Fast-swimming biohybrid OstraBot with self-trained high-strength muscles

Domain: biology
Published: 2026-03-18
Editorial outcome: minor_revision

Abstract
Limited muscle force generation remains a major bottleneck in developing stronger, faster, and more efficient biohybrid robots. We present a fully autonomous self-training platform that strengthens skeletal muscle tissues by harnessing their robust spontaneous contractions. This approach produced muscle actuators with a maximum force of 7.05 mN and a stress of 8.51 mN/mm2, the highest reported for C2C12-derived muscle actuators. To demonstrate their capabilities, we developed a twin-tail muscle-powered ostraciiform swimming robot, OstraBot, and guided its design using a physiology-based muscle contraction model. Model-informed analysis identified stiffness–frequency combinations that maximized muscle energy output, enabling a top speed of 467 mm/min (15.6 body lengths/min), significantly outperforming previously reported skeletal muscle-powered biohybrid robots. The robot demonstrated strong thrust generation and precise on–off controllability through sound-triggered clapping control. This work establishes a versatile platform for producing high-strength skeletal muscle actuators and quantitatively guiding the robotic design for high-performance biohybrid robots.

Introduction
In the search for actuator materials that are sustainable, energy-efficient, and capable of adaptive motion, engineered biological muscle tissues have emerged as a promising alternative to synthetic abiotic materials1–4. Skeletal muscle, in particular, provides on-demand contractility5 and modular scalability6, which has enabled the development of skeletal muscle-driven robots such as biohybrid walking machines7–9 and robotic manipulators10–14. Despite these demonstrations, progress in biohybrid robot research has been constrained by a common bottleneck: the limited force generation of engineered skeletal muscle tissues. This limitation reduces locomotive speed, payload capacity, and overall versatility, preventing biohybrid systems from achieving the complexity and performance seen in robots powered by conventional actuators.

The need to overcome this challenge has motivated numerous recent studies into enhancing the force output of muscle actuators. One approach is to use primary skeletal muscle cells from murine13,15 or human10,12 sources. Although they generate higher intrinsic forces, these cells are less sustainable, show batch-to-batch variability, and are difficult to culture at scale16,17. Immortalized myoblast lines, most notably C2C12, remain widely used in biohybrid robots, despite producing weaker active forces than primary cells under conventional maturation conditions. Efforts to enhance the strength of C2C12 muscle tissues have focused on introducing biological cues, such as growth factors18 or coculture with photosynthetic algae19, and on active training approaches, including mechanical and electrical stimulation20–23, which improve myotube alignment and functional maturation. Despite these strategies, most C2C12 muscle tissues generate <1 mN of active force, with only a few reports exceeding 2 mN, and corresponding stresses consistently remaining below 2 mN/mm2.

Here, we introduce an arm-wrestling-inspired self-training strategy that enables C2C12 skeletal muscle tissues to generate a maximum force of 7.05 mN (8.51 mN/mm2), markedly exceeding previously reported values for biohybrid robots. This autonomous, scalable training platform converts spontaneous contractions of early-developing muscle tissues into repeated lengthening–shortening cycles, increasing muscle force without specialized equipment or manual intervention. We also developed a physiology-based predictive model linking muscle mechanics to robotic performance, enabling model-informed design and control to fully exploit muscle contractile potential. Leveraging the strong muscle tissues and the contraction model, we built a biohybrid swimming robot, OstraBot, which achieved a swimming speed of 467 mm/min (15.6 body lengths/min)—the fastest locomotion reported for skeletal muscle-driven biohybrid robots.

Results
Arm-wrestling-inspired self-training mechanism
Just as humans build strength through exercise, engineered muscle tissues can also become stronger when mechanically trained24,25. Inspired by the dynamics of an arm-wrestling contest, we developed a self-training platform in which a pair of engineered skeletal muscle tissues is mechanically coupled to generate autonomous, reciprocal training cycles (Fig. 1a). After molding, the muscle rings underwent initial matrix remodeling and compaction in the polydimethylsiloxane (PDMS) molds (Supplementary Fig. 1). A pair of compacted muscle tissues was then transferred to the self-training platform, each with one end anchored to the stationary base and the other connected to the sliding block. As the muscle pair progressively differentiated, the two tissues began actively pulling against each other, generating continuous mechanical loading that strengthened and aligned both tissues over time. C2C12 skeletal muscle actuators produced using our self-training platform exhibited markedly higher force output and powered biohybrid robots with substantially greater locomotion speeds compared to previously reported systems7–9,15,21,23,26–34 (Fig. 1b, Supplementary Table 1).

The autonomy of the self-training behavior originates from the intrinsic spontaneous contraction of skeletal muscle tissues. We observed that C2C12 skeletal muscle tissues exhibited robust spontaneous contraction at early stages of differentiation, even in the absence of external stimulation (Fig. 1c). The spontaneous contraction behavior emerged 3 days after initiating differentiation, with contraction amplitude further strengthened at day 4 and plateaued by day 5–7. After day 7, the spontaneous contractions gradually diminished and became barely detectable by day 14 (Fig. 1d, Supplementary Movie 1). This evolution coincided with key morphological changes of the muscle tissues (Supplementary Fig. 2), with spontaneous twitching diminishing after day 7 as myotube formation and maturation progressed. This spontaneous contractile activity created a natural time window for autonomous self-training during early muscle development, precisely when mechanical stimulation is most effective for promoting myotube alignment and hypertrophy35. Conveniently, as the muscle tissues matured and spontaneous twitchings subsided, they transitioned into a stable state well-suited for externally controlled robotic actuation.

With the robust spontaneous contraction as the driving force, the proposed self-training platform translated this activity into reciprocal mechanical interactions between the paired muscle tissues. Analogous to an arm-wrestling contest, the spontaneous contraction of one muscle exerted a pulling force that lengthened its counterpart. In turn, contractions from the opposing muscle generated reciprocal loading, resulting in continuous alternating lengthening–shortening strain cycles across the muscle pair without the need for external mechanical input (Fig. 1e, Supplementary Movie 2). The maximum strain experienced by a single muscle during these cycles was approximately ±2.8% (peak to peak ~5.6%), which emerged spontaneously from force balance between the paired muscles rather than from externally imposed displacement (Fig. 1f). Notably, our self-training platform enabled balanced mechanical training within the muscle pair: each tissue experienced a similar number of shortening and lengthening phases per minute with no significant differences (Fig. 1g). After 7 days of differentiation, both muscles maintained comparable resting lengths (Supplementary Fig. 3), further confirming that the platform provided balanced and consistent mechanical stimulation throughout the self-training phase.

Importantly, the spontaneous contraction activity of C2C12 muscle tissues was consistently observed across batches when standard protocols were followed, confirming the reliability and reproducibility of our self-training mechanism (Supplementary Movie 3). Similar spontaneous contractility has also been reported in both C2C12-derived23,36–38 and primary myoblast-based muscle constructs39–43, further supporting its generalizability and robustness as a force enhancement strategy. Moreover, we observed effective self-training across muscle constructs of different sizes, highlighting the scalability of this approach (Supplementary Movie 4).

Enhanced contractile performance of self-trained skeletal muscles
To evaluate the effect of proposed self-training on muscle maturation and force generation, we compared the proposed self-training maturation platform (STMP) with two control configurations (Supplementary Fig. 4). The low stiffness maturation platform (LMP) represents the most commonly adopted maturation configuration in biohybrid robotics systems, in which muscle tissues are differentiated while attached to a compliant skeleton that permits actuation but provides limited mechanical support during maturation. This condition captures the trade-off between structural compliance and force development that constrains most existing biohybrid robots. The high-stiffness maturation platform (HMP) was introduced as an internal control to isolate the effect of self-training. In this configuration, the muscles were fixed between rigid posts to suppress self-induced length change while providing enhanced mechanical support. To assess and compare structural maturation of skeletal muscle tissues, we performed immunofluorescence staining for myosin heavy chain and nuclei (Fig. 2a) and further visualized the images with line integral convolution (Fig. 2b). Muscle tissues cultured on the LMP exhibited disorganized myotube formation with poor alignment (Fig. 2a(i)), while those matured on the HMP showed better alignment but still contained regions of misalignment and non-uniform fiber thickness (Fig. 2a(ii)). In contrast, tissues cultured on the STMP displayed highly aligned, densely packed myotubes (Fig. 2a(iii)). Notably, high-magnification imaging revealed clear sarcomeric striations within the STMP-cultured tissues (Fig. 2a(iv)), indicating advanced structural maturation and sarcomere reorganization44. Furthermore, quantitative orientation analysis revealed that STMP-cultured muscle tissues exhibited a sharp, narrow peak around 0°, indicating highly aligned myotube organization. In contrast, HMP-cultured tissues showed a broader distribution concentrated within 0–15°, suggesting moderate alignment, while LMP-cultured tissues displayed a widely dispersed orientation profile with no distinct directional preference (Fig. 2c). Myotube diameter analysis revealed the largest averaged diameters in the STMP group (~20 μm), followed by the LMP group (~16 μm), with the HMP group showing significantly smaller diameters (~10 μm) (Fig. 2d), indicating greater fiber hypertrophy after self-training. Importantly, although myotubes in the LMP group exhibited a larger average diameter than those in the HMP, LMP-cultured tissues showed inferior force output due to poor global alignment, which limits efficient summation of myotube contractile forces at the tissue level.

