape that deforms during the\nbending process (Supplementary Fig. S5), the –OH terminal groups on the surface of\nutrathin L-MXene are capable of forming hydrogen bonds with the P(VDF-TrFE) film,\nthereby establishing a tighter and more stable interface connection (Supplementary Fig.\nS6). This demonstrates that L-MXene exhibits excellent mechanical compatibility with\nthe P(VDF-TrFE) film, which in turn ensures superior cycling stability (Supplementary\nFig. S11). Moreover, introducing M-MXene, which has the same composition as L-\nMXene, into PENG helps enhance the tensile strength of the P(VDF-TrFE) film. (iii)\nFor AOS and CS-QLED, L-MXene is also used as the electrode material. Compared\nwith ITO, L-MXene has higher electrical conductivity and mechanical strength, and\ncan maintain good electrical conductivity even after 5000 bending cycles\n(Supplementary Fig. S18). This excellent performance is attributed to the hydrogen\nbond connection formed between L-MXene and the PET substrate,9 as well as the fact\nthat its layered structure can effectively disperse the stress generated during bending\nthrough sliding. Additionally, the OSMX material derived from S-MXene is utilized as\na functional layer in AOS, where the use of homologous materials helps mitigate\nmechanical mismatch effects.\nRegarding electrical interface matching, the utilization of homogeneous MXene\nmaterials not only simplifies the fabrication process and mitigates interfacial mismatch\nbut also reduces work function differences due to their intrinsic structural homogeneity,\nthereby substantially improving charge transfer efficiency and overall interface\ncompatibility. Electrical interface matching at both the inter-device and intra-device\nlevels: (i) MXene enables inter-device integration through interconnected nanosheets,\nwhich not only simplifies the fabrication process but also promotes the formation of\nmore uniform and coherent interfaces, thereby helping to reduce interface mismatch\nduring the connection of electrodes fabricated via different processes. (ii) At the intra-\ndevice level, the use of homogeneous materials contributes to minimizing work\nfunction differences. Specifically, (i) In the integrated system, L-MXene is used to\nconnect PENG, AOS, and CS-QLED devices. Compared with traditional metal\nelectrodes (such as ITO or other metals), the interface barrier between L-MXene\nnanosheets layers is lower, effectively reducing energy loss during charge transport. (ii)\nIn PENG, the tight hydrogen bond interface connection formed between L-MXene and\nthe P(VDF-TrFE) film significantly improves the charge collection efficiency and\ngenerates a current output higher than that reported in the literature.10 (iii) In AOS, the\nabundant hydrophilic groups on the surface of L-MXene allow ZnO NCs to be directly\ndeposited through various methods such as spin coating and atomic layer deposition\nwithout additional surface treatment steps.11 (iv) In CS-QLED, the tunable work\nfunction of L-MXene enables it to form a good interface contact with the hole transport\nlayer PEDOT:PSS, significantly reducing the hole injection barrier12 (Supplementary\nTable S1).\n3. For Figure 3i, it is recommended to add gridlines to both the horizontal and vertical\naxes to facilitate the reader's understanding of the specific numerical relationships\nacross different frequencies. For Figure 3j, the number of data points appears limited.\nAdditional data points should be included to enhance the reliability of the fitting and\nthe clarity of the trend.\nResponse: We greatly appreciate your valuable comments. As a response, gridlines\nhave been added to both the horizontal and vertical axes in Fig. 3i to facilitate the\nreader’s understanding of the specific numerical relationships across different\nfrequencies. We have added more data points in Fig. 3j to improve the reliability of the\nfitting and enhance the clarity of the trend, and have revised the corresponding\ndescriptions: “The time constants τ and τ for the photo-induced PPF were fitted to\n1 2\n0.241 s and 3.059 s, respectively (R2=0.998, calculation and fitting methods are\ndescribed in Supplementary Note S5).” in the manuscript accordingly.\nFig. 3. Characterization of synaptic properties of OSMX-based AOS.