Machine learning-assisted kinetic matching model for rational electrode design in aqueous zinc-ion batteries

Domain: chemistry
Published: 2025-12-25
Editorial outcome: major_revision

Abstract
Aqueous zinc-ion batteries offer inherent safety and low cost, yet performance is limited by unstable zinc metal negative electrodes and dissolution-prone positive electrodes, causing dendrite growth, sluggish ion transport, and rapid capacity decay. Replacing both electrodes with intercalation hosts provides a solution, but progress is slowed by the lack of a universal principle for selecting kinetically compatible pairs. Most existing efforts optimize single components rather than addressing the electrodes’ kinetic mismatch governing full-cell stability. Here we show a machine-learning-assisted kinetic-matching framework that quantitatively evaluates ion-transport compatibility in intercalation-type zinc-ion batteries electrodes. By correlating interlayer spacing with Zn2+ diffusion behavior, the model introduces two descriptors predicting synchronized ion flux for rational electrode pairing. Using this framework, an optimized Zn3V3O8||NH4V4O10 system achieves a specific capacity of 310 mAh g-1 and retains over 12,000 cycles at 5 Ag-1. The strategy further extends to deformable formats through conductive hydrogel architectures, enabling omnidirectionally stretchable, all-hydrogel zinc-ion batteries with an areal capacity of 1.2 mAh cm-2 and an energy density of 1070 μWh cm-2. These results provide a quantitative design route for next-generation zinc-ion batteries.

Introduction
Aqueous zinc-ion batteries (AZIBs) are among the most promising candidates for next-generation energy storage, offering intrinsic safety, low cost, and competitive energy and power densities1. However, their reliance on metallic zinc negative electrodes and traditional deposition-dissolution positive electrodes imposes fundamental electrochemical and mechanical constraints2. Zinc undergoes dendritic growth, corrosion, and parasitic side reactions, degrading Coulombic efficiency, accelerating capacity fade, and shortening cycle life—issues that interfacial coatings or electrolyte additives cannot fully resolve3–5. Meanwhile, positive electrode materials in AZIBs primarily operate via three distinct mechanisms: intercalation, dissolution-deposition, and conversion. Dissolution-deposition systems are highly representative because the Zn metal negative electrode—the most common and practical choice for AZIBs—offers high volumetric capacity and operates through a simple, highly reversible mechanism that enables uniform plating/stripping and improved cycling stability. Many positive electrodes, including MnO2, Fe, and organic quinone-based compounds, also rely on reversible dissolution-precipitation reactions6–8. Despite their attractive capacities and stability, dissolution-deposition electrodes can still face structural collapse, sluggish Zn2+ kinetics, and incomplete material utilization, which limit specific energy and power capability. In contrast, conversion-type electrodes involve complex multiphase transitions and differ fundamentally in system design, while intercalation and dissolution-deposition remain the two most prevalent and practically relevant categories for AZIBs9,10. Mechanically, the rigidity and limited ductility of zinc metal preclude the repeatable stretchability and omnidirectional flexibility required for flexible electronics. Structural innovations, such as mesh-like architectures and spring-inspired designs11, and conductive hydrogels, can partially accommodate strain12,13, but the continued use of zinc powder and deposition-dissolution positive electrodes perpetuates degradation pathways14. In hydrated environments, hydrogel-induced zinc oxidation further accelerates corrosion, and positive electrode dissolution undermines structural and compositional stability15–17. These intertwined challenges have thus far limited AZIB integration across a wide spectrum of applications18,19, from flexible wearables and implantable devices to autonomous Internet of Things sensors, portable power tools, and even stationary grid-level energy storage20–22, where reliable, high-performance aqueous batteries are critically needed23.

To surmount these barriers, dual-intercalation architectures have emerged, replacing both zinc and MnO2 with intercalation hosts for the negative and positive electrodes. Inspired by the “rocking-chair” mechanism of lithium-ion batteries24,25, this approach shuttles Zn2+ between two stable lattices, eliminating metal plating/stripping and mitigating dendrite, corrosion, and interface degradation. Compared with Li-ion batteries, where negative electrode overmatching prevents lithium plating, the dual-intercalation aqueous zinc-ion system requires closely synchronized kinetics. Both electrodes rely on intercalation, and Zn2+ transport is slower than Li+, so mismatched diffusion rates at either electrode create bottlenecks and limit performance (Fig. 1). Thus, well-matched intercalation kinetics are essential to maximize rate capability, minimize polarization, and enhance cycling stability. Layered compounds such as titanium phosphate have demonstrated over 10,000 stable cycles with improved safety26, and V-based oxides offer higher theoretical capacity and energy densities. Yet these systems still exhibit low specific capacity and sub-40 percent retention at high rates, highlighting kinetic mismatches between electrode pairs27. In the pursuit of developing desired AZIBs, substantial progress has been achieved in material design, electrode architecture, and interface regulation. However, a key factor that continues to limit full-cell performance is the mismatch in ion diffusion kinetics between intercalation-type positive and negative electrodes. This issue, initially observed by Wu et al. in lithium-ion battery and supercapacitor hybrid systems28, becomes particularly pronounced in dual-intercalation AZIBs, where both electrodes operate within constrained kinetic windows. When the ion transport rates of the two electrodes are not well aligned, the resulting concentration gradients lead to ion accumulation, distorted electric fields, increased polarization, and ultimately reduced energy efficiency and cycling stability29. To address this mismatch, structural modulation has emerged as a promising strategy to achieve diffusion compatibility. One effective method involves crystal facet engineering. For example, exposing the (011) planes of TiS2 improved diffusion kinetics and enabled better compatibility with MnO2 positive electrodes, achieving a high capacity of 71.9 mAh g−1 at 2 Ag−130. Alternatively, simultaneous lattice optimization of both CoVOH and O-MoO2 electrodes resulted in 90% capacity retention over 500 cycles31, confirming the value of synchronizing diffusion kinetics across both electrodes. These studies demonstrate that structural tuning can effectively improve rate capability and cycling stability. Nevertheless, many intercalation materials continue to exhibit inherently low specific capacities and structural instability, limiting further progress. This performance gap ultimately traces back to a fundamental problem where rational electrode selection criteria for full-cell design remain underdeveloped. Central to this problem is the compatibility of ion diffusion kinetics between the negative and positive electrodes, which serves not only as a prerequisite for maximizing the intrinsic properties of electrode materials but also as a decisive factor in performance optimization. Despite its importance, the field still lacks a universal theoretical framework or quantitative model to evaluate and predict kinetic compatibility. This deficiency forces reliance on empirical trial-and-error strategies and confines material exploration to a narrow range of familiar electrode pairs, thereby hindering innovation in next-generation battery systems. Therefore, developing comprehensive theoretical frameworks and predictive models for ion diffusion kinetics matching is crucial to accelerate the development of cutting-edge AZIB technologies. To address this long-standing challenge, machine learning serves as an indispensable tool for decoding complex electrode kinetics and validating physically derived hypotheses32,33. By integrating domain knowledge with data-driven modeling, machine learning provides a robust statistical framework to quantify and unravel the nonlinear relationships between material properties and electrochemical behavior. This approach enhances the predictability and generalizability of kinetic matching principles, complementing traditional experimental methods and opening pathways for the rational design of high-performance energy storage systems.

In this study, we explore the ionic diffusion kinetics matching and its impact on the electrochemical performance of intercalation-type negative electrode materials with varying diffusion coefficient distributions when paired with layered positive electrodes with controllable diffusion rates. Based on experimental validation across diverse intercalation systems and machine learning optimization, this work establishes a quantitative, machine learning-assisted kinetic matching model that links confined interlayer spacing to ion diffusion kinetics in intercalation electrodes. The model is universal because it systematically evaluates ion-diffusion compatibility across a wide spectrum of intercalation electrodes, rather than being limited to familiar electrode pairs. It is quantitative because it leverages machine learning to establish predictive correlations, replacing semi-empirical heuristics with data-driven rigor and enabling rational optimization of full-cell kinetics. This model introduces two descriptors, Composite Shift Index (CSI) and diffusion ratio (DR), that enable intuitive assessment of kinetic compatibility between electrode pairs. Effective matching is achieved when both conditions are simultaneously satisfied: 0.241 < CSI < 1.013 and 0.936 < DR < 1.020. Notably, the descriptors CSI and DR were derived through experience-guided feature engineering grounded in physical insights into intercalation kinetics. These features were not generated by machine learning but were intentionally designed by researchers to capture the fundamental kinetic characteristics of intercalation processes. Furthermore, by regulating the interlayer spacing and zinc-ion diffusion rate of NH4V4O10, a well-matched Zn3V3O8||NH4V4O10 (ZVO||NVO) full cell was designed, exhibiting a high specific capacity of 310 mAh g−1, along cycle life exceeding 12,000 cycles at 5 Ag−1, and notable rate capability. Guided by the proposed kinetic-matching framework, we developed a universal silver-poly(acrylic acid) conductive hydrogel that preserves the structural integrity and electrochemical performance of ZVO||NVO electrodes while reinforcing the mechanical resilience of the stretchable system. This work presents a practical demonstration of an omnidirectionally deformable electrode platform compatible with diverse intercalation materials, offering a generalizable strategy for constructing high-performance, dual-intercalation AZIBs. By integrating hydrogel electrolytes, we further construct an omnidirectionally stretchable AZIB system, achieving a high areal capacity of 1.2 mAh cm−2 and an energy density of 1070 µWh cm−2 (4 cm2). This battery meets the energy demands of wearable electronics (power consumption >2 mW)34, and provides intrinsic flexibility to accommodate omnidirectional deformation, thereby demonstrating the guiding value of the proposed formula. Importantly, the mathematical model for electrode matching provides a robust theoretical foundation and practical reference for electrode pair selection and full-cell performance prediction in Zn-metal-free AZIBs.

Results
Design principles of dual-intercalation AZIBs
As mentioned, intercalation-type electrode materials typically have narrow kinetic windows, making them prone to mismatches in ionic intercalation rates between the positive and negative electrodes. Additionally, the available positive electrode-negative electrode pairings for intercalation-based systems are more limited compared to those in conventional zinc-metal-based AZIBs, which further constrains the design space for achieving optimal kinetic matching. To systematically address the noted challenges of intercalation AZIBs, it is crucial to first elucidate the nature and consequences of kinetic mismatch between electrodes. A quantitative framework for defining “kinetic matching” must be established to guide material selection and device engineering. As schematically illustrated in Fig. 1, when the positive electrode facilitates rapid Zn2+ deintercalation (typically features a relatively larger interlayer spacing) compared to the slower intercalation kinetics of the negative electrode, charge processes induce significant ion accumulation at the negative electrode-electrolyte interface. Conversely, if the positive electrode possesses sluggish ion insertion kinetics (typically derived from a smaller interlayer spacing), Zn2+ congestion at the positive electrode-electrolyte interface arises during discharge. This phenomenon is referred to as kinetic mismatch between positive and negative electrodes. The kinetic mismatch between the electrodes leads to asynchronous ion intercalation and deintercalation processes. When the positive electrode deintercalates faster than the negative electrode can intercalate, Zn2+ accumulates at the negative electrode-electrolyte interface; conversely, slower positive electrode intercalation relative to negative electrode deintercalation causes ion congestion at the positive electrode-electrolyte interface. These local imbalances distort the uniform electric field within the cell, requiring higher overpotentials to drive ion migration and increasing overall polarization. Electrochemically, this manifests as voltage hysteresis, peak splitting in cyclic voltammetry (CV), and asymmetric current responses, reflecting inefficient redox reactions and compromised kinetics. Quantitative kinetic matching mitigates these effects by synchronizing ion flux, stabilizing interfacial conditions, and minimizing polarization, thereby improving rate performance, energy efficiency, and long-term cycling stability. In contrast, when the ionic intercalation kinetics of the electrodes are well-matched, the Zn2+ flux remains synchronized throughout charged-discharge cycling, maintaining dynamic equilibrium at the electrode-electrolyte interfaces. This balance promotes homogeneous electric field distribution, which is reflected in CV curves with minimal peak separation and near-unity anodic-to-cathodic current ratios. Therefore, rationally designing electrode materials with matched intercalation kinetics is essential for mitigating polarization, enhancing charge-transfer efficiency, and achieving stable long-term cycling.

Kinetic evaluation and structural stability of NVO intercalation electrode
To enable a quantitative evaluation of “kinetic matching”, it is essential to investigate the intrinsic intercalation kinetics of electrode materials in greater depth. In selecting a representative positive electrode model, NVO was chosen due to its two-dimensional layered structure and abundance of redox-active sites. The insertion of NH4+ ions acts as a structural pillar, stabilizing and expanding the V-O layers35, which enhances its capacity to accommodate Zn2+ ions (Fig. S1). The layered structure of NVO supports efficient ion insertion and extraction while ensuring structural stability, which helps maintain high and stable capacity even under rapid charge/discharge cycles, contributing to high-rate performance36. Therefore, NVO offers broad applicability, structural stability, high electrochemical reversibility, and favorable rate capability, making it a reliable positive electrode candidate selected for this study. To investigate the effect of NVO crystal structure on ion intercalation kinetics, the zinc-ion diffusion energy barrier distribution was simulated using Density Functional Theory (DFT) based on its lattice structure, as shown in Fig. 2a. To establish NVO models with varying lattice structures, the number of NH4+ ions and H2O molecules between the V-O layers within the unit cells was adjusted. Specifically, the number of H2O molecules was set to 4, 3, 2, and 1, corresponding to NH4+ ion counts of 12, 13, 14, and 15. This resulted in four NVO structures with different interlayer spacings, yielding c-lattice constants of 13.87, 14.44, 15.21, and 16.46 Å (the atomic coordinates of these models are provided in Supplementary Data 1–4). For NVO-2, NVO-3, and NVO-4, diffusion energy barriers were calculated using the most stable adsorption site at the ammonium vacancy. For NVO-1, which lacks intrinsic vacancies, multiple adsorption configurations were evaluated (Fig. S2), and the configuration with the lowest adsorption energy was selected to ensure accurate diffusion barrier determination. Comparative analysis of two possible diffusion paths, Path-1 and Path-2, revealed that Path-1 exhibits a lower Zn2+ diffusion barrier, confirming it as the most favorable diffusion pathway. These calculations ensure that the reported barriers accurately reflect the energetically preferred ion transport routes. As shown in Fig. 2b, DFT simulation results reveal that the zinc-ion diffusion energy barrier in NVO decreases initially and then increases with the gradual removal of ammonium ions and insertion of water molecules across four structural configurations. This trend is attributed to the evolution of Bader charge and adsorption energy, as presented in Fig. 2c and Table S1. The Bader charge analysis specifically quantifies the charge transfer associated with the Zn2+ ion when occupying different adsorption sites. Importantly, this charge redistribution exhibits an inverse correlation with the diffusion barrier: smaller absolute charge variation corresponds to more effective electrostatic stabilization of Zn2+ within the host lattice, which weakens Zn-host interactions, lowers the energy penalty for ion displacement, and thereby facilitates long-range migration. This analysis explicitly clarifies that it is the charge variation of the Zn2+ ion, rather than changes in the host framework, that underpins the observed correlation between Bader charge values and diffusion barriers. With increasing interlayer spacing, the charge transfer values calculated via Bader analysis of the four models are calculated as −1.11e, −1.05e, −0.77e, and −0.98e, respectively, accompanied by corresponding adsorption energies of −1.89 eV, −1.26 eV, −0.61 eV, and −2.22 eV. As the absolute adsorption energy of zinc ions on the V-O layers decreases, the diffusion energy barrier drops significantly from 2.57 to 0.77 eV. However, when the interlayer spacing becomes excessively large, the charge transfer values derived from Bader charge analysis increase again, causing zinc ions to favor diffusion along the layer edges, where the stronger adsorption enhances the energy barrier, elevating it to 1.868 eV. These simulation results demonstrate that the ion diffusion kinetics of NVO can be effectively tuned by modulating interlayer spacing, offering a viable strategy for optimizing intercalation dynamic performance.

An experimental strategy enabling real-time regulation of the actual intercalation rate of NVO allows for precise monitoring of the electrochemical response during the transition from a kinetically “mismatched” to a “matched” state, thereby providing a quantitative framework to define the functional range of kinetic matching. To investigate this, NVO was assembled into a half-cell configuration with zinc metal as the counter electrode for subsequent electrochemical studies. In-situ X-ray diffraction (XRD) analyses were conducted, revealing that the interlayer spacing of NVO undergoes reversible changes during the charge-discharge process. Specifically, in-situ XRD results revealed that the (001) diffraction peak of NVO, located in the 5–7° range, shifted to a lower angle during the charging process and gradually returned to a higher angle during discharge (Fig. S3a). According to DFT simulations, this behavior is attributed to the extraction of ammonium ions during charging, which reduces electrostatic interactions between the V–O layers and leads to an expansion of the interlayer spacing. During discharge, the insertion of Zn2+ ions into NH4V4O10 induces a contraction of the interlayer spacing, as evidenced by the shift of the (001) diffraction peak toward higher angles. This behavior contrasts with the gradual peak shift to lower angles reported for other vanadium oxides such as HNaV6O16·4H2O and Al-V2O5·1.6H2O, which is typically attributed to lattice expansion upon Zn2+ intercalation37,38. The distinct response of NH4V4O10 arises from the strong electrostatic interaction between the inserted Zn2+ ions and the negatively charged V-O layers, which effectively pulls the layers closer together and leads to lattice tightening. This contraction is further facilitated by the presence of ammonium ions, which stabilizes the layered structure and enhances the reversibility of the Zn2+ intercalation process. Importantly, this unusual contraction does not indicate a detrimental effect; instead, it reflects the ion-layer interactions in NH4V4O10, which contribute to its high structural stability during cycling. Recent studies on ammonium vanadate positive electrodes have reported similar interlayer contraction phenomena, supporting our observations and providing experimental validation for the proposed mechanism35,39,40. These studies highlight that NH4V4O10-based electrodes can accommodate Zn2+ intercalation without significant structural degradation, which is advantageous for long-term cycling performance and rate capability. Furthermore, ex situ XRD characterization over the initial three charge-discharge cycles (Fig. 2d) showed that the maximum interlayer spacing increased from 1.332 to 1.659 nm during charging to 2.0 V, while the minimum spacing decreased from 1.324 to 1.304 nm during discharging to 0.2 V, eventually stabilizing over time. These results demonstrate that the interlayer spacing of NVO can be effectively modulated by adjusting the charge-discharge cut-off voltage. Simultaneously, these results clearly indicate that the interlayer spacing of NVO remains in a dynamic state throughout electrochemical cycling. As such, it can only be characterized by defining a range between its maximum and minimum values, rather than by a single static value. Consequently, the interlayer spacing of NVO is dynamically modulated by the intercalation and deintercalation of Zn2+ and NH4+ ions during cycling, creating a distribution of diffusion barriers that governs ion transport. While conventional methods, such as adjusting temperature (Fig. S7) or other targeted interventions like SEI layers and surface engineering can influence overall kinetics, they cannot selectively balance the kinetic mismatch between positive and negative electrode or offer broad applicability. In contrast, controlling the cut-off voltage serves a dual purpose in ensuring positive electrode stability. Firstly, it regulates the range of interlayer spacing variation to tune Zn2+ diffusion pathways and surface flux. Secondly, it mitigates the notorious H+ insertion prevalent at higher potentials. Given the much smaller size of H+ compared with Zn2+ and interlayer NH4+, its insertion causes minimal spacing variation. This is critical, as excessive H+ insertion at high voltages leads to over-reduction of vanadium species and subsequent V–O bond breakage, a primary cause of structural dissolution41,42. The selected voltage window (0.2–1.8 V) effectively limits proton insertion per cycle, thereby mitigating its contribution to vanadium dissolution. Consequently, this strategy not only optimizes kinetic matching across electrodes but also enhances structural integrity by minimizing both lattice strain and H+-induced degradation.

Intercalation synergy regulation and compatibility verification of ZVO||NVO cell
To systematically evaluate the impact of positive electrode-negative electrode kinetic matching on the electrochemical performance of full cells, it is necessary to quantify the zinc-ion diffusion coefficients and identify the optimal voltage window for matching the intercalation dynamics of NVO with ZVO. Galvanostatic intermittent titration technique (GITT) measurements were performed on both NVO and ZVO electrodes, each assembled in a half-cell configuration with Zn metal as the counter electrode (Fig. S6). As shown in Fig. 2e, ZVO exhibits a faster zinc ion diffusion rate (log D = −11.22 to −9.39) compared to NVO (log D = −11.74 to −10.51). Further GITT tests on NVO at different upper cutoff voltages revealed that NVO displayed the largest ion diffusion coefficient (log D = −9.42 to −10.71) when the maximum cutoff voltage was set to 1.8 V, which aligns with the DFT simulation results and demonstrates a great match with the ZVO negative electrode. This indicates that the voltage window of 0.2–1.8 V facilitates ideal matching of the ion intercalation kinetics in the ZVO||NVO full cell. To further demonstrate the voltage control over NVO ion diffusion kinetics and explore its matching status with ZVO, electrochemical quartz crystal microbalance (EQCM) measurements were conducted. As shown in Fig. 2f, significant mass changes are observed at both the negative electrode and positive electrode during the discharge processes. The mass response of NVO with a cutoff voltage of 1.8 V closely mirrors that of ZVO, indicating consistent ion diffusion behaviors between the two electrode materials. XPS analysis of the three samples with different interlayer spacing initiated by cutoff charging voltages, shown in Fig. S3b, reveals that as the maximum cutoff voltage increases, the proportion of vanadium in the V5+-oxidation state also increases from 61.4 to 86.3%. This observation supports the explanation that the expansion of interlayer spacing is due to the extraction of ammonium ions, consistent with charge conservation. These findings demonstrate that increasing the cutoff voltage enhances the capacity of NVO by modulating the ion diffusion kinetics to match that of ZVO, although the higher vanadium oxidation states may increase resistance to zinc ion insertion during discharge.