To assess functional performance, we used an ultrasensitive force transducer to directly measure the active force output of the muscle tissues under varying stimulation frequencies. The force generation profile closely followed the stimulation frequency, with force summation occurring at increasing frequencies and full tetanus when above 20 Hz (Supplementary Fig. 5). For STMP-cultured muscle pair, the left and right tissues produced comparable active force amplitudes and activation profiles (Supplementary Fig. 6), further confirming balanced training shown in Fig. 1g. Across all stimulation frequencies tested, muscle tissues matured on the STMP generated significantly higher active forces compared to those on the HMP and LMP (Fig. 2e). Under 20 Hz stimulation, the STMP-cultured muscle tissues achieved peak tetanic forces of ~ 6.15±0.67 mN, whereas the HMP and LMP tissues produced maximum forces around 3.70±0.41 mN and 0.86±0.18 mN, respectively. To further assess the durability of the muscle contractility, we monitored active forces over 21 days (Fig. 2f). Both the STMP and HMP groups showed a similar trend of increasing active force during early differentiation, peaking around day 12, and remaining relatively stable without significant decline until day 17. However, the STMP group consistently exhibited higher active forces compared to the HMP group. Notably, on day 4, the active forces of STMP and HMP were at similar levels, but the STMP group showed a markedly larger increase in force thereafter. This suggests that the self-training contributed to enhanced strengthening starting from day 4, coinciding with the onset of robust spontaneous contraction activity. In contrast, the LMP group displayed an early force peak at day 7, followed by a continuous decline until day 21, when many samples ruptured due to excessive passive tension buildup. LIVE/DEAD staining performed at day 21 revealed that STMP-cultured muscle tissues exhibited predominantly live cells with minimal dead cell presence at the tissue surface and near-surface regions (Supplementary Fig. 7). While this imaging does not directly assess cell viability in the tissue core, the STMP tissues maintained consistent and strong force output at day 21 (Fig. 2f), indicating sustained contractile function over long-term culture. These results collectively demonstrate that the STMP platform not only enhances structural maturation but also significantly improves functional contractility and long-term durability of engineered skeletal muscle tissues. The differences in contractile performance among the three platforms can be attributed to their distinct spontaneous contraction profiles (Supplementary Fig. 8). On the HMP, the high stiffness restrains muscle contractions, limiting the magnitude of mechanical stimulation; on the LMP, although the compliant beams allowed larger spontaneous contractions, the progressively increasing passive tension caused the muscle length to shorten over time, leading to loss of structural integrity (Supplementary Fig. 9). In contrast, the STMP provided a dynamic equilibrium of developing passive forces between the muscle pair, maintaining muscle length under built-up tensions while enabling large spontaneous contraction amplitudes for self-training.

To contextualize these findings within the broader field of biohybrid robotics, we benchmarked the contractile performance of our self-trained muscle actuators against previously reported C2C12-based systems, using both absolute force and normalized muscle stress as metrics. Cross-sectional areas, measured from hematoxylin and eosin (H&E)–stained sections, averaged 0.83±0.15 mm2 (Supplementary Fig. 10). From these values, the STMP-cultured muscles produced maximum twitch and tetanic forces of 4.21 mN (5.08 mN/mm2) and 7.05 mN (8.51 mN/mm2), respectively—both substantially higher than those reported for muscle actuators in prior biohybrid robot studies7–9,14,19,21,23,26–30,32,45–47 (Fig. 2g, Supplementary Table 2), underscoring the ability of the self-training platform to overcome the long-standing low force generation bottleneck in this field.

Model-informed design of a twin-tail biohybrid swimming robot
To showcase the capabilities of the self-trained high-force muscle tissues, we designed a twin-tail biohybrid swimming robot—OstraBot. This design was inspired by the ostraciiform swimming of boxfish, a mode of locomotion distinct from the undulatory swimming used by most swimming robots48–50, which involves coordinated body and tail motion. In ostraciiform swimming, the body remains rigid while thrust is generated solely by oscillating the tails51, resulting in a mechanically simple structure capable of stable, energy-saving propulsion52. OstraBot consists of a rigid body for stabilization, a self-trained muscle as the driving force, a pair of flexible beams functioning as artificial tendons for force transmission, and two tails for propulsion (Fig. 3a). Upon electrical stimulation, the muscle contracts, deforming the artificial tendons and driving oscillatory motion of the tails to propel the robot forward.

Achieving high robotic performance requires not only strong muscle actuators but also a rational system design. To this end, we mechanically represented the biohybrid robot as an active muscle element connected to a linear elastic spring, representing the artificial tendon, and a viscous damper, representing the hydrodynamic drag (Fig. 3b). The mechanical properties of the spring and damper can be determined experimentally: force–deflection testing of the tendon beams confirmed highly linear bending behavior under small deflections (Fig. 3c), allowing modeling as a linear spring, while the damping coefficient could be obtained by fitting to experimental motion data. In contrast, the muscle actuator is inherently more challenging to model due to its dynamic and nonlinear force-generating characteristics53. In many biohybrid robot studies, the complexity of muscle behavior is simplified by approximating the muscle as a linear actuator54.

To better account for underlying muscle dynamics in robotic design and control, we developed a physiology-based contraction model that link electrical stimulation to force output through four interconnected biological stages: electric flux, calcium dynamics, muscle activation, and crossbridge cycling (Fig. 3d). Each stage is described by a set of differential equations55–57 (see Supplementary Note 1 for detailed derivations), enabling the model to take an input electrical stimulation signal, solve the resulting calcium and activation dynamics, and predict the complete time course of muscle force generation. In the contraction model, the active force generated by the skeletal muscle is governed by:

F active = F max · A(t) · f v (v) · f l (l) (1)

where F max is the maximum isometric force that the muscle can generate, A(t) is the stimulation-dependent activation level. f v (v) and f l (l) represent the force–velocity and force–length scaling functions, respectively. These two terms account for the inverse relationship between contraction velocity and force (Supplementary Fig. 11a) and the muscle’s optimal length range (Supplementary Fig. 11b). The scaling functions are independent and can be calibrated separately from force measurements under specific testing conditions (see Supplementary Note 2 for model calibration). Simulation results of the calibrated model closely matched experimental measurements (Supplementary Fig. 12a), reproducing peak force, stimulation-dependent contraction frequency, and key temporal features such as time to peak tension and half-relaxation time (Supplementary Fig. 12b), demonstrating its capability to accurately simulate the amplitude and dynamic profile of muscle force generation.

We then integrated the muscle contraction model into the muscle–tendon–load framework of the OstraBot to predict the robot’s performance (see Supplementary Note 3). For the robot to achieve high performance, the muscles should produce the greatest possible mechanical work in each contraction-relaxation cycle (Supplementary Fig. 13). Because the tendon always returns to its starting position, no elastic energy is stored between cycles; thus, in this idealized scenario, the muscle’s energy output is directly translated into robotic motion. Tendon stiffness is a key parameter governing the muscle’s energy output, as it determines how the muscle responds dynamically and how effectively muscle forces are transmitted. Similar to biological tendons, an appropriate tendon stiffness balances energy storage, force transmission, and overall efficiency58–60. Model simulations revealed that tendons that are too stiff limit deformation and produce minimal motion, while overly soft tendons allow large deformations but insufficient restoring force to complete the cycle, reducing useful work. Only tendons with intermediate stiffness produce the largest energy loop areas, indicating maximal mechanical work generation (Fig. 3e, Supplementary Fig. 14). In addition to tendon stiffness as a structural design parameter, stimulation frequency serves as a key operational control parameter influencing system performance. For a single contraction-relaxation cycle, higher stimulation frequencies lead to contraction summation, which reduces the mechanical energy output per cycle (Supplementary Fig. 15). When total energy output over time is considered, however, a stimulation frequency of 3 Hz yielded the highest simulated performance for tendons of intermediate stiffness, balancing energy per cycle with cycle repetition rate (Fig. 3f).

To examine the combined influence of tendon stiffness and stimulation frequency, we generated a heatmap of muscle energy output across varying stiffness–frequency combinations (Fig. 3g). We defined a high-performance range as the region where energy output exceeded 80% of the maximum, indicated by the overlaid circle. The simulation reveals a distinct tendon stiffness range of 1.0–4.5 N/m associated with maximal energy output. Interestingly, the simulation uncovered a stiffness–frequency matching phenomenon: softer tendons performed best at lower stimulation frequencies, whereas stiffer tendons benefited from higher stimulation frequencies. These simulation results highlight how both tendon stiffness (design parameter) and stimulation frequency (control parameter) can be tuned in concert to enhance biohybrid robot performance.

Experimental validation of stiffness design and frequency matching
To validate the simulation predictions, we fabricated OstraBot variants with soft, intermediate, and stiff tendons with stiffness values of 0.77 N/m, 2.88 N/m, and 12.64 N/m, respectively (Supplementary Fig. 16). At a stimulation frequency of 3 Hz, the self-trained muscle under STMP contracted robustly, producing oscillatory tail motion that propelled the robots forward. Swimming performance varied markedly with tendon stiffness. The intermediate-stiffness OstraBot achieved the greatest displacement, consistently outperforming both the soft- and stiff-tendon designs (Fig. 4a, Supplementary Movie 5). Quantitative displacement measurements (Fig. 4b) confirmed these trends: the intermediate-stiffness robot traveled 80.22 mm over 18 s, reaching a steady-state speed of 5.50 mm/s, compared to 20.09 mm (0.98 mm/s) for the soft-tendon design and 7.98 mm (0.36 mm/s) for the stiff-tendon design. Interestingly, only the intermediate-stiffness OstraBot exhibited a distinct acceleration phase, as seen in the displacement-time profiles and confirmed by instantaneous speed measurements (Fig. 4c). In each actuation cycle, the muscle accelerated the robot to a sufficiently high instantaneous speed to prevent a complete stop, thereby allowing momentum to accumulate over time. In contrast, the soft- and stiff-tendon OstraBots attained lower peak speeds and halted between actuation cycles, which precluded forward momentum buildup. Muscle displacement tracking (Supplementary Fig. 17) provided a mechanistic explanation consistent with model predictions: when the tendon was too soft, the tail oscillated over a small angular range despite large absolute displacement, producing limited effective thrust, whereas the stiff-tendon design generated minimal displacement due to restricted beam deformation, resulting in low thrust.

