Soft magnetic microrobots with remote sensing and communication capabilities

Domain: physics
Published: 2025-11-25
Editorial outcome: major_revision

Abstract
Remote communication in small-scale robotics offers a powerful way to enhance their capabilities, introducing options for state monitoring, multi-agent collaboration, and autonomous operation. Integrating common remote communication tools, such as antennas, into microrobots is challenging with conventional design and manufacturing techniques. We propose a concept that integrates shape-reconfigurable soft microrobots with flexible electronics, leveraging their elastic mechanical properties to enable remote communication. This approach, based on photolithography processes, is scalable and adaptable to various sensing applications. As a proof of concept, we present a microrobot, which integrates a thermoresponsive magnetic hydrogel, an anisotropic support structure, and a flexible dipole antenna into a cohesive three-layered design. The microrobot can morph from a helical shape at low temperatures to a planar shape at high temperatures. This shape transformation can be remotely detected by external radio communication receivers, enabling shape-state recognition and environmental temperature sensing. Furthermore, we show that the collective behavior of multiple microrobots enhances the recognition performance by amplifying the signal. The concept represents a significant advancement in co-engineering smart materials and flexible electronics, illustrating an approach of microrobotic embodied intelligence by integrating environmental monitoring, magnetic navigation, and remote signaling.

Introduction
Microorganisms, despite their microscopic size, exhibit robust intelligence that allows them to thrive in complex environments through adaptation and communication. For example, Escherichia coli uses membrane chemoreceptors to detect amino acids, adjusting flagellar rotation to navigate toward nutrient sources1. Additionally, through a communication process called quorum sensing, these bacteria monitor population density and exchange chemical signals to coordinate collective behaviors and regulate gene expression dynamically2. Certain bacteria can also respond to physical cues such as temperature and magnetic fields through thermotaxis or magnetotaxis3. Inspired by these natural strategies, roboticists have incorporated principles of microscale intelligence into microrobotic designs4. A central approach involves using functional materials that respond to varied stimuli, allowing microrobots to sense, process information, and navigate autonomously5–7. Despite the challenges associated with micro- and nanoscale fabrication, significant progress has already been made in developing intelligent microrobotic systems, ranging from soft7–11, to compound12, and reconfigurable designs13–20, as well as encodable17,19,21–25, multifunctional17,26,27, and integrated systems28,29. By incorporating anisotropic magnetic designs, researchers have introduced improved navigation and previously unobtainable functionalities into versatile microrobotic platforms8,17,19,21,30. These individual microrobots have further advanced to multiscale12, multimodal14,15, multi-agent31, hierarchical32,33, self-organizing22,34, and swarm behaviors13,14,35,36. And in doing so, they have expanded their applications in environmental remediation37, micromanipulation38, medicine39,40, and sensing41 contexts.

An important aspect of microscale intelligence focuses on communication and collective behavior. Through communication, simple local interactions can develop into elaborate complex group interactions, enabling sophisticated patterns and actions. This coordinated cooperation allows for the design of microrobots that are less complex individually but more skilled as a collective. Additionally, microrobots can further benefit from additional advantages over bacteria external agents, as their communication with external agents can enhance localization, environmental monitoring, and autonomy.

The integration of these technologies into microrobots became possible in the last few decades due to advances in microfabrication techniques, including those commonly used in microelectromechanical systems (MEMS)42–44 and complementary metal-oxide-semiconductor (CMOS)28,29,45, alongside emerging methods such as 3D direct laser writing7,24,27,46. For example, Bandari et al. used microfabrication to create inductively powered microrobots with a strain engineering-based form of chemical propulsion29, and Miskin et al. integrated photovoltaics and surface electrochemical actuators for a light-powered microrobot28. While advanced fabrication techniques have shown up possibilities for actuation methods, these implementations still struggle with a critical capability for real, practical utility. Namely, these microrobots cannot communicate real-time information about their environment with external systems.

Roboticists have attempted to solve this problem in numerous fashions, whether by incorporating commercial antenna chips (including RFID) or through tethered approaches. These methods, based on readily available, low-cost, and robust transmission technologies, can relay important information about their environment to external agents. For example, Li et al. demonstrated radio frequency (RF)-capable, magnetically navigated microrobots that wirelessly transmitted temperature and pH data47. However, both the chips and required auxiliary circuits force fundamental tradeoffs between signal quality and microrobot size while also requiring a rigid structure. Han and colleagues addressed some of these issues by using microfabrication and compressive buckling techniques to incorporate a flexible electronic sensor array within a balloon catheter that measured pressure and temperature48. These sensing functionalities can be enhanced using various flexible and stretchable electronic systems49–53, as these devices offer high mechanical compliance and multi-functionality through the integration of thin, cellular-scale semiconductor components. The soft nature of the flexible system better matches the mechanical properties seen in biology, but the setup still restricts the working range by tethered design, and the wireless alternatives require substantial external RF power transmitters. As such, the question remains whether an untethered can successfully integrate the key aspects of remote navigation, collective behavior, and wireless sensing into a single system while maintaining the key advantage of flexible soft microrobots.

This research seeks to answer that question with a microrobotic approach that integrates flexible electronics and a shape-reconfigurable soft microrobot into a single device. This leverages smart materials to create large, physical changes in the microrobot in response to local stimuli and a flexible electronic antenna design that can take advantage of the entire microrobotic surface to propagate its signal. By coupling these two together, the physical changes to the microrobotic structure produce equally dramatic changes in the antenna signals, enabling instant remote communication within a fully flexible microrobotic system. To achieve this, we developed an integration protocol to combine an anisotropic SU-8 passive layer with an iron oxide nanoparticle (IONP) embedded thermally-responsive hydrogel active layer, leading to a temperature-dependent helical-to-planar transformation function. Subsequently, we laminated a flexible dipole antenna for radio communication, completing the microrobot. This microrobot demonstrated magnetic navigation, RF communication-based shape detection, localization, and remote temperature sensing. The collective behavior of the multiple microrobots showed both enhanced RF signal sensitivity and improved reliability.

Results
Microrobots capable of shape perception
The microrobot consists of three main layers, as shown in Fig. 1a. The first corresponds to the flexible dipole antenna layer. The antenna is realized on an anisotropic support layer, which is attached to a thermoresponsive hydrogel composite layer comprising a poly-N-isopropylacrylamide (pNIPAM) with embedded iron oxide nanoparticles (detailed information on the layer design and parameters provided in the subsequent section). At low temperatures, the thermoresponsive hydrogel forms a porous mesh, allowing water molecules to penetrate the network, causing it to swell. This volumetric change in the hydrogel layer induces bending moments in layered structures (Supplementary Fig. 1a and Movie S1) and interacts with the anisotropic supporting layer to generate torque for twisting (Supplementary Fig. 1b). This results in a three-dimensional helical structure (Fig. 1b), optimized for magnetically guided corkscrew motion. The three-layer helical structure is shown in Fig. 1c.

Figure 1d provides an overview of the microrobotic concept and its intended use case. In its hydrated state, within a standard temperature range state, the microrobot’s helical shape enables propulsion and guidance through fluidic environments via rotating magnetic fields. When the environmental temperature exceeds 40 °C, surpassing the phase change threshold, the thermoresponsive hydrogel dehydrates and shrinks volumetrically, causing the microrobot to morph from a helical to a planar shape. As the microrobot changes shape, the laminated dipole antenna deforms accordingly (Supplementary Fig. 1c, d), altering its RF signal response in a way that can be detected by an external agent.

Flexible electronics and soft microrobot integration processes and layered structure design
Figure 2a outlines the process used to integrate flexible electronics into soft microrobots, with three key fabrication steps for the electronics layer, the passive layer, and the active layer (details provided in the “Methods” section). Initially, the electronics (i.e., the dipole antenna) are patterned onto the sacrificial layer (polymethyl methacrylate, PMMA)-coated silicon wafer (Fig. 2b). Next, the passive layer is formed by spin-coating SU-8 onto the electronics layer and structuring the rectangular and scaffold patterns via photolithography (Fig. 2c). Spacers are then added around the structures to provide room for the hydrogel composites to fill the space between the upper cover glass (i.e., photomask) and the lower silicon wafer. A photomask coated with a sacrificial layer (polyvinyl alcohol, PVA) is aligned with the spacer, forming a sandwich structure. Note that alignment markers are designed to precisely position the layered designs, including the electronics, scaffold structures, spacers, and photomask patterns (Supplementary Fig. 2). To ensure a strong bonding and prevent delamination during hydrogel swelling (Supplementary Movie S2), the SU-8 surface requires treatment after being patterned. Finally, the IONP/pNIPAM resin is added to fill the remaining gaps, while magnetic fields are applied to align the IONPs. After curing the hydrogel patterns with UV light, the layered structures are lifted off by removing both sacrificial layers (Fig. 2d). This microfabrication process demonstrates straightforward scalability, as it relies entirely on successive photolithography-based techniques (Fig. 2e). Furthermore, this integration concept is flexible and can be extended to accommodate other electronic components (Supplementary Fig. 3) or support alternative layer configurations.

Following this fabrication protocol, we designed the microrobot’s layered structure based on classical lamination theory (detailed angular design parameters are provided in Supplementary Fig. 4). The base layer consists of the electronics layer (thickness 100 nm, pattern orientation: 90°), followed by a 10 µm-thick supporting layer, and a 10 µm-thick anisotropic scaffold layer with +45° orientation. The top layer (up to 20 µm thick) is composed of a pNIPAM hydrogel composite with embedded IONPs aligned at −45°. While each layer has its own preferred directionality, the anisotropic scaffold layer induces the formation of a helical structure upon hydrogel swelling, as predicted by classical lamination theory. As a result, when the laminated layered structures swell, they form a helix (Fig. 2f, g), regardless of the orientation of the electronics or IONP/hydrogel layers. The microscopy images (Fig. 2h, i) reveal that the helical axis tends to align closely with the scaffold axis. Adjusting the IONP concentration in the hydrogel layer only slightly alters the angle between the helical axis and the scaffold axis, from 4.7° to 10.3° (Supplementary Fig. 5). This indicates that the scaffold layer plays a dominant role in determining the helical shape, fully transferring any shape change of the soft robotic structure to the dipole antenna. This helical design also ensures that, after shape morphing, the IONPs are vertically aligned with the helical axis, which is critical for generating corkscrew motion under a rotating magnetic field.

Design parameter study for effective magnetic navigation and shape reconfiguration
The formation of helical structures under swelling is additionally governed by the length and width dimensions of the planar form. To confirm which sizes were appropriate, we varied the dimensions between 1 and 2 mm of width, and 5, 10, and 15 mm lengthwise. As seen in Fig. 3a, planar structures of either 1 mm or 2 mm in width and at least 10 mm in length formed helical structures under swelling. Thus, 2 mm × 15 mm and 2 mm × 10 mm are adequate designs for magnetic navigation through corkscrew locomotion, which agrees with previous research showing that higher length-to-diameter ratio helical microrobots have an advantage in generating corkscrew locomotion under rotating magnetic fields54. Additionally, while longer microrobots of the same width resulted in more turns in the helical shape, wider microrobots of the same length exhibited a lower helical angle due to reduced bending moment and torque. As such, one should note that the aspect ratio (ratio length to width of the planar structure) plays an important role in converting volumetric change to bending moment and torque. The 2 mm × 15 mm microrobot showed the highest length-to-diameter ratio (~4.1) compared to others, whereas the 2 mm × 10 mm microrobot follows with a ratio of ~3.5.

We navigated the 2 mm × 15 mm microrobot along a zigzag-shaped water channel under a rotating magnetic field (40 mT at 4 Hz, Supplementary Movie S3), with an average speed of 0.6 mm s−1, using a magnetic navigation system consisting of three pairs of Helmholtz coils (details in the “Methods” section and Supplementary Fig. 6). Note that microrobots can also be manipulated in dragging motion using gradient fields, which can particularly be useful for controlling planar shape without relying on corkscrew motion capability (see Supplementary Fig. 7 and Movie S4). The average speed of 2 mm × 15 mm microrobot was 1.2 mm s-1 under a magnetic field of 10 mT and gradient of 120 mT mm-1.

We also confirmed the shape reconfiguration capability, which switches the shape from helical to planar in response to temperature change. A gradual increase in temperature to 40 °C resulted in a flattening of the shape across all designs (Supplementary Fig. 8), which was repeatable over several attempts (Supplementary Fig. 9 and Movie S5). Additionally, the repeatability of shape reconfiguration was evaluated by analyzing the average body length and its standard deviation. We found that the average body length of planar microrobots varied from 14.9 mm to 14.4 mm over two rounds of shape morphing, while that of helical microrobots ranged from 11.4 mm to 10.9 mm. When the average length of the planar microrobots is divided by the designed body length, it results in an error of ~0.6-4%, indicating that shape sensing can be repeatable. Furthermore, the consistency of shape morphing among multiple microrobots was assessed using the standard deviation-to-average ratio (i.e., coefficient of variation) of body length across three microrobot units. The planar shape shows coefficient of variation values of ~0.038 and ~0.063 over two rounds of shape morphing, indicating a stable shape reconfiguration across the microrobots in response to environmental temperature changes.

Shape detection performance through RF communication
We evaluated the shape detection performance through RF communication for the above microrobot designs. The design discussed above is intended to exhibit significant signal shifts based on the geometry state (helical or planar). The scattering transmission coefficient from port 2 to port 1 (i.e., S21 signal) was acquired using a vector network analyzer (VNA) under an experimental configuration described in Fig. 3b (details in the “Methods” section and Supplementary Fig. 10). The gap between coils was set to 10 cm. Figure 3c shows the comparative RF signal shifts upon shape change. In the study, microrobots with dimensions of 1 mm × 15 mm, 2 mm × 10 mm, and 2 mm × 15 mm demonstrated significant signal differences between helical and planar shapes as shown in Fig. 3c. Additionally, we observed that the microrobots had a RF resonance frequency near 12 GHz in air. Based on this observation, we took advantage of the resonance peak to distinguish the shape of microrobots over barriers by focusing our analysis around 12 GHz. Considering both shape reconfigurability and shape detection performance, 2 mm × 15 mm and 2 mm × 10 mm microrobots were selected for further evaluation.

Microrobots with greater area exhibited more distinct signal differences between helical and planar states. This phenomenon can be analytically validated to support the experimental results and to further refine antenna designs. Assuming an unobstructed line-of-sight propagation in free space, the Friis transmission equation describes the power transfer relationship between two antennas (i.e., transmitter and receiver coils) as:

Pr/Pt = Gt Gr (λ / 4πR)2 (1)

where Pr and Pt are the power received by the receiver and the power transmitted from the transmitter, respectively. Gt and Gr are the gains of the transmitter and receiver, respectively. λ is the wavelength, and R is the distance between the transmitter and receiver55. When the dipole antenna is placed between the transmitter and receiver coils, transmission in radio communication involves a two-step procedure, and the transmission coefficient (S21) is defined as:

|S21|2 = Pr/Pt = η2 Gt Gr (D0 λ2 / 4πR1 4πR2)2 (2)

where η, R1, R2, and D0 are the radiation efficiency of the dipole antenna, the distance between the transmitter and dipole antenna, the distance between the dipole antenna and receiver, and the maximum directivity of the dipole antenna, respectively. The maximum directivity (D0) is defined as:

D0 = 4π Aem / λ2 (3)

where Aem is the effective aperture of the antenna. Consequently, the shape transformation of microrobots from a helical to a planar shape increases the effective aperture (Aem), enhancing the maximum directivity (D0) and shifting the transmission coefficient (S21) curve (details for theoretical estimation in Supplementary Methods). Finally, these signal changes additionally scale with the absolute physical area of the microrobot. This was demonstrated by continuously tracking the RF signals as a single microrobot’s shape morphed from helical to planar (shown in Fig. 3d). During the shape transition, the amplitude at the resonance frequency gradually shifted by an order of 10-1, highlighting the microrobot’s capability for real-time shape sensing.

To make sure that the signal effects were primarily from the antenna, we experimentally confirmed that the IONPs had a negligible effect on RF signal change. Standalone electronics (i.e., antenna onto the SU-8 supporting layer) and the electronics integrated with microrobots (i.e., antenna with the SU-8 supporting layer and IONP/hydrogel composite layer) showed similar signals (Supplementary Fig. 11a), which indicates that the influence of IONPs on radio communication can be disregarded when designing microrobots.