To investigate the relationship between intercalation compatibility and electrochemical performance in dual-intercalation AZIBs, a ZVO||NVO full cell was constructed as a representative electrode intercalation synergy model. This system serves to reveal the underlying redox mechanisms and ion transport kinetics between the ZVO negative electrode and NVO positive electrode, offering insight into the impact of kinetic matching on overall cell performance. The energy storage mechanism and structural evolution of the ZVO and NVO electrodes were verified during the charge-discharge process in the ZVO||NVO full cell via ex-situ X-ray photoelectron spectroscopy (XPS). The peak area ratio in Fig. 2g shows that during the charge-discharge process, V5+ remains the dominant species in NVO. Additionally, after discharge, the proportion of V4+ increases from 9.1 to 38.6%, and this change occurs reversibly during the cycling process, demonstrating the stability of the redox mechanism. In Fig. 2h, the results of ZVO negative electrode indicate that vanadium predominantly exists in the +5-oxidation state during discharge and shifts to the +4-oxidation and +3-oxidation state upon charging, confirming a reversible redox reaction that significantly contributes to the high capacity. The alternating insertion and extraction of zinc ions between the two electrodes leads to the reversible rise and fall of the vanadium valence state, proving theoretical feasibility of the stable and reversible redox reaction between ZVO and NVO. Figure S8 shows the CV curves of ZVO within a voltage window of 0.2–1.6 V, displaying two reduction peaks at relatively low potentials of 0.4 and 0.8 V. Electrochemical reaction kinetics were further analyzed from CV curves at different scan rates (0.2, 0.4, 0.6, 0.8, and 1 mVs−1), yielding b-values of 0.794, 0.834, 0.838, and 0.815 for the corresponding oxidation-reduction peaks, indicating that both internal diffusion and surface reactions influence the reaction kinetics, leading to rapid and highly reversible dynamics. Galvanostatic charge-discharge (GCD) results demonstrate a specific capacity above 260 mAh g−1 at a specific current of 0.2 Ag−1 and 115 mAh g−1 at 2 Ag−1 (Fig. S9). Additionally, the Zn||ZVO half-cell exhibits high cycling stability, with capacity continuously increasing even after 300 cycles at 2 Ag−1 (Fig. S10). NVO has also been tested to possess a high specific capacity of 493 mAh g−1 within a voltage window of 0.2–1.8 V (Fig. S11). Additionally, under this voltage window, the CV curve analysis of the Zn||NVO half-cell reveals that the three pairs of redox peaks correspond to b-values of 0.826, 0.965, 0.845, 0.966, 0.957, and 0.858, indicating similar reaction kinetics with the ZVO negative electrode (Fig. S12). Through the systematic characterizations of the redox mechanisms and analysis of reaction kinetics in the ZVO||NVO full-cell, the results demonstrate the practical feasibility of constructing metal-free zinc-ion batteries using this paired electrode system. Its reversible electrode reaction can be expressed by the following equations:

Negative electrode: Zn3V3O8 + xZn2+ + 2xe− ↔ Zn3+xV3O8

Positive electrode: NH4V4O10 + xZn2+ + 2xe− ↔ Zn xNH4V4O10

Notably, to construct a durable metal-free battery system, intercalation electrodes were initially selected from literature-reported candidates27,44,45, but many of these proved highly electrolyte-dependent and prone to positive electrode-negative electrode mismatching. Therefore, the ZVO||NVO system was selected as a representative model, not only due to its well-documented advantages in the literature and more optimal overall performance in preliminary full-cell tests among various intercalation-type electrode pairs, but also because it serves as an exemplary template for high-performance intercalation-matched energy storage systems. This system offers valuable insights into the fundamental correlation between electrode matching and electrochemical behavior.

Electrochemical impact of intercalation kinetics matching
To theoretically elucidate the influence of intercalation kinetics matching status between the positive and negative electrodes on full-cell electrochemical performance, finite element simulations executed by COMSOL Multiphysics were conducted to visualize the spatial distributions of zinc ion concentration and electric field intensity under different kinetic scenarios. As illustrated in Fig. 3a, b, the white circles denote active electrode particles where interfacial intercalation reactions take place, as simulated using a finite element-based microstructure model consistent with established computational studies of battery electrodes46,47. When the ion diffusion rate of the positive electrode is lower than that of the negative electrode, Zn2+ ions tend to accumulate at the positive electrode surface, resulting in a steep concentration gradient and localized electric field intensification at the positive electrode-electrolyte interface. Theoretical calculations of the mismatched model reveal a concentration gradient difference of 0.48 mol m−3 at the positive electrode surface, while the negative electrode side shows only a 0.09 mol m−3 difference. This disparity results in a significant electric field gradient, ranging from 0.75 to 0.43 V m−1, at the positive electrode-electrolyte interface. In contrast, when the diffusion rates of the positive and negative electrodes are well matched, the Zn2+ concentration becomes more uniformly distributed throughout the cell, giving rise to a homogeneous electric field profile with the maximum field intensity located near the negative electrode surface. Consequently, the ion concentration variation at the positive electrode-electrolyte interface in the matched model is 2.2 times smaller than that in the mismatched model, leading to a reduced electric field gradient of 0.22 to 0.18 V m−1, which is seven times lower than that in the mismatched model. This electrodynamic uniformity facilitates more balanced ion transport and improved reaction kinetics. Correspondingly, as shown in Fig. 3c, the voltage differences between the main redox peaks for ZVO||NVO cells with cutoff voltages of 1.6 V, 1.8 V, and 2.0 V were 0.363 V, 0.319 V, and 0.393 V, respectively. The cell operating at 1.8 V, corresponding to the kinetically matched ZVO||NVO configuration, exhibited the smallest peak separation and thus the highest redox reversibility and minimal polarization. These findings underscore the critical role of positive electrode-negative electrode kinetic matching in achieving efficient, stable energy storage performance. As shown in Fig. 3d, the Nyquist plots of NVO subjected to different cutoff voltages reveal that the sample treated at 1.8 V exhibits the steepest low-frequency slope, indicating enhanced interfacial ion diffusion. Consistent with this observation, the Warburg impedance values were determined to be 16.65±0.31 Ω, 15.16±0.19 Ω, and 17.77±0.06 Ω for NVO-1.6V, NVO-1.8V, and NVO-2.0V, respectively. Statistical analysis using Tukey’s HSD test confirms the differences are significant. The NVO-1.8V electrode showed the lowest impedance, further supporting its faster ion transport kinetics within the full cell.

Figure 3e compares the performance rate of the full cells assembled with three NVO positive electrodes. It was observed that while the 1.8 V sample had a lower initial capacity than the 2.0 V sample at 0.2 Ag−1, it outperformed the other two samples at high currents, specifically at 10 Ag−1, demonstrating a desired rate performance. To quantitatively evaluate the trade-off between specific energy and rate performance resulting from the reduced upper cut-off voltage, we systematically compared the gravimetric energy densities of cells operated at 2.0 V and 1.8 V across a range of specific currents (Fig. S13). Although lowering the voltage window inevitably decreases the nominal specific energy (Energy=Capacity×Voltage), the discharge plateau remains largely unchanged, meaning that the actual energy loss is less severe than the nominal voltage reduction would suggest. At low specific currents (<2 Ag−1), the 2.0 V cell delivers higher specific energy owing to its larger specific capacity, with the 1.8 V cell showing a reduction of approximately 15.4–19.1%. However, at higher specific currents, the situation reverses: the 1.8 V cell exhibits higher specific energy because its significantly enhanced ion transport kinetics and reduced polarization enable more efficient utilization of active material. This rate-dependent crossover highlights the application-dependent balance point between specific energy and rate performance. For energy-oriented scenarios, the 2.0 V configuration remains advantageous, while for high-power and long-life applications, the 1.8 V configuration offers a more favorable compromise. Furthermore, the modeling framework developed in this study provides a generalizable means to identify such operating windows for other electrode systems by quantitatively balancing energy and power requirements under different working conditions. To validate the positive impact of the matching state of ZVO||NVO on the electrochemical performance of AZIBs, the battery was assembled with an NP ratio of 2 and tested under a voltage window of 0.2–1.8 V. As shown in Fig. 3f, the battery exhibits distinct pairs of oxidation-reduction peaks, with high reduction potentials at 0.75 V and 1.65 V. Additionally, the battery demonstrates a high specific capacity of 310 mAh g−1 at a specific current of 0.1 Ag−1, which surpasses all other reported Zn-metal-free AZIBs (Fig. 3g). Stable cycling performance was achieved in full-cell tests (Fig. 3h), with over 4500 cycles at 2 Ag−1 and more than 12,000 cycles at 5 Ag−1, maintaining over 80% of the initial capacity (Fig. S14). For a comprehensive comparison, Table S10 benchmarks this work against recently reported AZIBs based on zinc metal negative electrodes or dual-intercalation systems. The kinetically matched system developed here achieves a high initial specific capacity and sustained cycling stability across various specific currents, as quantified in the table, contributing to the development of AZIBs with balanced specific energy and durability. This highlights the benefits of a metal-free design and emphasizes the crucial role of dynamic matching between the positive and negative electrodes in determining battery performance.

To overcome the limitation of using a single electrode pairing (ZVO||NVO) in the aforementioned experiments and to verify the broader applicability of the kinetic matching theory across various electrode systems, VOPO4 (VOP) and V2O5·nH2O (VOH) were selected as intercalation-type positive electrodes with tunable interlayer spacing for electrochemical performance evaluation. By fixing ZVO as the negative electrode to control variables, this approach enables elucidation of the mathematical correlation between interlayer spacing in positive electrode materials and ionic diffusion kinetics matching. Both positive electrodes feature tunable interlayer spacing, which was adjusted through the intercalation of aniline molecules into VOP and potassium ions into VOH. The structural modifications were confirmed by XRD to verify the changes in interlayer spacing.

As shown in Fig. S15a, the XRD pattern reveals changes in the interlayer structure, where potassium ion intercalation into VOH induces a decrease in interlayer spacing from 1.147 to 1.067 nm due to electrostatic attraction between VOH and potassium ions. Consequently, the potassium ion-modified VOH (KVOH) exhibits an increased capacity of 3.56 times, as demonstrated in Fig. 4a. Additionally, the potential difference of the redox peaks significantly decreases from 0.147 to 0.003 V, which notably suppresses electrode polarization. The electrochemical impedance spectroscopy (EIS) curves shown in Fig. 4b exhibit a noticeable decrease in impedance, with the charge transfer resistance (Rct) reducing from 9.497 to 0.435 Ω, and the Warburg impedance (Rw) dropping from 11.500 to 5.413 Ω. Furthermore, the rate-performance comparisons in Fig. 4c confirm that regulating the interlayer spacing significantly enhances the rate capability. When the specific current increased from 0.1 to 10 Ag−1, the capacity retention increased from 55 to 77%. In contrast, Fig. S15b reveals that aniline intercalation causes the (001) peak of the modified VOP electrode (PA-VOP) to shift from 11.8° to 5.5°, indicating an expansion of the interlayer spacing (1.571 nm) compared to VOP (0.733 nm). In terms of optimizing electrochemical performance, VOP and PA-VOP exhibit a similar trend to VOH and KVOH. As shown in Fig. 4d, both VOP and PA-VOP exhibit a broad voltage window of 0.5–2.2 V in CV tests. A notable 3.5-fold capacity enhancement from 68.5 to 239.6 mAh g−1 is observed for PA-VOP following interlayer expansion, while KVOH exhibits a 3.6-fold enhancement in capacity when tested with the same ZVO. Quantitative analysis of the CV curves confirm that Zn2+ intercalation remains the dominant charge storage mechanism in the full cells, with the diffusion-controlled process contributing over 60% of the total capacity at 0.2 mVs−1. The observed improvements in capacity, together with the subtle shifts in redox peak positions, are primarily attributed to the expanded interlayer spacing of the positive electrode materials. This structural modification facilitates faster zinc-ion diffusion and reduces kinetic mismatches between the negative and positive electrodes, thereby enhancing overall ion transport efficiency. As a result, the electrochemical performance, including rate capability and cycle stability, is significantly improved. These findings underscore that the enhanced behavior is mainly driven by structural and kinetic optimizations, rather than by alternative charge storage contributions such as proton pseudocapacitance (Fig. S16). At the same time, the peak voltage difference decreased from 0.844 to 0.465 V, the Rct decreased from 32.170 to 8.837 Ω, and the Rw reduced from 288.900 to 5.628 Ω. Additionally, the capacity retention improved from 31 to 45% with the specific current increased from 0.1 to 10 Ag−1. Furthermore, the zinc ion intercalation kinetics of VOP and VOH before and after lattice regulation were quantitatively assessed using GITT, as shown in Fig. S17. Pre-intercalation significantly enhances the zinc ion diffusion coefficients of both VOH and VOP, where the natural logarithm of the diffusion coefficient for VOH increased from the range of [−12.280, −8.879] to [−10.890, −9.785], while the distribution range for VOP shifted from [−15.493, −9.215] to [−14.640, −8.879], aligning them more closely with the diffusion kinetics of ZVO. Consequently, the optimized electrodes exhibit both increased capacity, reduced polarization resistance, and enhanced rate performance. These findings confirm that the electrochemical performance of vanadium-based positive electrodes is critically governed by their kinetic compatibility with the ZVO negative electrode, thereby substantiating the broader applicability of the electrode kinetics matching framework.

Development of a semi-empirical model for electrode kinetic matching status in dual-intercalation AZIBs
Building on the consistent optimization effect of intercalation kinetics matching on electrochemical performance in the aforementioned systems, a rational, tunable strategy for optimizing electrode pairings in dual-intercalation AZIBs can be further developed. Notably, both the expansion and contraction of interlayer spacing result in enhanced electrochemical performance, indicating the existence of an optimal kinetic range for ionic diffusion. The degree of electrode matching status fundamentally hinges on the extent of overlap between the respective kinetic windows of the positive and negative electrode. Therefore, by modulating interlayer spacing to either accelerate or decelerate diffusion kinetics, the kinetic overlap between electrodes is increased relative to the initial state, thereby irreversibly improving the electrochemical enhancement. The varying degrees of overlap determine the extent of improvement in electrochemical performance. To further substantiate the hypothesis of kinetic range matching between electrodes, a series of positive electrode materials with varying interlayer spacings were investigated to quantify their corresponding Zn2+ diffusion coefficients relative to the ZVO negative electrode (Table S2). The derivation of the diffusion matching model is grounded in diffusion coefficients calculated from Fick’s second law48, which governs the temporal evolution of concentration profiles under diffusive transport and is mathematically expressed as Eq. (1):

∂C(x,t)/∂t = D ∂2C(x,t)/∂x2 (1)

Where C is the concentration of the diffusing Zn2+, t is time, and D denotes the diffusion coefficient. Then, to enable the comparison and eventual matching of diffusion coefficients between different electrodes within an electrochemical system, the solution of Fick’s second law is represented as Eq. (2):

Di = π/4 * (Vm * Z * F * S)^2 * (dE/dδ)^2 * (dE/dt)^2 (2)

Here, Di denotes the diffusion coefficient corresponding to a specific current value i; F is Faraday’s constant (96485 C mol−1); Vm is the molar volume of the electrode material; Z is the ionic charge number (for Zn2+, Z = 2); S is the contact area between the electrode and electrolyte; dE/dδ represents the slope of the Coulomb titration curve, reflecting the voltage response to compositional changes; and dE/dt captures the relationship between potential and the square root of time.

Balancing the ion diffusion kinetics between the negative and positive electrode and evaluating their impact on overall electrochemical performance requires a statistical analysis of the diffusion coefficients for each electrode material. To enable quantitative comparison across this broad range of diffusion coefficients, the base-10 logarithm (lgD) is applied. The mean (lgD) and standard deviation (σlgD) of the logarithmic diffusion coefficients over the entire charge-discharge cycle are calculated to evaluate both the overall diffusion level and its temporal stability. These are defined as Eqs. (3) and (4):

lgD = 1/n * Σ lgDi (3)

σlgD = sqrt(1/(n-1) * Σ (lgDi - lgD)^2) (4)

Where n is the number of data points, and lgDi denotes the logarithmic value of the zinc ion diffusion coefficient at each step of the charge-discharge process. The mean and standard deviation of lgD for each electrode material were used to construct a statistical distribution range for their respective intercalation kinetics. To quantitatively evaluate the degree of kinetic mismatch between two electrodes, a CSI was proposed, which serves as a metric for the divergence in their ion diffusion kinetics. A higher CSI value reflects a greater degree of kinetic disparity. The CSI is defined as Eq. (5):

CSI = |lgD+ - lgD-| + 2 * |σlgD+ - σlgD-| / (lgD+ * lgD-) (5)

This calculation formula provides a quantitative feature for evaluating the dynamic matching status between electrodes. The applicability and predictive capability of this approach are validated by the observed variations in CSI values across three representative positive electrode materials (NVO, VOP, and VOH), as summarized in Table S3, each showing distinct enhancement effects based on their kinetic alignment with ZVO.

Building upon the established set of kinetic evaluation equations, we further validated their universality by expanding the study to include four additional negative electrode materials beyond Zn3V3O8 (ZVO): TiS2, MXene, 3, 4, 9, 10-perylenetetracarboxylic diimide (PTCDI), and MoO2. These materials were systematically paired with the four positive electrode materials (Fig. 4a–g). To uncover deeper correlations, the XGBoost machine learning algorithm was employed to model the relationship between diffusion-derived features and electrochemical performance, particularly capacity retention under high specific currents (Fig. 5a). XGBoost was selected due to its well-established computational efficiency and predictive accuracy, which have supported its widespread application in data-rich scientific and industrial domains. To quantify feature relevance, we computed the correlation of each feature with electrochemical performance (Fig. 5b). In the resulting map, darker tones reflect stronger correlations. Among all features, the Diffusion Ratio (DR, defined as the ratio of mean ion diffusion coefficients) and the CSI exhibited the strongest correlations with electrochemical performance. These two metrics also emerged with the greatest feature importance in the XGBoost model (Fig. 5c, d), underscoring their dominant influence on electrochemical behavior. Notably, the significance of DR was consistent with insights from finite element simulations, which suggest that closer alignment of ion diffusion kinetics between electrodes facilitates more efficient charge transport and rate performance. The machine-learning-derived optimal DR range (0.936−1.020) is tightly centered near unity, reflecting the critical principle of kinetic balance between the negative and positive electrode. The slight asymmetry of this range may indicate a modest tolerance for slightly faster negative electrode kinetics, which arises naturally from the composition of the training dataset: negative electrode materials generally exhibit higher diffusion rates than positive electrodes, producing a minor skew of DR values below 1, as shown in Supplementary Table S9. Experimental validation of the DR descriptor across 30 distinct electrode pairs via GITT measurements confirms strong agreement with the predicted optimal range (0.936–1.020), as detailed in Table S9, supporting its utility for predicting and optimizing full-cell performance. Thus, while the model accommodates minor asymmetry, the overarching design guideline emphasizes the achievement of well-matched intercalation kinetics to ensure efficient ion flux, minimize polarization, and maximize both rate capability and cycling stability in AZIBs. The XGBoost model demonstrated high predictive accuracy for the DR and CSI descriptors, achieving a high coefficient of determination (R2=0.886) and a low root mean square error (RMSE=0.067) (Fig. 5e). To ensure the robustness of our modeling approach, we conducted a comparative evaluation of multiple machine-learning algorithms, including Linear Regression, Random Forest, Support Vector Regression (SVR), Lasso, and Ridge Regression. As summarized in Fig. S18, all of these models exhibited substantially lower predictive accuracy compared with XGBoost: their R2 values (0.528, 0.610, 0.443, 0.547, and 0.573) were considerably below that of XGBoost (0.886), while their RMSE values (0.136, 0.124, 0.148, 0.134, and 0.130) were consistently higher than XGBoost’s 0.067. This quantitative comparison highlights XGBoost’s robust predictive capability, attributable to its ability to effectively capture complex nonlinear feature interactions in our dataset. The inclusion of these comparative results in the Supporting Information not only provides readers with a broader methodological context but also reinforces the rationale for selecting XGBoost as the most suitable and robust framework for this study. Notably, the model identified electrode pairs with DR values in the range of 0.936–1.020 as those most likely to achieve the top 30% electrochemical performance. The CSI metric also proved effective, with high-performing systems predominantly falling within a CSI range of 0.241–1.013 (Fig. 5f). To evaluate the robustness of this design criterion, sensitivity analyses were performed using alternative thresholds of top 10% and top 50%. While the top 10% threshold yielded very narrow parameter ranges (DR: 0.946–0.956; CSI: 0.801–0.803) with insufficient data points for practical screening, the top 50% threshold produced broad ranges (DR: 0.936–1.114; CSI: 0.148–1.244) that introduced high performance variance and reduced predictive specificity. Crucially, the central tendencies and directional correlations between DR, CSI, and full-cell electrochemical performance remained consistent across all thresholds, with strong mutual overlap between the derived ranges. These results confirm that the proposed design rule is inherently robust, providing reliable guidance for electrode selection and screening while maintaining practical applicability for different material systems (Fig. S19). Introduced in this study as two quantitative descriptors, DR and CSI capture the degree of intercalation kinetics matching between electrode pairs. The CSI quantifies the distributional spread of Zn2+ diffusion coefficients across available pathways. Optimal values (0.241–1.013) reflect uniform diffusion landscapes that enable rapid kinetics, whereas values outside this range indicate inhomogeneous transport and localized bottlenecks that exacerbate polarization. The DR measures kinetic compatibility between electrodes, with values of 0.936–1.020 corresponding to balanced intercalation/deintercalation dynamics, and deviations beyond this range revealing electrode-specific kinetic mismatches. Machine-learning feature importance analysis identified CSI and DR as the most predictive descriptors, a conclusion further corroborated by experimental validation. Among the 30 combinations evaluated, those falling within the optimal DR range (0.936–1.020) and CSI range (0.241–1.013) consistently exhibited reduced redox peak separations, lower charge-transfer (Rct) and Warburg (Rw) resistances, higher specific capacities, and larger capacity retention as the specific current increased from 0.1 to 1.0 Ag−1 (Fig. 4g). More broadly, this work establishes a data-driven, formula-based approach that transforms the previously qualitative understanding of ion diffusion kinetics matching into a quantitative design rule, paving the way for rational electrode intercalation synergy and system-level optimization in advanced flexible energy storage technologies.