We next examined how stimulation frequency, a key operational control parameter, interacts with stiffness to modulate robot swimming performance. For the intermediate-tendon OstraBot, the distance–time curves exhibited a clear stepwise pattern, with step intervals corresponding directly to the stimulation period (Fig. 4d). Stimulation at 3 Hz produced the fastest forward motion, characterized by a distinct acceleration phase (Supplementary Movie 6), whereas stimulation at 2 Hz and 4 Hz resulted in significantly slower motion with no evident speed accumulation across actuation cycles. When performance was compared across all stiffness groups, a clear stiffness–frequency matching pattern emerged: for the soft-tendon robot, the highest speed (~2.23 mm/s) occurred at 2 Hz (Supplementary Movie 7), whereas with increasing tendon stiffness, the frequency yielding peak speed also increased. The intermediate-stiffness robot reached its maximum speed (~5.50 mm/s) at 3 Hz, while the stiff-tendon robot performed best (~1.72 mm/s) at 4 Hz (Fig. 4e). These findings demonstrate that matching actuation frequency to tendon stiffness is essential for maximizing biohybrid robot performance, in agreement with predictions from our muscle–tendon interaction model.

Finally, to showcase the importance and necessity of strong muscle actuators for achieving high performance, we compared OstraBot powered by HMP- and STMP-cultured muscles. The STMP-powered robot swam more than three times faster than its HMP-powered counterpart (Fig. 4f, Supplementary Movie 8), highlighting how increasing muscle force directly translates to improved biohybrid robot performance.

Demonstration of controllability and robust operation
Having validated how structural design and actuation control influence swimming performance, we next performed additional demonstrations to assess OstraBot’s controllability. While swimming speed can be tuned by stimulation frequency, this simultaneously alters stroke timing and the overall swimming pattern. In contrast, varying the electric field strength provides a more direct and continuous means of speed modulation by controlling muscle activation levels, without altering the underlying kinematics of the swimming motion. Increasing the field strength from 0.15 V/mm to 0.50 V/mm progressively enhanced swimming speed, whereas further increasing it to 0.70 V/mm resulted in a marked reduction (Fig. 5a, b, and Supplementary Movie 9). This decrease is likely due to overstimulation effects such as excessive charge accumulation, ionic imbalance, or early-onset muscle fatigue61, all of which can impair contractile performance. The maximum swimming speed reached 7.78 mm/s (0.26 body length/s) when stimulated at 0.50 V/mm and 3 Hz (Fig. 5c). We further assessed OstraBot’s robustness by introducing external disturbances during steady forward swimming. When momentarily pushed backward, the robot exhibited a brief reversal in displacement and a transient negative swimming speed (Fig. 5d). It rapidly regained forward motion within a few seconds (Fig. 5e), demonstrating strong recovery capability and resilience to mechanical interference. This rapid recovery is attributed to the system’s high thrust generation, enabled by the strong contractile output of the self-trained muscles and the model-informed mechanical design of the robot.

To further demonstrate OstraBot’s on–off controllability, we constructed a sound-triggered control system that activated and deactivated swimming in response to clapping signals (Supplementary Fig. 18). The robot was programmed to start swimming upon detecting two consecutive claps and to stop when a single clap was detected. In the absence of sound signals, the robot remained stationary. Upon receiving the start command, electrical stimulation was applied to the muscle actuator, and the robot immediately initiated swimming. Upon receiving the stop command, stimulation ceased, tail oscillations also stopped, and the robot glided for about 4 s before halting completely (Fig. 5f, Supplementary Movie 10). Notably, while these engineered muscles exhibit spontaneous contractions during early development, their amplitude diminishes substantially and the activity becomes random by the time they are integrated into the robot (Fig. 5g). As a result, spontaneous contractions cannot generate continuous thrust to overcome hydrodynamic resistance, ensuring precise on–off control of the robot.

Methods
Cell culture
The cells used in this study were the mouse myoblast cell line C2C12 (CRL-1772, ATCC) under passage 5. The cells were cultured and expanded in growth medium consisting of high-glucose Dulbecco’s modified Eagle’s medium (DMEM, Gibco) supplemented with 10% (v/v) fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Gibco). The cells were passaged at a 10:1 split ratio before reaching 70% confluency to avoid depletion of differentiation potential. To induce myogenic differentiation, the culture medium was changed to differentiation medium consisting of DMEM supplemented with 10% (v/v) horse serum (HS, Gibco), 1% penicillin-streptomycin (Gibco), 50 ng/mL insulin-like growth factor (IGF-LR3-1, PeproTech), and 1 mg/mL of 6-aminocaproic acid (Sigma-Aldrich).

Formation of skeletal muscle tissues and self-training
To construct the skeletal muscle tissues, PDMS injection models were first fabricated by molding against a 3D-printed ring-shaped polylactic acid (PLA) mold using a commercial fused deposition modeling (FDM) printer (Bambu Lab X1C). Prior to cell seeding, the PDMS molds were autoclaved, plasma-treated and coated with 1% wt F-127 Pluronic solution (Sigma-Aldrich) to facilitate muscle release. C2C12 cells were trypsinized, counted, centrifuged, and resuspended in growth medium to ensure a final concentration of 1.0 × 107 cells/mL in the mixture. 18 μL of cell suspension was sequentially mixed with 100 μL of fibrinogen (8 mg/mL, Sigma-Aldrich), 30 μL of Matrigel (Corning), and 2 μL of thrombin (100 U/mL, Sigma-Aldrich). After mixing, the cell-matrix mixture was seeded into the PDMS mold and incubated at 37 °C for 30 min to initiate gelation. The crosslinked cell-matrix mixture was cultured in growth medium supplemented with 1 mg/mL 6-aminocaproic acid for 2 days, allowing it to compact and gain mechanical strength before it could be transferred.

The HMP and STMP platforms were directly 3D printed using an FDM printer with PLA filaments. The LMP platform was molded using PDMS. Two days after molding, the compacted muscle tissues were carefully lifted from the mold and transferred onto the designated maturation platforms using a customized tool. Each well was filled with 5 mL of differentiation medium per muscle tissue, with daily medium changes. To assess and visualize the spontaneous contraction of the engineered muscle tissues, their motion was recorded using an inverted optical microscope (TE2000, Nikon) fitted with an incubation chamber. The temperature was maintained at 37 °C during testing. The recorded videos were analyzed using an in-house dense optical flow-based tracking algorithm.

Immunostaining
To assess and compare myogenic differentiation and alignment across samples differentiated on different platforms, tissues were processed for immunofluorescence staining. All muscle tissues were maintained on their respective maturation platforms prior to fixation to preserve structural integrity and native alignment. Tissues were washed three times with Dulbecco’s phosphate buffered saline (DPBS, Hyclone) and fixed in 4% paraformaldehyde (PFA, Bioss) for 30 min at room temperature, followed by three washes in DPBS. For immunostaining, samples were permeabilized with 0.2% (v/v) Triton X-100 in DPBS for 15 min, then washed and blocked with 2% bovine serum albumin (BSA). Muscle tissues were incubated with Alexa Fluor®−488-conjugated anti-myosin heavy chain II antibody (MF-20, eBioscience) diluted in 5% DPBS-BSA buffer and left overnight at 4 °C. After three washes to remove unbound antibodies, nuclei were counterstained with 1 μg/mL DAPI (Sigma-Aldrich), followed by washing three times with DPBS. Fluorescently labelled tissues were imaged using a laser confocal laser microscope (LSM700, Zeiss), with excitation wavelengths of 405 nm for DAPI and 488 nm for MF-20. Myotube orientation was visualized using line integral convolution and quantified using OrientationJ68.

LIVE/DEAD staining
To evaluate the viability of the engineered skeletal muscle tissues after long-term culture, LIVE/DEAD staining was performed on day 21. Briefly, the tissues were washed three times with DPBS and incubated with a staining solution containing 2 μM calcein-AM and 4 μM ethidium homodimer-1 for 30 min at 37 °C. Due to the limited optical penetration depth in thick 3D muscle constructs, confocal imaging was performed at the tissue surface and near-surface regions using a laser scanning confocal microscope.

Cryosectioning
To determine cross-sectional areas, the self-trained muscle tissues were sectioned and stained with hematoxylin and eosin. Following fixation as described above, samples were sequentially immersed in 30%, 50%, and 70% sucrose solutions at 4 °C for cryoprotection. The tissues were then embedded in optimal cutting temperature (OCT) compound, rapidly frozen in liquid nitrogen, and stored at –80 °C until sectioning. Transverse sections (10 μm thick) were prepared using a cryostat (CryoStar NX50, Thermo Fisher Scientific).