Enhanced shape detection performance through collective effect
We investigated whether the collective effect of many microrobots could improve the shape detection performance through RF communication, using the 2 mm × 15 mm sized microrobots (Fig. 3e and Supplementary Fig. 12a). Increasing the number of microrobots led to a greater distinctive difference in RF signal amplitude between helical and planar shapes at ~12.02 GHz and ~21.70 GHz. The maximum amplitude difference increased from 1.3 dB at 11.97 GHz to 4.1 dB at 12.07 GHz (near the first RF signal peak) and from 1.7 dB at 21.60 GHz to 5.2 dB at 21.80 GHz (near the second RF signal peak). Figure 3f shows the signal changes as a function of the number of the microrobots at 12.06 GHz. Additionally, similar results were observed with 2 mm × 10 mm sized microrobots (Supplementary Fig. 12b). The slope of trend lines, indicating sensitivity enhanced by the collective effect, further suggests that the 2 mm × 15 mm microrobots could provide improved performance in remote shape detecting through collective microrobots.

The collective effect of multiple dipole antennas on transmission performance is also theoretically supported. The maximum directivity D0 of an array of antenna components with small spacing (d≪λ) is defined as:

D0 ≈ 2N (Nkd / πλ) (4)

where N, k, and d are the number of antenna components, Boltzmann constant, and space between components, respectively. As the number of components (N) increases, the maximum directivity (D0) increases, enhancing the transmission coefficient S21 from Eq. (2). Additionally, we performed a full wave 3-D finite element method simulation to further confirm that multiple antennas’ shape transformation can induce the RF signal changes using COMSOL’s Multiphysics RF Module. Figure 3g shows a comparison of simulated RF responses of planar- and helical-shaped dipole antennas. The comparison with experimental data (Fig. 3e) reveals a similar trend in resonance frequency with an offset, confirming that experimental RF signal evaluation results are consistent with the analytical results.

In addition to the increase in RF signal differentiation, we also verified two important aspects of how the signal behavior changes from collective effects. First, the RF response from standalone dipole antennas were consistent with those observed from the integrated microrobotic systems (Supplementary Fig. 11b, c), which implies that the dipole antenna effects were dominant over any contribution from the IONPs. Secondly, we also took into consideration the role transmitter distance could play. Most notably, we observed that the distance between the transmitter and receiver is non-linearly related to the radio communication performance in collective microrobot signaling. For collections of 7 microrobots, we got better performance out of a receiver placed at 10 cm compared to 5 cm. Whereas for 5 or fewer microrobots, the 5 cm distance was more optimal (Supplementary Fig. 12c, d). This result could be explained by the far-field properties of dipole antennas and suggests that receiver distance should be optimized based on the number of microrobots for better shape detection performance.

Investigation of environmental effects
The performance of RF communication-based shape detection can be significantly influenced by environmental factors, including the medium (e.g., ion-containing aqueous solutions) and the surroundings (e.g., experimental setups). First, different media can shift the resonant frequency of RF signals. The basic mechanism of the dipole antenna’s resonance is based on the matched wavelength of the electromagnetic wave. The wavelength at the resonant frequency is defined as:

λ = c / fr,air (5)

where c and fr,air are the speed of light and the resonant frequency in air, respectively. In a specific medium, the speed of light vm is given by:

vm = c / n (6)

where n is the refractive index of the medium. Based on Maxwell’s electromagnetic field theory, the refractive index is given by n = √εr μr. For non-magnetic materials, the relative magnetic permeability index μr is ~1, meaning that the refractive index depends solely on the relative dielectric constant εr. Thus, the refractive index simplifies to n = √εr. Since the electromagnetic wavelength for the resonance of dipole antenna need to remain the same across different media (e.g., air and ion-containing aqueous solutions), Eqs. (5) and (6) can be rewritten as:

c / fr,air = vm / fr,medium = c / (n fr,medium) (7)

where fr,medium is the resonant frequency of the dipole antenna in a specific medium. Thus, the resonant frequency fr,medium is defined by Eq. (8).

fr,medium = fr,air / √εr (8)

Considering the developed 2 mm × 15 mm microrobots, they exhibited a resonant frequency of ~12 GHz in air. When immersed them in phosphate-buffered saline (PBS, relative permittivity: ~70), the resonant frequency is expected to shift to ~1.45 GHz. Figure 3h shows that experimentally measured resonant frequency shifts to ~1.52 GHz in the PBS medium, confirming the above analytical estimation is reasonable. This analytic estimation and experimental results also aligned with a simulation result, which shows a resonant frequency shifts to ~1.47 GHz (Supplementary Fig. 13).

We also examined the effect of the experimental setup as a method to investigate surrounding influences. In the setup, the sample holder (i.e., a plate to place microrobots in the experimental setup) can affect the measured RF signals, as it is positioned between the antennas and can modulate signals. Note that we excluded effects of antennas, VNA, and cables, as they are pre-calibrated, making their contributions to signal variability negligible. First, we compared RF signals in two configurations of the experimental setup, that is, with and without the sample holder. As a result, overall trends of RF signals were similar across both experiments, while slight amplitude shift was observed (Supplementary Fig. 14a). Additionally, RF signals from different sample holder designs also show negligible effects (Supplementary Fig. 14b), demonstrating consistent shape detection performance across different experimental setup configurations. These findings indicate that while the sample holder may introduce minor amplitude deviations, it does not cause additional signal artifacts (e.g., extra peaks or dips). Any small shifts caused by the setup can be readily corrected during data processing.

Microrobot localization
Wireless communication capability enables microrobot localization and local information perception. Figure 4a illustrates that planar microrobots can be localized through a real scanning using transmitter and receiver antennas. This antenna setup scanned a sample holder divided into a four-by-three array and detected a notable dip (at ~12.2 GHz) in the measured RF signal at coordinate position (3,2), where the planar microrobots were positioned (Fig. 4b; see also Supplementary Fig. 15a).

Additionally, when the microrobots were placed under a cover (Supplementary Fig. 15b, c), the planar and helical microrobots transmitted distinguishable RF signals, enabling shape detection (Fig. 4c). This demonstrates that planar microrobots can be detected via RF communication without the need for a vision system. Finally, these demonstrations imply that developed microrobots can be localized even when covered, allowing their use for a local shape sensing.

Demonstration of microrobots’ actuation and remote shape recognition
To demonstrate the complete operation of the proposed microrobot system, we constructed a functional example of the process (Fig. 5 and Supplementary Movie S6). We positioned three helical microrobots (2 mm × 15 mm) in a water channel with varying temperatures, simulating magnetic navigation, thermoresponsive shape reconfiguration, and RF-based shape recognition (Fig. 5a). The microrobots were actuated under rotating magnetic fields (40 mT at 4 Hz) using a magnetic navigation system (experimental configuration in Supplementary Fig. 6), propelled along the water channel from the low-temperature zone to the high-temperature zone via corkscrew motion (Fig. 5b). Upon reaching the high-temperature zone (~40 °C), the microrobots transformed from a helical to a planar shape (Fig. 5c).

The shape reconfiguration from helical to planar was evaluated as a function of temperature (Fig. 5d). The reconfiguration results were then correlated with the RF signal amplitude at the resonant frequency. The phase change temperature was set to 40 °C, as the shape reconfiguration from helical to fully planar was completed within a reasonable timeframe (less than 10 s) under this condition (Supplementary Fig. 16). Based on this analysis, we remotely detected the microrobot’s shape states (Fig. 5e, f) through their RF responses using the VNA. By comparing the measured RF responses (Fig. 5g) with our reference dataset, we confirmed the successful shape reconfiguration of the microrobots. Notably, the RF response corresponding to the fully planar shape (pink line in Fig. 5g) reflects deformation triggered by elevated temperatures (~39–41 °C), indicating that passive, binary inference of environmental temperature may be feasible based on the final microrobot shape. Note that experiments were performed sequentially for i) navigation and transformation and then ii) transformation and temperature sensing, as the Joule heating coils in the navigation setup can distort the radio communication signals.

Methods
Materials
SU-8 and Polymethyl methacrylate (PMMA) photoresists were purchased from Kayaku Advanced Materials, Inc. (USA). 30-nm diameter iron oxide nanoparticles (IONPs) coated with polyvinylpyrrolidone (PVP) were purchased from US Research Nanomaterials Inc. (USA). N-Isopropylacrylamide (NIPAM, 97%), N,N’-Methylenebis(Acrylamide) (BIS, 99%), 1-Butanol (99.9%), Poly(vinyl alcohol) (PVA, 80%, MW 9k), Propylene glycol monomethyl ether acetate (PGMEA, 99.5%), and Acrylic acid (AAc, 99%) were obtained from Sigma-Aldrich (USA). Poly-N-Isopropylacrylamide (pNIPAM, MW 300k) was purchased from Scientific Polymer Products Inc. (USA). The photoinitiator Omnirad 2022 was supplied by Su.K Hock GmbH (Germany). All chemicals were used as received.

Preparation of magnetic thermoresponsive hydrogel
The magnetic thermoresponsive hydrogel was prepared by mixing NIPAM (monomer), BIS, 1-Butanol, and AAc in a molar ratio of 118:5:365:12. The photoinitiator was added as 1 wt% of the entire hydrogel matrix. The mixture was stirred overnight. Before use, IONPs were mixed into the hydrogel at a concentration of 7 wt%.

Integration of flexible electronics and soft microrobot
A 4-inch silicon wafer was spin-coated with a 2 µm-thick PMMA acting as a sacrificial layer. A 100 nm-thick gold layer was deposited on the wafer using an electron beam deposition process, followed by photolithography, wet etching, and cleaning processes to fabricate antenna patterns (trace width 100 µm, trace gap 50 µm). Subsequently, two SU-8 layers (10 µm thickness each) were patterned sequentially to fabricate a supporting layer and scaffold layer using a mask aligner (MA/BA6 Gen4, SUSS). Then, a 3 µm-thick SU-8 layer was fabricated as a spacer to control the overall thickness of the microrobots. The SU-8 surface was functionalized by silanization and silicon oxide coating to enhance adhesion.

Afterward, a magnetic thermoresponsive hydrogel layer was fabricated through a magnetic field-assisted photolithography process. A photomask was prepared using a 4-inch glass wafer coated with chromium. The chromium layer was patterned with the hydrogel layer design and then coated with a 2 µm-thick PVA sacrificial layer on the patterned side. This photomask was placed on the spacer, aligning the markers (Supplementary Fig. 2) using a custom-built magnetic field-assisted photolithography device (Supplementary Fig. 22). The magnetic thermoresponsive hydrogel was then injected into the space, specifically between the silicon wafer and the glass wafer. This entire sandwich structure was then placed into a static uniform magnetic field of 10 mT for 4 min to align the IONPs, followed by exposure to UV light (wavelength: 365 nm, intensity: 3 mW cm-2) for 2 min while maintaining the magnetic field. After curing, this sandwich structure was sequentially placed into water and PGMEA to dissolve the sacrificial layers, releasing the integrated microrobots from the wafer. The detailed fabrication protocol flow chart is depicted in the Supplementary Fig. 23.

Characterization of antenna fabrication stability
The stability of the antenna fabrication was characterized using a probe station to measure the resistance of the antenna. By correlating the resistance with different designs, we ensured the consistency of the electronic properties (Supplementary Fig. 24) and achieved a 100% yield in electronics fabrication.

Evaluation of shape reconfiguration performance
The shape reconfiguration performance was evaluated by alternately placing the microrobots in 20 °C and 40 °C water. Optical images were captured using commercial CCDs (a 2A3840−45ucPRO and a cA1920-150uc, BASLER Inc., Germany) for image processing.

Actuation of microrobots through rotating magnetic fields
Rotating magnetic fields were generated using three pairs of Helmholtz coils in three-dimensional space (Supplementary Fig. 6). Magnetic fields were controlled by adjusting their strength (<46 mT), rotational frequency (<10 Hz), and direction.

Evaluation of shape detection performance by RF communication
RF communication was performed using a vector network analyzer (VNA, model N5247B, Keysight, USA) with a measuring frequency range of 10 MHz to 26 GHz. A pair of transmitting and receiving coils (diameter: 7 mm) were placed parallel to each other with a gap distance of 5 to 10 cm and were coaxially connected to the VNA. Samples were placed on an acrylic plate between the coils.

The VNA measured two-port scattering parameters, where S21 denotes the transmission coefficient from one port to the other. The S21 parameters were obtained under various experimental configurations, including measurements without microrobots (background signal), with helical microrobots, and with planar microrobots. We evaluated the signal change corresponding to the shape change by subtracting the signals with microrobot from the background signals. The collective effect was also evaluated through the same configuration.

Radiocommunication simulation
The simulation was conducted using COMSOL Multiphysics RF Module with a full wave 3-D finite element method. The helical- or planar-shaped dipole antenna was respectively placed in a bounding box with periodic boundary conditions to mimic multiple antennas in close proximity. Linearly polarized plane waves with frequencies ranging from 5 to 30 GHz were excited above the microrobot surface, and transmission of the electromagnetic wave with the same polarization was monitored below the antennas for all frequency sampling points.