Fabrication of all-hydrogel AZIBs with matched kinetics for flexible applications
To validate the practical applicability of the above empirical models in guiding electrode material selection for dual-intercalation flexible AZIBs, the previously characterized ZVO||NVO electrode pairing, which simultaneously satisfies the screening criteria for both DR (0.946) and CSI (0.321), was selected to construct a Zn-free intercalation-type full cell for subsequent electrochemical performance evaluation. To establish a kinetically matched state, the ionic diffusion kinetics matching rate was tuned to 68% via cutoff voltage modulation. Building upon this foundation and ensuring full electrode flexibility, both NVO and ZVO were integrated into a PAA-Ag hybrid hydrogel precursor solution to fabricate self-supporting, flexible hydrogel electrodes. Ag nanoflakes were incorporated as a conductive filler to form an efficient percolation network within the hydrogel electrode, substantially enhancing electronic conductivity. Four-probe measurements confirmed that the sheet resistance decreased from 1.580 kΩ to 307.4 mΩ following the addition of Ag nanoflakes (Fig. S20), demonstrating their crucial role in improving charge transfer kinetics. The SEM morphology of the hydrogel electrode (inset of Fig. 6a) reveals a porous network structure, which facilitates ion transport while maintaining good elasticity. The mechanical properties of these hydrogel electrodes are presented in Fig. 6a, demonstrating that both the hydrogel negative electrode and hydrogel positive electrode achieve an elongation rate of nearly 400% and a tensile strength of 100 kPa, surpassing even the pure hydrogel matrix without host materials (24 kPa breaking stress at 230% strain). This enhancement can be attributed to the hydrogen bonding interactions between hydroxyl groups on the surfaces of ZVO||NVO vanadate crystals and the hydrogel network. The incorporation of rigid crystalline domains helps to bear part of the mechanical stress and even undergoes orientation alignment under strain, leading to increased hydrogel strength. Additionally, the introduction of more non-covalent interactions further contributes to the improved stretchability of the network. Moreover, the influence of gel encapsulation on the intrinsic electrochemical properties of the host materials was minimized through concentration optimization. For the negative electrode (Fig. 6b), increasing the ZVO concentration to 300 mg mL−1 allowed the hydrogel electrode to achieve electrochemical behavior comparable to that of the pristine ZVO electrode. Similarly, for the positive electrode (Fig. 6c), raising the NVO concentration to 200 mg mL−1 resulted in a CV curve closely resembling that of the pristine NVO electrode, with well-defined oxidation-reduction peaks. Higher loadings, while enhancing tensile strength, markedly reduced elongation-at-break due to restricted polymer chain mobility. Excessive loadings further disrupted the crosslinking density of the PAA network, compromising structural cohesion and preventing the fabrication of robust free-standing electrodes. Therefore, mass loadings were optimized at 200 mg mL−1 for the positive electrode and 300 mg mL−1 for the negative electrode. Considering that the hydrogen bond-induced adhesion between the electrode and electrolyte improves interfacial contact, enhances synergy, and strengthens the connection, the host materials are firmly anchored to the electrolyte surface, reducing interfacial impedance and shortening ion transport pathways. Therefore, a PVA-based hydrogel electrolyte was integrated with PAA hydrogel-based electrodes to fabricate coplanar flexible AZIBs (Fig. S21). As shown in Fig. 6d, this coplanar cell exhibits stable CV curves within a voltage window of 0.2–1.8 V. The GCD curves in Fig. 6e indicate a high areal capacity of 1.2 mAh cm−2 at a current density of 0.5 mA cm−2. Long-term cycling tests (Fig. 6f) further demonstrate the significant cyclic stability of the fabricated coplanar cell, which retains over 80% of its initial capacity after more than 200 cycles. Notably, the all-hydrogel cell fabricated in this study no longer exhibits the typical deformation constraints seen in previously reported flexible AZIBs with zinc-metal negative electrodes and gel electrolytes, which can only undergo small amplitude deformations in specific directions49. Lateral deformation, bending, or twisting may cause metal plates to fracture or lead to interface delamination, resulting in battery failure. Due to the flexibility of the electrodes, this coplanar cell can adapt to deformations in all directions, including twisting and bending, and can maintain its power supply performance for an LED signboard during deformation, as illustrated in Fig. 6g and Supplementary Movie 1. Also, this intrinsically flexible battery can stably power devices without compromising their wearability, exhibiting adaptability to everyday human motions. For example, integration with a palm-conformable, printed flexible light-emitting device enables this battery to deliver stable power during dynamic hand motions. (Figs. 6h and S22; Supplementary Movie 2). This capability to deform omnidirectionally, while maintaining stable electrochemical performance under mechanical stress, is critical for powering wearable devices during dynamic motion. This flexible cell was subjected to 40% strain cycling to quantitatively validate conformance with wearable requirements. It retains stable electrochemical performance even after 120 stretching-releasing cycles (Fig. 6i), demonstrating its robust mechanical resilience and long-term durability under deformation. As shown in Fig. S23, the devices utilize the hydrogel electrolyte itself as an encapsulation layer, exhibiting good water retention with only 0.041 wt% loss after 288 h under ambient conditions, thereby ensuring operational stability without additional packaging. Moreover, the all-hydrogel cell (4 cm2) achieves an energy density of 1070 µWh cm−2 (0.8 mW cm−2), while maintaining a high-power density of 4.0 mW cm−2 (248.2 µWh cm−2), as shown in Fig. 6j. These performances metrics are among the high values reported for the Zn-metal-free AZIBs in terms of areal capacity (<500 µWh cm−2 at 0.1 mA cm−2) and cyclic lifespan (<100 cycles)50–61, highlighting the notable performance of this developed flexible AZIBs. Moreover, it substantiates the positive impact of kinetically matched electrode pairings, as defined by the empirical model proposed in this study, on the performance of dual-intercalation AZIBs. This provides a theoretical framework for electrode material selection and performance prediction in the design of not only dual-intercalation but also broader classes of flexible AZIBs.

In summary, this study quantitatively defines ionic diffusion kinetics matching status in dual-intercalation electrode systems through a semi-empirical formula, establishing mathematical criteria for efficient electrode intercalation synergy. Although not applicable to non-intercalation or hybrid systems, this strategy provides broadly useful guidance for electrode pairing in dual-intercalation AZIBs under standard conditions, underscoring the importance of system-specific kinetic considerations in future battery design. By correlating theoretical calculations with experimental results, it clarifies the relationship between electrode matching and full-cell performance, providing a reliable reference model for performance prediction and a guiding principle for optimizing dual-intercalation AZIBs. Building on this, the interlayer spacing of the NVO positive electrode was tuned via voltage excitation to achieve optimized matching with the widely applicable ZVO negative electrode. The resulting aqueous AZIBs exhibited a specific capacity of 310 mAh g−1 and notable cycling stability exceeding 12,000 cycles at 5 Ag−1. To further validate the applicability of the proposed empirical equation in guiding electrode selection for flexible Zn-metal-free systems, a kinetically matched ZVO||NVO electrode pair was embedded into a PAA hydrogel matrix and assembled with a PVA-based hydrogel electrolyte via hydrogen bonding, resulting in the construction of an all-hydrogel AZIB. This battery demonstrated omnidirectional deformability, high durability, and stable functionality under mechanical deformation, achieving an energy density of 1070 µWh cm−2 (4 cm2) while maintaining electrochemical stability over 120 stretching-releasing cycles. This intrinsically flexible, stretchable, and high-performance AZIB design presents a promising pathway for advancing flexible energy storage systems to power next-generation wearable and portable electronic devices, seamlessly adapting to a wider range of human motions and body curvatures.

Methods
Materials
Polyvinyl alcohol (PVA, Mw 89000-98000, 98-99% hydrolyzed), vanadium pentoxide (V2O5, ≥99.5%), aniline (≥99.5%), ammonium metavanadate (NH4VO3, ≥99%), N-Methylpyrrolidone (NMP, ≥99.5%), ammonium persulfate (APS, ≥98%), and N,N’-methylenebisacrylamide (MBAA, ≥99%) were purchased from Shanghai Aladdin Bio-Chem Technology Co. Ltd. Zinc acetate dihydrate (CH3COO)2Zn·2H2O (98%), ethylene glycol (EG, 98%), phosphoric acid (H3PO4, ≥85 wt.% in H2O, ≥99.99% metals basis), potassium sulfate (K2SO4, 99%), isopropanol (≥99%), ethanol (>99.9%), Iron(III) nitrate nonahydrate (Fe(NO3)3·9H2O, 99.9%) and acrylic acid (>99%) was purchased from Shanghai Macleans Biochemical Technology Co., Ltd. Oxalic acid (H2C2O4·2H2O, ≥99.5%) and hydrogen peroxide (H2O2, ≥30%) were purchased from Sinopharm Chemical Reagent Co., Ltd. Zinc trifluoromethanesulfonate (Zn(OTf)2, 98%) was purchased from Energy Chemical. Super P (>99.9%) was purchased from Feynman Biotechnology Tech Co., Ltd. Polyvinylidene fluoride (PVDF, average Mw ~1,100,000, ≥99.5%) was purchased from Nanjing Moges Energy Technology Co., Ltd. Silver nanoflakes (96.5%) was purchased from Jiangsu XFNANO Materials Tech Co., Ltd. Zinc plate (99.99%) was purchased from Chengshuo Metal Materials Co., Ltd., and deionized water was used for all experiments.

Preparation of electrode materials
Synthesis of Zn3V3O8. To synthesize the negative electrode host materials62, 10 mmol of (CH3COO)2Zn·2H2O and 10 mmol of NH4VO3 were added to 60 mL of deionized water and stirred magnetically at 90°C for 5.5 h to form a sol. The sol was then dried in an oven at 70°C for 72 h. After drying, the obtained bulk product was ground into a fine powder and annealed at 750°C for 9 h under a 5% Ar/H2 mixed gas atmosphere with a heating rate of 4°C min−1, then cooled naturally to obtain the target product.

Synthesis of layered NH4V4O10. To synthesize the positive electrode host materials63, 4.67 mmol of NH4VO3 and 0.71 mmol of H2C2O4·2H2O were dissolved in 70 mL of distilled water and stirred at 25°C for 1 h. The solution was then transferred to a hydrothermal autoclave and heated at 180°C for 2 h. After the reaction, the product was naturally cooled to 25°C, washed sequentially with deionized water and ethanol three times, and dried at 60°C to yield the target product.

Synthesis of VOP and PA-VOP. To synthesize VOP64, 2.4 g of V2O5 powder and 13.3 mL of H3PO4 were added to 57.7 mL of deionized water and stirred for 2 h. The mixture was then heated in a hydrothermal reactor at 120°C for 16 h. After the reaction, the product was centrifuged, washed, and dried at 60°C to obtain VOP.

Subsequently, PA-VOP was synthesized using an ultrasonic exfoliation and self-assembly process. For this, 100 mg of VOPO4 powder was dispersed in 7 mL of isopropanol, and the solution was sonicated for 30 min. After centrifugation, the exfoliated sheets were transferred into an aniline solvent and stirred for 24 h. The product was then centrifuged, washed, and dried under vacuum at 60°C.

Synthesis of KVOH and VOH. To synthesize KVOH65, 0.364 g of V2O5 was dissolved in 50 mL of deionized water, and 2 mL of H2O2 was added. Separately, 0.087 g of K2SO4 was dissolved in 30 mL of deionized water. The two solutions were mixed under magnetic stirring for 0.5 h and then heated at 120°C for 6 h. The resulting precipitate was washed three times with deionized water and ethanol and dried in an oven at 60°C to obtain the green powder KVOH. For VOH, the same procedure was followed without adding K2SO4.

Fabrication of hydrogel electrolytes and testing electrodes
To prepare the electrolyte56, 4.5 g of polyvinyl alcohol (PVA) powder was added to 40 mL of a mixed solution containing 1 M Zn(OTf)2 in water and ethylene glycol. The mixture was stirred at 90°C until it dissolved uniformly and then froze at −40°C for crosslinking to form the hydrogel.

For the testing electrode, the electrode slurry was formulated by thoroughly mixing the active material (lab-synthesized), Super P, and PVDF in a mass ratio of 7:2:1 within NMP solvent. This slurry was then manually single-side coated onto the titanium mesh (99.99%, 200 meshes, thickness: 0.5 mm, pore size: 0.077 mm) utilizing a coating brush. Following drying under 60°C, the electrode sheets were punched into circular disks with an electrode cutter (MSK-T10, Shenzhen Kejing Star Technology Co., Ltd.), achieving a targeted active material mass loading of 1–2 mg cm−2. Notably, the basic electrochemical performance of the electrode materials was tested by assembling coin cells (CR2032, 20×20×3 mm, stainless steel 304) with electrode sheets (φ=12 mm) and hydrogel electrolytes (φ=16 mm, volume: 0.26±0.05 cm3). For the half-cell test, zinc foil (thickness: 80 μm) was used as the negative electrode.

Fabrication of hydrogel electrodes and assembled AZIBs
A precursor solution was prepared with ethylene glycol and water in a 2:1 volume ratio, followed by the addition of 15 mL of acrylic acid to 35 mL of the mixed solution. To this, 150 mg of Fe(NO3)3·9H2O, 20 mg of MBAA, and 150 mg of APS were added. The mixture was stirred thoroughly, and ethanol (50 vol% relative to the total precursor volume) was added to retard the spontaneous polymerization process, yielding the polyacrylic acid (PAA) precursor solution.

The hydrogel electrodes were formulated by homogeneously mixing active electrode material (NH4V4O10 for the positive electrode or Zn3V3O8 for the negative electrode) with Ag nanoflakes in the PAA hydrogel precursor solution. The concentration of the active material was systematically varied from 60 to 300 mg mL−1, while the concentration of Ag nanoflakes was maintained constant at 400 mg mL−1 to ensure consistent electrical conductivity. The resulting mixtures were poured into Petri dishes and gelled at 60°C to form self-supporting electrode films with a uniform thickness of approximately 0.1 mm. These freestanding hydrogel electrodes were then cut into rectangular strips of desired dimensions (typically 10 mm×25 mm) using a surgical blade. The electrolyte and substrate consisted of the PVA hydrogel, prepared according to the procedure described in the previous section of hydrogel electrolyte. The PVA precursor solution was cast in a Petri dish and gelled at −40°C, forming a transparent, freestanding film with a thickness of approximately 1.0 mm. A coplanar battery architecture was constructed by arranging the discrete positive and negative hydrogel electrode strips in parallel on the same PVA hydrogel substrate, maintaining a defined insulating gap of 0.2 mm between them to prevent internal short-circuiting. The assembly was fully encapsulated by casting a second layer of PVA precursor solution over the electrodes and substrate, followed by a final gelling step at −40°C. This created a monolithic, all-hydrogel structure with integrated ionic continuity. External electrical connections were established by attaching flexible conductive adhesive tapes to the exposed terminals of the hydrogel electrodes, enabling electrochemical testing and the configuration of devices in series. The electrochemical properties of the electrodes were evaluated using CV, GCD, and EIS methods via an electrochemical workstation. Additionally, the mechanical properties of the hydrogel electrodes were measured and compared using a universal testing machine.

Characterizations
SEM was performed on a Sirion sweep electron microscope (FEI, Hillsboro, USA) with a 20 kV working voltage. The element valence of the electrode samples was analyzed by XPS on a 4800X X-ray photoelectron spectroscopy (Thermo Fisher, Waltham, USA). XRD curves were recorded using a DX-2700b X-ray diffractometer (XRD) (Haoyuan Instrument, Dandong, China) with Cu-Kα radiation at a step interval of 0.02° and a scanning speed of 8° min−1. The tensile and compression properties of hydrogel samples were measured using an MTS Universal Testing Machine (E43, Shenzhen, China). The electrochemical experiments were performed using a Biologic VSP workstation (Seyssinet-Pariset, France) in an ambient laboratory environment maintained at 25°C (no climatic/environmental chamber), while a CT2001A LAND battery test system (Wuhan, China) was used for galvanostatic charge-discharge testing at 25°C. EIS measurements were carried out using a potentiostatic signal with an amplitude of 10 mV. The frequency was swept from 100 kHz down to 100 mHz with an acquisition density of 6 points per decade. To ensure the measurements were carried out under a quasi-stationary state, the cells were left at open circuit for 30 min to reach a stable potential before the initiation of each frequency sweep. For ex situ analysis (XPS/XRD), cycled electrodes were harvested under ambient laboratory conditions (~25°C, air environment). The working electrode (slurry-coated titanium mesh) was carefully extracted from the electrochemical cell after reaching the target potential during galvanostatic cycling (1 Ag−1 in 1 M Zn(CF3SO3)2 aqueous electrolyte, with Zn metal or a compatible electrode as the counter electrode). The harvested electrode was immediately rinsed with anhydrous ethanol to remove residual electrolyte salts, followed by drying in air. The dried samples were stored in sealed containers under ambient conditions prior to analysis. All ex situ measurements were performed under controlled temperature conditions (25°C).

Density functional simulation
First-principles calculations based on density functional theory (DFT) were performed using the Vienna Ab-initio Simulation Package with projector-augmented wave pseudopotentials66. The exchange-correlation interactions were treated within the generalized gradient approximation using the Perdew-Burke-Ernzerhof functional67,68. A plane-wave energy cutoff of 500 eV was employed, and the Brillouin zone was sampled with a 3×3×3 k-point mesh. The electronic energy convergence criterion was set to 10−7 eV, while structural relaxations were performed until the maximum Hellmann-Feynman force on each atom was reduced below 0.005 eV Å−1. The nudged elastic band method was used to evaluate diffusion energy barriers69.

Finite element simulation
Finite element simulations were conducted using COMSOL Multiphysics 6.2 to investigate ion concentration profiles and the spatial distribution of the electric field within a dual-intercalation battery system. The simulation framework integrated the “Battery Module” and the “Transport of Diluted Species” interfaces in a coupled approach. Ion insertion and extraction at the electrode surfaces were governed by the Butler-Volmer kinetics, while ion diffusion within both the positive and negative electrode was modeled according to Fick’s law. In the electrolyte phase, ion migration under the influence of an electric field was described using the Nernst-Planck equation. For the simulation, the Zn2+ diffusion coefficients in the positive electrode were set to 1.0×10−11 m2 s−1 for the control group and 1.0×10−10 m2 s−1 for the experimental group. The initial Zn2+ ion concentration in the electrolyte was 1000 mol m−3. A constant charging current of 0.006 A was applied, with a maximum voltage limit of 1.8 V.

Machine learning implementation
An XGBoost model was employed to predict the electrochemical performance of dual-intercalation AZIBs based on diffusion-derived descriptors. The training dataset was obtained exclusively from experimentally measured data in this study. To quantitatively assess kinetic compatibility between electrode pairs, two key descriptors were defined: the CSI and the DR. CSI quantifies the divergence in ion diffusion kinetics between electrodes, reflecting the degree of synchronization in their intercalation behavior. DR is defined as the ratio of the mean apparent ion diffusion coefficients (logarithmic values) of the positive and negative electrode, providing a scale-invariant measure of relative diffusion rates. The respective formulas for calculating these descriptors are provided below:

CSI = |lgD+ - lgD-| + 2 * |σlgD+ - σlgD-| / (lgD+ * lgD-)

DR = lgD+ / lgD-

Where lgD+ and lgD- denote the logarithmic value of the zinc ion diffusion coefficient of positive and negative electrode at each step of the charge-discharge process. Computational analyses were performed on a dedicated workstation (Dell Precision T3680: Intel Core i9-14900K CPU, NVIDIA RTX 3090 GPU, 128 GB RAM). The total computation time dedicated to hyperparameter optimization and k-fold cross-validation procedures was 336 h.

Data availability
All data generated or analyzed during this study are included in this published article and its supplementary information files. Source data are provided with this paper.