Muscle force measurement
To accurately characterize the passive and active force generated by the muscle tissues, we built a customized force-measuring device (Supplementary Fig. 19). The measuring device was composed of an ultrasensitive force transducer (LSB200-10g, FUTEK) fitted with a 3D-printed adapter for anchoring muscle tissues and a pair of carbon electrodes for electrical stimulation. The force sensor was factory calibrated and independently validated using known weights prior to experiments (Supplementary Fig. 20). Electrical signals were generated using a stimulator (Master-9, AMPI), amplified by a high-power operational amplifier (OPA541, Texas Instruments), and delivered through two platinum wires.

When measuring the active force, muscle tissues were released from the maturation platforms, placed in Tyrode’s salt solution (Sigma-Aldrich), and connected to the force transducer via the adapter. When electrically stimulated, the tested muscle contracted, pulled the adaptor and transmitted the force to the transducer. The sampling rate of the transducer was set to 100 samples/second, and the average of a 5 s continuous measurement was taken as the final result.

For all electrical stimulation experiments carried out in this study, the pulse width was set to 20 ms and the electric field set to 0.25 V/mm unless specified.

Construction of the biohybrid OstraBot
The body of the OstraBot was fabricated using a FDM 3D printer (Bambu Lab X1C) with PLA filaments. The infill density was set to 20%, achieving an approximate density of 0.7 g/mm3 to provide sufficient buoyancy. The flexible beams were made of PDMS cast in 3D-printed molds, with stiffness tuned by varying both the beam geometry (Supplementary Fig. 21) and the precursor-to-curing-agent ratios (8:1 for stiff, 10:1 for intermediate, and 20:1 for soft tendons). The tails were cut from a 0.25 mm-thick polyimide film using an automated cutting machine (Cameo 4, Silhouette). All components were assembled using medical-grade adhesive (Loctite 4861, Henkel). Before muscle integration, the robot skeleton was sterilized in 70% ethanol for 30 min and exposed to UV light for an additional 30 min. Engineered skeletal muscle tissue was then mounted onto the robot by securing it around the muscle anchors. To initiate swimming, biphasic square-wave pulses (pulse width: 20 ms) with different electric field strength were applied and delivered through a pair of parallel carbon electrodes submerged in the buffer medium.

The motion of the OstraBots was recorded using a digital camera and analyzed for swimming speed, tail beat amplitude, and frequency using Tracker software.