\nPhotoresponse of the AOS device under illuminating with varied g time, h pulses, and\ni frequencies at a fixed wavelength of 365 nm. j Variation of PPF index as a function of\ndifferent light interval time (Δt).\n4. When optical and pressure stimuli are applied simultaneously and the optical input\ndominates, what will be the resulting output light signal? Or, the pressure input\ndominates, what will be the resulting output light signal? It is recommended to clearly\nexplain the decision logic or output mapping relationship under such multimodal input\nconditions.\nResponse: We appreciate the reviewer’s helpful comments. For applications that mimic\nthe adaptive color changes of octopuses in response to external stimuli, the designed\ncircuit can still function adequately when receiving only visual signal input without\ntactile input, or tactile input in the absence of visual input. This functionality is achieved\nthrough a signal acquisition and processing workflow (Supplementary Fig. S33). When\nonly visual signals are present (or when visual input dominates), the luminous intensity\nof the CS-QLED increases with both light intensity and the number of light pulses (Fig.\n4g); similarly, under exclusive tactile input (or when tactile input dominates), the\nluminous intensity rises proportionally with applied pressure and the number of\npressure pulses (Fig. 4g).\nAs a response, we have added the following descriptions to further clearly explain the\ndecision logic or output mapping relationship under such multimodal input conditions:\n“The decision logic of the FSPVS operates as follows: the PENG detects pressure\nsignals, and the AOS receives optical inputs. Both pressure and light stimuli can act on\nthe AOS to trigger the generation of EPSCs independently or synergistically. These\nEPSCs are then captured by a conversion unit in the peripheral circuit and transformed\ninto voltage with varying amplitudes, which effectively drive the CS-QLED to produce\nvisual output and enable color switching.” “When both light and pressure stimuli are\napplied simultaneously, with the light input being dominant (20 mW·cm-2 light stimulus\nand 1.5 N pressure stimulus), the CS-QLED emits yellow light that exceeds threshold\n1 (state 2). Conversely, when the pressure stimulus dominates (5 N pressure stimulus\nand 6 mW·cm-2 light stimulus), the CS-QLED initially emits yellow light above\nthreshold 1 and gradually transitions to green light surpassing threshold 2 (state 3: alarm\nmode) as the number of stimuli increases. When moderate pressure and light intensity\nare concurrently applied, the CS-QLED emits yellow light exceeding threshold 1 (state\n2). When both high pressure and high light intensity are simultaneously applied, the\nCS-QLED exceeds threshold 2 (state 3) and emits green light.” in the manuscript.\nFig. 4. Visualized dual-mode sensory for environment-adaptive application based\non FSPVS. g Light-emitting intensity of the CS-QLED varies in response to different\npressure and optical intensity stimuli.\nSupplementary Fig. S33. a Schematic diagram of the circuit inspired by the octopus.\nb Optical image of the circuit inspired by octopus (scale bar: 2 cm). c Optical images\nof the flexible PCB during the bending process (right), and a magnified view of the\nflexible PCB (left, scale bar: 500 μm).\nFor applications that mimic the adaptive color changes of octopuses in response to\nexternal stimuli, the designed circuit can still function adequately when receiving only\nvisual signal input without tactile input, or tactile input in the absence of visual input.\nThis functionality is achieved through a signal acquisition and processing workflow\n(Supplementary Fig. S33). When only visual signals are present (or when visual input\ndominates), the luminous intensity of the CS-QLED increases with both light intensity\nand the number of light pulses (Fig. 4g); similarly, under exclusive tactile input (or\nwhen tactile input dominates), the luminous intensity rises proportionally with applied\npressure and the number of pressure pulses (Fig. 4g).\n5. It is unclear whether the system requires an external 365 nm ultraviolet light source\nin the badminton application scenario, and if so, whether this may limit its practicality\nin real-world settings. Furthermore, the optical pulse appears to play a relatively\nlimited role in the final application (e.g., motion recognition and positioning). It is\nrecommended to highlight the specific contribution of optical signals within the\nprocessing pipeline. In addition, if a strong connection to the metaverse concept cannot\nbe clearly established, it may be preferable to avoid mentioning the term altogether in\nthe manuscript.