Code availability
The machine learning code supporting the findings of this study has been deposited in the GitHub repository at https://github.com/rachelyyr-llamama/ML/blob/main and is publicly available under the MIT License. The raw database used for model training comes from the experimental results as listed in Data availability. A permanent, citable version of the code and the complete training dataset has been assigned the DOI 10.5281/zenodo.17827952. The training dataset generated for and used in this study has also been deposited in the same repository70.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Recommendation: Major revision\nAqueous Zn-based batteries have attracted much attention associated to their intrinsic safety and abundant resources.\nHowever, there are restrained in cathode and anode issues, such as cathode dissolution, uncontrolled Zn dendrite at the\nanode, and limited sluggish ion transport. In this manuscript, the authors propose an intercalation-type electrode based on a\nmachine learning-assisted kinetic matching model, which offers a good quantitative framework for assessing ion-diffusion\ncompatibility between intercalation-type electrodes. On the whole, I recognized the soundness of the design configuration\nand clarity of the logical structure. However, the reliability of the foundational database requires substantial improvement. In\nits current form, the manuscript demands a more rigorous foundation for publication in this journal, and several critical issues\nmust be addressed before it can be considered.\n1. The introduction should further highlight the underlying novelty by expanding beyond the discussion of flexible design to\ninclude more substantial information on the machine-learning component. Apart from spatial factors, are there other factors\nthat influence the kinetics?\n2. It should be clarified whether the dissolution–deposition mechanism is representative in this context. If not, more specific\nand directly relevant literature should be referenced to support the discussion.\n3. The author should add the DFT modeling or configuration for the absorption energy and diffusion barrier as shown in\nFigure 2c. The different absorption configurations would yield different energy values. There is a mismatch for the diffusion\nbarrier values for NVO-4 materials (1.876 eV shown in Figure 2c, while 1.8676 eV in Table S1).\n4. The authors should provide DFT modeling or configurations for the adsorption energy and diffusion barrier, as shown in\nFigure 2c. Different adsorption configurations could lead to varying energy values. Additionally, there is a mismatch in the\ndiffusion barrier values for the NVO-4 material (1.876 eV in Figure 2c versus 1.8676 eV in Table S1).\n5. As shown in Figure S2a, the (001) diffraction changes during the discharge–charge process are barely discernible. While\nit is understood that NVO possesses low crystallinity, the XRD signal should be further enhanced to allow clearer\nobservation.\n6. According to previous reports on vanadium oxides (Adv. Energy Mater., 2024, 14, 2401493; Nano Energy, 2020, 70,\n104519), upon Zn²⁺ insertion, the (001) diffraction peaks are typically observed to gradually shift toward lower angles rather\nthan higher angles. However, I note that for NVO, the observed behavior can be considered acceptable within the framework\nof your proposed mechanism. Nevertheless, given the atypical nature of this phenomenon, additional supporting references\nshould be provided to strengthen the clarity and credibility of the explanation.\n7. If the anode is metallic zinc, NVO undergoes changes during charging and discharging. Therefore, it is difficult to attribute\nthe fast kinetics solely to spacing modification, given the presence of Zn intercalation, as you mentioned. More precisely, the\ninterlayer spacing of NVO can be effectively modulated by adjusting the zinc ion intercalation rather than by altering the\ncharge-discharge cut-off voltage.\n8. It is recommended to include a magnified view in Figure S4 to facilitate clearer recognition of the differences and variation\ntendencies.\n9. As mentioned regarding Figure 3d, the minor difference in Warburg impedance hardly supports your claim of superior ion\ntransport kinetics.\n10. From Figure S11, it is difficult to recognize this as demonstrating exceptional cycling stability in full-cell tests.\nFurthermore, the comparison in Figure 3h is not rigorous, as it omits the parameter of current density. In the same vein, it\nwould be more convincing to present specific capacity on the y-axis instead of capacity retention. This is because, at lower\nspecific capacities, batteries generally exhibit better cycling performance.\n11. From all the CV curve samples, the ZVO/VOH vs. ZVO/KVOH (Figure 4a) and ZVO/VOP vs. ZVO/PA-VOP (Figure 4d)\nconfigurations exhibit markedly different storage mechanisms. Therefore, it is difficult to attribute the observed results solely\nto enhanced matching between the anode and cathode. In particular, when aniline is introduced into VOP, could the\nelevated peaks at higher voltages in Figure 4d indicate a greater contribution from proton involvement rather than due to the\nspatial modification?", "reviewer_2": "The authors of this study sought to address the mismatch in ion diffusion rates between the cathode and anode, which is a\nkey cause of performance degradation in aqueous zinc-ion batteries. To this end, they introduced a machine learning\napproach and, for the first time, developed a “kinetic matching model” capable of quantitatively predicting the kinetic\ncompatibility of two electrodes. Using this model, they identified the optimal electrode pairing (ZVO||NVO) and successfully\nfabricated a battery with an outstanding lifespan of over 12,000 cycles. Furthermore, they applied this principle to hydrogel-\nbased flexible batteries, achieving both high energy density and mechanical stability. Ultimately, this work presents a new\nparadigm for data-driven, rational battery design. However, additional revisions addressing the following major questions\nare necessary for publication in Nature Communications.\n1. In the DFT-based NEB calculations, it is unclear why only NVO-1 exhibits migration along a different crystallographic\ndirection. It would be beneficial to also provide results in which all migration paths are aligned along the same direction for\nconsistency. Furthermore, while it is stated that the Bader charge results are related to the diffusion barrier, the explanation\ndoes not clearly specify whose charge variation is responsible for this correlation.\n2. The strategy of optimizing the kinetics by lowering the upper cut-off voltage from 2.0 V to 1.8 V is highly original. However,\nthis approach directly sacrifices the battery’s energy density (Energy = Capacity × Voltage). While the 1.8 V cell may exhibit\nsuperior rate capability and cycle life, a quantitative comparison is needed to determine how much energy density is lost\ncompared to the 2.0 V cell. From a practical application standpoint, where is the balance point between the benefits of\nimproved lifespan and rate performance and the drawbacks of reduced energy density? Furthermore, can the current model\nalso assist in identifying this optimal practical trade-off?\n3. In Fig. 5f, the optimal ranges of DR and CSI are determined based on a threshold of the “top 30%.” However, this “top\n30%” criterion may appear somewhat arbitrary. Have you examined how sensitive the proposed optimal design range\n(design rule) is to changes in this threshold, for example, if it were set to the “top 10%” or “top 50%”? If the robustness of this\ndesign rule is strongly dependent on the chosen threshold, wouldn’t that limit its general applicability?\n4. In Li-ion batteries, it is common practice to intentionally design the anode capacity to be greater than that of the cathode in\norder to prevent critical issues such as lithium metal plating. In the dual-insertion system studied here, is pursuing perfectly\nsymmetric kinetics (DR ≈ 1) always the optimal approach? Would it not be more stable and robust to intentionally design the\nanode kinetics to be slightly faster than the cathode, so that it can serve as a “buffer” to accommodate the incoming ion flux\nfrom the cathode during sudden high-power demands? Could the model’s optimal DR range of 0.936–1.020, which is not\nperfectly symmetric around 1.0, be hinting at the necessity of such asymmetry?\n5. The CSI and DR proposed in this study are highly effective features designed based on deep physical insights into battery\nkinetics. However, they represent a successful case of “feature engineering” grounded in the researchers’ expertise, rather\nthan features “learned” or “generated” autonomously by AI. Certain expressions in the manuscript (e.g., “ML-based model”)\ncould be interpreted as implying that AI itself is the agent explaining this phenomenon. Would it not convey the\nmethodological essence more accurately to clearly redefine the role of AI in this work as “a powerful statistical tool for\nvalidating the hypotheses of the researcher-proposed physical model and modeling complex nonlinear relationships”?\n6. It would be helpful to include in the Supporting Information the results of tests conducted using several other machine\nlearning methods.", "reviewer_3": "This work presents a machine learning-assisted kinetic matching model that enables the rational design of dual-intercalation aqueous zinc-ion batteries with synchronized ion transport, achieving high capacity, long-term cycling stability, and\nstretchable device integration. However, there are a few concerns:\n1. While the work introduces a novel ML-assisted kinetic matching model, the introduction would benefit from a clearer one-\nsentence summary of what sets this model apart from existing approaches (e.g., what \"universal\" and \"quantitative\" offer\nover past semi-empirical efforts).\n2. The acronyms Confined Spacing Index (CSI) and Diffusion Ratio (DR) are introduced without explanation of how they are\ncalculated or interpreted. A short clarification (e.g., \"CSI quantifies...\") would improve reader comprehension and model\ntransparency.\n3. There is no mention of the dataset or material pool used to develop or validate the kinetic matching model. Including this\nwould lend credibility to the ML framework and clarify its generalizability.\n4. The manuscript alludes to \"cathode-anode kinetic mismatch\" but does not clearly explain how this mismatch leads to\nperformance degradation. A brief mechanistic explanation would strengthen the argument for kinetic matching.\n5. Why was this specific dual-intercalation system chosen for demonstration? Clarifying whether it was selected by the\nmodel or based on prior literature would help showcase the utility of the model.\n6. The reported specific capacity and cycling performance are impressive, but a brief comparison to existing AZIB systems\n(e.g., in a parenthetical or supplementary table) would help readers appreciate the significance.\n7. While Ag nanoflakes are mentioned in the hydrogel matrix, the text does not explain their functon(e.g., enhancing\nconductivity, mechanical reinforcement). Adding this detail would provide insight into the stretchable battery design.\n8. The phrase “universal and quantifiable strategy” may come across as overreaching without discussing limitations or\nmaterial scope. Consider tempering this with a note on applicability or current constraints.\n9. The five cited references are highly relevant to the presented work as they collectively demonstrate the importance of\naddressing zinc anode challenges in aqueous zinc-ion batteries (AZIBs) through machine learning (ML),\nelectrolyte/interfacial engineering, and advanced material design—all of which align with and complement the current\nstudy’s focus on kinetic matching and dual-intercalation electrodes. Reference 1 (10.1039/D5EE00650C) employs machine\nlearning to optimize protective interface layers for Zn anodes, highlighting the effectiveness of ML in navigating complex\nmaterial–property relationships—thus directly supporting the rationale for the ML-assisted approach in this study. Reference\n2 (https://doi.org/10.1002/ange.202507504) introduces a novel halogenated TiBTx MBene protective layer that improves Zn\nanode performance through engineered surface functionalities, underscoring the importance of interfacial ion dynamics and\nZn2+ compatibility, which the current work aims to address from a system-level electrode matching perspective. Reference 3\n(https://doi.org/10.1016/j.est.2025.117300) showcases the role of electrolyte additives (DPAH) in regulating Zn2+ deposition\nbehavior and improving reversibility, emphasizing how chemical strategies can\nharmonize ion transport—analogous to the synchronized ion flux concept proposed here. Reference 4 (https://doi.org/10.1002/smtd.202500576) demonstrates the\nimpact of hydrogel electrolytes on battery performance across temperature extremes, directly supporting the current study’s\ndemonstration of omnidirectionally stretchable all-hydrogel AZIBs. Reference 5 (https://doi.org/10.1021/acsnano.4c13312) highlights an ML-based approach to screen electrolyte additives based on donor numbers, reinforcing the value of data-\ndriven methods in optimizing battery components—a foundational theme in this work’s kinetic-matching framework. Citing\nthese works collectively underscores the current study’s integration of ML, interfacial engineering, ion transport coordination,\nand flexible battery design, while situating it within the broader landscape of next-generation AZIB development.", "reviewer_4": "This study presents a compelling theoretical framework for evaluating ion diffusion compatibility between intercalation-type electrodes using machine learning-assisted computation. By facilitating the rapid screening and optimization of electrode\nmaterials for zinc-ion batteries, the work holds potential to accelerate material discovery in this field. The authors further\nverify their model through comprehensive experimental validation across various Zn-ion intercalation systems,\ndemonstrating enhanced electrochemical performance.\nWhile the study is scientifically meaningful and methodologically innovative, several points require clarification and further\nelaboration to solidify the conclusions. The manuscript will be acceptable upon well addressing the following issues:\n1. The empirical formula incorporates the CSI and DR parameters. Do these parameters carry explicit physical significance?\nPlease clarify their definitions and provide justification for their selection as computational descriptors.\n2. The manuscript should clearly specify the data sources used for machine learning training and validation. A complete list\nof all datasets included in the computational analysis is essential for transparency and reproducibility.\n3. In Figures 3a and 3b, the meaning of the white circles is unclear. Please clarify their significance and explain the\ntheoretical basis and design rationale of the simulation models used.\n4. Figure 4g highlights superior electrochemical performance in formula-compliant systems; however, it only presents\ncomparisons based on the CSI parameter. To more robustly validate the semi-empirical model, corresponding comparative\ndata for the DR parameter should also be included.\n5. The manuscript currently lacks sufficient experimental details to ensure reproducibility. Specifically, the hydrogel battery\nassembly process and the implementation details of the machine learning workflow should be described more thoroughly.\n6. Figures 6b and 6c suggest that increasing the concentration of active electrode material improves the capacity of the\nhydrogel electrodes. Please clarify why higher loadings were not explored further to maximize capacity, and whether there\nwere practical or mechanical limitations.\n7. Was encapsulation employed during the flexible device demonstrations? If not, please provide experimental data on\nwater content changes under ambient conditions to evaluate the water retention capability of the system."}, "author_response": "Manuscript ID: NCOMMS-25-56714A\nTitle: Machine Learning-Assisted Kinetic Matching Model for Rational Electrode\nDesign in Aqueous Zinc-Ion Batteries\nPlease note that the responses are presented in blue fonts, and the revisions are\npresented in orange fonts, which are also reflected in the revised manuscript and\nsupporting information with highlight.\nReviewer 1\nRecommendation: Major revision\nComments:\nAqueous Zn-based batteries have attracted much attention associated to their intrinsic\nsafety and abundant resources. However, there are restrained in cathode and anode\nissues, such as cathode dissolution, uncontrolled Zn dendrite at the anode, and limited\nsluggish ion transport. In this manuscript, the authors propose an intercalation-type\nelectrode based on a machine learning-assisted kinetic matching model, which offers a\ngood quantitative framework for assessing ion-diffusion compatibility between\nintercalation-type electrodes. On the whole, I recognized the soundness of the design\nconfiguration and clarity of the logical structure. However, the reliability of the\nfoundational database requires substantial improvement. In its current form, the\nmanuscript demands a more rigorous foundation for publication in this journal, and\nseveral critical issues must be addressed before it can be considered.\nResponse: We sincerely thank the reviewer for the valuable comments and suggestions\non our work. In response, we have made corresponding revisions and additions to\naddress the concerns raised and to further improve the overall quality of the manuscript.\nWe hope that the revised version will meet with the reviewer’s approval and support.\n(1) The introduction should further highlight the underlying novelty by expanding\nbeyond the discussion of flexible design to include more substantial information on the\nmachine-learning component. Apart from spatial factors, are there other factors that\ninfluence the kinetics?\nResponse: Thank you for the constructive suggestion regarding the need to further\nemphasize the underlying novelty of our work in the Introduction. In response, we have\nexpanded the discussion to more clearly highlight the role of the machine-learning\ncomponent, which functions as an advanced computational framework not only to\nvalidate hypotheses derived from the physical model but also to capture complex, non-\nlinear correlations that are difficult to resolve by conventional experimental or\nanalytical methods. This integration strengthens mechanistic understanding and\nenhances the predictive capacity of the model. Regarding the additional factors\ninfluencing electrode kinetics, beyond spatial parameters such as interlayer spacing and\ncrystallographic design, kinetic performance is also strongly affected by operational\nconditions, including temperature, local ion concentration, and interfacial electrical\nproperties. For example, elevated temperature significantly accelerates ion diffusion, as\nquantitatively verified by comparative GITT measurements at 60°C and 25°C. As\nshown in Figure S7, the logarithmic zinc-ion diffusion coefficients extracted from GITT\ncurves exhibit higher values at elevated temperature, underscoring the critical role of\nthermal effects in diffusion kinetics. Nevertheless, these parameters generally act as\nglobal factors that simultaneously influence both electrodes and the electrolyte in a full\ncell. In contrast, spatial engineering, such as lattice adjustment, pillar insertion, or\ninterlayer spacing modulation, enables selective, electrode-specific tuning of kinetic\nbehavior. This targeted approach is essential to deliberately balance ion intercalation\nkinetics between cathode and anode, which is key to achieving high-performance full-\ncell systems.\nFollowing the comment, the relevant discussion has been added into the Introduction\nand Results and Discussion sections on Page 5, Line 2 and Page 10, Line 13 in the\nrevised manuscript and Supporting Information, as shown below:\nPage 5\n“To address this long-standing challenge, machine learning serves as an indispensable\ntool for decoding complex electrode kinetics and validating physically derived\nhypotheses.[32,33] By integrating domain knowledge with data-driven modeling,\nmachine learning provides a robust statistical framework to quantify and unravel the\nnonlinear relationships between material properties and electrochemical behavior. This\napproach enhances the predictability and generalizability of kinetic matching\nprinciples, complementing traditional experimental methods and opening new\npathways for the rational design of high-performance energy storage systems.”\nReference\n[32] Dong, J., Yin, J., Ang, E. H. et al. Machine learning-assisted benign\ntransformation of three zinc states in zinc ion batteries. Energy Environ. Sci. 18, 4872-\n4882 (2025).\n[33] Luo, H., Gou, Q., Li, M. et al. Machine Learning-Assisted High-Donor-Number\nElectrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion\nBatteries. ACS Nano 19, 2, 2427-2443 (2025).\nPage 10\n“While conventional methods, such as adjusting temperature (Figure S7) or other\ntargeted interventions like SEI layers and surface engineering can influence overall\nkinetics, they cannot selectively balance the kinetic mismatch between cathode and\nanode or offer broad applicability. In contrast, controlling the cut-off voltage effectively\nregulates the range of interlayer spacing variation, directly tuning Zn2+ diffusion\npathways and surface flux. This strategy not only optimizes kinetic matching across\nelectrodes but also preserves structural stability by minimizing lattice strain, enabling\nfast, reversible Zn2+ intercalation and enhancing both performance and durability in\naqueous zinc-ion batteries.”\nPage 6 (Supporting Information)\nFigure S7. Comparison of logarithmic zinc-ion diffusion coefficients for Zn||NVO\nunder 60°C and 25°C.\n(2) It should be clarified whether the dissolution-deposition mechanism is\nrepresentative in this context. If not, more specific and directly relevant literature\nshould be referenced to support the discussion.\nResponse: We thank the reviewer for this valuable suggestion. Zinc metal is the most\nwidely used anode for AZIBs, offering high volumetric capacity, low redox potential,\nand intrinsic safety. Importantly, it operates via a straightforward dissolution-\ndeposition mechanism, which is highly reversible. This reversibility promotes uniform\nZn plating/stripping, mitigates dendrite formation, and significantly enhances cycling\nstability. On the cathode side, representative materials such as MnO , Fe, and organic\n2\nquinone-based compounds (Adv. Mater. 2022, 34, 2204681; Adv. Energy Mater. 2019,\n9, 1901838; Mater. Today Energy 2020, 16, 100396; Nano Res. 2025, 18, 94907708; J.\nMater. Chem. A, 2019, 7, 23378-23415.) also rely on reversible dissolution-deposition\nreactions. Together, these dissolution-deposition electrodes constitute a broad and\npractically important class of AZIBs, combining high capacity with enhanced cycling\nstability due to the intrinsically reversible reaction pathways. The combination of high\nvolumetric capacity and robust reversibility explains why dissolution-deposition-type\nAZIBs are widely investigated and considered representative. In contrast, conversion-\ntype electrodes (e.g., iodine- or bromine-based) involve complex multiphase transitions\nwith less predictable reversibility and are therefore less directly relevant. Given their\nmechanistic similarities and shared device-level challenges, comparing intercalation\nwith dissolution-deposition remains representative in the context of aqueous energy\nstorage. Our dual-intercalation strategy is therefore designed to address issues relevant\nto dissolution-deposition electrodes, while conversion-type electrodes fall outside the\nscope of this work. Following the comment, the corresponding clarification has been\nincorporated into the Introduction (Page 3, Line 9) as follows:\nPage 3\n“Meanwhile, cathode materials in AZIBs primarily operate via three distinct\nmechanisms: intercalation, dissolution-deposition, and conversion. Dissolution-\ndeposition systems are highly representative because the Zn metal anode—the most\ncommon and practical choice for AZIBs—offers high volumetric capacity and operates\nthrough a simple, highly reversible mechanism that enables uniform plating/stripping\nand improved cycling stability. Many cathodes, including MnO , Fe, and organic\n2\nquinone-based compounds, also rely on reversible dissolution-precipitation\nreactions.[6-8] Despite their attractive capacities and stability, dissolution-deposition\nelectrodes can still face structural collapse, sluggish Zn2+ kinetics, and incomplete\nmaterial utilization, which limit energy density and power capability. In contrast,\nconversion-type electrodes involve complex multiphase transitions and differ\nfundamentally in system design, while intercalation and dissolution–deposition remain\nthe two most prevalent and practically relevant categories for AZIBs.