Statistical analysis
Statistical analysis was performed using OriginPro software. For comparisons between two groups, an unpaired two-tailed Student’s t-test was used. For comparisons involving three groups, a one-way ANOVA followed by Tukey’s post hoc test was performed. Results are presented as means ± SD. The F and P values of Fig. 2d, e are listed in Supplementary Table 3.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents the development of a novel muscle stimulation platform capable of autonomously training engineered muscle tissue without external actuation. This represents an original and elegant design that stands out for its simplicity and potential for broad application. The integration of model-informed optimisation to enhance muscle performance for robotic implementation is interesting and highlights key principles of biorobotics system design, although the robotic system itself remains relatively conventional. Overall, the methodology appears sound and consistent with the standards of the field, but several aspects require clarification and revision.\nResults and Interpretation:\nThe primary concern lies in the interpretation of the data presented in Figure 2g. The reported maximum tetanic forces for the control platforms LMP and HMP (0.86 ± 0.18 mN and 3.70 ± 0.41 mN, respectively) are significantly lower than those of the STMP. Consequently, the superior performance of the STMP tissues relative to existing literature is attributed to the training provided by the platform. However, when the HMP tissues are included in the comparison with prior studies, in a similar manner as was done for the STMP tissue in Figure 2g, the HMP tissues also perform markedly better than all references (coordinates: 3.7 mN; 4.46 mN/mm²). Please note that the muscle stress (mN/mm²) for the HMP was estimated assuming a comparable overall surface as the STMP tissues. This observation suggests that the enhanced performance may not be exclusively due to the STMP training, thereby weakening the central claim that the platform itself is the primary factor driving improved muscle strength.\nThe authors are encouraged to re-examine this conclusion and to provide additional evidence to disentangle the effects of the training platform from other factors such as baseline tissue properties or potential impact of the force measurement method. This latter point is particularly critical, and the authors should clarify whether the custom force detection device was validated using known standards to confirm its calibration accuracy.\nDiscussion:\nAn additional discussion point worth considering relates to the strain imposed on the muscle during actuation, which is reported to be relatively low (below 3%). This contrasts with established findings in muscle tissue engineering, where higher and more physiologically relevant strain levels (more generally around 10%, but reaching up to 40%) have been shown to promote maturation of engineered muscle tissue [1], often resulting in improved structural organisation, functionality, and contractile performance. The authors should therefore clarify whether they propose that lower strains might offer some specific advantage, or whether these reduced strains could instead represent a limitation on the maximal force generation achievable with their system.\nMore broadly, the authors are encouraged to elaborate on which specific characteristics of the training system they believe are responsible for its success in generating skeletal muscle constructs capable of producing forces that exceed those typically reported in the literature. A clearer discussion of these contributing factors would help readers better understand the mechanisms driving the observed improvements and strengthen the overall interpretation of the results.\nMethods:\nThe experimental methods are generally robust and align with field standards. However, the mode of electrical stimulation delivery during the robot swimming experiments requires clearer presentation. While Supplementary Figure 18 depicts the use of a swimming channel with electrodes on both sides, this information should be explicitly integrated into the OstraBot Methods section to enhance clarity and reproducibility.\nIn addition, clarification is needed regarding the live/dead imaging protocol. Was the imaging performed at the surface of the constructs or within their mid-depth region? Adding this precision would strengthen the claim of long-term viability if no necrosis or cell death was observed not only at the surface but also at the core of the tissue matrix after 21 days of culture.\nOther:\nA few minor editorial and presentation issues should also be addressed to improve the manuscript’s clarity:\n- There is a typographical error on line 78 (“with contraction amplitude further strengthened at day 4 and plateaued by day^(5–7)”) that should be corrected.\n- Figure 3E would benefit from visual revision, as the overlapping semi-transparent ovals make the colour code difficult to interpret.\n- As the soft tendon was shown to perform less effectively than the intermediate tendon, Figure 4D should instead present the data for the intermediate tendon. The corresponding discussion should also be revised accordingly to reflect this substitution and thereby strengthen the overall argument.\nReferences:\n[1] Nakayama KH, Shayan M, Huang NF. Engineering Biomimetic Materials for Skeletal Muscle Repair and Regeneration. Adv Healthc Mater. 2019 Mar;8(5):e1801168. doi: 10.1002/adhm.201801168. Epub 2019 Feb 6. PMID: 30725530; PMCID: PMC6589032.", "reviewer_2": "This is an inspiring work aimed at achieving a higher force output in biohybrid actuator/robot systems by utilizing the dynamic equilibrium of developing passive forces between muscle pairs using a slide mechanism. It is autonomous, reproducible, scalable, and supports long-term cell viability. This work also thoroughly investigated the physiology-based model of muscle contraction. Overall, this work is comprehensive and impactful, making it a valuable contribution in the field.\nPlease refer to my comments below to enhance the clarity.\n(Figure 2d-e) The myotube diameter from LMP is larger than that from HMP. But why are the active force and tetanic force outputs from HMP higher than those from LMP?\n(Page 12. Stiffness-frequency matching phenomenon) Many aquatic animals actively adjust the stiffness of their flexible appendages (body parts that generate thrust) in response to swimming frequency, typically lowering stiffness at low beating frequencies and increasing it at high beating frequencies. Multiple literatures in biolocomotion and robotics have studied the same phenomenon, and I suggest citing some of these literatures to enrich the discussion.\n(Page 15) Scallop-theorem-like effect: add a reference citation, and how did you calculate the Reynolds number at 1, 2, and 3 Hz of swimming? Was the fluid medium Newtonian fluid or non-Newtonian fluid? In the case of a non-Newtonian fluid, the scallop theorem is not appropriate to explain the experimental results.\nWhat about the inertia and amplitude impact of the flapping tail? In Video S6, the tail amplitude at 2 Hz appears to be larger than in the cases of 1 Hz or 3 Hz actuation. Due to the elongated structure of the flapping tail, when the soft tendon was used, the optimal actuating frequency might be tuned to 2 Hz. Analyzing Strouhal number could add more in-depth analysis of the effect of swimming frequency.\n(Discussion section) I'm convinced that this work overcame the force output bottleneck and demonstrated much-improved results. What other open issues remained? (if any) One of the main reasons for studying biohybrid actuators is to develop an efficient motile robot, as an alternative to abiotic actuators. Does the force output improvement achieved in this work enable the robot to achieve comparable swimming efficiency to that of ostraciiform swimming fishes or existing robots?\n[Minor comments]\nFigure 2a(iii). The parameter 'θ(theta)' corresponds to the myotube orientation angle in Fig. 2c?\nFigure 2a(iv). Is this the highly magnified muscle cultured from STMP? Please clarify this in the figure caption as well. What are the meanings of the three white arrows?\nFigure 2g. Need more information to clarify the meaning of the different marker types (circle, triangle).\nFigure 3c. In the figure caption, 'the flexible beam' corresponds to which components in Fig. 3a? I recommend using consistent notation; if it is 'artificial tendon' in Fig. 3a, please use the same name.\nVideo S1. \"diminishes form day 7\" the word \"form\" might be a typo of from. Please check.\nVideo S2. Real-time of fast-forwarded video? Please specify it.\nVideo S3. Why are the colors of the four batches different? Do they mean something?\nVideo S4. The data reported in Fig. 1, which exhibited the highest force output, was obtained from a medium-sized muscle?\nFigure S4. This figure consists of three subpanels (a, b, and c), but no explanation has been provided for each panel.\nFigure S11b. The name of the vertical axis (active force) seems misleading, as it displays both active and passive forces.\nFigure 3e. Hard to tell which area corresponds to which stiffness (even hard to know for non color blind people). Increase the color contrast or annotate each area with the corresponding stiffness value.\n(Page 11 and Figure S14) Soft, intermediate, and stiff tendons correspond to which stiffness shown in Fig. 3e (among the five stiffness values)?\nFigure S15. The same comment as Figure 3e above.\nFigure S16. The color label in 16a seems reversed, the red marker should be stiff.\nFigure 5b. Please make it more color-blind friendly. Some of the green color graphs look the same and hard to tell which one corresponds to which electric field."}, "author_response": "Dear Reviewers,\nWe sincerely thank all reviewers for your time and attention to our manuscript\nNCOMMS-25-72868. We greatly appreciate the constructive comments from the two\nreviewers on our manuscript, which helped us furnish the manuscript with additional\ntechnical discussions to fully justify the significance of this work. We are highly\ngrateful for the appreciation of our work from\nReviewer #1 as “The manuscript presents the development of a novel muscle\nstimulation platform”, “This represents an original and elegant design that stands\nout for its simplicity and potential for broad application.”, “The integration of\nmodel-informed optimisation to enhance muscle performance for robotic\nimplementation is interesting and highlights key principles of biorobotics system\ndesign”.\nReviewer #2 as “This is an inspiring work aimed at achieving a higher force output\nin biohybrid actuator/robot systems”, “this work is comprehensive and impactful,\nmaking it a valuable contribution in the field”.\nFollowing the reviewers’ suggestions, we have provided additional experimental data\nand carefully and thoroughly revised the manuscript in wording/phrasing. Please see\nthe revised versions of the Manuscript and Supplementary Information marked in red.\nGiven our comprehensive responses and thorough revisions made to the manuscript,\nwe believe that all the concerns from the reviewers have been fully addressed. And we\nare open to any new questions you have.\nThank you all very much for re-evaluating our work in advance.\nYours sincerely,\nYu Jun Tan, PhD.\nAssistant Professor\nDepartment of Mechanical Engineering\nNational University of Singapore\nEmail: yujun.tan@nus.edu.sg\nReviewer #1\nThe primary concern lies in the interpretation of the data presented in Figure 2g. The\nreported maximum tetanic forces for the control platforms LMP and HMP (0.86 ± 0.18\nmN and 3.70 ± 0.41 mN, respectively) are significantly lower than those of the\nSTMP. Consequently, the superior performance of the STMP tissues relative to existing\nliterature is attributed to the training provided by the platform. However, when the\nHMP tissues are included in the comparison with prior studies, in a similar manner as\nwas done for the STMP tissue in Figure 2g, the HMP tissues also perform markedly\nbetter than all references (coordinates: 3.7 mN; 4.46 mN/mm²). Please note that the\nmuscle stress (mN/mm²) for the HMP was estimated assuming a comparable overall\nsurface as the STMP tissues. This observation suggests that the enhanced\nperformance may not be exclusively due to the STMP training, thereby weakening\nthe central claim that the platform itself is the primary factor driving improved muscle\nstrength.\nThe authors are encouraged to re-examine this conclusion and to provide additional\nevidence to disentangle the effects of the training platform from other factors such as\nbaseline tissue properties or potential impact of the force measurement method. This\nlatter point is particularly critical, and the authors should clarify whether the custom\nforce detection device was validated using known standards to confirm its calibration\naccuracy.