\nResponse: Thank you for the reviewer’s insightful comments and suggestions.\nRegarding the necessity and practicality of the 365 nm ultraviolet light source: Our\nAOS device is activated by 365 nm UV light sources, rendering this specific UV\nwavelength essential for system functionality. Furthermore, the detection and\nrecognition of invisible UV light are crucial for the development of advanced artificial\nvisual perception systems (Nat. Nanotechnol. 2019, 14, 776–782; Nat. Photonics 2023,\n17, 629–637). UV excitation not only effectively eliminates interference from visible\nlight but also efficiently promotes electron transitions in semiconductor materials due\nto its high photon energy. This ultraviolet sensing application has been successfully\ndemonstrated in other memristor systems and further extended to human body\nrecognition (Nat. Nanotechnol. 2024, 20, 93–103; Nat. Commun. 2025, 16, 2263),\nconfirming their technical feasibility and practical applicability. While future\noptimization through material selection may enable tunable responsiveness to the near-\ninfrared spectrum, the primary focus of this work is the development of an MXene-\nintegrated multimodal perception-processing-optical feedback system. As a response,\nwe have added the following description: “The detection and recognition of invisible\nultraviolet (UV) light can effectively avoid visible light interference, which is beneficial\nin artificial visual systems.13” in the manuscript.\nRegarding the role of optical pulses and their specific contribution: Optical pulses\nplay a critical role in processing and recognizing the swing trajectory of racket. To\neffectively integrate spatial and temporal information from consecutive frames into a\ncompact representation, each frame is divided into four sequential subframes (t , t , t ,\n0 1 2\nand t ), corresponding to key moments during the swing process. When the racket—\n3\nrepresented by yellow pixels—is detected, a light pulse is applied to the AOS of the\nassociated pixels; otherwise, when no motion is detected—indicated by blue pixels—\nno pulse is applied. The temporal evolution of each pixel is encoded as a 4-bit sequence\ns(t), which is mapped onto a 16-bit binary vector. This sequence of light pulses (ranging\nfrom (0000) to (1111)) is then fed as input to the physical reservoir of the memristor,\ninducing changes in its EPSC value. As a result, the temporal dynamics of each pixel\nare embedded into the conductance state of a single memristor, enabling effective\ncharacterization of object motion over time. As a response, we have revised the\nfollowing description to highlight the specific contribution of optical signals within the\nprocessing pipeline: “For racket swing trajectory recognition, optical pulses are\nessential for information processing. To integrate the spatial and temporal information\nfrom consecutive frames into a compact representation, we divide each frame image\ninto four consecutive subframes (t , t , t , and t ), representing four distinct moments of\n0 1 2 3\nthe racket swing.” in the manuscript.\nRegarding the metaverse concept: To avoid misleading readers and as a response to\nyour suggestion, we have substituted the related description of metaverse concept with\nmultisensory human-computer interaction in the manuscript and Supplementary Fig.\nS36.\nReferences\n1. Zhang, Y. et al. Highly stable flexible pressure sensors with a quasi-\nhomogeneous composition and interlinked interfaces. Nat. Commun. 13, 1317\n(2022).\n2. Ji, Y. et al. Ultralow energy adaptive neuromorphic computing using\nreconfigurable zinc phosphorus trisulfide memristors. Nat. Commun. 16, 6899\n(2025).\n3. Wei, H. et al. Redox MXene Artificial Synapse with Bidirectional Plasticity and\nHypersensitive Responsibility. Adv. Funct. Mater. 31, 2007232 (2020).\n4. Sokolov, A., Ali, M., Li, H., Jeon, Y. R., Ko, M. J. & Choi, C. Partially Oxidized\nMXene Ti C T Sheets for Memristor having Synapse and Threshold Resistive\n3 2 x\nSwitching Characteristics. Adv. Electron. Mater. 7, 2000866 (2020).