[9,10]”\nReferences\n[6] Guo, X., Zhou, J., Liang, S. et al. Zn/MnO Battery Chemistry with Dissolution-\n[7] Zhang, W., Li, H., Tao, Z. et al. Ultra-low cost and high Coulombic efficiency\naqueous zinc-ion battery. Nano Res. 18, 94907708 (2025).\n[8] Han, C., Lia, J., Li, B. et al. Organic quinones towards advanced electrochemical\nenergy storage: recent advances and challenges. J. Mater. Chem. A, 7, 23378-23415\n(2019).\n[9] Liu, Z., Luo, Z., Liang, S. et al. Balanced Interfacial Ion Concentration and\nMigration Steric Hindrance Promoting High-Efficiency Deposition/Dissolution Battery\nChemistry. Adv. Mater. 34, 2204681 (2022).\n[10] Liang, G., Mo, F., Zhi, C. et al. A Universal Principle to Design Reversible\nAqueous Batteries Based on Deposition-Dissolution Mechanism. Adv. Energy Mater. 9,\n1901838 (2019).\n(3) The author should add the DFT modeling or configuration for the absorption energy\nand diffusion barrier as shown in Figure 2c. The different absorption configurations\nwould yield different energy values. There is a mismatch for the diffusion barrier values\nfor NVO-4 materials (1.876 eV shown in Figure 2c, while 1.8676 eV in Table S1).\nResponse: We thank the reviewer for the insightful comment regarding the DFT\nmodeling of adsorption energies and diffusion barriers. For NVO-2, NVO-3, and NVO-\n4, the most stable adsorption site is consistently located at the ammonium vacancy,\nwhich was used as both the initial and final states for diffusion barrier calculations. This\nensures that the reported barriers uniquely reflect the most favorable diffusion pathway.\nFor NVO-1, which lacks intrinsic vacancies, multiple adsorption configurations were\nevaluated. Four distinct sites (A, B, C, D) were identified, with adsorption energies of\n–2.130 eV, –0.163 eV, –1.347 eV, and –2.130 eV, respectively. Sites A and D, having\nidentical minimum energies and symmetry-equivalent positions, were selected as\nendpoints for diffusion pathways. Comparative analysis of Path-1 (A→D) and Path-2\n(A→C, Figure S2) demonstrates that Path-1 exhibits a lower Zn2+ diffusion barrier,\nconfirming it as the most favorable pathway. Regarding the discrepancy noted for\nNVO-4 (1.876 eV in Figure 2c vs. 1.8676 eV in Table S1), we have identified a labeling\nerror in the original figure. The raw DFT data has been carefully reexamined, and the\ncorrected values are now included in the revised Figure 2c.\nTo improve clarity, we have added details of the adsorption configurations, energies,\nand the diffusion barrier calculation methodology to the Supporting Information\n(Figure S2), ensuring reproducibility and transparency of the DFT analysis. The\nrelevant discussion has been added into the Results and Discussion sections on Page\n8, Line 15 in the revised manuscript and Supporting Information, as shown below:\nPage 8\n“For NVO-2, NVO-3, and NVO-4, diffusion energy barriers were calculated using the\nmost stable adsorption site at the ammonium vacancy. For NVO-1, which lacks intrinsic\nvacancies, multiple adsorption configurations were evaluated (Figure S2), and the\nconfiguration with the lowest adsorption energy was selected to ensure accurate\ndiffusion barrier determination. Comparative analysis of two possible diffusion paths,\nPath-1 and Path-2, revealed that Path-1 exhibits a lower Zn2+ diffusion barrier,\nconfirming it as the most favorable diffusion pathway. These calculations ensure that\nthe reported barriers accurately reflect the energetically preferred ion transport routes.”\nPage 2 (Supporting Information)\nFigure S2. Different configurations for zinc-ion transport paths within the lattice of\nNVO-1 and the corresponding energy barrier distributions.\nPage 11\nFigure 2. (c) Adsorption energies and corresponding diffusion energy barriers of Zn2+\nin structurally distinct NVO variants.\n(4) The authors should provide DFT modeling or configurations for the adsorption\nenergy and diffusion barrier, as shown in Figure 2c. Different adsorption configurations\ncould lead to varying energy values. Additionally, there is a mismatch in the diffusion\nbarrier values for the NVO-4 material (1.876 eV in Figure 2c versus 1.8676 eV in Table\nS1).\nResponse: We thank the reviewer for the insightful comment regarding the DFT\nmodeling of adsorption energies and diffusion barriers. For NVO-2, NVO-3, and NVO-\n4, the most stable adsorption site is consistently located at the ammonium vacancy,\nwhich was used as both the initial and final state for diffusion barrier calculations. This\nensures that the reported barriers uniquely reflect the most favorable diffusion pathway.\nFor NVO-1, which lacks intrinsic vacancies, multiple adsorption configurations were\nevaluated. Four distinct sites (A, B, C, D) were identified, with adsorption energies of\n–2.130 eV, –0.163 eV, –1.347 eV, and –2.130 eV, respectively. Sites A and D, having\nidentical minimum energies and symmetry-equivalent positions, were selected as\nendpoints for diffusion pathways. Comparative analysis of Path-1 (A→D) and Path-2\n(A→C, Figure S2) demonstrates that Path-1 exhibits a lower Zn2+ diffusion barrier,\nconfirming it as the most favorable pathway. Regarding the discrepancy noted for\nNVO-4 (1.876 eV in Figure 2c vs. 1.8676 eV in Table S1), we have identified a labeling\nerror in the original figure. The raw DFT data has been carefully reexamined, and the\ncorrected values are now included in the revised Figure 2c.\nTo improve clarity, we have added details of the adsorption configurations, energies,\nand the diffusion barrier calculation methodology to the Supporting Information\n(Figure S2), ensuring reproducibility and transparency of the DFT analysis. The\nrelevant discussion has been added into the Results and Discussion sections on Page\n8, Line 15 in the revised manuscript and Page 2 in the Supporting Information, and\nFigure 2c on Page 11 has also been revised, as shown below:\nPage 8\n“For NVO-2, NVO-3, and NVO-4, diffusion energy barriers were calculated using the\nmost stable adsorption site at the ammonium vacancy. For NVO-1, which lacks intrinsic\nvacancies, multiple adsorption configurations were evaluated (Figure S2), and the\nconfiguration with the lowest adsorption energy was selected to ensure accurate\ndiffusion barrier determination. Comparative analysis of two possible diffusion paths,\nPath-1 and Path-2, revealed that Path-1 exhibits a lower Zn2+ diffusion barrier,\nconfirming it as the most favorable diffusion pathway. These calculations ensure that\nthe reported barriers accurately reflect the energetically preferred ion transport routes.”\nPage 2 (Supporting Information)\nFigure S2. Different configurations for zinc-ion transport paths within the lattice of\nNVO-1 and the corresponding energy barrier distributions.\nPage 11\nFigure 2. (c) Adsorption energies and corresponding diffusion energy barriers of Zn2+\nin structurally distinct NVO variants.\n(5) As shown in Figure S2a, the (001) diffraction changes during the discharge-charge\nprocess are barely discernible. While it is understood that NVO possesses low\ncrystallinity, the XRD signal should be further enhanced to allow clearer observation.\nResponse: We thank the reviewer for the helpful suggestion regarding the weak (001)\ndiffraction signal in Figure S2a. To address this, we have meticulously repeated the in-\nsitu XRD measurements under significantly optimized conditions. Specifically, the\nelectrode loading was increased to > 4 mg cm-2 to enhance scattering intensity, and the\ncharge-discharge rate was reduced to 1 A g-1 to improve the signal-to-noise ratio by\nproviding more stable measurement conditions. These adjustments yielded much\nclearer in-situ XRD results. The updated data, now presented as Figure S3a in the\nrevised Supporting Information (shown below), clearly reveal the evolution of the (001)\npeak during the discharge-charge process. Notably, the (001) diffraction peak shifts to\nhigher angles upon discharge and returns to lower angles upon charging, consistent with\nour earlier observations but now captured with significantly improved clarity.\nFollowing the comment, the original Figure S2a has been replaced with this improved\nversion in the revised Supporting Information (the figure is now numbered as Figure\nS3a), as shown below:\nPage 3 (Supporting Information)\nFigure S3. (a) In-situ XRD image of NVO.\n(6) According to previous reports on vanadium oxides (Adv. Energy Mater., 2024, 14,\n2401493; Nano Energy, 2020, 70, 104519), upon Zn2+ insertion, the (001) diffraction\npeaks are typically observed to gradually shift toward lower angles rather than higher\nangles. However, I note that for NVO, the observed behavior can be considered\nacceptable within the framework of your proposed mechanism. Nevertheless, given the\natypical nature of this phenomenon, additional supporting references should be\nprovided to strengthen the clarity and credibility of the explanation.\nResponse: We sincerely thank the reviewer for highlighting the apparent discrepancy\nin the (001) diffraction peak shift during Zn2+ insertion. While previous reports on\ncertain vanadium oxides (e.g., HNaV O ·4H O and Al-V O ·1.6H O) show a shift\n6 16 2 2 5 2\ntoward lower angles, our NH V O simulations consistently indicate a shift to higher\n4 4 10\nangles, corresponding to interlayer contraction. This behavior arises from the strong\nelectrostatic attraction between intercalated Zn2+ ions and the negatively charged V-O\nlayers, leading to lattice tightening. The cited materials differ in crystalline and\ncompositional structures, resulting in distinct ion-layer interactions and thus different\npeak-shift behaviors. To support our observations, we have included additional\nreferences reporting similar peak shifts to higher angles in NH V O -based electrodes\n4 4 10\n(Adv. Sci. 2023, 10, 2206836; Nano-Micro Lett. 2021, 13, 116; Nano Energy 2023, 114,\n108671). Following the comment, the relevant discussion has been added into the\nResults and Discussion sections on Page 9, Line 22 in the revised manuscript, as\nshown below:\nPage 9\n“During discharge, the insertion of Zn2+ ions into NH V O induces a contraction of\n4 4 10\nthe interlayer spacing, as evidenced by the shift of the (001) diffraction peak toward\nhigher angles. This behavior contrasts with the gradual peak shift to lower angles\nreported for other vanadium oxides such as HNaV O ·4H O and Al-V O ·1.6H O,\n6 16 2 2 5 2\nwhich is typically attributed to lattice expansion upon Zn2+ intercalation.[37,38] The\ndistinct response of NH V O arises from the strong electrostatic interaction between\n4 4 10\nthe inserted Zn2+ ions and the negatively charged V-O layers, which effectively pulls\nthe layers closer together and leads to lattice tightening. This contraction is further\nfacilitated by the presence of ammonium ions, which stabilizes the layered structure\nand enhances the reversibility of the Zn2+ intercalation process. Importantly, this\nunusual contraction does not indicate a detrimental effect; instead, it reflects the unique\nion-layer interactions in NH V O , which contribute to its high structural stability\n4 4 10\nduring cycling. Recent studies on ammonium vanadate cathodes have reported similar\ninterlayer contraction phenomena, supporting our observations and providing\nexperimental validation for the proposed mechanism.[39–41] These studies highlight that\nNH V O -based electrodes can accommodate Zn2+ intercalation without significant\n4 4 10\nstructural degradation, which is advantageous for long-term cycling performance and\nrate capability.”\nReferences\n[37] Zhang, P., Gong. Y., Ren, X. et al. Glutamic Acid Induced Proton Substitution of\nSodium Vanadate Cathode Promotes High Performance in Aqueous Zinc-Ion Batteries.\nAdv. Energy Mater. 14, 2401493 (2024).\n[38] Zheng, J., Meng, C., Cao, G. et al. Fast and reversible zinc ion intercalation in Al-\nion modified hydrated vanadate. Nano Energy, 70, 104519 (2020).\n[39] Li, S., Yao, S., Du, F. et al. Quantitative Regulation of Interlayer Space of\nNH V O for Fast and Durable Zn2+ and NH + Storage. Adv. Sci. 10, 2206836 (2023).\n4 4 10 4\n[40] Zong, Q., Du, W., Cao, D. et al. Enhanced Reversible Zinc Ion Intercalation in\nDefcient Ammonium Vanadate for High-Performance Aqueous Zinc-Ion Battery. Nano-\nMicro Lett. 13, 116 (2021).\n[41] Fang, K., Wang, G., Yang, H. et al. Electrochemical activation strategy enabled\nammonium vanadate cathodes for all-climate zinc-ion batteries. Nano Energy 114,\n108671 (2023).\n(7) If the anode is metallic zinc, NVO undergoes changes during charging and\ndischarging. Therefore, it is difficult to attribute the fast kinetics solely to spacing\nmodification, given the presence of Zn intercalation, as you mentioned. More precisely,\nthe interlayer spacing of NVO can be effectively modulated by adjusting the zinc ion\nintercalation rather than by altering the charge-discharge cut-off voltage.\nResponse: We thank the reviewer for the insightful comment regarding the origin of\nfast kinetics in NVO cathodes. The interlayer spacing of NVO is dynamically\nmodulated by the intercalation and deintercalation of both Zn2+ and NH₄+ ions during\ncharge-discharge cycling, resulting in a distribution of diffusion barriers rather than a\nsingle fixed value and reflecting variable ion transport pathways within the layered\nstructure. By adjusting the cut-off voltage, the upper and lower bounds of this spacing\nvariation can be effectively controlled, tailoring the distribution of Zn2+ diffusion rates\nand optimizing overall ion transport kinetics. This approach enables practical\nmodulation of kinetic behavior without altering the intrinsic crystal structure, providing\na flexible strategy to achieve fast charge-discharge performance. While other methods,\nsuch as varying reaction temperature, tuning the hydrated radius of Zn2+, or adjusting\nthe applied cell voltage, can influence ion transport, they generally affect the system\nglobally and cannot selectively balance the kinetic mismatch between cathode and\nanode. Targeted interventions, including artificial SEI layers, increasing intercalation\nsite density, or surface morphology engineering, can finely regulate Zn2+ kinetics for a\nspecific electrode, but these approaches are electrode-specific and lack general\napplicability. In contrast, controlling the interlayer spacing via the cut-off voltage\nprovides a relatively universal and practical strategy: by tuning the voltage, the extent\nof layer expansion or contraction can be freely adjusted, directly influencing\nintercalation pathways and surface ion flux, allowing controlled amplification or\nsuppression of Zn2+ diffusion kinetics and enabling kinetic matching across electrodes.\nImportantly, this dynamic interlayer adjustment also enhances structural stability. As\nthe spacing adapts to accommodate Zn2+ ions, lattice strain is minimized, reducing the\nrisk of layer collapse or mechanical degradation during cycling. The reversible\nexpansion and contraction facilitate fast, reversible Zn2+ intercalation and\ndeintercalation, explaining the observed high-rate capability. This dual effect, enhanced\nkinetics and improved structural robustness, demonstrates that cut-off voltage control\nis an effective and practical means to tune both performance and durability in NVO-\nbased aqueous zinc-ion batteries. Following the reviewer’s advice, we have added the\nrelevant discussion into the Results and Discussion sections on Page 10, Line 10 in\nthe revised manuscript, as shown below:\nPage 10\n“Consequently, the interlayer spacing of NVO is dynamically modulated by the\nintercalation and deintercalation of Zn2+ and NH₄+ ions during cycling, creating a\ndistribution of diffusion barriers that governs ion transport. While conventional\nmethods, such as adjusting temperature (Figure S7) or other targeted interventions like\nSEI layers and surface engineering can influence overall kinetics, they cannot\nselectively balance the kinetic mismatch between cathode and anode or offer broad\napplicability. In contrast, controlling the cut-off voltage effectively regulates the range\nof interlayer spacing variation, directly tuning Zn2+ diffusion pathways and surface flux.\nThis strategy not only optimizes kinetic matching across electrodes but also preserves\nstructural stability by minimizing lattice strain, enabling fast, reversible Zn2+\nintercalation and enhancing both performance and durability in aqueous zinc-ion\nbatteries.”\nPage 6 (Supporting Information)\nFigure S7. Comparison of logarithmic zinc-ion diffusion coefficients for Zn||NVO\nunder 60°C and 25°C.\n(8) It is recommended to include a magnified view in Figure S4 to facilitate clearer\nrecognition of the differences and variation tendencies.\nResponse: We thank the reviewer for the helpful suggestion. In response, we have\nadded a magnified view of Figure S4, focusing on the (001) diffraction peak shifts, to\nmore clearly illustrate their variation tendencies during the electrochemical process.\nThis enhanced view highlights the subtle structural changes and reinforces the\ndiscussion of the material’s structural evolution. The revised figures have been included\nas Figure S5 in the Supporting Information, also shown below:\nPage 4 (Supporting Information)\nFigure S5. Ex-situ XRD patterns of NVO under different voltage windows.\n(9) As mentioned regarding Figure 3d, the minor difference in Warburg impedance\nhardly supports your claim of superior ion transport kinetics.\nResponse: We thank the reviewer for highlighting the interpretation of Warburg\nimpedance in Figure 3d. To address this, we performed repeated EIS measurements on\nfull cells incorporating NVO cathodes with different interlayer spacings (NVO-1.6V,\nNVO-1.8V, and NVO-2.0V). The Warburg impedance values were extracted from\nmultiple independent tests and statistically analyzed, yielding mean values of\n16.65 ± 0.31 Ω, 15.16 ± 0.19 Ω, and 17.77 ± 0.06 Ω, respectively. These data were\nfurther evaluated using Tukey’s HSD test, confirming that the differences are\nstatistically significant. While the variations appear subtly in the original Nyquist plots,\nthe statistical analysis demonstrates a consistent trend of enhanced ion transport\nkinetics for the optimized NVO-1.8V sample. To improve clarity and visual\ninterpretation, we adjusted the axis scaling in Figure 3d and added a complementary\nbar chart comparing the Warburg impedance values across samples. These\nmodifications provide a clear representation of the differences, enabling more intuitive\nunderstanding of the correlation between interlayer spacing and ion transport\nperformance. The relevant discussion and revised Figure 3d have been added into the\nResults and Discussion sections on Page 15, Line 1 and Page 16 in the revised\nmanuscript, as shown below:\nPage 15\n“Consistent with this observation, the Warburg impedance values were determined to\nbe 16.65 ± 0.31 Ω, 15.16 ± 0.19 Ω, and 17.77 ± 0.06 Ω for NVO-1.6V, NVO-1.8V, and\nNVO-2.0V, respectively. Statistical analysis using Tukey’s HSD test confirms the\ndifferences are significant. The NVO-1.8V electrode showed the lowest impedance,\nfurther supporting its superior ion transport kinetics within the full cell.”\nPage 16\nFigure 3. (d) EIS curves of the full cell at different voltage windows (inset: the\ncorresponding Warburg impedance comparison).\n(10) From Figure S11, it is difficult to recognize this as demonstrating exceptional\ncycling stability in full-cell tests. Furthermore, the comparison in Figure 3h is not\nrigorous, as it omits the parameter of current density. In the same vein, it would be more\nconvincing to present specific capacity on the y-axis instead of capacity retention. This\nis because, at lower specific capacities, batteries generally exhibit better cycling\nperformance.\nResponse: We thank the reviewer for the careful evaluation of the cycling performance\ndata. In response, Figure S11 has been modified to present specific capacity on the y-\naxis instead of capacity retention, providing a clearer and more meaningful\nvisualization of electrochemical performance. Regarding Figure 3h, we agree that\nomitting the current density could limit the rigor of the comparison. To address this, we\nhave included Table S4 in the Supporting Information, which correlates current\ndensity, initial capacity, and cycle number, enabling a more comprehensive and fair\nperformance evaluation across different conditions. The relevant discussion has been\nadded into the Results and Discussion sections on Page 15, Line 36 in the revised\nmanuscript and Page 9, Page 18 in the Supporting Information, as shown below:\nPage 15\n“Exceptional cycling stability was achieved in full-cell tests (Figure 3h), with over\n4,500 cycles at 2 A g-1 and more than 12,000 cycles at 5 A g-1, maintaining over 80% of\nthe initial capacity (Figure S14). For a comprehensive comparison, Table S4\nbenchmarks this work against recently reported AZIBs using zinc metal anodes or\nkinetically mismatched dual-intercalation systems. The kinetically matched system\ndeveloped here demonstrates significantly higher initial specific capacity and superior\ncycling stability across various current densities, highlighting its competitive\nadvantage in both energy density and long-term durability.”\nPage 9 (Supporting Information)\nFigure S14. Cycle performance of ZVO//NVO cell under current densitis of 2 A g-1\nand 5 A g-1.\nPage 18 (Supporting Information)\nTable S4. Performance comparison with recently reported AZIBs.\nElectrode Electrolyte Current Inital Cycle References\nsystem density capacity\n（A/g） number\n(mAh/g）\nNa TiS /CuSe | Zn(OTf) 0.1 68 100 Adv. Energy Mater. 2022,\n1.6 2 2 2\n|ZnxMn O 12, 2202182.\n2 4\nTiS ||p-MnO ZnSO 0.1 212.2 150 Adv. Energy Mater. 2024,\n2 2 4\n14, 2400724.\nZVO||NVO PVA/EG/Zn(OTf) 0.1 310.0 171 This work\n0.14 2 2\nn O 1900993.\n2 4\nZn||MnO PVA-GO 0.3 175 1500 Sci. Adv. 2021, 7, eabl3742.\n3 2 8\n32, 2112540.\nZn||Na V O Agr-PEG 1.0 137.1 500 Adv. Funct. Mater. 2022,\n5 12 32\n32, 2206653.\nZn||NH V O TA-SA 2.0 225.5 900 Sci. Bull. 2022, 67, 955-\n4 4 10\n962.\nZn||MnO PAM/NFC/G 2.0 170 1000 Energy Storage Mater.\n2\n2022, 51, 286-293.\nZVO||NVO PVA/EG/Zn(OTf) 2.0 216.7 1510 This work\n2\n2019, 12, 706-715.\nZn||rGO/MnO cellulose-TEOS 3.0 190 2000 Adv. Mater. 2021, 33,\n2\n2007559.\nZn||P-NVO PAM-PVA 3.0 172.3 3000 Adv. Funct. Mater. 2024,\n34, 2310966.\nZn||V O PAM-Zn(OTf) 5.0 191 3500 Adv. Mater. 2023, 35,\n2 5 2\n2301996.\nZVO||NVO PVA/EG/Zn(OTf) 5.0 136.7 12600 This work\n2\n(11) From all the CV curve samples, the ZVO/VOH vs. ZVO/KVOH (Figure 4a) and\nZVO/VOP vs. ZVO/PA-VOP (Figure 4d) configurations exhibit markedly different\nstorage mechanisms. Therefore, it is difficult to attribute the observed results solely to\nenhanced matching between the anode and cathode. In particular, when aniline is\nintroduced into VOP, could the elevated peaks at higher voltages in Figure 4d indicate\na greater contribution from proton involvement rather than due to the spatial\nmodification?\nResponse: We thank the reviewer for raising this important point regarding the possible\ncontribution of alternative charge storage mechanisms. We agree that factors beyond\nsimple anode-cathode matching could influence the observed differences in CV curves.\nTo clarify, we conducted a comparative analysis of the half-cell CV curves for VOH vs.\nKVOH and VOP vs. PA-VOP, which revealed highly similar redox peak positions and\nonly moderate differences in capacity relative to the full-cell measurements. This\nindicates that intercalation modification did not fundamentally alter the charge storage\nmechanism. Furthermore, we quantitatively deconvoluted the current response using\nDunn’s method to separate contributions from Zn2+ intercalation and proton\npseudocapacitance in the ZVO||PA-VOP system across multiple scan rates. Specifically,\nas the scan rate increases from 0.2 to 1.0 mV s-1, the diffusion contribution ratios for\nVOP are 69.1%, 62.9%, 61.0%, 51.7%, 48.6%, respectively, while those for PA-VOP\nare 62.5%, 57.1%, 54.9%, 49.7%, 45.6%, indicating a similar trend and comparable\nproportions between the two materials. The analysis confirmed that diffusion-\ncontrolled Zn2+ intercalation remains the dominant mechanism. Although aniline\nintercalation could potentially promote proton involvement, the actual mass of aniline\ninserted is minimal and contributes negligibly to the overall capacity. Importantly, the\nobserved shifts in redox peaks in Figure 4d are consistent with enhanced zinc-ion\ndiffusion kinetics due to interlayer expansion and improved anode-cathode matching,\nrather than significant proton contributions. Therefore, the enhanced electrochemical\nbehavior is primarily attributed to structural and kinetic improvements rather than a\nchange in the fundamental charge storage mechanism. Following the reviewer’s\ncomment, the relevant discussion has been added into the Results and Discussion\nsections on Page 17, Line 28 in the revised manuscript and Page 10 in the Supporting\nInformation, also shown below:\nPage 17\n“Quantitative analysis of the CV curves confirms that Zn2+ intercalation remains the\ndominant charge storage mechanism in the full cells, with the diffusion-controlled\nprocess contributing over 60% of the total capacity at 0.2 mV s-1. The observed\nimprovements in capacity, together with the subtle shifts in redox peak positions, are\nprimarily attributed to the expanded interlayer spacing of the cathode material. This\nstructural modification facilitates faster zinc-ion diffusion and reduces kinetic\nmismatches between the anode and cathode, thereby enhancing overall ion transport\nefficiency. As a result, the electrochemical performance, including rate capability and\ncycle stability, is significantly improved. These findings underscore that the enhanced\nbehavior is mainly driven by structural and kinetic optimizations, rather than by\nalternative charge storage contributions such as proton pseudocapacitance (Figure\nS16).”