\nWe thank the reviewer for this insightful comment, which has helped us clarify both\nthe interpretation of Fig. 2g and the distinction between the different maturation\nplatforms used in this study.\n(1) Validation of force measurement accuracy\nWe strongly agree that accurate force measurement is critical for interpreting\nabsolute force values, and that the use of high-sensitivity force sensors represents a\nreliable and widely adopted approach in the field (Vesga-Castro C. et al. Elife 11,\ne77204, 2022; Morita T. et al. Sci. Adv. 11, eadu9962, 2025; Yang L. et al. Adv.\nFunct. Mater. 35, 2410334, 2025). The force sensor used in this work is a high-\nsensitivity strain-based force sensor with a factory calibration certificate. In response\nto the reviewer’s concern, we have now performed an independent validation of the\nforce measurement system following calibration approaches commonly adopted in\nprevious studies (Morita T. et al. Sci. Adv. 11, eadu9962, 2025). The validation\nconfirms a linear and repeatable response with good agreement between applied\nand measured forces across the force range reported in this study.\nThe calibration procedure, results, and the factory calibration certificate have been\nadded to the supplementary information (Supplementary Fig. 20), supporting the\naccuracy and reliability of the muscle force measurements reported here.\n(page 23, line 473–475) The force sensor was factory calibrated and independently\nvalidated using known weights prior to experiments (Supplementary Fig. 20).\nSupplementary Fig. 1.\nCalibration of the force sensor. (a) Factory-provided calibration data covering a\nload range from 0 to 10 g. (b) Photograph of the experimental setup used for sensor\nvalidation. The force sensor was mounted vertically, and glass beads of known mass\nwere placed in a tray attached to the sensor. (c) Table comparing the measured\nweights obtained from the force sensor with the corresponding known weights used\nfor calibration. Experiment performed at 22 °C.\n(2) Clarification of high-stiffness maturation platform (HMP)\nWe would like to clarify that the high-stiffness maturation platform (HMP) is not\nintended to represent common practice in the field, and we acknowledge that the\noriginal manuscript may have inadvertently suggested otherwise. In typical biohybrid\nrobot systems, muscle tissues are differentiated directly after molding while attached\nto a compliant skeleton. This skeleton must be soft enough to allow actuation, yet stiff\nenough to support the increasing passive tension of the developing muscle, creating\nan inherent mechanical trade-off. As a result, mechanical support during\ndifferentiation is often suboptimal, which we represent with the low-stiffness\nmaturation platform (LMP).\nBy contrast, the HMP was specifically devised in this study as an internal control by\nfixing the sliding block to eliminate self-training while maintaining constant muscle\nlength. This configuration provides improved mechanical support during\ndifferentiation, a factor that has been widely reported to be critical for muscle force\ndevelopment (Hu X. et al. Biomaterials 32, 8979-8989, 2011; Ren K. et al. Adv.\nFunct. Mater. 18, 1378-1389, 2008). This explains why HMP tissues exhibit force\noutputs that exceed most previously reported values.\nImportantly, we performed additional experiments to directly examine the mechanical\nconstraint imposed by the LMP and HMP configurations. Due to the intrinsic\nspontaneous contraction of differentiating muscle tissues, muscles cultured on the\nLMP exhibited pronounced and rhythmic shortening behavior (see attached video\nbelow). This compliance-enabled deformation provides a form of intrinsic mechanical\nactivity that can promote local myotube growth and hypertrophy. However, the low\nstiffness of the LMP provides limited mechanical constraints and fails to align the\nmyotubes well.\nIn contrast, in the HMP, fixation of the muscle ends restricts global length change.\nAs a result, only regions of the tissue not in direct contact with the posts remain partially\nunconstrained and exhibit limited spontaneous contractions (see attached video\nbelow). This stiffer mechanical support enhances global myotube alignment, thereby\nimproving force transmission, but does not provide sustained cyclic deformation that\nwould drive hypertrophy.\nCritically, while HMP isolates the effect of improved passive mechanical support,\nit\nlacks the dynamic length modulation and cyclic mechanical feedback that are unique\nto the STMP. The STMP, therefore, introduces an additional active stimulus—self-\ninduced cyclic loading during maturation—that is absent in both LMP and HMP.\nThe\nsubstantially higher force output observed in STMP tissues thus reflects the specific\ncontribution of platform-enabled self-training, rather than differences in baseline\ntissue properties or force measurement methodology.\nWe have revised the description in the corresponding text, as shown below:\n(page 6, line 119–127) To evaluate the effect of proposed self-training on muscle\nmaturation and force generation, we compared the proposed self-training maturation\nplatform (STMP) with two control configurations (Supplementary Fig. 4). The low\nstiffness maturation platform (LMP) represents the most commonly adopted\nmaturation configuration in biohybrid robotic systems, in which muscle tissues are\ndifferentiated while attached to a compliant skeleton that permits actuation but\nprovides limited mechanical support during maturation. This condition captures the\ntrade-off between structural compliance and force development that constrains most\nexisting biohybrid robots. The high-stiffness maturation platform (HMP) was\nintroduced as an internal control to isolate the effect of self-training. In this\nconfiguration, the muscles were fixed between rigid posts to suppress self-induced\nlength change while providing enhanced mechanical support.\n(page 19&20, line 372–385) More broadly, the enhanced force output achieved using\nthe STMP can be attributed to several defining features of the training strategy. The\nplatform enables a maturation-then-assembly approach, decoupling muscle\ndifferentiation from the mechanical constraints of the final robotic skeleton and\nthereby overcoming the stiffness trade-off that limits force development in\nconventional biohybrid systems. In addition, the STMP establishes a dynamically\nbalanced mechanical environment by pairing muscle with reciprocal mechanical\nopposition. Unlike low-stiffness platforms that allow contraction but lead to\nprogressive shortening, or rigid platforms that maintain muscle length while\nsuppressing movement, deformation in the STMP emerges naturally from force\nbalance between opposing muscles coupled through the sliding block. Finally,\nmechanical stimulation in the STMP is contraction-driven and bidirectional,\nencompassing both shortening and subsequent lengthening phases. Although the\ninstantaneous strain amplitude experienced by individual muscles is relatively\nmodest (±2–3%), this force-regulated stimulation is applied continuously and adjusts\nnaturally as muscle force develops. This self-training configuration differs\nfundamentally from displacement-controlled systems that impose intermittent,\nexternally prescribed strain63.\nAn additional discussion point worth considering relates to the strain imposed on the\nmuscle during actuation, which is reported to be relatively low (below 3%). This\ncontrasts with established findings in muscle tissue engineering, where higher and\nmore physiologically relevant strain levels (more generally around 10%, but reaching\nup to 40%) have been shown to promote maturation of engineered muscle tissue [1],\noften resulting in improved structural organisation, functionality, and contractile\nperformance. The authors should therefore clarify whether they propose that lower\nstrains might offer some specific advantage, or whether these reduced strains could\ninstead represent a limitation on the maximal force generation achievable with their\nsystem.\nReferences:\n[1] Nakayama KH, Shayan M, Huang NF. Engineering Biomimetic Materials for\nSkeletal Muscle Repair and Regeneration. Adv Healthc Mater. 2019\nMar;8(5):e1801168. doi: 10.1002/adhm.201801168. Epub 2019 Feb 6. PMID:\n30725530; PMCID: PMC6589032.\nWe thank the reviewer for this question. We acknowledge that many engineered 3D\nskeletal muscle systems employ externally imposed strain amplitudes on the order of\n~10% to promote maturation (Fan T. et al. Biofabrication 17, 022013, 2025).\nHowever, the mechanical regime in the present system differs fundamentally from\nthese approaches.\nFirst, the strain in STMP is contraction-driven and bidirectional, encompassing both\nshortening and re-lengthening phases. In contrast, most existing mechanical training\nplatforms rely on externally actuated stretch, in which deformation is prescribed by\nthe actuator and the muscle is primarily subjected to lengthening-only loading; as a\nresult, active concentric shortening against a mechanical load is not typically\nsupported.\nSecond, while the instantaneous strain amplitude is relatively small (±3%), the\nmechanical stimulation is applied continuously with a high duty cycle throughout the\nentire differentiation period, rather than intermittently for limited durations per day\n(Nakayama K.H. et al. Advanced healthcare materials 8.5 (2019): 1801168). As a\nresult, the cumulative mechanical exposure experienced by the muscle is substantial\ndespite the lower strain magnitude.\nFinally, muscle maturation is a dynamic process in which both active force generation\nand passive tension evolve continuously over time. In displacement-controlled\ntraining, a fixed strain is imposed regardless of the maturation state of the tissue,\nwhich may not be optimal throughout the entire differentiation process (Acta\nBiomaterialia 94 (2019): 232-242). In contrast, the STMP allows deformation to be\ndetermined by muscle-generated force acting against the platform stiffness, such that\nearly-stage tissues experience limited deformation while progressively stronger\ntissues generate larger deformation as maturation proceeds.\nTherefore, we do not consider the lower strain amplitude to be a limitation on\nmaximal force generation; rather, it reflects a distinct mechanical conditioning regime\nin which deformation is regulated by muscle-generated force rather than imposed\ndisplacement.\nWe have added this discussion in the manuscript as follows:\nIn the Results section:\n(page 4, line 92–95) The maximum strain experienced by a single muscle during\nthese cycles was approximately ± 2.8% (peak to peak ~ 5.6%), which emerged\nspontaneously from force balance between the paired muscles rather than from\nexternally imposed displacement (Fig. 1f).\nIn the Discussion section:\n(page 19&20, line 372–385) More broadly, the enhanced force output achieved using\nthe STMP can be attributed to several defining features of the training strategy. The\nplatform enables a maturation-then-assembly approach, decoupling muscle\ndifferentiation from the mechanical constraints of the final robotic skeleton and\nthereby overcoming the stiffness trade-off that limits force development in\nconventional biohybrid systems. In addition, the STMP establishes a dynamically\nbalanced mechanical environment by pairing muscle with reciprocal mechanical\nopposition. Unlike low-stiffness platforms that allow contraction but lead to\nprogressive shortening, or rigid platforms that maintain muscle length while\nsuppressing movement, deformation in the STMP emerges naturally from force\nbalance between opposing muscles coupled through the sliding block. Finally,\nmechanical stimulation in the STMP is contraction-driven and bidirectional,\nencompassing both shortening and subsequent lengthening phases. Although the\ninstantaneous strain amplitude experienced by individual muscles is relatively\nmodest (±2–3%), this force-regulated stimulation is applied continuously and adjusts\nnaturally as muscle force develops. This self-training configuration differs\nfundamentally from displacement-controlled systems that impose intermittent,\nexternally prescribed strain63.\nMore broadly, the authors are encouraged to elaborate on which specific\ncharacteristics of the training system they believe are responsible for its success in\ngenerating skeletal muscle constructs capable of producing forces that exceed those\ntypically reported in the literature. A clearer discussion of these contributing factors\nwould help readers better understand the mechanisms driving the observed\nimprovements and strengthen the overall interpretation of the results.\nWe believe the enhanced force output of the muscle tissues matured using the\nSTMP arises from several distinctive features of the training strategy. First, the\nplatform enables a maturation-then-assembly approach for biohybrid robot\nconstruction, decoupling muscle maturation from the mechanical constraints of the\nfinal robotic skeleton. This overcomes the inherent stiffness dilemma in conventional\nbiohybrid systems, where overly stiff skeletons limit actuation while overly compliant\nskeletons provide insufficient mechanical support during muscle maturation.