Data availability
All the relevant data are available within the Article and Supplementary Information. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The integration of actuation, sensing, and communication in one small-scale robot is challenging and critical for promoting the intelligence of small-scale robot. Current works tend to focus on the programmable deformation and designable actuation, the function of sensing and communication are less explored. In this paper, the authors integrate antennas, magnetic hydrogels, and anisotropic elements onto a micro-scale robot using photolithography techniques. The robot can be precisely propelled to the target position, respond to changes in external temperature and magnetic fields, and provide feedback on the perceived environmental temperature via radio frequency, which exhibit more capabilities and enhanced intelligence. Furthermore, the proposed manufacturing method for the robot is versatile and sensing modules can be extended from temperature to humidity, light, chemistry, and other areas, laying the solid foundation for future biomedical applications. However, in-depth research is needed when regarding the robot's actuation and retrieval after detection, and there are some problems required to be solved before the acceptance.\n(1) In the introduction, the authors mentioned that small-scale robots have made progress in coding and multimodality. The magnetic programming, anisotropic programming, and multi-function integration should be noted, just as exhibited in Sci. Adv.8, eabn8932(2022) and Nat Commun 10, 4087 (2019).\n(2) In the part of the communication experiment, it was noted that an experimental platform exists between the transmitter and the receiver in addition to the robot. Whether the experimental platform affect the experimental results?\n(3) New experiment environment should be designed to make the control of robot form a closed loop for better reflecting the advantages of magnetic field actuation and the importance of communication when the robot cannot be directly observed\n(4) The author proposed that different types of electronics may be involved in the manufacturing process to highlight the scalability of the manufacturing method. Will these different electronics have variable signal effects during communication? If there are different responses, does it mean that the communication process is able to recognize multiple robots with different designs? Corresponding experiment could be designed to demonstrate this point.\n(5) In the final experiment, the robot is flattened due to the increase of temperature and the signal is captured by an external device, whether the flattened robot continue to respond to external magnetic field for subsequent recovery?", "reviewer_2": "This manuscript presents a magnetically guided soft microrobot with integrated communication and environmental sensing capabilities. A proof of concept was demonstrated using a remotely controlled temperature-sensing microrobot. There have been numerous studies employing NIPAM-polymer for transformation and temperature sensing, rendering this work lacking in novelty. Consequently, I am unable to recommend this paper for publication in Nature Communications. Furthermore, there are several issues within the manuscript that require attention and revision by the authors:\n1. In the section reviewing related work, the manuscript mentions previous studies but fails to provide an in-depth comparative analysis of these works. A clearer discussion of how this study builds on or improves upon existing research is needed to emphasize its innovation and advantages.\n2. The temperature within the human body remains relatively stable, making it unlikely that a soft robot with temperature sensing capabilities would be necessary for human applications. Additionally, the size of the robot appears to be too large for such purposes. This prompts the question of which specific environment would require a soft robot with temperature sensing capabilities, further questioning its intended purpose. Additionally, it is unclear why magnetic control is necessary for this device.\n3. The robot undergoes deformation in response to temperature stimuli. Is the degree of deformation consistent across trials? How much unexpected variation in deformation impacts the accuracy of the sensing signal?\n4. Deformation is triggered when the temperature exceeds a certain threshold. With such conditions, how can the robot be deployed into a real system?\n5. In complex environments, such as natural or living settings, how can the sensitivity and accuracy of the sensor signals be ensured?\n6. While using multiple robots enhances the sensing signal, the impact of inter-robot distance and relative orientation on signal quality requires further evaluation.\n7. The simplified experimental conditions used in this study may not fully capture the challenges and limitations that microrobots could encounter in real-world applications. A broader range of scenarios should be tested to assess these factors more comprehensively.\n8. The manuscript is insufficient to verify the practical application of the robot. Although biomedical applications are mentioned, the challenges of operating in complex biological environments, as well as biocompatibility issues, are not sufficiently addressed. There is also a lack of discussion or data on potential health risks, such as immune responses or toxicity, that microrobots might pose in living organisms.\n9. The manuscript does not propose specific strategies or plans for mass production of the microrobots or for their integration into existing systems or processes. Guidance on how to scale production and facilitate practical deployment would be beneficial.", "reviewer_4": "Reviewer Comments for Manuscript NCOMMS-24-77214-T: Soft Magnetic Microrobots with Remote Sensing and Communication Capabilities\nThis article reports on the fabrication and testing of a new class of mm scale lithographically fabricated robot which combines the functionalities of locomotion, temperature sensing, and data transmission via RF signals, in a single device platform. The key advance is the combination of a temperature sensitive bending magnetic hydrogel/SU8 bilayer with a gold antennae layer, to realize a trilayer device can sense and report information about temperature via deformation of the antennae by the hydrogel. The authors outline the concept of operation for the device, the fabrication protocol by which the devices are built, the effect that bending has on RF signal amplitude, how the effect increases with number of devices, and demonstrate that devices can locomote using external magnetic fields to regions of varying temperature. Overall, the biggest strength of this paper is taking up the task of integrating multiple functionalities into a single robot. The following changes would likely improve the quality of the manuscript:\n1. Remote sensing generally denotes that one can infer the specific value of temperature from the action of the device. It would be a lot easier to judge this work if data were included that quantifies how the detected radio signal depends on environmental temperature. Specifically, we feel that somewhere in the main text there needs to be a graph that shows temperature vs the authors preferred measurement from the antennae (i.e. resonance amplitude, resonance frequency shift). All we could find was the statement that the hydrogel undergoes a phase transition around 35C, and the data in 4F where robots are stated to unfold at 40C. Depending on the width of this transition, these devices could report across biomedically relevant temperatures, or be completely unsuited as a body temperature of 40C constitutes a medical emergency. Some data about how temperature can be extracted and quantified based on the device behavior would dramatically improve this work. Better still would be a thoughtful discussion about device accuracy and precision, given this is a completely analog reporting system.\n2. 1-10 mm is a large length scale for microelectronics, allowing for a range of off-the-shelf sensing and communication modalities. For instance, the chips used to control commercial RFID antennas are typically just mm in size (i.e., ignoring the antenna, the control circuit that packages data can be well under the size of this robot). Likewise, there are numerous publications demonstrating CMOS temperature sensors and chips well under 1 mm in size. This begs the question: why this schema? Why not use an integrated circuit to measure and modulate RF signal directly? Or, for that matter, why not use acoustic communication, such as in neural dust? These alternatives would have significant benefits in signal reliability and accuracy; the authors should explain the merits of their approach, as compared to equally large devices.\n3. The paper could use more discussion of the role of the environment in communication. 10 GHz RF waves penetrate weakly into water and by extension tissue (see Fig 2, https://arxiv.org/abs/1306.5709). Typically, the penetration depth is on the order of a mm. This seems like a major problem: if the best-case signal change for temperature reporting is on the order of a 1-10dB, the same effect could be produced by the device (or transmitter) moving roughly 1 mm (i.e. ~1 body length) deeper in tissue. How would this get de-convolved in practice? Likewise, some discussion of the power needed to operate the device at a given depth, and whether they would be safe, would be helpful. A 1 mm penetration depth would imply that nearly 43 orders of magnitude in power would be lost to reach 10 cm into tissue. How could this be overcome, especially for biomedical applications?\n4. The device design and fabrication protocol are nice, especially the integration of the magnetic material. This is one of the best parts of the paper, but the extensive discussion of the specifics should be relegated to the supplemental information, in favor of a briefer overview. Additionally, many statements claim standard fabrication techniques are key advances and should be done away with entirely. For instance: “Key advancements included ensuring precise layer alignment”, i.e. using a mask aligner? “Improving adhesion between layers”, i.e. using industry-standard adhesion promoters? “Optimizing the anisotropic layer and IONP pattern orientation”, i.e.magnetizing in the correct direction? “We develop a versatile microfabrication process that incorporates flexible electronics onto a soft microrobotic chassis using combination of thin-film deposition, photolithography, and wet etching techniques”, the versatility of microfabrication is well established. Similar statements appear throughout the manuscript and should be removed.\n5. The authors forgo what might be an illuminating discussion about the fact that devices loose motility in high temperature regimes. One could argue that this is a feature, and that the device could be configured to operate in a “seek and destroy” scheme, where they travel around an environment until they reach a region of desired temperature, then planarize and remain their indefinitely. Given the vast expertise of the authors, especially in biomedical applications of microrobots, we’d love to hear them expand on the nuanced ways in which the interweaving of sensing and locomotion may be beneficial.\n6. The discussion section could be sharpened. For example: “Previous approaches using smaller antennas faced challenges with inefficient radio communication, unreliable remote sensor powering, and poor signal differentiation in environmental monitoring due to inert sensing mechanisms.” It is not demonstrated in this work that any of these problems were solved. “Our research highlights the microrobots’ potential for biomedical applications requiring operation within the body’s enclosed cavities or conduits.” Only cavities that are large enough to accommodate the size of one or several devices (a few cm as seen in figure 4), and shallow enough that the RF signal can penetrate at order 10 GHz. “This approach could also be extended to develop interactive microrobots that respond to both internal and external stimuli, gather data, communicate with each other, and autonomously manage their functions.” It’s not clear how most of these behaviors will be achieved with the presented platform. If these statements are true, they need clarification and expansion. If not, they should be removed."}, "author_response": "Responses to Reviewers’ Comments\nReviewer 1:\nGeneral comments: The integration of actuation, sensing, and communication in one small-scale robot\nis challenging and critical for promoting the intelligence of small-scale robot. Current works tend to\nfocus on the programmable deformation and designable actuation, the function of sensing and\ncommunication are less explored. In this paper, the authors integrate antennas, magnetic hydrogels, and\nanisotropic elements onto a micro-scale robot using photolithography techniques. The robot can be\nprecisely propelled to the target position, respond to changes in external temperature and magnetic\nfields, and provide feedback on the perceived environmental temperature via radio frequency, which\nexhibit more capabilities and enhanced intelligence. Furthermore, the proposed manufacturing method\nfor the robot is versatile and sensing modules can be extended from temperature to humidity, light,\nchemistry, and other areas, laying the solid foundation for future biomedical applications. However, in-\ndepth research is needed when regarding the robot's actuation and retrieval after detection, and there\nare some problems required to be solved before the acceptance.\nResponse:\nWe are thankful to the reviewer for the positive assessment of our work and for the insightful\nsuggestions that helped clarify the paper’s contributions. We have carefully addressed each comment,\nrefining the manuscript to emphasize our core innovations in integrating flexible electronics with soft\nmicrorobots to achieve actuation, sensing, and communication at a small scale. Through these revisions,\nwe provide a clearer account of the robust integration method and remote communication mechanism,\nwhile also acknowledging current limitations and avenues for future exploration. We greatly appreciate\nthe reviewer’s detailed feedback, which has strengthened and sharpened our presentation of this\nresearch.\nComment 1: In the introduction, the authors mentioned that small-scale robots have made progress in\ncoding and multimodality. The magnetic programming, anisotropic programming, and multi-function\nintegration should be noted, just as exhibited in Sci. Adv. 8, eabn8932 (2022) and Nat. Commun. 10, 4087\n(2019).\nResponse:\nWe appreciate the reviewer’s emphasis on magnetic programming, anisotropic programming,\nand multi-function integration in small-scale robotics. In response, we have revised our Introduction to\nincorporate these important aspects, including references to the suggested studies (Sci. Adv. 8,\neabn8932 (2022) and Nat. Commun. 10, 4087 (2019)), and expanded the discussion to highlight how\nsuch programming strategies and alignments provide multifunctional capabilities in microrobots.\n1\npage 3 (lines 50-51)\nBy incorporating anisotropic magnetic designs, researchers have introduced improved navigation and\npreviously unobtainable functionalities into versatile microrobotic platforms8, 17, 19, 21, 30.\nComment 2: In the part of the communication experiment, it was noted that an experimental platform\nexists between the transmitter and the receiver in addition to the robot. Whether the experimental\nplatform affect the experimental results?\nResponse:\nWe agree with the reviewer’s concern regarding the potential influence of the experimental\nplatform on our RF communication experiments. In response, we conducted additional tests with\ndifferent configurations of platform and media to quantify any variations in amplitude or trends of RF\nsignals. Our findings indicate that while the holder can introduce minor amplitude shifts, these changes\ndo not alter the overall signal trends and minor amplitude shifts can be subtracted via data processing.\nTherefore, the configurations of experimental platform do not affect to the experimental results. We\nhave added these results to the manuscript.\nPage 12 (lines 283-285)\nThe performance of RF communication-based shape detection can be significantly influenced by\nenvironmental factors, including the medium (e.g., ion-containing aqueous solutions) and the\nsurroundings (e.g., experimental setups).\nPage 13 (lines 308-319)\nWe also examined the effect of the experimental setup as a method to investigate surrounding influences.\nIn the setup, the sample holder (i.e., a plate to place microrobots in the experimental setup) can affect\nthe measured RF signals, as it is positioned between the antennas and can modulate signals. Note that\nwe excluded effects of antennas, VNA, and cables, as they are pre-calibrated, making their contributions\nto signal variability negligible. First, we compared RF signals in two configurations of the experimental\nsetup, that is, with and without the sample holder. As a result, overall trends of RF signals were similar\n were similar\nacross both experiments, while slight amplitude shift was observed (Supplementary Fig. 14a).\nAdditionally, RF signals from different sample holder designs also show negligible effects\n(Supplementary Fig. 14b), demonstrating consistent shape detection performance across different\nexperimental setup configurations. These findings indicate that while the sample holder may introduce\nminor amplitude deviations, it does not cause additional signal artifacts (e.g., extra peaks or dips). Any\nsmall shifts caused by the setup can be readily corrected during data processing.\nsignals recorded with and without the holder. b. RF communication signals recorded using a well-type\nholder and a plate-type holder. While the overall trends remain similar across different conditions,\nslight amplitude shifts are observed. These signals are superimposed on background noise and post-\nprocessed to isolate the environmental effect.\nComment 3: New experiment environment should be designed to make the control of robot form a\nclosed loop for better reflecting the advantages of magnetic field actuation and the importance of\ncommunication when the robot cannot be directly observed.\nResponse:\nWe are grateful to the reviewer for suggesting a closed-loop experiment for situations where\ndirect observation is not feasible, as it could better highlight the advantages of both magnetic actuation\nand RF communication. In response, we performed additional experiments to demonstrate microrobot\nlocalization on a four-by-three array platform. This demonstration confirms that RF communications\ncan detect the microrobots’ position even under a shielding cover, reinforcing the potential of closed-\nloop control to enhance microrobot functionality through remote sensing and tracking in scenarios with\nlimited visibility. We have revised the manuscript to include the result of this additional experiment.\nPage 14 (lines 321-332)\nMicrorobot localization\nWireless communication capability enables microrobot localization and local information perception.\nFigure 4a illustrated that planar microrobots can be localized through areal scanning using transmitter\nand receiver antennas. This antenna setup scanned a sample holder divided into a four-by-three array\nand detected a notable dip (at ~12.2 GHz) in the measured RF signal at coordinate position (2, 3),\nwhere the planar microrobots were positioned (Fig. 4b; see also Supplementary Fig. 15a).\nAdditionally, when the microrobots were placed under a cover (Supplementary Fig. 15b,c), the planar\nand helical microrobots transmitted distinguishable RF signals, enabling shape detection (Fig. 4c).\nThis demonstrates that planar microrobots can be detected via RF communication without the need for\n3\na vision system. Finally, these demonstrations imply that developed microrobots can be localized even\nwhen covered, allowing their use for a local shape sensing.