\n5. Mullani, N. B. et al. Surface Modification of a Titanium Carbide MXene\nMemristor to Enhance Memory Window and Low-Power Operation. Adv. Funct.\nMater. 33, 2300343 (2023).\n6. Ma, H., Fang, H., Xie, X., Liu, Y., Tian, H. & Chai, Y. Optoelectronic Synapses\nBased on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-\nOlfactory Crossmodal Perception. Nano-Micro Lett. 16, 104 (2024).\n7. Sha, S. et al. In Situ Oxidized MXene-Based Flexible Ultraviolet Optoelectronic\nSynapse for Neuromorphic Computing and Multimodal Perception. ACS\nPhotonics 12, 4709–4722 (2025).\n8. Qu, Y. et al. 2D Vanadium Carbide/Oxide Heterostructure-Based Artificial\nSensory Neuron for Multi-Color Near-Infrared Object Recognition. Adv. Mater.,\n10.1002/adma.202512238, e12238 (2025).\n9. Abdolhosseinzadeh, S., Jiang, X., Zhang, H., Qiu, J. & Zhang, C. Perspectives\non solution processing of two-dimensional MXenes. Mater. Today 48, 214–240\n(2021).\n10. Kamysbayev, V. et al. Covalent surface modifications and superconductivity of\ntwo-dimensional metal carbide MXenes. Science 369, 979–983 (2020).\n11. Wang, Z., Kim, H. & Alshareef, H. N. Oxide Thin-Film Electronics using All-\nMXene Electrical Contacts. Adv Mater 30, e1706656 (2018).\n12. Zhou, H. et al. Overcoming the Limitations of MXene Electrodes for Solution-\nProcessed Optoelectronic Devices. Adv. Mater. 34, 2206377 (2022).\n13. Zhou, F. et al. Optoelectronic resistive random access memory for\nneuromorphic vision sensors. Nat. Nanotechnol. 14, 776–782 (2019)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The paper has shown substantial improvement after revision and is now suitable for publication in Nature Communications.", "reviewer_3": "The authors have provided point-by-point responses to the comments, and the manuscript has been improved through the revisions. However, several problems still remain and should be addressed:\n1. While the authors have supplemented bending test, the performance was only tested when the bending radius was reduced to 5 mm and lacked long-term cyclic bending evaluations. Cyclic bending tests should be added. Furthermore, for consistency, the bending radius used throughout the manuscript should be unified. For instance, the bending radius in Supplementary Fig. S18 is 3 mm, whereas elsewhere it is 5 mm.\n2. Regarding energy consumption, the authors only provided data for the AOS unit. However, since the key innovation of this work lies in the MXene-based integration of both the PENG and AOS, the overall energy consumption of the entire integrated system (PENG + AOS) should be evaluated and discussed.\n3. The authors compared the performance of individual AOS devices, but the comparison with existing integrated systems (PENG + AOS) is missing. Such a comparison would better highlight the overall innovation and advantages of the proposed system."}, "author_response": "Point-by-point responses to the reviewers’ comments\n(Comments are in italics, responses in blue, and manuscript corrections in yellow.)\nThe authors would like to thank the reviewers for their helpful comments to improve\nthe article’s quality. The manuscript has been corrected accordingly, and point-by-point\nresponses to the reviewers’ comments are as follows:\nReviewer #1\nComments:\nThe paper has shown substantial improvement after revision and is now suitable for\npublication in Nature Communications.\nResponse: We sincerely appreciate your review of our manuscript and your recognition\nof the revisions we have made.\nReviewer #3\nComments:\nThe authors have provided point-by-point responses to the comments, and the\nmanuscript has been improved through the revisions. However, several problems still\nremain and should be addressed:\nResponse: We sincerely thank you for the positive comments and valuable suggestions.\nAs a response, we have conducted the following additional work: (1) performed a more\ncomprehensive evaluation of the device’s flexible performance; (2) carried out\ncalculations and (3) comparative analysis of the overall energy consumption of\nPENG+AOS integrated system. We believe that the revised manuscript can adequately\naddress the reviewer’s concerns.\nOur point-by-point responses to the reviewer’s comments are as follows:\n1. While the authors have supplemented bending test, the performance was only tested\nwhen the bending radius was reduced to 5 mm and lacked long-term cyclic bending\nevaluations. Cyclic bending tests should be added. Furthermore, for consistency, the\nbending radius used throughout the manuscript should be unified. For instance, the\nbending radius in Supplementary Fig. S18 is 3 mm, whereas elsewhere it is 5 mm.