\nSecond, the STMP provides a dynamically balanced mechanical environment by\npairing muscles with evenly matched mechanical opposition. In contrast to the low-\nstiffness maturation platform (LMP), which allows contraction but provides limited\nresistance and leads to progressive muscle shortening, and the high-stiffness\nmaturation platform (HMP), which maintains muscle length but restricts movement,\nthe STMP supports active contraction while preserving mechanical support. As a\nresult, muscle deformation emerges from the balance between opposing muscle\nforces rather than being externally imposed or fully constrained.\nThird, the mechanical stimulation in STMP is contraction-driven and bidirectional,\nencompassing both concentric shortening and subsequent lengthening of the\nmuscle. This mode of training differs from most externally actuated platforms, which\npredominantly impose lengthening-only deformation, and more closely reflects\nphysiological muscle use during force generation.\nFollowing the reviewer’s suggestion, we have added a dedicated discussion in the\nDiscussion section elaborating on the key characteristics of the training system that\ncontribute to the enhanced force output as follows:\n(page 19&20, line 372–385) More broadly, the enhanced force output achieved using\nthe STMP can be attributed to several defining features of the training strategy. The\nplatform enables a maturation-then-assembly approach, decoupling muscle\ndifferentiation from the mechanical constraints of the final robotic skeleton and\nthereby overcoming the stiffness trade-off that limits force development in\nconventional biohybrid systems. In addition, the STMP establishes a dynamically\nbalanced mechanical environment by pairing muscle with reciprocal mechanical\nopposition. Unlike low-stiffness platforms that allow contraction but lead to\nprogressive shortening, or rigid platforms that maintain muscle length while\nsuppressing movement, deformation in the STMP emerges naturally from force\nbalance between opposing muscles coupled through the sliding block. Finally,\nmechanical stimulation in the STMP is contraction-driven and bidirectional,\nencompassing both shortening and subsequent lengthening phases. Although the\ninstantaneous strain amplitude experienced by individual muscles is relatively\nmodest (±2–3%), this force-regulated stimulation is applied continuously and adjusts\nnaturally as muscle force develops. This self-training configuration differs\nfundamentally from displacement-controlled systems that impose intermittent,\nexternally prescribed strain63.\nThe experimental methods are generally robust and align with field standards.\nHowever, the mode of electrical stimulation delivery during the robot swimming\nexperiments requires clearer presentation. While Supplementary Figure 18 depicts\nthe use of a swimming channel with electrodes on both sides, this information should\nbe explicitly integrated into the OstraBot Methods section to enhance clarity and\nreproducibility.\nWe thank the reviewer for this helpful suggestion. In response, we have revised the\nMethods section to explicitly describe the electrical stimulation conditions used during\nthe OstraBot swimming experiments. Specifically, to initiate swimming, biphasic\nsquare-wave pulses (pulse width: 20 ms) were applied and delivered through a pair\nof parallel carbon electrodes submerged in the buffer medium, as illustrated in\nSupplementary Fig. 18.\n(page 23, line 493–495) To initiate swimming, biphasic square-wave pulses (pulse\nwidth: 20 ms) with different electric field strength were applied and delivered through\na pair of parallel carbon electrodes submerged in the buffer medium.\nIn addition, clarification is needed regarding the live/dead imaging protocol. Was the\nimaging performed at the surface of the constructs or within their mid-depth region?\nAdding this precision would strengthen the claim of long-term viability if no necrosis\nor cell death was observed not only at the surface but also at the core of the tissue\nmatrix after 21 days of culture.\nWe thank the reviewer for this important point. Due to the technical difficulty of optical\nimaging through thick 3D muscle tissues, LIVE/DEAD staining and imaging were\nperformed at the tissue surface and near-surface regions, rather than at the mid-\ndepth of the constructs.\nWe acknowledge that this limits direct visualization of cell viability in the tissue core.\nAccordingly, we have revised the manuscript to explicitly state the imaging depth and\nto avoid overstating whole-construct viability based solely on LIVE/DEAD imaging.\nImportantly, we note that the tissues exhibit consistent and strong force output at day\n21 (Fig. 2f), indicating sustained contractile function over long-term culture. While not\na direct measure of cell viability at the core, this functional stability provides indirect\nevidence that a substantial fraction of the muscle tissue remains viable and functional\nat late time points.\nWe have revised the description in the corresponding text, as shown below:\nIn Results:\n(page 8, line 164–169) LIVE/DEAD staining performed at day 21 revealed that\nSTMP-cultured muscle tissues exhibited predominantly live cells with minimal dead\ncell presence at the tissue surface and near-surface regions (Supplementary Fig. 7).\nWhile this imaging does not directly assess cell viability in the tissue core, the STMP\ntissues maintained consistent and strong force output at day 21 (Fig. 2f), indicating\nsustained contractile function over long-term culture.\nIn Methods:\n(page 23, line 460–461) Due to the limited optical penetration depth in thick 3D\nmuscle constructs, confocal imaging was performed at the tissue surface and near-\nsurface regions using a laser scanning confocal microscope.\nIn the Supplementary Fig. 7 caption:\nSupplementary Fig. 7.\nCell viability of self-trained muscle cultured for 21 days. LIVE/DEAD staining\nreveals that the majority of cells remained live (green), with few dead cells (red) at\nthe tissue surface and near-surface regions, confirming the long-term biocompatibility\nof the platform. Scale, 50 μm.\nA few minor editorial and presentation issues should also be addressed to improve\nthe manuscript’s clarity:\n- There is a typographical error on line 78 (“with contraction amplitude further\nstrengthened at day 4 and plateaued by day^(5–7)”) that should be corrected.\nWe thank the reviewer for pointing out this typographical error. The text has been\ncorrected to “day 5–7” instead of “day^(5–7)”.\nFigure 3E would benefit from visual revision, as the overlapping semi-transparent\novals make the colour code difficult to interpret.\nWe thank the reviewer for the suggestion. We have revised Fig. 3e by changing the\ncolor and rearranging the layered regions to enhance contrast and improve\nidentification of the different stiffness conditions.\nAs the soft tendon was shown to perform less effectively than the intermediate\ntendon, Figure 4D should instead present the data for the intermediate tendon. The\ncorresponding discussion should also be revised accordingly to reflect this\nsubstitution and thereby strengthen the overall argument.\nWe agree with the reviewer’s suggestion. Fig. 4d has been updated to present the\ndata obtained from the OstraBots with intermediate tendon instead of the soft tendon.\nThe corresponding text in the Results has been revised accordingly to reflect this\nchange and to strengthen the overall argument.\nThe revised figure and text is as follows:\n(page 14&15, line 297–301) For the intermediate-tendon OstraBot, the displacement-\ntime curves exhibited a clear stepwise pattern, with step intervals corresponding\ndirectly to the stimulation period (Fig. 4d). Stimulation at 3 Hz produced the fastest\nforward motion, characterized by a distinct acceleration phase (Supplementary Movie\n6), whereas stimulation at 2 Hz and 4 Hz resulted in significantly slower motion with\nno evident speed accumulation across actuation cycles.\nReviewer #2\n(Figure 2d-e) The myotube diameter from LMP is larger than that from HMP. But why\nare the active force and tetanic force outputs from HMP higher than those from LMP?\nWe thank the reviewer for this insightful question. At the tissue level, force output is\ngoverned not only by myotube size but, more critically, by the degree of myotube\nalignment and the efficiency of force transmission along a common contraction axis.\nHighly aligned myotubes enable constructive summation of contractile forces,\nwhereas misalignment leads to vector cancellation and reduced net force, a\nphenomenon widely reported in engineered skeletal muscle systems (Advanced\nMaterials 28.48 (2016): 10588-10612).\nIn the LMP, although the compliant platform allows greater tissue deformation and\nspontaneous contraction, it provides insufficient passive mechanical support during\ndifferentiation, thereby impeding the establishment of global anisotropic alignment\n(Fig. 2c). Consequently, contractile forces generated by individual myotubes are\npoorly coordinated and cannot be effectively summed at the tissue level, despite the\nlarger average myotube diameter.\nBy contrast, the HMP provides greater passive mechanical support during\ndifferentiation, promoting tissue compaction and improved myotube alignment, even\nthough the average myotube diameter is smaller. This enhanced structural\norganization facilitates more effective force transmission along the principal\ncontraction direction, leading to higher active and tetanic force outputs compared\nwith the LMP.\nWe have added the following description:\n(page 7, line 142–145) Importantly, although myotubes in the LMP group exhibited a\nlarger average diameter than those in the HMP, LMP-cultured tissues showed inferior\nforce output due to poor global alignment, which limits efficient summation of\nmyotube contractile forces at the tissue level.\n(Page 12. Stiffness-frequency matching phenomenon) Many aquatic animals actively\nadjust the stiffness of their flexible appendages (body parts that generate thrust) in\nresponse to swimming frequency, typically lowering stiffness at low beating\nfrequencies and increasing it at high beating frequencies. Multiple literatures in\nbiolocomotion and robotics have studied the same phenomenon, and I suggest citing\nsome of these literatures to enrich the discussion.\nWe thank the reviewer for this insightful comment. We agree that body or appendage\nstiffness is a key parameter in aquatic locomotion, and that many animals actively\nmodulate stiffness in response to swimming frequency to improve propulsive\nefficiency.\nFrom a biomechanical perspective, classical analyses have shown that the cost of\nundulatory propulsion depends strongly on the relationship between driving\nfrequency ad the natural frequency of the body, which is governed by body stiffness\n(American Zoologist 36.6 (1996): 678-694). It has therefore been proposed that\nswimmers can benefit from increasing effective body stiffness at higher tailbeat\nfrequencies to maintain stiffness–frequency matching and reduce the energetic cost\nof body bending.\nComplementary evidence from computational and robotic models further supports\nthis principle. Systematic variations of body stiffness in undulatory swimmers\ndemonstrate strong resonance effects, with swimming performance and efficiency\npeaking when stiffness and actuation frequency are appropriately matched\n(Bioinspiration & Biomimetics 17.1 (2021): 011002). Motivated by these insights,\ntunable-stiffness strategies have been explicitly implemented in bioinspired robotic\nswimmers to optimize performance across different swimming regimes (Science\nRobotics 6.57 (2021): eabe4088; Science Robotics 6.50 (2021): eabd2971), rather\nthan relying on a single fixed stiffness design.\nWe have revised the manuscript to incorporate these representative studies and to\nplace our discussion within this broader framework of stiffness–frequency matching\nin biological and robotic aquatic locomotion.\n(page 20, line 390–392) Notably, this stiffness–frequency matching behavior is\nconsistent with prior studies in aquatic locomotion64, in which frequency-dependent\nbody or appendage stiffness has been shown to play a key role in efficient propulsion\nin both biological swimmers and bioinspired robotic systems65-67.\n(Page 15) Scallop-theorem-like effect: add a reference citation, and how did you\ncalculate the Reynolds number at 1, 2, and 3 Hz of swimming? Was the fluid medium\nNewtonian fluid or non-Newtonian fluid? In the case of a non-Newtonian fluid, the\nscallop theorem is not appropriate to explain the experimental results.\nWhat about the inertia and amplitude impact of the flapping tail? In Video S6, the tail\namplitude at 2 Hz appears to be larger than in the cases of 1 Hz or 3 Hz actuation.