\nPage 10 (Supporting Information)\nFigure S16. Comparative half-cell CV analysis of VOH/KVOH and VOP/PA-VOP and\ntheir respective capacity contributions in full cells.\nReviewer 2\nComments:\nThe authors of this study sought to address the mismatch in ion diffusion rates\nbetween the cathode and anode, which is a key cause of performance degradation in\naqueous zinc-ion batteries. To this end, they introduced a machine learning approach\nand, for the first time, developed a “kinetic matching model ” capable of\nquantitatively predicting the kinetic compatibility of two electrodes. Using this model,\nthey identified the optimal electrode pairing (ZVO||NVO) and successfully fabricated\na battery with an outstanding lifespan of over 12,000 cycles. Furthermore, they applied\nthis principle to hydrogel-based flexible batteries, achieving both high energy density\nand mechanical stability. Ultimately, this work presents a new paradigm for data-driven,\nrational battery design. However, additional revisions addressing the following major\nquestions are necessary for publication in Nature Communications.\nResponse: We appreciate the comments from reviewer, and we have tried our best to\naddress his/her suggestions in the following section and incorporated them into the\nrevised manuscript.\n(1) In the DFT-based NEB calculations, it is unclear why only NVO-1 exhibits\nmigration along a different crystallographic direction. It would be beneficial to also\nprovide results in which all migration paths are aligned along the same direction for\nconsistency. Furthermore, while it is stated that the Bader charge results are related to\nthe diffusion barrier, the explanation does not clearly specify whose charge variation is\nresponsible for this correlation.\nResponse: We thank the reviewer for these insightful comments regarding the DFT-\nbased NEB calculations. Regarding the first point, the migration path of Zn2+ ions is\nintrinsically dictated by the most favorable adsorption sites within each NVO structure.\nIn NVO-2, NVO-3, and NVO-4, the existence of NH₄+ vacancies creates low-energy\nadsorption centers, guiding Zn2+ diffusion along vacancy-vacancy pathways. By\ncontrast, NVO-1 lacks intrinsic vacancies, and therefore, we systematically evaluated\nmultiple potential adsorption configurations. Four distinct adsorption sites (denoted A,\nB, C, and D) were identified, with calculated adsorption energies of –2.130 eV, –0.163\neV, –1.347 eV, and –2.130 eV, respectively. Among these, sites A and D are symmetry-\nequivalent and correspond to the most stable adsorption states. Consequently, the A→D\npathway was selected for NEB analysis, as it minimizes the migration barrier. For\ncomparison, we also examined an alternative pathway (A→C), which aligns more\nclosely with the migration direction observed in other NVO samples. However, as\nshown in Fig. S2, Path-1 (A→D) yields a substantially lower diffusion barrier than\nPath-2 (A→C), confirming that enforcing the same crystallographic direction as other\nNVOs would artificially overestimate the barrier. Thus, the reported NVO-1 pathway\nreflects the most energetically realistic migration route.\nRegarding the second point, the Bader charge analysis specifically refers to the charge\ntransfer associated with the Zn2+ ion when occupying different adsorption sites. A lower\nabsolute Bader charge value indicates more effective charge stabilization at the\nadsorption site, which weakens the electrostatic interaction between Zn2+ and the host\nlattice. This stabilization directly correlates with a reduced diffusion barrier, as the Zn2+\nion encounters less resistance during migration. We have revised the manuscript to\nexplicitly clarify that the Zn2+ charge variation, rather than changes in the host\nframework, underpins the observed correlation between Bader charge values and\ndiffusion barriers.\nFollowing this, the relevant discussion has been added into the Results and Discussion\nsections on Page 8, Line 15 and Line 28 in the revised manuscript and Page 2 in the\nSupporting Information, as shown below:\nPage 8, Line 15\n“For NVO-2, NVO-3, and NVO-4, diffusion energy barriers were calculated using the\nmost stable adsorption site at the ammonium vacancy. For NVO-1, which lacks intrinsic\nvacancies, multiple adsorption configurations were evaluated (Figure S2), and the\nconfiguration with the lowest adsorption energy was selected to ensure accurate\ndiffusion barrier determination. Comparative analysis of two possible diffusion paths,\nPath-1 and Path-2, revealed that Path-1 exhibits a lower Zn2+ diffusion barrier,\nconfirming it as the most favorable diffusion pathway. These calculations ensure that\nthe reported barriers accurately reflect the energetically preferred ion transport routes.”\nPage 2 (Supporting Information)\nFigure S2. Different configurations for zinc-ion transport paths within the lattice of\nNVO-1 and the corresponding energy barrier distributions.\nPage 8, Line 28\n“The Bader charge analysis specifically quantifies the charge transfer associated with\nthe Zn2+ ion when occupying different adsorption sites. Importantly, this charge\nredistribution exhibits an inverse correlation with the diffusion barrier: smaller\nabsolute charge variation corresponds to more effective electrostatic stabilization of\nZn2+ within the host lattice, which weakens Zn-host interactions, lowers the energy\npenalty for ion displacement, and thereby facilitates long-range migration. This\nanalysis explicitly clarifies that it is the charge variation of the Zn2+ ion, rather than\nchanges in the host framework, that underpins the observed correlation between Bader\ncharge values and diffusion barriers.”\n(2) The strategy of optimizing the kinetics by lowering the upper cut-off voltage from\n2.0 V to 1.8 V is highly original. However, this approach directly sacrifices the battery’\ns energy density (Energy = Capacity × Voltage). While the 1.8 V cell may exhibit\nsuperior rate capability and cycle life, a quantitative comparison is needed to determine\nhow much energy density is lost compared to the 2.0 V cell. From a practical application\nstandpoint, where is the balance point between the benefits of improved lifespan and\nrate performance and the drawbacks of reduced energy density? Furthermore, can the\ncurrent model also assist in identifying this optimal practical trade-off?\nResponse: We sincerely thank the reviewer for raising this critical point regarding the\ntrade-off between kinetic optimization and energy density resulting from the reduced\nupper cut-off voltage. Indeed, lowering the voltage window from 2.0 V to 1.8 V\ninevitably reduces the nominal energy density (Energy = Capacity × Voltage). However,\nas shown in Figure 3c, the discharge plateau (reduction peak) remains largely\nunchanged, which means the actual loss is less pronounced than might be expected from\nthe nominal voltage shift. To quantitatively assess this trade-off, we have compared the\ngravimetric energy densities of the NVO-2.0V and NVO-1.8V cells under different\ncurrent densities (Figure S13). The results reveal that at relatively low current densities\n(< 2 A g-1), the 2.0 V cell delivers higher energy density, with the 1.8 V configuration\nincurring an energy density loss of approximately 15.4-19.1%. By contrast, at higher\ncurrent densities, the 1.8 V cell achieves superior energy density, as the improved\nkinetics and reduced polarization enable more efficient utilization of the active material.\nThis crossover behavior illustrates that the optimal operating window is application-\ndependent: for energy-oriented applications, a wider voltage window is preferable,\nwhile for high-power applications, the 1.8 V configuration offers a better overall\ncompromise between energy, power, and long-term stability. Importantly, the modeling\nframework developed in this study can be extended to systematically identify such\nbalance points for different electrode systems, thereby serving as a practical tool to\nguide optimization of energy-power trade-offs in Zn-metal-free systems. Following this,\nthe relevant discussion has been added into the Results and Discussion sections on\nPage 15, Line 10 in the revised manuscript and Page 9 in the Supporting\nInformation, as shown below:\nPage 15\n“To quantitatively evaluate the trade-off between energy density and rate performance\nresulting from the reduced upper cut-off voltage, we systematically compared the\ngravimetric energy densities of cells operated at 2.0 V and 1.8 V across a range of\ncurrent densities (Figure S13). Although lowering the voltage window inevitably\ndecreases the nominal energy density (Energy = Capacity × Voltage), the discharge\nplateau remains largely unchanged, meaning that the actual energy density loss is less\nsevere than the nominal voltage reduction would suggest. At low current densities (< 2\nA g-1), the 2.0 V cell delivers higher energy density owing to its larger specific capacity,\nwith the 1.8 V cell showing a reduction of approximately 15.4-19.1%. However, at\nhigher current densities, the situation reverses: the 1.8 V cell exhibits superior energy\ndensity because its significantly enhanced ion transport kinetics and reduced\npolarization enable more efficient utilization of active material. This rate-dependent\ncrossover highlights the application-dependent balance point between energy density\nand rate performance. For energy-oriented scenarios, the 2.0 V configuration remains\nadvantageous, while for high-power and long-life applications, the 1.8 V configuration\noffers a more favorable compromise. Furthermore, the modeling framework developed\nin this study provides a generalizable means to identify such operating windows for\nother electrode systems by quantitatively balancing energy and power requirements\nunder different working conditions.”\nPage 9 (Supporting Information)\nFigure S13. Energy density comparison of NVO cells with upper cut-off voltages of\n2.0 V and 1.8 V at various current densities.\n(3) In Fig. 5f, the optimal ranges of DR and CSI are determined based on a threshold of the “top 30%.” However, this “top 30%” criterion may appear somewhat\narbitrary. Have you examined how sensitive the proposed optimal design range (design\nrule) is to changes in this threshold, for example, if it were set to the “top 10%” or\n“top 50%”? If the robustness of this design rule is strongly dependent on the chosen\nthreshold, wouldn’t that limit its general applicability?\nResponse: We sincerely thank the reviewer for raising this important point regarding\nthe selection of the “top 30%” threshold for defining optimal DR and CSI ranges. To\nassess the sensitivity of our design rule, we conducted additional analyses using both\nstricter (top 10%) and more relaxed (top 50%) thresholds. The top 10% threshold\nyielded very narrow ranges (DR: 0.946-0.956; CSI: 0.801-0.803), providing too few\ndata points for practical screening. In contrast, the top 50% threshold produced broad\nranges (DR: 0.936-1.114; CSI: 0.148-1.244), which introduced substantial performance\nvariance and reduced predictive specificity. Importantly, the central trends and\ncorrelations between DR, CSI, and full-cell performance remained consistent across all\nthresholds, with strong overlap in the derived optimal ranges. This confirms that the\nproposed design rule is robust, and the top 30% threshold represents a balanced\ncompromise, offering both statistical reliability and practical applicability for electrode\nscreening across diverse material systems. Following this comment, the relevant\ndiscussion has been added into the Results and Discussion sections on Page 22, Line\n10 in the revised manuscript and Page 15 in the Supporting Information, as shown\nbelow:\nPage 22\n“To evaluate the robustness of this design criterion, sensitivity analyses were performed\nusing alternative thresholds of top 10% and top 50%. While the top 10% threshold\nyielded very narrow parameter ranges (DR: 0.946-0.956; CSI: 0.801-0.803) with\ninsufficient data points for practical screening, the top 50% threshold produced broad\nranges (DR: 0.936-1.114; CSI: 0.148-1.244) that introduced high performance\nvariance and reduced predictive specificity. Crucially, the central tendencies and\ndirectional correlations between DR, CSI, and full-cell electrochemical performance\nremained consistent across all thresholds, with strong mutual overlap between the\nderived ranges. These results confirm that the proposed design rule is inherently robust,\nproviding reliable guidance for electrode selection and screening while maintaining\npractical applicability for different material systems (Figure S19).”\nPage 15 (Supporting Information)\nFigure S19. The predicted value numerical range corresponding to the DR and CSI\nranges with (a) a performance threshold of 10% and (b) a performance threshold of\n50%.\n(4) In Li-ion batteries, it is common practice to intentionally design the anode capacity\nto be greater than that of the cathode in order to prevent critical issues such as lithium\nmetal plating. In the dual-insertion system studied here, is pursuing perfectly\nsymmetric kinetics (DR ≈ 1) always the optimal approach? Would it not be more stable and robust to intentionally design the\nanode kinetics to be slightly faster than the cathode, so that it can serve as a “buffer” to accommodate the incoming ion flux\nfrom the cathode during sudden high-power demands? Could the model’s optimal DR range of 0.936-1.020, which is not\nperfectly symmetric around 1.0, be hinting at the necessity of such asymmetry?\nResponse: We sincerely thank the reviewer for raising this insightful point regarding\nthe potential advantage of intentionally asymmetric kinetics. Indeed, in conventional\nLi-ion batteries, anode overmatching is often employed as a safety buffer to prevent\nlithium plating. However, in the dual-intercalation aqueous zinc-ion system studied\nhere, both electrodes operate via intercalation, and Zn2+ transport is inherently slower\nthan Li+. Therefore, maintaining close kinetic synchrony between the anode and\ncathode is crucial, as a significant mismatch in diffusion rates at either electrode would\ncreate a rate-limiting bottleneck, limiting overall cell performance, as conceptually\nillustrated in Figure 1.\nThe machine-learning-derived optimal DR range (0.936-1.020) is tightly centered near\nunity, reflecting this principle of kinetic balance. The slight asymmetry of the range,\nas noted by the reviewer, may indeed hint at a modest tolerance for slightly faster anode\nkinetics. This bias arises naturally from the composition of the training dataset: the\nanode materials generally exhibit higher diffusion rates than the cathodes, leading to a\nsmall skew in DR values below 1, as shown in Supplementary Table S3g. Thus, while\nthe model accommodates minor asymmetry, the overarching design guideline remains\nthe achievement of well-matched intercalation kinetics to ensure efficient ion flux,\nminimize polarization, and maximize rate capability and cycling stability in dual-\nintercalation AZIBs. The corresponding discussion is presented on Page 3, Line 38 and\nPage 21, Line 17 in the revised manuscript and Page 14 in the Supporting\nInformation, also shown below:\nPage 3\n“Compared with Li-ion batteries, where anode overmatching prevents lithium plating,\nthe dual-intercalation aqueous zinc-ion system requires closely synchronized kinetics.\nBoth electrodes rely on intercalation, and Zn²⁺ transport is slower than Li⁺, so\nmismatched diffusion rates at either electrode create bottlenecks and limit performance\n(Figure 1). Thus, well-matched intercalation kinetics are essential to maximize rate\ncapability, minimize polarization, and enhance cycling stability.”\nPage 21\n“The machine-learning-derived optimal DR range (0.936-1.020) is tightly centered\nnear unity, reflecting the critical principle of kinetic balance between the anode and\ncathode. The slight asymmetry of this range may indicate a modest tolerance for slightly\nfaster anode kinetics, which arises naturally from the composition of the training\ndataset: anode materials generally exhibit higher diffusion rates than cathodes,\nproducing a minor skew of DR values below 1, as shown in Supplementary Table S3g.\nExperimental validation of the DR descriptor across 30 distinct electrode pairs via\nGITT measurements confirms strong agreement with the predicted optimal range\n(0.936-1.020), as detailed in Table S3g, supporting its utility for predicting and\noptimizing full-cell performance. Thus, while the model accommodates minor\nasymmetry, the overarching design guideline emphasizes the achievement of well-\nmatched intercalation kinetics to ensure efficient ion flux, minimize polarization, and\nmaximize both rate capability and cycling stability in dual-intercalation aqueous zinc-\nion batteries.”\nPage 14 (Supporting Information)\nTable S3g. Summary of experimentally measured DR values (matched electrode pairs\nare highlighted in bold).\nDR value ZVO TS MXene PTCDI MoO\nPAVOP 1.037 0.962 1.048 1.026 0.930\nKVOH 1.014 0.940 1.024 1.003 0.909\nNVO-2.0V 1.058 0.981 1.068 1.046 0.948\nVOP 1.098 1.018 1.109 1.086 0.984\nVOH 1.094 1.014 1.105 1.082 0.981\n(5) The CSI and DR proposed in this study are highly effective features designed based\non deep physical insights into battery kinetics. However, they represent a successful\ncase of “feature engineering” grounded in the researchers’ expertise, rather than\nfeatures “learned” or “generated” autonomously by AI. Certain expressions in the\nmanuscript (e.g., “ML-based model”) could be interpreted as implying that AI itself\nis the agent explaining this phenomenon. Would it not convey the methodological\nessence more accurately to clearly redefine the role of AI in this work as “a powerful\nstatistical tool for validating the hypotheses of the researcher-proposed physical model\nand modeling complex nonlinear relationships”?\nResponse: We sincerely thank the reviewer for this insightful comment on the role of\nML in our study. We agree that the descriptors CSI and DR are outcomes of experience-\nguided feature engineering rooted in physical understanding of intercalation kinetics,\nrather than autonomously generated by AI. To more accurately convey the\nmethodological essence, we have revised the manuscript to emphasize that ML is\nemployed here as a powerful statistical tool: it validates researcher-proposed physical\nhypotheses and captures complex nonlinear relationships between material properties\nand electrochemical performance. This clarification highlights that the physical insights\nare researcher-driven, while ML provides robust validation and predictive modeling\nsupport. Following this, the relevant discussion has been added into the Introduction\nsection on Page 5, Line 2 and Line 26 in the revised manuscript, as shown below:\nPage 5, Line 2\n“To address this long-standing challenge, machine learning serves as an indispensable\ntool for decoding complex electrode kinetics and validating physically derived\nhypotheses.[32,33] By integrating domain knowledge with data-driven modeling,\nmachine learning provides a robust statistical framework to quantify and unravel the\nnonlinear relationships between material properties and electrochemical behavior. This\napproach enhances the predictability and generalizability of kinetic matching\nprinciples, complementing traditional experimental methods and opening new\npathways for the rational design of high-performance energy storage systems.”\nReference\n[32] Dong, J., Yin, J., Ang, E. H. et al. Machine learning-assisted benign\ntransformation of three zinc states in zinc ion batteries. Energy Environ. Sci. 18, 4872-\n4882 (2025).\n[33] Luo, H., Gou, Q., Li, M. et al. Machine Learning-Assisted High-Donor-Number\nElectrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion\nBatteries. ACS Nano 19, 2, 2427-2443 (2025).\nPage 5, Line 26\n“Notably, the descriptors CSI and DR were derived through experience-guided feature\nengineering grounded in physical insights into intercalation kinetics. These features\nwere not generated by machine learning but were intentionally designed by researchers\nto capture the fundamental kinetic characteristics of intercalation processes.”\n(6) It would be helpful to include in the Supporting Information the results of tests\nconducted using several other machine learning methods.\nResponse: We thank the reviewer for the valuable suggestion to include results from\nadditional machine learning models. In our preliminary analysis, we indeed evaluated\nseveral alternatives, including Linear Regression, Random Forest, Support Vector\nRegression (SVR), Least Absolute Shrinkage and Selection Operator (Lasso), and\nRidge Regression. As shown in Figure S18, these models all exhibited substantially\nlower predictive accuracy compared to XGBoost: their R2 values (0.528, 0.610, 0.443,\n0.547, and 0.573) were far below those of XGBoost (0.886), while their RMSE values\n(0.136, 0.124, 0.148, 0.134, and 0.130) were consistently higher than XGBoost’s 0.067.\nThis quantitative comparison highlights the superior performance of XGBoost, which\ncan be attributed to its effective handling of nonlinear feature interactions and its\nrobustness for our dataset. We have now included these comparative results in the\nSupporting Information to provide readers with a broader context for model selection\nand to justify the use of XGBoost in this work. Following this, the relevant discussion\nhas been added into the Results and Discussion sections on Page 21, Line 33 in the\nrevised manuscript and Page 15 in the Supporting Information, also shown below:\nPage 21\n“To ensure the robustness of our modeling approach, we conducted a comparative\nevaluation of multiple machine-learning algorithms, including Linear Regression,\nRandom Forest, Support Vector Regression (SVR), Lasso, and Ridge Regression. As\nsummarized in Figure S18, all of these models exhibited substantially lower predictive\naccuracy compared with XGBoost: their R2 values (0.528, 0.610, 0.443, 0.547, and\n0.573) were considerably below that of XGBoost (0.886), while their RMSE values\n(0.136, 0.124, 0.148, 0.134, and 0.130) were consistently higher than XGBoost’s 0.067.\nThis quantitative comparison highlights XGBoost’s superior predictive capability,\nattributable to its ability to effectively capture complex nonlinear feature interactions\nin our dataset. The inclusion of these comparative results in the Supporting Information\nnot only provides readers with a broader methodological context but also reinforces the\nrationale for selecting XGBoost as the most suitable and robust framework for this\nstudy.”\nPage 15 (Supporting Information)\nFigure S18. Single-target prediction results of various machine learning models.\nReviewer 3\nComments:\nThis work presents a machine learning-assisted kinetic matching model that\nenables the rational design of dual-intercalation aqueous zinc-ion batteries with\nsynchronized ion transport, achieving high capacity, long-term cycling stability, and\nstretchable device integration. However, there are a few concerns:\nResponse: We thank reviewers for positive feedback and valuable suggestions that\nhave been instrumental in improving the quality of our work. We are going to carefully\nanswer your questions and incorporate your suggestions into our revised manuscript.\n(1) While the work introduces a novel ML-assisted kinetic matching model, the\nintroduction would benefit from a clearer one-sentence summary of what sets this\nmodel apart from existing approaches (e.g., what \"universal\" and \"quantitative\" offer\nover past semi-empirical efforts).\nResponse: We thank the reviewer for this constructive suggestion. To more clearly\nhighlight the novelty of our model, we have revised the Abstract and Introduction to\nexplicitly emphasize why it is both universal and quantitative. The universality arises\nfrom its ability to systematically explore ion-diffusion compatibility across a wide\nrange of electrode combinations, far beyond the narrow sets typically studied in semi-\nempirical approaches. Its quantitative nature lies in leveraging machine learning to\nestablish predictive correlations, thereby replacing empirical trial-and-error with data-\ndriven rigor. These revisions distinguish our framework from prior efforts and better\nconvey its methodological essence. Following this, the relevant discussion has been\nadded into the Abstract on Page 2, Line 11 and Introduction on Page 5, Line 14 in\nthe revised manuscript, as shown below:\nPage 2 (Abstract)\n“Here, we introduce the first universal and quantitative machine learning–assisted\nkinetic matching model for assessing ion-diffusion compatibility between intercalation\nelectrodes. Unlike conventional semi-empirical approaches that rely on empirical trial-\nand-error and are constrained to narrow sets of familiar electrode pairs, this\nframework enables broad exploration of diverse electrode combinations while\nproviding quantitative predictive power, thereby explicitly distinguishing it from prior\nefforts.”