\nResponse: We greatly appreciate your valuable comment. To ensure consistency, we\nhave conducted additional tests at a bending radius of 3 mm and cyclic bending\nevaluations to more comprehensively demonstrate the device’s mechanical flexibility\nand bending performance.\nAs a response, we have (1) added additional tests at a bending radius of 3 mm in\nSupplementary Fig. S28, cyclic bending evaluations in Supplementary Fig. S29, and\nthe corresponding description: “When the bending radius ranging from 10 to 3 mm, the\nelectrical pulse conductance modulation performance (Supplementary Fig. S28a) and\nthe optical pulse response current (Supplementary Fig. S28b) of the AOS did not show\nsignificant changes. In addition, at the bending radius of 3 mm, the electrical\nconductance modulation performance and the optical pulse response maintain stability\nafter 1000 bending cycles (Supplementary Fig. S29), further demonstrating the bending\nresistance of the device.” in the manuscript.\nSupplementary Fig. S28. a Continuous conductance modulation under different\nbending radii by 15 positive electrical pulses at a fixed amplitude (1.9 V) and 15\nnegative electrical pulses at a fixed amplitude (–0.3 V), with both pulse width and\ninterval set to 0.37 s. b Photoresponse of AOS under different bending radii at a fixed\nwavelength of 365 nm.\nSupplementary Fig. S29. a Continuous conductance modulation under different\nbending cycles (bending radius: 3 mm) by 15 positive electrical pulses at a fixed\namplitude (1.9 V) and 15 negative electrical pulses at a fixed amplitude (–0.3 V), with\nboth pulse width and interval set to 0.37 s. b Photoresponse of AOS under different\nbending cycles (bending radius: 3 mm) at a fixed wavelength of 365 nm.\n(2) added additional tests at a bending radius of 3 mm and cyclic bending evaluations\nin Supplementary Fig. S34, and the corresponding description: “To investigate the\nmechanical flexibility of the CS-QLED, we performed bending tests under varying radii\nand bending cycles (Supplementary Fig. S34). The results demonstrate that the current\ndensity–voltage characteristics remain nearly constant as the bending radius decreases\nfrom 10 mm to 3 mm (Supplementary Fig. S34a). Furthermore, after 1000 cycles of\ndynamic bending at a radius of 3 mm (Supplementary Fig. S34b), the current density–\nvoltage characteristics curve does not show marked degradation, indicating preferable\nmechanical flexibility and bending tolerance.” in the manuscript.\nSupplementary Fig. S34. Current density–voltage characteristics of CS-QLED a with\ndifferent bending radii and b with different bending cycle numbers at a bending radius\nof 3 mm.\n2. Regarding energy consumption, the authors only provided data for the AOS unit.\nHowever, since the key innovation of this work lies in the MXene-based integration of\nboth the PENG and AOS, the overall energy consumption of the entire integrated system\n(PENG+AOS) should be evaluated and discussed.\nResponse: Thank you very much for your important comment. We fully acknowledge\nthe importance of evaluating and discussing the overall energy consumption of the\nintegrated system (PENG + AOS), and have conducted the following experiments and\nanalysis.\nWhen the PENG and AOS integrated system is subjected to pressure stimulation, the\nAOS actively responds to the piezoelectric signals generated by the PENG, enabling\nself-powered operation without any external energy supply and resulting in zero system\nenergy consumption. Under light stimulation, the energy consumption per single light\npulse is calculated to be as low as 1.437 fJ, demonstrating exceptional energy efficiency.\nAs a response, we have added Supplementary Fig. S30 in the Supplementary\nInformation and corresponding description to analyze the energy consumption: “We\nfurther analyzed the overall energy consumption of the PENG and AOS system\n(Supplementary Figure S30). The result demonstrated that the PENG and AOS system\ncan operate in a self-powered mode (0 J) under pressure stimulation via PENG, and\nachieve a single-pulse energy consumption as low as 1.437 fJ under optical stimulation.”