\nPage 17 (lines 393-402)\nContinuous tracking of the microrobot’s shape demonstrates the feasibility of this approach for real-\ntime, remote monitoring of the microrobot’s morphological state. This enables remote recognition of\nthe microrobot’s configuration (e.g., helical vs. planar or folded vs. unfolded) during operation,\naddressing one of the key challenges in the practical use of microrobots. For example, configuration\nrecognition can be advantageous in targeted delivery applications, where active shape reconfiguration\n(e.g., via magnetic hyperthermia induced by alternating magnetic fields) is required to adapt to complex\nenvironments. In such cases, remote communication can inform successful shape transformation,\nallowing the microrobot to proceed to its next task. Additionally, our microrobot localization\nexperiments demonstrate that the system can reliably detect both position and shape, suggesting its\npotential for local temperature sensing applications.\nFigure 4. RF communication-based microrobot localization. a. Schematic of RF communication-\nbased localization, consisting of a scanning system and a sample holder divided into four-by-three\narray. The localization system consists of transmitter and receiver coils positioned opposite to each\nother, with scanning performed along a predefined path. b. RF signals across the array exhibit a\ndistinctive signal at position (3,2) corresponding to the location of the planar microrobots. c. RF signals\nfrom seven units of 2×15 microrobots placed behind a barrier. The clearly distinct signals between\nhelical and planar shapes demonstrate that the microrobots can be detected through the barrier.\n4\na\ny\nx\nb c\nTransmitter\nBarrier\n(Paper)\nReceiver Microrobots\nSupplementary Fig. 15 Localization setup and shielded experimental setup. a. The experimental\nplatform, divided into a 4 × 3 array. Two transmission coils were connected to the VNA, and the\nmicrorobots were positioned at (3,2). b. A shielded paper cover was placed between the microrobots\nand the transmitter coil, with an enlarged view shown in (c).\nComment 4: The author proposed that different types of electronics may be involved in the\nmanufacturing process to highlight the scalability of the manufacturing method. Will these different\nelectronics have variable signal effects during communication? If there are different responses, does it\nmean that the communication process is able to recognize multiple robots with different designs?\nCorresponding experiment could be designed to demonstrate this point.\nResponse:\nWe are thankful to the reviewer for the valuable advice regarding the varying communication\nperformance across different types of electronics. In response, we further analyzed the RF signals from\nthe dipole antennas of different sizes. We figured out that microrobots (i.e., dipole antennas) of different\nsizes (e.g., 2 mm × 15 mm and 2 mm × 10 mm) exhibited a resonant frequency shift of ~300 MHz,\nindicating that design parameters can vary the signal response. Additionally, we performed a finite\nelement method simulation to investigate the resonant frequency of alternative designs, such as spiral\nantennas. This simulation also shows the resonant frequency tuning capability through design change.\nAccordingly, theses suggest that antenna design can be leveraged to recognize multiple microrobots (or\nmultiple sets of the same design microrobots). Meanwhile, we would like to clarify that we optimized\nthe dipole antenna design to a size of 2 mm × 15 mm to ensure better applicability within our\ndemonstration scenario. We have revised the manuscript accordingly.\nAdditionally, we explored various pathways for improving the functionality of the developed\nmicrorobots. For example, different antenna designs produced measurably shifted resonant frequencies.\nSignals from two types of microrobots (2 mm × 15 mm and 2 mm × 10 mm with a 10 cm gap between\nthe transmitter and receiver antennas) displayed a difference in resonant frequency shifts of ~300 MHz\n(Supplementary Fig. 12a(iv) and 12b(iv)). Furthermore, S varied with different antenna designs. As shown in Supplementary\nclear that careful antenna design can be used as a tool to distinguish microrobots, which when used in\ndenser distributions, could enhance the signal sensitivity, as shown in Supplementary Fig. 18.\nSupplementary Fig. 17. Resonant frequency of the new spiral antenna in air. The resonant frequency\nof the spiral antenna is ~ 20.6 GHz.\nSupplementary Fig. 18. Effect of microrobot distribution density on RF communication using\nCOMSOL simulation. A 33% increase in microrobot distribution density resulted in a threefold\nenhancement of the S amplitude at the resonant frequency. (Unit area: 1 cm2)\nsignal is captured by an external device, whether the flattened robot continue to respond to external\nmagnetic field for subsequent recovery?\nResponse:\nWe are thankful to the reviewer for raising the concern regarding the subsequent recovery of\nplanar microrobots. While rotating magnetic fields are typically preferred to generate rotational or\n6\ncorkscrew motion in microrobots, gradient magnetic fields can also be employed to actuate the\nmicrorobots through dragging motion. In this study, the corkscrew locomotion using a helical shape\nwas selected due to its advantage in achieving precise control. Nevertheless, magnetic field gradients\ncan be used for magnetic guidance of microrobots. To address this point, we performed additional\nexperiments demonstrating magnetic guidance of planar microrobots using gradient magnetic fields.\nThese confirmed magnetic navigation capability for two different sizes of planar microrobots. We have\nincluded these results and revised the manuscript accordingly.\nPage 8 (line 177-180)\nNote that microrobots can also be manipulated in dragging motion using gradient fields, which can\nparticularly be useful for controlling planar shape without relying on corkscrew motion capability (see\nSupplementary Fig. 7 and Movie S4). The average speed of 2 mm × 15 mm microrobot was 1.2 mm s-1\nunder a magnetic field of 10 mT and gradient of 120 mT mm-1.\na 0 s b 0 s\n12 s 20s\n24 s 40s\nSupplementary Fig. 7. Navigation of planar microrobots using a magnetic gradient field. A 2 mm ×\n15 mm planar microrobot (a) and a 2 mm × 5 mm planar microrobot (b) were guided by the magnetic\ndragging force. The magnetic field and gradient were set to 10 mT and 120 mT mm-1, respectively. The\naverage speeds of 2 mm × 15 mm and 2 mm × 5 mm microrobot were 1.2 mm s-1 and 0.7 mm s-1,\nrespectively (Scale bar: 10 mm).\nGeneral comments: This manuscript presents a magnetically guided soft microrobot with integrated\ncommunication and environmental sensing capabilities. A proof of concept was demonstrated using a\nremotely controlled temperature-sensing microrobot. There have been numerous studies employing\nNIPAM-polymer for transformation and temperature sensing, rendering this work lacking in novelty.\nConsequently, I am unable to recommend this paper for publication in Nature Communications.\nFurthermore, there are several issues within the manuscript that require attention and revision by the authors:\nResponse:\nWe appreciate the reviewer’s thorough evaluation of our work. However, we partially disagree\nwith the concern regarding the novelty of our research. While previous studies have employed NIPAM\nhydrogels for temperature sensing, they typically rely on tethered systems, such as using electric wires\n(e.g., doi.org/10.1016/j.orgel.2020.105818) or optical fibers (e.g., doi.org/10.1021/acsami.7b00049).\nThese systems inherently constrain the mobility and practical deployment of microrobots due to their\ndependence on external connections. Additionally, although NIPAM hydrogels have been utilized in\nmicrorobots for temperature sensing via photonic gel colorimetric responses (e.g.,\ndoi.org/10.1002/aisy.202100248), these methods face significant challenges in practical applications,\nparticularly in achieving data readout in non-transparent environments. In contrast, our study presents\na wireless, radio-communication-based temperature sensing microrobot that integrates flexible\nelectronics with shape-morphable soft microrobots. To our knowledge, this represents the first\ndemonstration of a fully untethered soft microrobot that combines instant environmental sensing with\nwireless communication, all while maintaining high mobility and adaptability.\nIn response to the reviewer’s feedback, we have revised the manuscript to more clearly\nhighlight our novel incorporation of flexible electronics for real-time communication and\nenvironmental sensing, thereby distinguishing our work from earlier efforts aimed at enhancing\nmicrorobot intelligence. Furthermore, we have conducted additional experiments and refined our\ndiscussion to better articulate the practical applications and significance of this technology. We are\ngrateful for the reviewer’s comments, which have greatly helped us strengthen both the clarity and rigor\nof our manuscript.\nComment 1: In the section reviewing related work, the manuscript mentions previous studies but fails\nto provide an in-depth comparative analysis of these works. A clearer discussion of how this study\nbuilds on or improves upon existing research is needed to emphasize its innovation and advantages.\nResponse:\nWe are thankful to the reviewer for highlighting the need for a clearer comparison with existing\nwork. In response, we have revised the introduction to underscore our core innovation: the seamless\n8\nintegration of flexible electronics into soft microrobots, which preserves inherent flexibility while\nproviding a versatile platform for added functionalities on soft microrobots. By leveraging the direct\nconversion of smart material properties into readily interpretable electrical signals for remote\ncommunication, our approach not only addresses the limitations of more rigid or tethered designs but\nalso establishes a robust framework for future enhancements, thereby advancing beyond the current\nstate of the art in wireless, multifunctional microrobot research.\nPage 3 (lines 50-54)\nBy incorporating anisotropic magnetic designs, researchers have introduced improved navigation and\npreviously unobtainable functionalities into versatile microrobotic platforms8, 17, 19, 21, 30. These\nindividual microrobots have further advanced to multiscale12, multimodal14, 15, multi-agent31,\nhierarchical32, 33, self-organizing22, 34, and swarm behaviors13, 14, 35, 36. And in doing so, they have\nexpanded their applications in environmental remediation37, micromanipulation38, medicine39, 40, and\nsensing41 contexts.\nPage 4 (lines 63-100)\nThe integration of these technologies into microrobots became possible in the last few decades due to\nadvances in microfabrication techniques, including those commonly used in microelectromechanical\nsystems (MEMS)42, 43, 44 and complementary metal-oxide-semiconductor (CMOS)28, 29, 45, alongside\nemerging methods such as 3D direct laser writing7, 24, 27, 46. For example, Bandari et al. used\nmicrofabrication to create inductively powered microrobots with a strain engineering based form of\nchemical propulsion29, and Miskin et al. integrated photovoltaics and surface electrochemical actuators\nfor a light powered microrobot28. While advanced fabrication techniques have opened up new\npossibilities for actuation methods, these implementations still struggle with a critical capability for\nreal practical utility. Namely, these microrobots cannot communicate real-time information about their\nenvironment with external systems.\nRoboticists have attempted to solve this problem in numerous fashions, whether by incorporating\ncommercial antenna chips (including RFID) or through tethered approaches. These methods, based on\nreadily available, low cost, and robust transmission technologies, can relay important information\nabout their environment to external agents. For example, Li et al. demonstrated radiofrequency (RF)-\ncapable, magnetically navigated microrobots that wirelessly transmitted temperature and pH data47.\nHowever, both the chips and required auxiliary circuits force fundamental tradeoffs between signal\nquality and microrobot size while also requiring a rigid structure. Han and colleagues addressed some\nof these issues by using microfabrication and compressive buckling techniques to incorporate a flexible\nelectronic sensor array within a balloon catheter that measured pressure and temperature48. The soft\nnature of the flexible system better matches the mechanical properties seen in biology, but the tethered\n9\nsetup still restricts the working range. As such, the question remains whether an untethered can\nsuccessfully integrate the key aspects of remote navigation, collective behavior, and wireless sensing\ninto a single system while maintaining the key advantage of flexible soft microrobots.\nThis research seeks to answer that question with a new microrobotic approach that, as its key innovation,\nintegrates flexible electronics and a shape reconfigurable soft microrobot into a single device. This\nleverages smart materials to create large, physical changes in the microrobot in response to local\nstimuli and a flexible electronic antenna design that can take advantage of the entire microrobotic\nsurface to propagate its signal. By coupling the two together, the physical changes to the robotic\nstructure produce equally dramatic changes in the antenna signal character, enabling instant remote\ncommunication within a fully flexible microrobotic system. To achieve this, we developed an integration\nprotocol to combine an anisotropic SU-8 passive layer with an iron oxide nanoparticle (IONP)\nembedded thermally-responsive hydrogel active layer, leading to a temperature dependent helical-to-\nplanar transformation function. We then laminated a flexible dipole antenna for radio communication,\ncompleting the microrobot. In this work, we explain the new fabrication route and demonstrate\nmicrorobot magnetic navigation, RF communication-based shape detection, localization, and remote\ntemperature sensing. We also show that the collective behavior of multiple microrobots enhances both\nRF signal sensitivity and reliability.\nComment 2: The temperature within the human body remains relatively stable, making it unlikely that\na soft robot with temperature sensing capabilities would be necessary for human applications.\nAdditionally, the size of the robot appears to be too large for such purposes. This prompts the question\nof which specific environment would require a soft robot with temperature sensing capabilities, further\nquestioning its intended purpose. Additionally, it is unclear why magnetic control is necessary for this\ndevice.\nResponse:\nWe are thankful for the reviewer’s concern. First, we would like to clarify the purpose behind\nthe development of this microrobot. In many microrobotic applications, real-time recognition of the\nmicrorobot’s state is challenging due to its small size and the limited resolution of conventional\nlocalization/imaging tools. For example, one important aspect is detection of the shape configuration\n(e.g., helical vs. planar) in targeted delivery applications. To navigate complex environments, the\nmicrorobot may need to actively change its shape (e.g., via programming its physical/chemical behavior\nor on-command by applying an external energy source, i.e., alternating magnetic fields to induce\nmagnetic hyperthermia of IONP-hydrogel composites). Accordingly, it is important to ensure that the\nshape transformation has been accomplished before proceeding to the next task. To address this\n10\nchallenge, our work proposes the integration of a flexible antenna into a soft microrobot, capable of\nreal-time shape detection through remote communication.\nAdditionally, the shape detection functionality can be extended to temperature sensing by\nleveraging passive shape changes induced by environmental temperature variations. For example, this\napproach could be applied in human body environments to monitor localized temperature changes.\nWhile the overall body temperature remains relatively stable, localized temperature variations can occur.\nFor example, temperature elevations of 0.5-2 °C near tumor sites and 1-3 °C in regions of inflammation\nhave been reported. Additionally, the size of the microrobot is compatible with anatomical dimensions\ntypically found in the gastrointestinal (GI) tract, supporting its potential feasibility for biomedical\napplications in such environments. As magnetic fields are widely used to actuate microrobots across\nbarriers, we employed this method for microrobot control.\nTo better communicates our findings on the microrobot’s shape recognition functionality, we\nhave revised the manuscript.\nPage 2 (lines 25-31, abstract)\nAs a proof of concept, we present a microrobot, which integrates a thermoresponsive magnetic hydrogel,\nan anisotropic support structure, and a flexible dipole antenna into a cohesive three-layered design.\nThe microrobot can morph from helical shape at low-temperatures to planar shape at high-temperatures.\nThis shape transformation can be remotely detected by external radio communication receivers,\nenabling shape-state recognition and environmental temperature sensing. Furthermore, we show that\nthe collective behavior of multiple microrobots enhances the recognition performance by\namplifying the signal.\nPage 4 (lines 84-86)\nAs such, the question remains whether an untethered can successfully integrate the key aspects of remote\nnavigation, collective behavior, and wireless sensing into a single system while maintaining the key\nadvantage of flexible soft microrobots.\nPage 15 (lines 356-361)\nWe demonstrated remote communication with soft, shape-reconfigurable microrobots, featuring a key\ninnovation in the integration of a flexible dipole antenna. The microrobot’s temperature-responsive\nshape-changing ability directly translates into antenna reconfiguration, providing a clear mechanism\nfor detecting its morphological state remotely. By designing the antenna to match the microrobot’s\ndimensions, we significantly enhance the sensitivity and reliability of radio communication signals. This\napproach overcomes challenges in poor signal sensitivity common in previous attempts with\nsimilar micro antennas.\nContinuous tracking of the microrobot’s shape demonstrates the feasibility of this approach for real-\ntime, remote monitoring of the microrobot’s morphological state. This enables remote recognition of\nthe microrobot’s configuration (e.g., helical vs. planar or folded vs. unfolded) during operation,\naddressing one of the key challenges in the practical use of microrobots. For example, configuration\nrecognition can be advantageous in targeted delivery applications, where active shape reconfiguration\n(e.g., via magnetic hyperthermia induced by alternating magnetic fields) is required to adapt to complex\nenvironments. In such cases, remote communication can inform successful shape transformation,\nallowing the microrobot to proceed to its next task. Additionally, our microrobot localization\nexperiments demonstrate that the system can reliably detect both position and shape, suggesting its\npotential for local temperature sensing applications.\nThe used hydrogels in this study swell at the 34-37 °C of lower critical solution temperature (LCST)\nwith a narrow LCST range 62, which can be further tuned by adding hydrophilic and hydrophobic\nmonomers and/or modifying the polymer architecture. By manipulating the swelling behavior, the\ndetectable temperature range can be varied63. Based on this, the temperature sensing accuracy, which\nis originally limited by the intrinsic nature of hydrogel-based temperature sensing, may be further\nimproved through fuzzy logic approach, leveraging the collective effect of microrobots designed with\nvarying phase change temperature. Additionally, the type of hydrogels can be extended to various\nstimulus-responsive hydrogels (e.g., pH, light, electric, chemical) beyond the heat-detection\ndemonstrated here. Thus, this microrobotic concept has the potential to be expanded into a wide range\nof devices for monitoring diverse stimuli at the microscale.