\nPage 5 (Introduction)\n“Based on experimental validation across diverse intercalation systems and machine\nlearning optimization, this work establishes the first quantitative, machine learning-\nassisted kinetic matching model that links confined interlayer spacing to ion diffusion\nkinetics in intercalation electrodes. The model is universal because it systematically\nevaluates ion-diffusion compatibility across a wide spectrum of intercalation electrodes,\nrather than being limited to familiar electrode pairs. It is quantitative because it\nleverages machine learning to establish predictive correlations, replacing semi-\nempirical heuristics with data-driven rigor and enabling rational optimization of full-\ncell kinetics.”\n(2) The acronyms Confined Spacing Index (CSI) and Diffusion Ratio (DR) are\nintroduced without explanation of how they are calculated or interpreted. A short\nclarification (e.g., \"CSI quantifies...\") would improve reader comprehension and\nmodel transparency.\nResponse: We appreciate the reviewer’s insightful query regarding the physical\nmeaning and calculation of the Confined Spacing Index (CSI) and Diffusion Ratio (DR).\nWhile the original manuscript briefly introduced DR as the ratio of mean ion diffusion\ncoefficients and outlined the computation process for CSI, we agree that these\ndefinitions were not explained with sufficient clarity. To improve transparency and\nreproducibility, we have revised the Methods section to explicitly define and interpret\nboth descriptors, supported by their mathematical formulations. Specifically, CSI\nquantifies the spread of Zn2+ diffusion coefficients, with optimal values (0.241–1.013)\nindicating homogeneous diffusion pathways that enable rapid kinetics, whereas values\noutside this range reflect localized bottlenecks that promote electrochemical\npolarization. DR, by contrast, characterizes kinetic compatibility between electrodes: it\nrepresents the asymmetry in Zn2+ intercalation capability, with the optimal range\n(0.936–1.020) corresponding to well-balanced insertion/extraction dynamics.\nValues below or above this range reflect electrode-specific limitations that induce cathode–\nanode kinetic mismatch. Both descriptors were identified as dominant features through\nXGBoost analysis and further validated experimentally, underscoring their robustness\nand physical relevance. The relevant discussion has been added into the Results and\nDiscussion and Methods sections on Page 22, Line 23 and Page 30, Line 34 in the\nrevised manuscript, as shown below:\nPage 22\n“The CSI quantifies the distributional spread of Zn2+ diffusion coefficients across\navailable pathways. Optimal values (0.241-1.013) reflect uniform diffusion landscapes\nthat enable rapid kinetics, whereas values outside this range indicate inhomogeneous\ntransport and localized bottlenecks that exacerbate polarization. The DR measures\nkinetic compatibility between electrodes, with values of 0.936-1.020 corresponding to\nbalanced intercalation/deintercalation dynamics, and deviations beyond this range\nrevealing electrode-specific kinetic mismatches. Machine-learning feature importance\nanalysis identified CSI and DR as the most predictive descriptors, a conclusion further\ncorroborated by experimental validation.”\nPage 30\n“Machine Learning Implementation\nAn XGBoost model was employed to predict the electrochemical performance of\ndual-intercalation AZIBs based on diffusion-derived descriptors. The training dataset\nwas obtained exclusively from experimentally measured data in this study. To\nquantitatively assess kinetic compatibility between electrode pairs, two key descriptors\nwere defined: the Composite Shift Index (CSI) and the Diffusion Ratio (DR). CSI\nquantifies the divergence in ion diffusion kinetics between electrodes, reflecting the\ndegree of synchronization in their intercalation behavior. DR is defined as the ratio of\nthe mean apparent ion diffusion coefficients (logarithmic values) of the cathode and\nanode, providing a scale-invariant measure of relative diffusion rates. The respective\nformulas for calculating these descriptors are provided below:\n+ + -\n|𝑙̅̅𝑔̅ ̅𝐷̅̅̅−𝑙̅𝑔̅̅ ̅𝐷̅̅−̅|+2∙|𝜎𝑙𝑔𝐷 −𝜎𝑙𝑔𝐷|\n𝐶𝑆𝐼 =\n𝜎𝑙𝑔𝐷+ +𝜎𝑙𝑔𝐷−\n𝑙̅𝑔̅̅ ̅𝐷̅̅+̅\n𝐷𝑅 =\nWhere and denote the logarithmi 𝑙̅c 𝑔̅̅ v̅𝐷̅a̅−l̅ue of the zinc ion diffusion coefficient\nof cathode and anode at each step of the charge-discharge process.\n𝑙𝑔𝐷+ 𝑙𝑔𝐷−\nComputational\nanalyses were performed on a dedicated workstation (Dell Precision T3680: Intel Core\ni9-14900K CPU, NVIDIA RTX 3090 GPU, 128 GB RAM). The total computation time\ndedicated to hyperparameter optimization and k-fold cross-validation procedures was\n336 hours.”\n(3) There is no mention of the dataset or material pool used to develop or validate the\nkinetic matching model. Including this would lend credibility to the ML framework\nand clarify its generalizability.\nResponse: We sincerely appreciate the reviewer’s emphasis on computational\ntransparency and reproducibility. In response, we have added dedicated subsections in\nthe Methods section detailing the dataset used to develop and validate the kinetic\nmatching model. The dataset was constructed entirely from our own experimental\nmeasurements of Zn2+ diffusion coefficients and electrochemical performance across\ndiverse electrode materials. This ensured broad coverage of both cathode and anode\ncandidates, enabling robust model training and validation. Furthermore, all executable\ncode and processed datasets have been deposited on GitHub, ensuring full transparency,\nreproducibility, and accessibility for the community. Following this, the relevant\ndiscussion has been added into the Methods section on Page 31, Line 15 in the revised\nmanuscript, as shown below:\nPage 31\n“Data availability\nAll data generated or analyzed during this study are included in this published\narticle and its supplementary information files. All text, images, and videos in this\nmanuscript were produced or experimentally obtained by the authors and are not\nderived from large language models or AI tools. Source data are provided with this\npaper.\nCode availability\nThe code used for the Machine-learning-analysis has been deposited in Github\nand can be accessed here: https://github.com/rachelyyr-llamama/ML.git. The database\nused for model training comes from the experimental results as listed in Data\navailability.”\n(4) The manuscript alludes to \"cathode-anode kinetic mismatch\" but does not clearly\nexplain how this mismatch leads to performance degradation. A brief mechanistic\nexplanation would strengthen the argument for kinetic matching.\nResponse: We thank the reviewer for prompting a more detailed mechanistic\nexplanation of how cathode-anode kinetic mismatch contributes to performance\ndegradation. Kinetic mismatch occurs when the intercalation/deintercalation rates of\nZn²⁺ ions in the cathode and anode are misaligned. If the cathode’s deintercalation is\nfaster than the anode’s intercalation, Zn2+ accumulates at the anode-electrolyte interface\nduring charging, causing localized concentration polarization. Conversely, if the\ncathode intercalates more slowly than the anode deintercalates, ion congestion builds\nat the cathode-electrolyte interface during discharge. Such mismatches distort the\nelectric field distribution, increasing the overpotential required for ion migration and\nelevating overall cell polarization. This manifests experimentally as voltage hysteresis,\nCV peak separation, and asymmetric current responses, ultimately reducing energy\nefficiency, accelerating capacity fade, and limiting rate capability. In contrast, well-\nmatched kinetics ensure synchronous ion flux, maintain interfacial equilibrium,\npromote uniform electric field distribution, and minimize polarization, thereby\nenhancing reversibility, rate performance, and cycling stability. Following this, the\nrelevant discussion has been added into the Results and Discussion sections on Page\n6, Line 27 in the revised manuscript, as shown below:\nPage 6\n“The kinetic mismatch between the electrodes leads to asynchronous ion intercalation\nand deintercalation processes. When the cathode deintercalates faster than the anode\ncan intercalate, Zn2+ accumulates at the anode-electrolyte interface; conversely, slower\ncathode intercalation relative to anode deintercalation causes ion congestion at the\ncathode-electrolyte interface. These local imbalances distort the uniform electric field\nwithin the cell, requiring higher overpotentials to drive ion migration and increasing\noverall polarization. Electrochemically, this manifests as voltage hysteresis, peak\nsplitting in cyclic voltammetry, and asymmetric current responses, reflecting inefficient\nredox reactions and compromised kinetics. Quantitative kinetic matching mitigates\nthese effects by synchronizing ion flux, stabilizing interfacial conditions, and\nminimizing polarization, thereby improving rate performance, energy efficiency, and\nlong-term cycling stability.”\n(5) Why was this specific dual-intercalation system chosen for demonstration?\nClarifying whether it was selected by the model or based on prior literature would help\nshowcase the utility of the model.\nResponse: We thank the reviewer for this insightful question regarding the choice of\nthe ZVO//NVO dual-intercalation system. Our initial motivation for developing a dual-\nintercalation configuration was to address the limitations of conventional aqueous zinc-\nion batteries, namely the dependence on metallic zinc anodes that hinders flexibility\nand accelerates parasitic side reactions. Early electrode pair selections were guided by\nliterature reports (Adv. Funct. Mater. 2021, 31, 2103210; Nano Energy, 2022, 100,\n107520; Small 2024, 20, 2306111); however, these systems were often highly\nelectrolyte-specific, and cathode-anode pairs frequently exhibited poor kinetic\ncompatibility, with performance deteriorating sharply upon substitution of either\ncomponent. To systematically resolve this issue, we conducted a comprehensive\nelectrode-matching analysis based on intercalation materials reported in the literature.\nGuided by the established matching principles and the predictive power of the DR and\nCSI descriptors, the ZVO//NVO system was ultimately selected. This system not only\nshows favorable DR and CSI values and strong literature support but also demonstrated\noutstanding overall electrochemical performance in our preliminary full-cell screening.\nTherefore, ZVO//NVO serves as an ideal representative model to validate and showcase\nthe utility of our machine learning–assisted kinetic matching framework. The relevant\ndiscussion has been added into the Results and Discussion sections on Page 13, Line\n36 in the revised manuscript, as shown below:\nPage 13\n“Notably, to construct a durable metal-free battery system, intercalation electrodes\nwere initially selected from literature-reported candidates,[43-45] but many of these\nproved highly electrolyte-dependent and prone to cathode-anode mismatching.\nTherefore, the ZVO//NVO system was selected as a representative model, not only due\nto its well-documented advantages in the literature and superior overall performance\nin preliminary full-cell tests among various intercalation-type electrode pairs, but also\nbecause it serves as an exemplary template for high-performance intercalation-\nmatched energy storage systems. This system offers valuable insights into the\nfundamental correlation between electrode matching and electrochemical behavior.”\nReference\n[43] Wang, X., Wang, Y., Yuan, G. et al. Tailoring Ultrahigh Energy Density and Stable\nDendrite-Free Flexible Anode with Ti C T MXene Nanosheets and Hydrated\n3 2 x\nAmmonium Vanadate Nanobelts for Aqueous Rocking-Chair Zinc Ion Batteries. Adv.\nFunct. Mater. 31, 2103210 (2021).\n[44] Zhao, D., Chen, S., Chen, Z. et al. A stable \"rocking-chair\" zinc-ion battery\nboosted by low-strain Zn V (PO ) cathode. Nano Energy, 100, 107520 (2022).\n3 4 4 6\n[45] Bai, Y., Yan, L., Li, C. et al. Advances of Zn Metal-Free “Rocking-Chair”-Type\nZinc Ion Batteries: Recent Developments and Future Perspectives. Small 20, 2306111\n(2024).\n(6) The reported specific capacity and cycling performance are impressive, but a brief\ncomparison to existing AZIB systems (e.g., in a parenthetical or supplementary table)\nwould help readers appreciate the significance.\nResponse: We appreciate the reviewer’s suggestion to contextualize the performance\nof our system. In response, we have added a new supplementary table (Table S4)\ncomparing the initial specific capacities and cycling stabilities of our ZVO//NVO full\ncell with those of recently reported aqueous zinc-ion batteries based on intercalation-\ntype electrodes. This comparison provides a clear benchmark, allowing readers to more\ndirectly appreciate the advances in capacity, cycling durability, and rate performance\nenabled by our kinetically matched electrode design. The relevant discussion has been\nadded into the Results and Discussion sections on Page 15, Line 38 in the revised\nmanuscript and Page 18 in the Supporting Information, as shown below:\nPage 15\n“For a comprehensive comparison, Table S4 benchmarks this work against recently\nreported AZIBs using zinc metal anodes or kinetically mismatched dual-intercalation\nsystems. The kinetically matched system developed here demonstrates significantly\nhigher initial specific capacity and superior cycling stability across various current\ndensities, highlighting its competitive advantage in both energy density and long-term\ndurability.”\nPage 18 (Supporting Information)\nTable S4. Performance comparison with recently reported AZIBs.\nElectrode Electrolyte Current Inital Cycle References\nsystem density capacity\n（A/g） number\n(mAh/g）\nNa TiS /CuSe | Zn(OTf) 0.1 68 100 Adv. Energy Mater. 2022,\n1.6 2 2 2\n|ZnxMn O 12, 2202182.\n2 4\nTiS ||p-MnO ZnSO 0.1 212.2 150 Adv. Energy Mater. 2024,\n2 2 4\n14, 2400724.\nZVO||NVO PVA/EG/Zn(OTf) 0.1 310.0 171 This work\n0.14 2 2\nn O 1900993.\n2 4\nZn||MnO PVA-GO 0.3 175 1500 Sci. Adv. 2021, 7, eabl3742.\n3 2 8\n32, 2112540.\nZn||Na V O Agr-PEG 1.0 137.1 500 Adv. Funct. Mater. 2022,\n5 12 32\n32, 2206653.\nZn||NH V O TA-SA 2.0 225.5 900 Sci. Bull. 2022, 67, 955-\n4 4 10\n962.\nZn||MnO PAM/NFC/G 2.0 170 1000 Energy Storage Mater.\n2\n2022, 51, 286-293.\nZVO||NVO PVA/EG/Zn(OTf) 2.0 216.7 1510 This work\n2\n2019, 12, 706-715.\nZn||rGO/MnO cellulose-TEOS 3.0 190 2000 Adv. Mater. 2021, 33,\n2\n2007559.\nZn||P-NVO PAM-PVA 3.0 172.3 3000 Adv. Funct. Mater. 2024,\n34, 2310966.\nZn||V O PAM-Zn(OTf) 5.0 191 3500 Adv. Mater. 2023, 35,\n2 5 2\n2301996.\nZVO||NVO PVA/EG/Zn(OTf) 5.0 136.7 12600 This work\n2\n(7) While Ag nanoflakes are mentioned in the hydrogel matrix, the text does not explain\ntheir functon(e.g., enhancing conductivity, mechanical reinforcement). Adding this\ndetail would provide insight into the stretchable battery design.\nResponse: We thank the reviewer for highlighting the need to clarify the function of\nAg nanoflakes in the stretchable hydrogel electrode. Ag nanoflakes act as a conductive\nfiller within the hydrogel matrix, compensating for the inherently low electronic\nconductivity of the active materials. Their addition establishes a percolation network\nthat facilitates rapid electron transport throughout the electrode, thereby improving\noverall charge transfer kinetics. Quantitative evaluation via four-probe measurements\ndemonstrates a dramatic reduction in sheet resistance from 1.580 kΩ to 307.4 mΩ,\nunderscoring the critical role of Ag nanoflakes in enabling high electrochemical\nperformance in stretchable zinc-ion batteries. Following this, the relevant discussion\nhas been added into the Results and Discussion sections on Page 24, Line 5 in the\nrevised manuscript and Page 16 in the Supporting Information, as shown below:\nPage 24\n“Ag nanoflakes were incorporated as a conductive filler to form an efficient percolation\nnetwork within the hydrogel electrode, substantially enhancing electronic conductivity.\nFour-probe measurements confirmed that the sheet resistance decreased from 1.580 kΩ\nto 307.4 mΩ following the addition of Ag nanoflakes (Figure S20), demonstrating their\ncrucial role in improving charge transfer kinetics.”\nPage 16 (Supporting Information)\nFigure S20. Sheet resistance of the hydrogel electrode before and after the addition of\nAg nanoflakes, measured by a four-probe method.\n(8) The phrase “ universal and quantifiable strategy ” may come across as\noverreaching without discussing limitations or material scope. Consider tempering this\nwith a note on applicability or current constraints.\nResponse: We thank the reviewer for emphasizing the need to clarify the scope and\nlimitations of the proposed strategy. This approach is specifically tailored for dual-\nintercalation aqueous zinc-ion batteries, where balanced Zn2+ intercalation kinetics\nbetween cathode and anode are essential to minimize polarization and maintain\nperformance. Notably, the screening and kinetic matching were conducted under\nstandard conditions; under extreme environments, such as very high or low\ntemperatures or elevated pressures, intercalation kinetics may deviate from nominal\nbehavior, potentially affecting the model’s predictive accuracy. Therefore, this strategy\nis not directly applicable to batteries that do not rely on intercalation mechanisms or to\nhybrid battery chemistries. The relevant discussion has been added into the Abstract\nsection on Page 2, Line 27 and the Conclusions section on Page 27, Line 1 in the\nrevised manuscript, as shown below:\nPage 2 (Abstract)\n“These devices exhibited an areal capacity of 1.2 mAh cm-2 and an energy density of\n1070 μWh cm-2, demonstrating a quantitative and broadly applicable strategy for\nguiding electrode pairing in dual-intercalation aqueous zinc-ion batteries, where\nprecise matching of Zn2+ intercalation kinetics between cathode and anode is critical,\nwhile remaining tailored to standard operating conditions and not directly applicable\nto non-intercalation or hybrid systems.”\nPage 27 (Conclusions)\n“Although not applicable to non-intercalation or hybrid systems, this strategy provides\nbroadly useful guidance for electrode pairing in dual-intercalation AZIBs under\nstandard conditions, underscoring the importance of system-specific kinetic\nconsiderations in future battery design.”\n(9) The five cited references are highly relevant to the presented work as they\ncollectively demonstrate the importance of addressing zinc anode challenges in aqueous\nzinc-ion batteries (AZIBs) through machine learning (ML), electrolyte/interfacial\nengineering, and advanced material design—all of which align with and complement\nthe current study’s focus on kinetic matching and dual-intercalation electrodes.\nReference 1 (10.1039/D5EE00650C) employs machine learning to optimize protective\ninterface layers for Zn anodes, highlighting the effectiveness of ML in navigating\ncomplex material-property relationships—thus directly supporting the rationale for the\nML-assisted approach in this study. Reference 2\n(https://doi.org/10.1002/ange.202507504) introduces a novel halogenated TiBTx\nMBene protective layer that improves Zn anode performance through engineered\nsurface functionalities, underscoring the importance of interfacial ion dynamics and\nZn2+ compatibility, which the current work aims to address from a system-level\nelectrode matching perspective. Reference 3 (https://doi.org/10.1016/j.est.2025.117300)\nshowcases the role of electrolyte additives (DPAH) in regulating Zn2+ deposition\nbehavior and improving reversibility, emphasizing how chemical strategies can\nharmonize ion transport—analogous to the synchronized ion flux concept proposed\nhere. Reference 4 (https://doi.org/10.1002/smtd.202500576) demonstrates the impact\nof hydrogel electrolytes on battery performance across temperature extremes, directly\nsupporting the current study’s demonstration of omnidirectionally stretchable all-\nhydrogel AZIBs. Reference 5 (https://doi.org/10.1021/acsnano.4c13312) highlights an\nML-based approach to screen electrolyte additives based on donor numbers, reinforcing\nthe value of data-driven methods in optimizing battery components—a foundational\ntheme in this work’s kinetic-matching framework. Citing these works collectively\nunderscores the current study’s integration of ML, interfacial engineering, ion transport\ncoordination, and flexible battery design, while situating it within the broader landscape\nof next-generation AZIB development.\nResponse: We sincerely thank the reviewer for suggesting these highly relevant and\nvaluable references. After careful evaluation, we have incorporated them into the\nrevised manuscript to better position this work within the recent advances in AZIB\nresearch. These references enrich the discussion by highlighting complementary\nstrategies such as ML-guided interface design, electrolyte additive engineering, and\nadvanced hydrogel electrolytes for stable Zn anodes. Their inclusion strengthens the\ncontextualization of this study, underscoring how the proposed kinetic-matching\nframework for dual-intercalation electrodes aligns with and extends ongoing efforts\ntoward next-generation AZIBs. The relevant discussion has been added into the\nIntroduction section on Page 3, Line 6 and Page 5, Line 2 in the revised manuscript,\nas shown below:\nPage 3\n“Zinc undergoes dendritic growth, corrosion, and parasitic side reactions, degrading\nCoulombic efficiency, accelerating capacity fade, and shortening cycle life-issues that\ninterfacial coatings or electrolyte additives cannot fully resolve.[3-5]”\nReference\n[3] Shen, Q., Ang, E. H., Wang, J. et al. Heteroanionic Halogenated TiBTx MBene\nProtective Layer With Dual-Functional Zincophilic and Hydrophobic Characteristics\nfor Dendrite-Free Zinc Anode. Angew. Chem. Int. Ed. 137, 29, e202507504 (2025).\n[4] Rui, K., Ang, E. H., Wang, Y. et al. Anion-cation synergy of D-pantothenic acid\nhemicalcium for dendrite-free zinc anodes in aqueous zinc-ion batteries. J. Energy\nStorage 129, 117300 (2025).\n[5] Zhang, M., Wang, Y., Zhu, J. et al. Hydrogel Electrolyte With Ultrahigh Water-\nLocking Capability for Quasi-Solid Zinc-Ion Batteries with Extreme Environmental\nSafety. Small Methods 2500576 (2025).\nPage 5\n“To address this long-standing challenge, machine learning serves as an indispensable\ntool for decoding complex electrode kinetics and validating physically derived\nhypotheses.[32,33] ”\n[32] Dong, J., Yin, J., Ang, E. H. et al. Machine learning-assisted benign\ntransformation of three zinc states in zinc ion batteries. Energy Environ. Sci. 18, 4872-\n4882 (2025).\n[33] Luo, H., Gou, Q., Li, M. et al. Machine Learning-Assisted High-Donor-Number\nElectrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion\nBatteries. ACS Nano 19, 2, 2427-2443 (2025).\nReviewer 4\nComments:\nThis study presents a compelling theoretical framework for evaluating ion diffusion\ncompatibility between intercalation-type electrodes using machine learning-assisted\ncomputation. By facilitating the rapid screening and optimization of electrode materials\nfor zinc-ion batteries, the work holds potential to accelerate material discovery in this\nfield. The authors further verify their model through comprehensive experimental\nvalidation across various Zn-ion intercalation systems, demonstrating enhanced\nelectrochemical performance.\nWhile the study is scientifically meaningful and methodologically innovative, several\npoints require clarification and further elaboration to solidify the conclusions. The\nmanuscript will be acceptable upon well addressing the following issues:\nResponse: We would like to thank the reviewer for the comments and acknowledge\nhis/her recognition of our work.\n(1) The empirical formula incorporates the CSI and DR parameters. Do these\nparameters carry explicit physical significance? Please clarify their definitions and\nprovide justification for their selection as computational descriptors.\nResponse: We appreciate the reviewer’s insightful query regarding the physical\nsignificance of the Composite Shift Index (CSI) and Diffusion Ratio (DR). These\nparameters were rigorously designed to quantify critical aspects of intercalation kinetics\nin electrode materials. The CSI evaluates the homogeneity of zinc-ion diffusion\npathways by analyzing the distribution of diffusion energy barriers. Values within the\noptimal range (from 0.241 to 1.013) indicate uniform ion transport, while deviations\nreflect heterogeneous diffusion environments that can generate localized kinetic\nbottlenecks and increased polarization. The DR represents the kinetic compatibility\nbetween the cathode and anode by comparing their effective diffusion coefficients. A\nDR value between 0.936 and 1.020 indicates well-synchronized\nintercalation/deintercalation rates, whereas values outside this range reveal kinetic\nasymmetry, potentially causing ion accumulation and inefficient charge/discharge\nbehavior. Both descriptors were identified as dominant features through an XGBoost-\nbased machine learning feature importance analysis and subsequently validated\nexperimentally, confirming their critical roles in predicting and optimizing full-cell\nperformance.