\nin the manuscript.\nSupplementary Fig. S30. Energy consumption of the PENG+AOS system under\noptical stimulation.\nThe PENG+AOS system can operate in a self-powered mode under pressure\nstimulation via PENG. When the system is stimulated by light, the energy consumption\n(E) can be calculated using the following formula:\n𝑡1\n𝐸 = ∫ 𝑉 ∙𝐼(𝑡)𝑑𝑡\n𝑡0\nwhere t\n0\n(t\n1\n) is the start (end) time of the light stimulation, I is the device current, and V\nis the voltage applied to the device.1\nWe integrated the instantaneous power over the light pulse period, which\nencompassed the 200 μs light pulse under a read voltage of 0.01 V. The resulting energy\nconsumption was 1.437 fJ. Further reductions in energy consumption can be achieved\nby lowering the read voltage or minimizing the electrode area.\n3. The authors compared the performance of individual AOS devices, but the\ncomparison with existing integrated systems (PENG + AOS) is missing. Such a\ncomparison would better highlight the overall innovation and advantages of the\nproposed system.\nResponse: We greatly appreciate your valuable comments. According to the\nsuggestions, we have added a comparison with existing integrated PENG + memristor\nsystems (Supplementary Table S4). The PENG + AOS system proposed in this work\nexhibits significant advantages over existing PENG + memristor systems. First, it\nenables multimodal visual-tactile sensing capabilities. Second, the use of MXene\nelectrodes allows for monolithic integration, thereby improving structural compactness\nand conferring excellent mechanical flexibility. Third, the system demonstrates strong\npotential for future applications in multi-modal human–machine interaction. Together,\nthese attributes highlight the overall innovation and technical competitiveness of our\nresearch.\nAs a response, we have added Supplementary Table S4 in the Supplementary\nInformation and corresponding description: “Compared to existing PENG and\nmemristor integrated systems, our PENG and AOS integrated system exhibits distinct\nadvantages in terms of device integration, multimodal sensing capabilities, and\nversatility in functional applications (Supplementary Table S4).” in the manuscript.\nSupplementary Table S4. Comparison of the state-of-the-art PENG and\nmemristor systems.\nMultimodal Energy\nDevice structure of Device structure of Monolithic\nFlexibility response consumption Application Ref.\nPENG memristor integration\ncapability (J) \nPENG:\nConductive flexible;\nfabric/KNbO3/TiN\nTiN−Si/KNbO3/Pt No\nmemristor:\nPressure Pressure: 0 - 2\nrigid\nAu/(Na0.5K0.5)NbO3/Au TiN/(Na0.5K0.5)NbO3/Pt No Yes Pressure Pressure: 0 Metaplasticity 3\nITO/ZnO/Al ITO/ZnO/Yb2O3/Au No Yes Pressure Pressure: 0 - 4\nIdentify different\nITO/(R)-3- ITO/(R)-3-\namplitudes and\nQuinuclidinol/P(VDF- Quinuclidinol/P(VDF- No Yes Pressure Pressure: 0 5\nfrequencies of\nTrFE)-40/ITO TrFE)-40/Ag\npressure\nPENG:\nIdentify different\nCu/Te flexible;\nAu/tellurene/Au No Pressure Pressure: 0 amplitudes of 6\nnanoflakes/PDMS/Cu memristor:\npressure\nrigid\nStrike type\nrecognition,\nPressure: 0;\nL-MXene/MXP/ L-MXene/ZnO/OSMX- Pressure and dynamic racket This\nYes Yes light:\nL-MXene PMMA/ZnO/Al light swing trajectory work\n1.43710-15\nrecognition, and\nposition recognition\nReferences\n1. Wang, J. et al. Technology and Integration Roadmap for Optoelectronic\nMemristor. Adv. Mater. 36, 2307393 (2023).\n2. Lee, T.-H. et al. Low-Temperature-Grown KNbO Thin Films and Their\nACS Appl. Mater. Interfaces 9, 43220-43229 (2017).\n3. Kim, B.-Y. et al. Nanogenerator-induced synaptic plasticity and metaplasticity\nof bio-realistic artificial synapses. NPG Asia Mater. 9, e381 (2017).\n4. Khan, M. U., Abbas, Y., Rezeq, M. d., Alazzam, A. & Mohammad, B.\nUnidirectional Neuromorphic Resistive Memory Integrated with Piezoelectric\nNanogenerator for Self-Power Electronics. Adv. Funct. Mater. 34, 2305869\n(2023).\n5. Yao, H. et al. Biomimetic assembly of organic ferroelectrics induced tailorable\ndomain reconfiguration for high-performance self-powered neuromorphic\nsystem. Chem. Eng. J. 523, 168247 (2025).\n6. Bian, J. et al. A self-powered artificial tactile perception system with self-\nprotection functionality based on tellurene threshold switching memristor. Sci.\nChina Mater. 68, 2488-2496 (2025)."}]

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