\nTaking the remotely controlled actuation and local environmental sensing together, one begins to see\nan interesting use case for the developed microrobotic platform in biomedical applications (e.g., binary\ntemperature sensing in localized diagnostic applications. Regions of tumor or inflammation exhibit\nhigher temperatures, typically elevated by 0.5–3 °C).\nPage 18 (lines 430-431)\nCombined, this wireless communication-capable microrobotic platform holds promise for future medical use.\nPage 19 (lines 441-449)\nIn summary, our proposed microrobot design and manufacturing protocol, which integrates flexible\nelectronics with soft microrobots, enables the development of fully flexible, wireless-sensing\nmicrorobots capable of magnetic navigation and collective behavior. The ultra-thin dipole antenna\nremains fully functional under the mechanical strain induced by shape transformation of the\nmicrorobots, transmitting different radio communication signals between planar and helical shapes.\nand ensuring reliability in RF-based temperature sensing. We expect that this advancement could open\nnew possibilities in embodied micro-intelligence that can interact to environment and gather data,\nthereby paving the way for the autonomous operation of small-scale robots.\nComment 3: The robot undergoes deformation in response to temperature stimuli. Is the degree of\ndeformation consistent across trials? How much unexpected variation in deformation impacts the\naccuracy of the sensing signal?\nResponse:\nWe appreciate the reviewer’s inquiry regarding the consistency of the microrobot’s deformation\nand its impact on sensing accuracy. To address this, we additionally evaluated the dimensions of the\nplanar and helical microrobots across the temperature variation, figuring out that the shape-morphing\nwas achieved with minimal variation. This consistency supports our focus on a binary communication\nparadigm, where differentiation between the planar and helical configurations is the significant factor.\nWe have revised the manuscript accordingly to include these findings and clarify the deformation\nconsistency.\nPage 8 (lines 185-194)\nAdditionally, the repeatability of shape reconfiguration was evaluated by analyzing the average body\nlength and its standard deviation. We found that the average body length of planar microrobots varied\nfrom 14.9 mm to 14.4 mm over two rounds of shape morphing, while that of helical microrobots ranged\nfrom 11.4 mm to 10.9 mm. When the average length of the planar microrobots is divided by designed\nbody length, it results in an error of ~0.6-4%, indicating that shape sensing is repeatable. Furthermore,\nthe consistency of shape morphing among multiple microrobots was assessed using the standard\ndeviation-to-average ratio (i.e., coefficient of variation) of body length across three microrobot units.\nThe planar shape shows coefficient of variation values of ~0.038 and ~0.063 over two rounds of shape\nmorphing, indicating a stable shape reconfiguration across the microrobots in response to\nenvironmental temperature changes.\nComment 4: Deformation is triggered when the temperature exceeds a certain threshold. With such\nconditions, how can the robot be deployed into a real system?\nResponse:\nWe are thankful to the reviewer for this insightful suggestion. To clarify the conditions, we\nperformed additional experiments to analyze shape deformation as a function of temperature. The\nhydrogels used in this research have 34-37 °C of lower critical solution temperature (LCST), while our\nanalysis identified the phase change temperature to be approximately 40 °C. Based on these results, we\n13\nmay propose a potential use-case scenario in which the microrobots can be deployed in environments\nwith temperatures not exceeding 43 °C, with the aim of detecting whether the local temperature\nsurpasses 39 °C (for example, in localized temperature sensing within human body).\nAdditionally, in our other study, we observed that variation in bilayer structures (e.g., using\n[the same hydrogel]/[SU8] with different thickness combinations or using [different hydrogel]/[SU8]\nwith the same thickness combinations) can increase the phase change temperature (e.g., up to 60 °C).\nAlso, it is well known that the LCST can be tuned by the hydrogel types\n(doi.org/10.1021/acs.langmuir.5b02948, doi.org/10.1039/D3LP00114H). Thus, by tuning the phase\nchange temperature, the microrobots can be deployed in environments requiring local binary\ntemperature sensing, such as temperature-responsive micromanipulation, environmental monitoring,\nand diagnostic temperature-sensing in medical applications.\nWithin these tunable temperature ranges, microrobots can be deployed in microrobotic\napplications discussed in our Response to Comment 2. We have revised the manuscript accordingly.\nPage 15 (lines 342-346)\nAdditionally, the shape deformation from helical to planar was evaluated as a function of temperature\n(Fig. 5d). Note that the phase change temperature was set to 40 °C, as the shape deformation from\nhelical to fully planar was completed within a reasonable timeframe (less than 10 seconds) under this\ncondition (Supplementary Fig. 16). These deformation results were then correlated with the RF signal\namplitude at the resonant frequency.\nPage 17 (lines 393-418)\nContinuous tracking of the microrobot’s shape demonstrates the feasibility of this approach for real-\ntime, remote monitoring of the microrobot’s morphological state. This enables remote recognition of\nthe microrobot’s configuration (e.g., helical vs. planar or folded vs. unfolded) during operation,\naddressing one of the key challenges in the practical use of microrobots. For example, configuration\nrecognition can be advantageous in targeted delivery applications, where active shape reconfiguration\n(e.g., via magnetic hyperthermia induced by alternating magnetic fields) is required to adapt to complex\nenvironments. In such cases, remote communication can inform successful shape transformation,\nallowing the microrobot to proceed to its next task. Additionally, our microrobot localization\nexperiments demonstrate that the system can reliably detect both position and shape, suggesting its\npotential for local temperature sensing applications.\nThe used hydrogels in this study swell at the 34-37 °C of lower critical solution temperature (LCST)\nwith a narrow LCST range 62, which can be further tuned by adding hydrophilic and hydrophobic\nmonomers and/or modifying the polymer architecture. By manipulating the swelling behavior, the\ndetectable temperature range can be varied63. Based on this, the temperature sensing accuracy, which\n14\nis originally limited by the intrinsic nature of hydrogel-based temperature sensing, may be further\nimproved through fuzzy logic approach, leveraging the collective effect of microrobots designed with\nvarying phase change temperature. Additionally, the type of hydrogels can be extended to various\nstimulus-responsive hydrogels (e.g., pH, light, electric, chemical) beyond the heat-detection\ndemonstrated here. Thus, this microrobotic concept has the potential to be expanded into a wide range\nof devices for monitoring diverse stimuli at the microscale.\nTaking the remotely controlled actuation and local environmental sensing together, one begins to see\nan interesting use case for the developed microrobotic platform in biomedical applications (e.g., binary\ntemperature sensing in localized diagnostic applications. Regions of tumor or inflammation exhibit\nhigher temperatures, typically elevated by 0.5–3 °C).\nFigure 5. Demonstration of magnetic navigation, thermoresponsive shape reconfiguration, and\nremote-temperature sensing with three microrobots. d. Shape deformation from helical (0%) to planar\n(100%) as a function of PBS temperature. The signal amplitude at resonant frequency is estimated from\nthe shape.\nComment 5: In complex environments, such as natural or living settings, how can the sensitivity and\naccuracy of the sensor signals be ensured?\nResponse:\nWe are thankful to the reviewer for highlighting the importance of sensing performance in\ncomplex environments. First, we would like to clarify that the demonstration of our microrobots in\nnatural or living settings is beyond scope of this work. However, we agree with that the sensitivity and\naccuracy of the sensing signals should be evaluated in more complex, realistic environments.\nAccordingly, we conducted additional experiments and finite element method simulations to\nevaluate shape detection performance in different media (e.g., phosphate-buffered saline (PBS), which\nclosely mimics the ionic composition of body fluids). Theoretical analysis, computational simulations,\n15\nand experimental measurements confirm that the resonant frequency of the planar antenna is observed\nwith a shift from ~12.0 GHz in air to ~1.5 GHz in ion-rich media. These results indicate that our system\ncan maintain reliable sensing and communication for distinguishing between planar and helical shapes\nin more naturalistic environments.\nWe have revised the manuscript accordingly to include these results. Additionally, we have\nclarified that our work is not solely focused on designing a temperature-sensing microrobot for\nbiomedical applications and have removed any potentially confusing references in the manuscript.\nPage 12 (lines 282-306)\nInvestigation of environmental effects\nThe performance of RF communication-based shape detection can be significantly influenced by\nenvironmental factors, including the medium (e.g., ion-containing aqueous solutions) and the\nsurroundings (e.g., experimental setups). First, different media can shift the resonant frequency of RF\nsignals. The basic mechanism of the dipole antenna’s resonance is based on the matched wavelength\nof the electromagnetic wave. The wavelength at the resonant frequency is defined as:\n𝑐\n𝜆 = (5)\n𝑓\n!,#$!\nwhere c and 𝑓 are the speed of light and the resonant frequency in air, respectively. In a specific\n!,#$!\nmedium, the speed of light 𝑣 is given by:\n%\n𝑐\n𝑣 = (6)\n% 𝑛\nwhere 𝑛 is the refractive index of the medium. Based on Maxwell’s electromagnetic field theory, the\nrefractive index is given by 𝑛 = √𝜀\n!\n𝜇\n!\n. For non-magnetic materials, the relative magnetic permeability\nindex 𝜇 is approximately 1, meaning that the refractive index depends solely on the relative dielectric\n!\nconstant 𝜀\n!\n. Thus, the refractive index simplifies to 𝑛 = √𝜀\n!\n. Since the electromagnetic wavelength for\nthe resonance of dipole antenna needs to remain the same across different media (e.g., air and ion-\ncontaining aqueous solutions), Equation (5) and (6) can be rewritten as:\n𝑐 𝑣 𝑐/𝑛\n%\n= = (7)\n𝑓 𝑓 𝑓\n!,#$! !,%&'$(% !,%&'$(%\nwhere 𝑓 is the resonant frequency of the dipole antenna in a specific medium. Thus, the\n!,%&'$(%\nresonant frequency 𝑓 is defined by Equation (8).\n!,%&'$(%\n𝑓\n!,#$!\n𝑓 = (8)\n!,%&'$(%\n√𝜀\n!\nConsidering the developed 2 mm × 15 mm microrobots, they exhibited a resonant frequency of ~12\nGHz in air. When immersed them in phosphate-buffered saline (PBS, a relative permittivity: ~70), the\nresonant frequency is expected to shift to ~1.45 GHz. Figure 3h shows that experimentally measured\n16\nresonant frequency shifts to ~1.52 GHz in the PBS medium, confirming above analytical estimation is\nreasonable. This analytic estimation and experimental results also aligned with a simulation result,\nwhich shows a resonant frequency shifts to ~1.47 GHz (Supplementary Fig. 13).\nPage 16 (lines 384-391)\nWe also theoretically and experimentally confirmed that the use of microrobots in the ion-containing\naqueous solution medium significantly lowered the resonant frequency and enhanced the RF\ncommunication performance, where one-order reduction in resonant frequency enhances the\npenetration capability of RF signals. Comparing the RF signals of the dipole antenna in PBS at 2 mm\nand 4 mm depths shows a clearer signal distinction between signals at a greater depth. (Supplementary\nFig. 19). This is probably because the increased medium thickness absorbs more RF signals 61,\nenhancing signal sensitivity. Additionally, since human body fluids are similarly ion-containing\naqueous solutions, there is potential applicability for these microrobots in biomedical applications.\nFigure 3. Design parameter study and shape detecting performance through RF communication. h.\nRF signal shifts according to shape reconfiguration from helical to planar of seven units of 2×15\nmicrorobots immersed in PBS solution. The resonant frequency of the microrobots in PBS solution\nshifts to ~1.5 GHz, whereas ~12 GHz in air.\nSupplementary Figure 13. Simulation of medium effects on RF communication. The COMSOL\nsimulation with a PBS medium shows a resonant frequency shift to a lower region, consistent with\nexperimental results.\nresonant frequency in PBS. As the depth increases, communication performance improves, leading to\na more significant absolute amplitude difference.\nComment 6: While using multiple robots enhances the sensing signal, the impact of inter-robot distance\nand relative orientation on signal quality requires further evaluation.\nResponse:\nWe are thankful to the reviewer for this insightful suggestion. We agree that the distance and\nrelative orientation of the multiple microrobots can influences radio communication performance.\nTheoretically, a denser distribution of microrobots is expected to be more effective, as a higher number\nof microrobots leads to increased signal amplitude.\nTo support this, we performed finite element method simulations comparing the signal strength\nbetween two different microrobot densities. In result, the array with 33% higher density showed a\nthreefold increase in signal difference, reaching to -30 dB (as shown in supplementary Fig. 17). These\nfindings are consistent with our experimental results observed in collective microrobot behaviors,\nfurther validating the impact of microrobot density on communication performance.\nRegarding microrobot orientation, we acknowledge that RF signal transmission and reception\nare dependent on the relative orientations of the transmitter, receiver, and antenna. However, in our\nsystem, the relative orientation of microrobots can be actively controlled via an external magnetic field,\nensuring alignment across all units. Therefore, we believe similar results would be achieved in practical\napplications where uniform orientation is maintained. We have revised the manuscript accordingly.\nPage 16 (lines 380-382)\nTaken together, it becomes clear that careful antenna design can be used as a tool to distinguish\nmicrorobots, which when used in denser distributions, could enhance the signal sensitivity, as shown\nin Supplementary Fig. 18.\nCOMSOL simulation. A 33% increase in microrobot distribution density resulted in a threefold\nenhancement of the S amplitude at the resonant frequency. (Unit area: 1 cm2)\nchallenges and limitations that microrobots could encounter in real-world applications. A broader range\nof scenarios should be tested to assess these factors more comprehensively.\nResponse:\nWe are thankful to the reviewer for emphasizing the need to evaluate a broader spectrum of\noperational scenarios. In response, we conducted additional experiments to demonstrate the microrobots’\nremote localization and signal detection capabilities, including tests under a shielding cover and across\na three-by-four array platform. These experiments serve as proof-of-concept for the functionality of our\nintegrated flexible electronics and soft microrobot system in more complex and less accessible\nenvironments, thereby offering an initial foundation for further exploration of real-world applications.\nWe have revised the manuscript to include these results.\nPage 14 (lines 321-332)\nMicrorobot localization\nWireless communication capability enables microrobot localization and local information perception.\nFigure 4a illustrated that planar microrobots can be localized through areal scanning using transmitter\nand receiver antennas. This antenna setup scanned a sample holder divided into a four-by-three array\nand detected a notable dip (at ~12.2 GHz) in the measured RF signal at coordinate position (2, 3),\nwhere the planar microrobots were positioned (Fig. 4b; see also Supplementary Fig. 15a).\nAdditionally, when the microrobots were placed under a cover (Supplementary Fig. 15b,c), the planar\nand helical microrobots transmitted distinguishable RF signals, enabling shape detection (Fig. 4c).\nThis demonstrates that planar microrobots can be detected via RF communication without the need for\na vision system. Finally, these demonstrations imply that developed microrobots can be localized even\nwhen covered, allowing their use for a local shape sensing.\nbased localization, consisting of a scanning system and a sample holder divided into four-by-three\narray. The localization system consists of transmitter and receiver coils positioned opposite to each\nother, with scanning performed along a predefined path. b. RF signals across the array exhibit a\ndistinctive signal at position (3,2) corresponding to the location of the planar microrobots. c. RF signals\nfrom seven units of 2×15 microrobots placed behind a barrier. The clearly distinct signals between\nhelical and planar shapes demonstrate that the microrobots can be detected through the barrier.\na\ny\nx\nb c\nTransmitter\nBarrier\n(Paper)\nReceiver Microrobots\nSupplementary Fig. 15 Localization setup and shielded experimental setup. a. The experimental\nplatform, divided into a 4 × 3 array. Two transmission coils were connected to the VNA, and the\nmicrorobots were positioned at (3,2). b. A shielded paper cover was placed between the microrobots\nand the transmitter coil, with an enlarged view shown in (c).\nbiomedical applications are mentioned, the challenges of operating in complex biological environments,\nas well as biocompatibility issues, are not sufficiently addressed. There is also a lack of discussion or\ndata on potential health risks, such as immune responses or toxicity, that microrobots might pose in\nliving organisms.\nResponse:\nWe appreciate the reviewer’s concerns regarding the practical application and biocompatibility\nof our microrobots. While we do provide preliminary biocompatibility test results (Supplementary Fig.\n19), our primary goal is to showcase the seamless integration of flexible electronics with soft\nmicrorobots rather than to develop a dedicated biomedical device at this stage. To address potential\nchallenges in complex biological settings, we conducted additional experiments in phosphate-buffered\nsaline (PBS) with a similar ionic environment of body fluids. These results demonstrate our system’s\nbroader applicability and lay the groundwork for future investigations, including more extensive safety\nand biocompatibility assessments in realistic physiological conditions. We have revised the manuscript\naccordingly.\nPage 12 (lines 281-306, Duplicated from Response 5 for the reviewer's reference.)\nInvestigation of environmental effects\nThe performance of RF communication-based shape detection can be significantly influenced by\nenvironmental factors, including the medium (e.g., ion-containing aqueous solutions) and the\nsurroundings (e.g., experimental setups). First, different media can shift the resonant frequency of RF\nsignals. The basic mechanism of the dipole antenna’s resonance is based on the matched wavelength\nof the electromagnetic wave. The wavelength at the resonant frequency is defined as:\n𝑐\n𝜆 = (5)\n𝑓\n!,#$!\nwhere c and 𝑓 are the speed of light and the resonant frequency in air, respectively. In a specific\n!,#$!\nmedium, the speed of light 𝑣 is given by:\n%\n𝑐\n𝑣 = (6)\n% 𝑛\nwhere 𝑛 is the refractive index of the medium. Based on Maxwell’s electromagnetic field theory, the\nrefractive index is given by 𝑛 = √𝜀\n!\n𝜇\n!\n. For non-magnetic materials, the relative magnetic permeability\nindex 𝜇 is approximately 1, meaning that the refractive index depends solely on the relative dielectric\n!\nconstant 𝜀\n!\n. Thus, the refractive index simplifies to 𝑛 = √𝜀\n!\n. Since the electromagnetic wavelength for\nthe resonance of dipole antenna needs to remain the same across different media (e.g., air and ion-\ncontaining aqueous solutions), Equation (5) and (6) can be rewritten as:\n𝑐 𝑣 𝑐/𝑛\n%\n= = (7)\n𝑓 𝑓 𝑓\n!,#$! !,%&'$(% !,%&'$(%\n21\nwhere 𝑓 is the resonant frequency of the dipole antenna in a specific medium. Thus, the\n!,%&'$(%\nresonant frequency 𝑓 is defined by Equation (8).