\nDue to the elongated structure of the flapping tail, when the soft tendon was used,\nthe optimal actuating frequency might be tuned to 2 Hz. Analyzing Strouhal number\ncould add more in-depth analysis of the effect of swimming frequency.\nWe thank the reviewer for this comment. We agree that the scallop theorem alone\ncannot rigorously explain the results in Fig. 4d, and we did not intend to claim it as a\ngoverning mechanism but simply trying to analyze potential reason for the backward\nmotion.\nThe purpose of this section is to establish stimulation frequency as an important\ndesign parameter for improving robot performance, rather than to provide a detailed\nhydrodynamic analysis. As also suggested by Reviewer 1, since the intermediate\ntendon was identified as the better configuration in Fig. 4b, we have replaced Fig. 4d\nto show the frequency-dependent performance of the intermediate-tendon OstraBot\ninstead of the soft-tendon case. As a result, the corresponding Reynolds number–\nbased discussion has been removed, as it is not central to the main argument of this\nwork.\nRegarding the apparent difference in tail motion amplitude observed in the video S6\n(now renumbered as Supplementary Video S7), we have now performed a\nquantitative analysis of tail displacement, as shown below. This analysis shows that\nthe absolute tail displacement (defined in Supplementary Fig. 14) at 1, 2, and 3 Hz is\ncomparable. The visually smaller motion at 3 Hz arises from incomplete muscle\nrelaxation at higher stimulation frequencies, which reduces the effective\ndisplacement contributing to propulsion, rather than from changes in structural inertia\nor hydrodynamic effects. This finding further supports our conclusion that stimulation\nfrequency critically influences muscle force transmission and overall robot\nperformance.\n1.34\n)\nm0.67\nm\nnt\n(0.00\ne\nm1.34\ne\nc\npl\na0.67\ns\ndi0.00\nail 1 Hz\nT1.34\n0.67\n0.00\n0 2 4 6 8 10\nTime (s)\nWe agree that the Strouhal number is an important dimensionless parameter for\nassessing swimming efficiency, as it captures the interaction between flapping\nfrequency, amplitude, and forward speed. Swimming efficiency generally reflects two\ncoupled factors in biohybrid robots: (i) muscle force generation and transmission\nefficiency, and (ii) hydrodynamic structure–fluid interaction determined by body\ngeometry. In the present work, we focus on the former by optimizing muscle strength,\ntendon stiffness, and stimulation frequency to enhance force transmission. A\nsystematic exploration of Strouhal number–based hydrodynamic optimization would\nrequire controlled variation of body geometry and stimulation conditions, which is\nbeyond the scope of this study but represents an important direction for future work.\n(Discussion section) I'm convinced that this work overcame the force output\nbottleneck and demonstrated much-improved results. What other open issues\nremained? (if any) One of the main reasons for studying biohybrid actuators is to\ndevelop an efficient motile robot, as an alternative to abiotic actuators. Does the force\noutput improvement achieved in this work enable the robot to achieve comparable\nswimming efficiency to that of ostraciiform swimming fishes or existing robots?\nWe thank the reviewer for this insightful comment. We agree that, beyond force\noutput, important open challenges remain in biohybrid robotics, particularly in\nachieving high swimming efficiency comparable to biological swimmers or state-of-\nthe-art abiotic robots.\nIn this work, we aimed to address two major and widely recognized bottlenecks in\nbiohybrid robots: (i) the intrinsically low force output of engineered skeletal muscle\nactuators, and (ii) the lack of rational, mechanics-based design principles linking\nactuator performance to robotic motion. Through continuous self-training and model-\nguided design, we substantially enhanced muscle force output and achieved a\nswimming speed of 467 mm/min, outperforming previously reported skeletal muscle–\ndriven biohybrid swimming robots.\nHowever, we do not claim that the current robot achieves optimal or biologically\ncomparable swimming efficiency. Swimming efficiency is a system-level property that\ndepends on additional factors such as body morphology, tail geometry, and robot\nsize etc., which were not systematically optimized in the present study.\nWe therefore view the present work as establishing a necessary foundation:\novercoming the force-output bottleneck and providing a rational design framework\nwithout which further efficiency optimization would not be feasible. Future studies can\nbuild upon this platform by integrating morphological optimization and data-driven\ndesign approaches, such as those demonstrated in recent biohybrid swimming\nrobots using machine learning-based optimization (Zimmerman J.F. et al. Sci. Robot.\n10, eadr6472, 2025), to further improve swimming efficiency.\nAccordingly, we have added the following content to the Discussion:\n(page 20, line 396–403) Despite these advances, further improvements in swimming\nefficiency remain an important direction for biohybrid robotic systems. While\novercoming the force-output bottleneck is a necessary prerequisite for high-\nperformance locomotion, swimming efficiency is a system-level property governed by\nadditional factors such as tail size, tail geometry, and overall body morphology.\nRecent studies have shown that jointly optimizing morphology and actuation using\nmachine learning-based approaches can substantially improve swimming efficiency\nin biohybrid fish robots68. In this context, the self-trained high-force muscles and the\nstiffness–frequency matching framework established here provide a robust and\nscalable foundation upon which such system-level efficiency optimization can be\naccomplished.\n[Minor comments]\nFigure 2a(iii). The parameter 'θ(theta)' corresponds to the myotube orientation angle\nin Fig. 2c?\nWe thank the reviewer for pointing this out. The parameter θ indeed corresponds to\nthe myotube orientation angle shown in Fig. 2c, and we have revised the figure\ncaption to indicate it.\nFigure 2a(iv). Is this the highly magnified muscle cultured from STMP? Please clarify\nthis in the figure caption as well. What are the meanings of the three white arrows?\nWe thank the reviewer for this comment. Yes, Figure 2a(iv) shows a highly magnified\nimage of muscle tissue cultured on the STMP. The three white arrows indicate\nrepresentative sarcomeric striation structures within the STMP-cultured muscle\ntissue, which are characteristic of organized myofibrillar architecture and muscle\nmaturation. We have revised the figure caption to explicitly clarify the tissue source\nand the meaning of the arrows to enhance clarity.\nThe revised caption is as follows:\nFig. 2 | Functional evaluation of muscle tissues cultured on different\nmaturation platforms. a Immunostaining images of muscle tissues cultured on (i)\nLMP, (ii) HMP, (iii) STMP, and (iv) a high-magnification view of the STMP-cultured\nmuscle tissue highlighting sarcomeric striations (indicated by three arrows) at day 7.\nGreen: myosin heavy chain; blue: nuclei. θ denotes the myotube orientation angle\nrelative to the longitudinal axis of the construct. Scale bars, (i-iii) 50 μm; (iv) 10 μm.\nFigure 2g. Need more information to clarify the meaning of the different marker types\n(circle, triangle).\nWe thank the reviewer for this comment. We acknowledge that the original legend\ncould be confusing. The legend in Fig. 2g has been revised to clearly define the\nmeaning of the different marker types (red circle for tetanic force and blue triangle for\ntwitch force).\nFigure 3c. In the figure caption, 'the flexible beam' corresponds to which components\nin Fig. 3a? I recommend using consistent notation; if it is 'artificial tendon' in Fig. 3a,\nplease use the same name.\nWe thank the reviewer for pointing this out. The term “flexible beam” in the caption of\nFig. 3c indeed corresponds to the artificial tendon shown in Fig. 3a. We have revised\nthe figure caption to use consistent terminology throughout the manuscript for clarity.\nThe revised caption is as follows:\nFig. 3 | Model-informed design of a biohybrid OstraBot. a Image of the fabricated\nbiohybrid OstraBot. Scale bar, 5 mm. b Mechanical representation of the biohybrid\nsystem. c Measured force–deflection relation of the artificial tendon.\nVideo S1. \"diminishes form day 7\" the word \"form\" might be a typo of from. Please\ncheck.\nWe thank the reviewer for pointing out this typographical error. The word “form” has\nbeen corrected to “from” in Video S1.\nVideo S2. Real-time of fast-forwarded video? Please specify it.\nWe thank the reviewer for this comment. Video S2 is presented in real time, and we\nhave now explicitly specified the play speed in the video to avoid ambiguity.\nVideo S3. Why are the colors of the four batches different? Do they mean\nsomething?\nWe thank the reviewer for this question. The color differences observed among the\nfour batches in Video S3 arise from variations in filming conditions between\nexperiments, such as lighting and camera exposure settings. The colors do not\nrepresent any experimental variable or encoding and have no physical or biological\nsignificance.\nVideo S4. The data reported in Fig. 1, which exhibited the highest force output, was\nobtained from a medium-sized muscle?\nWe thank the reviewer for this question. Yes, the data reported in Fig. 1 that\nexhibited the highest force output were obtained from a medium-sized muscle. We\nhave clarified this information in the caption of Video S4 to improve clarity.\nFigure S4. This figure consists of three subpanels (a, b, and c), but no explanation\nhas been provided for each panel.\nWe thank the reviewer for pointing this out. We have revised the caption of Fig. S4 to\ninclude explicit descriptions for subpanels (a), (b), and (c), clarifying the three\ndifferent types of muscle maturation platforms shown in the figure.\nThe revised caption is as follows:\nSupplementary Fig. 4.\nDifferent maturation platforms. Representative images of the (a) low-stiffness\nmaturation platform (LMP) with stiffness of approximately 1 mN/mm, (b) high-\nstiffness maturation platform (HMP) with the muscle anchored to two fixed posts,\n(c)\nself-training maturation platform (STMP).\nFigure S11b. The name of the vertical axis (active force) seems misleading, as it\ndisplays both active and passive forces.\nWe thank the reviewer for pointing this out. The vertical axis label in Fig. S11b has\nbeen revised from “active force” to “muscle force” to more accurately reflect that both\nactive and passive force components are included.\nFigure 3e. Hard to tell which area corresponds to which stiffness (even hard to know\nfor non color blind people). Increase the color contrast or annotate each area with the\ncorresponding stiffness value.\nWe have revised Fig. 3e by changing the color and rearranging the layered regions\nto enhance contrast and improve identification of the different stiffness conditions.\n(Page 11 and Figure S14) Soft, intermediate, and stiff tendons correspond to which\nstiffness shown in Fig. 3e (among the five stiffness values)?\nWe thank the reviewer for this question. The stiffness of soft, intermediate, and stiff\ntendon in Supplementary Fig. 14 is 0.2 N/m, 3.0 N/m, and 20 N/m. We have modified\nthe caption of Supplementary Fig. 14 to include this information.\nFigure S15. The same comment as Figure 3e above.\nWe have revised Supplementary Fig. 15 by changing the color and rearranging the\nlayered regions to enhance contrast and improve identification of the different\nstimulation frequencies.\n3.5 5 Hz\n3.0 3 Hz\nN) 2 Hz\nm2.5 1 Hz\nc e\n(\n2.0\nor\ne\nf\n1.5\nv\nA\ncti\n1.0\n0.5\n0.0\n-0.5 -0.4 -0.3 -0.2 -0.1 0.0\nMuscle displacement (mm)\nFigure S16. The color label in 16a seems reversed, the red marker should be stiff.\nWe thank the reviewer for pointing this error out. The color label in Fig. S16a has\nbeen corrected.\nFigure 5b. Please make it more color-blind friendly. Some of the green color graphs\nlook the same and hard to tell which one corresponds to which electric field.\nWe have revised Fig. 5b by updating the color scheme to be more color-blind friendly\nand to improve the distinction between curves corresponding to different electric field\nconditions."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Dear Authors,\nThank you very much for the time you took to thoroughly answer all of my questions. I am satisfied with the responses and clarifications given, and I am convinced of the significance of this work. I have no further questions at this stage and I recommend the manuscript for publication.", "reviewer_2": "Thank you for the authors' effort to address my comments, and I'm satisfied with the revised version. I have no additional comments."}, "author_response": ""}]

Decision letter