\nThe relevant discussion has been added into the Results and Discussion and Methods\nsections on Page 22, Line 23 and Page 30, Line 37 in the revised manuscript, as\nshown below:\nPage 22\n“The CSI quantifies the distributional spread of Zn2+ diffusion coefficients across\navailable pathways. Optimal values (0.241-1.013) reflect uniform diffusion landscapes\nthat enable rapid kinetics, whereas values outside this range indicate inhomogeneous\ntransport and localized bottlenecks that exacerbate polarization. The DR measures\nkinetic compatibility between electrodes, with values of 0.936-1.020 corresponding to\nbalanced intercalation/deintercalation dynamics, and deviations beyond this range\nrevealing electrode-specific kinetic mismatches. Machine-learning feature importance\nanalysis identified CSI and DR as the most predictive descriptors, a conclusion further\ncorroborated by experimental validation.”\nPage 30\n“To quantitatively assess kinetic compatibility between electrode pairs, two key\ndescriptors were defined: the Composite Shift Index (CSI) and the Diffusion Ratio (DR).\nCSI quantifies the divergence in ion diffusion kinetics between electrodes, reflecting the\ndegree of synchronization in their intercalation behavior. DR is defined as the ratio of\nthe mean apparent ion diffusion coefficients (logarithmic values) of the cathode and\nanode, providing a scale-invariant measure of relative diffusion rates. The respective\nformulas for calculating these descriptors are provided below:\n+ + -\n|𝑙̅̅𝑔̅ ̅𝐷̅̅̅−𝑙̅𝑔̅̅ ̅𝐷̅̅−̅|+2∙|𝜎𝑙𝑔𝐷 −𝜎𝑙𝑔𝐷|\n𝐶𝑆𝐼 =\n𝜎𝑙𝑔𝐷+ +𝜎𝑙𝑔𝐷−\n𝑙̅𝑔̅̅ ̅𝐷̅̅+̅\n𝐷𝑅 =\nWhere and denote the logarithm 𝑙̅i 𝑔̅c̅ ̅v 𝐷̅a̅−̅lue of the zinc ion diffusion coefficient\nof cathode and anode at each step of the charge-discharge process.”\n𝑙𝑔𝐷+ 𝑙𝑔𝐷−\n(2) The manuscript should clearly specify the data sources used for machine learning\ntraining and validation. A complete list of all datasets included in the computational\nanalysis is essential for transparency and reproducibility.\nResponse: We sincerely appreciate the reviewer’s emphasis on computational\ntransparency and reproducibility. In response, we have added dedicated subsections in\nthe Methods section that comprehensively document the data sources used for machine\nlearning training and validation. The dataset was constructed entirely from our own\nexperimental results. To ensure full transparency and reproducibility, all executable\ncode has been deposited on GitHub. The relevant information has been added into the\nMethods section on Page 31, Line 15 in the revised manuscript, as shown below:\nPage 31\n“Data availability\nAll data generated or analyzed during this study are included in this published\narticle and its supplementary information files. All text, images, and videos in this\nmanuscript were produced or experimentally obtained by the authors and are not\nderived from large language models or AI tools. Source data are provided with this\npaper.\nCode availability\nThe code used for the Machine-learning-analysis has been deposited in Github\nand can be accessed here: https://github.com/rachelyyr-llamama/ML.git. The database\nused for model training comes from the experimental results as listed in Data\navailability.”\n(3) In Figures 3a and 3b, the meaning of the white circles is unclear. Please clarify their\nsignificance and explain the theoretical basis and design rationale of the simulation\nmodels used.\nResponse: We thank the reviewer for requesting clarification regarding the COMSOL\nsimulations in Figures 3a and 3b. The white circles represent active electrode particles\nwhere interfacial intercalation reactions occur, highlighting regions of pronounced ion\ninsertion and extraction during operation. The simulation model was constructed using\nestablished finite element methodologies widely applied for battery electrode\nmicrostructure representation within the COMSOL Multiphysics environment, as\nsupported by prior studies (Energy Storage Mater. 2022, 51, 476–485; Energy 2025,\n319, 135006). This approach allows visualization of internal ion transport and its spatial\ndistribution within the intercalation electrodes, providing mechanistic insight into the\nkinetic behavior that governs full-cell performance. The relevant discussion has been\nadded into the Results and Discussion sections on Page 14, Line 13 in the revised\nmanuscript, as shown below:\nPage 14\n“As illustrated in Figures 3a and 3b, the white circles denote active electrode particles\nwhere interfacial intercalation reactions take place, as simulated using a finite element-\nbased microstructure model consistent with established computational studies of\nbattery electrodes.[46,47]”\nReference\n[46] Guo, Y., Li, X., Yan, G., et al. Visualization of concentration polarization in thick\nelectrodes. Energy Storage Mater. 51, 476-485 (2022).\n[47] Huang, Y., Wu, T., Xu, B. et al. Modelling of heterogeneous structure and particle-\nscale analysis of LiFePO4 electrode. Energy 319, 135006 (2025).\n(4) Figure 4g highlights superior electrochemical performance in formula-compliant\nsystems; however, it only presents comparisons based on the CSI parameter. To more\nrobustly validate the semi-empirical model, corresponding comparative data for the DR\nparameter should also be included.\nResponse: We thank the reviewer for this insightful suggestion regarding the\nexperimental validation of the DR parameter. In response, we conducted\ncomprehensive validation across 30 distinct electrode pairs encompassing a wide range\nof cathode–anode combinations. For each electrode pair, the DR value was calculated\nas the ratio of the average Zn2+ diffusion coefficients (logarithmic values) of the cathode\nmaterial to those of the anode. These average values, derived from GITT measurements\nat multiple states of charge, are summarized in Table S3f, and the resulting DR values\nare listed in Table S3g of the Supporting Information. The results show excellent\nagreement with the model-predicted optimal DR range of 0.936 to 1.020, confirming\nboth the physical relevance and predictive accuracy of the DR descriptor. This\nexperimental validation underscores the robustness of DR as a reliable metric for\nevaluating and optimizing kinetic compatibility in full-cell systems. The relevant\ndiscussion has been added into the Results and Discussion sections on Page 21, Line\n23 in the revised manuscript and Page 14 in the Supporting Information, as shown\nbelow:\nPage 21\n“Experimental validation of the DR descriptor across 30 distinct electrode pairs via\nGITT measurements confirms strong agreement with the predicted optimal range\n(0.936-1.020), as detailed in Table S3g, supporting its utility for predicting and\noptimizing full-cell performance.”\nPage 14 (Supporting Information)\nTable S3f. The average value and the standard deviation of diffusion coefficients of\ndiffenrent electrodes.\nElectrode\nZVO -10.29423 0.36592\nTiS -11̅𝑙.̅𝑔1̅̅ 0̅𝐷3̅25 0. 𝜎 5 𝑙 7 𝑔 9 𝐷 35\nMoO -11.48469 0.46507\nNVO-2.0V -10.88714 0.27136\nNVO-1.8V -10.3098 0.22075\nVOH -11.26269 0.65550\nKVOH -10.43430 0.25055\nVOP -11.29965 0.60348\nPA-VOP -10.67776 0.44610\nTable S3g. Summary of experimentally measured DR values (matched electrode pairs\nare highlighted in bold).\nDR value ZVO TS MXene PTCDI MoO\nPAVOP 1.037 0.962 1.048 1.026 0.930\nKVOH 1.014 0.940 1.024 1.003 0.909\nNVO-2.0V 1.058 0.981 1.068 1.046 0.948\nVOP 1.098 1.018 1.109 1.086 0.984\nVOH 1.094 1.014 1.105 1.082 0.981\n(5) The manuscript currently lacks sufficient experimental details to ensure\nreproducibility. Specifically, the hydrogel battery assembly process and the\nimplementation details of the machine learning workflow should be described more\nthoroughly.\nResponse: We thank the reviewer for emphasizing the importance of methodological\nclarity to ensure reproducibility. In response, we have comprehensively revised the\nMethods section to provide greater detail on both the hydrogel battery assembly and\nthe machine learning implementation. For the hydrogel battery assembly, we now\nexplicitly describe the co-planar configuration. The device architecture employs a cast\nhydrogel electrolyte substrate as the base layer. Discrete cathode and anode hydrogel\ncomponents are precisely positioned in parallel on this substrate, separated by a defined\ninsulating gap to prevent electrical shorting. The assembly is then encapsulated with a\ntop hydrogel electrolyte layer to ensure full ionic continuity and mechanical integrity.\nExternal electrical connections are established using flexible conductive adhesive tapes\naffixed to the electrode terminals, which enable reliable electrochemical testing and\nallow facile integration of series cell configurations.\nRegarding the machine learning framework, we now provide explicit descriptions of\nthe employed model, the mathematical definitions of the descriptors, and the\ncomputational environment used. Specifically, an XGBoost model was implemented to\npredict electrochemical performance of dual-intercalation AZIBs based on diffusion-\nderived features. The training dataset was fully derived from our experimentally\nmeasured results, ensuring consistency between computation and experiment. To\nquantify kinetic compatibility between electrode pairs, two key descriptors were\nintroduced: the Composite Shift Index (CSI), which captures the homogeneity and\nsynchronization of ion transport by evaluating the distribution of diffusion barriers,\nand the Diffusion Ratio (DR), which provides a scale-invariant measure of relative diffusion\nrates. Both descriptors are mathematically defined in the Methods section on Page 29,\nLine 22 and Page 30, Line 34 in the revised manuscript, also shown below:\nPage 29\n“A co-planar architecture was implemented utilizing a cast hydrogel electrolyte\nsubstrate. Discrete cathode and anode hydrogel components were precisely arranged\nin parallel on this substrate, maintaining a defined insulating gap to prevent short-\ncircuiting. The assembly was encapsulated with a top hydrogel electrolyte layer to\nensure ionic continuity. External electrical connections were established using flexible\nconductive adhesive tapes applied to the electrode terminals, enabling electrochemical\ntesting and the formation of series configurations.”\nPage 30\n“Machine Learning Implementation\nAn XGBoost model was employed to predict the electrochemical performance of\ndual-intercalation AZIBs based on diffusion-derived descriptors. The training dataset\nwas obtained exclusively from experimentally measured data in this study. To\nquantitatively assess kinetic compatibility between electrode pairs, two key descriptors\nwere defined: the Composite Shift Index (CSI) and the Diffusion Ratio (DR). CSI\nquantifies the divergence in ion diffusion kinetics between electrodes, reflecting the\ndegree of synchronization in their intercalation behavior. DR is defined as the ratio of\nthe mean apparent ion diffusion coefficients (logarithmic values) of the cathode and\nanode, providing a scale-invariant measure of relative diffusion rates. The respective\nformulas for calculating these descriptors are provided below:\n+ + -\n|𝑙̅̅𝑔̅ ̅𝐷̅̅̅−𝑙̅𝑔̅̅ ̅𝐷̅̅−̅|+2∙|𝜎𝑙𝑔𝐷 −𝜎𝑙𝑔𝐷|\n𝐶𝑆𝐼 =\n𝜎𝑙𝑔𝐷+ +𝜎𝑙𝑔𝐷−\n𝑙̅𝑔̅̅ ̅𝐷̅̅+̅\n𝐷𝑅 =\nWhere and denote the logarithm 𝑙̅i 𝑔̅c̅ ̅v 𝐷̅a̅−̅lue of the zinc ion diffusion coefficient\nof cathode and anode at each step of the charge-discharge process. Computational\n𝑙𝑔𝐷+ 𝑙𝑔𝐷−\nanalyses were performed on a dedicated workstation (Dell Precision T3680: Intel Core\ni9-14900K CPU, NVIDIA RTX 3090 GPU, 128 GB RAM). The total computation time\ndedicated to hyperparameter optimization and k-fold cross-validation procedures was\n336 hours.”\n(6) Figures 6b and 6c suggest that increasing the concentration of active electrode\nmaterial improves the capacity of the hydrogel electrodes. Please clarify why higher\nloadings were not explored further to maximize capacity, and whether there were\npractical or mechanical limitations.\nResponse: We thank the reviewer for raising this critical point regarding active material\nloading optimization. The decision to limit mass loading to 200 mg mL-1 for the cathode\nand 300 mg mL-1 for the anode was guided by fundamental constraints of the hydrogel\nmatrix. Mechanical characterization revealed that while higher loading improved\ntensile strength, it simultaneously led to a pronounced reduction in elongation-at-break\ndue to restricted chain mobility within the polymer network. More importantly, loadings\nbeyond these thresholds disrupted the crosslinking density of the PAA framework,\nundermining structural cohesion and preventing the fabrication of mechanically robust,\nfree-standing electrodes. As a result, we prioritized maintaining toughness and\nstructural integrity over maximizing specific capacity, which represents a necessary\ncompromise to achieve balanced mechanical and electrochemical performance in\ndeformable battery architectures. The relevant discussion has been added into the\nResults and Discussion sections on Page 24, Line 28 in the revised manuscript, also\nshown below:\nPage 24\n“Higher loadings, while enhancing tensile strength, markedly reduced elongation-at-\nbreak due to restricted polymer chain mobility. Excessive loadings further disrupted the\ncrosslinking density of the PAA network, compromising structural cohesion and\npreventing the fabrication of robust free-standing electrodes. Therefore, mass loadings\nwere optimized at 200 mg mL-1 for the cathode and 300 mg mL-1 for the anode.”\n(7) Was encapsulation employed during the flexible device demonstrations? If not,\nplease provide experimental data on water content changes under ambient conditions\nto evaluate the water retention capability of the system.\nResponse: We thank the reviewer for raising this crucial point regarding environmental\nstability. In our flexible device design, we implemented an integrated encapsulation\nstrategy in which the in-situ polymerized PVA/EG-Zn(OTf) hydrogel simultaneously\n2\nfunctions as both the ionic conduction medium and a self-encapsulating protective layer,\nthereby obviating the need for external packaging. This multifunctionality arises from\nthe strong hydrogen-bonding interactions between PVA chains and the water-ethylene\nglycol mixed solvent phase, which effectively suppresses water evaporation and\nensures long-term hydration stability. This approach is consistent with previously\nreported hydrogel-based self-protection strategies (Adv. Energy Mater. 2024, 14,\n2303592), while further extending the concept toward packaging-free device operation.\nTo quantitatively assess environmental stability, accelerated aging tests were performed\nunder ambient conditions (25°C, 60% RH). The hydrogel demonstrated remarkable\nwater retention, with only 0.041 wt.% water loss observed after 288 hours, underscoring\nits robustness against dehydration. These results confirm that the hydrogel electrolyte\nitself provides both high ionic conductivity and intrinsic encapsulation, thereby\nensuring stable device operation without the requirement of additional packaging.\nFollowing this, the relevant discussion has been added into the Results and Discussion\nsections on Page 25, Line 23 in the revised manuscript and Page 17 in the Supporting\nInformation, as shown below:\nPage 25\n“As shown in Figure S23, the devices utilize the hydrogel electrolyte itself as an\nencapsulation layer, exhibiting excellent water retention with only 0.041 wt% loss after\n288 hours under ambient conditions, thereby ensuring operational stability without\nadditional packaging.”\nPage 17 (Supporting Information)\nFigure S23. Water retention performance of the hydrogel ZIB under ambient\nconditions."}, {"round": 2, "reviewer_comments": {"reviewer_1": "After revision, this manuscript has been significantly improved. I recommend that it can be accepted after the following minor concerns are addressed:\nAs the authors mentioned, adjusting the cut-off voltage can effectively regulate the upper and lower limits of interlayer spacing variation, thereby tailoring the distribution of Zn²⁺ diffusion rates and optimizing overall ion transport kinetics. However, whether this reversibility is simultaneously attributed to the suppression of notorious proton insertion remains uncertain. To enhance the logical coherence and precision of the manuscript, particularly regarding the role of cut-off voltage in controlling interlayer spacing variation, the potential suppression of proton insertion should also be considered, or at least highlighted in discussion section, as reported by previous studies on cut-off voltage modification and high-voltage reaction inhibition strategies addressing vanadium dissolution in vanadium oxide–based electrodes for ZIBs (Energy Environ. Sci., 2025, DOI: 10.1039/D5EE03635F; Adv. Mater., 2024, 36, 2310645).\n(Remarks on code availability)\nPlease unify the language used in the code.", "reviewer_2": "The manuscript meets all the requirements for publication in the journal.\n(Remarks on code availability)\nAll necessary data are adequately provided, and I have confirmed that the XGBoost-related code functions properly when executed with them.", "reviewer_3": "The authors answered to the questions appropriately\n(Remarks on code availability)", "reviewer_4": "In this revision, the authors have addressed my concerns, the manuscript is thus recommended for acceptance.\n(Remarks on code availability)"}, "author_response": "Reviewer 1\nAfter revision, this manuscript has been significantly improved. I recommend that it\ncan be accepted after the following minor concerns are addressed.\nResponse: We sincerely thank the reviewer for the valuable comments and suggestions\non our work. In response, we have made corresponding revisions and additions to\naddress the concerns raised and to further improve the overall quality of the manuscript.\nWe hope that the revised version will meet with the reviewer’s approval and support.\n1、(Remarks to the Author): As the authors mentioned, adjusting the cut-off voltage\ncan effectively regulate the upper and lower limits of interlayer spacing variation,\nthereby tailoring the distribution of Zn2+ diffusion rates and optimizing overall ion\ntransport kinetics. However, whether this reversibility is simultaneously attributed to\nthe suppression of notorious proton insertion remains uncertain. To enhance the logical\ncoherence and precision of the manuscript, particularly regarding the role of cut-off\nvoltage in controlling interlayer spacing variation, the potential suppression of proton\ninsertion should also be considered, or at least highlighted in discussion section,\nas reported by previous studies on cut-off voltage modification and high-voltage reaction\ninhibition strategies addressing vanadium dissolution in vanadium oxide-based\nelectrodes for ZIBs (Energy Environ. Sci., 2025, DOI: 10.1039/D5EE03635F; Adv.\nMater., 2024, 36, 2310645).\nResponse: We sincerely thank the reviewer for this insightful and constructive\ncomment. We fully agree that suppression of proton (H+) insertion could act\nsynergistically with interlayer spacing regulation to enhance the observed reversibility.\nAs correctly pointed out by the reviewer and supported by previous studies (Energy\nEnviron. Sci., 2025, DOI: 10.1039/D5EE03635F; Adv. Mater., 2024, 36, 2310645), H+\ninsertion is a voltage-dependent process. In our study, the CV profiles (Fig. 3c) display\na reduction peak around 1.7 V, which can be attributed to H⁺ insertion, consistent with\nliterature reports. The potential difference (~1.7 V in our system vs. ~1.3 V in the\nreference) is likely associated with variations in electrode composition, electrolyte\nenvironment, and reference configuration, which together define the thermodynamic\nand kinetic boundaries of H+ intercalation.\nImportantly, the small hydrated radius of H+ (≈ 1.0 Å) means that its insertion induces\nonly limited lattice expansion compared with Zn2+ (≈4.3 Å) or the internally\ncoordinated NH + ions. As a result, while H+ insertion may occur at moderate potentials,\n4\nits contribution to interlayer spacing variation is much less significant than that of Zn2+\ndiffusion.\nCollectively, these structural and electrostatic factors, combined with our controlled\nvoltage window, effectively mitigate excessive H+ insertion and its cumulative\nstructural damage. Although complete exclusion of H+ participation is impractical\nunder full-cell conditions, limiting the upper potential within 1.8 V successfully reduces\nthe extent of proton-driven vanadium reduction, V-O bond cleavage, and subsequent\ndissolution. Therefore, our voltage regulation strategy not only optimizes Zn2+ transport\nkinetics and interlayer dynamics but also simultaneously suppresses the deleterious\nefarious\neffects of H+ intercalation, reinforcing the rationale behind our voltage window design.\nFollowing this valuable comment, the relevant discussion has been added into the\nResults and Discussion sections on Page 10, Line 16 in the revised manuscript, as\nshown below:\nPage 10\n“In contrast, controlling the cut-off voltage serves a dual purpose in ensuring cathode\nstability. Firstly, it regulates the range of interlayer spacing variation to tune Zn2+\ndiffusion pathways and surface flux. Secondly, it mitigates the notorious H+ insertion\nprevalent at higher potentials. Given the much smaller size of H+ compared with Zn2+\nand interlayer NH +, its insertion causes minimal spacing variation. This is critical, as\n4\nexcessive H+ insertion at high voltages leads to over-reduction of vanadium species and\nsubsequent V-O bond breakage, a primary cause of structural dissolution.[42,43] The\nselected voltage window (0.2-1.8 V) effectively limits proton insertion per cycle, thereby\nmitigating its contribution to vanadium dissolution. Consequently, this strategy not only\noptimizes kinetic matching across electrodes but also enhances structural integrity by\nminimizing both lattice strain and H+-induced degradation.”\nReference\n[42] Xie, X., Chen, N., Li, Z. et al. Capturing failure mechanisms toward the rational\ndesign of reversible vanadium oxide-based zinc batteries. Energy Environ. Sci. DOI:\n10.1039/D5EE03635F (2025).\n[43] Dai, Y., Mai, L., He, G. et al. Inhibition of Vanadium Cathodes Dissolution in\nAqueous Zn-Ion Batteries. Adv. Mater. 36, 2310645 (2024).\n2、(Remarks on code availability): Please unify the language used in the code.\nResponse: We thank the reviewer for this suggestion. We have now unified the\nprogramming language throughout the code, with the following specific revisions:\n All variable names, figure titles, axis labels, and comments have been standardized\ninto English for consistency and readability.\n The file names and saved image names have been replaced with English terms.\n The code now follows a unified commenting style (# English comment) to improve\nclarity and professional appearance.\nThe revised code has been uploaded to GitHub and is publicly accessible at:\nhttps://github.com/rachelyyr-llamama/ML/blob/main/Revised-GBOOSTmodels.ipynb.\nThese revisions ensure the code is fully consistent in language and format, enhancing\nreproducibility and international readability. Following this, the relevant discussion has\nbeen revised in the Methods section on Page 32, Line 18 in the revised manuscript, as\nshown below:\nPage 32\n“Code availability\nThe database used for model training comes from the experimental results as listed\nin Data availability. The code used for the Machine-learning-analysis has been\ndeposited in Github and can be accessed here: https://github.com/rachelyyr-\nllamama/ML/blob/main/Revised-GBOOSTmodels.ipynb.”\nReviewer 2\n(Remarks to the Author): The manuscript meets all the requirements for publication\nin the journal.\n(Remarks on code availability): All necessary data are adequately provided, and I\nhave confirmed that the XGBoost-related code functions properly when executed with\nthem.\nResponse: We sincerely thank the reviewer for his/her positive feedback. We are also\ngrateful for their verification of the code functionality and data availability. The\nreviewer’s thorough review has been very helpful in ensuring the quality of our work.\nReviewer 3\n(Remarks to the Author): The authors answered to the questions appropriately.\nResponse: We greatly appreciate the reviewer's positive feedback on our responses.\nThe reviewer’s questions were instrumental in enhancing the overall quality of our\nwork.\nReviewer 4\n(Remarks to the Author): In this revision, the authors have addressed my concerns,\nthe manuscript is thus recommended for acceptance.\nResponse: We extend our sincere gratitude to the reviewer for his/her positive\nrecommendation regarding the acceptance of our manuscript. We greatly appreciate\ntheir contribution, which has enhanced the quality of our work."}]

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