\n!,%&'$(%\n𝑓\n!,#$!\n𝑓 = (8)\n!,%&'$(%\n√𝜀\n!\nConsidering the developed 2 mm × 15 mm microrobots, they exhibited a resonant frequency of ~12\nGHz in air. When immersed them in phosphate-buffered saline (PBS, a relative permittivity: ~70), the\nresonant frequency is expected to shift to ~1.45 GHz. Figure 3h shows that experimentally measured\nresonant frequency shifts to ~1.52 GHz in the PBS medium, confirming above analytical estimation is\nreasonable. This analytic estimation and experimental results also aligned with a simulation result,\nwhich shows a resonant frequency shifts to ~1.47 GHz (Supplementary Fig. 13).\nPage 16 (lines 384-391, Duplicated from Response 5 for the reviewer's reference.)\nWe also theoretically and experimentally confirmed that the use of microrobots in the ion-containing\naqueous solution medium significantly lowered the resonant frequency and enhanced the RF\ncommunication performance, where one-order reduction in resonant frequency enhances the\npenetration capability of RF signals. Comparing the RF signals of the dipole antenna in PBS at 2 mm\nand 4 mm depths shows a clearer signal distinction between signals at a greater depth. (Supplementary\nFig. 19). This is probably because the increased medium thickness absorbs more RF signals 61,\nenhancing signal sensitivity. Additionally, since human body fluids are similarly ion-containing\naqueous solutions, there is potential applicability for these microrobots in biomedical applications.\nPage 18 (lines 415-431)\nTaking the remotely controlled actuation and local environmental sensing together, one begins to see\nan interesting use case for the developed microrobotic platform in biomedical applications (e.g., binary\ntemperature sensing in localized diagnostic applications. Regions of tumor or inflammation exhibit\nhigher temperatures, typically elevated by 0.5–3 °C). As an initial step, we examined the feasibility of\nusing this microrobot in bio-settings by testing the material biocompatibility with MTT assays\n(Supplementary Fig. 20). When exposed to human umbilical vein endothelial cell (HUVEC) cultures\nfor 24 and 72 hours, the microrobots demonstrated over 80% of cell viability, indicating acceptable\nlevels of biocompatibility. Meanwhile, studying radio communication in bio-settings reveals both\npromising aspects and ongoing challenges. For example, the penetration depth of RF electromagnetic\nwaves (1-5 GHz) through tissues falls within a reasonable range (16-60 mm). However, signal\nreflection at the air-tissue interface remains a limitation, indicating the need for further optimization\n22\nof external transmitter and receiver coil designs. In addition, while increasing transmitting coil power\ncan help mitigate signal attenuation over these depths, human exposure to RF electromagnetic field\nshould be considered. Although research is ongoing, power levels can be increased within safe limits\ndefined by the tissue heating capacity, measured by the specific absorption rate (SAR), which is\ngenerally accepted to be less than 2 W kg-1 for localized exposure.64 Combined, this wireless\ncommunication-capable microrobotic platform holds promise for future medical use.\nFigure 3. Design parameter study and shape detecting performance through RF communication. h.\nRF signal shifts according to shape reconfiguration from helical to planar of seven units of 2×15\nmicrorobots immersed in PBS solution. The resonant frequency of the microrobots in PBS solution\nshifts to ~1.5 GHz, whereas ~12 GHz in air.\nSupplementary Figure 13. Simulation of medium effects on RF communication. The COMSOL\nsimulation with a PBS medium shows a resonant frequency shift to a lower region, consistent with\nexperimental results.\nresonant frequency in PBS. As the depth increases, communication performance improves, leading to\na more significant absolute amplitude difference.\nSupplementary Fig. 20. Biocompatibility of microrobots. Using MTT assays with human umbilical\nvein endothelial cells (HUVEC), the microrobots were subjected to cell culturing conditions for 24 and\n72 hours, demonstrating sufficient biocompatibility.\nComment 9: The manuscript does not propose specific strategies or plans for mass production of the\nmicrorobots or for their integration into existing systems or processes. Guidance on how to scale\nproduction and facilitate practical deployment would be beneficial.\nResponse:\nWe are thankful to the reviewer for highlighting the importance of scalable fabrication. In\nresponse, we have provided a detailed fabrication protocol flowchart (Supplementary Fig. 21) to\nillustrate how our microrobots can be produced via established semiconductor manufacturing processes.\nBy leveraging wafer-scale techniques, we demonstrate high-yield batch production, with each run\nachieving an average of 80% yield on a four-inch wafer. This approach underscores the intrinsic\ncompatibility of our design with current industry-standard fabrication methods, facilitating reliable\nintegration of flexible electronics into soft microrobots at scale.\nThe detailed fabrication protocol flow chart is depicted in the Supplementary Fig. 22 and written in the\nSupplementary Methods section.\nPre-Clean the Silicon Wafer\nPMMA Spin-coating\nPMMA Layer Preparation\nPMMA Baking\nGold Layer Deposition\nAZ Resin Patterning\nElectronics Fabrication\nWet Etching\nStripping and Cleaning\nSilica Preparation SU-8 Spin-coating\nAdhesion Promotion\nAdhesion Promoter Treating SU-8 Prebaking\nFirst Isotropic Layer Fabrication UV Patterning\nPassive Layers Fabrication\nSecond Anisotropic Layer Fabrication\n(Same process as first layer) SU-8 Postbaking\nHydrogel Resin Injection into the Mold Resin Developing\nActive Layers Fabrication External Magnetic Field Application\nUV Crosslinking\nMask Release in Water\nFinal Release\nMicrorobots Release in PGMEA\nMicrorobot Collection\nSupplementary Fig. 22. Fabrication and integration workflow chart.\nGeneral comments: This article reports on the fabrication and testing of a new class of mm scale\nlithographically fabricated robot which combines the functionalities of locomotion, temperature sensing,\nand data transmission via RF signals, in a single device platform. The key advance is the combination\nof a temperature sensitive bending magnetic hydrogel/SU8 bilayer with a gold antennae layer, to realize\na trilayer device can sense and report information about temperature via deformation of the antennae\nby the hydrogel. The authors outline the concept of operation for the device, the fabrication protocol by\nwhich the devices are built, the effect that bending has on RF signal amplitude, how the effect increases\nwith number of devices, and demonstrate that devices can locomote using external magnetic fields to\nregions of varying temperature. Overall, the biggest strength of this paper is taking up the task of\nintegrating multiple functionalities into a single robot. The following changes would likely improve the\nquality of the manuscript:\nResponse:\nWe appreciate the reviewer’s valuable assessment of our work and are grateful for the\nconstructive suggestions provided. In response, we have conducted additional experiments and revised\nthe manuscript to address each comment in detail. These results not only reaffirm our core findings but\nalso clarify and highlight the novel integration of flexible electronics into the soft microrobots to achieve\ncommunication. We believe these revisions significantly strengthen the manuscript and enhance its\noverall readability.\nComment 1: Remote sensing generally denotes that one can infer the specific value of temperature\nfrom the action of the device. It would be a lot easier to judge this work if data were included that\nquantifies how the detected radio signal depends on environmental temperature. Specifically, we feel\nthat somewhere in the main text there needs to be a graph that shows temperature vs the authors\npreferred measurement from the antennae (i.e. resonance amplitude, resonance frequency shift). All we\ncould find was the statement that the hydrogel undergoes a phase transition around 35C, and the data in\n4F where robots are stated to unfold at 40C. Depending on the width of this transition, these devices\ncould report across biomedically relevant temperatures, or be completely unsuited as a body\ntemperature of 40C constitutes a medical emergency. Some data about how temperature can be\nextracted and quantified based on the device behavior would dramatically improve this work. Better\nstill would be a thoughtful discussion about device accuracy and precision, given this is a completely\nanalog reporting system.\nResponse:\nWe are thankful to the reviewer for highlighting the need to clarify how the radio signal depends\non environmental temperature. In response, we performed additional experiments to analyze shape\ndeformation as a function of temperature. The hydrogels used in this research have 34-37 °C of lower\n26\ncritical solution temperature (LCST), while our analysis identified the phase change temperature to be\napproximately 40 °C. The shape deformation results were then correlated with the RF signal amplitude\nat the resonant frequency.\nWe also would like to explain the purpose behind the development of this microrobot. In many\nmicrorobotic applications, real-time recognition of the microrobot’s state is challenging due to its small\nsize and the limited resolution of conventional localization/imaging tools. For example, one important\naspect is detection of the shape configuration (e.g., helical vs. planar) in targeted delivery applications.\nTo navigate complex environments, the microrobot may need to actively change its shape (e.g., via\nprogramming its physical/chemical behavior or on-command by applying an external energy source,\ni.e., alternating magnetic fields to induce magnetic hyperthermia of IONP-hydrogel composites).\nAccordingly, it is important to ensure that the shape transformation has been accomplished before\nproceeding to the next task. To address this challenge, our work proposes the integration of a flexible\nantenna into a soft microrobot, capable of real-time shape detection through remote communication.\nAdditionally, the shape detection functionality can be extended to temperature sensing by\nleveraging passive shape changes induced by environmental temperature variations. It needs to note that\nthe temperature sensing accuracy of our system is lower than that of conventional digital sensors, due to\nthe intrinsic nature of the hydrogel-based sensing mechanism. Therefore, the use-case scenario needs to\nbe defined accordingly. For example, we may propose that these microrobots can be deployed in\nenvironments where temperatures do not exceed 43 °C, with the specific aim of detecting whether the\nlocal temperature surpasses 39 °C. While the overall body temperature remains relatively stable, localized\ntemperature variations can occur. For example, temperature elevations of 0.5-2 °C near tumor sites and 1-\n3 °C in regions of inflammation have been reported. Thus, the microrobots may be well suited for binary\ntemperature sensing in localized diagnostic applications.\nAlternatively, fuzzy logic approaches could be applied using microrobots designed with\nvarying phase change temperature. In our other study, we observed that variation in bilayer structures\n(e.g., using [the same hydrogel]/[SU8] with different thickness combinations or using [different\nhydrogel]/[SU8] with the same thickness combinations) can increase the phase change temperature (e.g.,\nup to 60 °C). Also, it is well known that the LCST can be tuned by the hydrogel types\n(doi.org/10.1021/acs.langmuir.5b02948, doi.org/10.1039/D3LP00114H).\nWe have included these findings and the related discussion into the revised manuscript.\nPage 2 (lines 25-31, abstract)\nAs a proof of concept, we present a microrobot, which integrates a thermoresponsive magnetic hydrogel,\nan anisotropic support structure, and a flexible dipole antenna into a cohesive three-layered design.\nThe microrobot can morph from helical shape at low-temperatures to planar shape at high-\ntemperatures. This shape transformation can be remotely detected by external radio communication\nreceivers, enabling shape-state recognition and environmental temperature sensing. Furthermore, we\nshow that the collective behavior of multiple microrobots enhances the recognition performance by\namplifying the signal.\nPage 10 (lines 230-233)\nThis was demonstrated by continuously tracking the RF signals as a single microrobot’s shape morphed\nfrom planar to helical (shown in Fig. 3d). During the shape transition, the amplitude at the resonance\nfrequency gradually shifted by an order of 10-1, highlighting the microrobot’s capability for real-time\nshape sensing.\nPage 15 (lines 334-353)\nRemote temperature-sensing\nTo demonstrate the full process for thermal measurement via radio communication-based shape\ndetection, we constructed a functional example of the process (Fig. 5 and Supplementary Movie S6).\nWe positioned three helical microrobots (2 mm × 15 mm) in a water channel with varying temperatures,\nsimulating the conditions of the targeted temperature-sensing application (Fig. 5a). The microrobots\nwere actuated under rotating magnetic fields (40 mT at 4 Hz) using a magnetic navigation system\n(experimental configuration in Supplementary Fig. 6), propelled along the water channel from the low-\ntemperature zone to the high-temperature zone via corkscrew motion (Fig. 5b). Upon reaching the high-\ntemperature zone (~40 °C), the microrobots transformed from a helical to a planar shape (Fig. 5c).\nAdditionally, the shape deformation from helical to planar was evaluated as a function of temperature\n(Fig. 5d). Note that the phase change temperature was set to 40 °C, as the shape deformation from\nhelical to fully planar was completed within a reasonable timeframe (less than 10 seconds) under this\ncondition (Supplementary Fig. 16). These deformation results were then correlated with the RF signal\namplitude at the resonant frequency. Based on this analysis, we remotely detected the shape\ntransformations (Fig. 5e,f) through RF signals using the VNA. Comparing the RF signal from these\nmicrorobots (Fig. 5g) with our existing data set, we could estimate whether the environmental\ntemperature exceeded the phase change temperature. The pink line in Fig. 5g corresponds to the fully\nplanar shape, indicating a temperature in the range of 39-41 °C. Note that experiments were performed\nsequentially for i) navigation and transformation and then ii) transformation and temperature sensing,\nas the Joule heating coils in the navigation setup can distort the radio communication signals.\nPage 15 (lines 357-359)\nThe microrobot’s temperature-responsive shape-changing ability directly translates into antenna\nreconfiguration, providing a clear mechanism for detecting its morphological state remotely.\nContinuous tracking of the microrobot’s shape demonstrates the feasibility of this approach for real-\ntime, remote monitoring of the microrobot’s morphological state. This enables remote recognition of\nthe microrobot’s configuration (e.g., helical vs. planar or folded vs. unfolded) during operation,\naddressing one of the key challenges in the practical use of microrobots. For example, configuration\nrecognition can be advantageous in targeted delivery applications, where active shape reconfiguration\n(e.g., via magnetic hyperthermia induced by alternating magnetic fields) is required to adapt to complex\nenvironments. In such cases, remote communication can inform successful shape transformation,\nallowing the microrobot to proceed to its next task. Additionally, our microrobot localization\nexperiments demonstrate that the system can reliably detect both position and shape, suggesting its\npotential for local temperature sensing applications.\nThe used hydrogels in this study swell at the 34-37 °C of lower critical solution temperature (LCST)\nwith a narrow LCST range 62, which can be further tuned by adding hydrophilic and hydrophobic\nmonomers and/or modifying the polymer architecture. By manipulating the swelling behavior, the\ndetectable temperature range can be varied63. Based on this, the temperature sensing accuracy, which\nis originally limited by the intrinsic nature of hydrogel-based temperature sensing, may be further\nimproved through fuzzy logic approach, leveraging the collective effect of microrobots designed with\nvarying phase change temperature. Additionally, the type of hydrogels can be extended to various\nstimulus-responsive hydrogels (e.g., pH, light, electric, chemical) beyond the heat-detection\ndemonstrated here. Thus, this microrobotic concept has the potential to be expanded into a wide range\nof devices for monitoring diverse stimuli at the microscale.\nFigure 3. Design parameter study and shape detecting performance through RF communication. d.\nReal-time RF signal monitoring during continuous shape deformation from helical to planar, showing\nsignal amplitude increase throughout the transition.\nremote-temperature sensing with three microrobots. d. Shape deformation from helical (0%) to planar\n(100%) as a function of PBS temperature. The signal amplitude at resonant frequency is estimated from\nthe shape.\nSupplementary Fig. 16. Time for shape deformation from helical to planar as a function of\ntemperature. Note that the exact deformation time may vary depending on factors such as magnetic\nnanoparticle density, crosslinking density, and film thickness. However, the overall trend remains\nconsistent.\nComment 2: 1-10 mm is a large length scale for microelectronics, allowing for a range of off-the-shelf\nsensing and communication modalities. For instance, the chips used to control commercial RFID\nantennas are typically just mm in size (i.e., ignoring the antenna, the control circuit that packages data\ncan be well under the size of this robot). Likewise, there are numerous publications demonstrating\nCMOS temperature sensors and chips well under 1 mm in size. This begs the question: why this schema?\nWhy not use an integrated circuit to measure and modulate RF signal directly? Or, for that matter, why\nnot use acoustic communication, such as in neural dust? These alternatives would have significant\nbenefits in signal reliability and accuracy; the authors should explain the merits of their approach, as\ncompared to equally large devices.\nWe are thankful to the reviewer for the suggestion to include a discussion on alternative sensing\nand communication approaches, such as commercial RFID chips, integrated circuits, or acoustic\nmethods. While we agree on the potential of microelectronics to expand the functionalities of\nmicrorobots, we believe their use is limited within the scope of our approaches for several reasons.\nFlexibility (i.e., softness) is a desirable feature in microrobots, as it enables compatibility with\nenvironments of varying stiffness and allows the microrobots to conform to complex geometries. As\ndemonstrated in our work, flexibility also enables shape-morphing capabilities that would be difficult\nto achieve with rigid components. Although CMOS sensor technologies offer robust performance at the\nmillimeter scale, their inherently rigid structures and auxiliary circuits can restrict their suitability for\nintegration with soft microrobots. Similarly, acoustic communication methods typically require rigid\nresonators, which are incompatible with the mechanical compliance needed for soft microrobots.\nIn addition, the use of commercial electronic devices with RF signal modulation is constrained\nby challenges in power delivery, particularly when using smaller RF antennas. Furthermore, to the best\nof our knowledge, most commercial RF antennas are of similar or larger size than our developed\nmicrorobots (with the exception of certain ceramic-based antennas), making them unsuitable for direct\nintegration.\nWhile incorporating microelectronic components may complement or enhance our approach in\nthe future, it lies beyond the scope of the present study, which is primarily focused on the development\nof fully flexible microrobots. We are grateful for the reviewer’s insight and have included a discussion\ncomparing our approach with CMOS-based and acoustic communication solutions to highlight the merit\nof our approach. We have revised the manuscript accordingly.\nPage 4 (lines 63-86)\nThe integration of these technologies into microrobots became possible in the last few decades due to\nadvances in microfabrication techniques, including those commonly used in microelectromechanical\nsystems (MEMS)42, 43, 44 and complementary metal-oxide-semiconductor (CMOS)28, 29, 45, alongside emerging\nmethods such as 3D direct laser writing7, 24, 27, 46. For example, Bandari et al. used microfabrication to create\ninductively powered microrobots with a strain engineering based form of chemical propulsion29, and Miskin\net al. integrated photovoltaics and surface electrochemical actuators for a light powered microrobot28. While\nadvanced fabrication techniques have opened up new possibilities for actuation methods, these\nimplementations still struggle with a critical capability for real practical utility. Namely, these microrobots\ncannot communicate real-time information about their environment with external systems.\nRoboticists have attempted to solve this problem in numerous fashions, whether by incorporating\ncommercial antenna chips (including RFID) or through tethered approaches. These methods, based on\nreadily available, low cost, and robust transmission technologies, can relay important information\n31\nabout their environment to external agents. For example, Li et al. demonstrated radiofrequency (RF)-\ncapable, magnetically navigated microrobots that wirelessly transmitted temperature and pH data47.\nHowever, both the chips and required auxiliary circuits force fundamental tradeoffs between signal\nquality and microrobot size while also requiring a rigid structure. Han and colleagues addressed some\nof these issues by using microfabrication and compressive buckling techniques to incorporate a flexible\nelectronic sensor array within a balloon catheter that measured pressure and temperature48. The soft\nnature of the flexible system better matches the mechanical properties seen in biology, but the tethered\nsetup still restricts the working range. As such, the question remains whether an untethered can\nsuccessfully integrate the key aspects of remote navigation, collective behavior, and wireless sensing\ninto a single system while maintaining the key advantage of flexible soft microrobots.\nPage 18 (lines 433-439)\nAs the proposed microrobotic scheme utilizes flexible electronics, it offers advantages over commercial\nCMOS and RFID devices in terms of flexibility and adaptability, making it safer and more compatible\nfor use in various environments. Acoustic sensing and communication represent a potential\nalternative;65, 66 however, acoustic sensing tends to acquire information over a broad area, making it\nunsuitable for localized perception. Additionally, existing acoustic communication schemes often rely\non rigid implantable structures and are primarily focused on localization, without the ability to\nexternally transmit localized sensory data.\nComment 3: The paper could use more discussion of the role of the environment in communication.\nhttps://arxiv.org/abs/1306.5709). Typically, the penetration depth is on the order of a mm. This seems\nlike a major problem: if the best-case signal change for temperature reporting is on the order of a 1-\n10dB, the same effect could be produced by the device (or transmitter) moving roughly 1 mm (i.e. ~1\nbody length) deeper in tissue. How would this get de-convolved in practice? Likewise, some discussion\nof the power needed to operate the device at a given depth, and whether they would be safe, would be\nhelpful. A 1 mm penetration depth would imply that nearly 43 orders of magnitude in power would be\nlost to reach 10 cm into tissue. How could this be overcome, especially for biomedical applications?\nResponse:\nWe are grateful to the reviewer for emphasizing the role of environment in RF communication.\nWhile we agree that 10 GHz range have limitations in penetrating water and tissue, we would like to\nclarify that the signal attenuation we observed, on the order of 1-10 dB, was measured using a vector\nnetwork analyzer, which operates with relatively low transmission power.\nAdditionally, we found that phosphate-buffered saline (PBS), which closely mimics the ionic\ncomposition of bodily fluids, causes the resonant frequency shift from ~12.0 GHz to ~1.5 GHz.\nThis\n32\nshift is beneficial, as lower frequency RF signals exhibit greater penetration depth, enabling reliable\ndifferentiation between planar and helical shapes. This benefit of shift to lower resonant frequency is\nalso supported by several studies reporting that RF waves in the 1-5 GHz can be utilized in aqueous and\ntissue media (i.e., in brain stimulation), demonstrating penetration depths of 16-60 mm\n(doi.org/10.1098/rsta.2021.0020).\nIn summary, this challenge can be addressed by increasing the transmission power and\noperating the microrobots in ion-rich solutions. Our revised manuscript now includes a more explicit\ndiscussion of these findings.\nPage 12 (lines 281-306)\nInvestigation of environmental effects\nThe performance of RF communication-based shape detection can be significantly influenced by\nenvironmental factors, including the medium (e.g., ion-containing aqueous solutions) and the\nsurroundings (e.g., experimental setups). First, different media can shift the resonant frequency of RF\nsignals. The basic mechanism of the dipole antenna’s resonance is based on the matched wavelength\nof the electromagnetic wave. The wavelength at the resonant frequency is defined as:\n𝑐\n𝜆 = (5)\n𝑓\n!,#$!\nwhere c and 𝑓 are the speed of light and the resonant frequency in air, respectively. In a specific\n!,#$!\nmedium, the speed of light 𝑣 is given by:\n%\n𝑐\n𝑣 = (6)\n% 𝑛\nwhere 𝑛 is the refractive index of the medium. Based on Maxwell’s electromagnetic field theory, the\nrefractive index is given by 𝑛 = √𝜀\n!\n𝜇\n!\n. For non-magnetic materials, the relative magnetic permeability\nindex 𝜇 is approximately 1, meaning that the refractive index depends solely on the relative dielectric\n!\nconstant 𝜀\n!\n. Thus, the refractive index simplifies to 𝑛 = √𝜀\n!\n. Since the electromagnetic wavelength for\nthe resonance of dipole antenna needs to remain the same across different media (e.g., air and ion-\ncontaining aqueous solutions), Equation (5) and (6) can be rewritten as:\n𝑐 𝑣 𝑐/𝑛\n%\n= = (7)\n𝑓 𝑓 𝑓\n!,#$! !,%&'$(% !,%&'$(%\nwhere 𝑓 is the resonant frequency of the dipole antenna in a specific medium. Thus, the\n!,%&'$(%\nresonant frequency 𝑓 is defined by Equation (8).\n!,%&'$(%\n𝑓\n!,#$!\n𝑓 = (8)\n!,%&'$(%\n√𝜀\n!\nConsidering the developed 2 mm × 15 mm microrobots, they exhibited a resonant frequency of ~12\nGHz in air. When immersed them in phosphate-buffered saline (PBS, a relative permittivity: ~70), the\nresonant frequency is expected to shift to ~1.45 GHz. Figure 3h shows that experimentally measured\n33\nresonant frequency shifts to ~1.52 GHz in the PBS medium, confirming above analytical estimation is\nreasonable. This analytic estimation and experimental results also aligned with a simulation result,\nwhich shows a resonant frequency shifts to ~1.47 GHz (Supplementary Fig. 13).\nPage 16 (lines 384-391)\nWe also theoretically and experimentally confirmed that the use of microrobots in the ion-containing\naqueous solution medium significantly lowered the resonant frequency and enhanced the RF\ncommunication performance, where one-order reduction in resonant frequency enhances the\npenetration capability of RF signals. Comparing the RF signals of the dipole antenna in PBS at 2 mm\nand 4 mm depths shows a clearer signal distinction between signals at a greater depth. (Supplementary\nFig. 19). This is probably because the increased medium thickness absorbs more RF signals 61,\nenhancing signal sensitivity. Additionally, since human body fluids are similarly ion-containing\naqueous solutions, there is potential applicability for these microrobots in biomedical applications.\nPage 18 (lines 422-431)\nMeanwhile, studying radio communication in bio-settings reveals both promising aspects and ongoing\nchallenges. For example, the penetration depth of RF electromagnetic waves (1-5 GHz) through tissues\nfalls within a reasonable range (16-60 mm). However, signal reflection at the air-tissue interface\nremains a limitation, indicating the need for further optimization of external transmitter and receiver\ncoil designs. In addition, while increasing transmitting coil power can help mitigate signal attenuation\nover these depths, human exposure to RF electromagnetic field should be considered. Although\nresearch is ongoing, power levels can be increased within safe limits defined by the tissue heating\ncapacity, measured by the specific absorption rate (SAR), which is generally accepted to be less than 2\nW kg-1 for localized exposure.64 Combined, this wireless communication-capable microrobotic platform\nholds promise for future medical use.\nFigure 3. Design parameter study and shape detecting performance through RF communication. h.\nRF signal shifts according to shape reconfiguration from helical to planar of seven units of 2×15\nmicrorobots immersed in PBS solution. The resonant frequency of the microrobots in PBS solution\nshifts to ~1.5 GHz, whereas ~12 GHz in air.\nsimulation with a PBS medium shows a resonant frequency shift to a lower region, consistent with\nexperimental results.\nSupplementary Fig. 19. Experimental comparison of microrobots’ RF signal response at the\nresonant frequency in PBS. As the depth increases, communication performance improves, leading to\na more significant absolute amplitude difference.\nComment 4: The device design and fabrication protocol are nice, especially the integration of the\nmagnetic material. This is one of the best parts of the paper, but the extensive discussion of the specifics\nshould be relegated to the supplemental information, in favor of a briefer overview. Additionally, many\nstatements claim standard fabrication techniques are key advances and should be done away with\nentirely. For instance: “Key advancements included ensuring precise layer alignment”, i.e. using a mask\naligner? “Improving adhesion between layers”, i.e. using industry-standard adhesion promoters?\n“Optimizing the anisotropic layer and IONP pattern orientation”, i.e.magnetizing in the correct direction?\n“We develop a versatile microfabrication process that incorporates flexible electronics onto a soft\nmicrorobotic chassis using combination of thin-film deposition, photolithography, and wet etching\ntechniques”, the versatility of microfabrication is well established. Similar statements appear\nthroughout the manuscript and should be removed.\nResponse:\nWe appreciate the reviewer’s positive feedback on our device design and fabrication protocol.\nIn response to suggestions for better clarity, we have revised the main text to present only a concise\noverview of these methods, while adding the detailed fabrication and integration protocol to the\nSupplementary Materials. We have also removed redundant statements describing industry-standard\ntechniques (e.g., mask alignment, adhesion promoters, anisotropic layer orientation) to ensure a focused\npresentation of our unique integration approach.\nPage 5 (lines 88-100)\nThis research seeks to answer that question with a new microrobotic approach that, as its key innovation,\nintegrates flexible electronics and a shape reconfigurable soft microrobot into a single device. This\nleverages smart materials to create large, physical changes in the microrobot in response to local\nstimuli and a flexible electronic antenna design that can take advantage of the entire microrobotic\nsurface to propagate its signal. By coupling the two together, the physical changes to the robotic\nstructure produce equally dramatic changes in the antenna signal character, enabling instant remote\ncommunication within a fully flexible microrobotic system. To achieve this, we developed an integration\nprotocol to combine an anisotropic SU-8 passive layer with an iron oxide nanoparticle (IONP)\nembedded thermally-responsive hydrogel active layer, leading to a temperature dependent helical-to-\nplanar transformation function. We then laminated a flexible dipole antenna for radio communication,\ncompleting the microrobot. In this work, we explain the new fabrication route and demonstrate\nmicrorobot magnetic navigation, RF communication-based shape detection, localization, and remote\ntemperature sensing. We also show that the collective behavior of multiple microrobots enhances both\nRF signal sensitivity and reliability.\nPage 16 (lines 363-373)\nIn our manufacturing process, we enhanced the spacer-method-based microfabrication method\npreviously developed by our group8 to integrate a flexible dipole antenna into soft microrobotic\nstructures. Specifically, functionalizing the SU-8 surface through techniques like bond cleavage and\nsilanization was crucial for achieving complete shape transformation between the planar and helical\nstates. Since our manufacturing protocols are based on the well-established microfabrication processes,\nthey are compatible with different semiconductor technologies51, including MEMS52, 53 and flexible\nelectronics54, 55 processes by combining the electronic elements prior to fabricating the microrobotic\nstructures. This means other electronic devices (e.g., thin-film transistors56, 57, sensors58,\nelectroluminescent59, and energy harvester60) can be used to expand the microrobot’s functionality and\ntake advantage of even higher levels of intelligence (e.g., processing, computing). Also, the\nmicrofabrication processes are scalable and suitable for mass manufacturing with batch-processing\noptions, producing microrobots of varying sizes and designs.\nPage 20 (lines 487-488)\nThe detailed fabrication protocol flow chart is depicted in the Supplementary Fig. 22 and written in the\nSupplementary Methods section.\nPMMA Spin-coating\nPMMA Layer Preparation\nPMMA Baking\nGold Layer Deposition\nAZ Resin Patterning\nElectronics Fabrication\nWet Etching\nStripping and Cleaning\nSilica Preparation SU-8 Spin-coating\nAdhesion Promotion\nAdhesion Promoter Treating SU-8 Prebaking\nFirst Isotropic Layer Fabrication UV Patterning\nPassive Layers Fabrication\nSecond Anisotropic Layer Fabrication\n(Same process as first layer) SU-8 Postbaking\nHydrogel Resin Injection into the Mold Resin Developing\nActive Layers Fabrication External Magnetic Field Application\nUV Crosslinking\nMask Release in Water\nFinal Release\nMicrorobots Release in PGMEA\nMicrorobot Collection .\nSupplementary Fig. 22. Fabrication and integration workflow chart.\nComment 5: The authors forgo what might be an illuminating discussion about the fact that devices\nloose motility in high temperature regimes. One could argue that this is a feature, and that the device\ncould be configured to operate in a “seek and destroy” scheme, where they travel around an environment\nuntil they reach a region of desired temperature, then planarize and remain their indefinitely. Given the\nvast expertise of the authors, especially in biomedical applications of microrobots, we’d love to hear\nthem expand on the nuanced ways in which the interweaving of sensing and locomotion may be\nbeneficial.\nResponse:\nWe are thankful to the reviewer for this insightful suggestion. Gradient magnetic fields can\nactuate the microrobots even in their planar state, within the elevated temperature regime. To\ndemonstrate the mobility of planar microrobots, we performed additional experiments.\nAs mentioned in Response to Comment 1, the shape sensing functionality is important for\npractical microrobotic applications, including locomotion capability. In biomedical contexts, given the\nsize of the microrobots, we can envision their potential use in diagnostic local temperature sensing\n37\napplications within gastrointestinal (GI) tract. In this scenario, the microrobots could be naturally\nextracted, thus retrieval may not be required.\nIn addition, to expand the potential of microrobots capabilities in both sensing and locomotion,\nwe have included additional experimental results demonstrating microrobotic localization using a four-\nby-three array sample holder. We have revised the manuscript accordingly.\nPage 2 (lines 25-31, abstract)\nAs a proof of concept, we present a microrobot, which integrates a thermoresponsive magnetic hydrogel,\nan anisotropic support structure, and a flexible dipole antenna into a cohesive three-layered design.\nThe microrobot can morph from helical shape at low-temperatures to planar shape at high-\ntemperatures. This shape transformation can be remotely detected by external radio communication\nreceivers, enabling shape-state recognition and environmental temperature sensing. Furthermore, we\nshow that the collective behavior of multiple microrobots enhances the recognition performance by\namplifying the signal.\nPage 8 (lines 177-180)\nNote that microrobots can also be manipulated in dragging motion using gradient fields, which can\nparticularly be useful for controlling planar shape without relying on corkscrew motion capability (see\nSupplementary Fig. 7 and Movie S4). The average speed of 2 mm × 15 mm microrobot was 1.2 mm s-1\nunder a magnetic field of 10 mT and gradient of 120 mT mm-1.\nPage 14 (lines 321-332)\nMicrorobot localization\nWireless communication capability enables microrobot localization and local information perception.\nFigure 4a illustrated that planar microrobots can be localized through areal scanning using transmitter\nand receiver antennas. This antenna setup scanned a sample holder divided into a four-by-three array\nand detected a notable dip (at ~12.2 GHz) in the measured RF signal at coordinate position (2, 3),\nwhere the planar microrobots were positioned (Fig. 4b; see also Supplementary Fig. 15a).\nAdditionally, when the microrobots were placed under a cover (Supplementary Fig. 15b,c), the planar\nand helical microrobots transmitted distinguishable RF signals, enabling shape detection (Fig. 4c).\nThis demonstrates that planar microrobots can be detected via RF communication without the need for\na vision system. Finally, these demonstrations imply that developed microrobots can be localized even\nwhen covered, allowing their use for a local shape sensing.\n38\na 0 s b 0 s\n12 s 20s\n24 s 40s\nSupplementary Fig. 7. Navigation of planar microrobots using a magnetic gradient field. A 2 mm ×\n15 mm planar microrobot (a) and a 2 mm × 5 mm planar microrobot (b) were guided by the magnetic\ndragging force. The magnetic field and gradient were set to 10 mT and 120 mT mm-1, respectively. The\naverage speeds of 2 mm × 15 mm and 2 mm × 5 mm microrobot were 1.2 mm s-1 and 0.7 mm s-1,\nrespectively (Scale bar: 10 mm).\nFigure 4. RF communication-based microrobot localization. a. Schematic of RF communication-\nbased localization, consisting of a scanning system and a sample holder divided into four-by-three\narray. The localization system consists of transmitter and receiver coils positioned opposite to each\nother, with scanning performed along a predefined path. b. RF signals across the array exhibit a\ndistinctive signal at position (3,2) corresponding to the location of the planar microrobots. c. RF signals\nfrom seven units of 2×15 microrobots placed behind a barrier. The clearly distinct signals between\nhelical and planar shapes demonstrate that the microrobots can be detected through the barrier.\n39\na\ny\nx\nb c\nTransmitter\nBarrier\n(Paper)\nReceiver Microrobots\nSupplementary Fig. 15 Localization setup and shielded experimental setup. a. The experimental\nplatform, divided into a 4 × 3 array. Two transmission coils were connected to the VNA, and the\nmicrorobots were positioned at (3,2). b. A shielded paper cover was placed between the microrobots\nand the transmitter coil, with an enlarged view shown in (c).\nComment 6: The discussion section could be sharpened. For example: “Previous approaches using\nsmaller antennas faced challenges with inefficient radio communication, unreliable remote sensor\npowering, and poor signal differentiation in environmental monitoring due to inert sensing mechanisms.”\nIt is not demonstrated in this work that any of these problems were solved. “Our research highlights the\nmicrorobots’ potential for biomedical applications requiring operation within the body’s enclosed\ncavities or conduits.” Only cavities that are large enough to accommodate the size of one or several\ndevices (a few cm as seen in figure 4), and shallow enough that the RF signal can penetrate at order 10\nGHz. “This approach could also be extended to develop interactive microrobots that respond to both\ninternal and external stimuli, gather data, communicate with each other, and autonomously manage\ntheir functions.” It’s not clear how most of these behaviors will be achieved with the presented platform.\nIf these statements are true, they need clarification and expansion. If not, they should be removed.\nResponse:\nWe are thankful to the reviewer for highlighting the need to refine our discussion section. In\nresponse, we have carefully revised the text to clarify or remove any statements not directly supported\nby our findings. We have also narrowed the scope of our conclusions to emphasize the demonstrated\nfunctionalities rather than proposing specific microrobotic applications, while explicitly acknowledging\nthe limitations of operating at high RF frequencies. We have revised the manuscript accordingly.\nWe demonstrated remote communication with soft, shape-reconfigurable microrobots, featuring a key\ninnovation in the integration of a flexible dipole antenna. The microrobot’s temperature-responsive\nshape-changing ability directly translates into antenna reconfiguration, providing a clear mechanism\nfor detecting its morphological state remotely. By designing the antenna to match the microrobot’s\ndimensions, we also significantly enhance the sensitivity and reliability of radio communication signals.\nThis approach overcomes challenges in poor signal sensitivity common in previous attempts with\nsimilar micro antennas.\nIn our manufacturing process, we enhanced the spacer-method-based microfabrication method\npreviously developed by our group8 to integrate a flexible dipole antenna into soft microrobotic\nstructures. Specifically, functionalizing the SU-8 surface through techniques like bond cleavage and\nsilanization was crucial for achieving complete shape transformation between the planar and helical\nstates. Since our manufacturing protocols are based on the well-established microfabrication processes,\nthey are compatible with different semiconductor technologies51, including MEMS52, 53 and flexible\nelectronics54, 55 processes by combining the electronic elements prior to fabricating the microrobotic\nstructures. This means other electronic devices (e.g., thin-film transistors56, 57, sensors58,\nelectroluminescent59, and energy harvester60) can be used to expand the microrobot’s functionality and\ntake advantage of even higher levels of intelligence (e.g., processing, computing). Also, the\nmicrofabrication processes are scalable and suitable for mass manufacturing with batch-processing\noptions, producing microrobots of varying sizes and designs.\nAdditionally, we explored various pathways for improving the functionality of the developed\nmicrorobots. For example, different antenna designs produced measurably shifted resonant frequencies.\nSignals from two types of microrobots (2 mm × 15 mm and 2 mm × 10 mm with a 10 cm gap between\nthe transmitter and receiver antennas) displayed a difference in resonant frequency shifts of ~300 MHz\n(Supplementary Fig. 12a(iv) and 12b(iv)). Furthermore, S varied with different antenna designs. As\n21\nshown in Supplementary Fig. 17, the square spiral antenna exhibited a resonant frequency of ~20.6\nGHz. Taken together, it becomes clear that careful antenna design can be used as a tool to distinguish\nmicrorobots, which when used in denser distributions, could enhance the signal sensitivity, as shown\nin Supplementary Fig. 18.\nWe also theoretically and experimentally confirmed that the use of microrobots in the ion-containing\naqueous solution medium significantly lowered the resonant frequency and enhanced the RF\ncommunication performance, where one-order reduction in resonant frequency enhances the\npenetration capability of RF signals. Comparing the RF signals of the dipole antenna in PBS at 2 mm\nand 4 mm depths shows a clearer signal distinction between signals at a greater depth. (Supplementary\nenhancing signal sensitivity. Additionally, since human body fluids are similarly ion-containing\naqueous solutions, there is potential applicability for these microrobots in biomedical applications.\nContinuous tracking of the microrobot’s shape demonstrates the feasibility of this approach for real-\ntime, remote monitoring of the microrobot’s morphological state. This enables remote recognition of\nthe microrobot’s configuration (e.g., helical vs. planar or folded vs. unfolded) during operation,\naddressing one of the key challenges in the practical use of microrobots. For example, configuration\nrecognition can be advantageous in targeted delivery applications, where active shape reconfiguration\n(e.g., via magnetic hyperthermia induced by alternating magnetic fields) is required to adapt to complex\nenvironments. In such cases, remote communication can inform successful shape transformation,\nallowing the microrobot to proceed to its next task. Additionally, our microrobot localization\nexperiments demonstrate that the system can reliably detect both position and shape, suggesting its\npotential for local temperature sensing applications.\nThe used hydrogels in this study swell at the 34-37 °C of lower critical solution temperature (LCST)\nwith a narrow LCST range 62, which can be further tuned by adding hydrophilic and hydrophobic\nmonomers and/or modifying the polymer architecture. By manipulating the swelling behavior, the\ndetectable temperature range can be varied63. Based on this, the temperature sensing accuracy, which\nis originally limited by the intrinsic nature of hydrogel-based temperature sensing, may be further\nimproved through fuzzy logic approach, leveraging the collective effect of microrobots designed with\nvarying phase change temperature. Additionally, the type of hydrogels can be extended to various\nstimulus-responsive hydrogels (e.g., pH, light, electric, chemical) beyond the heat-detection\ndemonstrated here. Thus, this microrobotic concept has the potential to be expanded into a wide range\nof devices for monitoring diverse stimuli at the microscale.\nTaking the remotely controlled actuation and local environmental sensing together, one begins to see\nan interesting use case for the developed microrobotic platform in biomedical applications (e.g., binary\ntemperature sensing in localized diagnostic applications. Regions of tumor or inflammation exhibit\nhigher temperatures, typically elevated by 0.5–3 °C). As an initial step, we examined the feasibility of\nusing this microrobot in bio-settings by testing the material biocompatibility with MTT assays\n(Supplementary Fig. 20). When exposed to human umbilical vein endothelial cell (HUVEC) cultures\nfor 24 and 72 hours, the microrobots demonstrated over 80% of cell viability, indicating acceptable\nlevels of biocompatibility. Meanwhile, studying radio communication in bio-settings reveals both\npromising aspects and ongoing challenges. For example, the penetration depth of RF electromagnetic\nwaves (1-5 GHz) through tissues falls within a reasonable range (16-60 mm). However, signal\nreflection at the air-tissue interface remains a limitation, indicating the need for further optimization\n42\nof external transmitter and receiver coil designs. In addition, while increasing transmitting coil power\ncan help mitigate signal attenuation over these depths, human exposure to RF electromagnetic field\nshould be considered. Although research is ongoing, power levels can be increased within safe limits\ndefined by the tissue heating capacity, measured by the specific absorption rate (SAR), which is\ngenerally accepted to be less than 2 W kg-1 for localized exposure.64 Combined, this wireless\ncommunication-capable microrobotic platform holds promise for future medical use.\nAs the proposed microrobotic scheme utilizes flexible electronics, it offers advantages over commercial\nCMOS and RFID devices in terms of flexibility and adaptability, making it safer and more compatible\nfor use in various environments. Acoustic sensing and communication represent a potential\nalternative;65, 66 however, acoustic sensing tends to acquire information over a broad area, making it\nunsuitable for localized perception. Additionally, existing acoustic communication schemes often rely\non rigid implantable structures and are primarily focused on localization, without the ability to\nexternally transmit localized sensory data.\nIn summary, our proposed microrobot design and manufacturing protocol, which integrates flexible\nelectronics with soft microrobots, enables the development of fully flexible, wireless-sensing\nmicrorobots capable of magnetic navigation and collective behavior. The ultra-thin dipole antenna\nremains fully functional under the mechanical strain induced by shape transformation of the\nmicrorobots, transmitting different radio communication signals between planar and helical shapes.\nThe collective behavior further enhances the signal differentiation, improving remote shape detection\nand ensuring reliability in RF-based temperature sensing. We expect that this advancement could open\nnew possibilities in embodied micro-intelligence that can interact to environment and gather data,\nthereby paving the way for the autonomous operation of small-scale robots.\nSupplementary Fig. 17. Resonant frequency of the new spiral antenna in air. The resonant frequency\nof the spiral antenna is ~ 20.6 GHz.\nCOMSOL simulation. A 33% increase in microrobot distribution density resulted in a threefold\nenhancement of the S amplitude at the resonant frequency. (Unit area: 1 cm2)\nresonant frequency in PBS. As the depth increases, communication performance improves, leading to\na more significant absolute amplitude difference.\n44"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The Authors have addressed all my concerns, I recommend the acceptance of this manuscript.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Reviewer Response for Manuscript NCOMMS-24-77214-T: Soft Magnetic Microrobots with Remote Sensing and Communication Capabilities\nWe appreciate the author’s detailed responses to our comments. Unfortunately, their response and changes made to the manuscript have not resolved our concerns and in some cases created further questions.\nRebuttal to Comment 1 Response: Here the authors explained that the absence of more detailed temperature sensing data is due to the fundamental properties of the deformable hydrogel sensing modality, i.e. that it is primarily supposed to sense binary temperature change above or below a small, biologically relevant temperature window of 36-40 degrees C.\nNonetheless, the authors also provide a graph showing the change in the response of the antenna as a function of deformation %, but offer no quantification of the change as a function of temperature. This leaves lingering questions about one could reliably infer temperature based on these partial deformations, given the small change in the RF response and the possibility that unaccounted forces in the robot’s environment could impede the continuous deformation response to temperature needed to achieve differentiation between sensed values or manipulate the robot directly, thus obscuring the measurement. These points need to be addressed to lend credibility to the system’s usefulness.\nRebuttal to Comment 2 Response: The authors contend that despite the vast improvement in temperature sensing capabilities in existing mm scale CMOS architectures, such systems are “incompatible” in vivo because they are rigid, a problem which is remedied by their device flexibility. This is false, as there is an extensive body of work on flexible, biocompatible, electronics platforms which can sense and respond to temperature [D. Kim, et al. “Stretchable and Foldable Silicon Integrated Circuits”, Science 320-5675, 2008] [J. Rogers, et al. “Materials and Mechanics for Stretchable Electronics”, Science 327-5973, 2010] [J. Viveneti. et al. “Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo”, Nature Neuroscience 14, 2011] [T. Kim, et al. “Injectable, Cellular-Scale Optoelectronics with Applications for Wireless Optogenetics”, Science 340-6129, 2013][G. Hills, et al. “Modern microprocessor built from complementary carbon nanotube transistors”, Nature 572, 2019]. One of the key findings shown by these platforms is that while the modulus of the materials used may be higher than the materials in the authors’ devices, the bending stiffness can be matched by making the device layer sufficiently thin, allowing useful materials for electronics, such as metals and semiconductors, to be incorporated into flexible structures. As such, we maintain that the authors should explain why their electing to use a deformation-based temperature sensor is advantageous, when there exist alternatives with extremely high degrees of functionality.\nRebuttal to Comment 3 Response: We are pleased to see that the devices operate better in environments more similar to biological media, but are still not convinced that in vivo sensing at useful depths will be possible at safe radiation levels. It seems to us that even when assuming a penetration depth of 60 mm at the resonance frequency, the order -10 dB signal differentiation between the helical and planar states (.316 intensity ratio) is the same signal differentiation that would be achieved simply by moving the antenna 70 mm deeper into tissue (or a variety of other obstructions). Given that the robot is designed to move around its environment, potentially at varying depths to search for high temperature areas, it is not clear to us how temperature sensing information could be reliably inferred in the dissipative media of the body, given the demonstrated signal response. One could plausibly increase the transmitter power to achieve better signal differentiation at these depths, but as the authors note there is a hard cap on this workaround set by radiative heating of the media which is both medically dangerous and self-defeating as a temperature sensor, because the media could heat due to the RF signals. Similarly, one could argue that the robot could be tracked to determine its depth, but this undercuts the value of RF sensing (why not just image the shape directly at that point). We feel that these serious drawbacks to an RF communication schema must be addressed or refuted in a more substantial way in the manuscript."}, "author_response": "Responses to Reviewers’ Comments\nReviewer 4:\nGeneral comments: We sincerely appreciate the reviewer’s thorough evaluation of our work and the valuable comments and feedback, which have helped us improve the manuscript and better highlight the novelty of our research. We provide a point-by-point response to address the concerns raised as follows.\nComment 1: It would be to better clarify what's being presented in their new supplemental section on page 24: namely it is unclear if these experiments were performed in air or in water (or a tissue phantom). We recommend the authors spell out these experiments more clearly, especially since if they were done in air, the argument is not very compelling.\nResponse:\nWe thank the reviewer for pointing out the unclear description in the supplementary section.\nWe have revised the text to clarify that these experiments were performed in air. In addition, we note\nthat the two-hop relay system can enhance the observed signal strength, as shown in Supplementary\nFig. 19. The coupled effects of absorption by the dipole antenna and attenuation in water contribute to\na larger signal difference between the two microrobot states. However, as the water depth increases\nbeyond ~4 mm, the overall signal level decreases significantly, which may limit the applicability of this\nRF concept in such conditions. While we acknowledge these limitations, a more comprehensive\ninvestigation of the complex interactions in RF communication, such as attenuation in multi-layered\nmedia and interface effects, would be an important topic for future work, though it is beyond the scope\nof the present study.\nRevision:\nOverall, the signal variation due to depth is roughly half the magnitude of that caused by morphological\nchanges in air, which can reach to −4 dB (Supplementary Fig. 20).\nComment 2: The power safety argument feels disingenuous. The typical IEEE limit for safe incident power density in RF in the GHz range (surface or whole body) is around 1mW/cm2 (c.f. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8859679). If you're emitting 1mW from a 120mm transmitter, it sure seems like you've over the safe exposure limit. Likewise, a comparison to tissue ablation is not reasonable, since in that case you're deliberately trying to produce a thermal effect. This claim should be tempered: state what you power use and put that in the context of the *standard* RF limits for sensing, measurement, and communication.\nResponse:\nWe thank the reviewer for raising this important point. The transmitter was powered using a\nhigh-frequency VNA, which operates at approximately 1 mW output power. This power is not\nconcentrated within a confined 1 cm2 area but instead radiates outward and attenuates rapidly in the\nmedium, resulting in much lower local exposure levels. We would also clarify that this system is\nintended for operation in localized and restricted environments. According to the references\nrecommended by the reviewer, the local exposure reference level at 10 GHz is approximately 182\nmW/cm2, which is substantially higher than the levels used in our study. When combined with the\nadditional spreading loss in the medium, this confirms that our operating conditions are well within\ninternationally accepted safety guidelines. We are grateful to the reviewer for us to clarify this point\nmore explicitly.\nRevision:\nAccording to the IEEE standard for safety levels with respect to human exposure to electromagnetic\nfields, the local exposure reference level at 10 GHz is approximately 182 mW cm-², which is substantially\nhigher than the levels used in this study 71.\n71. IEEE Standard for Safety Levels with Respect to Human Exposure to Electric, Magnetic, and\nElectromagnetic Fields, 0 Hz to 300 GHz. In: IEEE Std C95.1-2019 (Revision of IEEE Std C95.1-2005/\nIncorporates IEEE Std C95.1-2019/Cor 1-2019)) (2019)."}, {"round": 3, "reviewer_comments": {"reviewer_4": "Generally we feel that the authors have addressed our concerns with their added content. Although we're still unsure about the value of this system vs. other sensing modalities or its practicality in a real world setting, the authors now make factually correct claims and have improved their placement of their work in the broader context of research.\nWe give two final suggestion to improve the paper quality.\n1) It would be to better clarify what's being presented in their new supplemental section on page 24: namely it is unclear if these experiments were performed in air or in water (or a tissue phantom). We recommend the authors spell out these experiments more clearly, especially since if they were done in air, the argument is not very compelling.\n2) The power safety argument feels disingenuous. The typical IEEE limit for safe incident power density in RF in the GHz range (surface or whole body) is around 1mW/cm2 (c.f. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8859679). If you're emitting 1mW from a 120mm transmitter, it sure seems like you've over the safe exposure limit. Likewise, a comparison to tissue ablation is not reasonable, since in that case you're deliberately trying to produce a thermal effect. This claim should be tempered: state what you power use and put that in the context of the *standard* RF limits for sensing, measurement, and communication."}, "author_response": ""}]

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